Agent‐based distributed and economic automatic generation control for droop‐controlled AC microgrids

Abstract

Microgrids (MGs) are presented as a cornerstone of smart grid, which can integrate renewable energy sources environmentally, friendly, and reliably. Hierarchical control strategies, i.e. primary droop control, second automatic generation control (AGC), and tertiary economic dispatch (ED), are widely used to control and optimise an MG. However, the gap between large time-scale ED and small time-scale AGC may decrease the economical efficiency of an MG. To address this problem, this study proposes a distributed economic AGC (EAGC) algorithm. The algorithm is fully distributed, as each distributed generator is assigned with an agent and the agents only require the local measurements and communication with its neighbours. Compared with centralised algorithms, the distributed control algorithm is easy to fulfil the plug-and-play requirements and reduces costs to modify the architecture of an MG. Furthermore, the proposed EAGC algorithm is suitable for all possible operation modes of an MG, i.e. islanded mode, grid-connected mode, and transition mode. To improve the robustness against agent loss or communication link fault, the topology of agents' communication network is designed to satisfy the N − 1 rule. The effectiveness of the proposed algorithm is demonstrated through the simulation tests which involve plenty of case studies.

Optimal sizing and energy scheduling of isolated microgrid considering the battery lifetime degradation

“…The microgrid consists of 68 kW photovoltaic and 37 kW wind turbine system. The data for the diesel generators are taken from [33] and is shown in Table 2. The battery capital cost, maintenance cost, interest rate and lifetime had been taken from [34].…”

Fully Distributed Hierarchical Control of Parallel Grid-Supporting Inverters in Islanded AC Microgrids

“…where U nom represents the nominal voltage value, and K AVC P and K AVC I are the parameters for a PI controller. In summary, the proposed secondary control is based on the proposed EAGC and AVC algorithms, in which only the (N − 1)-rule-based communication topology design is related to [53], and the distributed-consensus-based model solving strategy for AGC is related to [54]- [56], while most of the other parts are different from the previously published works. The innovation of the proposed method contains the following: 1) both the AGC and AVC are researched and fulfilled based on the same multiagent system architecture to avoid redundant construction cost; and 2) the proposed algorithm is fully distributed; there is no need for a specialized/central agent to coordinate the operations of the other agents.…”

“…When the algorithm is converged, the incremental cost λ i for all the GSIs is equal, P refi for i = 1, 2, ..., n is the optimal solution of (23), and all GSIs operate at the optimal active power dispatch [21]. Detailed about how to solve (26) by using the distributed consensus algorithm is available in [56].…”

Adaptive Fuzzy Droop Control for Optimized Power Sharing in an Islanded Microgrid

“…Traditionally, economic dispatch is added to droop control minimize the operation cost based on the cost function. Reference [10] proposes a hierarchical distributed economic control algorithm by adjusting the droop coefficient based on cost function and [11] proposes a cost-based droop scheme to reduce generation cost considering various DG operating characteristics. However, as for different kinds of RES, there is no unified cost function, the parameters are uncertain which should be designed according to RES characteristics.…”