Abstract
An energy optimization strategy is proposed to minimize operation cost and carbon emission with and without demand response programs (DRPs) in the smart grid (SG) integrated with renewable energy sources (RESs). To achieve optimized results, probability density function (PDF) is proposed to predict the behavior of wind and solar energy sources. To overcome uncertainty in power produced by wind and solar RESs, DRPs are proposed with the involvement of residential, commercial, and industrial consumers. In this model, to execute DRPs, we introduced incentive-based payment as price offered packages. Simulations are divided into three steps for optimization of operation cost and carbon emission: (i) solving optimization problem using multi-objective genetic algorithm (MOGA), (ii) optimization of operating cost and carbon emission without DRPs, and (iii) optimization of operating cost and carbon emission with DRPs. To endorse the applicability of the proposed optimization model based on MOGA, a smart sample grid is employed serving residential, commercial, and industrial consumers. In addition, the proposed optimization model based on MOGA is compared to the existing model based on multi-objective particle swarm optimization (MOPSO) algorithm in terms of operation cost and carbon emission. The proposed optimization model based on MOGA outperforms the existing model based on the MOPSO algorithm in terms of operation cost and carbon emission. Experimental results show that the operation cost and carbon emission are reduced by 24% and 28% through MOGA with and without the participation of DRPs, respectively.
Highlights
Energy optimization is an indispensable task in energy management of the smart grid (SG) [1,2].Optimal energy optimization is possible only by actively engaging consumers in demand response programs (DRPs) offered by electric utility companies (ECUs) [3]
This paper proposes three techniques genetic algorithm (GA), Pigeon Inspired
The carbon emission generated by grid is calculated as follows: CEGrid (t) = EmissionCO2 Grid × Po Grid (t) where EmissionCO2 Grid is carbon emissions produced by grid during power generation and Po Grid (t) is the output power produced by grid in time period t, respectively
Summary
Energy optimization is an indispensable task in energy management of the smart grid (SG) [1,2]. In this research [25], few methods are used for energy optimization, such as TOU to reduce economic cost by shifting load from peak to off peak hours, real time pricing (RTP), and DRPs. The proposed model is beneficial for both electricity market and consumers. The concept of incentive-based DRPs is introduced as price offer packages to overcome the uncertainty factor in power generation by RESs like solar and wind In this method, end-users can select an offered price package to participate in energy optimization. End-users can select an offered price package to participate in energy optimization In this model, the Rayleigh PDF is proposed to model variation in energy generation caused by RESs like solar and wind.
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