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HOA DINH NGUYEN Last modified date:2022.02.21

Associate Professor / International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), and Institute of Mathematics for Industry (IMI)
Multiscale Science and Engineering for Energy and the Environment Thrust
International Institute for Carbon-Neutral Energy Research

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Academic Degree
Doctor of Philosophy
Country of degree conferring institution (Overseas)
Field of Specialization
Control Systems, Applied Mathematics, Smart Grid, Energy Systems
ORCID(Open Researcher and Contributor ID)
Total Priod of education and research career in the foreign country
Outline Activities
My current research mainstream is Applied Mathematics for Energy Systems whose philosophy is to develop innovative and interdisciplinary approaches to resolve challenges on the current and future energy systems from a mathematical viewpoint. This research theme intersects between control engineering, applied mathematics, and energy systems, where various techniques from Multi-Agent System, Nonlinear and Switched Systems, Robust and Optimal Control, Nonlinear and Stochastic Optimization, etc., are utilized for the modeling, analysis, control, and optimization of energy systems. The key challenges to be solved are as follows:
- Maximum integration of intermittent and stochastic Renewable Energy Sources into the power grid while guaranteeing the stability, security, and performance of the whole system.
- Design of a decentralized or fully distributed smart power grid with real-time power scheduling, real-time pricing mechanism, and efficient Demand Response, to replace the current centralized power grid with day-ahead power scheduling and 30-minute pricing scheme.
- Efficiently coordinated control of large-scale and complex energy systems with very different dynamics/behaviors of distinct participants.
- Simultaneous consideration of environmental, economics, and societal factors into the modeling, analysis, and design of energy systems.
Research Interests
  • Decentralized/Distributed Control and Optimization of Energy Systems for Integration of Distributed Renewable Energy Resources
    keyword : Control System, Multi-Agent Systems, Smart Grid, Complex Network, Control and Optimization of Energy Systems
Current and Past Project
  • This research studies a novel bottom-up approach called direct energy sharing and trading (DEST) in which power is generated from renewable sources located at or very near to end-users, which is directly shared and traded between them. Thus, it is expected that energy losses are significantly reduced, and much more renewable energy is integrated while avoding effects to grid, e.g. frequency variation,or net load steep ramps. Hence, the energy efficiency and security are enhanced. Further, new business models can be created from this new DEST system. Tools from control engineering, power engineering, optimization, and complex network are used to resolve the problems of direct coordination and trading in the DEST system.
Academic Activities
1. Dinh Hoa Nguyen, Electric Vehicle – Wireless Charging-Discharging Lane Decentralized Peer-to-Peer Energy Trading, IEEE Access, 10.1109/ACCESS.2020.3027832, 8, 179616-179625, 2020.09, [URL], 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..
2. Dinh Hoa Nguyen, Tatsumi Ishihara, Distributed Peer-to-Peer Energy Trading for Residential Fuel Cell Combined Heat and Power Systems", International Journal of Electrical Power and Energy Systems, International Journal of Electrical Power and Energy Systems, 10.1016/j.ijepes.2020.106533, 2021.02, [URL], 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..
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4. Dinh Hoa Nguyen, A Novel Optimization Model for Integrating Carbon Constraint with Demand Response and Real-time Pricing, International Journal of Mathematics for Industry, 10.1142/S2661335220500057, 2020.07, [URL], 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..
5. Thinh Thanh Doan, Subhonmesh Bose, Hoa Dinh Nguyen, Carolyn L. Beck, Convergence of the Iterates in Mirror Descent Methods, IEEE Control Systems Letters, 10.1109/LCSYS.2018.2854889, 3, 1, 114-119, 2019.01, 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..
6. Javad Khazaei, Dinh Hoa Nguyen, Distributed Consensus for Output Power Regulation of DFIGs With On-Site Energy Storage, IEEE Transactions on Energy Conversion, 10.1109/TEC.2018.2871575, 34, 2, 1043-1051, 2019.06, 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..
7. Hoa Dinh Nguyen, Javad Khazaei, Multi-Agent Time-Delayed Fast Consensus Design for Distributed Battery Energy Storage Systems, IEEE Transactions on Sustainable Energy, 10.1109/TSTE.2017.2785311, 9, 3, 1397-1406, 2017.12, 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..
8. Dinh Hoa Nguyen, Andrew Chapman, Hadi Farabi-Asl, Nation-wide emission trading model for economically feasible carbon reduction in Japan, Applied Energy, 10.1016/j.apenergy.2019.113869, 255, 2019.12, 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..
9. Dinh Hoa Nguyen, Shun Ichi Azuma, Toshiharu Sugie, Novel control approaches for demand response with real-time pricing using parallel and distributed consensus-based ADMM, IEEE Transactions on Industrial Electronics, 10.1109/TIE.2018.2881938, 66, 10, 7935-7945, 2019.10, 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..
10. Hoa Dinh Nguyen, Tatsuo Narikiyo, Michihiro Kawanish, Optimal Demand Response and Real-time Pricing by a Sequential Distributed Consensus-based ADMM Approach, IEEE Transactions on Smart Grid, 10.1109/TSG.2017.2676179, 2017.03, 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..
11. Hoa Dinh Nguyen, Reduced-Order Distributed Consensus Controller Design via Edge Dynamics, IEEE Transactions on Automatic Control, 10.1109/TAC.2016.2554279, 62, 1, 475-480, 2017.01, 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..
12. Hoa Dinh Nguyen, Tatsuo Narikiyo, Michihiro Kawanishi, Robust consensus analysis and design under relative state constraints or uncertainties, IEEE Transactions on Automatic Control, 10.1109/TAC.2017.2752843, 63, 6, 1694-1700, 2018.06, 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..
Membership in Academic Society
  • IEEE Power and Energy Society
  • IEEE Smart Grid Community
  • IEEE Control System Society
  • Dynamic Environmental Economic Dispatch: A Distributed Solution based on an Alternating Direction Method of Multipliers
  • Robust Iterative Learning Control for Linear Systems with Iteration-Varying Parametric Uncertainties
  • Robust Iterative Learning Control for Linear Systems with Time-Invariant Parametric Uncertainties