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- Energy Resource Scheduling Optimization for Smart Power Distribution Grids - Hour-Ahead HorizonPublication . Canizes, Bruno; Soares, João; Almeida, José; Paris, Wanderley; Vale, ZitaAs the use of renewable energy sources grows, the energy aggregator company plays an increasingly significant role in ensuring extremely flexible supply and demand, as requested by the smart grid architecture. This study presents a model for the problem of intraday energy resource scheduling (hour-ahead). The model is solved using the CPLEX solver and is developed as mixed integer linear programming. A distribution network with 180 buses located in Portugal considering high distributed energy resources penetration is used to demonstrate the application of the proposed model. The findings indicate how forecast errors and contractual restrictions with energy storage systems and electric car charging stations affect hour-ahead 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.
- 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.
- Evolutionary Algorithms for Energy Scheduling under uncertainty considering Multiple AggregatorsPublication . Almeida, José; Soares, João; Canizes, Bruno; Lezama, Fernando; Fotouhi Ghazvini, Mohammad Ali; Vale, ZitaThe ever-increasing number of electric vehicles (EVs) circulating on the roads and renewable energy production to achieve carbon footprint reduction targets has brought many challenges to the electrical grid. The increasing integration of distributed energy resources (DER) in the grid is causing severe operational challenges, such as congestion and overloading for the grid. Active management of distribution network using the smart grid (SG) technologies and artificial intelligence (AI) techniques can support the grid's operation under such situations. Implementing evolutionary computational algorithms has become possible using SG technologies. This paper proposes an optimal day-ahead resource scheduling to minimize multiple aggregators' operational costs in a SG, considering a high DER penetration. The optimization is achieved considering three metaheuristics (DE, HyDE-DF, CUMDANCauchy++). Results show that CUMDANCauchy++ and HyDE-DF present the best overall results in comparison to the standard DE.
- 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.
- 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.
- 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.
- 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.