Apoio à decisão para participação em mercados de energia elétrica

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Publications

Learning Bidding Strategies in Local Electricity Markets using a Nature-Inspired Algorithm

Publication . Lezama, Fernando; Soares, João; Faia, Ricardo; Faria, Pedro; Vale, Zita

Local electricity markets (LEM) are a promising idea to foster the efficiency and use of renewable energy at the distribution level. However, how these local markets will be integrated into existing market structures, and to make the most profit from them, is still unclear. In this work, we propose a LEM framework based on bi-level optimization. In the upper level, end-users aim at maximizing profits, while the lower level represents the clearing market process. Moreover, a cascade integration to the wholesale market through an aggregator that acts after the LEM has been cleared is considered. Learning strategies using only available information can be a powerful tool to take the most advantage of LEM. To this end, we advocate the use of ant colony optimization (ACO), a nature-inspired technique, similar to that employed in machine learning. By using ACO, consumers, producers and prosumers, can learn the best strategies to maximize their profits without sharing private information and based solely on their experience.

2020Conference object

Open access

Fair Remuneration of Energy Consumption Flexibility Using Shapley Value

Publication . Faia, R.; Pinto, Tiago; Vale, Zita

This paper proposes a new methodology for fair remuneration of consumers participation in demand response events. With the increasing penetration of renewable energy sources with a high variability; the flexibility from the consumers’ side becomes a crucial asset in power and energy systems. However, determining how to effectively remunerate consumers flexibility in a fair way is a challenging task. Current models tend to apply over-simplistic and non-realistic approaches which do not incentivize the participation of the required players. This paper proposes a novel methodology to remunerate consumers flexibility, in a fair way. The proposed model considers different aggregators, which manage the demand response requests within their coalition. After player provide their flexibility, the remuneration is calculated based on the flexibility amount provided by the players, the previous participation in demand response programs, the localization of the players, the type of consumer, the effort put in the provided flexibility amount, and the contribution to the stability of the coalition structure using the Shapley value. Results show that by assigning different weights to the distinct factors that compose the calculation formulation, players remuneration can be adapted to the needs and goals of both the players and the aggregators.

2019Book part

Open access

Distribution Network Expansion Planning Considering the Flexibility Value for Distribution System Operator

Publication . Faia, R.; Canizes, Bruno; Faria, Pedro; Vale, Zita

The electric power system has undergone numerous changes over the years. The transformation of the end-users from passive actors to active actors brings implications for the electric power system. The distribution system operator will be able to guide its operations in the function of the active role of the end-users. In many situations, the distribution system operator is carried out to avoid congestion in the distribution networks, and when it happens the distribution system operator is obliged to compensate the affected end-users. This paper presents a model in which distribution system operator can take advantage of the flexibility of the end-users in order to minimize the costs of the investments in distribution network expansion. The investment cost with the presented methodology as show the results has a reduction of 5.77%.

2019Conference object

Open access

An Optimization Model for Energy Community Costs Minimization Considering a Local Electricity Market between Prosumers and Electric Vehicles

Publication . Faia, Ricardo; Soares, João; Vale, Zita; Corchado, Juan Manuel

Electric 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

2021Journal article

Open access

A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market Problem

Publication . Vieira, Miguel; Faia, Ricardo; Lezama, Fernando; Canizes, Bruno; Vale, Zita

Energy community markets have emerged to promote prosumers' active participation and empowerment in the electrical power system. These initiatives allow prosumers to transact electricity locally without an intermediary such as an aggregator. However, it is necessary to implement optimization methods that determine the best transactions within the energy community, obtaining the best solution under these models. Particle Swarm Optimization (PSO) fits this type of problem well because it allows reaching results in short optimization times. Furthermore, applying this metaheuristic to the problem is easy compared to other available optimization tools. In this work, we provide a sensitivity analysis of the impact of different parameters of PSO in solving an energy community market problem. As a result, the combination of parameters that lead to the best results is obtained, demonstrating the effectiveness of PSO solving different case studies.

2022Conference object

Open access