Abstract
Building automation systems contribute to reduce electricity costs by managing distributed energy resources in an efficient way. However, a large share of consumption cannot be optimized through automation alone, since it mainly depends on human interactions. Gamification can be used as one form of changing users’ behaviours [1], but its implementation does require assumptions on the behaviour patterns that need to be identified, encouraged, or discouraged. To tackle this problem, we propose a framework that joins building automation solutions with gamification techniques to enable behavioural demand response.

Abstract
This article addresses the developments ongoing in SENSIBLE, an H2020 funded project focused on energy storage and energy management, which demonstration occurs in Évora-Portugal, Nottingham, UK and Nuremberg, Germany. Currently, the presented study focuses on the concepts and developments necessary in order to make possible that residential clients can participate in a market environment with their electrical flexibility, also considering distribution system operator (DSO) needs when gird is under stress caused by any technical constraint. Moreover, than the concept behind it is necessary to consider several developments: (i) a low layer where residential assets will live in customer’s houses; (ii) a high-level layers where market tools and DSO management tools will live; (iii) an intermediate layer, which bridge the gap between the low layer and high layer. These developments are a result of the ongoing works under one of SENSIBLE use cases which demonstrations occurs in a small village in Évora district in Portugal. © 2017 Institution of Engineering and Technology. All rights reserved.

Abstract
This paper presents an algorithm developed for the optimization of Low Voltage (LV) grids that takes advantage of Distributed Energy Resources (DER) such as storage devices and flexible loads. The proposed approach is based on a multi-temporal Optimal Power Flow (OPF) algorithm that feeds from forecasting tools for load and renewable generation, which means the optimization model looks at a 24-hours horizon with hourly resolution. Specific constraints to the OPF are added to adequately model storage devices, namely their State-of-Charge (SOC) limits as well as their charging and discharging efficiencies. Moreover, a full three-phase model was built due to the unbalanced nature of LV grids motivated by the presence of single-phase load and generation. The algorithm developed has been extensively tested through simulation using a real LV Portuguese network data to illustrate the performance of the algorithm in different scenarios with good results.

Abstract
Power systems have been through deep changes, with their operation in the scope of competitive electricity markets (EM) and the increasingly intensive use of renewable energy sources and distributed generation. This requires new business models able to cope with the new opportunities. Virtual Power Players (VPPs) are a new player type which allows aggregating a diversity of players (distribution Generation, storage units, electrical vehicles, and consumers) to participate in the markets and to provide a set of new services promoting generation and consumption efficiency and to improving players’ benefits. A major task of VPPs is the remuneration of generation and of the services (e.g. market operation costs, and energy reserves) as well as charging energy consumption. This PhD research will contribute by developing fair and strategic remuneration and tariff methodologies, able to allow efficient VPP operation and VPP goals accomplishment in EM. © Springer International Publishing AG 2018.

Abstract

Abstract
Proliferation of wind power generation is increasingly making this power source an important asset in designs of energy and reserve markets. Intuitively, wind power producers will require the development of new offering strategies that maximize the expected profit in both energy and reserve markets while fulfilling the market rules and its operational limits. In this paper, we implement and exploit the controllability of the proportional control strategy. This strategy allows the splitting of potentially available wind power generation in energy and reserve markets. In addition, we take advantage of better forecast information from the different day-ahead and balancing stages, allowing different shares of energy and reserve in both stages. Under these assumptions, different mathematical methods able to deal with the uncertain nature of wind power generation, namely, stochastic programming, with McCormick relaxation and piecewise linear decision rules are adapted and tested aiming to maximize the expected revenue for participating in both energy and reserve markets, while accounting for estimated balancing costs for failing to provide energy and reserve. A set of numerical examples, as well as a case study based on real data, allow the analysis and evaluation of the performance and behavior of such techniques. An important conclusion is that the use of the proposed approaches offers a degree of freedom in terms of minimizing balancing costs for the wind power producer strategically to participate in both energy and reserve markets. Copyright (c) 2017 John Wiley & Sons, Ltd.