This paper proposes a distributed control approach for optimal power dispatch towards environment-friendly power grids. To do so, an optimization problem called environmental economic dispatch, which is subjected to physical constraints of the considered power system, is solved. Consequently, we propose an approach based on the Alternating Direction Method of Multipliers (ADMM) to obtain the globally optimal solution of
that optimization problem in a distributed manner under a fast convergence. As a result, each generation unit can derive by itself an optimal generated power that minimizes with compromising both fuel and emission costs while satisfying both global and local physical constraints caused by the whole system and by its own. The most distinguished feature of this approach is that the power balance constraint is always guaranteed during the execution of the algorithm. Finally, the performance of our proposed approach is illustrated through the simulation to a realistic power system and the comparison with another method.

This paper presents a novel algorithm of the robust iterative learning control for linear systems subject to time-invariant parametric uncertainties. The design problem is formulated as a min-max problem with a quadratic performance criterion. Then, we derive an upper-bound of the worst-case performance. Applying Lagrange duality to the minimization problem leads to a dual problem which can be reformulated as an optimization problem over linear matrix inequalities. An algorithm is given afterward and its convergence properties are proved. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

In this paper, a new robust Iterative Learning Control (ILC) algorithm has been proposed for linear systems in the presence of iteration-varying parametric uncertainties. The robust ILC design is formulated as a min-max problem using a quadratic performance criterion subject to constraints of the control input update. An upper bound of the maximization problem is derived, then, the solution of the min-max problem is achieved by solving a minimization problem. Applying Lagrange duality to this minimization problem results in a dual problem which can be reformulated as a convex optimization problem over linear matrix inequalities (LMls).
Next, we present an LMI-based algorithm for the robust ILC design and prove the convergence of the control input and the error. Finally, the proposed algorithm is applied to a flexible link to demonstrate its effectiveness.

Language：English   Publishing type：Research paper (scientific journal)  

Building on the carbon reduction targets agreed in the Paris Agreements, many nations have renewed their efforts toward achieving carbon neutrality by the year 2050. In line with this ambitious goal, nations are seeking to understand the appropriate combination of technologies which will enable the required reductions in such a way that they are appealing to investors. Around the globe, solar and wind power lead in terms of renewable energy deployment, while carbon capture and storage (CCS) is scaling up toward making a significant contribution to deep carbon cuts. Using Japan as a case study nation, this research proposes a linear optimization modeling approach to identify the potential contributions of renewables and CCS toward maximizing carbon reduction and identifying their economic merits over time. Results identify that the combination of these three technologies could enable a carbon dioxide emission reduction of between 55 and 67 percent in the energy sector by 2050 depending on resilience levels and CCS deployment regimes. Further reductions are likely to emerge with increased carbon pricing over time. The findings provide insights for energy system design, energy policy making and investment in carbon reducing technologies which underpin significant carbon reductions, while identifying potential regional social co-benefits.

DOI： 10.3390/su151813447

Other Link： https://www.mdpi.com/2071-1050/15/18/13447

Language：English   Publishing type：Research paper (scientific journal)  

This paper investigates stochastic market clearing solutions in peer-to-peer (P2P) energy markets as two parameters of prosumers’ cost functions are flexibly and randomly chosen in certain intervals to compensate uncertainties and guarantee their energy preferences. For tractability, the scenarios in which one parameter is varied while the other is fixed are considered. It is then shown that a few distributions, namely normal, Cauchy, and gamma distributions, are invariant for P2P energy markets, i.e., if prosumer cost function parameters follow one of those distributions, then market clearing solutions will follow the same type of distribution. Explicit and analytical formulas for probability density functions of market clearing price and trading powers in P2P energy markets are derived for each type of the above-mentioned distributions. As such, variations on P2P energy market clearing solutions can be assessed and predicted when parameters in the stochastic distribution of prosumer cost function parameters are changed. Simulations are carried out for a modified IEEE European Low Voltage Test Feeder system whose results validate the obtained theoretical characterizations.

DOI： 10.1016/j.ijepes.2023.109014

Other Link： https://www.sciencedirect.com/science/article/pii/S0142061523000716

Language：English   Publishing type：Research paper (scientific journal)  

This paper studies the problem of tracking a moving ground object that can be found in a number of applications such as surveillance, criminal and military situations. Ground vehicles can be employed for the tracking, but are greatly affected by surrounding obstacles, e.g. building blocks, roadside objects, other vehicles and passengers, etc. Hence, their cooperation with an aerial vehicle, e.g. a drone, which has a better vision from above, is proposed. This is inspired from the wolf-raven cooperative hunting behavior in nature, where the aerial vehicle acts as the raven and ground vehicles act as wolfs. However, the position and velocity sensing as well as the vision of ground and aerial vehicles are still perturbed by the surrounding environment. Therefore, a distributed robust control design is proposed for the ground-aerial vehicle cooperation to suppress the effects of the aforementioned disturbances. Conditions on the controller parameter are derived so that the proposed controller is robust in the H∞ or H2 sense, depending on the disturbance types. Furthermore, the bounded input constraint, as often encountered in reality, is investigated, revealing that the proposed controller is input-to-state stable with environment disturbances. Numerical simulations are then carried out to illustrate the effectiveness of the proposed nature-inspired distributed robust controller design.

DOI： 10.1109/TITS.2022.3229336

Other Link： https://ieeexplore.ieee.org/document/9994634

Language：English   Publishing type：Research paper (scientific journal)  

This paper studies the optimal clearing problem for prosumers in peer-to-peer (P2P) energy markets. It is proved that if no trade weights are enforced and the communication structure between successfully traded peers is connected, then the optimal clearing price and total traded powers in P2P market are the same with that in the pool-based market. However, if such communication structure is unconnected, then the P2P market is clustered into smaller P2P markets. If the trade weights are imposed, then the derived P2P market solutions can be significantly changed. Next, a novel decentralized optimization approach is proposed to derive a trading mechanism for P2P markets, based on the alternating direction method of multipliers (ADMM) which naturally fits into the bidirectional trading in P2P energy systems and converges reasonably fast. Analytical formulas of variable updates reveal insightful relations for each pair of prosumers on their individually traded prices and powers with their total traded powers. Further, based on those formulas, decentralized learning schemes for tuning parameters of prosumers cost functions are proposed to attain successful trading with total traded power amount as desired. Case studies on a synthetic system and the IEEE European Low Voltage Test Feeder are then carried out to verify the proposed approaches.

DOI： 10.1109/TPWRS.2020.3021474

Other Link： https://ieeexplore.ieee.org/document/9186203

Language：English   Publishing type：Research paper (scientific journal)  

This paper studies the optimal energy management in a group of dwellings having micro fuel cell combined heat and power systems. To increase the self-sufficiency and resilience of such local community, a P2P energy trading system between dwellings is proposed in which output powers from fuel cells working under their rated powers can be sold to those already reach their rated outputs but still lack powers. The arising optimization problem from this optimal P2P energy trading system is non-convex due to the nonlinear dependence of power and heat efficiencies on fuel cell output power. Therefore, a linearization method is proposed to convexify the problem. Consequently, a distributed ADMM approach is introduced to solve the convexified optimization problem in parallel at each dwelling. A case study for a group of six dwellings based on realistic electric consumption data is then presented to demonstrate the proposed approach performance and positive impacts of the P2P energy trading system. More specifically, the proposed distributed ADMM approach is reasonably fast in convergence and is scalable well with system size. In addition, P2P electricity trading system helps operate fuel cells at a higher efficiency and increase the self-sufficiency of such dwellings.

DOI： 10.1016/j.ijepes.2020.106533

Other Link： https://www.sciencedirect.com/science/article/pii/S0142061520313478

Language：English   Publishing type：Research paper (scientific journal)  

This paper investigates the problem of bidirectional energy exchange between electric vehicles (EVs) and road lanes embedded with wireless power transfer technologies called wireless charging-discharging lanes (WCDLs). As such, EVs could provide better services to the grid, especially for balancing the energy supply-demand, while bringing convenience for EV users, because no cables and EV stops are needed. To enable this EV–WCDL energy exchange, a novel decentralized peer-to-peer (P2P) trading mechanism is proposed, in which EVs directly negotiate with a WCDL to reach consensus on the energy price and amounts to be traded. Those energy price and amounts are solutions of an optimization problem aiming at optimizing private cost functions of EVs and WCDL. The negotiation process between EVs and WCDL is secured by a privacy-preserving consensus protocol. Further, to assure successful trading with desired energy price and amounts, an analytical and systematic method is proposed to select cost function parameters by EVs and WCDL in a decentralized manner. Simulations are then carried out to validate the developed theoretical results, which confirm the effectiveness and scalability of the proposed algorithm.

DOI： 10.1109/ACCESS.2020.3027832

Other Link： https://ieeexplore.ieee.org/document/9210076

Language：English   Publishing type：Research paper (scientific journal)  

Since the global warming recently becomes more severe that causes many serious changes on the weather, economy, and society world-wide, lots of efforts have been put forward to prevent it. As one of the most important energy sectors, electric power grid needs to improve itself to ad-dress the challenge of suppressing the carbon emission during electric generation especially that utilizes fossil-based fuels, whilst increasing the use of renewable and clean sources. This paper hence presents a novel optimization model for tackling the problems of optimal power scheduling and real-time pricing in presence of a carbon constraint while taking into account a demand response possibility, which may provide a helpful method to limit the carbon emission from conventional generation while promoting renewable generation.The keys here are to explicitly integrate the cost of emission to the total generation cost of conventional generation and to combine with the consumer satisfaction function. As such, conventional generation units must carefully schedule their power generation for their profits, while consumers, with the help from renewable energy sources, are willing to adjust their consumption to change the peak demand. Overall, a set of compromised solution called the Pareto front is derived upon which the conventional generating units choose their optimal generation profile to satisfy a given carbon constraint.

DOI： 10.1142/S2661335220500057

Other Link： https://www.worldscientific.com/doi/abs/10.1142/S2661335220500057

Language：English   Publishing type：Research paper (scientific journal)  

The issue of climate change and the development of international agreements around carbon targets such as the Paris agreement have engendered the prospect of a carbon constrained future. As a result, individual nations who are signatory to the Paris Agreement have developed ambitious carbon reduction targets in order to restrict temperature rises to two degrees Celsius compared to pre-industrial levels. To achieve these ambitious goals, nations have a variety of policy approaches at their disposal including feed in tariffs, fossil fuel restrictions, carbon capture and storage, renewable portfolio standards and carbon trading regimes. This study investigates carbon trading, and, using Japan as a case study assesses the economic feasibility and environmental efficiency of a carbon trading scheme underpinned by renewable energy deployment. The model employed uses an optimization approach, cognizant of technological, geographic and economic constraints. Findings identify that such an approach incorporating the 47 prefectures of Japan could engender a 42% reduction in emissions without resilience constraints and 34% incorporating a best-mix, resilient approach. Both approaches prove feasible at moderate carbon prices, considering international norms. The findings underpin policy implications for a future national Japanese emission trading scheme to improve previous single prefecture attempts which did not engender carbon trading.

DOI： 10.1016/j.apenergy.2019.113869

Language：English   Publishing type：Research paper (scientific journal)  

This paper studies the automated demand response (DR) problem in smart grids equipped with information and communication technology networks, where power generating and consuming units can exchange information as a multiagent system (MAS), and a real-time pricing (RTP) scheme is proposed. When the communication graph among agents is connected, a novel parallel and distributed consensus-based algorithm is proposed to derive an RTP scheme to facilitate DR, and when communication uncertainties exist, a robust consensus algorithm is proposed to cease the effect of uncertainties. Next, this paper proposes a novel control mechanism to tackle the problem of disconnected communication among agents, e.g., under cyber-attacks, by employing the so-called mixed communication-broadcast control architecture where the underlying ideas are twofold. First, each area in the grid associated with a connected subgraph is controlled by a MAS to guarantee the power balance and to reach consensus on the local electric price for that area. Second, a supervisory unit observes those local electric prices to calculate the global electric price for the whole grid and then broadcasts to all units so that they can properly adjust their output powers. Simulations are carried out on the IEEE 39-bus system to validate the proposed control mechanisms.

DOI： 10.1109/TIE.2018.2881938

Language：English   Publishing type：Research paper (scientific journal)  

This paper proposes a novel distributed control architecture for output power regulation of doubly fed induction generator (DFIG) based wind turbines (WTs) with on-site battery energy storage systems (BESSs). The proposed distributed control architecture receives information from adjacent WTs+BESSs to control the DFIG's grid side converter (GSC) and the storage unit for output active/reactive power regulation and energy management purposes. The proposed method guarantees the equal active power sharing and energy management of WTs+BESSs in various wind speed conditions. A distributed controller is also designed to respond to the reactive power demand and share the reactive load demand between the GSC and BESS controllers based on their capacities. Furthermore, a controller is designed to account for the communication delays caused by the communication systems based on the IEEE standards. The proposed method is tested on the modified IEEE 14-bus power system with 10 WTs+BESSs.

DOI： 10.1109/TEC.2018.2871575

Language：English   Publishing type：Research paper (scientific journal)  

We consider centralized and distributed mirror descent (MD) algorithms over a finite-dimensional Hilbert space, and prove that the problem variables converge to an optimizer of a possibly nonsmooth function when the step sizes are square summable but not summable. Prior literature has focused on the convergence of the function value to its optimum. However, applications from distributed optimization and learning in games require the convergence of the variables to an optimizer, which is generally not guaranteed without assuming strong convexity of the objective function. We provide numerical simulations comparing entropic MD and standard subgradient methods for the robust regression problem.

DOI： 10.1109/LCSYS.2018.2854889

Language：English   Publishing type：Research paper (scientific journal)  

This paper proposes a novel approach to analyze and design distributed robust consensus control algorithms for general linear leaderless multiagent systems (MASs) subjected to relative-state constraints or uncertainties represented by a locally or a globally sector-bounded condition. First, we show that the MAS robust consensus design under relative-state constraints or uncertainties is equivalent to the robust stability design under state constraints or uncertainties of a transformed MAS, which has lower dimensions. Next, the transformed MAS under state constraints or uncertainties is reformulated as a networked Lur'e system. By employing the S-procedure and Lyapunov theory, sufficient conditions for robust consensus and the designs of robust consensus controller gain are derived from solutions of distributed linear matrix inequality (LMI) convex problems. Finally, numerical examples are introduced to illustrate the effectiveness of the proposed theoretical approach.

DOI： 10.1109/TAC.2017.2752843

Language：English   Publishing type：Research paper (scientific journal)  

This paper proposes a novel distributed control architecture for synchronization of the active/reactive power sharing, energy levels, frequency/voltage of distributed battery energy storage systems (BESSs) using inter-BESS communications. The local information of each BESS is exchanged with a few other neighboring BESSs to achieve a consensus. The consensus speed is significantly improved by introducing an inner control loop for energy levels. The droop frequency/voltage control is also added to regulate the active/reactive power sharing and the frequency/voltage of BESSs. Furthermore, self delays and communication delays are explicitly considered in the design. As a result, the derived consensus algorithms are fully distributed with fast consensus speed in both contexts of nonexistence and existence of time delays, which have not been addressed in the existing research for BESSs. To validate the design, several simulation case studies are carried out.

DOI： 10.1109/TSTE.2017.2785311

Language：English   Publishing type：Research paper (scientific journal)  

This paper proposes a novel optimization model and a novel approach to derive new Demand Response and Real-time Pricing schemes for Smart Grid in which Renewable Energy and power losses are taken into account. In our proposed optimization model, a time-varying load constraint is introduced to better capture the consumption variation of customers and hence gives our approach an adaptive feature as well as facilitates Demand Response. Then our approach enables all generation and demand units to actively collaborate in a distributed manner to obtain the optimal electric price and their optimal power updates in real-time while achieving their best profits. To do so, the total welfare in the grid is maximized and the optimization problem is analytically solved using the Alternating Direction Method of Multipliers and consensus theory for Multi-agent Systems. Moreover, the power balance constraint is guaranteed in every iteration of the proposed algorithm. Next, the effects of Renewable Energy to conventional generation, consumer consumption, and electric price are theoretically revealed which show the essential role of Renewable Energy for peak load shifting. Finally, simulations on the IEEE 39-bus system are introduced to illustrate the effectiveness of the proposed approach.

DOI： 10.1109/TSG.2017.2676179

Language：English   Publishing type：Research paper (scientific journal)  

This technical note proposes a novel approach to design fully distributed reduced-order consensus controllers for multi-agent systems (MASs) with identical general linear dynamics of agents. A new model namely edge dynamics representing the differences on connected agents' states is first presented. Then the distributed consensus controller design is shown to be equivalent to the synthesis of a distributed stabilizing controller for this edge dynamics. Consequently, based on LQR approach, the globally optimal and locally optimal distributed stabilizing controller designs are proposed, of which the locally optimal distributed stabilizing design for the edge dynamics results in a fully distributed consensus controller for the MAS with no conservative bound on the coupling strength. This approach is then further developed to obtain reduced-order distributed consensus controllers for linear MASs. Finally, a numerical example is introduced to demonstrate the theoretical results.

DOI： 10.1109/TAC.2016.2554279

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2024.3520206

Language：English   Publishing type：Research paper (scientific journal)  

Algal cultivation is a sustainable approach which can be used not only for carbon dioxide sequestration but also for making useful products in many industries. To facilitate the widespread adoption of this approach, the current research studies the stable control of closed photobioreactors (PBRs) cultivating microalgae. More specifically, a proportional–integral (PI) controller is employed for the tracking of the microalgal concentration to a desired reference corresponding to a required amount of sequestrated carbon dioxide. In the presence of the practically positive and bounded constraints of the dilution rate, the stability and reference tracking of the closed-loop PBR system needs to be assessed. This work then derives conditions under which a unique equilibrium point exists and the closed-loop PBR system is asymptotically stable around such an equilibrium point. The derived theoretical results are validated and illustrated through numerical simulations for PBRs of the microalgae Chlorella vulgaris.

DOI： 10.3390/su152115269

Other Link： https://www.mdpi.com/2071-1050/15/21/15269

Language：English   Publishing type：Research paper (scientific journal)  

Optical wireless power transfer (OWPT) has been employed in the literature as a wireless powering approach for implanted and wearable devices. However, most of the existing studies on this topic have not studied the performances of OWPT systems when light is transmitted through clothing. This research therefore contributes to investigate the effects of clothing on OWPT performances from both theoretical and experimental perspectives. An obtained experimental result indicates that a single light-emitting diode (LED) transmitter is able to perform the OWPT through white cotton clothing, but failed with another dark cotton clothing, even at a small transmitting distance. Hence, this research proposes to employ LED arrays as optical transmitters to improve the OWPT system capability in terms of the wirelessly transmitted power, transmitting distance and system tolerance to misalignments, whilst keeping the system safety, low cost and simplicity. Consequently, a theoretical formula for the power transmission efficiency made by an LED array through clothing is proposed and then is verified with experimental results. Furthermore, the important role of multiple light reflections at the surfaces of clothing and the LED array transmitter is pointed out.

DOI： 10.3390/su15108146

Other Link： https://www.mdpi.com/2071-1050/15/10/8146

Language：English   Publishing type：Research paper (scientific journal)  

Uninterruptible power battery (UPS) is an important part to ensure the stable operation of data center. Its security is related to the reliability and stability of power system. Among them, the state of health (SOH) prediction is a key issue of the valve regulated lead–acid (VRLA) battery operation and maintenance in data center. In this work, the battery SOH is predicted by the correlation between the nadir voltage value of Coup De Fouet (CDF) phenomenon and SOH. Then, the CDF phenomenon is combined with popular data-driven methods, such as linear regression, regression tree, support-vector machine, Gaussian process, neural network, to predict battery SOH through 215 features. Finally, the above method is verified with the real discharge dataset of UPS battery in data center. The experimental results show that the data-driven method combining big data has higher accuracy than the simple prediction of battery SOH based on the nadir voltage value of CDF phenomenon and its variants.

DOI： 10.1016/j.egyr.2023.04.264

Other Link： https://www.sciencedirect.com/science/article/pii/S2352484723006303

Language：English   Publishing type：Research paper (scientific journal)  

This research proposes and analyzes a dynamic optical wireless power transfer (OWPT) system for wireless charging of aerial and ground electric vehicles (EVs). In this system, an overhead facility is proposed to locate laser transmitters, renewable energy resources, and energy storage devices. There are laser transmitters on the facility’s roof pointing upward to wirelessly charge aerial EVs, while the other laser transmitters on the facility’s ceiling pointing downward to wirelessly charge ground EVs. All laser transmitters are able to rotate around the normal direction to track and continuously charge aerial and ground EVs while they are moving, due to the equipped tracking cameras. Analytical mathematical formulas are then derived for the wirelessly transmitted power and energy. Based on those formulas, the unique existence of maximum power and energy points are proved. Furthermore, those maximum points are shown to be inversely linearly dependent on the environment attenuation coefficient, i.e. on weather conditions. Numerical simulations are carried out to validate and illustrate the obtained theoretical results. Finally, implications of those results on the design of ground EVs are introduced, based on which a comparison with another wireless power transfer technology reveals that the proposed dynamic OWPT system is more effective.

DOI： 10.1109/ACCESS.2023.3234577

Other Link： https://ieeexplore.ieee.org/document/10007816

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.mlwa.2022.100265

Other Link： https://www.sciencedirect.com/science/article/pii/S2666827022000093

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/JPHOT.2022.3146365

Other Link： https://ieeexplore.ieee.org/document/9695200/

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1246/bcsj.20210323

Other Link： http://www.journal.csj.jp/doi/pdf/10.1246/bcsj.20210323

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2021.3126424

Other Link： https://ieeexplore.ieee.org/document/9606722

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2021.3125031

Other Link： https://ieeexplore.ieee.org/document/9599658

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/tste.2021.3091976

Other Link： https://ieeexplore.ieee.org/document/9463752

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1080/00207179.2021.1992671

Other Link： https://www.tandfonline.com/doi/abs/10.1080/00207179.2021.1992671

Language：English   Publishing type：Research paper (scientific journal)  

Different wireless power transfer (WPT) technologies using inductive, capacitive, or optical coupling, and microwaves have been theoretically investigated and many have been employed in commercial products. WPT technologies have their own advantages and drawbacks and have been individually studied. This article envisions a concept of universal and ubiquitous wireless power transfer (U2WPT), in which power can be wirelessly transferred between any entity, whether stationary or in-motion, as long as they are equipped with appropriate energy transmitters and receivers. The realisation of such a U2WPT concept allows for the analysis of the sustainability of existing WPT systems in a unified manner, and to potentially overcome their limitations and engender greater energy mobility, flexibility, and sustainability. In addition, market mechanisms for U2WPT systems are introduced, along with an analysis of the benefits engendered in terms of economic, environmental, human and social outcomes, and improvement of energy and transportation systems. Finally, a discussion on the realisation of the U2WPT concept including policy implications, and recommendations for future research directions essential to their deployment is presented.

DOI： 10.1080/19397038.2021.1988187

Other Link： https://www.tandfonline.com/doi/abs/10.1080/19397038.2021.1988187

Language：English   Publishing type：Research paper (scientific journal)  

This chapter develops a foundation for stability analysis of storage-integrated wind farms and delves into the concept of distributed control design to provide ancillary services to the grid using storage-integrated wind turbines. The stability analysis is performed for a double-fed induction generator (DFIG)-based wind turbine integrated with a stand-alone battery energy storage system (BESS). Dynamics of the induction generator, AC-side filters, phase-locked loop (PLL), rotor-side converter (RSC), grid side converter (GSC), and BESS controllers are considered. Eigenvalue analysis is performed to evaluate the stability of the integrated systems. A distributed controller is designed to provide active and reactive power-sharing and energy synchronization capabilities to the BESS units in an IEEE 14-bus system integrated with a wind farm composed of 10 storage-integrated wind turbines.

DOI： https://doi.org/10.1049/PBPO171E_ch12

Other Link： https://digital-library.theiet.org/content/books/10.1049/pbpo171e_ch12

Language：English   Publishing type：Research paper (scientific journal)  

The occupancy of residential energy consumers is an important subject to be studied to account for the changes on the load curve shape caused by paradigm shifts to consumer-centric energy markets or by significant energy demand variations due to pandemics, such as COVID-19. For non-intrusive occupancy analysis, multiple types of sensors can be installed to collect data based on which the consumer occupancy can be learned. However, the overall system cost will be increased as a result. Therefore, this research proposes a cheap and lightweight machine learning approach to predict the energy consumer occupancy based solely on their electricity consumption data. The proposed approach employs a support vector machine (SVM), in which different kernels are used and compared, including positive semi-definite and conditionally positive definite kernels. Efficiency of the proposed approach is depicted by different performance indexes calculated on simulation results with a realistic, publicly available dataset. Among SVM models with different kernels, those with Gaussian (rbf) and sigmoid kernels have the highest performance indexes, hence they may be most suitable to be used for residential energy consumer occupancy prediction.

DOI： 10.3390/su13158321

Other Link： https://www.mdpi.com/2071-1050/13/15/8321

Language：English   Publishing type：Research paper (scientific journal)  

This paper proposes a novel conceptual system of optical wireless power transfer (OWPT) between objects, which is different from the existing OWPT systems such that a single device—an optical transceiver—is employed. This optical transceiver, which is capable of both absorbing and emitting light, is fabricated from a metal halide perovskite known for its superior features that can help significantly reduce the whole system size and cost. The proposed system contributes to realizing a thing-to-thing OWPT network, in which surfaces of objects/things are covered by perovskite transceivers (fully or partially), enabling them to wirelessly charge or discharge from the others.

DOI： 10.3389/fenrg.2021.679125

Other Link： https://www.frontiersin.org/articles/10.3389/fenrg.2021.679125/full

Language：English   Publishing type：Research paper (scientific journal)  

One of the major challenges of existing highly distributed smart grid system is the centralized supervisory control and data acquisition (SCADA) system, which suffers from single point of failure. This chapter introduces a novel distributed control algorithm for distributed energy storage devices in smart grids that can communicate with the neighboring storage units and share information in order to achieve a global objective. These global objectives include voltage regulation, frequency restoration, and active/reactive power sharing (demand response). Consensus theory is used to develop controllers for multiple energy storage devices in a cyber-physical environment, where the cyber layer includes the communication system between the storage devices and the physical layer includes the actual control and closed-loop system. Detailed proof of designs is introduced to ensure the stability and convergence of the proposed designs. Finally, the designed algorithms are validated using time-domain simulations in IEEE 14-bus system using MATLAB software.

DOI： 10.5772/intechopen.93409

Other Link： https://www.intechopen.com/chapters/73045

Language：English   Publishing type：Research paper (scientific journal)  

This article proposes a distributed consensus-based demand response control for permanent magnet synchronous generator (PMSG)-based wind turbines using standalone distributed battery energy storage systems (BESSs). The proposed controller cooperatively regulates the output power of individual wind turbines and BESSs in a wind farm to deliver active and reactive powers to the load in varying wind speed conditions. Capability curves of the wind turbines and BESSs are considered in the design. In addition, a virtual leader is designed to regulate the voltage and frequency of the combined PMSG-BESS units at the point of common coupling. Simulations on modified IEEE 14-bus power system are performed to validate the proposed design.

DOI： 10.1109/TSTE.2019.2954796

Other Link： https://ieeexplore.ieee.org/document/8908722

Language：English   Publishing type：Research paper (scientific journal)  

This research details outcomes from a global model which estimates future hydrogen penetration into a carbon constrained energy system to the year 2050. Focusing on minimum and maximum penetration scenarios, an investigation of global fuel cell vehicle (FCV) deployment is undertaken, cognizant of optimal economic deployment at the global level and stakeholder preferences in a case study of Japan. The model is mathematically formulated as a very large-scale linear optimization problem, aiming to minimize system costs, including generation type, fuel costs, conversion costs, and carbon reduction costs, subject to the
constraint of carbon dioxide reductions for each nation. Results show that between approximately 0.8% and 2% of global energy consumption needs can be met by hydrogen out to the year 2050, with city gas and transport emerging as significant use cases. Passenger FCVs and hydrogen buses account for almost all of the hydrogen-based transportation sector, leading to a global deployment of approximately 120 million FCVs by 2050. Hydrogen production is reliant on fossil fuels, and OECD nations are net importers – especially Japan with a 100% import case. To underpin hydrogen production from fossil fuels, carbon capture and storage (CCS) is required in significant quantities when anticipating a large fleet of FCVs. Stakeholder engagement suggests optimism toward FCV deployment while policy issues identified include necessity for large-scale future energy system investment and rapid technical and economic feasibility progress for renewable energy technologies and electrolyzers to achieve a hydrogen economy that is not reliant on fossil fuels.

Language：English   Publishing type：Research paper (scientific journal)  

This paper studies the formation control problem in clustered network systems composing of linear agents that are subjected to state constraints. In each cluster, there exists an agent called a leader who can communicate with other leaders outside of its cluster at some specific discrete instants. Moreover, the continuous-time communication structure in each cluster is represented by a fixed and undirected graph. A robust formation control protocol is proposed to deal with the hybrid communication described above and the constraints on states of agents. It is next shown that the hybrid robust formation control design for clustered multi-agent networks can be indirectly solved through the robust stabilization design of an equivalent system obtained by matrix theory and algebraic graph theory. Then, a robust controller is designed for the initial clustered network system in terms of linear matrix inequalities. Finally, a formation design for unmanned aerial vehicles is carried out and simulated to illustrate the effectiveness of the proposed hybrid formation control design method.

DOI： 10.1016/j.sysconle.2020.104689

Other Link： https://www.sciencedirect.com/science/article/pii/S0167691120300700

Language：English   Publishing type：Research paper (scientific journal)  

This paper presents a design of robust consensus for homogeneous leader-follower multiagent systems (MAS). Each agent of MAS is described by a linear time-invariant dynamic model subject to parametric uncertainty. The agents are interconnected through a static interconnection matrix over an undirected graph to cooperate and share information with their neighbours. The consensus design of MAS can be transformed to stability analysis by using decomposition technique. We apply Lyapunov theorem to derive the sufficient condition to ensure the consensus of all independent subsystems. In addition, we design a robust distributed state feedback gain based on linear quadratic regulator (LQR) setting. Controller gain is computed via solving a linear matrix inequality. As a result, we provide a robust design procedure of a cooperative LQR control to achieve consensus objective and maximize the admissible bound of the uncertainty. Finally, we give numerical examples to illustrate the effectiveness of the proposed consensus design. The results show that the response for MAS in presence of uncertainty using robust consensus design follows the response of the leader and is better than that of the existing nominal consensus design.

DOI： 10.4186/ej.2020.24.3.169

Language：English   Publishing type：Research paper (other academic)  

This paper focuses on the output consensus problem in networks divided into clusters of heterogeneous agents. In each cluster, there exists an agent called a leader, who can instantly communicate with other leaders outside of its cluster. Moreover, each cluster is represented by a fixed and undirected graph. By introducing a dynamic internal reference model for each agent, that takes into account the continuous-time communications among internal reference models in virtual clusters and discrete information exchanges between virtual clusters, an adaptive distributed consensus control protocol is proposed. It is shown that the output consensus problem is indirectly solved through the consensus of the virtual references. Then, a sufficient condition for the internal reference models in virtual clusters is proposed. Next, a sufficient and necessary condition is derived for the output consensus of linear heterogeneous agents in the considered clustered network. Finally, an illustrative example is given to show the effectiveness of the proposed theoretical results.

DOI： 10.1109/CDC40024.2019.9029998

Language：English   Publishing type：Research paper (other academic)  

In this paper, we present our work on operational cost optimization problem of a microgrid with renewable energy and EV batteries. The sequential distributed consensus alternative direction method of multipliers which utilizes the average consensus of multi-agent system is applied to solve the problem in a distributed way. We also adopt an EV charging strategy with 3 charging scenarios to ensure a sufficient EV energy for EV trips. Furthermore, Toyota demonstration data is used for case studies. The simulation results show the usefulness of the distributed approach and characteristic of each EV charging scenario.

DOI： 10.1109/VPPC46532.2019.8952464

Language：English   Publishing type：Research paper (other academic)  

This paper proposes a novel approach based on the edge dynamics to design a distributed formation controller for multi-agent systems with generic linear dynamics. Consequently, sufficient conditions are derived under which a desired formation pattern can be achieved. The obtained results are then utilized to design controllers for a group of unmanned front-wheel steering vehicles with nonlinear models such that they achieve an expected formation. Simulation results on a vehicle group shows the efficiency of the proposed approach.

DOI： 10.1109/VPPC46532.2019.8952226

Language：English   Publishing type：Research paper (other academic)  

Balancing the power supply and demand is one of the most fundamental and important problems for the operation and control of any electric power grid. There are multiple ways to guarantee the supply-demand balance, but in this research we focus on one specific method to facilitate it namely the prediction of electricity consumption, which is widely used by utility companies or system operators. It is known that this prediction is challenging because of many reasons, for example, inexact weather forecasts, uncertain consumers' behaviors, etc. Hence, analytical and linear models of electricity consumption might not be able to deal with such issues well. This paper therefore presents a machine learning-based approach to predict electricity consumption, in which an improved radial basis function neural network (iRBF-NN) is proposed, whose inputs are time sampling points, temperature, and humidity associated with the consumption. The parameters of this iRBF-NN are sought by solving an optimization problem where four types of cost functions are used and compared on their performances and computational costs. Afterward, the derived model is employed to predict the future electricity consumption based on the hourly forecasts of temperature and humidity. Finally, simulation results for realistic data in Tokyo are presented to illustrate the efficiency of the proposed approach.

Language：English   Publishing type：Research paper (other academic)  

Different types of power grids have different characteristics which require proper control methods to be implemented. Typically, droop control schemes are utilized at the primary control level due to their simplicity and quick response. Nevertheless, those droop methods can be varied depending on the power grid impedance, and hence, various secondary control protocols might be needed. This research proposes a distributed design approach for distributed battery energy storage systems (BESSs) at the higher control level corresponding to three different droop schemes implemented at the lower control level for various grid conditions. The designed controllers use multi-agent system and consensus theory to regulate the voltage/frequency and synchronize the energy level as well as active and reactive power sharing among BESSs. A modified IEEE 57-bus power system is used in the simulation for validation purpose.

DOI： 10.1109/GTDAsia.2019.8715853

Language：English   Publishing type：Research paper (other academic)  

Standalone battery energy storage systems (BESS) have widely been integrated to wind turbines recently to enhance the power quality and reliability of wind generation systems. This research analyzes the stability of a doubly fed induction generator (DFIG)-type wind turbines with stand alone battery storage using small-signal analysis. For the wind turbine, the induction generator model, grid side converter (GSC) controller, AC dynamics, and rotor side converter (RSC) controller are considered. For the BESS, the AC filters, and BESS controller are considered in the modeling. Eigenvalue analysis is performed to study the sensitivity of parameters of the system and control parameters on the stability of the combined wind turbine+BESS system. MATLAB/Simulink is used to analyze the system and validate the design.

DOI： 10.1109/GTDAsia.2019.8715961

Language：English   Publishing type：Research paper (other academic)  

This paper studies the day-ahead prediction of electricity consumption for power supply-demand balance in electric power networks. To handle the uncertainties in weather forecast and the nonlinearity relation between the electricity consumption and the weather conditions, this paper proposes a Radial Basis Function like Artificial Neural Network (RBF-like ANN) model with temperature, humidity, and sampling times as inputs. Then the Least Absolute Deviation, i.e., the L₁ norm condition, is employed as the optimization cost which is minimized in the model training process. To solve the L₁ optimization problem, two approaches, namely least square (L₂) based and alternating direction method of multipliers (ADMM), are utilized and compared. The simulations on real data collected in California shows that the latter approach performs better, and the number of neurons does not affect much to the prediction performance of the latter approach while it does influence on that of the former approach. Further, the proposed RBF-like ANN model equipped with ADMM solving approach provides reasonably good prediction of the electricity consumption in spite of the imprecise weather forecast.

DOI： 10.23919/SICEISCS.2019.8758717

Language：English   Publishing type：Research paper (scientific journal)  

When controlling a Dual-Active-Bridge (DAB) DC/DC converter, the high frequency terminal current is usually measured for use in the current feedback controller. In order to measure that current, a wide bandwidth sensor accompanied with high-speed amplifiers are required. Furthermore, a high Analog-to-Digital sampling rate is also necessary for sampling and processing the measured data. To avoid those expensive requirements, this paper proposes an alternative control method for the DAB converter. In the proposed method, the terminal current is estimated by a reduced-order proportional integral observer. A technique is also proposed to reduce the phase drift effect when the voltages at two terminals are not matched. Afterwards, a combined current feedforward—voltage feedback control system is developed to enhance the system dynamics and to regulate the output voltage. This control system needs only the information of the terminal voltages and no current sensor is required. Experimental results show that the observer can estimate the terminal current very quickly with the accuracy of more than 98 % . In addition, the output voltage is well regulated with a fluctuation of less than ± 2.6 % and a settling time of less than 6.5 ms in the presence of a 30 % load change.

DOI： 10.3390/en11040931

Language：English   Publishing type：Research paper (scientific journal)  

In this paper, we propose a new and systematic design framework for output consensus in heterogeneous multiinput multi-output (MIMO) general nonlinear multi-agent systems (MASs) subjected to directed communication topology. First, the input-output feedback linearization method is utilized assuming that the internal dynamics is input-to-state stable (ISS) to obtain linearized subsystems of agents. Consequently, we propose local dynamic controllers for agents such that the linearized subsystems have an identical closed-loop dynamics which has a single pole at the origin whereas other poles are on the open left-half complex plane. This allows us to deal with distinct agents having arbitrarily vector relative degrees and to derive rank-1 cooperative control inputs for those homogeneous linearized dynamics which results in a minimum rank distributed dynamic consensus controller for the initial nonlinear MAS. Moreover, we prove that the coupling strength in the consensus protocol can be arbitrarily small but positive and, hence, our consensus design is nonconservative. Next, our design approach is further strengthened by tackling the problem of randomly switching communication topologies among agents where we relax the assumption on the balance of each switched graph and derive a distributed rank-1 dynamic consensus controller. Finally, a numerical example is introduced to illustrate the effectiveness of our proposed framework.

DOI： 10.1109/TCNS.2016.2580908

Language：English   Publishing type：Research paper (scientific journal)  

This paper proposes a novel approach to design fully distributed consensus controllers for heterogeneous linear Multiagent Systems subjected to randomly switching directed topologies and model uncertainties. The appealing features of this approach are as follows. First, it uses the mildest assumption for the randomly switching topologies that the union of switched graphs has a spanning tree. Second, the consensus is achieved under a class of state multiplicative uncertainties. Moreover, the proposed consensus controllers are low-rank and have nonconservative coupling strengths. Finally, a numerical example is presented to illustrate the effectiveness of the proposed theoretical approach.

DOI： 10.1155/2018/8081264

Language：English   Publishing type：Research paper (scientific journal)  

In Dual-Active-Bridge converter control system, high frequency terminal current is usually measured then fed-back to the current mode controller. However, the measurement requires wide bandwidth sensors, fast and precise operational amplifier, and high sampling rate Analog to Digital conversion. This paper proposes a Proportional Integral Observer to estimate such the current to eliminate the need of current sensors. A combined feedforward-feedback control system is then developed to enhance the system dynamics and to regulate the output voltage. The control system needs only the information of terminal voltages. Simulation and experiment results show that the observer performs fast and accurately with acceptable observation error; and the output voltage is well regulated regardless of load changes.

DOI： 10.1016/j.ifacol.2018.11.724

Language：English   Publishing type：Research paper (other academic)  

This paper investigates a distributed consensus control design for heterogeneous energy storage devices in smart grids. Using communications between energy storage devices in the system, primary and secondary voltage/frequency control are achieved. The primary voltage and frequency synchronization is achieved by equally sharing the active and reactive powers among energy storage devices. The secondary voltage and frequency restoration is achieved by selecting one energy storage as a virtual leader, and other energy storage devices will act as followers to follow the leader energy storage system. The uniqueness of the proposed consensus design is the use of nominal values of grid voltage and frequency instead of their measured values. The proposed design is validated using modified IEEE 14-bus system in MATLAB.

DOI： 10.1109/ICRERA.2017.8191101

Language：English   Publishing type：Research paper (other academic)  

This paper proposes a design of Iterative Learning Control (ILC) based on Alternating Direction Method of Multipliers (ADMM) with application to building temperature control system (BTCS). BTCS is modeled by linear multi-agent system subjected to undirected communication topology. The main advantage of the proposed scheme is that it gives good performance despite existing the interconnection among rooms. In this paper, the ILC design is formulated as computing control input updating. Moreover, we apply ILC to a four-connected room model. It aims that the temperature of each room in building is controlled by a central controller. Finally, numerical results illustrate the effectiveness of ILC.

DOI： 10.1109/ECTICon.2017.8096363

Language：English   Publishing type：Research paper (scientific journal)  

This paper proposes a distributed control architecture for battery energy storage systems (BESSs) based on multi-agent system (MAS) framework. The active/reactive power sharing, the frequency/voltage, and the energy of BESSs are synchronized by exchanging local information with a few other neighboring BESSs. Two consensus algorithms namely leaderless and leader-follower are proposed. The proposed control architecture offers unique features. Firstly, the heterogeneous nature of BESSs is explicitly taken into account in BESS models and consensus designs, while it is usually ignored in the literature. Secondly, the proposed designs bring the plug-and-play capability to the smart grid system by operating in both islanded and grid-connected modes. Next, the nominal frequencies and nominal voltage magnitudes of BESSs are used in the consensus design instead of their frequencies and voltage magnitudes. This makes the proposed structure much easier to be implemented in the real power grids. Lastly, an additional control input is designed to synchronize the energy levels of BESSs directly, whereas the energy levels of BESSs are synchronized indirectly through their powers in other existing research. Time-domain simulations on a modified IEEE 57-bus power system are then carried out to validate the proposed control structure and the consensus designs.

DOI： 10.1109/TSG.2017.2765241

Language：English   Publishing type：Research paper (other academic)  

This paper proposes a systematic approach to derive analytical and explicit certificates for transient stability analysis of inverter-based microgrids. We first derive a line-based model of microgrids with general mesh structure. Then by employing Lyapunov stability theory with a quadratic Lyapunov function, the region of attraction of any post-fault stable equilibrium point (EP) is estimated by a large domain which contains the well-known principle region inside, if a low-dimension convex LMI problem is feasible. Accordingly, it provides a very efficient and robust transient stability certificate which can be calculated off-line, easily, and very fast. Moreover, it can be applied to microgrids with any structure. Finally, numerous tests on a microgrid are then given to illustrate the effectiveness of the proposed approach.

DOI： 10.1109/CCTA.2017.8062694

Language：English   Publishing type：Research paper (scientific journal)  

This paper proposes a novel approach to design distributed consensus controllers for linear MASs subjected to fixed and randomly switching directed topologies and model uncertainties. Employing the idea of selective pole shift based on LQR method, our approach provides a systematic framework to synthesize fully distributed consensus controllers which have low rank and non-conservative coupling strengths. In addition, our approach enables to broaden the class of randomly switching topologies in which the switching component graphs need not to be balanced. Next, we show that all aforementioned remarkable features also apply to the context that dynamics of agents are heterogeneous caused by parametric multiplicative uncertainties in their models. Finally, a numerical example is presented to illustrate the effectiveness of the proposed theoretical approach.

DOI： 10.1016/j.ifacol.2017.08.411

Language：English   Publishing type：Research paper (scientific journal)  

The Fukushima accident has pushed Japan to further develop its renewables initiative, particularly the biomass energy commodity. Their projection for the 2030 energy mix includes a biomass share of 4%. Further, a policy was introduced in 2002 called the Biomass Nippon Strategy. This was revised in 2006, fortifying the creation of Biomass Towns. Another major step forward came in 2009 with the Basic Act for the Promotion of Biomass Utilization. New goals were set with the introduction of the Basic Energy Plan. To meet the target, an agenda for the supply of domestic and imported biomass is needed. Domestic supply, such as wood pellets and agricultural residue has a small future potential. However for import scemes including wood and Palm Kernel Shell (PKS) from Indonesia and Malaysia are currently in place. There are also several future potential sources of biomass as yet untapped. In the future, the supply of biomass energy commodity could be increased to meet the target of 4% of the energy mix including comoddities such as biodiesel from sunflower, Jatropha Curcas as well as EFB (Empty fruit bunch), Sugarcane, Bagasse, Algae, Cotton seed, Coconut oil, Coconut Shell.

DOI： 10.12720/sgce.6.2.119-126

Language：English   Publishing type：Research paper (other academic)  

This paper aims to design decentralized iterative learning control (ILC) for building temperature control system (BTCS). The BTCS is described by a large-scale interconnected dynamic equation and modeled as a linear multiagent system subjected to undirected communication topology using graph theory. Typically, there are two types of control strategies for BTCS, namely, distributed and decentralized schemes. The main idea of designing for the decentralized scheme is to separate the whole system into a number of subsystems. In this research, we formulate and apply decentralized ILC to a four-connected-room model. In particular, we design D-type ILC for each room in the building separately, and each controller does not communicate with other controllers. The main task of the local controller is to achieve the tracking objective, i.e., the temperature of each room tracks its own desired reference temperature. Finally, numerical results illustrate the effectiveness of decentralized ILC and are compared with that of distributed consensus controller (DCC). The results show that output responses of both controllers can track trapesoidal reference and consume the same amount of total power at steady state. Decentralized ILC gives response without overshoot, and its convergence is faster than that of DCC. Convergence analysis reveals that tracking speed depends on the choice of learning gain.

Language：English   Publishing type：Research paper (other academic)  

This paper proposes an approach for examining the small-signal stability of transmission power grids in presence of a power line removal. Employing this approach, we derive an algebraic certificate for directly checking the existence of a new stable equilibrium point after the outage of a power line. This criterion is obtained based on the power network structure and power flow parameters without requiring the inertia parameters of generators, calculating the new equilibrium point, and the linearization technique. Therefore, the computational cost is much less than that of the well-known eigenvalue analysis approach for evaluating the small-signal stability of power systems, and the derived certificate gives insights into the power system's structure and parameters for achieving stability. A numerical test is then introduced to illustrate the proposed method.

DOI： 10.1109/ICSET.2016.7811798

Language：English   Publishing type：Research paper (other academic)  

This paper proposes a unified approach to analyze and synthesize consensus control laws for general linear leaderless multi-agent systems (MASs) subjected to input or state constraints. First, the problem of consensus under input constraint is investigated where the MAS is reformulated as a network of Lur'e systems. As a result, a sufficient condition for consensus and the design of consensus controller gain are derived from solutions of a distributed LMI convex problem. Next, the problem of consensus with state constraint is transformed into an equivalent problem of consensus under input constraint. Therefore, a similar method to the previous problem can be employed for this scenario, however an additional LMI constraint should be satisfied. Finally, a numerical example is introduced to illustrate the proposed theoretical approach.

DOI： 10.1109/ECC.2016.7810340

Language：English   Publishing type：Research paper (other academic)  

This paper proposes a distributed control approach for optimal power dispatch towards environment-friendly power grids. To do so, an optimization problem called environmental economic dispatch, which is subjected to physical constraints of the considered power system, is solved. Consequently, we propose an approach based on the Alternating Direction Method of Multipliers (ADMM) to obtain the globally optimal solution of that optimization problem in a distributed manner under a fast convergence. As a result, each generation unit can derive by itself an optimal generated power that minimizes with compromising both fuel and emission costs while satisfying both global and local physical constraints caused by the whole system and by its own. The most distinguished feature of this approach is that the power balance constraint is always guaranteed during the execution of the algorithm. Finally, the performance of our proposed approach is illustrated through the simulation to a realistic power system and the comparison with another method.

DOI： 10.1109/ICSET.2016.7811747

Language：English   Publishing type：Research paper (other academic)  

This paper presents a new approach to obtain an optimal energy management in Smart Grids from both generation and demand sides where generation and demand units collaborate with others in a distributed manner to simultaneously obtain their optimal powers that maximize the total welfare in the grid. The proposed approach is developed based on an optimization method called Alternating Direction Method of Multipliers (ADMM). The significant differences with existing methods such as gradient-based ones are that the power balance constraint is always satisfied during the running of proposed algorithm and the convergence speed is faster. Furthermore, the ADMM is very suitable for distributed implementation, which facilitates its applications in Smart Grids. A simulation on the benchmark IEEE 39-bus system is introduced to illustrate the effectiveness of the proposed approach.

DOI： 10.1109/ISGT-Asia.2016.7796480

Language：English   Publishing type：Research paper (scientific journal)  

This paper presents a brief survey of recent works on Iterative Learning Control (ILC) of Energy Management System (EMS) based on a framework of Multi-Agent System (MAS). ILC is a control methodology which is especially suitable for dynamical systems whose control tasks are executed in a finite time interval and are repeated over and over. The key idea of ILC is to take available system information in the past and current runs, to generate the control input for the next run. EMS is a computer-based system to monitor energy consumption, control operation, and optimize energy supplies and demands. EMS can be naturally modeled as MAS since each power-generated or powerconsumed component of EMS can be cast as agent. Each agent of MAS is a dynamical system itself and has its own target such as tracking desired trajectory and minimizing energy. Moreover, there are common objectives of EMS which aim to attain its energy efficiency, reliability and optimality. Then one agent can cooperate with other agents to achieve some global objectives, in addition to their own local goals, by exchanging information with other agents. Lastly, we will explore some open research problems and their potential applications.

DOI： 10.4186/ej.2016.20.5.1

Language：English   Publishing type：Research paper (other academic)  

This paper aims at proposing a distributed control approach for optimal power dispatch towards environment-friendly power grids. In order to do so, both electric generation and pollutant emission costs are properly incorporated into a unique objective function of an optimization problem subjected to physical constraints of the considered power system. Consequently, we propose an approach based on the Alternating Direction Method of Multipliers (ADMM) to obtain the globally optimal solution of that optimization problem in a distributed manner under a fast convergence. As a result, each generation unit in a power system can derive by itself an optimal generated power that minimizes with compromising both fuel and emission costs while satisfying both global and local physical constraints caused by the whole system and by its own. The most distinguished feature of this approach is that the power balance constraint is always guaranteed during the execution of the algorithm. Finally, the performance of our proposed approach is illustrated through the simulation to a realistic power system and the comparison with another method.

DOI： 10.1109/SICE.2016.7749182

Language：English   Publishing type：Research paper (other academic)  

This paper presents a systematic design approach for output consensus in a class of heterogeneous uncertain nonlinear multi-agent systems (MASs). By employing a feedback linearization technique, the output consensus problem of heterogeneous nonlinear MASs can be recast as a state consensus problem for homogeneous linear MASs. Consequently, a sub-optimal consensus design for linear MASs is presented to provide a constructive method for solving the initial output consensus problem in heterogeneous nonlinear MASs. Finally, the proposed approach is verified through a secondary voltage control problem in smart grids revealing its effectiveness.

DOI： 10.1109/CDC.2015.7402781

Language：English   Publishing type：Research paper (scientific journal)  

This paper proposes a unified approach to analyze and synthesize consensus control laws for nonlinear leaderless multi-agent systems (MASs) subjected to output constraints. First, we employ the input-output feedback linearization method to derive the linearized models of agents. Accordingly, the consensus problem under output constraints for the initial nonlinear MAS is transformed into an equivalent consensus problem under state constraints for the linearized MAS, which is then reformulated as a network of Lur'e systems. Next, a sufficient condition for consensus and the design of consensus controller gain are derived from solutions of a distributed LMI convex problem. Finally, a numerical example is introduced to illustrate the proposed theoretical approach.

DOI： 10.1016/j.ifacol.2016.10.380

Language：English   Publishing type：Research paper (other academic)  

This paper proposes a novel approach to design distributed optimal controllers for the power coordination problem in Wind Power Plants (WPPs). Based on LQR method, we present a systematic procedure to design a fully distributed optimal stabilizing controller for the linearized model of Wind Turbines (WTs) in a WPP. Next, the reduced-order distributed optimal controller is derived by elaborating more on the selection of weighting matrices in the LQR performance cost. Furthermore, the effect of disturbances to the system performance is evaluated through the H_∞ norm computation. Finally, the simulation results for a linearized model of WPP are shown to illustrate the effectiveness of the proposed approach.

DOI： 10.1109/CCA.2015.7320744

Language：English   Publishing type：Research paper (scientific journal)  

This paper is concerned with systematic ways of designing hierarchical decentralized controllers for heterogeneous multi-agent dynamical systems. Given a bunch of independent agents or subsystems with a class of information networks, the aim of the paper is to propose a systematic design procedure for hierarchical decentralized controllers, where each subsystem cooperatively interacts with each other as well as controls it locally to achieve both the local and global goals in some senses. It is shown that employing the LQR (Linear Quadratic Regulator) method with properly chosen weighting matrices in the performance index, both the local and global objectives can be achieved by the desired hierarchical decentralized structure which fits the given information network. The effectiveness of the proposed design method is confirmed through an illustrative example and its application to a velocity consensus problem in vehicle platoons.

DOI： 10.9746/jcmsi.8.295

Language：English   Publishing type：Research paper (scientific journal)  

This paper presents a new and systematic procedure to design sub-optimal hierarchical feedback controllers for the leader-follower consensus problem in homogeneous multi-agent systems. First, the given multi-agent system is treated as a two-layer hierarchical system where the agents perform local actions in the lower layer and interact with others in the upper layer to achieve some global goals. Then the consensus controller design is formulated as a hierarchical state feedback control problem. Employing LQR approach with an appropriately selected performance index, an optimal hierarchical state feedback controller is derived which includes two terms namely local and global terms. Consequently, by removing the local term, the remaining global term is proved to make the multi-agent system consensus which results in a sub-optimal hierarchical consensus controller. Finally, some numerical examples are introduced to illustrate the effectiveness of the proposed method.

DOI： 10.1016/j.automatica.2015.02.037

Language：English   Publishing type：Research paper (scientific journal)  

In this paper, we present a systematic approach based on harmonic balance method to study the entrained oscillations in a class of Goodwin-type oscillator networks forced by external periodic signals consisting of high order harmonics. First, a necessary condition and a conjecture for entrainment of network oscillations are presented. Next, the authors reveal an estimation for the profile of entrained oscillations in one situation and the monotone dependence of the amplitude and phase shift of entrained oscillations to the external input in other contexts. The theoretical results are then illustrated through some examples including a practical model for circadian rhythm in Neurospora crassa.

DOI： 10.9746/jcmsi.7.337

Language：English   Publishing type：Research paper (other academic)  

This paper proposes a systematic method to design hierarchical, decentralized, stabilizing controllers for homogeneous hierarchical dynamical networks. Based on LQR approach with a properly chosen performance index including global and local objectives with control input penalty, an obtained optimal LQR feedback gain gives the closed-loop system a prescribed desirable hierarchical structure. In addition, the undesirable eigenvalues of the given homogeneous network can be selectively shifted by further selecting the weighting matrices based on the left eigenvectors associated with those eigenvalues. Finally, the proposed method is summarized into a systematic design procedure with an illustrative numerical example to show its effectiveness.

Language：English   Publishing type：Research paper (other academic)  

In this paper, we present a systematic approach based on harmonic balance method to study the induced oscillations in a class of Goodwin oscillator networks forced by external periodic signals. Analytical expressions on the dependence of the phases and amplitudes of network oscillations to those of forcing inputs are revealed. Based on those expressions, we further show that under some specific conditions, the amplitude and phase shift of synchronized oscillations in networks of Goodwin oscillators monotonically depend on the amplitude of exciting inputs. The theoretical results are then illustrated through some examples.

Language：English   Publishing type：Research paper (other academic)  

This paper presents a new optimization-based iterative learning control (ILC) framework for multiple-point tracking control. Conventionally, one demand prior to designing ILC algorithms for such problems is to build a reference trajectory that passes through all given points at given times. In this paper, we produce output curves that pass close to the desired points without considering the reference trajectory. Here, the control signals are generated by solving an optimal ILC problem with respect to the points. As such, the whole process becomes simpler; key advantages include significantly decreasing the computational cost and improving performance. Our work is then examined in both continuous and discrete systems.

DOI： 10.1109/CDC.2011.6160494

Language：English   Publishing type：Research paper (scientific journal)  

In this paper, we present a new robust iterative learning control (ILC) design for a class of linear systems in the presence of time-varying parametric uncertainties and additive input/output disturbances. The system model is described by the Markov matrix as an affine function of parametric uncertainties. The robust ILC design is formulated as a minmax problem using a quadratic performance criterion subject to constraints of the control input update. Then, we propose a novel methodology to find a suboptimal solution of the minmax optimization problem. First, we derive an upper bound of the worst-case performance. As a result, the minmax problem is relaxed to become a minimization problem in the form of a quadratic program. Next, the robust ILC design is cast into a convex optimization over linear matrix inequalities (LMIs) which can be easily solved using off-the-shelf optimization solvers. The convergences of the control input and the error are proved. Finally, the robust ILC algorithm is applied to a physical model of a flexible link. The simulation results reveal the effectiveness of the proposed algorithm.

DOI： 10.1016/j.automatica.2011.05.022

Language：English   Publishing type：Research paper (scientific journal)  

This paper presents the design of a robust Iterative Learning Control (ILC) algorithm for linear systems in the presence of parametric uncertainties and repet-itive disturbances. The robust ILC design is formu-lated as a min-max problem with a quadratic perfor-mance index subjected to constraints of the control input. Employing Lagrange duality, we can refor-mulate the robust ILC design as a convex optimiza-tion problem over linear matrix inequalities (LMIs). An LMI algorithm for the robust ILC design is then given. Finally, the effectiveness of the proposed ro-bust ILC algorithm is demonstrated through a nu-merical example.

Language：English   Publishing type：Research paper (scientific journal)  

In this paper, a new robust iterative learning control (ILC) algorithm has been proposed for linear systems in the presence of iteration-varying parametric uncertainties. The robust ILC design is formulated as a min-max problem using a quadratic performance criterion subject to constraints of the control input update. An upper bound of the maximization problem is derived, then, the solution of the min-max problem is achieved by solving a minimization problem. Applying Lagrangian duality to this minimization problem results in a dual problem which can be reformulated as a convex optimization problem over linear matrix inequalities (LMIs). Next, we present an LMI-based algorithm for the robust ILC design and prove the convergence of the control input and the error. Finally, the proposed algorithm is applied to a distillation column to demonstrate its effectiveness.

DOI： 10.1002/asjc.266

Language：English   Publishing type：Research paper (other academic)  

In this paper, we present an iterative learning control (ILC) algorithm to track specified desired multiple terminal points at given time instants. A framework to update the desired trajectories from given points is developed based on the interpolation technique. The approach shows better rate of convergence of the errors. The simulation with a satellite antenna control model is demonstrated to show the effectiveness of our approach.

DOI： 10.1109/ISIC.2011.6045393

Language：English   Publishing type：Research paper (scientific journal)  

This article presents a novel robust iterative learning control algorithm (ILC) for linear systems in the presence of multiple time-invariant parametric uncertainties.The robust design problem is formulated as a min-max problem with a quadratic performance criterion subject to constraints of the iterative control input update. Then, we propose a new methodology to find a sub-optimal solution of the min-max problem. By finding an upper bound of the worst-case performance, the min-max problem is relaxed to be a minimisation problem. Applying Lagrangian duality to this minimisation problem leads to a dual problem which can be reformulated as a convex optimisation problem over linear matrix inequalities (LMIs). An LMI-based ILC algorithm is given afterward and the convergence of the control input as well as the system error are proved. Finally, we apply the proposed ILC to a generic example and a distillation column. The numerical results reveal the effectiveness of the LMI-based algorithm.

DOI： 10.1080/00207179.2010.531398

Language：English   Publishing type：Research paper (other academic)  

This paper presents the design of a robust Iterative Learning Control (ILC) algorithm for linear systems in the presence of parametric uncertainties and repetitive disturbances. The robust ILC design is formulated as a min-max problem with a quadratic performance index subjected to constraints of the control input. Employing Lagrange duality, we can reformulate the robust ILC design as a convex optimization problem over linear matrix inequalities (LMIs). An LMI algorithm for the robust ILC design is then given. Finally, the effectiveness of the proposed robust ILC algorithm is demonstrated through a numerical example.

Language：English   Publishing type：Research paper (other academic)  

This paper presents a novel algorithm of the robust iterative learning control for linear systems subject to timeinvariant parametric uncertainties. The design problem is formulated as a min-max problem with a quadratic performance criterion. Then, we derive an upper-bound of the worst-case performance. Applying Lagrange duality to the minimization problem leads to a dual problem which can be reformulated as an optimization problem over linear matrix inequalities. An algorithm is given afterward and its convergence properties are proved. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

Language：English   Publishing type：Research paper (other academic)  

In this paper, a new robust Iterative Learning Control (ILC) algorithm has been proposed for linear systems In the presence of iteration-varying parametric uncertainties. The robust ILC design is formulated as a min-max problem usmg a quadratic performance criterion subject to constraints of the control input update. An upper bound of the maximization problem is derived, then, the solution of the min-max problem is achieved by solving a minimization problem. Applying Lagrange duality to this minimization problem results in a dual problem which can be reformulated as a convex optimization problem over linear matrix inequalities (LMls). Next, we present an LMI-based algorithm for the robust ILC design and prove the convergence of the control input and the error. Finally, the proposed algorithm is applied to a flexible link to demonstrate its effectiveness.

Language：English   Publishing type：Research paper (other academic)  

In this paper, we present a robust Iterative Learning Control (ILC) design for linear systems in the presence of time-varying parametric uncertainties. The robust ILC design is formulated as a min-max problem using a quadratic performance criterion subject to constraints of the control input update where the system model contains time-varying parametric uncertainties. An upper bound of the worst-case performance is employed in the min-max problem. Subsequently, applying Lagrangian duality to the min-max problem, we derive a dual problem which is reformulated as a convex optimization over linear matrix inequalities (LMIs). As a result, iterative input updates can be obtained by solving a series of LMI problems. We give an LMI algorithm for the robust ILC design and prove the convergence of the control input and the error. Finally, a numerical example is presented to illustrate the effectiveness of the proposed algorithm.

DOI： 10.1109/CDC.2009.5399615

Language：English   Publishing type：Research paper (other academic)  

In this paper, we present the design of a robust Iterative Learning Control (ILC) algorithm for a single flexible link in the presence of parametric uncertainty. The robust ILC design is formulated as a min-max problem with a quadratic performance index. An upper bound of the worst-case performance is employed in the min-max problem. Applying Lagrange duality to the min-max problem, we can reformulate the robust ILC design as a convex optimization over linear matrix inequalities (LMIs). An LMI algorithm for the robust ILC design is given. Finally, the simulation results for a single flexible link are presented to illustrate the effectiveness of the proposed robust ILC algorithm.

DOI： 10.1109/ECTICON.2009.5137030

Language：English   Publishing type：Research paper (scientific journal)  

This paper presents the design of iterative learning control based on quadratic performance criterion (Q-ILC) for linear systems subject to additive uncertainty. The robust Q-ILC design can be cast as a min-max problem. We propose a novel approach which employs an upper bound of the worst-case performance, then formulates a non-convex quadratic minimization problem to get the update of iterative control inputs. Applying Lagrange duality, the Lagrange dual function of the non-convex quadratic problem is equivalent to a convex optimization over linear matrix inequalities (LMIs). An LMI algorithm with convergence properties is then given for the robust Q-ILC design. Finally, we provide a numerical example to illustrate the effectiveness of the proposed method.

DOI： 10.1016/j.jprocont.2008.12.004

Language：English   Publishing type：Research paper (scientific journal)  

This paper presents the design of iterative learning control based on quadratic performance criterion (Q-ILC) for linear systems subject to additive uncertainty. The robust Q-ILC design can be cast as a min-max problem. We propose a novel approach which employs an upper bound of the worst-case performance, then formulates a non-convex quadratic minimization problem to get the update of iterative control inputs. Applying Lagrange duality, the Lagrange dual function of the non-convex quadratic problem is equivalent to a convex optimization over linear matrix inequalities (LMIs). An LMI algorithm with convergence properties is then given for the robust Q-ILC design. Finally, we provide a numerical example to illustrate the effectiveness of the proposed method.

DOI： 10.1016/j.jprocont.2008.12.004

Language：English   Publishing type：Research paper (other academic)  

This paper presents the design of Iterative Learning Control based on Quadratic performance criterion (Q-ILC) for linear systems subject to additive uncertainty. Robust Q-ILC design can be cast as a min-max problem. We propose a novel approach which employs an upper bound of the worst-case error, then formulates a nonconvex quadratic minimization problem to get the update of iterative control inputs. Applying Langrange duality, the Lagrange dual function of the nonconvex quadratic problem is equivalent to a convex optimization over linear matrix inequalities (LMIs). An LMI algorithm with convergence properties is then given for the robust Q-ILC. Finally, we provide a numerical example to illustrate the effectiveness of the proposed method.

DOI： 10.1109/ICARCV.2008.4795802

Event date： 2024.9

Language：English   Presentation type：Poster presentation  

Venue：Kualar Lumpur, Malaysia   Country：Malaysia  

Event date： 2024.7

Language：English   Presentation type：Oral presentation (general)  

Venue：Seattle, Washington, USA   Country：United States  

Event date： 2024.6 - 2024.7

Language：English   Presentation type：Oral presentation (general)  

Venue：Kitakyushu   Country：Japan  

Event date： 2024.3

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Yokohama   Country：Japan  

This paper introduces an analysis for the monotonic dependence of the OWPT efficiency in the air on the distance between the transmitter and receiver planes, where the transmitter is a row of LEDs and the receiver is relatively aligned to the LED row center. A critical value of such inter-plane distance is derived such that beyond which the OWPT efficiency is monotonically decreasing. This is shown to be quite accurate compared to numerical simulation results

Other Link： https://owpt.opicon.jp/

Event date： 2024.3

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Hiroshima   Country：Japan  

Event date： 2023.11 - 2023.12

Language：English  

Venue：Chiang Mai, Thailand   Country：Japan  

In this paper, an iterative learning control (ILC) problem of electric vehicle (EV) battery state-of-charge (SoC) profile is exploited, because of the identical driving routes daily. Due to the variations on departure and return times as well as traffic conditions, the daily SoC profile is not the same, but could be slightly different from one working day to the other. This paper, therefore, proposes a novel ILC algorithm to deal with such daily-varying characteristic of battery SoC profiles, namely a quadratic ILC (Q-ILC) law with an iteration-varying weighting matrix for the control input update. It is then proved that the proposed ILC law is rapidly converged after just one iteration, under suitable selections of the Q-ILC weighting matrices. Numerical simulations are carried out, whose results validate the superior performance of the proposed ILC algorithm.
As such, the proposed ILC scheme has the great potential for rapidly achieving a desired, predicted SoC profile of the EV battery in the next day, and hence, would help save the EV energy consumption.

Event date： 2023.9

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Nagoya University   Country：Japan  

This paper investigates the problem of enhancing the energy efficiency of buildings which eventually contributes to the carbon neutrality and sustainability. To achieve that, two strategies are proposed based on the exploitation of the functioning of building windows for thermal losses prevention and renewable power generation. More specifically, the first strategy proposes an integrated control scheme for lighting and heating, ventilation, and air conditioning (HVAC) systems under controllable blinds or curtains. Numerical simulations and calculations are then carried out to show the effectiveness of this strategy. Next, in the second strategy, it is proposed to employ the so-called solar windows to obtain clean power generation via solar cells attached to building windows. A realistic experiment is performed for an office building, which reveals the viability of such solar power generation strategy.

Event date： 2023.7

Language：English   Presentation type：Oral presentation (general)  

Venue：Yokohama   Country：Japan  

Other Link： https://www.ifac2023.org/

Event date： 2023.4

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Yokohama   Country：Japan  

This paper presents a study on optical wireless power transfer (OWPT) through water environments utilizing a red laser transmitter, a solar cell receiver, and a clear plastic box as the water container. Distinct scenarios are considered, including both transmitter and receiver are inside of the water container, and one is outside whereas the other is inside. The obtained results show differences on the OWPT performance between those scenarios, hence revealing meaningful insights for conducting underwater OWPT.

Event date： 2022.11

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Nara   Country：Japan  

This paper presents a preliminary study on the use of optical wireless power transfer (OWPT) for supplying power without wires to wearables even when they are covered. System description is first given, then theoretical backgrounds on OWPT systems with covered optical receivers are introduced. Next, experimental results on simple tests for an OWPT are provided, in which the optical transmitter is an LED and the optical receiver is a silicon solar cell. The solar cell is covered by two different materials namely a cotton shirt sleeve and a double-layer paper napkin in order to compare the OWPT performance. It is then shown from experimental results that the transparency, i.e. the optical transmittance, of the paper napkin is much better than that of the cotton shirt sleeve. Lastly, additional experiments are carried out for different numbers of paper napkin layers that evaluate the theoretical optical transmittance formula. It then turns out that the Beer-Lambert law is invalid to capture the efficiency of OWPT in the considering context. The obtained results also give hints for choosing suitable clothing materials and for system design in order to have a high performance OWPT system for clothing-covered wearables.

Other Link： https://ieeexplore.ieee.org/document/9971481

Event date： 2022.11

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Nagoya   Country：Japan  

Implanted and wearable devices, e.g. biosensors, implanted cardiac pacemakers, etc., are very important for health monitoring and medical therapy. Traditionally, batteries are embedded inside bodies together with implanted devices to keep them operated. Nevertheless, such batteries must be replaced after a few years, hence requiring expensive surgeries and bring discomfort to users [1]. For wearable devices, wired energy supply is inconvenient and does not fully support system mobility. Thus, much attention has been paid on employing wireless power transfer (WPT) for implanted and wearable devices so that less or even no batteries and wires are needed. Among distinct WPT technologies, optical WPT (OWPT) is a promising candidate owing to several advantages it offers at the small scales of implanted and wearable devices, including higher efficiency, more stable output, lighter weight, etc. [2]–[4]. Additionally, OWPT can be combined with optical communication to wirelessly send out useful biomedical information, see e.g. [5].

This work investigates the possibility of OWPT for implanted and wearable devices, in which solar cells are put on skin. Power can then be supplied to implanted devices by different methods such as using microneedles or skin electrodes. This brings several advantages, one of which is the potential for simplifying system installation and maintenance. Experimental systems are depicted in Figure 1 under the scenarios of free-space and under-clothes OWPT. A commercial solar cell with dimensions of 25mm × 25mm × 3mm was used. A small LED is employed as a visual indicator which is on if OWPT is successful, and vice versa, is off if OWPT is unsuccessful. A 100V 7.1W white LED (Panasonic Corp.) producing visible light is used as the light source. Rechargeable batteries or capacitors can be added, but are temporarily removed for direct powering of the indicating LED.

Event date： 2022.11

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Online   Country：United States  

This paper explores the possibility of implementing distributed optimization through peer-to-peer energy (P2P) trading platform while keeping the temperature of batteries in electric vehicles below the safe threshold. While most thermal management schemes consider a battery as a point to simplify the computation, this work proposes to model and regulate the battery’s temperature profile over its cells. This more granular
access to the battery’s temperature allows for more effective management as the temperature distribution over cells is not homogeneous. As a result, a linear discrete-time model is derived for the cell-level temperature vector and incorporated into the P2P energy trading platform. A multi-period P2P policy is developed to maximize the EV’s owner benefits while preventing battery cells’ temperature from rising too high. Numerical results with a set of 15 agents which are a part of IEEE 33-bus radial distribution test feeder are presented to demonstrate the performance of the proposed algorithm on P2P trading and battery temperatures.

Event date： 2022.11

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Online   Country：United States  

Inspired by the wolf-raven cooperative hunting in nature, this paper studies a problem of ground-aerial vehicle cooperation for tracking a moving ground object. In the considered problem, a group of ground vehicles is guided by an aerial vehicle to finally trace and catch the moving ground object. To achieve that, a distributed PI controller design is proposed and proved to be effective against a certain class of disturbances in the inter- vehicle and vehicle-object sensing. Simulations are carried out to demonstrate the effectiveness of the proposed control design.

Event date： 2022.1

Language：English   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Venue：Fukuoka   Country：Japan  

Event date： 2022.1

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Online   Country：Japan  

Other Link： https://isarob.org/symposium/

Event date： 2021.12

Language：English   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Venue：Online   Country：Japan  

AI and optimization are often considered from different perspectives by different research communities. However, it is undeniable that they are closely related to each other, where optimization is a core part of many machine learning algorithms, while AI can be employed to support optimization schemes. This talk presents examples of such mutuality with illustrations in energy systems. Moreover, directions for future research are also introduced.

Other Link： https://apcmfi.org/fmfi2021/index.html

Event date： 2021.5

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Virtual (Zoom)   Country：Japan  

Event date： 2021.3

Language：English   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Venue：Virtual (Zoom)   Country：Japan  

Event date： 2020.7

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Virtual   Country：Japan  

Other Link： https://www.sciencedirect.com/science/article/pii/S2405896320314348

Event date： 2020.3

Language：English   Presentation type：Oral presentation (general)  

Venue：Kyoto   Country：Japan  

Other Link： https://ieeexplore.ieee.org/document/9081113

Event date： 2020.3

Language：English   Presentation type：Oral presentation (general)  

Venue：Tokushima   Country：Japan  

Other Link： https://ieeexplore.ieee.org/document/9083493

Event date： 2019.12

Language：English   Presentation type：Oral presentation (general)  

Venue：Nice   Country：France  

Other Link： https://ieeexplore.ieee.org/document/9029998

Event date： 2019.12

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Hanoi   Country：Viet Nam  

Other Link： https://ieeexplore.ieee.org/document/8952398

Event date： 2019.11

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Suwon   Country：Korea, Republic of  

Event date： 2019.10

Language：English   Presentation type：Oral presentation (general)  

Venue：Hanoi   Country：Viet Nam  

Other Link： https://ieeexplore.ieee.org/document/8952226

Event date： 2019.10

Language：English   Presentation type：Oral presentation (general)  

Venue：Hanoi   Country：Viet Nam  

Other Link： https://ieeexplore.ieee.org/document/8952464

Event date： 2019.4

Language：English   Presentation type：Oral presentation (general)  

Venue：KitaKyushu   Country：Japan  

Event date： 2019.4

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Kumamoto   Country：Japan  

Event date： 2019.4

Language：English   Presentation type：Symposium, workshop panel (public)  

Country：Japan  

Event date： 2019.4

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2019.4

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2019.4

Language：English   Presentation type：Symposium, workshop panel (public)  

Country：Japan  

Event date： 2019.4

Language：English   Presentation type：Oral presentation (general)  

Venue：KitaKyushu   Country：Japan  

Event date： 2018.12

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Yangon   Country：Myanmar  

Event date： 2018.7

Language：English   Presentation type：Oral presentation (general)  

Venue：Nagoya   Country：Japan  

Other Link： http://www.cpese.net/index.html

Event date： 2018.7

Language：English   Presentation type：Symposium, workshop panel (public)  

Venue：Michigan State University, East Lansing, MI, USA   Country：United States  

Event date： 2018.7

Language：English   Presentation type：Oral presentation (general)  

Venue：Portland, Oregon, USA   Country：United States  

Other Link： http://www.pes-gm.org/2018/

Event date： 2018.7

Language：English   Presentation type：Oral presentation (general)  

Venue：Tokyo   Country：Japan  

Other Link： https://www.cpes2018.com/

Event date： 2017.11

Language：English  

Venue：San Diego   Country：United States  

This paper investigates a distributed consensus control design for heterogeneous energy storage devices in smart grids. Using communications between energy storage devices in the system, primary and secondary voltage/frequency control are achieved. The primary voltage and frequency synchronization is achieved by equally sharing the active and reactive powers among energy storage devices. The secondary voltage and frequency restoration is achieved by selecting one energy storage as a virtual leader, and other energy storage devices will act as followers to follow the leader energy storage system. The uniqueness of the proposed consensus design is the use of nominal values of grid voltage and frequency instead of their measured values. The proposed design is validated using modified IEEE 14-bus system in MATLAB.

Event date： 2017.8

Language：English  

Venue：Kohala Coast   Country：United States  

This paper proposes a systematic approach to derive analytical and explicit certificates for transient stability analysis of inverter-based microgrids. We first derive a line-based model of microgrids with general mesh structure. Then by employing Lyapunov stability theory with a quadratic Lyapunov function, the region of attraction of any post-fault stable equilibrium point (EP) is estimated by a large domain which contains the well-known principle region inside, if a low-dimension convex LMI problem is feasible. Accordingly, it provides a very efficient and robust transient stability certificate which can be calculated off-line, easily, and very fast. Moreover, it can be applied to microgrids with any structure. Finally, numerous tests on a microgrid are then given to illustrate the effectiveness of the proposed approach.

Event date： 2017.6

Language：English  

Venue：Phuket   Country：Thailand  

This paper proposes a design of Iterative Learning Control (ILC) based on Alternating Direction Method of Multipliers (ADMM) with application to building temperature control system (BTCS). BTCS is modeled by linear multi-agent system subjected to undirected communication topology. The main advantage of the proposed scheme is that it gives good performance despite existing the interconnection among rooms. In this paper, the ILC design is formulated as computing control input updating. Moreover, we apply ILC to a four-connected room model. It aims that the temperature of each room in building is controlled by a central controller. Finally, numerical results illustrate the effectiveness of ILC.

Event date： 2017.3

Language：English  

Venue：Okayama   Country：Japan  

This paper aims to design decentralized iterative learning control (ILC) for building temperature control system (BTCS). The BTCS is described by a large-scale interconnected dynamic equation and modeled as a linear multiagent system subjected to undirected communication topology using graph theory. Typically, there are two types of control strategies for BTCS, namely, distributed and decentralized schemes. The main idea of designing for the decentralized scheme is to separate the whole system into a number of subsystems. In this research, we formulate and apply decentralized ILC to a four-connected-room model. In particular, we design D-type ILC for each room in the building separately, and each controller does not communicate with other controllers. The main task of the local controller is to achieve the tracking objective, i.e., the temperature of each room tracks its own desired reference temperature. Finally, numerical results illustrate the effectiveness of decentralized ILC and are compared with that of distributed consensus controller (DCC). The results show that output responses of both controllers can track trapesoidal reference and consume the same amount of total power at steady state. Decentralized ILC gives response without overshoot, and its convergence is faster than that of DCC. Convergence analysis reveals that tracking speed depends on the choice of learning gain.

Event date： 2016.11 - 2016.12

Language：English  

Venue：Melbourne   Country：Australia  

This paper presents a new approach to obtain an optimal energy management in Smart Grids from both generation and demand sides where generation and demand units collaborate with others in a distributed manner to simultaneously obtain their optimal powers that maximize the total welfare in the grid. The proposed approach is developed based on an optimization method called Alternating Direction Method of Multipliers (ADMM). The significant differences with existing methods such as gradient-based ones are that the power balance constraint is always satisfied during the running of proposed algorithm and the convergence speed is faster. Furthermore, the ADMM is very suitable for distributed implementation, which facilitates its applications in Smart Grids. A simulation on the benchmark IEEE 39-bus system is introduced to illustrate the effectiveness of the proposed approach.

Event date： 2016.11

Language：English  

Venue：Hanoi   Country：Viet Nam  

This paper proposes a distributed control approach for optimal power dispatch towards environment-friendly power grids. To do so, an optimization problem called environmental economic dispatch, which is subjected to physical constraints of the considered power system, is solved. Consequently, we propose an approach based on the Alternating Direction Method of Multipliers (ADMM) to obtain the globally optimal solution of that optimization problem in a distributed manner under a fast convergence. As a result, each generation unit can derive by itself an optimal generated power that minimizes with compromising both fuel and emission costs while satisfying both global and local physical constraints caused by the whole system and by its own. The most distinguished feature of this approach is that the power balance constraint is always guaranteed during the execution of the algorithm. Finally, the performance of our proposed approach is illustrated through the simulation to a realistic power system and the comparison with another method.

Event date： 2016.11

Language：English  

Venue：Hanoi   Country：Viet Nam  

This paper proposes an approach for examining the small-signal stability of transmission power grids in presence of a power line removal. Employing this approach, we derive an algebraic certificate for directly checking the existence of a new stable equilibrium point after the outage of a power line. This criterion is obtained based on the power network structure and power flow parameters without requiring the inertia parameters of generators, calculating the new equilibrium point, and the linearization technique. Therefore, the computational cost is much less than that of the well-known eigenvalue analysis approach for evaluating the small-signal stability of power systems, and the derived certificate gives insights into the power system's structure and parameters for achieving stability. A numerical test is then introduced to illustrate the proposed method.

Event date： 2016.9

Language：English  

Venue：Tsukuba   Country：Japan  

This paper aims at proposing a distributed control approach for optimal power dispatch towards environment-friendly power grids. In order to do so, both electric generation and pollutant emission costs are properly incorporated into a unique objective function of an optimization problem subjected to physical constraints of the considered power system. Consequently, we propose an approach based on the Alternating Direction Method of Multipliers (ADMM) to obtain the globally optimal solution of that optimization problem in a distributed manner under a fast convergence. As a result, each generation unit in a power system can derive by itself an optimal generated power that minimizes with compromising both fuel and emission costs while satisfying both global and local physical constraints caused by the whole system and by its own. The most distinguished feature of this approach is that the power balance constraint is always guaranteed during the execution of the algorithm. Finally, the performance of our proposed approach is illustrated through the simulation to a realistic power system and the comparison with another method.

Event date： 2016.6 - 2016.7

Language：English  

Venue：Aalborg   Country：Denmark  

This paper proposes a unified approach to analyze and synthesize consensus control laws for general linear leaderless multi-agent systems (MASs) subjected to input or state constraints. First, the problem of consensus under input constraint is investigated where the MAS is reformulated as a network of Lur'e systems. As a result, a sufficient condition for consensus and the design of consensus controller gain are derived from solutions of a distributed LMI convex problem. Next, the problem of consensus with state constraint is transformed into an equivalent problem of consensus under input constraint. Therefore, a similar method to the previous problem can be employed for this scenario, however an additional LMI constraint should be satisfied. Finally, a numerical example is introduced to illustrate the proposed theoretical approach.

Event date： 2015.12

Language：English  

Venue：Osaka   Country：Japan  

This paper presents a systematic design approach for output consensus in a class of heterogeneous uncertain nonlinear multi-agent systems (MASs). By employing a feedback linearization technique, the output consensus problem of heterogeneous nonlinear MASs can be recast as a state consensus problem for homogeneous linear MASs. Consequently, a sub-optimal consensus design for linear MASs is presented to provide a constructive method for solving the initial output consensus problem in heterogeneous nonlinear MASs. Finally, the proposed approach is verified through a secondary voltage control problem in smart grids revealing its effectiveness.

Event date： 2015.9

Language：English  

Venue：Sydney   Country：Australia  

This paper proposes a novel approach to design distributed optimal controllers for the power coordination problem in Wind Power Plants (WPPs). Based on LQR method, we present a systematic procedure to design a fully distributed optimal stabilizing controller for the linearized model of Wind Turbines (WTs) in a WPP. Next, the reduced-order distributed optimal controller is derived by elaborating more on the selection of weighting matrices in the LQR performance cost. Furthermore, the effect of disturbances to the system performance is evaluated through the H_∞ norm computation. Finally, the simulation results for a linearized model of WPP are shown to illustrate the effectiveness of the proposed approach.

Event date： 2014.8

Language：English  

Venue：Cape Town   Country：South Africa  

This paper proposes a systematic method to design hierarchical, decentralized, stabilizing controllers for homogeneous hierarchical dynamical networks. Based on LQR approach with a properly chosen performance index including global and local objectives with control input penalty, an obtained optimal LQR feedback gain gives the closed-loop system a prescribed desirable hierarchical structure. In addition, the undesirable eigenvalues of the given homogeneous network can be selectively shifted by further selecting the weighting matrices based on the left eigenvectors associated with those eigenvalues. Finally, the proposed method is summarized into a systematic design procedure with an illustrative numerical example to show its effectiveness.

Event date： 2013.7

Language：English  

Venue：Zurich   Country：Switzerland  

In this paper, we present a systematic approach based on harmonic balance method to study the induced oscillations in a class of Goodwin oscillator networks forced by external periodic signals. Analytical expressions on the dependence of the phases and amplitudes of network oscillations to those of forcing inputs are revealed. Based on those expressions, we further show that under some specific conditions, the amplitude and phase shift of synchronized oscillations in networks of Goodwin oscillators monotonically depend on the amplitude of exciting inputs. The theoretical results are then illustrated through some examples.

Event date： 2011.12

Language：English  

Venue：Orlando, FL   Country：United States  

This paper presents a new optimization-based iterative learning control (ILC) framework for multiple-point tracking control. Conventionally, one demand prior to designing ILC algorithms for such problems is to build a reference trajectory that passes through all given points at given times. In this paper, we produce output curves that pass close to the desired points without considering the reference trajectory. Here, the control signals are generated by solving an optimal ILC problem with respect to the points. As such, the whole process becomes simpler; key advantages include significantly decreasing the computational cost and improving performance. Our work is then examined in both continuous and discrete systems.

Event date： 2011.9

Language：English  

Venue：Denver, CO   Country：United States  

In this paper, we present an iterative learning control (ILC) algorithm to track specified desired multiple terminal points at given time instants. A framework to update the desired trajectories from given points is developed based on the interpolation technique. The approach shows better rate of convergence of the errors. The simulation with a satellite antenna control model is demonstrated to show the effectiveness of our approach.

Event date： 2010.5

Language：English  

Venue：Chiang Mai   Country：Thailand  

This paper presents the design of a robust Iterative Learning Control (ILC) algorithm for linear systems in the presence of parametric uncertainties and repetitive disturbances. The robust ILC design is formulated as a min-max problem with a quadratic performance index subjected to constraints of the control input. Employing Lagrange duality, we can reformulate the robust ILC design as a convex optimization problem over linear matrix inequalities (LMIs). An LMI algorithm for the robust ILC design is then given. Finally, the effectiveness of the proposed robust ILC algorithm is demonstrated through a numerical example.

Event date： 2009.12

Language：English  

Venue：Shanghai   Country：China  

In this paper, we present a robust Iterative Learning Control (ILC) design for linear systems in the presence of time-varying parametric uncertainties. The robust ILC design is formulated as a min-max problem using a quadratic performance criterion subject to constraints of the control input update where the system model contains time-varying parametric uncertainties. An upper bound of the worst-case performance is employed in the min-max problem. Subsequently, applying Lagrangian duality to the min-max problem, we derive a dual problem which is reformulated as a convex optimization over linear matrix inequalities (LMIs). As a result, iterative input updates can be obtained by solving a series of LMI problems. We give an LMI algorithm for the robust ILC design and prove the convergence of the control input and the error. Finally, a numerical example is presented to illustrate the effectiveness of the proposed algorithm.

Event date： 2009.8

Language：English  

Venue：Hong Kong   Country：China  

In this paper, a new robust Iterative Learning Control (ILC) algorithm has been proposed for linear systems In the presence of iteration-varying parametric uncertainties. The robust ILC design is formulated as a min-max problem usmg a quadratic performance criterion subject to constraints of the control input update. An upper bound of the maximization problem is derived, then, the solution of the min-max problem is achieved by solving a minimization problem. Applying Lagrange duality to this minimization problem results in a dual problem which can be reformulated as a convex optimization problem over linear matrix inequalities (LMls). Next, we present an LMI-based algorithm for the robust ILC design and prove the convergence of the control input and the error. Finally, the proposed algorithm is applied to a flexible link to demonstrate its effectiveness.

Event date： 2009.8

Language：English  

Venue：Fukuoka   Country：Japan  

This paper presents a novel algorithm of the robust iterative learning control for linear systems subject to timeinvariant parametric uncertainties. The design problem is formulated as a min-max problem with a quadratic performance criterion. Then, we derive an upper-bound of the worst-case performance. Applying Lagrange duality to the minimization problem leads to a dual problem which can be reformulated as an optimization problem over linear matrix inequalities. An algorithm is given afterward and its convergence properties are proved. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

Event date： 2009.5

Language：English  

Venue：Chonburi   Country：Thailand  

In this paper, we present the design of a robust Iterative Learning Control (ILC) algorithm for a single flexible link in the presence of parametric uncertainty. The robust ILC design is formulated as a min-max problem with a quadratic performance index. An upper bound of the worst-case performance is employed in the min-max problem. Applying Lagrange duality to the min-max problem, we can reformulate the robust ILC design as a convex optimization over linear matrix inequalities (LMIs). An LMI algorithm for the robust ILC design is given. Finally, the simulation results for a single flexible link are presented to illustrate the effectiveness of the proposed robust ILC algorithm.

Event date： 2008.12

Language：English  

Venue：Hanoi   Country：Viet Nam  

This paper presents the design of Iterative Learning Control based on Quadratic performance criterion (Q-ILC) for linear systems subject to additive uncertainty. Robust Q-ILC design can be cast as a min-max problem. We propose a novel approach which employs an upper bound of the worst-case error, then formulates a nonconvex quadratic minimization problem to get the update of iterative control inputs. Applying Langrange duality, the Lagrange dual function of the nonconvex quadratic problem is equivalent to a convex optimization over linear matrix inequalities (LMIs). An LMI algorithm with convergence properties is then given for the robust Q-ILC. Finally, we provide a numerical example to illustrate the effectiveness of the proposed method.

Type：Competition, symposium, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Academic society, research group, etc. 

Type：Peer review 

Proceedings of International Conference Number of peer-reviewed papers：3

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Peer review 

Proceedings of International Conference Number of peer-reviewed papers：3

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Type：Competition, symposium, etc. 

Electric power grids have been witnessing an evolution to be cleaner, smarter and more flexible. There are many factors enforcing this evolution of power grids, such as the environment, technology, economics and society, which rise up new challenges to be resolved for power grids.

As one may know, the traditional power grid is operated and controlled in a centralized manner, i.e. all the electric utility companies received the commands from a central unit called control center about the power output they need to generate and simultaneously send their actual generated powers to that control center. Meanwhile, the control center will collect the actual load demands based on which it will calculate the optimal power outputs for utility companies and send that information to them so that they can adjust their outputs accordingly. This type of centralized operation and control puts a very high stress on the control center for processing a huge amount of data, and is not good from a technological point of view, as well as is not economic. On the other hand, the power generation based on exhausting fossil sources, such as coal, gas, is very harmful for the environment and is one of the causes for the global warming. Therefore, the renewable energy sources, such as solar, wind, ocean, which are environmentally friendly and possibly never-ending, have been more and more utilized to generate clean powers worldwide. It should be noted that renewable energy sources have distributed nature, i.e. they are geographically located at different places, e.g. rooftop houses, farms, etc., with different amount of possible energy generation. As a result, renewable-based power generation cannot be operated and control in the same way as with fossil-based one.

In order to resolve the aforementioned challenges, distributed optimization and control is a promising approach which fits into the natural topological structure of power grids, especially in presence of renewable energy sources, distributed storage systems, and smart mobility. This lecture therefore aims at introducing the basic ideas and examples of the distributed optimization and control approach for Smart Grid, which has gained much attention recently from both the academy and the industry.

With the development of science and technology as well as environmental and social perception changes, energy systems are witnessing significant concerns from governments, societies, and researchers.
Particularly, many interesting but challenging interdisciplinary research problems on energy systems rise up for both academy and industry. This talks aims at presenting some of those challenges from the viewpoint of control engineering, and then illustrating by some detailed problems. As will be seen, challenges are also opportunities for control engineers to make prominent contributions to the field.