Abstract
Distribution system operators (DSO) are currently moving towards active distribution grid management. One goal is the development of tools for operational planning of flexibility from distributed energy resources (DER) in order to solve potential (predicted) congestion and voltage problems. This work proposes an innovative flexibility management function based on stochastic and chance-constrained optimization that copes with forecast uncertainty from renewable energy sources (RES). Furthermore, the model allows the decision-maker to integrate its attitude towards risk by considering a trade-off between operating costs and system reliability. RES forecast uncertainty is modeled through spatial-temporal trajectories or ensembles. An AC-OPF linearization that approximates the actual behavior of the system is included, ensuring complete convexity of the problem. McCormick and big-M relaxation methods are compared to reformulate the chance-constrained optimization problem. The discussion and comparison of the proposed models is carried out through a case study based on actual generation data, where operating costs, system reliability and computer performance are evaluated.

Abstract
Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers' consumption elasticity. It determines the flexibility of each client under the considered DR program and identifies whether the tariffs offered by the DR program affect the consumers' usual consumption or not. The aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. We identify a set of critical clients who actively participate in DR events along with the most responsive and least responsive clients for the considered DR program. We find that the percentage of DR consumers who actively participate seem to be much less than expected by retailers, indicating that not all consumers' elasticity is effectively utilized.

Abstract

Abstract
This paper presents numerical analysis of two alternative optimization approaches intended to support an EV aggregation agent in optimizing buying bids for the day-ahead electricity market. A study with market data from the Iberian electricity market is used for comparison and validation of the forecasting and optimization performance of the global and divided optimization approaches. The results show that evaluating the forecast quality separately from its impact in the optimization results is misleading, because a forecast with a low error might result in a higher cost than a forecast with higher error. Both bidding approaches were also compared with an inflexible EV load approach where the EV are not controlled by an aggregator and start charging when they plug-in. Results show that optimized bids allow a considerable cost reduction when compared to an inflexible load approach, and the computational performance of the algorithms satisfies the requirements for operational use by a future real EV aggregation agent.

Abstract
Engaging the residential consumers and providing the best tariffs for their randomized behavior is one of the major barriers to demand response (DR) implementation. Additionally, DR offers submitted by aggregators or retailers are not consumer-specific, which turns it even more difficult for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causal inference between dynamic DR tariffs and observed residential electricity consumption (resolution of 30 minutes) to estimate consumers' consumption elasticity. Ultimately, the aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs.

Abstract
This paper analyzes the electricity prices of the MIBEL electricity spot market with respect to a set of possible explanatory variables. Understanding the main drivers of the electricity price is a key aspect in understanding price formation and in developing forecasting models, which are essential for the selling and buying strategies of market agents. For this analysis, different techniques have been applied in this work, including standard and lasso regression models, causal analysis based on bayesian networks and classification trees. Results from the different approaches are coherent and show strong dependency of the electricity prices with the Portuguese imported coal for lower non-dispatchable net demands, which has been progressively replaced by gas for larger non-dispatchable net demands. Hydro reservoirs and hydro production are also main explanatory variables of the electricity price for all non-dispatchable net demand levels.