Abstract
The increasing use of renewable energy sources and distributed generation brought deep changes in power systems, namely with the operation of competitive electricity markets. With the eminent implementation of micro grids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a new type of player, which allows aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players' benefits. In order to achieve this objective, it is necessary to define tariff structures that benefit or penalize agents according to their behavior. In this paper a method for determining the tariff structures has been proposed, optimized for different load regimes. Daily dynamic tariff structures were defined and proposed, on an hourly basis, 24 hours day-Ahead from the characterization of the typical load profile, the value of the electricity market price and considering the renewable energy production. © 2018 IEEE.

Abstract
The advent of more proactive consumers, the so-called "prosumers", with production and storage capabilities, is empowering the consumers and bringing new opportunities and challenges to the operation of power systems in a market environment. Recently, a novel proposal for the design and operation of electricity markets has emerged: these so-called peer-to-peer (P2P) electricity markets conceptually allow the prosumers to directly share their electrical energy and investment. Such P2P markets rely on a consumer-centric and bottom-up perspective by giving the opportunity to consumers to freely choose the way they buy their electric energy. A community can also be formed by prosumers who want to collaborate, or in terms of operational energy management. This paper contributes with an overview of these new P2P markets that starts with the motivation, challenges, market designs moving to the potential future developments in this field, providing recommendations while considering a test-case.

Abstract
The continuous proliferation of distributed energy resources (DER), mainly from renewable energy sources (RES) is changing the operational planning of distribution grids. Microgrids (MGs) as a small part of distribution grids are characterized by their ability to partially/fully self-producing their energy needs, and for the ability to trade different energy products (e.g. energy and reserve). This paper, addresses the energy and reserve market problem within the MG environment considering the RES uncertain production. Thus, a two-stage stochastic programming was modelled, minimizing the energy and reserve costs of the MG operator. A DC Optimal Power Flow (OPF) was incorporated to mitigate potential congestion that may occur in the MG. The assessment of the model is carried out through a test case based on actual generation data, considering a 37-bus distribution grid. The performance and accuracy of the model is determined based on the expected value of perfect information (EVPI) and value of stochastic solution (VSS).

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
The charging behavior of electric vehicles (EVs) is a key concern to system operators and retailers given a massive adoption these resources. System operators and retailers may profit from the handling of EVs as flexible loads. The former has interest to move their charging into periods without congestion and voltage problems. Similarly, the latter wants them to only charge at periods when energy is cheaper. This paper addresses the problem by modelling a real-time tariff to encourage flexible EVs charging behavior. More precisely, different tariffs are modelled based on the relation between wind power generation, load consumption and spot price, while assuming Denmark as showcase. The EVs behavior entails three different patterns and a socioeconomic term that defines the anxiety of EVs users' to be responsive to the tariffs. An important conclusion is that a proper real-time tariff design can reduce the energy costs for the retailer and EVs.

Abstract
Increasing power injection of distributed energy resources (DER) (including prosumers) has been changing the way the distribution system is operated and managed. Thus, conventional network usage tariffs are no longer fair enough to distribute the network costs to the various system participants. Within this scope, this work studies innovative cost allocation models that fairly distribute fixed, network usage and power losses costs to all system participants. A three-stage model is designed, in which: (i) an alternating current optimal power flow (AC OPF) for the distribution grid is performed; (ii) two different power tracing models (namely, the Abdelkader's and Bialek's tracing methods) are implemented and compared; and (iii) the distribution of costs through a MW-mile variant. The model is tested and validated in a 33-node distribution network considering high penetration of DER. © 2019 IEEE.