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- Large-scale optimization of households with photovoltaic-battery system and demand responsePublication . Lezama, Fernando; Faia, Ricardo; Abrishambaf, Omid; Faria, Pedro; Vale, ZitaThe adoption of distributed resources by households, e.g., storage units and renewables, open the possibility of self-consumption (on-site generation), sell energy to the grid as a small producer, or do both according to the context of operation. In this paper, a framework capturing the interactions between an aggregator and a large number of households is envisaged. We consider households equipped with distributed resources and simple smart technologies that look for the reduction of energy bills and can perform demand response actions. A mixed-integer linear programming formulation that provides optimal scheduling of household devices and minimal operation costs is developed. Results show that the model can be applied considering up to 10000 households. Moreover, households can reduce up to 20% of their energy bill on average using storage units and demand response. Besides, the aggregator can attain profits by offering the resulting flexibility to upper-level players of the energy chain, such as the distribution system operator.
- Portfolio Optimization for Electricity Market Participation with NPSO-LRSPublication . Faia, R.; Pinto, Tiago; Vale, ZitaMassive changes in electricity markets have occurred during the last years, as a consequence of the massive introduction of renewable energies. These changes have led to a restructuring process that had an impact throughout the electrical industry. The case of the electricity markets is a relevant example, where new forms of trading emerged and new market entities were created. With these changes, the complexity of electricity markets increased as well, which brought out the need from the involved players for adequate support to their decision making process. Artificial intelligence plays an important role in the development of these tools. Multi-agent systems, in particular, have been largely explored by stakeholders in the sector. Artificial intelligence also provides intelligent solutions for optimization, which enable troubleshooting in a short time and with very similar results to those achieved by deterministic techniques, which usually result from very high execution times. The work presented in this paper aims at solving a portfolio optimization problem for electricity markets participation, using an approach based on NPSO-LRS (New Particle Swarm Optimization with Local Random Search). The proposed method is used to assist decisions of electricity market players.
- Differential Evolution Aplication in Portfolio optimization for Electricity MarketsPublication . Faia, R.; Lezama, Fernando; Soares, João; Vale, Zita; Pinto, Tiago; Corchado, Juan ManuelSmart Grid technologies enable the intelligent integration and management of distributed energy resources. Also, the advanced communication and control capabilities in smart grids facilitate the active participation of aggregators at different levels in the available electricity markets. The portfolio optimization problem consists in finding the optimal bid allocation in the different available markets. In this scenario, the aggregator should be able to provide a solution within a timeframe. Therefore, the application of metaheuristic approaches is justified, since they have proven to be an effective tool to provide near-optimal solutions in acceptable execution times. Among the vast variety of metaheuristics available in the literature, Differential Evolution (DE) is arguably one of the most popular and successful evolutionary algorithms due to its simplicity and effectiveness. In this paper, the use of DE is analyzed for solving the portfolio optimization problem in electricity markets. Moreover, the performance of DE is compared with another powerful metaheuristic, the Particle Swarm optimization (PSO), showing that despite both algorithms provide good results for the problem, DE overcomes PSO in terms of quality of the solutions.
- Decision Support for Negotiations among Microgrids Using a Multiagent ArchitecturePublication . Pinto, Tiago; Fotouhi Ghazvini, Mohammad Ali; Soares, João; Faia, Ricardo; Corchado, Juan; Castro, Rui; Vale, ZitaThis paper presents a decision support model for negotiation portfolio optimization considering the participation of players in local markets (at the microgrid level) and in external markets, namely in regional markets, wholesale negotiations and negotiations of bilateral agreements. A local internal market model for microgrids is defined, and the connection between interconnected microgrids is based on nodal pricing to enable negotiations between nearby microgrids. The market environment considering the local market setting and the interaction between integrated microgrids is modeled using a multi-agent approach. Several multi-agent systems are used to model the electricity market environment, the interaction between small players at a microgrid scale, and to accommodate the decision support features. The integration of the proposed models in this multi-agent society and interaction between these distinct specific multi-agent systems enables modeling the system as a whole and thus testing and validating the impact of the method in the outcomes of the involved players. Results show that considering the several negotiation opportunities as complementary and making use of the most appropriate markets depending on the expected prices at each moment allows players to achieve more profitable results.
- A Local Electricity Market Model for DSO Flexibility TradingPublication . Faia, R.; Pinto, Tiago; Vale, Zita; Corchado, Juan ManuelThe necessity of end-user engagement in power systems have become a reality in recent times. One of the solutions to this engagement is the creation of local energy markets. The distribution systems operators are compelled to investigate and optimize their asset investment cost in reinforcement of grids by introducing smart grid functionalities in order to avoid investments. The congestion management is the one of the most promising strategies to deal with the network issues. This paper presents a local electricity market or flexibility negotiation as a strategy in order to help the distribution system operator in congestion management. The local market is performed considering an asymmetric action model and is coordinated by an aggregator. A case study is presented considering a simulation that uses a low voltage network with 17 buses, which includes 9 consumers and 3 prosumers, all domestic users. Results show that using the proposed market model, the network congestion is avoided by taking advantage from the trading of consumers flexibility.
- Optimal Distribution Grid Operation Using Demand ResponsePublication . Faia, Ricardo; Canizes, Bruno; Faria, Pedro; Vale, Zita; Terras, Jose M.; Cunha, Luis V.This paper presents a model in which the distribution system operator can take advantage of the flexibility of end-users to overcome some operation issues on low voltage networks. Distributed generation based on renewable energy sources connected to these kinds of networks is challenging the conventional control and operation framework designed for passive distribution networks. Considering this, the proposed research work presents a non-linear problem using the flexibility available in the end-users to minimize the operation costs of the distribution system operator. For this goal, it is seeking the minimization of power losses and flexibility acquisition costs. The presented case study considers a realistic low voltage network with 256 bus and 96 connected end-users to show the approach effectiveness..
- Bidding in local electricity markets with cascading wholesale market integrationPublication . Lezama, Fernando; Soares, João; Faia, Ricardo; Vale, Zita; Kilkki, Olli; Repo, Sirpa; Segerstam, JanLocal electricity markets are a promising idea to foster the efficiency and use of renewable energy at the distribution level. However, as such a new concept, how these local markets will be designed and integrated into existing market structures, and make the most profit from them, is still unclear. In this work, we propose a local market mechanism in which end-users (consumers, small producers, and prosumers) trade energy between peers. Due to possible low liquidity in the local market, the mechanism assumes that end-users fulfill their energy demands through bilateral contracts with an aggregator/retailer with access to the wholesale market. The allowed bids and offers in the local market are bounded by a feed-in tariff and an aggregator tariff guaranteeing that end-users get, at most, the expected cost without considering this market. The problem is modeled as a multi-leader single-follower bi-level optimization problem, in which the upper levels define the maximization of agent profits. In contrast, the lower level maximizes the energy traded in the local market. Due to the complexity of the matter, and lack of perfect information of end-users, we advocate the use of evolutionary computation, a branch of artificial intelligence that has been successfully applied to a wide variety of optimization problems. Throughout three different case studies considering end-users with distinct characteristics, we evaluated the performance of four different algorithms and assessed the benefits that local markets can bring to market participants. Results show that the proposed market mechanism provides overall costs improvements to market players of around 30–40% regarding a baseline where no local market is considered. However, the shift to local markets in energy procurement can affect the conventional retailer/aggregator role. Therefore, innovative business models should be devised for the successful implementation of local markets in the future.
- A New Hybrid-Adaptive Differential Evolution for a Smart Grid Application Under UncertaintyPublication . Lezama, Fernando; Soares, João; Faia, Ricardo; Pinto, Tiago; Vale, ZitaPower systems are showing a dynamic evolution in the last few years, caused in part by the adoption of smart grid technologies. The integration of new elements that represent a source of uncertainty, such as renewables generation, electric vehicles, variable loads and electricity markets, poses a higher degree of complexity causing that traditional mathematical formulations struggle in finding efficient solutions to problems in the smart grid context. In some situations, where traditional approaches fail, computational intelligence has demonstrated being a very powerful tool for solving optimization problems. In this paper, we analyze the application of Differential Evolution (DE) to address an energy resource management problem under uncertain environments. We perform a systematic parameter tuning to determine the best set of parameters of four state-of-the-art DE strategies. Having knowledge of the sensitivity of DE to the parameter selection, self-adaptive parameter control DE algorithms are also implemented, showing that competitive results can be achieved without the application of parameter tuning methodologies. Finally, a new hybrid-adaptive DE algorithm, HyDE, which uses a new “DE/target - to - perturbed_best/1” strategy and an adaptive control parameter mechanism, is proposed to solve the problem. Results show that DE strategies with fixed parameters, despite very sensitive to the setting, can find better solutions than some adaptive DE versions. Overall, our HyDE algorithm excelled all the other tested algorithms, proving its effectiveness solving a smart grid application under uncertainty.
- Genetic Algorithms for Portfolio Optimization with Weighted Sum ApproachPublication . Faia, Ricardo; Pinto, Tiago; Vale, Zita; Corchado, Juan Manuel; Soares, João; Lezama, FernandoThe use of metaheuristics to solve real-life problems has increased in recent years since they are easy to implement, and the problems become easy to model when applying metaheuristic approaches. However, arguably the most important aspect is the simulation time since results can be obtained from metaheuristic methods in a much smaller time, and with a good approximation to the results obtained with exact methods. In this work, the Genetic Algorithm (GA) metaheuristic is adapted and apphed to solve the optimization of electricity markets participation portfolios. This work considers a multiobjective model that incorporates the calculation of the profit and the risk incurred in the electricity negotiations. Results of the proposed approach are compared to those achieved with an exact method, and it can be concluded that the proposed GA model can achieve very close results to those of the deterministic approach, in much quicker simulation time.
- Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy managementPublication . Faia, R.; Pinto, Tiago; Abrishambaf, Omid; Fernandes, Filipe; Vale, Zita; Corchado, Juan ManuelThis paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k-Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users.