A survey of distributed optimization

The microgrid concept is gaining popularity with the proliferation of distributed generation. Control techniques in the microgrid are an evolving research topic in the area of microgrids. A large volume of survey articles focuses on the control techniques of the microgrid; however, a systematic survey of the hierarchical control techniques based on different microgrid architectures is addressed very little. The hierarchy of control in microgrid comprises three layers, which are primary, secondary, and tertiary control layers. A review of the primary and secondary control strategies for the ac, dc, and hybrid ac–dc microgrid is addressed in this paper. Furthermore, it includes the highlights of the state-of-the-art control techniques and evolving trends in the microgrid research.

Control Techniques in AC, DC, and Hybrid AC–DC Microgrid: A Review

Energy storage technologies are used in modern grids for a variety of applications and with different techniques. The range of applications and technologies is very broad, and finding the right storage solution for the job at hand can be difficult. In order to simplify the selection, this paper presents a matrix of the available technologies and applications. Along with proposing the matrix, the technologies and applications of Energy Storage Systems (ESSs) are described thoroughly and are compared on the basis of many different parameters, such as capacity, storage power, response time, discharge time, and life time. Moreover, the structure of energy storage, which is constituted of different steps and parts, is investigated. Since the implementing of an ESS is expensive, this paper also analyzes the possibility of integrating different types of ESSs and presents a comprehensive diagram to show the ESS technologies that can be integrated together in order to provide the needed performance in a cost-optimal way. Finally, the key results of this comprehensive study are summarized in a number of tables.

Energy storage systems in modern grids—Matrix of technologies and applications

A microgrid is a promising approach to provide clean, renewable, and reliable electricity by integrating various distributed generations and energy storage systems into power systems. However, highly intermittent renewable generations and various load demands pose new challenges to the optimal resource management in a microgrid. This paper proposes a fully distributed control strategy based on the consensus algorithm for the optimal resource management in an islanded microgrid. The proposed strategy is implemented through a multiagent system framework, which only requires information exchange among neighboring agents through a local network. The objective is achieved through a two-level control strategy. The upper control level is a consensus-based optimization algorithm that discovers the reference of optimal power generation or demand while maintaining the supply–demand balance. The lower control level is responsible for reference tracking of the associated component. Simulation results in the IEEE 14- and 162-bus systems are presented to demonstrate the effectiveness of the proposed control strategy.

Distributed Optimal Resource Management Based on the Consensus Algorithm in a Microgrid

The increasing integration of the distributed renewable energy sources highlights the requirement to design various control strategies for microgrids (MGs) and microgrid clusters (MGCs). The multiagent system (MAS)-based distributed coordinated control strategies show the benefits to balance the power and energy, stabilize voltage and frequency, achieve economic and coordinated operation among the MGs and MGCs. However, the complex and diverse combinations of distributed generations (DGs) in MAS increase the complexity of system control and operation. In order to design the optimized configuration and control strategy using MAS, the topology models and mathematic models such as the graph topology model, noncooperative game model, the genetic algorithm, and particle swarm optimization algorithm are summarized. The merits and drawbacks of these control methods are compared. Moreover, since the consensus is a vital problem in the complex dynamical systems, the distributed MAS-based consensus protocols are systematically reviewed. On the other hand, the communication delay issue, which is inevitable no matter in the low- or high-bandwidth communication networks, is crucial to maintain the stability of the MGs and MGCs with fixed and random delays. Various control strategies to compensate the effect of communication delays have been reviewed, such as the neural network-based predictive control, the weighted average predictive control, the gain scheduling scheme, and synchronization schemes based on the multitimer model for the case of fixed communication delay, and the generalized predictive control, networked predictive control, model predictive control, Smith predictor, $H_{\infty}$ -based control, sliding mode control for the random communication delay scenarios. Furthermore, various control methods have been summarized to describe switching topologies in MAS with different objectives, such as the plug-in or plug-out of DGs in an MG, and the plug-in or plug-out of MGs in an MGC, and multiagent-based energy coordination and the economic dispatch of the MGC. Finally, the future research directions of the multiagent-based distributed coordinated control and optimization in MGs and MGCs are also presented.

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MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

For a microgrid with high renewable energy penetration to work autonomously, it must maintain its own supply-demand balance of active power. Maximum peak power tracking algorithms, which emphasize high renewable energy utilization, may cause a supply-demand imbalance when the available renewable generation is more than demanded, especially for autonomous microgrids. Currently, droop control is one of the most popular decentralized methods for sharing active and reactive loads among the distributed generators. However, conventional droop control methods suffer from slow and oscillating dynamic response and steady state deviations. To overcome these problems, this paper proposes a distributed subgradient-based solution to coordinate the operations of different types of distributed renewable generators in a microgrid. By controlling the utilization levels of renewable generators, the supply-demand balance can be well maintained and the system dynamic performance can be significantly improved. Simulation results demonstrate the effectiveness of the proposed control solution.

Distributed Subgradient-Based Coordination of Multiple Renewable Generators in a Microgrid

Traditionally, economic dispatch and demand response (DR) are considered separately, or implemented sequentially, which may degrade the energy efficiency of the power grids. One important goal of optimal energy management (OEM) is to maximize the social welfare through the coordination of the suppliers’ generations and customers’ demands. Thus, it is desirable to consider the interactive operation of economic dispatch and DR, and solve them in an integrated way. This paper proposes a fully distributed online OEM solution for smart grids. The proposed solution considers the economic dispatch of conventional generators, DR of users, and operating conditions of renewable generators all together. The proposed distributed solution is developed based on a market-based self-interests motivation model since this model can realize the global social welfare maximization among system participants. The proposed solution can be implemented with multiagent system with each system participant assigned with an energy management agent. Based on the designed distributed algorithms for price updating and supply–demand mismatch discovery, the OEM among agents can be achieved in a distributed way. Simulation results demonstrate the effectiveness of the proposed solution.

Haibin Yu

Papers

Distributed Subgradient-Based Coordination of Multiple Renewable Generators in a Microgrid

Distributed Online Optimal Energy Management for Smart Grids