Environmental Economic Load Dispatch Considering Demand Response Using a New Heuristic Optimization Algorithm

Environmental Economic Load Dispatch Considering Demand Response Using a New Heuristic Optimization Algorithm

Karthik N., Arul Rajagopalan, Prakash V. R., Oscar Danilo Montoya, Sowmmiya U., Kanimozhi R.
DOI: 10.4018/978-1-6684-8816-4.ch013
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The aim of demand response concept is to mitigate the consumption of electric utility consumers to a reasonable consumption level so as to balance the demand-supply requirements. In this current research paper, demand response is considered as a multi-objective optimal economic emission dispatch issue to be addressed. The authors propose a novel nature-inspired optimization algorithm in the name of improved circle search algorithm (ICSA) to find the optimum combination between two generations, such as traditional generators, and demand response as a demand resource. The proposed ICSA was incorporated in MATLAB environment whereas the proposed optimization algorithm was evaluated using two standard test systems such as IEEE-30 bus system and 6-bus system. The optimization results substantiate that the proposed optimization algorithm is capable of mitigating both operation costs as well as test system emissions in a significant manner, while it was also achieving these results overcoming the specified constraints.
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The search for newer alternatives, to replace the conventional power generation processes, is a continuous process among power system (PS) operators. Conventional power generation techniques have a lot of drawbacks in terms of power operation such as carbon dioxide emission, uninterrupted demand for fuel supply, high maintenance costs and unprecedented outages. Most power grids today use Economic Load Dispatch (ELD) as a standard tool. The goal of this tool is to reduce the expenditures associated with generating electricity by optimising the fine-tuning of the real output power, produced, and distributed by the power generating units, to meet the demand over a specified time without compromising certain drawbacks (Karthik et al., 2022). Nonlinear optimization has been hypothesised to be at the heart of the ELD problem (Karthik et al., 2019, 2017).

Demand response (DR) techniques yield efficient outcomes for different types of PS issues such as reliability issues, congestion problems during generation, transmission and distribution, huge operational costs, high emissions and the peak to average ratio during high demand. In literature (Jordehi, 2019), a comprehensive review was carried out on the applications of various optimization techniques on DR in electric PS. An extensive survey was conducted earlier (Yan et al., 2018) comprising different price-driven DR methods. The review concluded that price signals remain an effective market technique in terms of mitigating energy costs, carbon emissions and peak demand saving and risk and reliability management, due to the addition of smart meters at electric infrastructure in residential households. In the study conducted earlier (Liu et al., 2020), the authors formulated an industrial virtual power plant (VPP) optimization model. In this model, an industrial park’s supply and demand-side resources are integrating together. The authors hypothesized the industrial park as the one that deals with day-ahead market.

In the comprehensive review conducted earlier (Naval & Yusta, 2021), different types of virtual power plant models were audited for several types of electricity markets. Following Kitchenham's recommendations, the authors of (Rouzbahani et al., 2021) conducted a thorough review of the scheduling challenges inherent to the VPP concept. The aim of this review was to overcome the challenges faced during scheduling process in VPPs. The study considered different aspects of the scheduling process such as technical, economic and general, when trying to elucidate the energy management problem. Furthermore, simulation studies were also carried out with different types of uncertainty. The study proposed to develop a customer information model so as to impart customer response features in DR and detail about the pattern of participation in energy market (Kwag & Kim, 2012). Using the demand sources as virtual generation resources, this study introduced a new concept for determining the optimal combined scheduling of DR generation in PS.

As well as showcasing consumer data, this model also expresses the marginal cost function for virtual generation resources. A novel sine cosine algorithm (Gonidakis & Vlachos, 2019) was used to find the optimal generating allocation of thermal units for three PS with different characteristics. In this research work, the authors considered emission and fuel cost as objective functions. In the study conducted earlier by the current study author, the roles played by RES and plug-in EVs upon transportation industry and electrical energy have been extensively discussed in terms on overall Green House Gas emissions and net generation cost (Behera et al., 2021). In literature, the authors proposed modified version of Grey Wolf Optimization algorithm to overcome the CEED problem (Halbhavi et al., 2017). As evidence of the supreme robustness of the proposed optimization algorithm, a simulated test system using a combination of renewable energy sources was run. In another study conducted earlier (Sasaki et al., 2019) to overcome the dynamic economic load dispatch (DELD) problem, a new real-time generation schedule was proposed in addition to renewable energy sources.

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