Genetic Algorithm to Solve Demand Side Management and Economic Dispatch Problem

Mellouk, Lamyae; Boulmalf, Mohammed; Aaroud, Abdessadek; Zine-Dine, Khalid; Benhaddou, Driss

doi:10.1016/j.procs.2018.04.111

Cited by 28 publications

(19 citation statements)

References 10 publications

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“…Regarding DED, many optimization algorithms have been applied to solve the DED problem, such as symbiotic organisms search (SOS) algorithm, which combines GA, PSO, and SOS in a tri-base population [25]. In [26], the GA algorithm is implemented to optimize the demand side management and the DED as a complementary stage. An accelerated approach is proposed in [27] to solve the DED problem with high computational speed.…”

Section: Introductionmentioning

confidence: 99%

Improved Salp–Swarm Optimizer and Accurate Forecasting Model for Dynamic Economic Dispatch in Sustainable Power Systems

et al. 2020

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Worldwide, the penetrations of photovoltaic (PV) and energy storage systems are increased in power systems. Due to the intermittent nature of PVs, these sustainable power systems require efficient managing and prediction techniques to ensure economic and secure operations. In this paper, a comprehensive dynamic economic dispatch (DED) framework is proposed that includes fuel-based generators, PV, and energy storage devices in sustainable power systems, considering various profiles of PV (clear and cloudy). The DED model aims at minimizing the total fuel cost of power generation stations while considering various constraints of generation stations, the power system, PV, and energy storage systems. An improved optimization algorithm is proposed to solve the DED optimization problem for a sustainable power system. In particular, a mutation mechanism is combined with a salp–swarm algorithm (SSA) to enhance the exploitation of the search space so that it provides a better population to get the optimal global solution. In addition, we propose a DED handling strategy that involves the use of PV power and load forecasting models based on deep learning techniques. The improved SSA algorithm is validated by ten benchmark problems and applied to the DED optimization problem for a hybrid power system that includes 40 thermal generators and PV and energy storage systems. The experimental results demonstrate the efficiency of the proposed framework with different penetrations of PV.

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Section: Introductionmentioning

confidence: 99%

Improved Salp–Swarm Optimizer and Accurate Forecasting Model for Dynamic Economic Dispatch in Sustainable Power Systems

et al. 2020

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“…The authors used energy rates as a function of the overall power demand of customers. Mellouk et al (2018) have used a genetic algorithm method to solve DSM and dynamic economic dispatch problems.…”

Section: Introductionmentioning

confidence: 99%

Optimal renewable resources mix for low carbon production energy system in Morocco

et al. 2020

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The production of electrical energy has always been a subject of debate to fight against climate change and preserve natural resources. Several countries, including Morocco, have proposed ambitious policies to develop renewable energy sources of production in the short and medium term. The major problem of renewable sources production is their intermittent nature. Using storage units, particularly PETS' and molten salt technologies, will increase the penetration rate of renewable energies. The balance between production and consumption will optimize production, and it represents an important aspect of smart grids. In this article, an electrical energy consumption prediction was developed for the period 2019-2025, and optimization of the renewable energies injected within the grid was made by introducing the algorithm used. The results show that the use of storage units will increase the rate of renewable energies integration to 47.189% of the total installed capacity.

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“…In recent years, artificial intelligence algorithms have been widely used in dynamic economic dispatching models. For example, genetic algorithm (GA) [3][4][5][6][7][8][9], simulated annealing algorithm (SA) [10], tabu search algorithm (TS) [11,12], differential evolution algorithm (DE) [13][14][15][16][17][18][19], particle swarm optimization (PSO) [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34], artificial bee colony algorithm (ABC) [35], artificial immune system algorithm (AIS) [36,37], evolutionary programming algorithm (EP) [38][39][40], complementary quadratic programming algorithm (cQP) [41], biogeography-based optimization algorithm (BBO) [42,43], teaching learning-based optimization algorithm (TLBO) [44,45], charged system search algorithm (CSSA) [46], flower pollination algorithm (FPA) [47], rooted tree optimiz...…”

Section: Introductionmentioning

confidence: 99%

Modified Antipredatory Particle Swarm Optimization for Dynamic Economic Dispatch with Wind Power

Chen

Han

Wang

et al. 2019

Mathematical Problems in Engineering

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A modified antipredatory particle swarm optimization (MAPSO) algorithm with evasive adjustment behavior is proposed to solve the dynamic economic dispatch problem of wind power. The algorithm adds the social avoidance inertia weight to the conventional antipredatory particle swarm optimization (APSO) speed update formula. The size of inertia weight is determined by the distance between the global worst particle and other particles. After normalizing the distance, the inertia weight is controlled within the ideal range by using the characteristics of sigmoid function and linear decreasing method, which improves the ability of particles to avoid the worst solution. Then, according to the characteristics of the acceleration coefficient which can adjust the local and global searching ability of particles, acceleration coefficients of nonlinear change strategy is proposed to improve the searching ability of the algorithm. Finally, the proposed algorithm is applied to several benchmark functions and power grid system models, and the results are compared with those reported using other algorithms, which prove the effectiveness and superiority of the proposed algorithm.

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Genetic Algorithm to Solve Demand Side Management and Economic Dispatch Problem

Cited by 28 publications

References 10 publications

Improved Salp–Swarm Optimizer and Accurate Forecasting Model for Dynamic Economic Dispatch in Sustainable Power Systems

Improved Salp–Swarm Optimizer and Accurate Forecasting Model for Dynamic Economic Dispatch in Sustainable Power Systems

Optimal renewable resources mix for low carbon production energy system in Morocco

Modified Antipredatory Particle Swarm Optimization for Dynamic Economic Dispatch with Wind Power

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