A demand side management control strategy using Whale optimization algorithm

Abstract

In recent years, demand side management programs are in the spotlight due to the evolution of the smart grid and consumer-centric policies. Demand side management program contains many objectives one of the prime objective is to manage energy demand by certain change in consumer demand. This can be achieved by various methods such as financial discount and change in behavior through imparting education to support the stressed conditions of the grid. This paper demonstrates demand side management strategies based upon strategic conservation, peak clipping and load shifting techniques for future smart grids. The grid contains large number of controllable devices. The day before strategic conservation, peak clipping and load shifting techniques discussed in this paper are mathematically derived for minimization problem. A heuristic-based Whale optimization algorithm (WOA) was developed for solving this problem of minimization. Simulations are conducted on a test smart grid that contains a variation in loads in two service areas, one with residential consumers, and another with commercial consumers. WOA proves its efficacy by comparing the results with spider monkey optimization and biogeography based optimization. The simulation results show that proposed demand side management strategies achieve substantial savings, while reducing the peak load demand of the smart grid.

Industrial Demand-Side Management by Means of Differential Evolution Considering Energy Price and Labour Cost

“…Genetic algorithm and particle swarm optimisation are traditional ones that have been widely applied for managing electric vehicles loads [20], optimising micro-market strategies [21], and operating smart grids [22]. More advanced algorithms have been applied to DSM, such as biogeography-based optimisation and whale optimisation algorithm [23,24].…”

Impact of Divergence in BBO on Efficient Energy Strategy of Demand Side Management

“…By using GA [10] minimized the Peak to Average Ratio (PAR) to increase the overall efficiency of smart grid, in [11] authors developed a DSM system for building using GA, [12] solved the economic load dispatch problem under the demand response and energy consumption scheduling held in [13] and [14] which gave the demand response optimization model for home appliances. Other examples of these algorithms are Evolutionary Algorithms [15,16] and Biogeography Based Optimization Algorithm [17][18][19]. Evolutionary game theory has been used to analyze the different factors that influencing the demand participation of users [15] and in [16] evolutionary algorithm is used for home energy management.…”

“…To solve power scheduling problems Biogeography Based Optimization has been used in smart home [17], acquired efficient energy management in smart grid [18] and to gain countable reduction in cost, while lowering the peak load [19]. If talking about these algorithms, the flow starts with the initial solution set and successive increments in runs with incorporation of various operators.…”

Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks

“…In this regard, scheduling problems need a smart controller for the VPP system using optimization algorithms. Thus, several optimization algorithms have been established by researchers recently, such as the genetic algorithm [7], gravitational search algorithm [9][10][11], butterfly algorithm [12], herd-related optimization approaches [13], whale optimization algorithm [14], cat swarm optimization [15], practical swarm optimization (PSO) [16], etc. The energy management duties are to ensure security; use a mixture of energy, generation, transmission, and distribution resources; and minimize losses and increase profit [17][18][19][20][21].…”