DP-EPSO: Differential privacy protection algorithm based on differential evolution and particle swarm optimization

Gao, Qiang; Sun, Han; Wang, Zhifang

doi:10.1016/j.optlastec.2023.110541

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…As objective functions, Kweon et al [17] considered environmental costs, operational costs, and safety when optimizing microgrids operating in island mode. Last but not least, Rivadulla et al [18] utilized particle swarm optimization (PSO) to develop a model for AC/DC hybrid microgrids.…”

Section: Current Status Of Microgrid Optimal Scheduling Researchmentioning

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: Current Status Of Microgrid Optimal Scheduling Researchmentioning

confidence: 99%

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

Guan,

Wang,

et al. 2024

Energies

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Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids

Vafaeva,

Raju,

Ballabh

et al. 2024

E3S Web Conf.

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This study investigates the optimization of the size of a solar-wind hybrid microgrid using Particle Swarm Optimization (PSO) to improve energy production efficiency, economic feasibility, and overall sustainability. By using past solar and wind resource data, load demand profiles, and system component specifications, the PSO algorithm effectively maximized the capabilities of solar panels and wind turbines. The findings indicate a significant rise in daily energy production, with a 15% enhancement in solar panel capability and a 12% boost in wind turbine capability. The increased energy production plays a crucial role in dealing with the natural irregularity of renewable resources, hence enhancing the resilience and self-reliance of the microgrid. The economic calculations demonstrate significant improvements in the economic feasibility of the microgrid designs. The Levelized Cost of Energy (LCOE) undergoes a significant 10% decrease, suggesting a more economically efficient energy generation. Moreover, the payback time for the original expenditure is reduced by 15%, indicating faster returns on investment. The economic improvements highlight the practical advantages of using PSO for microgrid size, in line with the goal of creating sustainable energy solutions while minimizing economic costs. The improved performance of Particle Swarm Optimization (PSO) is shown by a thorough comparison study with other optimization approaches, such as Genetic Algorithms (GA) and Simulated Annealing (SA). The superior convergence rate of PSO, together with a 15% enhancement in solution quality relative to GA and SA, underscores the efficiency and efficacy of PSO in traversing the complex solution space associated with microgrid size. PSO’s comparative advantage makes it an effective tool for tackling the intricacies of integrating renewable energy, highlighting its potential for extensive use in microgrid design and optimization. The sensitivity evaluations demonstrate that the solutions optimized by the PSO are resilient even when important parameters vary, thereby highlighting the stability and dependability of the approach. In addition to technical and economic factors, the study evaluates the environmental consequences and social aspects of the optimum microgrid designs. The land use efficiency has seen a 10% enhancement, demonstrating the optimum application of area for renewable energy infrastructure. In addition, there is a 7% improvement in community approval, which demonstrates the algorithm’s ability to effectively handle social aspects and promote a comprehensive and socially acceptable approach to renewable energy projects.

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DP-EPSO: Differential privacy protection algorithm based on differential evolution and particle swarm optimization

Cited by 2 publications

References 9 publications

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

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

Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids

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