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

Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience

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

Uncertainty models for stochastic optimization in renewable energy applications

This paper suggests a new energy management system for a grid-connected microgrid with various renewable energy resources including a photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT) and battery energy storage system (BESS). For the PV system operating in the microgrid, an innovative mathematical modelling is presented. In this model, the effect of various irradiances in different days and seasons on day-ahead scheduling of the microgrid is evaluated. Moreover, the uncertainties in the output power of the PV system and WT, load demand forecasting error and grid bid changes for the optimal energy management of microgrid are modelled via a scenario-based technique. To cope with the optimal energy management of the grid-connected microgrid with a high degree of uncertainties, a modified bat algorithm (MBA) is employed. The proposed algorithm leads to a faster computation of the best location and more accurate result in comparison with the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The simulation results demonstrate that the use of practical PV model in a real environment improve the accuracy of the energy management system and decreases the total operational cost of the grid-connected microgrid.

Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty

Conventionally, community energy management system (CEMS) is provided with the information of surplus and shortage amounts only at each time interval. This limited information may lead to an increase in the operational cost of the multimicrogrid (MMG) systems. This paper suggests informing the CEMS about the adjustable power also, in addition to the surplus and shortage information. This additional information will result in a variety of options for the CEMS to fulfill the load demands of its network. CEMS will choose among various available options, which include trading with the power grid, buying from a controllable distributed generation plant, buying from a community battery energy storage system (CBESS), or controlling the adjustable power: increasing or decreasing the generation of controllable units. CBESS can either be controlled by CEMS or can act as an autonomous entity. The effects of both the operational options have been analyzed and economically efficient mode is suggested for MMG systems. Demand response (DR) is also considered in the proposed model. The incorporation of DR will ensure the supply reliability of the MMG system in addition to the reduction in operational cost. In contrast to the conventional single or two-step multimicrogrid optimization algorithms, a multistep hierarchical optimization algorithm based on a multiagent system is proposed in this paper. Easy to implement and computationally inexpensive mixed integer linear programming models are developed for each step.

A Multiagent-Based Hierarchical Energy Management Strategy for Multi-Microgrids Considering Adjustable Power and Demand Response

This paper presents an energy management strategy for day-ahead scheduling of networked microgrids and is named as nested energy management system . In the proposed nested energy management strategy, the surplus power existing in the inner level microgrids is reflected as a resource and deficit as a load to the outer level microgrids. The operation cost of networked microgrids with nested energy management systems is lesser as compared to conventional decentralized or hybrid energy management strategies. Similarly, due to the inclusion of data (surplus/deficit) from inner level microgrids, customer privacy also increases. In islanded mode, the resilient performance of disconnected microgrids is enhanced due to their ability to form sub-groups . The probability of subgrouping for L-layers of microgrids is given by (L-2)/L. In grid-connected mode, various cases with diverse generation costs at each microgrid level along with the time-of-use market price signals have been evaluated. In islanded mode, the effect of battery energy storage system and disconnection of microgrids from the network have been evaluated. Hybrid ac/dc microgrids have also been considered to evaluate the effectiveness of the proposed approach. Simulation results have proved the viability of the proposed approach.

A Resilient and Privacy-Preserving Energy Management Strategy for Networked Microgrids

$Q$ -learning-based operation strategies are being recently applied for optimal operation of energy storage systems, where, a $Q$ -table is used to store $Q$ -values for all possible state-action pairs. However, $Q$ -learning faces challenges when it comes to large state space problems, i.e., continuous state space problems or problems with environment uncertainties. In order to address the limitations of $Q$ -learning, this paper proposes a distributed operation strategy using double deep $Q$ -learning method. It is applied to managing the operation of a community battery energy storage system (CBESS) in a microgrid system. In contrast to $Q$ -learning, the proposed operation strategy is capable of dealing with uncertainties in the system in both grid-connected and islanded modes. This is due to the utilization of a deep neural network as a function approximator to estimate the $Q$ -values. Moreover, the proposed method can mitigate the overestimation that is the major drawback of the standard deep $Q$ -learning. The proposed method trains the model faster by decoupling the selection and evaluation processes. Finally, the performance of the proposed double deep $Q$ -learning-based operation method is evaluated by comparing its results with a centralized approach-based operation.

Double Deep $Q$ -Learning-Based Distributed Operation of Battery Energy Storage System Considering Uncertainties

A resilience-oriented optimization strategy is proposed in this paper by considering feasible islanding in normal operation and survivability of critical loads during emergency period. A resilience index is proposed for assessing the capability of each microgrid to feed local critical loads during sudden power disruptions. Based on the values of the proposed resilience index, local optimization results are revised by changing the commitment status of dispatchable generators and energy storage elements. The uncertainties associated with renewable generations and loads are realized via robust optimization method. The adjustable power bounds, suggested by each microgrid for minimizing the operation cost of the network, are also revised through the proposed resilience index. In emergency mode, decision between feeding of lesser critical loads and battery charging along with a strategy for minimization of load curtailment during switching of scheduling windows is considered. These two considerations assure the survivability of critical loads during emergency period. Finally, an incremental cost consensus algorithm is used for optimal allocation of surplus power among the connected microgrids having unserved loads. Three different network topologies are considered in emergency mode for assessing the performance of the proposed approach.

Van-Hai Bui

Papers

A Multiagent-Based Hierarchical Energy Management Strategy for Multi-Microgrids Considering Adjustable Power and Demand Response

Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience

A Resilient and Privacy-Preserving Energy Management Strategy for Networked Microgrids

Double Deep $Q$ -Learning-Based Distributed Operation of Battery Energy Storage System Considering Uncertainties

Resilience-Oriented Optimal Operation of Networked Hybrid Microgrids