In this paper, an agent-based decentralized control model for islanded microgrids is proposed, which consists of a two-layer control structure. The bottom layer is the electrical distribution microgrid, while the top layer is the communication network composed of agents. An agent is regarded as a local control processor together with communication devices, so agents can collect present states of distributed generators and loads, when communication lines are added between two layers. Moreover, each agent can also exchange information with its neighboring agents of the network. After information is processed according to control laws, agents adjust the production of distributed generators to which they connect. The main contributions of this paper are (i) an agent-based model for decentralized secondary control is introduced and the rules to establish the communication network are given; (ii) a systematic method is presented, which can be used to derive a set of control laws for agents from any given communication network, where only local information is needed. Furthermore, it has been seen that the output power supplied by distributed generators satisfies the load demand in the microgrid, when agents use the proposed control laws. Finally, the simulation results show that frequency and voltage fluctuations are small and meet the requirements. ).Manuscript received XX, 2014; revised XX, 2015. between two layers, and then exchanges the information with its neighboring agents. After all the information is processed according to control laws, agents adjust the output power of the DGs at the next time step in order to balance the supplies and demands in the MG.Further, we formulate the rules for how a communication network is constructed. Once an MG is given, many communication networks may be built in terms of the rules, and apparently the control laws for agents on each communication network are different. Therefore, we present a systematic method to derive a set of control laws for agents from any given communication network, where only local information is needed. Furthermore, we prove a theorem that shows the output power supplied by DGs equals the load demand in the MG, if each agent applies the control law that is derived. To evaluate the performance of our control laws, four cases are designed, in which the illumination intensity, the wind speed or/and the load demand change over time. Finally, simulations are carried out in MATLAB/Simulink and the results show that the frequency and the voltage satisfy the requirements, and the system remains stable, even in extreme conditions. Compared with a centralized control method, the proposed decentralized method only needs local information, which reduces the communication complexity.The rest of the paper is organized as follows. In Section II, the two layer control model with an agent-based communication network is introduced in detail. Using the steps and rules given, one can construct an agent-based communication network as the top layer of the control model, and ...

In this paper, cost based droop schemes are proposed, to minimize the total active power generation cost in an islanded microgrid (MG), while the simplicity and decentralized nature of the droop control are retained. In cost based droop schemes, the incremental costs of distributed generators (DGs) are embedded into the droop schemes, where the incremental cost is a derivative of the DG cost function with respect to output power. In the steady state, DGs share a single common frequency, and cost based droop schemes equate incremental costs of DGs, thus minimizing the total active power generation cost, in terms of the equal incremental cost principle. Finally, simulation results in an islanded MG with high penetration of intermittent renewable energy sources are presented, to demonstrate the effectiveness, as well as plug and play capability of the cost based droop schemes.Index Terms-Microgrid (MG), incremental cost, generation cost, cost based droop schemes, decentralized control.

In this paper, a two-layer network and distributed control method is proposed, where there is a top layer communication network over a bottom layer microgrid. The communication network consists of two subgraphs, in which the first is composed of all agents, while the second is only composed of controllable agents. The distributed control laws derived from the first subgraph guarantee the supply-demand balance, while further control laws from the second subgraph reassign the outputs of controllable distributed generators, which ensure active and reactive power are dispatched optimally. However, for reducing the number of edges in the second subgraph, generally a simpler graph instead of a fully connected graph is adopted. In this case, a near-optimal dispatch of active and reactive power can be obtained gradually, only if controllable agents on the second subgraph calculate set points iteratively according to our proposition. Finally, the method is evaluated over seven cases via simulation. The results show that the system performs as desired, even if environmental conditions and load demand fluctuate significantly. In summary, the method can rapidly respond to fluctuations resulting in optimal power sharing.

In islanded microgrids (MGs), the reactive power cannot be shared proportionally among distributed generators (DGs) with conventional droop control, due to the mismatch in feeder impedances. For the purpose of proportional reactive power sharing, a multiagent system (MAS) based distributed control model for droop-controlled MGs is proposed. The proposed control model consists of two layers, where the bottom layer is the electrical distribution MG, while the top layer is a communication network composed of agents. Moreover, agents on the communication network exchange the information acquired from DGs with neighbors, and calculate set points for DGs they connect to, according to the control laws. Further, a theorem is demonstrated, which yields a systematic method to derive the control laws from a given communication network. Finally, three cases are carried out to test the performance of the control model, in which the uncertainty of intermittent DGs, variations in load demands, as well as impacts of time delays are considered. The simulation results demonstrate the effectiveness of the control model in proportional reactive power sharing, and the plug and play capability of the control model is also verified. Index Terms-Microgrids (MGs), multiagent system (MAS), distributed control, reactive power sharing, plug and play.

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Battery energy storage system (BESS) is a pivotal component to increase the penetration of renewable generation and to strengthen the stability and reliability of the power system. In this paper, for the purpose of the state of charge (SOC) balancing and reactive power sharing, a multiagent system (MAS)-based distributed control model, which contains a top layer communication network built by agents and a bottom-layer microgrid composed of BESSs, distributed generators (DGs), and Loads, is provided. Next, a systematic method is designed to build the control laws for agents from any given network, where each agent on the top communication network collects the states of BESSs, DGs it connects and exchanges information with its neighboring agents. Moreover, two theorems, which provide guidelines to design distributed control laws for SOC balancing and reactive power sharing between BESSs, are proposed to show the convergent property of the proposed control laws. Furthermore, several simulation cases are employed to validate the effectiveness of the proposed control model when environmental conditions and time-varying load demands are considered. Finally, the simulation results verify the effectiveness of the proposed control model, i.e., the SOC balancing and proportional reactive power sharing are achieved as expected. Furthermore, our approach has the fast convergent speed of SOC balancing of BESSs, compared to the existed method. INDEX TERMS Battery energy storage system (BESS), distributed control, state of charge (SOC) balancing, reactive power sharing, microgrids.

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