Percorrer por autor "Faia, Ricardo"
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- Adaptive Portfolio Optimization for Multiple Electricity Markets ParticipationPublication . Pinto, Tiago; Morais, Hugo; Sousa, Tiago M.; Sousa, Tiago; Vale, Zita; Praça, Isabel; Faia, Ricardo; Pires, Eduardo José SolteiroThe increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources’ particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players’ participation in electric power negotiations while improving energy efficiency. The opportunity for players’ participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator—MIBEL.
- An Ad-Hoc Initial Solution Heuristic for Metaheuristic Optimization of Energy Market Participation PortfoliosPublication . Faia, Ricardo; Pinto, Tiago; Vale, Zita; Corchado, JuanThe deregulation of the electricity sector has culminated in the introduction of competitive markets. In addition, the emergence of new forms of electric energy production, namely the production of renewable energy, has brought additional changes in electricity market operation. Renewable energy has significant advantages, but at the cost of an intermittent character. The generation variability adds new challenges for negotiating players, as they have to deal with a new level of uncertainty. In order to assist players in their decisions, decision support tools enabling assisting players in their negotiations are crucial. Artificial intelligence techniques play an important role in this decision support, as they can provide valuable results in rather small execution times, namely regarding the problem of optimizing the electricity markets participation portfolio. This paper proposes a heuristic method that provides an initial solution that allows metaheuristic techniques to improve their results through a good initialization of the optimization process. Results show that by using the proposed heuristic, multiple metaheuristic optimization methods are able to improve their solutions in a faster execution time, thus providing a valuable contribution for players support in energy markets negotiations.
- An Optimization Model for Energy Community Costs Minimization Considering a Local Electricity Market between Prosumers and Electric VehiclesPublication . Faia, Ricardo; Soares, João; Vale, Zita; Corchado, Juan ManuelElectric vehicles have emerged as one of the most promising technologies, and their mass introduction may pose threats to the electricity grid. Several solutions have been proposed in an attempt to overcome this challenge in order to ease the integration of electric vehicles. A promising concept that can contribute to the proliferation of electric vehicles is the local electricity market. In this way, consumers and prosumers may transact electricity between peers at the local community level, reducing congestion, energy costs and the necessity of intermediary players such as retailers. Thus, this paper proposes an optimization model that simulates an electric energy market between prosumers and electric vehicles. An energy community with different types of prosumers is considered (household, commercial and industrial), and each of them is equipped with a photovoltaic panel and a battery system. This market is considered local because it takes place within a distribution grid and a local energy community. A mixed-integer linear programming model is proposed to solve the local energy transaction problem. The results suggest that our approach can provide a reduction between 1.6% to 3.5% in community energy costs
- 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.
- Case-based reasoning using expert systems to determine electricity reduction in residential buildingsPublication . Faia, Ricardo; Pinto, Tiago; Vale, Zita; Corchado, Juan ManuelCase-based reasoning enables solving new problems using past experience, by reusing solutions for past problems. The simplicity of this technique has made it very popular in several domains. However, the use of this type of approach to support decisions in the power and energy domain is still rather unexplored, especially regarding the flexibility of consumption in buildings in response to recent environmental concerns and consequent governmental policies that envisage the increase of energy efficiency. In order to determine the amount of consumption reduction that should be applied in a building, this article proposes a methodology that adapts the past results of similar cases in order to achieve a decision for the new case. A clustering methodology is used to identify the most similar previous cases, and an expert system is developed to refine the final solution after the combination of the similar cases results. The proposed CBR methodology is evaluated using a set of real data from a residential building. Results prove the advantages of the proposed methodology, demonstrating its applicability to enhance house energy management systems by determining the amount of reduction that should be applied in each moment, thus allowing such systems to carry out the reduction through the different loads of the building.
- 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.
- Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary AlgorithmsPublication . Lezama, Fernando; Faia, Ricardo; Faria, Pedro; Vale, ZitaHouseholds equipped with distributed energy resources, such as storage units and renewables, open the possibility of self-consumption of on-site generation, sell energy to the grid, or do both according to the context of operation. In this paper, a model for optimizing the energy resources of households by an energy service provider is developed. We consider houses equipped with technologies that support the actual reduction of energy bills and therefore perform demand response actions. A mathematical formulation is developed to obtain the optimal scheduling of household devices that minimizes energy bill and demand response curtailment actions. In addition to the scheduling model, the innovative approach in this paper includes evolutionary algorithms used to solve the problem under two optimization approaches: (a) the non-parallel approach combine the variables of all households at once; (b) the parallel-based approach takes advantage of the independence of variables between households using a multi-population mechanism and independent optimizations. Results show that the parallel-based approach can improve the performance of the tested evolutionary algorithms for larger instances of the problem. Thus, while increasing the size of the problem, namely increasing the number of households, the proposed methodology will be more advantageous. Overall, vortex search overcomes all other tested algorithms (including the well-known differential evolution and particle swarm optimization) achieving around 30% better fitness value in all the cases, demonstrating its effectiveness in solving the proposed problem
- Differential Evolution Optimization for a Residential Demand Response ApplicationPublication . Faia, Ricardo; Lezama, Fernando; Faria, Pedro; Vale, ZitaIn the smart grid era, when the power system is under stress, demand response (DR) is considered a viable and practical solution for smoothing the demand curve. DR is a procedure that is applied to provide changes in consumers power consumption. These changes can be obtained by optimization techniques producing solutions for the management of power profiles of consumers. In general, optimization techniques can be divided into two groups: the exact methods and the approximate methods. In this paper, an optimization DR problem is formulated and solved using an approximate method based on evolutionary computation. The differential evolution (DE) and one variant called hybrid-adaptive DE (HyDE), as well as the Particle swarm optimization (PSO) algorithms are used and their performance is compared. The results show that DE algorithms are superior to PSO for this application and their performance is close to that obtained with an exact method.
- Dynamic Fuzzy Clustering Method for Decision Support in Electricity Markets NegotiationPublication . Faia, Ricardo; Pinto, Tiago; Vale, ZitaArtificial Intelligence (AI) methods contribute to the construction of systems where there is a need to automate the tasks. They are typically used for problems that have a large response time, or when a mathematical method cannot be used to solve the problem. However, the application of AI brings an added complexity to the development of such applications. AI has been frequently applied in the power systems field, namely in Electricity Markets (EM). In this area, AI applications are essentially used to forecast / estimate the prices of electricity or to search for the best opportunity to sell the product. This paper proposes a clustering methodology that is combined with fuzzy logic in order to perform the estimation of EM prices. The proposed method is based on the application of a clustering methodology that groups historic energy contracts according to their prices’ similarity. The optimal number of groups is automatically calculated taking into account the preference for the balance between the estimation error and the number of groups. The centroids of each cluster are used to define a dynamic fuzzy variable that approximates the tendency of contracts’ history. The resulting fuzzy variable allows estimating expected prices for contracts instantaneously and approximating missing values in the historic contracts.
- Exploiting flexibility of end-users at DSO-TSO levels acting in dynamic coalitionsPublication . Lezama, Fernando; Pinto, Tiago; Faia, Ricardo; Vale, ZitaIn this paper, we propose and analyze a coalitional game in which end-users with different characteristics and preferences can cooperate to offer their flexibility to upper-level entities such as the DSO and TSO. We formulate the problem as a cooperative game in which users need to decide the percentage of flexibility they want to offer to DSO and TSO, while searching for the coalition structures that maximize their utilities. Finding the optimal coalition structure that maximizes the sum of payoffs (i.e., social welfare) is a complex combinatorial problem that cannot be efficiently solved for large instances. Therefore, we evoke the use of computational intelligence algorithms to find near-optimal solutions that can provide higher pay-offs in acceptable times than, for example, the grand coalition and independent coalitions.
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