Co-optimization of energy and reserve capacity considering renewable energy unit with uncertainty

This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability

Particle swarm optimization algorithm for the solution of nonconvex economic dispatch problem with valve point eect

Abstract In this study, particle swarm optimization (PSO) algorithm has been used for the solution of the economic dispatch problem with valve point effect. In these kind of problems, fuel cost curve increases as sinusoidal oscillations. In the solution of the problem B loss matrix has been used for the calculation of the transmission line losses. Total fuel cost rate has been minimized under electrical constraints

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Role of Metaheuristics in Optimizing Microgrids Operating and Management Issues::A Comprehensive Review

The increased interest in renewable-based microgrids imposes several challenges, such as source integration, power quality, and operating cost. Dealing with these problems requires solving nonlinear optimization problems that include multiple linear or nonlinear constraints and continuous variables or discrete ones that require large dimensionality search space to find the optimal or sub-optimal solution. These problems may include the optimal power flow in the microgrid, the best possible configurations, and the accuracy of the models within the microgrid. Metaheuristic optimization algorithms are getting more suggested in the literature contributions for microgrid applications to solve these optimization problems. This paper intends to thoroughly review some significant issues surrounding microgrid operation and solve them using metaheuristic optimization algorithms. This study provides a collection of fundamental principles and concepts that describe metaheuristic optimization algorithms. Then, the most significant metaheuristic optimization algorithms that have been published in the last years in the context of microgrid applications are investigated and analyzed. Finally, the employment of metaheuristic optimization algorithms to specific microgrid issue applications is reviewed, including examples of some used algorithms. These issues include unit commitment, economic dispatch, optimal power flow, distribution system reconfiguration, transmission network expansion and distribution system planning, load and generation forecasting, maintenance schedules, and renewable sources max power tracking

Metaheurísticas aplicadas ao problema de despacho econômico de energia elétrica

Resumo: Nesta dissertação é abordado um dos problemas de otimização em sistemas elétricos de potência, mais especificamente, o problema de despacho econômico de energia elétrica. Este é um problema bem estabelecido e conhecido em estudos de sistemas elétricos. Suas formulações simplificadas são facilmente resolvidas pelo método de otimização de Newton e suas variantes como o método dos pontos interiores primal-dual. Entretanto, variações destes problemas foram criadas com o intuito de tornar a modelagem mais realista, i.e., mais próxima das condições reais de operação dos sistemas modelados e portanto, mais complexa. Estas variações incluem taxas limites de rampa, zonas de operação proibidas, reserva de giro e funções de custo não-suaves, criando um espaço de busca altamente nãolinear, descontínuo, não-convexo e fortemente multimodal, onde o método de otimização de Newton falha em convergir. Por outro lado, métodos estocásticos de otimização, as metaheurísticas, livres de derivadas, são capazes de incorporar restrições e também de acomodar características nas funções de custo sem impedimentos de complexidade matemática, embora não possuam uma garantia de solução ótima. O objetivo principal desta dissertação é o levantamento de desempenho de metaheurísticas, através da aplicação e comparação em problemas de despacho econômico. Para isto, foi necessária a implementação de metaheurísticas como: algoritmo genético, evolução diferencial, otimização por enxame de partículas, algoritmo de seleção clonal, algoritmo de otimização por fogos de artifício, otimização big bang - big crunch, covariance matrix adaptation - evolution strategy, busca incremental baseada em população e simulated annealing. Estas metaheurísticas foram aplicadas a nove estudos de caso de despacho econômico de energia elétrica com efeito de ponto de válvula conhecidos na literatura, com o objetivo de otimização do custo de combustível dos geradores. A análise dos resultados obtidos compara o desempenho destes através de métricas como tempo de avaliação e melhor média obtida em diversos experimentos de otimização. Para validar estes resultados e verificar a significância de diferença entre os mesmos, foi utilizado o teste estatístico de Wilcoxon, que testa a hipótese nula que dados de duas amostras são amostras independentes de distribuições contínuas idênticas. Os resultados obtidos mostram que o Covariance Matrix Adaptation - Evolution Strategy e o Differential Evolution obtém os melhores resultados na otimização de problemas do despacho econômico. Dois pequenos experimentos foram adicionados a dissertação, um mostrando bons resultados na utilização de um gerador de folga variável e o outro a vantagem de processar avaliações da função objetivo no processador gráfico

Virtual power plant models and electricity markets - A review

In recent years, the integration of distributed generation in power systems has been accompanied by new facility operations strategies. Thus, it has become increasingly important to enhance management capabilities regarding the aggregation of distributed electricity production and demand through different types of virtual power plants (VPPs). It is also important to exploit their ability to participate in electricity markets to maximize operating profits. This review article focuses on the classification and in-depth analysis of recent studies that propose VPP models including interactions with different types of energy markets. This classification is formulated according to the most important aspects to be considered for these VPPs. These include the formulation of the model, techniques for solving mathematical problems, participation in different types of markets, and the applicability of the proposed models to real case studies. From the analysis of the studies, it is concluded that the most recent models tend to be more complete and realistic in addition to featuring greater diversity in the types of electricity markets in which VPPs participate. The aim of this review is to identify the most profitable VPP scheme to be applied in each regulatory environment. It also highlights the challenges remaining in this field of study

MOHRES, a Software Tool for Analysis and Multiobjective Optimisation of Hybrid Renewable Energy Systems : An Overview of Capabilities

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