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This study proposes a decentralised robust fuzzy control strategy for islanded operation of an AC microgrid with voltage source inverters. The objective is to design a robust controller for regulating the load voltage and sharing power among distributed generators (DGs) in the presence of uncertainties in the system and non-linear loads. The AC microgrid consists of parallel DGs connected to a main AC bus. A Takagi-Sugeno fuzzy approach is developed in this article to achieve stability and desired performance in dealing with non-linearities in the islanded microgrid and H∞ criterion is used to obtain a robust control strategy in presence of uncertainties raised by unmodelled and high frequency dynamics. Therefore, a non-convex condition in H∞ optimisation problem is converted to a convex linear matrix inequality (LMI) condition and is solved by MATLAB LMI toolbox. In order to develop a decentralized system, P-f and Q-V droop controllers are used to specify set-points for local inverter controllers in each DG. The effectiveness of the proposed control strategy in presence of constant power load and DG accidental outage is validated by the simulation of a microgrid test system in MATLAB SimPowerSystems toolbox. Comparison with cascaded proportional integral controller shows advantages of robust T-S controller. [ABSTRACT FROM AUTHOR]

This study proposes a decentralised robust fuzzy control strategy for islanded operation of an AC microgrid with voltage source inverters. The objective is to design a robust controller for regulating the load voltage and sharing power among distributed generators (DGs) in the presence of uncertainties in the system and non‐linear loads. The AC microgrid consists of parallel DGs connected to a main AC bus. A Takagi‐Sugeno fuzzy approach is developed in this article to achieve stability and desired performance in dealing with non‐linearities in the islanded microgrid and H∞ criterion is used to obtain a robust control strategy in presence of uncertainties raised by unmodelled and high frequency dynamics. Therefore, a non‐convex condition in H∞ optimisation problem is converted to a convex linear matrix inequality (LMI) condition and is solved by MATLAB LMI toolbox. In order to develop a decentralized system, P–f and Q–V droop controllers are used to specify set‐points for local inverter controllers in each DG. The effectiveness of the proposed control strategy in presence of constant power load and DG accidental outage is validated by the simulation of a microgrid test system in MATLAB SimPowerSystems toolbox. Comparison with cascaded proportional integral controller shows advantages of robust T‐S controller. [ABSTRACT FROM AUTHOR]

In a competitive electricity market, suppliers seek the optimal bidding strategy, maximising their individual profit. Due to the uncertainties, unknown parameters and dynamics of electricity market, the optimal bidding strategies cannot be attained through straightforward optimisation methods. In this study, the supply function equilibrium model is considered together with uniform price market clearing mechanism. The interaction among suppliers is modelled as an incomplete information game problem and multi-agent reinforcement learning (MARL) is utilised to find the optimal bidding strategy of the suppliers in a non-cooperative game. The proposed framework is an extension, from single agent Markov decision process to Markov game process, which is suitable for studying multi-agent decision making problems in stochastic environments. In this study, a Markov game model of the electricity market is proposed and a new state-action and Markov model of electricity market is proposed to built up the MARL environment. In addition, a learning-based non-cooperative MARL method is utilised for learning the optimal bidding strategies of the suppliers in a day-ahead electricity market. The proposed method is successfully applied to IEEE-30-bus power system. To examine the statistical significance of the results, the T-test method is applied to compare the competitive behaviour of the players in both multi- and single-agent frameworks. [ABSTRACT FROM AUTHOR]

In a competitive electricity market, suppliers seek the optimal bidding strategy, maximising their individual profit. Due to the uncertainties, unknown parameters and dynamics of electricity market, the optimal bidding strategies cannot be attained through straightforward optimisation methods. In this study, the supply function equilibrium model is considered together with uniform price market clearing mechanism. The interaction among suppliers is modelled as an incomplete information game problem and multi‐agent reinforcement learning (MARL) is utilised to find the optimal bidding strategy of the suppliers in a non‐cooperative game. The proposed framework is an extension, from single agent Markov decision process to Markov game process, which is suitable for studying multi‐agent decision making problems in stochastic environments. In this study, a Markov game model of the electricity market is proposed and a new state‐action and Markov model of electricity market is proposed to built up the MARL environment. In addition, a learning‐based non‐cooperative MARL method is utilised for learning the optimal bidding strategies of the suppliers in a day‐ahead electricity market. The proposed method is successfully applied to IEEE‐30‐bus power system. To examine the statistical significance of the results, the T‐test method is applied to compare the competitive behaviour of the players in both multi‐ and single‐agent frameworks. [ABSTRACT FROM AUTHOR]