An Introduction to MultiAgent Systems.

The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.

Trends in Microgrid Control

Advanced control strategies are vital components for realization of microgrids. This paper reviews the status of hierarchical control strategies applied to microgrids and discusses the future trends. This hierarchical control structure consists of primary, secondary, and tertiary levels, and is a versatile tool in managing stationary and dynamic performance of microgrids while incorporating economical aspects. Various control approaches are compared and their respective advantages are highlighted. In addition, the coordination among different control hierarchies is discussed.

Hierarchical Structure of Microgrids Control System

The significant benefits associated with microgrids have led to vast efforts to expand their penetration in electric power systems. Although their deployment is rapidly growing, there are still many challenges to efficiently design, control, and operate microgrids when connected to the grid, and also when in islanded mode, where extensive research activities are underway to tackle these issues. It is necessary to have an across-the-board view of the microgrid integration in power systems. This paper presents a review of issues concerning microgrids and provides an account of research in areas related to microgrids, including distributed generation, microgrid value propositions, applications of power electronics, economic issues, microgrid operation and control, microgrid clusters, and protection and communications issues.

State of the Art in Research on Microgrids: A Review

Photovoltaic (PV) energy has grown at an average annual rate of 60% in the last five years, surpassing one third of the cumulative wind energy installed capacity, and is quickly becoming an important part of the energy mix in some regions and power systems. This has been driven by a reduction in the cost of PV modules. This growth has also triggered the evolution of classic PV power converters from conventional singlephase grid-tied inverters to more complex topologies to increase efficiency, power extraction from the modules, and reliability without impacting the cost. This article presents an overview of the existing PV energy conversion systems, addressing the system configuration of different PV plants and the PV converter topologies that have found practical applications for grid-connected systems. In addition, the recent research and emerging PV converter technology are discussed, highlighting their possible advantages compared with the present technology. Solar PV energy conversion systems have had a huge growth from an accumulative total power equal to approximately 1.2 GW in 1992 to 136 GW in 2013 (36 GW during 2013) [1]. This phenomenon has been possible because of several factors all working together to push the PV energy to cope with one important position today (and potentially a fundamental position in the near future). Among these factors are the cost reduction and increase in efficiency of the PV modules, the search for alternative clean energy sources (not based on fossil fuels), increased environmental awareness, and favorable political regulations from local governments (establishing feed-in tariffs designed to accelerate investment in renewable energy technologies). It has become usual to see PV systems installed on the roofs of houses or PV farms next to the roads in the countryside. Grid-connected PV systems account for more than 99% of the PV installed capacity compared to

An Overview of Recent Research and Emerging PV Converter Technology

Microgrids are a new concept for future energy distribution systems that enable renewable energy integration and improved energy management capability. Microgrids consist of multiple distributed generators (DGs) that are usually integrated via power electronic inverters. In order to enhance power quality and power distribution reliability, microgrids need to operate in both grid-connected and island modes. Consequently, microgrids can suffer performance degradation as the operating conditions vary due to abrupt mode changes and variations in bus voltages and system frequency. This paper presents controller design and optimization methods to stably coordinate multiple inverter-interfaced DGs and to robustly control individual interface inverters against voltage and frequency disturbances. Droop-control concepts are used as system-level multiple DG coordination controllers, and control theory is applied to device-level inverter controllers. Optimal control parameters are obtained by particle-swarm-optimization algorithms, and the control performance is verified via simulation studies.

Control Methods of Inverter-Interfaced Distributed Generators in a Microgrid System

Once a fault in microgrids has been cleared, it is necessary to restore the unfaulted but out-of-service loads as much as possible in a timely manner. This paper proposes a novel fully distributed multiagent based load restoration algorithm. According to the algorithm, each agent makes synchronized load restoration decision according to discovered information. During the information discovery process, agents only communicate with their direct neighbors, and the global information is discovered based on the Average-Consensus Theorem. In this way, total net power, indexes and demands of loads that are ready for restoration can be obtained. Then the load restoration problem can be modeled and solved using existing algorithms for the 0-1 Knapsack problem. To achieve adaptivity and stability, a distributed algorithm for coefficient setting is proposed and compared against existing algorithms and a particle swarm optimization based algorithm. Theoretically, the proposed load restoration algorithm can be applied to systems of any size and structure. Simulation studies with power systems of different scale demonstrate the effectiveness of the proposed algorithm.

Novel Multiagent Based Load Restoration Algorithm for Microgrids

Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network

In traditional power system, economic dispatch and generation control are separately applied. Online generation adjustment is necessary to regulate generation reference for real-time control to realize economic operation of power systems. Since most economic dispatch solutions are centralized, they are usually expensive to implement, susceptible to single-point-failures, and inflexible. To address the above-mentioned problems, this paper proposed a multi-agent system based distributed control solution that can realize optimal generation control. The solution is designed based upon an improved distributed gradient algorithm, which can address both equality and inequality constraints. To improve the reliability of multi-agent system, the N-1 rule is introduced to design the communication network topology. Compared with centralized solutions, the distributed control solution not only can achieve comparable solutions but also can respond timely when the system experiences change of operating conditions. MAS based real-time simulation results demonstrate the effectiveness of the proposed solution.

Online Optimal Generation Control Based on Constrained Distributed Gradient Algorithm

If generation in a power system is insufficient to power all loads, efficient load shedding operations may need to be deployed to maintain the supply-demand balance. This paper proposes a distributed multi-agent-based load shedding algorithm, which can make efficient load shedding decision based on discovered global information. During the information discovery process, only communications between immediate neighboring agents are used. The information discovery algorithm is represented as a discrete time linear system and the stability of which is analyzed according to average-consensus theorem. According to rigorous stability analysis, convergence of the designed algorithm can be guaranteed. To improve the speed of the algorithm, particle swarm optimization (PSO) is used to optimize the coefficients for information exchange so that the second largest eigenvalue of the iteration matrix is minimized. According to the designed algorithm, total net active power and operating status of loads can be discovered accurately even with faults. Based on the discovered information, coordinated load shedding decision can be made.

Wenxin Liu

Papers

Control Methods of Inverter-Interfaced Distributed Generators in a Microgrid System

Novel Multiagent Based Load Restoration Algorithm for Microgrids

Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network

Online Optimal Generation Control Based on Constrained Distributed Gradient Algorithm

Stable Multi-Agent-Based Load Shedding Algorithm for Power Systems