Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization

Abstract

Demand response programs can effectively handle the smart grid’s increasing energy demand and power imbalances. In this regard, price-based DR (PBDR) and incentive-based DR (IBDR) are two broad categories of demand response in which incentives for consumers are provided in IBDR to reduce their demand. This work aims to implement the IBDR strategy from the perspective of the service provider and consumers. The relationship between the different entities concerned is modelled. The incentives offered by the service provider (SP) to its consumers and the consumers’ reduced demand are optimized using Stackelberg–particle swarm optimization (SPSO) as a bi-level problem. Furthermore, the system with a grid operator, the industrial consumers of the grid operator, the service provider and its consumers are analyzed from the service provider’s viewpoint as a tri-level problem. The benefits offered by the service provider to its customers, the incentives provided by the grid operator to its industrial customers, the reduction of customer demand, and the average cost procured by the grid operator are optimized using SPSO and compared with the Stackelberg-distributed algorithm. The problem was analyzed for an hour and 24 h in the MATLAB environment. Besides this, sensitivity analysis and payment analysis were carried out in order to delve into the impact of the demand response program concerning the change in customer parameters.

Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization

“…IBDR was found to be effective in reducing the overall operational cost of microgrids [14][15][16]. In Reference [17] IBDR with one selling price among two industrial consumers was analyzed with variation in discomfort weight factor. Here, also IBDR was found to be effective in solving demand deficit issues.…”

“…Among the analytical approaches, game theory [8], linear programming (LP) [11,13,34], Monte-Carlo simulation [15], and mixed integer nonlinear programming (MINLP) [31,33,34] were used to solve this problem.. The NI algorithms include particle swarm optimization (PSO) [7,17], grey wolf optimizer [9], Genetic Algorithm (GA) [10,26], Jaya algorithm [22], Artificial Bee Colony Algorithm (ABC) [26], and NSGA-III [28]. The NI technique is found to be very effective for the solution of nonlinear, discontinuous, and multi-modal functions, and it mostly provides a reasonable solution efficiently [38,39].…”

Development of Operation Strategy for Battery Energy Storage System into Hybrid AC Microgrids

“…The hybridization of microgrids integrated with photovoltaic generation systems plays an important role as an alternative to reduce diesel consumption and consequent reduction in operating costs [1]. In this context, energy storage systems act as enablers and enhancers of this integration, since several instability factors can be found when only the photovoltaic system is introduced, mainly due to the lack of control of the solar source [2].…”

Demand Response in HEMSs Using DRL and the Impact of Its Various Configurations and Environmental Changes

“…It identifies what works better with a particular environment by assigning a numeric reward or penalty to the action taken after receiving feedback from the environment. In contrast to the performance of RL, the conventional and model-based DR approaches, such as mixed interlinear programming [5,6], mixed integer non-linear programming (MINLP) [7], particle swarm optimization (PSO) [8], and Stackelberg PSO [9], require accurate mathematical models and parameters, the construction of which is challenging because of the increasing system complexities and uncertainties.…”