Multi-objective Optimal Planning of Distributed Energy Resources Using SPEA2 Algorithms Considering Multi-agent Participation

Ney, None Rafael C.; Canha, Luciane Neves; Adeyanju, Olatunji Matthew; Arend, G.

doi:10.1109/upec.2019.8893569

Cited by 8 publications

(2 citation statements)

References 17 publications

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“…In [135], the augmented epsilon constraint method is used to find the Pareto optimality region to analyse a complex mixed integer linear programming model. The developed multi-objective optimization method in [136] adopts the strength Pareto evolutionary algorithm 2 technique to promote and distribute the benefits of distributed new energy, and is expected to promote the update of current power industry regulatory proposals. An iterative constraint-based search method is presented to optimize the microgrid DRE configuration [137], while an improved teachinglearning optimization algorithm is used to enhance the performance of the algorithm in global search [138].…”

Section: Planning Of Dre In the Iesmentioning

confidence: 99%

“…Traditional algorithm Sequential least squares [125] Minimize the sum of error squares Iterative bi-layer optimization algorithm [127,139] Nonlinear and non-convex function and constraints Augmented epsilon constraint algorithm [135] The most used algorithm for multi-objective optimization Constraint-based iterative search algorithm [137] Based on maximum reliability and minimum cost, the optimal solution result is moderate Intelligent algorithm Improved PSO algorithm based on map-reduce [131] Reduce the particle search scope of a single evolutionary algorithm Multi-objective PSO algorithm [134] Use random selection and adaptive grid method Strength Pareto evolutionary algorithm 2 [136] Use a set of chromosome number chain solutions.…”

Section: Classification Algorithm References Characteristicmentioning

confidence: 99%

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The development of distributed renewable energy, such as photovoltaic power and wind power generation, makes the energy system cleaner, and is of great significance in reducing carbon emissions. However, weather can affect distributed renewable energy power generation, and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy. Energy systems with high penetration of distributed renewable energy involve the high-dimensional, nonlinear dynamics of large-scale complex systems, and the optimal solution of the uncertainty model is a difficult problem. From the perspective of statistical machine learning, the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.

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Section: Planning Of Dre In the Iesmentioning

confidence: 99%

Section: Classification Algorithm References Characteristicmentioning

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Fitting nonlinear mathematical models to the cost function of the quadrafilar helix antenna optimization problem

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Optimal sizing and location of grid-interfaced PV, PHES, and ultra capacitor systems to replace LFO and HFO based power generations

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et al. 2024

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The impacts of climate change, combined with the depletion of fossil fuel reserves, are forcing human civilizations to reconsider the design of electricity generation systems to gradually and extensively incorporate renewable energies. This study aims to investigate the technical and economic aspects of replacing all heavy fuel oil (HFO) and light fuel oil (LFO) thermal power plants connected to the electricity grid in southern Cameroon. The proposed renewable energy system consists of a solar photovoltaic (PV) field, a pumped hydroelectric energy storage (PHES) system, and an ultra-capacitor energy storage system. The economic and technical performance of the new renewable energy system was assessed using metrics such as total annualized project cost (TAC), loss of load probability (LOLP), and loss of power supply probability (LPSP). The Multi-Objective Bonobo Optimizer (MOBO) was used to both size the components of the new renewable energy system and choose the best location for the solar PV array. The results achieved using MOBO were superior to those obtained from other known optimization techniques. Using metaheuristics for renewable energy system sizing necessitated the creation of mathematical models of renewable energy system components and techno-economic decision criteria under MATLAB software. Based on the results for the deficit rate (LPSP) of zero, the installation of the photovoltaic field in Bafoussam had the lowest TAC of around 52.78 × 106€ when compared to the results for Yaoundé, Bamenda, Douala, and Limbe. Finally, the project profitability analysis determined that the project is financially viable when the energy produced by the renewable energy systems is sold at an average price of 0.12 €/kWh.

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Multi-objective Optimal Planning of Distributed Energy Resources Using SPEA2 Algorithms Considering Multi-agent Participation

Cited by 8 publications

References 17 publications

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Fitting nonlinear mathematical models to the cost function of the quadrafilar helix antenna optimization problem

Optimal sizing and location of grid-interfaced PV, PHES, and ultra capacitor systems to replace LFO and HFO based power generations

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