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- Ancillary service market simulationPublication . Vale, Zita; Ramos, Carlos; Faria, Pedro; Soares, João; Canizes, Bruno; Khodr, H. M.Power systems operation in a liberalized environment requires that market players have access to adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper deals with ancillary services negotiation in electricity markets. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of ancillary services using two different methods (Linear Programming and Genetic Algorithm approaches) is included in the paper.
- Estratégia de flexibilidade de veículos elétricos para alívio de congestionamento em redes de distribuiçãoPublication . Gomes, Lucas B. G.; Soares, João; Canizes, Bruno; Aranha Neto, Edison A. C.Devido à crescente preocupação com as questões ambientais e de sustentabilidade, o sistema de transporte está a passar por importantes mudanças em seu paradigma, com a crescente substituição de veículos de combustão interna por elétricos. Consequentemente, os sistemas elétricos precisam se adaptar à carga cada vez maior exigida da rede de distribuição e ao desafio de identificar padrões de comportamento dos utilizadores de veículos elétricos. Para preparar a rede para essas mudanças, é necessário estudar o comportamento dos usuários de VEs e desenvolver estratégias para lidar com a crescente demanda de veículos elétricos. Sabendo que os veículos elétricos passam por longos períodos estacionados nas estações de carregamento (acima do necessário para recarregar completamente a sua bateria), este trabalho de pesquisa propõe uma estratégia de carregamento de VEs, que visa explorar esses longos tempos estacionados nas estações de carregamento de maneira inteligente. Essa metodologia é aplicada em uma cidade inteligente realista com alta penetração de veículos elétricos para investigar melhor sua aplicação e resultados.
- Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity marketsPublication . Canizes, Bruno; Soares, João; Faria, Pedro; Vale, ZitaAncillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.
- Optimal expansion planning considering storage investment and seasonal effect of demand and renewable generationPublication . Canizes, Bruno; Soares, João; Lezama, Fernando; Silva, Cátia; Vale, Zita; Corchado, Juan ManuelA new era of cleaner distributed generators, like wind and solar, dispersed along the distribution network are gaining great importance and contributing to the environment and political goals. However, the variability and intermittency of those generators pose new complexities and challenges to the network planning. This research paper proposes an innovative stochastic methodology to deal with the expansion planning of large distribution networks in a smart grid context with high penetration of distributed renewable energy sources and considering the seasonal impact. Also, new power lines locations and types, the size and the location of energy storage systems are considered in the optimization. A distribution network with 180 buses located in Portugal considering high distributed generators penetration is used to illustrate the application of the proposed methodology. The results demonstrate the advantage of the stochastic model when compared with a deterministic formulation, avoiding the need for larger investments in new lines and energy storage systems.
- Fuzzy Monte Carlo model for transmission power systems reliability based decision makingPublication . Canizes, Bruno Miguel da Rocha; Vale, Zita; Khodr, H. M.This thesis presents the Fuzzy Monte Carlo Model for Transmission Power Systems Reliability based studies (FMC-TRel) methodology, which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states. This is followed by a remedial action algorithm, based on Optimal Power Flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. For the system states that cause load curtailment, an optimization approach is applied to reduce the probability of occurrence of these states while minimizing the costs to achieve that reduction. This methodology is of most importance for supporting the transmission system operator decision making, namely in the identification of critical components and in the planning of future investments in the transmission power system. A case study based on Reliability Test System (RTS) 1996 IEEE 24 Bus is presented to illustrate with detail the application of the proposed methodology.
- Production scheduling considering dynamic electricity price in energy-efficient factoriesPublication . Soares, João; Canizes, Bruno; Faria, Pedro; Vale, Zita; Ramos, CarlosFactories account for more than 42% of global energy consumption. In order to contribute to reduce carbon footprint and increase energy efficiency, it is important to optimize the tasks and time of product manufacturing according to the renewable generation and lower prices of the grid but without compromising production quality and output. This paper aims to develop flexible optimization platform for industrial production processes. The proposed production scheduling model is formulated as a 15-minute interval of one week time-span adopting mixed-integer linear optimization model and solved in TOMLAB. The model considers general production constraints for different products and takes into account with the photovoltaic generation of the factory as well as the dynamic price of the grid. The results are compared with a reference case without photovoltaic and where the dynamic price is not considered. The energy cost savings can amount up to 29% or 100 € in the considered example.
- ANN based day-ahead ancillary services forecast for electricity market simulationPublication . Faria, Pedro; Vale, Zita; Soares, João; Khodr, H. M.; Canizes, BrunoAdequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.
- Hybrid fuzzy Monte Carlo and logic programming model for distribution network reconfiguration in the presence of outagesPublication . Vale, Zita; Canizes, Bruno; Soares, João; Oliveira, Pedro; Sousa, Tiago; Silva, Marco; Soeiro, Alexandre; Khodr, H. M.This paper presents a methodology for distribution networks reconfiguration in outage presence in order to choose the reconfiguration that presents the lower power losses. The methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. Once obtained the system states by Monte Carlo simulation, a logical programming algorithm is applied to get all possible reconfigurations for every system state. In order to evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation a distribution power flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology to a practical case, the paper includes a case study that considers a real distribution network.
- Logic programming and fuzzy Monte Carlo for distribution network reconfigurationPublication . Vale, Zita; Canizes, Bruno; Soares, João; Oliveira, Pedro; Sousa, Tiago; Pinto, TiagoThis paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzyprobabilistic models allows catching both randomness and fuzziness of component outage parameters. A logic programming algorithm is applied, once obtained the system states by Monte Carlo Simulation, to get all possible reconfigurations for each system state. To evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation an AC load flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 115 buses distribution network.
- Smart City: A GECAD-BISITE Energy Management Case StudyPublication . Canizes, Bruno; Pinto, Tiago; Soares, João; Vale, Zita; Chamoso, Pablo; Santos, DanielThis paper presents the demonstration of an energy resources management approach using a physical smart city model environment. Several factors from the industry, governments and society are creating the demand for smart cities. In this scope, smart grids focus on the intelligent management of energy resources in a way that the use of energy from renewable sources can be maximized, and that the final consumers can feel the positive effects of less expensive (and pollutant) energy sources, namely in their energy bills. A large amount of work is being developed in the energy resources management domain, but an effective and realistic experimentation are still missing. This work thus presents an innovative means to enable a realistic, physical, experimentation of the impacts of novel energy resource management models, without affecting consumers. This is done by using a physical smart city model, which includes several consumers, generation units, and electric vehicles.