This paper performs an extensive review on control schemes and architectures applied to dc microgrids (MGs). It covers multilayer hierarchical control schemes, coordinated control strategies, plug-and-play operations, stability and active damping aspects, as well as nonlinear control algorithms. Islanding detection, protection, and MG clusters control are also briefly summarized. All the mentioned issues are discussed with the goal of providing control design guidelines for dc MGs. The future research challenges, from the authors’ point of view, are also provided in the final concluding part.

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Review on Control of DC Microgrids and Multiple Microgrid Clusters

This paper presents an overview of the state of the art control strategies specifically designed to coordinate distributed energy storage (ES) systems in microgrids. Power networks are undergoing a transition from the traditional model of centralised generation towards a smart decentralised network of renewable sources and ES systems, organised into autonomous microgrids. ES systems can provide a range of services, particularly when distributed throughout the power network. The introduction of distributed ES represents a fundamental change for power networks, increasing the network control problem dimensionality and adding long time-scale dynamics associated with the storage systems’ state of charge levels. Managing microgrids with many small distributed ES systems requires new scalable control strategies that are robust to power network and communication network disturbances. This paper reviews the range of services distributed ES systems can provide, and the control challenges they introduce. The focus of this paper is a presentation of the latest decentralised, centralised and distributed multi-agent control strategies designed to coordinate distributed microgrid ES systems. Finally, multi-agent control with agents satisfying Wooldridge’s definition of intelligence is proposed as a promising direction for future research.

https://orbit.dtu.dk/files/190879836/C_Users_Thomas_Documents_UNSW_PhD_Research_Paper_Review_Control_for_Microgrids_with_Distributed_Energy_Storage_Systems_Draft_Final_160612_Review_MA_MG_ES.dvi.pdf

Control Strategies for Microgrids With Distributed Energy Storage Systems: An Overview

Microgrids (MGs), featured by distributed energy resources, consumption and storage, are designed to significantly enhance the self-sustainability of future electric distribution grids. In order to adapt to this new and revolutionary paradigm, it is necessary to control MGs in intelligent and coordinated fashion. To this aim, a new generation of advanced Microgrid Supervisory Controllers (MGSC) and Energy Management Systems (EMS) has emerged. The aim of this paper is to summarize the control objectives and development methodologies in the recently proposed MGSC/EMS. At first, a classification of control objectives is made according to the definition of hierarchical control layers in MGs. Then, focusing on MGSC/EMS related studies, a detailed methodology review is given with emphasis on representative applications and research works. Finally, the conclusions are summarized and the proposals of future research directions in this area are given.

Microgrid supervisory controllers and energy management systems: A literature review

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

The optimal operation programming of electrical systems through the minimization of the production cost and the market clearing price, as well as the better utilization of renewable energy resources, has attracted the attention of many researchers. To reach this aim, energy management systems (EMSs) have been studied in many research activities. Moreover, a demand response (DR) expands customer participation to power systems and results in a paradigm shift from conventional to interactive activities in power systems due to the progress of smart grid technology. Therefore, the modeling of a consumer characteristic in the DR is becoming a very important issue in these systems. The customer information as the registration and participation information of the DR is used to provide additional indexes for evaluating the customer response, such as consumer's information based on the offer priority, the DR magnitude, the duration, and the minimum cost of energy. In this paper, a multiperiod artificial bee colony optimization algorithm is implemented for economic dispatch considering generation, storage, and responsive load offers. The better performance of the proposed algorithm is shown in comparison with the modified conventional EMS, and its effectiveness is experimentally validated over a microgrid test bed. The obtained results show cost reduction (by around 30%), convergence speed increase, and the remarkable improvement of efficiency and accuracy under uncertain conditions. An artificial neural network combined with a Markov chain (ANN-MC) approach is used to predict nondispatchable power generation and load demand considering uncertainties. Furthermore, other capabilities such as extendibility, reliability, and flexibility are examined about the proposed approach.

/pdf/an-optimal-energy-management-system-for-islanded-microgrids-3eom3ssgcx.pdf

An Optimal Energy Management System for Islanded Microgrids Based on Multiperiod Artificial Bee Colony Combined With Markov Chain

Hybrid power systems and microgrids may employ a mixture of dispatchable (conventional) and nondispatchable (renewable) generators alongside storage. Whether in grid-connected or grid-isolated (islanded) modes of operation, these systems may face multiple competing objectives when managing diverse installed assets. Power management of hybrid energy systems, therefore, involves operational tradeoffs amongst Pareto-optimal solutions. These attributes, including the ready implementation of distributed renewable generation and the incorporation of methods to locally manage power-networked assets, make them a unique area of study for pursing better sustainable performance. In part I of this paper, storage system round-trip efficiency and operational cost concepts were formulated for use in real-time dispatch decisions towards yielding improved performance of overall system objectives. In this paper (part II), the concepts of part I are implemented with a decentralized multiagent system (MAS). This MAS is employed for power management of a hybrid (diesel-storage battery) microgrid in grid-connected and islanded modes. This paper highlights the development and implementation of an MAS suitable for hybrid and microgrid system applications, as well as presenting an important discussion about the tradeoffs associated with multiobjective design for power management. The simulation results presented demonstrate improvement in sustainable performance of the hybrid system.

Improving Sustainability of Hybrid Energy Systems Part II: Managing Multiple Objectives With a Multiagent System

Power management of grid-tied microgrids including distributed generations (DGs) and storage devices can be challenging due to the intermittent and uncontrollable nature of many types of DGs, load variations in time, different grid power tariffs, availability of different options to balance the electric supply and demand, and other parameters. In order to operate a microgrid efficiently, the management system should accomplish two tasks. 1) It needs to be adaptive and optimize the microgrid's performance by defining long-term (daily-based) directives or control strategies. 2) The management system should be able to operate and control the microgrid in real time and satisfy all operational constraints. To address the above-mentioned tasks, a comprehensive power management system that includes two control layers is developed in this paper. Concept for the proposed power management policy was demonstrated on an experimental microgrid system composed of lead-acid batteries, photovoltaic (PV) system, 3-kW peak load, and a utility connection.

Experimental Demonstration of a Tiered Power Management System for Economic Operation of Grid-Tied Microgrids

Because of different technical and economical concerns, battery is happened to be an inevitable part of a microgrid as well as the most expensive component. This fact brings up the necessity of a real-time power management to guarantee the maximum possible battery lifetime based on the final cost of energy. In this way, this study attempts to present a real-time management framework for a grid-tied microgrid based on battery life and cost estimation. In order to verify the effectiveness of the proposed framework, a grid-tied commercial microgrid, which is equipped with wind turbine, PV solar panels and Li-Ion battery package, is optimally sized by HOMER® and dynamic models of different components have been developed in MATLAB/Simulink®. Then, simulation study has been carried out for a year on the system. All data such as load demand, wind, temperature, solar radiation, and time-based electricity tariff are grasped from different places for a year. Results show that the proposed framework effectively extends the battery lifetime while slightly decreases the cost of energy for customer.

A framework for real-time power management of a grid-tied microgrid to extend battery lifetime and reduce cost of energy

This paper proposes a multi-objective energy management method for microgrids which include local generation sources, grid connection, energy storage units and various loads. Minimization of the energy cost and maximization of batterys lifetime in a microgrid are considered as two main objectives which are optimized simultaneously. To achieve these objectives, microgrids central controller must find the best pattern for charging and discharging the battery. To this purpose, there is a need to have information about time-of-use (TOU) grid electricity rates, forecasted load profile and renewable generation levels. Model predictive control (MPC) policy is then utilized for solving the optimization problem and real-time implementation in a closed-loop framework. The performance and effectiveness of the proposed method is verified by simulating a microgrid model with real yearly data for the demand and renewable generation profiles and TOU rates. It is shown that the saving in energy cost can be increased considerably by applying the proposed MPC algorithm instead of a static energy management approach. Furthermore, the proposed algorithm is capable of regulating the battery usage based on the expected lifetime by considering the battery life span maximization objective.

A novel cost-aware multi-objective energy management method for microgrids

Systems and methods to perform multi-objective energy management of micro-grids include determining, by an advisory layer with Model Predictive Control (MPC) using a processor, long-term power management directives that include a charging threshold that characterizes one or more power sources, where the advisory layer provides optimal set points or reference trajectories to reduce a cost of energy; and determining real-time actions based on the charging threshold to adaptively charge a battery from the one or more power sources or to discharge the battery.

Babak Asghari

Papers

Improving Sustainability of Hybrid Energy Systems Part II: Managing Multiple Objectives With a Multiagent System

Experimental Demonstration of a Tiered Power Management System for Economic Operation of Grid-Tied Microgrids

A framework for real-time power management of a grid-tied microgrid to extend battery lifetime and reduce cost of energy

A novel cost-aware multi-objective energy management method for microgrids

Tiered power management system for microgrids