Abstract
This paper proposes one of the optimization methods based on atmospheric motion. It is a global optimization nature-inspired method such as Wind Driven Optimization (WDO) approach to solve the Optimal Power Flow (OPF) and Emission Index (EI) in electric power systems. Our main aim is to minimize an objective function necessary for a best balance between the energy production and its consumption, which is presented as a nonlinear function, taking into account of the equality and inequality constraints. The WDO approach is nature-inspired, population based iterative heuristic optimization algorithm for multi-dimensional and multi-modal problems. WDO method have been examined and tested on the standard IEEE 30-bus system and IEEE 57-bus system with different objectives that reflect total active power generation cost, the active power losses and the emission index. The results of used method have been compared and validated with known references published recently. The results are promising and show the effectiveness and robustness of proposed approach.
Highlights
Electric power systems engineering has the longest history of development compared to the various fields of engineering
This paper proposes one of the optimization methods based on atmospheric motion
The proposed Wind Driven Optimization (WDO)-based algorithm for solving Optimal Power Flow (OPF) and Emission Index (EI) problems has been applied to the IEEE 30bus and IEEE 57-bus test systems
Summary
Electric power systems engineering has the longest history of development compared to the various fields of engineering. In the last two decades, and in order to solve the OPF and EI problems, several methods of optimization are formulated such as Artificial bee colony (ABC) and Incremental artificial bee colony [17-25], Bacterial foraging algorithms (BFA) and hybrid fuzzy based Bacterial foraging algorithm [26-27], Artificial neutral networks (ANN) [29, 30], Harmony search (HS) [31, 32], Cuckoo search algorithm (CSA) [33], Evolution programming (EP) [34, 35], Differential evaluation (DE) [36-39], Modified differential evaluation (MDE) [40-44], Tabu search (TS) [45-47], Simulated annealing (SA) [4849], Gravitational search algorithms (GSA) [50-52], Evolutionary algorithm [53-55], Genetic algorithms (GA) [56-60], Particle swarm optimization (PSO) [61-69], Modified Particle swarm optimization (MPSO) [70-72], Distributed Sobol Particle swarm optimization (DSPSO) [73], Ant colony optimization (ACO) [74-79], Firefly Algorithm (FFA) [80-82], Tree-seed algorithm (TSA) [83], Sine-cosine algorithm (SCA) [84], Crow search algorithm (CSA) [85], Hybrid particle swarm optimization-differential evolution (FAHSPSO) [86], Modified imperialist competitive algorithm (MICA) [87], Grey wolf optimizer (GWO) [3, 38, 88-96], Shuffled frog leaping algorithm (SFLA) and Modified SFLA [48, 97][98], Electromagnetism-like mechanism method (ELM) [99], Ant-lion optimizer [100], Interior search algorithm [101], and more recently the Wind driven optimization (WDO) method [102-112] were successfully utilized since their introduction to the literature as single objective optimization algorithm, Machine Learning and Modified grasshopper optimization Algorithms [113,114], Rao Algorithm [115], Hamiltonian Technique [116], Artificial Eco System optimization [117-118], Teaching-Learning-Studying-Based Optimization [119] and Combining Deep Learning [120], Artificial Fish Swarm Algorithm [121] Variants of these algorithms were proposed to handle multi-objective functions in electric power systems. This promising algorithm is implemented firstly to solve the electromagnetic problems in communication engineering studies [106]
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