Optimization Algorithms in Smart Grids: A Systematic Literature Review

Abstract--Electrical smart grids are units that supply electricity from power plants to the users to yield reduced costs, power failures/loss, and maximized energy management. Smart grids (SGs) are well-known devices due to their exceptional benefits such as bi-directional communication, stability, detection of power failures, and inter-connectivity with appliances for monitoring purposes. Hence, the importance of SGs as a research field is increasing with every passing year. This paper focuses on novel features and applications of smart grids in domestic and industrial sectors. Specifically, we focused on Genetic algorithm, Particle Swarm Optimization, and Grey Wolf Optimization to study the efforts made up till date for maximized energy management and cost minimization in SGs. Many counter Smart grids refers to an electric grid that delivers the attack solutions such as secure data collectors, broadcast authentication, electricity from utility (power generator sources/company) to and secure DoS-resistant broadcast authentication the users (residential/industrial). A simple smart grid connection protocols have been studied to secure the data collection and is shown in Figure 1, with bi-directional communication coping the demands of users in efficient ways [9], [10]. The process of electricity other challenges are faced by both utility and users (energy delivery is capable of monitoring, modeling, controlling, data supply and energy demand) such as energy management, filtering, and data processing with help of number of intelligent cost efficiency, reducing power losses, and reducing pollutant features such as Artificial Intelligence (AI) or Computational emissions [11], [12]. The aforementioned challenges can be Intelligence (CI) as shown in Figure 2. SGs allow users to addressed using optimization techniques in SGs to maximize schedule the appliances depending upon pricing hours and the profit (for both users and utility) by managing electricity its demand that helps in saving energy, increasing reliability, distribution and reducing emissions. Furthermore, SGs support Optimization in SGs is employed to find the conditions with bidirectional power line communications such as Home Area maximum benefits while (at the same time) minimizing the Network (HAN) or Wide Area Network (WAN), and wireless electricity wastage and cost [13]. Hence, optimization problem communications such as ZigBee, 6LowPAN, Z-wave, IoT in SGs is defined as a scenario (i.e., an objective function) that networks, etc. [3]-[6]. For future work, we aim to expand our research for other optimization algorithms (i.e., ABC, ACO). Our contributions in this paper are: fluenced by a set of variables and/or constraints.