Optimal probabilistic framework for integration of high penetration of distributed energy resources in electrical distribution systems operation

• Autor(a) principal: Cordero Bautista, Luis Gustavo
• Data de Publicação: 2023
• Tipo de documento: Tese
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/250736
• topic: Central limit theorem; Cumulant method; Electric vehicle; Edgeworth expansion; Overvoltage probability constraint; Point estimate method; Probabilistic maximization of PV generation; Probabilistic power flow; Probabilistic optimization framework; Algoritmo ótimo probabilístico; Expansão de Edgeworth; Fluxo de potência probabilístico; Método dos cumulantes; Método de estimação de pontos; Otimização probabilística da geração fotovoltaica; Restrição de probabilidade de sobretensão; Teorema do limite central; Veículos elétricos

Worldwide clean energy policies are accelerating the deployment of distributed energy resources (DERs) integration into the distribution networks aiming to zero-emission targets, system support benefits and cost-effective grid services. Although clean DERs brings opportunities to customers and utilities, they can be unpredictable due to their stochastic nature leading to an increase of the probability of thermal overloading, overvoltage and reverse power flow. Therefore, traditional distribution network operation becomes more complex due to the increasing in variability and uncertainty of high penetration of DERs. It becomes imperative then take into account its stochastic nature to optimize the use and potential value of the DERs to provide crucial statistical information and evaluate their impacts without compromising the grid operation. In this pathway, this thesis explores and develops a novel optimal probabilistic framework to cater for the uncertainties of PV generation, aggregated demand and EVs with the goal to probabilistically maximize PV generation while evaluating and reducing the probability of technical challenges such as overvoltage using confidence interval constraint. Different probabilistic techniques are implemented through this thesis such as the Monte Carlo simulation, the Point Estimate Method, the Cumulant method with probability distribution reconstruction techniques of the Central Limit Theorem and Edgeworth expansion whose statistical information is employed within the optimization process. The efficiency and accuracy of the Cumulant method with Central Limit Theorem and Edgeworth expansion, 2m+1 PEM with Central Limit Theorem and Edgeworth expansion are presented with a detailed comparison in their performances with the Monte Carlo simulation. The validation of the proposed model is carried out using the IEEE 33 bus system, IEEE 69 bus system and a real electrical distribution system 202 bus system with high penetration of PV generation and EVs. Moreover, the application of the novel optimal probabilistic framework is tested in each test system to probabilistically maximize PV generation while effectively reducing overvoltage probability. The proposed method yielded satisfactory results to probabilistically maximize PV generation in terms of higher fitting accuracy of statistical information and optimal probability distribution for voltage, current, power flow through lines, PV generation with an efficient computational processing with promising application to industries.