Microgrids energy management systems: A critical review on methods, solutions, and prospects

Smart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities, and the uncertainty of the smart grid and high volume of information being collected, artificial intelligence techniques represent some of the enabling technologies for its future development and success. Owing to the decreasing cost of computing power, the profusion of data, and better algorithms, AI has entered into its new developmental stage and AI 2.0 is developing rapidly. Deep learning (DL), reinforcement learning (RL) and their combination-deep reinforcement learning (DRL) are representative methods and relatively mature methods in the family of AI 2.0. This article introduces the concept and status quo of the above three methods, summarizes their potential for application in smart grids, and provides an overview of the research work on their application in smart grids.

Review on the research and practice of deep learning and reinforcement learning in smart grids

Modified PSO algorithm for real-time energy management in grid-connected microgrids

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

Evolution of microgrids with converter-interfaced generations: Challenges and opportunities

This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid’s system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.

Dynamic Energy Management System for a Smart Microgrid

To characterize the power performance of wind turbine generators (WTGs), the International Electrotechnical Commission provides a power curve for each one based on ten minutes average. This approach causes the problem of systematic errors because of the nonlinear relationship between power and wind speed. In this paper, recurrent neural networks are introduced as an alternative approach to model this nonlinear relationship. Based on actual wind speed input and wind power output, an input-output relationship is established for a permanent magnet synchronous machine based wind power generator. Experimental studies are carried out to the develop power curve that characterize dynamic power performance of the wind turbine generator. These dynamic power performance models of wind turbine generators can be used as operational and planning models in control centers. Some preliminary results on the integration of a neural network wind generator model in a micro-grid simulation is presented.

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Prajwal K. Gautam

Papers

Dynamic Energy Management System for a Smart Microgrid

Dynamic performance model of wind turbine generators