Adaptive Decision support for Agents negotiation in electricity market and smart grid Power Transactions

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Strategic Particle Swarm Inertia Selection for Electricity Markets Participation Portfolio Optimization

Publication . Faia, Ricardo; Pinto, Tiago; Vale, Zita; Corchado, Juan Manuel

The portfolio optimization is a well-known problem in the areas of economy and finance. This problem has also become increasingly important in electrical power systems, particularly in the area of electricity markets, mostly due to the growing number of alternative/complementary market types that are being introduced to deal with important issues, such as the massive integration of renewable energy sources in power systems. The optimization of electricity market players’ participation portfolio comprises significant time constraints, which cannot be satisfied by the use of deterministic techniques. For this reason, meta-heuristic solutions are used, such as particle swarm optimization. The inertia is one of the most important parameter in this method, and it is the main focus of this paper. This paper studies 18 popular inertia calculation strategies, by comparing their performance in the portfolio optimization problem. A strategic methodology for the automatic selection of the best inertia calculation method for the needs of each optimization is also proposed. Results show that the proposed approach is able to automatically adapt the inertia parameter according to the needs in each execution.

2018Journal article

Open access

Multi-Objective Portfolio Optimization of Electricity Markets Participation

Publication . Faia, Ricardo; Pinto, Tiago; Vale, Zita; Manuel Corchado, Juan

Power and energy systems are being subject to relevant changes, mostly due to the large increase of distributed generation. These changes include the deregulation of electricity markets, which has become a more competitive marketplace due to the increase of the number of players based on renewable energy sources. This paper proposes a new portfolio optimization model for the participation in multiple alternative/complementary market opportunities, considering the risk management. The proposed model considers electricity as the asset to be negotiated. The risk is measured using the prediction error of electricity prices. A case study based on real data from Iberian electricity market-MIBEL assesses the results of the proposed model, using a particle swarm based optimization. Results show that using the proposed portfolio optimization model, market players are able to balance their market participation strategies depending on their risk aversion and profit seeking nature.

2018Conference object

Open access

Optimizing Opponents Selection in Bilateral Contracts Negotiation with Particle Swarm

Publication . Silva, Francisco; Faia, Ricardo; Pinto, Tiago; Praça, Isabel; Vale, Zita

This paper proposes a model based on particle swarm optimization to aid electricity markets players in the selection of the best player(s) to trade with, to maximize their bilateral contracts outcome. This approach is integrated in a Decision Support System (DSS) for the pre-negotiation of bilateral contracts, which provides a missing feature in the state-of-art, the possible opponents analysis. The DSS determines the best action of all the actions that the supported player can take, by applying a game theory approach. However, the analysis of all actions can easily become very time-consuming in large negotiation scenarios. The proposed approach aims to provide the DSS with an alternative method with the capability of reducing the execution time while keeping the results quality as much as possible. Both approaches are tested in a realistic case study where the supported player could take almost half a million different actions. The results show that the proposed methodology is able to provide optimal and near-optimal solutions with an huge execution time reduction.

2018Conference object

Open access

Differential Evolution Aplication in Portfolio optimization for Electricity Markets

Publication . Faia, R.; Lezama, Fernando; Soares, João; Vale, Zita; Pinto, Tiago; Corchado, Juan Manuel

Smart 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.

2018Conference object

Open access

Stochastic interval-based optimal offering model for residential energy management systems by household owners

Publication . Shokri Gazafroudi, Amin; Soares, João; Fotouhi Ghazvini, Mohammad Ali; Pinto, Tiago; Vale, Zita; Corchado, Juan Manuel

This paper proposes an optimal bidding strategy for autonomous residential energy management systems. This strategy enables the system to manage its domestic energy production and consumption autonomously, and trade energy with the local market through a novel hybrid interval-stochastic optimization method. This work poses a residential energy management problem which consists of two stages: day-ahead and real-time. The uncertainty in electricity price and PV power generation is modeled by interval-based and stochastic scenarios in the day-ahead and real-time transactions between the smart home and local electricity market. Moreover, the implementation of a battery included to provide energy flexibility in the residential system. In this paper, the smart home acts as a price-taker agent in the local market, and it submits its optimal offering and bidding curves to the local market based on the uncertainties of the system. Finally, the performance of the proposed residential energy management system is evaluated according to the impacts of interval optimistic and flexibility coefficients, optimal bidding strategy, and uncertainty modeling. The evaluation has shown that the proposed optimal offering model is effective in making the home system robust and achieves optimal energy transaction. Thus, the results prove that the proposed optimal offering model for the domestic energy management system is more robust than its non-optimal offering model. Moreover, battery flexibility has a positive effect on the system’s total expected profit. With regarding to the bidding strategy, it is not able to impact the smart home’s behavior (as a consumer or producer) in the day-ahead local electricity market.

2019Journal article

Open access