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Top1. Introduction
Nowadays, there is a paradigm shift in the energy sector to meet the increased demand for continuous, secure, reliable and quality electric power. The transformation of traditional grid to a self- healing smart grid involves the integration of intelligent monitoring, control and communication technologies. This helps in promoting the increased integration of renewable energy based DG sources and electric vehicles resulting in reduced green-house gas emissions leading to sustainable energy environment. Energy management systems (EMS) optimize the energy usage with the help of advanced technologies for measurement, monitoring and analysis of data at homes, buildings and even at grid. Uncertainty due to the intermittent nature of renewable energy sources and the loads can be taken care of using EMS. The major objective of EMS is reduction in energy consumption leading to cost minimization. It also helps in managing the energy storage systems based on the renewable energy output. To meet the supply demand balance, flexibility at the load end can be effected with the help of demand response programs through EMS. Micro grid energy management systems can also be realized by implementing DSM using various pricing mechanisms (Shen et al., 2016). Figure 1 depicts the components involved in an energy management system.
Figure 1. Components of an Energy Management System
Based on the methodology used to achieve the objectives, EMS can be broadly classified into (i) Rule based; (ii) Optimization based and (iii) Learning based EMS (Tran et al., 2020). Rule based EMS optimizes the energy usage on the basis of a set of rules in accordance with an algorithm or fuzzy logic. Optimization based EMS employs classical or metaheuristic algorithms to achieve the objectives under a set of constraints. Learning based EMS uses the historical data for training the system with the help of machine learning and artificial intelligence techniques.
In this paper, the various nature inspired algorithms used by Optimization based EMS are discussed. The application of these algorithms to optimize energy usage in residential homes, buildings and in a smart grid is discussed. The various objectives considered along with the constraints are explained in detail. The most commonly used algorithms include Particle Swarm Optimization, Genetic Algorithm, Ant Colony Optimization, Bat Algorithm, Cuckoo Search Algorithm, Artificial Bee Colony Algorithm etc. In order to reap the benefits of different algorithms, few authors have optimized the objectives of EMS using hybrid algorithms as well. Recently developed algorithms like Dragonfly Algorithm, Wind-Driven Optimization, Grasshopper Optimization Algorithm, Moth Flame Optimization etc. are also being used for developing efficient, effective EMS.
TopThe objectives considered for realizing EMS are manifold in different contexts. Minimization of cost, energy consumption, emissions, frequency of interruptions, losses, peak to average ratio (PAR), electricity bill etc. and maximization of user comfort, reliability, grid sustainability, income etc. are few of the most common objectives considered by different authors. Certain authors have considered multiple objectives, mostly by combining different single objectives using the weighted sum method. The constraints taken for the optimization problem include technical, economical, operational and other system constraints. Figure 2 represents the objectives involved in the Energy Management System in homes, buildings and microgrid Ahmad et al. (2017) proposed home energy management system (HEMS) which reduces the electricity bill and PAR by optimally scheduling the home appliances and energy storage systems (ESS) with the help of demand side management (DSM).
Figure 2. Energy Management System in homes, buildings and microgrid