Abstract
Large scale integration of micro-generation, together with active loads and energy storage devices, under micro-grid and multi micro-grid concepts, requires the adoption of advanced control strategies at different distribution network levels. This paper presents advanced control functionality to be housed at high voltage (HV)/medium voltage (MV) substations and to be used to manage micro-generation, active loads and energy storage, subject to different constraints. Some of these constraints involve inter-temporal relations, such as the ones related with energy storage levels in consecutive time moments. This functionality is specially oriented to deal with stressed MV network operation involving overload and excessive voltage drops situations.

Abstract
The charging flexibility of electric vehicles (EV) when aggregated by a market agent creates an opportunity for selling manual reserve in the electricity market. This paper describes a new optimization algorithm for optimizing manual reserve bids. Furthermore, two operational management algorithms covering alternative gate closures (i.e., day-ahead and hour-ahead) are also described. These operational algorithms coordinate EV charging for mitigating forecast errors. A case-study with data from the Iberian electricity market and synthetic EV time series is used for evaluating the algorithms.

Abstract
This paper addresses the bidding problem faced by an electric vehicles (EV) aggregation agent when participating in the day-ahead electrical energy market. Two alternative optimization approaches, global and divided, with the same goal (i.e. solve the same problem) are described. The difference is on how information about EV is modeled. The global approach uses aggregated values of the EV variables and the optimization model determines the bids exclusively based on total values. The divided approach uses individual information from each EV. In both approaches, statistical forecasting methods are formulated for the EV variables. After the day-ahead bidding, a second phase (named operational management) is required for mitigating the deviation between accepted bids and consumed electrical energy for EV charging. A sequential linear optimization problem is formulated for minimizing the deviation costs. This chain of algorithms provides to the EV aggregation agent a pathway to move to the smart-grid paradigm where load dispatch is a possibility.

Abstract
This paper proposes a method to determine the availability of loads to provide tertiary Reserve Services (RS) in the framework of Smart Grids. The method uses Physically-Based Load Models (PBLM) and considers the remuneration paid for the RS provision. The concept of availability is also discussed in the paper and the method is applied to three different categories of domestic thermal appliances: electric water heater, air conditioners and refrigerators.

Abstract
In re-regulated markets, the maximization of profits creates the tendency to postpone investments in the network infrastructure, with negative effects on losses. In order to oppose this tendency, several countries adopt regulation directives that reward the distributors if losses are reduced and penalize them if losses increase. This is the case of Portugal which adopted loss penalty/reward scheme may be found in [1]. Given the current framework, EDP Distribuição (EDP Group), Portugal, a Distribution System Operator (DSO), has established a loss reduction program, which includes line reinforcement investments, among other actions. The main idea is to make the best investments in HV and MV network lines, considering the trade-off between benefits and costs. The ideal scenario would be, of course, to analyse all HV and MV networks and simulate possible reinforcement alternatives. However, the large number of MV feeders (about 4,000) makes this alternative unworkable. Thus, the first phase consists of developing a procedure to rank MV networks according to their potential to reduce losses. The highest scored networks are then analysed using a power system simulator. This analysis takes into account the different reinforcement alternatives and evaluates the investments costs and the saved energy over a period of 30 years - The economic time span usually considered by EDP Distribuição for this kind of operation. For the HV case, all networks were analysed. This paper synthetizes the main results obtained in these studies.

Abstract
The aim of this work is to demonstrate a novel fuzzy-based expert system for selecting renewable energy technologies (RET). Fuzzy multi-rules and fuzzy multi-sets are used to evaluate the main operational characteristics of six types of RET fuelled by biogas from municipal solid waste (MSW) landfills. The construction of the fuzzy multi-rules and fuzzy multi-sets is based on the following method: Mamdani controller using the Max-Min (inference process) and Center of Gravity (defuzzification process). Several criteria are used for the investigation: costs, efficiency, cogeneration, life-cycle and environmental impacts. The fuzzy-based expert system considers three different settings with two different constraints: costs and environmental impacts. One of the most relevant aspects presented by this work is about the previous criteria rank. It was created according to the different relevance observed among the attributes. The purpose of the proposed arrangement is to facilitate the understanding of the methodology and to increase the possibility of incorporating the decision makers' preferences on the decision-aid process. These aspects are essential to strengthen the final decision.