Microgrids (MG) are anticipated to be important players in the future smart grid. For proper operation of MGs an Energy Management System (EMS) is essential. The EMS of an MG could be rather complicated when renewable energy resources (RER), energy storage system (ESS) and demand side management (DSM) need to be orchestrated. Furthermore, these systems may belong to different entities and competition may exist between them. Nash equilibrium is most commonly used for coordination of such entities however the convergence and existence of Nash equilibrium can not always be guaranteed. To this end, we use the correlated equilibrium to coordinate agents, whose convergence can be guaranteed. In this paper, we build an energy trading model based on mid-market rate, and propose a correlated Q-learning (CEQ) algorithm to maximize the revenue of each agent. Our results show that CEQ is able to balance the revenue of agents without harming total benefit. In addition, compared with Q-learning without correlation, CEQ could save 19.3% cost for the DSM agent and 44.2% more benefits for the ESS agent.
This paper proposes a decentralized dynamic state estimation scheme for microgrids. The approach employs the voltage and current measurements in the dq0 reference frame through phasor synchronization to be able to exclude orthogonal functions from their relationship formulas. Based on that premise, we utilize a Kalman filter to dynamically estimate states of microgrids. The decoupling of measurement values to state and input vectors reduces the computational complexity. The Kalman filter considers the process noise covariances, which are modified with respect to the covariance of measured inpu…
We consider the problem of energy management in microgrid networks. A microgrid is capable of generating a limited amount of energy from a renewable resource and is responsible for handling the demands of its dedicated customers. Owing to the variable nature of renewable generation and the demands of the customers, it becomes imperative that each microgrid optimally manages its energy. This involves intelligently scheduling the demands at the customer side, selling (when there is a surplus) and buying (when there is a deficit) the power from its neighboring microgrids depending on its current …
Typically, a large portion of microgrid generating capacity is from variable renewable resources that are greatly impacted by the environment and can be intermittent as well as stochastic. This would result in uncertainty of microgrid net-load, and negatively affect the grid reliability. A two-phase real-time energy management strategy for networked microgrid is proposed in this paper to address microgrid internal fluctuation internally, which enables a microgrid to become grid-friendly. The proposed strategy is based on coordination between the real-time dispatch (RTD) phase and the real-time…
In this study, a cooperative game model is presented to schedule the day-ahead operation of multi-microgrid (MMG) systems. In the proposed model, microgrids are scheduled to achieve a global optimum for the cost of the multi-microgrid system. The minimum cost is achieved by transactions of microgrids with each other. Also, price-based demand response is implemented in the model to build a cost-reducing opportunity for consumers. Applying Shapley value, the optimum cost of the MMG system is fairly allocated between microgrids. To enhance the confidence level of results, data uncertainties are i…
The microgrids design for remote locations represents one of the most important and critical applications of the microgrid concept. It requires the correct sizing and the proper utilization of the different sources to guarantee the economical feasibility and the reliability of the supply. This study illustrates an efficiency-based power management strategy, designed for an undergoing microgrid project, where the sizing process of the resources (diesel generators, battery energy storage system, and PV plant) is obtained using a mixed-integer optimization algorithm. The proposed power management…
The control and managing of power demand and supply become very crucial because of penetration of renewables in the electricity networks and energy demand increase in residential and commercial sectors. In this paper, a new approach is presented to bridge the gap between Demand-Side Management (DSM) and microgrid portfolio, sizing and placement optimization. Although DSM helps energy consumers to take advantage of recent developments in utilization of Distributed Energy Resources (DERs) especially microgrids, a huge need of connecting DSM results to microgrid optimization is being felt. Conseq…
A decentralized scalable approach to voltage control of DC islanded microgrids
Università degli Studi di Pavia（帕维亚大学）；United Technologies Research Center Ireland（联合技术研究中心爱尔兰）；Institute of Energy Technology, Aalborg University（奥尔堡大学能源技术研究所）
An Overview of the Prospects and Challenges of Using Artificial Intelligence for Energy Management Systems in Microgrids
Department of Electrical Engineering, University of Sharjah（电气工程系，谢贾哈大学）；Department of Electric, Electronic and Computer Engineering, University of Pretoria（电气电子与计算机工程系，普里蒂亚大学）
This paper addresses the problem of management and coordination of energy resources in a typical microgrid, including smart buildings as flexible loads, energy storages, and renewables. The overall goal is to provide a comprehensive and innovative framework to maximize the overall benefit, still accounting for possible requests to change the load profile coming from the grid and leaving every single building or user to balance between servicing those requests and satisfying his own comfort levels. The user involvement in the decision-making process is granted by a management and control soluti…
Microgrid (MG) system, which is composed of renewable resources with the utility grid, energy storage unit, electric vehicles, and loads, acts as a single controllable entity. To get efficient and low-cost energy, need to manage power flow within MG depending on renewable resources and load demand. This paper proposes an energy management system (EMS) for grid-connected photovoltaic (PV) and energy storage system (ESS) based on AC MG. The proposed EMS regulates power flow through the AC bus considering next-day solar irradiance prediction, tariff profile, and day-ahead load demand to minimize …
This paper addresses the issue of power flow control for partially faulty microgrids. In microgrid control systems, faults may occur in both electrical and communication layers. This may have severe effects on the operation of microgrids. In addition, disturbances always coexist with faults in microgrids, which may further deteriorate system performance. To address the faults and disturbances simultaneously, a model predictive control (MPC) method based on set-membership estimation (SME) that transmits information via a communication network is proposed. When electrical devices are nonfunction…
This paper presents a supervised multi-agent safe policy learning (SMAS-PL) method for optimal power management of networked microgrids (MGs) in distribution systems. While conventional reinforcement learning (RL) algorithms are black-box decision models that could fail to satisfy grid operational constraints, our proposed method is constrained by AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of operational constraints to ensure that the optimal control policy functions generate safe and feasible decisions. Furthermore,…
Lately, increasing number of electric vehicles (EVs) in residential parking station has become an important issue, because excessive number of EVs can destabilize the power system during peak hours with high charging power requested. When the power system of the residential parking station takes the structure of microgrid (MG), power provision for the EVs requires efficient power management scheme. To minimize the maintenance cost of the MG and maintain the grid stability, the MG needs to balance the charging/discharging power of EVs in the parking station. To achieve these goals, this paper p…
Microgrid (MG) energy management is an important part of MG operation. Various entities are generally involved in the energy management of an MG, e.g., energy storage system (ESS), renewable energy resources (RER) and the load of users, and it is crucial to coordinate these entities. Considering the significant potential of machine learning techniques, this paper proposes a correlated deep Q-learning (CDQN) based technique for the MG energy management. Each electrical entity is modeled as an agent which has a neural network to predict its own Q-values, after which the correlated Q-equilibrium …
This paper presents an Energy Management System (EMS) that considers power exchanges between a set of interconnected microgrids (MGs) and the main grid, in the context of Multi-MG (MMG) systems. The model is first formulated as a centralized optimization problem, which is then decomposed into subproblems corresponding to each MG, using Lagrangian relaxation, and solved through a distributed approach using a subgradient method. The proposed model determines the power exchanges minimizing the operation cost of each MG, considering grid constraints and preserving the privacy of each MG by not rev…
This paper presents a load switching group based energy management system (LSG-EMS) for operating microgrids on a distribution feeder powered by one or multiple grid-forming distributed energy resources. Loads on a distribution feeder are divided into load switching groups that can be remotely switched on and off. The LSG-EMS algorithm, formulated as a mixed-integer linear programming (MILP) problem, has an objective function of maximizing the served loads while minimizing the total number of switching actions. A new set of topology constraints are developed for allowing multiple microgrids to…
A neural network-based energy management system (NN-EMS) has been proposed in this paper for islanded ac microgrids fed by multiple PV-battery based distributed generators (DG). The stochastic and unequal irradiation results in unequal PV output, which causes an unequal state-of-charge (SoC) among the batteries of the DGs. This effect may cause the difference in the SoCs to increase considerably over time, leading to some batteries reaching their SoC limits. These batteries would no longer be able to control the dc-link of the hybrid grid forming DG. The proposed NN-EMS ensures SoC balancing b…
In recent years, there has been significant growth of distributed energy resources (DERs) penetration in the power grid. The stochastic and intermittent features of variable DERs such as roof top photovoltaic (PV) bring substantial uncertainties to the grid on the consumer end and weaken the grid reliability. In addition, the fact that numerous DERs are widespread in the grid makes it hard to monitor and manage DERs. To address this challenge, this paper proposes a novel real-time grid-supporting energy management (GSEM) strategy for grid-supporting microgrid (MG). This strategy can not only p…
As multi-microgrids become readily available, some limited models have been proposed that study operational and power quality constraints with local energy markets independently. This paper proposes a convex optimization model of an energy management system with operational and power quality constraints and interactions in a Local Energy Market (LEM) for unbalanced microgrids (MGs). The LEM consists of a pre-dispatch step and an energy transactions step (ETS). The ETS combines the MGs' objectives while considering two strategies: minimize the cost of buyers or maximize the revenue of sellers. …
In this paper, we propose a minimax model predictive control (MPC)-based energy management system that is robust with respect to uncertainties in renewable infeed and load. The MPC formulation includes a model of low-level droop control with saturation at the power and energy limits of the units. Robust MPC-based energy management systems tend to under-utilize the renewable energy sources to guarantee safe operation. In order to mitigate this effect, we further consider droop control of renewable energy sources. For a microgrid with droop-controlled units, we show that enhancing droop feedback…
This paper proposes a cyber-physical cooperative mitigation framework to enhance power systems resilience under extreme events, e.g., earthquakes and hurricanes. Extreme events can simultaneously damage the physical-layer electric power infrastructure and the cyber-layer communication facilities. Microgrid (MG) has been widely recognised as an effective physical-layer response to such events, however, the mitigation strategy in the cyber lay is yet to be fully investigated. Therefore, this paper proposes a resilience-oriented centralised-to-decentralised framework to maintain the power supply …
This paper proposes a model predictive control (MPC)-based energy management system to address communication failures in an islanded microgrid (MG). The energy management system relies on a communication network to monitor and control power generation in the units. A communication failure to a unit inhibits the ability of the MPC to change the power of the unit. However, this unit is electrically connected to the network and ignoring this aspect could have adverse effect in the operation of the microgrid. Therefore, this paper considers an MPC design that includes the electrical connectivity o…
Forming a microgrid on a distribution system with large scale outage after a severe weather event is emerging as a viable solution to improve resiliency at the distribution level. This option becomes more attractive when the distribution system has high levels of distributed PV. The management of such feeder-level microgrid has however many challenges, such as limited resources that can be deployed on the feeder quickly, and the limited real-time monitoring and control on the distribution system. Effective use of the distributed PV is also challenging as they are not monitored and controlled. …
