VPP Self-Scheduling Strategy Using Multi-Horizon IGDT, Enhanced Normalized Normal Constraint, and Bi-Directional Decision-Making Approach

This paper presents a new robust self-scheduling strategy for virtual power plants (VPPs) considering the uncer-tainty sources of electricity prices, wind generations, and loads. Multi-horizon information-gap decision theory (MH-IGDT) as a non-deterministic and non-probabilistic uncertainty modeling framework is proposed here to specifically model the uncertainty sources considering their various uncertainty horizons. Since each uncertain parameter tends to optimize its uncertainty horizon competitively for a particular value of the uncertainty budget, the proposed MH-IGDT model is formulated as a multi-objective op-timization problem. To solve this multi-objective problem, en-hanced normalized normal constraint (ENNC) method is pre-sented, which can obtain efficient uniformly-distributed Pareto optimal solutions. The proposed ENNC includes augmented nor-malized normal constraint method and lexicographic optimiza-tion technique to enhance the search performance in the objective space. To address the unsolved issue of being risk-averse or risk-seeker for a VPP in the market, a bi-directional decision-making approach is presented. This decision maker comprises an ex-ante performance evaluation method and a forward-backward dy-namic programming approach to hourly find the best Pareto so-lution within the generated risk-averse and risk-seeker Pareto frontiers. Simulation results of the proposed self-scheduling strat-egy are presented for a VPP including dispatchable/non-dispatch-able units, storages, and loads

Resiliency-oriented operation of distribution networks under unexpected wildfires using multi-horizon information-gap decision theory

Extreme events may trigger cascading outages of different components in power systems and cause a substantial loss of load. Forest wildfires, as a common type of extreme events, may damage transmission/distribution lines across the forest and disconnect a large number of consumers from the electric network. Hence, this paper presents a robust scheduling model based on the notion of information-gap decision theory (IGDT) to enhance the resilience of a distribution network exposed to wildfires. Since the thermal rating of a transmission/distribution line is a function of its temperature and current, it is assumed that the tie-line connecting the distribution network to the main grid is equipped with a dynamic thermal rating (DTR) system aiming at accurately evaluating the impact of a wildfire on the ampacity of the tie-line. The proposed approach as a multi-horizon IGDT-based optimization problem finds a robust operation plan protected against the uncertainty of wind power, solar power, load, and ampacity of tie-lines under a specific uncertainty budget (UB). Since all uncertain parameters compete to maximize their robust regions under a specific uncertainty budget, the proposed multi-horizon IGDT-based model is solved by the augmented normalized normal constraint (ANNC) method as an effective multi-objective optimization approach. Moreover, a posteriori out-of-sample analysis is used to find (i) the best solution among the set of Pareto optimal solutions obtained from the ANNC method given a specific uncertainty budget, and (ii) the best resiliency level by varying the uncertainty budget and finding the optimal uncertainty budget. The proposed approach is tested on a 33-bus distribution network under different circumstances. The case study under different conditions verifies the effectiveness of the proposed operation planning model to enhance the resilience of a distribution network under a close wildfire. © 2022 The Author(s

Multi-fidelity surrogate-based optimal design of road vehicle suspension systems

Ride comfort is a relevant performance for road vehicles. The suspension system can filter vibration caused by the uneven road to improve ride comfort. Optimization of the road vehicle suspension system has been extensively studied. As detailed models require significant computational effort, it becomes increasingly important to develop an efficient optimization framework. In this work, a multi-fidelity surrogate-based optimization framework based on the Approximate Normal Constraint method and Extended Kernel Regression surrogate modeling method is proposed and applied. An analytical model and a multi-body model of the suspension system are used as the low-fidelity and high-fidelity models, respectively. Compared with other well-known methods, the proposed method can provide good accuracy and high efficiency. In addition, the proposed method is applied to different types of vehicle suspension optimization problems and shows good robustness and efficiency

Modelo computacional baseado na teoria de decisão com lacunas de informação orientado a dados para a programação diária da operação de microrredes

Orientador: Prof. Dr. Clodomiro Unsihuay-VilaDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 22/02/2022Inclui referênciasResumo: Os sistemas de distribuição de energia elétrica vêm se modernizando cada vez mais, através da inserção dos recursos energéticos distribuídos (REDs), os quais incluem a geração distribuída, sistemas de armazenamento de energia, veículos elétricos, resposta da demanda, entre outros e também tornam possível a formação de microrredes. Entretanto, ao considerar a inserção de tais recursos nos sistemas de distribuição, novos desafios vêm à tona, por conta da natureza incerta relacionada à geração distribuída com fontes renováveis variáveis (FRVs) e à demanda, por exemplo. Estas incertezas impactam diretamente no problema do planejamento da operação, que visa, como outros objetivos, a minimização dos custos operacionais. Ao inserir as incertezas na modelagem do problema, é possível investigar seu impacto sobre os custos diretamente. Dadas as circunstâncias mencionadas, neste trabalho é desenvolvido um modelo computacional baseado na teoria de decisão com lacunas de informação (TDLI) (do inglês, information gap decision theory - IGDT) orientada a dados para a programação da operação diária de microrredes considerando incertezas relacionadas à geração fotovoltaica e à demanda. O modelo da programação diária da operação de microrredes considera sistemas de armazenamento (baterias), resposta da demanda, geração térmica e um modelo de fluxo de potência CA linearizado. Além disso, a abordagem orientada a dados proposta é incluída com o intuído de reduzir o aspecto conservador do modelo TDLI robusto e o aspecto otimista não realista do modelo TDLI através da função de oportunidade. Para este fim, dados reais de 14 anos discretizados em base horária foram utilizados. O modelo é resolvido por: um pré-processamento orientado a dados para se definir os limites das variáveis de incerteza, uma primeira etapa que é o modelo determinístico, a fim de se obter o menor custo de operação com dados previstos, e a etapa que considera as incertezas através do TDLI. Para validação da metodologia proposta, quatro microrredes teste foram utilizadas, considerando diferentes arranjos, como sistemas de 6 a 18 barras, além da alocação de geração fotovoltaica e baterias em diferentes pontos. As análises de risco mostram que quanto maior o nível de robustez das variáveis incertas, maior o custo de operação. Entretanto, com a implementação do pré-processamento, foi possível encontrar um ponto de saturação das variáveis incertas, e consequentemente, um limite para o aumento ou redução de custos, dependendo da estratégia TDLI adotada. Os resultados mostram a efetividade e utilidade do modelo proposto, pois foi demonstrado para os casos apresentados que o maior nível de incerteza utilizando a função de robustez que a geração fotovoltaica atingiu foi de 93,2%, o que quer dizer que o pior cenário seria uma redução deste valor de geração. Já para a demanda, o máximo nível de incerteza encontrado foi de 26,97%, ou um aumento máximo para a demanda. Similarmente, para a função de oportunidade, os maiores níveis dos parâmetros de incerteza da geração fotovoltaica e da demanda foram respectivamente de 79,41% e 37,94%.Abstract: Electric power distribution systems have been increasingly modernized by the distributed energy resources (DERs) insertion, such as distributed generation, energy storage systems, electric vehicles, demand response, and others. These resources make it possible for the formation of microgrids. However, when considering the inclusion of such resources in distribution systems, new challenges arise, due to the uncertain nature related to distributed generation with variable renewable sources and load demand, for example. These uncertainties have a direct impact on the operation planning problem, which aims to minimize operating costs, along with other objectives. By inserting uncertainties into the problem modeling, it is possible to investigate their impact on costs, directly. Given the preceding circumstances, this work develops a data-driven computational model based on information gap decision theory for programming the day-ahead operation of microgrids considering uncertainties related to photovoltaic generation and load demand. The day-ahead operation planning model of microgrid considers storage systems (batteries), demand response, thermal generation, and a linearized AC power flow model. Furthermore, the proposed data-driven approach is included to reduce the conservativeness of the robust IGDT model and the unrealistic optimistic aspect of the IGDT model through the opportunity function. For this purpose, existing data from 14 years, discretized on an hourly basis, were used. The model is solved with three different stages: a data-driven pre-processing to define the upper or lower bounds of the uncertainty variables, a first step to obtain the lowest cost of operation with predicted data, which is the deterministic model, and the step that considers uncertainties through IGDT. To validate the proposed methodology, four test microgrids were used, considering different arrangements, such as systems with 6 to 18 buses, in addition to the allocation of photovoltaic generation and batteries at different points. Risk analyzes show that the higher the level of robustness of the uncertain variables, the higher the cost of operation. However, with the preprocessing implementation, it was possible to find a saturation point for the uncertain variables and, consequently, a limit for the increase or reduction of costs, depending on the IGDT strategy adopted. The results show the effectiveness and applicability of the proposed model, as it was shown for the presented cases that the highest level of uncertainty using the robustness function that the photovoltaic generation reached was 93.2%, which means that the worst scenario would be a reduction in the average generation value. As for load demand, the maximum level of uncertainty found was 26.97%, or a maximum increase for load demand. Similarly, for the opportunity function, the highest uncertainty parameters levels for photovoltaic generation and demand were 79.41% and 37.94%, respectively

Forecasting and Risk Management Techniques for Electricity Markets

This book focuses on the recent development of forecasting and risk management techniques for electricity markets. In addition, we discuss research on new trading platforms and environments using blockchain-based peer-to-peer (P2P) markets and computer agents. The book consists of two parts. The first part is entitled “Forecasting and Risk Management Techniques” and contains five chapters related to weather and electricity derivatives, and load and price forecasting for supporting electricity trading. The second part is entitled “Peer-to-Peer (P2P) Electricity Trading System and Strategy” and contains the following five chapters related to the feasibility and enhancement of P2P energy trading from various aspects