Nature-Inspired Algorithms for Energy Management Systems

Abstract

The electric grid is being increasingly integrated with renewable energy sources whose output is mostly fluctuating in nature. The load demand is also increasing day by day, mainly due to the increased interest in electric vehicles and other automated devices. An energy management system helps in maintaining the balance between the available generation and the load demand and thus optimizes the energy usage. It also helps in reducing the peak load, green-house gas emissions, and the operational cost. Energy management can be performed at different levels and is essential for realizing smart homes, smart buildings, and even smart grid. The different objectives considered for designing energy management systems are reduction of emissions, energy cost, operational cost, peak demand, etc. Many traditional and hybrid nature-inspired algorithms are used for optimizing these various objectives. This paper intends to give an overview about the various nature-inspired algorithms used for optimizing energy management systems in homes, buildings, and micro grid.

Quantum Computing Approach for Energy Optimization in a Prosumer Community

“…These algorithms perform better than exact approaches, from the computational point of view, but they do not guarantee to provide the optimal solution [6]. A recent review of nature-inspired techniques for energy management systems is provided in [32], while the work in [33] compares and evaluates a number of self-organizing algorithms for the implementation of residential demand response (DR) techniques, where the goal is to schedule the usage of devices in periods of low demand and coordinate them in order to reduce costs and avoid peaks. More recently, data-driven and machine-learning methods have been investigated, since they often perform better than traditional approaches.…”