An enhanced multi‐objective differential evolution algorithm for dynamic environmental economic dispatch of power system with wind power

Bai, Yingjie; Wu, Xuedong; Xia, Aiming

doi:10.1002/ese3.827

Cited by 26 publications

(6 citation statements)

References 37 publications

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“…Essentially, the compromise solution is picked once the maximum number of iterations has been reached. For this process, a fuzzy decision-making technique is applied through two consecutive stages [46,47]. In the first stage, a fuzzification process is executed to convert each non-dominated solution into a membership function.…”

Section: Best Compromise Selection Based On Fuzzy Decision-making Tec...mentioning

confidence: 99%

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

et al. 2022

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This paper proposes a multi-objective teaching–learning studying-based algorithm (MTLSBA) to handle different objective frameworks for solving the large-scale Combined Heat and Power Economic Environmental Dispatch (CHPEED) problem. It aims at minimizing the fuel costs and emissions by managing the power-only, CHP and heat-only units. TLSBA is a modified version of TLBA to increase its global optimization performance by merging a new studying strategy. Based on this integrated tactic, every participant gathers knowledge from someone else randomly to improve his position. The position is specified as the vector of the design variables, which are the power and heat outputs from the power-only, CHP and heat-only units. TLSBA has been upgraded to include an extra Pareto archiving to capture and sustain the non-dominated responses. The objective characteristic is dynamically adapted by systematically modifying the shape of the applicable objective model. Likewise, a decision-making approach based on the fuzzy concept is used to select the most suitable CHPEED solution for large-scale dispatching of combined electrical power and heat energies. The proposed MTLSBA is assigned to multiple testing of 5-unit, 7-unit and 96-unit systems. It is contrasted with other reported techniques in the literature. According to numerical data, the suggested MTLSBA outperforms the others in terms of effectiveness and robustness indices. For the 5-unit system, the proposed MTLSBA achieves improvement in the fuel costs of 0.6625% and 0.3677% and reduction in the emissions of 2.723% and 7.4669% compared to non-dominated sorting genetic algorithm (NSGA-II) and strength Pareto evolutionary algorithm (SPEA 2), respectively. For the 7-unit system, the proposed MTLSBA achieves improvement in the fuel costs of 2.927% and 3.041% and reduction in the emissions of 40.156% and 40.050% compared to NSGA-II and SPEA 2, respectively.

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Section: Best Compromise Selection Based On Fuzzy Decision-making Tec...mentioning

confidence: 99%

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

et al. 2022

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“…In addition, the nodal power balance according to shift factor sensitivity matrices is considered in the model by Equation (25). The up and down reserve capacity allocations for GFGs are restricted by maximum and minimum generation capacity, as given in Equations ( 26) and (27), respectively [27,28]. Similarly, Equations ( 28) and (29) are used to limit TGs' capacity from the viewpoint of generation and reserve.…”

Section: Proposed Robust Scuc Problemmentioning

confidence: 99%

Flexibility Optimization in Robust Co-Optimization of Combined Power System and Gas Networks Using Transmission Lines’ Switching

2022

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The operational flexibility in electricity networks with a high penetration rate of renewable resources has received a great deal of attention. In several research works, gas network constraints have been studied in operational flexibility studies because specific characteristics of the gas-fired units play a major role in procuring flexibility. However, the literature shows that the interval range of the uncertainty set has been assumed to be deterministic in most of the available research works. This paper tries to fill the research gap about stochastic modeling of the interval range of the uncertainty set. It considers the uncertainty set’s interval range an uncertain parameter, allowing optimal scheduling with less conservative assumptions. In addition, transmission lines’ switching is considered to optimize the network topology when the N-1 reliability measure is adopted. As a nonconvex problem, the recourse decision level would be a mixed-integer program (MIP), for which an exact nested column-and-constraint generation algorithm is used to find the best solutions. The proposed method was applied to IEEE six-bus and 39-bus test systems, interconnecting their corresponding gas networks. The test results infer that in a specified risk level for the IEEE six-bus and 39-bus test systems, with a rather slight increase (7.3% and 065%) in the operation cost, a large reduction (89% and 99%) in the out-of-sample cost could be obtained, respectively. The test results illustrate the advantages of the proposed method, using the variable interval range of the uncertainty set and corrective transmission lines’ switching. In addition, it was shown that if transmission switching is not considered, the total cost rises up to 18.8%.

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“…An improved multi-objective differential evolution method (EMODE) was proposed for solving dynamic CEED of thermal-wind power production systems. To improve the optimization impact, the suggested technique employs the pre-eminence of viable solution and non-subjugated sorting (NSS) [17]. A cross-entropy optimization practice was proposed for solving CEED with renewable energy integration with conventional power plants.…”

Section: Introductionmentioning

confidence: 99%

4-E (Energy-Emission-Environment-Economic) Dispatch Problem in Solar PV Energy Using Hybrid PSOTLBO Search Algorithm

Kaushal

Kaur²

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Renewable energy sources have evolved into a technologically and economically feasible choice for use by electrical utilities. Furthermore, the widespread usage of renewable energy sources is driving policymakers and utilities to increase green energy’s role to ensure long-term economic growth. The most influential non-conventional energy source for generating power is solar energy. Electric power systems must be designed, built, and run in such a way that the majority of energy demand for loads is supplied reliably, cost-effectively, and in an environmentally responsible manner. In 4-E dispatch, the term “energy” refers to generated power in the scheduled period, the term “emission” refers to the amount of emission released in the scheduled period, and the term “environment” refers to health and environmental damage costs in the scheduled period and the term “economic” refers to power generating cost in the scheduled period. In this paper a hybrid of particle swarm optimization (PSO) and teaching learning-based optimization (TLBO), named as PSOTLBO is proposed, developed, and effectively applied to attain the best or optimum solutions for the 4-E (Energy-Emission-Environment-Economic) dispatch problem for scenarios involving ten thermal power plants and thirteen solar photovoltaic (PV) plants.

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An enhanced multi‐objective differential evolution algorithm for dynamic environmental economic dispatch of power system with wind power

Cited by 26 publications

References 37 publications

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

Flexibility Optimization in Robust Co-Optimization of Combined Power System and Gas Networks Using Transmission Lines’ Switching

4-E (Energy-Emission-Environment-Economic) Dispatch Problem in Solar PV Energy Using Hybrid PSOTLBO Search Algorithm

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