Tiago João Abreu

Abstract
Long-term storage expansion planning has usually employed representative days and intra-annual time series aggregation methodologies to reduce the computation complexity. This paper proposes a shift on the approach to the economic evaluation of these systems by implementing an intra-annual time series cost evaluation that considers different uncertainty trajectories. This methodology aims to determine the best possible investment strategies for the available computational budget using strategy game-based decision-making models, as Monte Carlo tree search. The proof of concept is illustrated by a single-bus equivalent test system and compared to a deterministic evaluation for a limited uncertainty model.

Abstract
The current coordination between the transmission system operator (TSO) and the distribution system operator (DSO) is changing mainly due to the continuous integration of distributed energy resources (DER) in the distribution system. The DER technologies are able to provide reactive power services helping the DSOs and TSOs in the network operation. This paper follows this trend by proposing a methodology for the reactive power management by the DSO under renewable energy sources (RES) forecast uncertainty, allowing the DSO to coordinate and supply reactive power services to the TSO. The proposed methodology entails the use of a stochastic AC-OPF, ensuring reliable solutions for the DSO. RES forecast uncertainty is modeled by a set of probabilistic spatiotemporal trajectories. A 37-bus distribution grid considering realistic generation and consumption data is used to validate the proposed methodology. An important conclusion is that the methodology allows the DSO to leverage the DER full capabilities to provide a new service to the TSO.

Abstract
This paper presents a model for supporting the investment planning decision-making from the perspective of an independent energy provider that wants to integrate Battery Energy Storage Systems (BESS) in distribution networks. For supporting the decision, a conditional set of economically viable optimal solutions for the business model of buying and selling energy is identified in order to allow other decision criteria (e.g. loss reduction, reliability, ancillary services, etc.) to be evaluated to enhance the economic benefits as the result of the synergy between different applications of BESS. For this, a novel approach optimization model based on the metaheuristic Differential Evolutionary Particle Swarm Optimization (DEEPSO) and the Group Method Data Handling (GMDH) neural network is proposed for sizing, location, and BESS operation schedule. Experimental results indicate that after identifying the breakeven cost of the business model, a good conditional decision set can be obtained for assessing then other business alternatives.

Abstract
Challenges in the coordination between the transmission system operator (TSO) and the distribution system operator (DSO) have risen continuously with the integration of distributed energy resources (DER). These technologies have the possibility to provide reactive power support for system operators. Considering the Portuguese reactive power policy as an example of the regulatory framework, this paper proposes a methodology for proactive reactive power management of the DSO using the renewable energy sources (RES) considering forecast uncertainty available in the distribution system. The proposed method applies a stochastic sequential alternative current (AC)-optimal power flow (SOPF) that returns trustworthy solutions for the DSO and optimizes the use of reactive power between the DSO and DER. The method is validated using a 37-bus distribution network considering real data. Results proved that the method improves the reactive power management by taking advantage of the full capabilities of the DER and by reducing the injection of reactive power by the TSO in the distribution network and, therefore, reducing losses.

Abstract
This paper presents a hybrid optimization model based on the metaheuristic Evolutionary Particle Swarm Optimization (EPSO) and Linear Programming for solving the problems of sizing, location and network interface technology selection of battery energy storage system (BESS). The batteries are integrated in a distribution network that has dispersed photovoltaic (PV) generation. Thus, a stochastic scenario generation model is also proposed for creating a database for the PV generation, load and energy prices curves considering historical data. The proposed approach is applied with success to the CIGRE MV benchmark network in the European configuration. Several tests were carried out in order to evaluate the EPSO approach for planning and operate BESS into the modern distribution networks. Experimental results indicate that dispersed solutions to locate the batteries throughout of the network were privileged over concentrated solutions.