To sustain the complexity of growing demand, the conventional grid (CG) is incorporated with communication technology like advanced metering with sensors, demand response (DR), energy storage systems (ESS), and inclusion of electric vehicles (EV). In order to maintain local area energy balance and reliability, microgrids (MG) are proposed. Microgrids are low or medium voltage distribution systems with a resilient operation, that control the exchange of power between the main grid, locally distributed generators (DGs), and consumers using intelligent energy management techniques. This paper gives a brief introduction to microgrids, their operations, and further, a review of different energy management approaches. In a microgrid control strategy, an energy management system (EMS) is the key component to maintain the balance between energy resources (CG, DG, ESS, and EVs) and loads available while contributing the profit to utility. This article classifies the methodologies used for EMS based on the structure, control, and technique used. The untapped areas which have scope for investigation are also mentioned.
A microgrid energy management system (EMS) with several generation and storage units is crucial in attaining stable and reliable operation. Optimal scheduling of energy resources in EMS becomes arduous due to uncertainty in the forecasting of intermittent renewable sources, electricity pricing, and load demand. However, with the demand response (DR) approaches the operational benefits in the EMS framework can be maximized. In order to improve the cost-effectiveness of the microgrid, a novel day-ahead energy management strategy is proposed for optimal energy allocation of the distributed generators with environmental consideration. An incentive load control-based demand response program is developed to improve the operational results. The forecasting uncertainties are handled using probability-based Hong’s 2 m approximation method. The suggested approach uses a metaheuristic genetic algorithm (GA) to solve the constrained convex problem in determining optimal load shifting. Incentive pricing is developed to adapt to the demand shifting for the benefit of the customers and utility operators. Two case studies with grid-connected and islanded modes are studied to assess the strategy. Results indicate that the proposed technique reduces the overall cost fitness by 12.28% and 18.91% in the two cases, respectively. The consistency in operational parameters with popular methods confirms the effectiveness and robustness of the method for day-ahead energy management.
With increasing demand in energy, efficiency, reliability, and resiliency in the power systems, integration of distributed generation (DG) or renewable energy sources in the local distribution system is trending nowadays. The power system optimization in distribution networks plays a prominent role in planning reliable supply to the users. This paper presents a meta-heuristic approach to improve the system performance by considering: a) voltage profile improvement, b) minimize the active power loss, and c) overall operation cost as the objective functions of the distribution system reconfiguration (DSR). Modified Newton Raphson (MNR) is used for a load flow analysis of the 33 bus balanced test system and 25 bus unbalanced distribution system. To study the performance of the system objective functions, three different scenarios are considered: i) with DG integrated, ii) with DSR performed, and iii) DG placed along with DSR. These scenarios are compared with the base system configuration parameters along with convergence comparison of harmony search algorithm (HAS) and particle swarm optimization (PSO) with firefly algorithm (FFA) of the distribution systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.