A multi-objective energy optimization in smart grid with high penetration of renewable energy sources. (1st October 2021)

• Record Type: Journal Article
• Authors: Ullah, Kalim; Hafeez, Ghulam; Khan, Imran; Jan, Sadaqat; Javaid, Nadeem
• Is Part Of: Applied energy. Volume 299(2021)
• Publication Date: 2021-10-01
• Subjects: Smart grid -- Multi-objective energy optimization -- Solar -- Wind -- Demand response programs -- Incline block tariff; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.117104
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: Energy optimization plays a vital role in energy management, economic savings, effective planning, reliable and secure power grid operation. However, energy optimization is challenging due to the uncertain and intermittent nature of renewable energy sources (RES) and consumer's behavior. A rigid energy optimization model with assertive intermittent, stochastic, and non-linear behavior capturing abilities is needed in this context. Thus, a novel energy optimization model is developed to optimize the smart microgrid's performance by reducing the operating cost, pollution emission and maximizing availability using RES. To predict the behavior of RES like solar and wind probability density function (PDF) and cumulative density function (CDF) are proposed. Contrarily, to resolve uncertainty and non-linearity of RES, a hybrid scheme of demand response programs (DRPS) and incline block tariff (IBT) with the participation of industrial, commercial, and residential consumers is introduced. For the developed model, an energy optimization strategy based on multi-objective wind-driven optimization (MOWDO) algorithm and multi-objective genetic algorithm (MOGA) is utilized to optimize the operation cost, pollution emission, and availability with/without the involvement in hybrid DRPS and IBT. Simulation results are considered in two different cases: operating cost and pollution emission, and operating cost and availability with/without participating in the hybrid scheme of DRPS and IBT. Simulation results illustrate that the proposed energy optimization model optimizes the performance of smart microgrid in aspects of operation cost, pollution emission, and availability compared to the existing models with/without involvement in hybrid scheme of DRPS and IBT. Thus, results validate that the proposed energy optimization model's performance is outstanding compared to the existing models. Highlights: A novel multi-objective energy optimization strategy in a smart grid with high penetration of RES is introduced. Probabilistic model is introduced for prediction of wind speed and solar irradiance. Hybrid scheme of DRPS and IBT is used to overcome uncertainty caused by the solar and wind. MOWDO and MOGA methods with Pareto criterion using fuzzy mechanism are introduced to solve multi-objective energy optimization problem.