Abstract
The increasing integration of renewable energy sources (RES) at different voltage levels of the distribution grid has led to technical challenges, namely voltage and congestion problems. Conversely, the integration of new Distributed Energy Resources (DER) provides the necessary flexibility to accommodate higher RES integration levels. This work describes the development of innovative functional modules, based on optimal power flow calculations and grid forecasting, dedicated to the predictive management of the distribution grid considering DER flexibility, which are integrated into a commercial SCADA/DMS solution. © The Institution of Engineering and Technology 2023.

Abstract
Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating energy storage systems (ESSs) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This article compares the performance of hybrid energy storage systems (HESSs) to a single battery, evaluating their energy supply cost and environmental impact through optimization problems. The optimization model is based on a MILP incorporating the energy and degradation terms. It generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load. Seven technologies are assessed, with an example applied to an industrial site combining a vanadium redox flow battery (VRFB) and lithium battery considering the demand of a local load (building). The results indicate that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.

Abstract
Microgrids are poised to become the building blocks of the future control architecture of electric power systems. As the number of controllable points in the system grows exponentially, traditional control and optimization algorithms become inappropriate for the required operation time frameworks. Reinforcement learning has emerged as a potential alternative to carry out the real-time dispatching of distributed energy resources. This paper applies one of the continuous action-space algorithms, proximal policy optimization, to the optimal dispatch of a battery in a grid-connected microgrid. Our simulations show that, though suboptimal, RL presents some advantages over traditional optimization setups. Firstly, it can avoid the use of forecast data and presents a lower computational burden, therefore allowing for implementation in distributed control devices.

Abstract
Balancing services are used for maintaining the continuous balance between generation and load in the system and keep the frequency stable on its nominal value. The demand for balancing services is increasing with the growing penetration of wind generation into the electricity industry. It is clearly seen that a major challenge of the coming environment for the electricity market is reducing the procurement cost of balancing services. This paper presents a probabilistic spot market model based on integration of day-ahead spot market and the real-time balancing market which aims to trade off preventive actions in the day-ahead spot market with corrective actions in the real-time balancing market. The proposed model is formulated as a bi-level optimization problem. To solve it, the inner optimization problem (reflecting the real-time balancing market) was substituted by its equivalent Karush-Kuhn-Tucker optimality conditions. Conventional spot market model is used as a benchmark in this study. The proposed and conventional spot market designs are applied to modified Nordic 32-bus example system. Comparison of results point out the benefits of the proposed approach over the traditional model.