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Browsing ISEP – GECAD – Artigos by Author "Almeida, José"
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- Coordination strategies in distribution network considering multiple aggregators and high penetration of electric vehiclesPublication . Almeida, José; Soares, João; Canizes, Bruno; Vale, ZitaGiven the current state of the society in which we live, in terms of energy pollution, several objectives have been set to try to reduce environmental problems. Some of these goals include an exponential increase in production through renewable energy, and Electric Vehicles (EVs) circulating on roads. Due to this high penetration of distributed energy resources in the electricity grid, several problems may exist: grid congestion, causing severe energy systems damage. Innovative coordination strategies must be developed to mitigate these situations. This paper proposes a methodology to minimize this problem in a smart grid with high penetration of Distributed Generation (DG) and EVs, taking into account multiple aggregators. Initially, the proposed model calculates each aggregator’s profit through some business models and analyzes the network without any congestion strategy. This analysis is then done in the presence of Distribution Locational Marginal Pricing (DLMPs), which the aggregator receives from the Distributed System Operator (DSO). The DSO gets these prices after running the Optimal Power Flow (OPF), where these prices involve the market price, the cost of losses, and the cost of congestion at a given point in the network. Here the aggregators react according to these costs, such as trying to buy flexibility from their customers. In this study, the results prove that dynamic prices are more viable for the power grid by reducing congestion by analyzing each aggregator’s profit.
- Electric vehicles local flexibility strategies for congestion relief on distribution networksPublication . Soares, João; Almeida, José; Gomes, Lucas; Canizes, Bruno; Vale, Zita; Neto, EdisonDue to the rising concern for the environment and sustainability issues, the transportation system is experiencing important changes to its paradigm, with the increasing replacement of internal combustion vehicles by electric ones. Consequently, the electric systems need to adapt to the ever-increasing load demand from the grid and the challenge to identify driving patterns in electric vehicle users’ behavior. To prepare the grid for these changes, it is necessary to study the behavior of EV users and develop strategies to cope with the growing demand for electric vehicles. Knowing that electric vehicles experience long-parked periods at the charging stations (more than necessary to fully recharge the battery), this research paper proposes an EV charging strategy that intelligently explores these long-parked times. It interrupts charging of EVs that have enough charge to start their trip from certain charging stations to alleviate problems in the network in exchange for a certain incentive. This methodology is then applied in a realistic smart city to investigate its application. The results show that the proposed methodology brings benefits to the distribution network to relieve line congestion and improve the voltage magnitude at the network buses.
- Electric vehicles local flexibility strategies for congestion relief on distribution networksPublication . Soares, João; Almeida, José; Gomes, Lucas; Canizes, Bruno; Vale, Zita; Aranha Neto, EdisonDue to the rising concern for the environment and sustainability issues, the transportation system is experiencing important changes to its paradigm, with the increasing replacement of internal combustion vehicles by electric ones. Consequently, the electric systems need to adapt to the ever-increasing load demand from the grid and the challenge to identify driving patterns in electric vehicle users’ behavior. To prepare the grid for these changes, it is necessary to study the behavior of EV users and develop strategies to cope with the growing demand for electric vehicles. Knowing that electric vehicles experience long-parked periods at the charging stations (more than necessary to fully recharge the battery), this research paper proposes an EV charging strategy that intelligently explores these long-parked times. It interrupts charging of EVs that have enough charge to start their trip from certain charging stations to alleviate problems in the network in exchange for a certain incentive. This methodology is then applied in a realistic smart city to investigate its application. The results show that the proposed methodology brings benefits to the distribution network to relieve line congestion and improve the voltage magnitude at the network buses.
- Evolutionary Algorithms applied to the Intraday Energy Resource Scheduling in the Context of Multiple AggregatorsPublication . Almeida, José; Soares, João; Lezama, Fernando; Canizes, Bruno; Vale, ZitaThe growing number of electric vehicles (EVs) on the road and renewable energy production to meet carbon reduction targets has posed numerous electrical grid problems. The increasing use of distributed energy resources (DER) in the grid poses severe operational issues, such as grid congestion and overloading. Active management of distribution networks using the smart grid (SG) technologies and artificial intelligence (AI) techniques by multiple entities. In this case, aggregators can support the grid's operation, providing a better product for the end-user. This study proposes an effective intraday energy resource management starting with a day-ahead time frame, considering the uncertainty associated with high DER penetration. The optimization is achieved considering five different metaheuristics (DE, HyDE-DF, DEEDA, CUMDANCauchy++, and HC2RCEDUMDA). Results show that the proposed model is effective for the multiple aggregators with variations from the day-ahead around the 6 % mark, except for the final aggregator. A Wilcoxon test is also applied to compare the performance of the CUMDANCauchy++ algorithm with the remaining. CUMDANCauchy++ shows competitive results beating all algorithms in all aggregators except for DEEDA, which presents similar results.
- Hour-ahead energy resource scheduling optimization for smart power distribution networks considering local energy marketPublication . Canizes, Bruno; Soares, João; Almeida, José; Vale, ZitaEnergy resource management is a concept that should be considered in energy systems due to the significant penetration of dispersed energy resources. Thus, the efficiency in the electrical network operation can be improved and the end-user costs reduced. In this way, an energy resource aggregator plays an important role in managing the demand and generation flexibility which is meant for small producers under market-oriented environments. This research paper presents an energy resource management in intraday (hour-ahead) time horizon considering local market transactions between players. The optimization model is formulated as mixed-integer linear programming and solved in a deterministic way. To exemplify the implementation of the proposed model, a realistic medium voltage distribution network with 180 buses, high penetration of distributed energy resources, energy storage systems, and electric vehicle charging stations is considered. The results show the impact of the forecast errors as well as the contractual constraints between the aggregator and energy storage systems and electric vehicle charging stations in the intraday scheduling costs.
- Intraday Energy Resource Scheduling for Load Aggregators Considering Local MarketPublication . Almeida, José; Soares, João; Canizes, Bruno; Razo-Zapata, Ivan; Vale, ZitaDemand response (DR) programs and local markets (LM) are two suitable technologies to mitigate the high penetration of distributed energy resources (DER) that is vastly increasing even during the current pandemic in the world. It is intended to improve operation by incorporating such mechanisms in the energy resource management problem while mitigating the present issues with Smart Grid (SG) technologies and optimization techniques. This paper presents an efficient intraday energy resource management starting from the day-ahead time horizon, which considers load uncertainty and implements both DR programs and LM trading to reduce the operating costs of three load aggregator in an SG. A random perturbation was used to generate the intraday scenarios from the day-ahead time horizon. A recent evolutionary algorithm HyDE-DF, is used to achieve optimization. Results show that the aggregators can manage consumption and generation resources, including DR and power balance compensation, through an implemented LM.
- Optimizing Energy Consumption of Household Appliances Using PSO and GWOPublication . Tavares, Inês; Almeida, José; Soares, João; Ramos, Sérgio; Vale, Zita; Foroozandeh, ZahraDue to the increasing electricity consumption in the residential sector, new control systems emerged to control the demand side. Some techniques have been developed, such as shaping the curve’s load peaks by planning and shifting the electricity demand for household appliances. This paper presents a comparative analysis for the energy consumption optimization of two household appliances using two Swarm Intelligence (SI) algorithms: Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). This problem’s main objective is to minimize the energy cost according to both machines’ energy consumption, respecting the restrictions applied. Three scenarios are presented: changing the energy market price during the day according to three types of energy tariffs. The results show that the user in the cheapest periods could switch on both machines because both techniques presented the highest energy consumption values. Regarding the objective function analysis, PSO and GWO obtained the best (more economical) values for the simple tariff due to its lower energy consumption. The GWO technique also presented more diverging values from the average objective function value than the PSO algorithm.
- Preliminary results of advanced heuristic optimization in the risk-based energy scheduling competitionPublication . Almeida, José; Lezama, Fernando; Soares, João; Vale, Zita; Canizes, BrunoIn this paper, multiple evolutionary algorithms are applied to solve an energy resource management problem in the day-ahead context involving a risk-based analysis corresponding to the proposed 2022 competition on evolutionary computation. We test numerous evolutionary algorithms for a risk-averse day-ahead operation to show preliminary results for the competition. We use evolutionary computation to follow the competition guidelines. Results show that the HyDE algorithm obtains a better solution with lesser costs when compared to the other tested algorithm due to the minimization of worst-scenario impact.
- Robust Energy Resource Management Incorporating Risk Analysis Using Conditional Value-at-RiskPublication . Almeida, José; Soares, Joao; Lezama, Fernando; Vale, ZitaThe energy resource management (ERM) problem in today’s energy systems is complex and challenging due to the increasing penetration of distributed energy resources with uncertain behavior. Despite the improvement of forecasting tools, and the development of strategies to deal with this uncertainty (for instance, considering Monte Carlo simulation to generate a set of different possible scenarios), the risk associated with such variable resources cannot be neglected and deserves proper attention to guarantee the correct functioning of the entire system. This paper proposes a risk-based optimization approach for the centralized day-ahead ERM taking into account extreme events. Risk-neutral and risk-averse methodologies are implemented, where the risk-averse strategy considers the worst scenario costs through the conditional value-at-risk ( CVaR ) method. The model is formulated from the perspective of an aggregator that manages multiple technologies such as distributed generation, demand response, energy storage systems, among others. The case study analysis the aggregator’s management inserted in a 13-bus distribution network in the smart grid context with high penetration of renewable energy and electric vehicles. Results show an increase of nearly 4% in the day-ahead operational costs comparing the risk-neutral to the risk-averse strategy, but a reduction of up to 14% in the worst-case scenario cost. Thus, the proposed model can provide safer and more robust solutions incorporating the CVaR tool into the day-ahead management.