scholarly journals A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2338
Author(s):  
Emad M. Ahmed ◽  
Rajarajeswari Rathinam ◽  
Suchitra Dayalan ◽  
George S. Fernandez ◽  
Ziad M. Ali ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Smart Grid ◽  
Demand Response ◽  
Optimization Algorithm ◽  
Dynamic Pricing ◽  
Particle Swarm ◽  
Modern World ◽  
Swarm Optimization ◽  
Pricing Scheme ◽  
Load Pattern

In the modern world, the systems getting smarter leads to a rapid increase in the usage of electricity, thereby increasing the load on the grids. The utilities are forced to meet the demand and are under stress during the peak hours due to the shortfall in power generation. The abovesaid deficit signifies the explicit need for a strategy that reduces the peak demand by rescheduling the load pattern, as well as reduces the stress on grids. Demand-side management (DSM) uses several algorithms for proper reallocation of loads, collectively known as demand response (DR). DR strategies effectively culminate in monetary benefits for customers and the utilities using dynamic pricing (DP) and incentive-based procedures. This study attempts to analyze the DP schemes of DR such as time-of-use (TOU) and real-time pricing (RTP) for different load scenarios in a smart grid (SG). Centralized and distributed algorithms are used to analyze the price-based DR problem using RTP. A techno-economic analysis was performed by using particle swarm optimization (PSO) and the strawberry (SBY) optimization algorithms used in handling the DP strategies with 109, 1992, and 7807 controllable industrial, commercial, and residential loads. A better optimization algorithm to go along with the pricing scheme to reduce the peak-to-average ratio (PAR) was identified. The results demonstrate that centralized RTP using the SBY optimization algorithm helped to achieve 14.80%, 21.7%, and 21.84% in cost reduction and outperformed the PSO.

Flexible energy load identification in intelligent manufacturing for demand response using a neural network integrated particle swarm optimization

2020 ◽  
pp. 095440622093365
Author(s):  
Md Monirul Islam ◽  
Zeyi Sun ◽  
Ruwen Qin ◽  
Wenqing Hu ◽  
Haoyi Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Demand Response ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
System Stability ◽  
Management Tool ◽  
Swarm Optimization ◽  
Demand Response Programs

Various demand response programs have been widely established by many utility companies as a critical load management tool to balance the demand and supply for the enhancement of power system stability in smart grid. While participating in these demand response programs, manufacturers need to develop their optimal demand response strategies so that their energy loads can be shifted successfully according to the request of the grid to achieve the lowest energy cost without any loss of production. In this paper, the flexibility of the electricity load from manufacturing systems is introduced. A binary integer mathematical model is developed to identify the flexible loads, their degree of flexibility, and corresponding optimal production schedule as well as the power consumption profiles to ensure the optimal participation of the manufacturers in the demand response programs. A neural network integrated particle swarm optimization algorithm, in which the learning rates of the particle swarm optimization algorithm are predicted by a trained neural network based on the improvement of the fitness values between two successive iterations, is proposed to find the near optimal solution of the formulated model. A numerical case study on a typical manufacturing system is conducted to illustrate the effectiveness of the proposed model as well as the solution approach.

scholarly journals A MOPSO-Based Optimal Demand Response Management System for the Integration of Wind-PV-FC-Battery Smart Grid

2019 ◽  
Vol 8 (4) ◽  
pp. 4402-4410
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Smart Grid ◽  
Demand Response ◽  
Particle Swarm ◽  
Response Strategy ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simulation Results ◽  
The Impact

This paper proposes the Multi-Objective Particle Swarm Optimization to optimize the performance of hybrid WindPV-FC-Battery smart grid to minimize operating costs and emissions. The demand response strategy based on the real-time pricing program with the participation of all kinds of consumers such as residential, commercial and industrial consumers is utilized in order to resolve the power generation uncertainty of renewable energy sources. The multi-objective particle swarm optimization based energy management programming model will be leveraged to reduce the operation costs, emission of pollutants, increase the micro grid operator’s demand response benefits and at the same time satisfying the load demand constraints amongst the others. For the purpose of validating the proposed model, the simulation results are considered for different cases for the optimization of operational costs and emissions with/without the involvement of demand response. The simulation results precisely concluded the impact created by the demand side management in reducing the effects of uncertainty that prevails in forecasted power generation through solar cells and wind turbines.

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