Ricardo Silva

Abstract
This paper proposes a three-stage model for managing energy communities for local energy sharing and providing grid flexibility services to tackle local distribution grid constraints. The first stage addresses the minimization of each prosumer’s individual energy bill by optimizing the schedules of their flexible resources. The second stage optimizes the energy bill of the whole energy community by sharing the prosumers’ energy surplus internally and re-dispatching their batteries, while guaranteeing that each prosumer’s new energy bill is always be equal to or less than the bill that results for this prosumer from stage one. This collective optimization is designed to ensure an additional collective benefit, without loss for any community member. The third stage, which can be performed by the distribution system operator (DSO), aims to solve the local grid constraints by re-dispatching the flexible resources and, if still necessary, by curtailing local generation or consumption. Stage three minimizes the impact on the schedule obtained at previous stages by minimizing the loss of profit or utility for all prosumers, which are furthermore financially compensated accordingly. This paper describes how the settlement should be performed, including the allocation coefficients to be sent to the DSO to determine the self-consumed and supplied energies of each peer. Finally, some case studies allow an assessment of the performance of the proposed methodology. Results show, among other things, the potential benefits of allowing the allocation coefficients to take negative values to increase the retail market competition; the importance of stage one or, alternatively, the need for a fair internal price to avoid unfair collective benefit sharing among the community members; or how stage three can effectively contribute to grid constraint solving, profiting first from the existing flexible resources.

Abstract

Abstract
This paper presents a model predictive control (MPC) framework for battery energy storage systems (BESS) management considering models for battery degradation, system efficiency and V-I characteristics. The optimization framework has been tested for microgrids with different renewable generation and load mix considering several operation strategies. A comparison for one-year simulations between the proposed model and a naïve BESS model, show an increase in computation times that still allows the application of the framework for real-time control. Furthermore, a trade-off between financial revenue and reduced BESS degradation was evaluated for the yearly simulation, considering the degradation model proposed. Results show that a conservative BESS usage strategy can have a high impact on the asset's lifetime and on the expected system revenues, depending on factors such as the objective function and the degradation threshold considered. © 2022 IEEE.

Abstract
Power systems rely on ancillary services (ASs) to ensure system security and stability. Until recently, only the conventional power generation resources connected to the transmission grids were allowed to provide these ASs managed by the transmission system operators (TSOs), while distribution system operators (DSOs) had a more passive role, focused on guaranteeing distribution capacity to bring power to final consumers with enough quality. Now, with the decarbonization, digitalization and decentralization processes of the electrical networks, the growing integration of distributed energy resources (DERs) in distribution grids are displacing conventional generation and increasing the complexity of distribution networks' operation, requiring the implementation of new active and coordinated management strategies between TSOs and DSOs. In this context, DERs are becoming potential new sources of flexibility for both TSOs and DSOs in helping to manage the power system. This paper proposes a systematic characterization of both traditional and potentially new ASs for TSOs, and newly expected DSO local system services to support the new distribution grid operation paradigm, reviewing, in addition, the main TSO-DSO coordination mechanisms.

Abstract