An integrated approach for cost-and emission optimal planning of coastal microgrid with demand-side management

Dawoud, Samir M.; Elkadeem, Mohamed R.; Abido, Mohammad A.; Atiya, Eman G.; Lin, Xiangning; Alzahrani, Atif Saeed; Kotb, Kotb M.

doi:10.1016/j.scs.2023.105149

Cited by 5 publications

(1 citation statement)

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“…Moreover, some remote When considering the practical implications of optimal microgrid scheduling, this approach is not only beneficial to users as it reduces electricity costs and demand-side power consumption but also assists in reducing environmental pollution at the power generation stage from the supply side. Consequently, the grid becomes more stable, and energy loss is reduced [4]. In relation to environmental protection, the development of photovoltaic and wind power can be a significant contributor to reducing the severity of environmental and energy crises.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm

Guan,

Wang,

et al. 2024

Energies

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Microgrid optimization scheduling, as a crucial part of smart grid optimization, plays a significant role in reducing energy consumption and environmental pollution. The development goals of microgrids not only aim to meet the basic demands of electricity supply but also to enhance economic benefits and environmental protection. In this regard, a multi-objective optimization scheduling model for microgrids in grid-connected mode is proposed, which comprehensively considers the operational costs and environmental protection costs of microgrid systems. This model also incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation, and it utilizes the improved algorithm to solve the optimization model. Simulation results demonstrate that this model can effectively reduce electricity costs for users and environmental pollution, promoting the optimized operation of microgrids and verifying the superior performance of the improved PSO algorithm. After algorithmic improvements, the optimal total cost achieved was CNY 836.23, representing a decrease from the pre-improvement optimal value of CNY 850.

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Section: Introductionmentioning

confidence: 99%