An improved adaptive differential evolution optimizer for non-convex Economic Dispatch Problems

Hamdi, Mounira; Idomghar, Lhasanne; Chaoui, Mondher; Kachouri, Abdennaceur

doi:10.1016/j.asoc.2019.105868

Cited by 37 publications

(10 citation statements)

References 39 publications

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“…Several research studies have addressed the significance of the objective function in maximizing power system reliability in economic dispatch. This objective is accomplished by integrating constraints related to power loss into the optimization problem while solving the EDP [60][61][62][63][64]. Table 3 presents a comprehensive summary of single objective function approaches in EDPs.…”

Section: Single Objective Optimizationmentioning

confidence: 99%

Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review

Marzbani,

Abdelfatah

2024

Energies

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Economic Dispatch Problems (EDP) refer to the process of determining the power output of generation units such that the electricity demand of the system is satisfied at a minimum cost while technical and operational constraints of the system are satisfied. This procedure is vital in the efficient energy management of electricity networks since it can ensure the reliable and efficient operation of power systems. As power systems transition from conventional to modern ones, new components and constraints are introduced to power systems, making the EDP increasingly complex. This highlights the importance of developing advanced optimization techniques that can efficiently handle these new complexities to ensure optimal operation and cost-effectiveness of power systems. This review paper provides a comprehensive exploration of the EDP, encompassing its mathematical formulation and the examination of commonly used problem formulation techniques, including single and multi-objective optimization methods. It also explores the progression of paradigms in economic dispatch, tracing the journey from traditional methods to contemporary strategies in power system management. The paper categorizes the commonly utilized techniques for solving EDP into four groups: conventional mathematical approaches, uncertainty modelling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, a predominant focus on single-case studies that limit the generalizability of findings, and the challenge of comparing research due to arbitrary system choices and formulation variations. The present paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field.

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Section: Single Objective Optimizationmentioning

confidence: 99%

Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review

Marzbani,

Abdelfatah

2024

Energies

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“…They are adaptable and can return several solutions to a single problem in a single simulation run. Several well-known optimization techniques have attempted to overcome these issues, including: Genetic algorithm (GA) [25], moth swarm optimization algorithm (MSA) [26],differential evolution (DE) [27], [28],simulated annealing (SA) [29], particle swarm optimization (PSO) [30], [31], spider monkey optimization (SMO) [32], grey wolf optimizer (GWO) [33], gravitational search algorithm (GSA), [34],fire fly algorithm (FFA) [35], spiral optimization algorithm (SOA) [36], harmony search algorithm (HSA) [37], [38], harris hawks optimization (HHO) [39], squirrel search algorithm (SSA) [40], artificial bee colony (ABC) [41], sine-cosine algorithm (SCA) [42], differential evolution (DE) [43], bacterial forging algorithm (BFA) [44], Fluid search optimization (FSO) [45], improved ABC (IABC) [46], modified BFA (MBFA) [47], hybrid hierarchical evolution (HHE) [48], whale optimization algorithm (WOA) [49], chaos turbo PSO (CTPSO) [50], hybrid particle swarm gravitational search algorithm (PSOGSA) [51], multi-objective PSO (MOPSO) [52], new global PSO (NGPSO) [53], quantum inspired glowworm swarm optimization (QGSO) [54], multi-objective DE based PSO (MODE/PSO) [55], combination of continuous greedy randomized adaptive search procedure and modified differential evolution (CGRASP-MDE), combination of continuous greedy randomized adaptive search procedure and self-adaptive differential evolution (C-GRASP-SaDE) [56], successful history-based adaptive DE variants with linear population size reduction (L-SHADE) and improved L-SHADE (IL-SHADE) [57].…”

Section: Literature Reviewmentioning

confidence: 99%

Economic Emission and Energy Scheduling for Renewable Rich Network Using Bio-Inspired Optimization

et al. 2022

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This paper deals with the combined economic emission load dispatch (CEELD) problem with and without the integration of renewable energy sources (RESs), in some more rational test scenarios of single CEELD and multi-objective CEELD (MO-CEELD) optimization. Hence, an efficient and coherent approach is presented to minimize the generation and emission cost using one of the bio-inspired metaheuristic algorithms named flower pollination algorithm (FPA). The evolution of a power system along with the integration of RESs demands equal advancement in the operation and control algorithms of the power grid. Therefore, the proposed approach in this paper offers an evolutionary single and multiobjective optimization process based on a bio-inspired FPA. Further, it has been validated by achieving the best compromise solution (BCS) using the Pareto categorizing process and fuzzy membership function. Moreover, different study cases comprising eleven and fifteen thermal units with and without considering RESs are tested with the proposed technique. Finally, the effectiveness of the proposed approach is tested by comparing the simulation results with some already existing techniques in terms of overall fuel and emission cost. Significantly, it has been noticed from the results that it outperforms all the previously presented approaches like PSO, DE, GSA, AEO, BA, and dBA, thus justifying its applicability.INDEX TERMS Combined economic emission dispatch, fuzzy membership function, multi-objective flower pollination algorithm, Pareto categorizing process, price penalty factor approach.

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“…In the literature, metaheuristic algorithms have been classified into four categories [10]: evolution-based algorithms (EA), such as EMO [11], IL-SHADE [12], MPDE [13], DPADE [14], L-HMDE [15], SDO [16], RA [17], BSA [18,19], DSC-ATMAES [20], etc. ; swarm-based algorithms (SA)-QPGPSO-w [21], MABC [22], CCSO, iCSPM [23,24], CBA [25], AGWO [26], ISSO [27], HHO [28], etc.…”

Section: Introductionmentioning

confidence: 99%

Social small group optimization algorithm for large-scale economic dispatch problem with valve-point effects and multi-fuel sources

Secui,

Secui

2024

Appl Intell

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Economic dispatch is an important issue in the management of power systems and is the current focus of specialists. In this paper, a new metaheuristic optimization algorithm is proposed, named Social Small Group Optimization (SSGO), inspired by the psychosocial processes that occur between members of small groups to solve real-life problems. The starting point of the SSGO algorithm is a philosophical conception similar to that of the social group optimization (SGO) algorithm. The novelty lies in the introduction of the small group concept and the modeling of individuals’ evolution based on the social influence between two or more members of the small group. This conceptual framework has been mathematically mapped through a set of heuristics that are used to update the solutions, and the best solutions are retained by employing a greedy selection strategy. SSGO has been applied to solve the economic dispatch problem by considering some practical aspects, such as valve-point loading effects, sources with multiple fuel options, prohibited operating zones, and transmission line losses. The efficiency of the SSGO algorithm was tested on several mathematical functions (unimodal, multimodal, expanded, and composition functions) and on power systems of varying sizes (ranging from 10-units to 1280-units). The SSGO algorithm was compared with SGO and other algorithms belonging to various categories (such as: evolution-based, swarm-based, human behavior-based, hybrid algorithms, etc.), and the results indicated that SSGO outperforms other algorithms applied to solve the economic dispatch problem in terms of quality and stability of the solutions, as well as computation time.

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An improved adaptive differential evolution optimizer for non-convex Economic Dispatch Problems

Cited by 37 publications

References 39 publications

Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review

Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review

Economic Emission and Energy Scheduling for Renewable Rich Network Using Bio-Inspired Optimization

Social small group optimization algorithm for large-scale economic dispatch problem with valve-point effects and multi-fuel sources

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