Reliability-based Probabilistic Network Pricing with Demand Uncertainty

The future energy system embraces growing flexible demand and generation, which bring large-scale uncertainties and challenges to current deterministic network pricing methods. This paper proposes a novel reliability-based probabilistic network pricing method considering demand uncertainty. Network reliability performance, including probabilistic contingency power flow (PCPF) and tolerance loss of load (TLoL), are used to assess the impact of demand uncertainty on actual network investment cost, where PCPF is formulated by the combined cumulant and series expansion. The tail value at risk (TVaR) is used to generate analytical solutions to determine network reinforcement horizons. Then, final network charges are calculated based on the core of the Long-run incremental cost (LRIC) algorithm. A 15-bus system is employed to demonstrate the proposed method. Results indicate that the pricing signal is sensitive to both demand uncertainty and network reliability, incentivising demand to reduce uncertainties. This is the first-ever network pricing method that determines network investment costs considering both supply reliability and demand uncertainties. It can guide better sitting and sizing of future flexible demand in distribution systems to minimise investment costs and reduce network charges, thus enabling a more efficient system planning and cheaper integration.</p

Market model for clustered microgrids optimisation including distribution network operations

This paper proposes a market model for the purpose of optimisation of clustered but sparse microgrids (MGs). The MGs are connected with the market by distribution networks for the sake of energy balance and to overcome emergency situations. The developed market structure enables the integration of virtual power plants (VPPs) in energy requirement of MGs. The MGs, internal service providers (ISPs), VPPs and distribution network operator (DNO) are present as distinct entities with individual objective of minimum operational cost. Each MG is assumed to be present with a commitment to service its own loads prior to export. Thus an optimisation problem is formulated with the core objective of minimum cost of operation, reduced network loss and least DNO charges. The formulated problem is solved by using heuristic optimization technique of Genetic Algorithm. Case studies are carried out on a distribution system with multiple MGs, ISP and VPPs which illustrates the effectiveness of the proposed market optimisation strategy. The key objective of the proposed market model is to coordinate the operation of MGs with the requirements of the market with the help of the DNO, without decreasing the economic efficiency for the MGs nor the distribution network. © The Institution of Engineering and Technology 2019

Performance Optimisation of Standalone and Grid Connected Microgrid Clusters

Remote areas usually supplied by isolated electricity systems known as microgrids which can operate in standalone and grid-connected mode. This research focus on reliable operation of microgrids with minimal fuel consumption and maximal renewables penetration, ensuring least voltage and frequency deviations. These problems can be solved by an optimisation-based technique. The objective function is formulated and solved with a Genetic Algorithm approach and performance of the proposal is evaluated by exhaustive numerical analyses in Matlab

Computacional models for expansion planning of electric power distribution systems containing multiple microgrids and distributed energy resources under uncertainties

Orientador: Prof. Dr. Clodomiro Unsihuay VilaTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 05/12/2022Inclui referênciasResumo: Sistemas de distribuição incorporaram diversas tecnologias nos últimos anos, e a introdução de recursos energéticos distribuídos (DERs) proporcionam desafios para o planejamento da sua expansão e operação, como a inserção de geração distribuída (DG), sistemas de armazenamento de energia (ESSs), resposta da demanda (DR) e incertezas associadas com fontes de energia renováveis. Além disso, sistemas de distribuição modernos com múltiplas microrredes (MMG) e tecnologias para automação e monitoramento possibilitam a criação de um mercado local para comercialização de energia entre as microrredes. Devido às incertezas e ambientes competitivos gerados por microrredes que podem tomar suas próprias decisões e comercializar energia, a decisão para expansão do sistema é uma tarefa complexa feita pelo operador do sistema de distribuição (DSO). Dessa forma, essa tese propõe quatro modelos computacionais para o planejamento da expansão de sistemas elétricos de distribuição contendo MMGs e DERs sob incertezas. Cada modelo é projetado para um cenário específico: o primeiro soluciona o planejamento robusto para casos sem MMGs, não usa dados históricos e inclui confiabilidade; o segundo insere MMGs e contingências; o terceiro considera cenários com dados históricos disponíveis; e o quarto inclui o mercado de comercialização de energia para MMG em ambientes competitivos. Todos os modelos buscam minimizar os custos com investimento e operação na perspectiva do DSO, alocando novos componentes (DG, ESSs, capacitores e linhas) sob incertezas, considerando a coordenação das DERs, DR e confiabilidade/contingências. Eles são formulados como problemas multiníveis e solucionados através de decomposições multiestágio usando abordagens como adaptative robust optimization, distributionally robust optimization e column-andconstraint generation. Os métodos propostos são ilustrados usando uma versão modificada do sistema teste IEEE 123-bus. Os resultados mostram que os aspectos apresentados neste trabalho são importantes no planejamento da expansão de redes de distribuição pois reduzem o custo total ao mesmo tempo que melhor representam redes modernas. Incluir contingências melhora significativamente os índices de confiabilidade, reduzindo em 70% do índice EENS no caso de estudo. Cenários considerando mercado de energia tendem a aumentar o custo total do plano em ambientes de competição, 14% no caso de estudo. Ao mesmo tempo, esse caso tem a vantagem do compartilhamento de riscos entre o DSO e investidores. Ademais, os métodos propostos que usam dados históricos se comportam similarmente a métodos robustos ou estocásticos, dependendo do volume de dados disponível.Abstract: Power distribution systems have incorporated many new technologies in recent years, and the introduction of distributed energy resources (DERs) provides challenges for their operation and expansion planning, such as the inclusion of distributed generation (DG), energy storage systems (ESSs), demand response (DR), and uncertainties associated with renewable power sources. Moreover, modern distribution networks with multiple microgrids (MMG) and automation/monitoring technologies enable the creation of a market framework for local energy trading among the microgrids. Due to uncertainties and the competitive environment where microgrids can make their own decisions and trade energy, the expansion decision is a complex task performed by the distribution system operator (DSO). Thus, this dissertation proposes four computational models for the expansion planning of electric power distribution systems containing multiple microgrids and DERs under uncertainties. Each model is designed for specific scenarios: the first one solves a robust plan for cases without MMGs, has no historical data, and includes reliability; the second model inserts MMGs and contingencies; the third one considers cases having historical data; and the last model formulates the energy trading market for MMGs in competitive environments. All models aim to minimize the investment and operational costs from the DSO's perspective, placing new components (DG, ESSs, capacitors, and lines) under uncertainties, considering the DERs' optimal daily operation, DR, and reliability/contingency. They are formulated as multi-level problems and solved through multi-stage decompositions using robust adaptative optimization, distributionally robust optimization, and column-and-constraint approaches. The proposed methods are illustrated using a modified version of the IEEE 123-bus test system. The results show that the aspects presented in this work are important for the expansion planning of distribution networks since they reduce the total cost and better represent modern new tasks. Including contingencies significantly improves reliability indexes, reducing by 70% the EENS (Expected Energy Not Served) index in the study case. Scenarios considering energy trade markets tend to increase the total cost of the plan in competitive environments, 14% in the case study; at the same time, they have the benefit of sharing the investment risks among investors and the DSO. Furthermore, the proposed methods that use historical data behaved similarly to robust or stochastic approaches, depending on the amount of data available