Bruno Peixoto Barbosa, PUC-RIO – BSc

Short-term power markets are environments in which the energy transacted in power systems in valued. Volumes and prices of the transactions result from the so-called economic dispatch, which aims to cover demand in the most efficient way – using either forecasted supply and demand conditions (ex-ante) or effectively observed data (ex-post). However, the increasing penetration of intermittent renewable sources has fueled the dissemination of multisettlement mechanisms, in which the energy allocation and price formation occurs in several moments, gradually adjusting expectations as the operation approaches. In this context, this work analyzes the impacts of a two-settlement scheme, comparing it to single-settlement mechanisms. The economic dispatch problem is modeled both in the ex-ante planning and in the real-time operation, considering renewable generation forecast errors and unit commitment thermal inflexibilities. The results of the application of this methodology in an example system are analyzed.

João Paulo Leite, PUC-RIO – BSc

The world is undergoing an energy transition. Variable renewable sources are increasing their share in energy systems worldwide due to their clean and cheap energy. However, these energy sources generation is intermittent and uncertain, which requires a greater amount of ancillary services, such as an operating reserve, to ensure the security of energy supply. In this sense, storage systems, especially batteries, stand out as good providers of these services, and are future candidates to allow a safe energy transition. However, to provide these services, batteries fail to capture revenue in the energy market. Thus, this work proposes a methodology for calculating the bidding strategy in competitive operating reserve markets, based on the agent's opportunity cost in the energy market. This strategy is calculated using a stochastic optimization model developed in this work. Finally, the methodology was applied in a case study for battery storage systems in Brazil.

Gabriella Dantas, PUC-RIO – BSc

With the tendency of growth in the demand of the Free Contracting Environment (ACL), it is essential to have financing options that assure the feasibility of projects trading energy in this market. In this context, the BNDES launched, in 2019, a credit assessment methodology for projects committed to the ACL. In parallel, new financing sources have become more relevant, such as the issuance of debentures and loans granted by Banco do Nordeste do Brasil (BNB). Thus, this work aims to assess the attractiveness of energy trading by wind and solar plants in the ACL, considering different financing arrangements. Using the Discounted Cash Flow methodology, the probability distribution of the Internal Rate of Return (IRR) is calculated, based on different spot price scenarios. The IRR obtained in the Regulated Contracting Environment (ACR) is used as a benchmark. The results indicate that is possible to obtain considerably higher returns in the ACL.

Camila Schuck Corrêa, UFRJ – BSc

The global efforts to reduce carbon emissions and adopt sustainable technologies for countries’ energetic development have driven the expansion of renewable energy sources and distributed generation. As a result, new challenges were introduced in the planning and operation of the electricity sector, mainly due to the intermittency of renewable sources and the presence of generation sources nearby consumption centers. Thereby, it is essential to analyze the effects of distributed generation based on renewable energy sources on electrical distribution networks. It is important to consider the variability and uncertainty of renewable energy sources in these analyses, in order to better capture the effect of distributed generation in the electrical system. Therefore, this work proposes to develop a methodology for the analysis of electrical distribution networks with photovoltaic distributed generation insertion, using multiple generation scenarios with intra-hour discretization. The methodology developed uses the OpenDSS software and the Python programming language as a base to perform three-phase power flow simulations and probabilistic analysis of the results.