Abstract
The large-scale deployment of distributed energy resources will produce reverse power flows, voltage, and congestion problems in the distribution networks. This paper proposes a novel optimization model to support distribution system operators planning future medium voltage distribution networks characterized by high penetration of behind-the-meter distributed energy resources. The optimization model defines the optimal mix, placement, and size of on-load tap charger transformers and energy storage devices with the objectives of mitigating network technical problems and minimizing both investment and operation costs. The proposed optimization model relaxes the non-convex formulation of the optimal power flow to a constrained second-order cone programming model and exactly linearizes the non-linear model of the on-load tap changer transformer via binary expansion scheme and big-M method. These two transformations reduce the computational burden of the optimization allowing it to be applicable to real-scale distribution grids, as demonstrated by the results. The numerical results also show that the joint optimization of energy storage devices and on-load tap changer transformers produces a more affordable and flexible planning strategy than the individual optimization of the technologies.

Abstract
The foreseen participation of aggregators of prosumers in the electricity markets will require the development of computational tools to support them in the definition and delivery of market products. This paper proposes a new hierarchical model predictive control (MPC) to support an aggregator in the delivery of multiple market products through the real-time control of heterogeneous flexible resources. The hierarchical MPC covers the participation of an aggregator in both energy and secondary reserve markets. The results show that the aggregator is capable of delivering several combinations of energy and secondary reserve without compromising the comfort and preferences of its clients.

Abstract
The integration of Renewable Energy Sources, mainly in distribution grids, has been changing the paradigm of power systems operation. This constitutes an opportunity to make use of flexible energy resources to support DSOs and TSOs on network operation activities. The amount of total flexibility available in the system can be quantified starting from lower voltage levels considering the implementation of demand response schemes. This work is focused on the development of an expeditious methodology to assess the total flexibility of low voltage consumers and aggregate it by means of a bottom-up approach until reaching the transmission network nodes.

Abstract

Abstract
Office buildings consume a significant amount of energy that can be reduced through behavioral change. Gamification offers the means to influence the energy consumption related to the activities of the office users. This paper presents a new mobile gamification platform to foster the adoption of energy efficient behaviors in office buildings. The gamification platform is a mobile application with multiple types of dashboards, such as (1) an information dashboard to increase the awareness of the users about their energy consumption and footprint, (2) a gaming dashboard to engage users in real-time energy efficiency competitions, (3) a leaderboard to promote peer competition and comparison, and (4) a message dashboard to send tailor-made messages about energy efficiency opportunities. The engagement and gamification strategies embedded in these dashboards exploit economic, environmental, and social motivations to stimulate office users to adopt energy efficient behaviors without compromising their comfort and autonomy levels. The gamification platform was demonstrated in an office building environment. The results suggest electricity savings of 20%. © 2020 Elsevier B.V.

Abstract
The large-scale deployment of smart home technologies will unlock the flexibility of prosumers, which in turn will be transformed into electricity market services by aggregators. This paper proposes a new network-constrained bidding optimization strategy to coordinate the participation of aggregators of prosumers in the day-ahead energy and secondary reserve markets. This bidding optimization strategy consists of a decentralized approach based on the alternating direction method of multipliers, where aggregators negotiate with the distribution system operator to obtain network-constrained energy and secondary reserve bids. For a case study of 2 aggregators and 1 distribution system operator, the results show that the network-constrained bidding strategy computes cost-effective and network-feasible energy and secondary reserve bids, as opposed to a network-free bidding strategy. In addition, the network-constrained bidding strategy preserves the independent roles of aggregators and the distribution system operator, and the data privacy of all agents.