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This paper focuses on multi-agent-system-based bi-level bidding strategy of microgrid to make all power producers participate in electricity allocation as rational agents under electricity market transaction. Firstly, the multi-agent-system-based bidding system is constructed as bi-level MAS. Then, the bidding model is formulated as a bi-level optimization problem, which makes the best of renewable energy source, and subjects it to the object of obtaining maximum profits for all power producers. According to this model, electricity market is divided into two subsidiary markets, which involve the upper and the lower electricity market. The lower electricity market is composed of microgrids, which ally together in the upper electricity market to offset their shortages compared to traditional power producers, such as efficient market influence, the reliability of the energy supplying. Additionally, game theory is introduced for modeling and Nash equilibrium as the solution of bidding optimization problem is found through a series of iterative process based on the bi-level MAS. Finally, both the upper market and the lower market are illustrated in the case studies and corresponding proper conclusions are received. [ABSTRACT FROM AUTHOR]

For distributed energy resources (DERs) in islanded microgrid (MG), to improve the control effect on the output voltages and frequency from droop control, a novel distributed control strategy is proposed in this article based on the concept of cyber physical system (CPS). The novel control structure is consisted by the cyber and the physical layers. In the cyber layer: firstly, the communication structures of overall microgrid and single DER are constructed; then, based on the communication structures, the influence of time-delay (delay) and packet loss (loss) on consensus control is analyzed in this article; And then, an event-triggered mechanism combined with adapt virtual leader-following consensus control (AVLFCC) and predictive compensation is proposed in this layer. It is used to solve the problem of time-varying delay and loss rate in the process of consensus control. In the physical layer: firstly, the droop control is used as the primary control on the output voltages and frequency of DERs; then, a novel secondary control method is proposed by using the event-triggered mechanism. And finally, the simulation results confirm the effectiveness of the novel distributed control strategy under communication problems (delay and loss) and during plug-and-play operation. [ABSTRACT FROM AUTHOR]

For the load frequency control (LFC) of the interconnected power system, a two-level coordinated control frame based on multi-agent system is proposed to improve the frequency control performance of the power system. In the control frame, the upper-level agent, in a centralized manner, coordinates the lower-level LFC agents to deal with the conflict between narrowing the area control error and stabilizing the system frequency. The lower-level LFC agents, in the distributed cooperative manner, realize the mutual power support among the neighboring areas to guarantee the frequency stability in an emergency. To verify the validity of the proposed method, the numerical simulations are performed on the three-area interconnected power systems. [ABSTRACT FROM AUTHOR]

This article focuses on multi-agent system based hierarchical energy management strategies for maximum economic and environmental benefits for microgrids. First, a two-level multi-agent based energy management system is constructed, which consists of an upper-level EMA in the view of whole system, multiple lower-level unit agents in a distributed manner, and their interactions based on communication. Second, in the upper-level agent, the energy management strategies are mainly designed by constructing multi-objective functions and by using a particle swarm optimization method based on hybrid probabilistic forecasting of renewable energy sources and loads. Third, in lower-level renewable energy source and load agents, the forecasting approach regarding renewable energy sources and loasd is mainly researched by means of the ensemble empirical mode decomposition combined with sparse Bayesian learning, called the hybrid probabilistic forecast approach. Moreover, in lower-level schedulable generation unit agents, local control strategies are also presented to regulate the output power to satisfy the reference power that is set by the upper-level agent. Finally, the validity of the proposed multi-agent based energy management strategies is demonstrated by means of simulation results. [ABSTRACT FROM AUTHOR]