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.

Abstract
The increasing penetration of distributed energy resources, along with developments in house management systems, are supporting prosumers to play an active role in the electricity market. In particular, a peer-to-peer (P2P) electricity market is an attractive framework to allow direct transactions between prosumers. However, this may raise several challenges in the operation and management of the distribution grid. In this context, the main contribution of this paper is the design of P2P electricity markets taking into account grid characteristics while solving potential congestion and voltage problems. The paper proposes a coordination methodology between P2P markets and distribution system operator (DSO) allocating a grid tariff to the prosumers that cause grid limit violations. Such tariff is defined based on the euclidean distance among trading prosumers, which is implemented in the P2P market through product differentiation approach. The proposed methodology is compared to a benchmark model in a representative distribution grid with 138 nodes and 109 prosumers. An important conclusion is that the proposed methodology is capable of achieving a trade-off between prosumers P2P transactions and grid operation.

Abstract
This paper estimates the profit of the joint operation of a wind farm and a li-ion battery energy storage system (BESS) in the Iberian electricity market (MIBEL). The day-ahead and first intraday energy markets, and the tertiary regulation market are considered to optimize the joint operation of both assets. A rolling window combined with a non-linear optimization model are used to design the operation strategy. The BESS lifetime (as a function of the depth of discharge) is considered in the optimization problem, and different BESS capacities and initial state of charge values are tested to determine their approximate optimum values. The model and strategy designed were tested using real data from the MIBEL market and predicted data from Sotavento wind farm. The resulting incomes were compared to the BESS investments costs to determine, for a given capacity, when the project becomes viable. © 2019 IEEE.