Abstract
The Brazilian Power System is mainly composed of renewable generation from hydroelectric and wind. Hence, spot and forward electricity prices tend to represent the inherently stochastic nature of these resources, while risk management is a measure taken by agents, especially hydro power plants (HPPs) to hedge against deep financial losses. A HPP goal is to maximize its profit considering uncertainties in forward electricity prices, spot prices, and generation scaling factor (GSF) for years ahead. Therefore, the objective of this work is to simulate the real decision-making process of a HPP, where they need to have a perspective of the forward market and future spot price assessment to negotiate forward electricity contracts. To do so, the present work models the uncertainty in electricity forward prices via two-stage stochastic programming, assessing the benefits of the stochastic solution in comparison to the deterministic one. In addition, different risk aversion levels are assessed using conditional value at risk (CVaR). An important conclusion is that the results show that the greater the HPP risk aversion is, the greater the energy selling via electricity forward contracts. Moreover, the proposed model has benefits in comparison to a deterministic approach.

Abstract
Electric vehicle (EV) sales and shared mobility are increasing worldwide. Despite its challenges, e-carsharing has an opportunity to still profit in periods of low rental demand compared to traditional carsharing. The purpose of this paper is to assess the profitability of an e-carsharing company based on distribution local marginal price (DLMP) and vehicle-to-grid (V2G) that cooperates with the distribution system operator (DSO) through a two -stage stochastic model. The AC optimal power flow (ACOPF) is modeled using second-order cone program-ming (SOCP) linearized by the global polyhedral approximation. The IEEE 33 bus test system and a real Kernel distribution for the EV rental demands are used in four planning cases in the GAMS environment. The results indicate that the proposed methodology does not affect EV user satisfaction. Moreover, the planning disregarding the power grid perspective is the most profitable, but the operation may not be possible in real applications due to the high-power flows via V2G. Finally, the e-carsharing planning considering the DSO perspective increased the charging cost by 1.66 % but also reduced the DLMP peak, losses, and peak demand by 2.5 %, 1.5 %, and 5.1 %, respectively. One important conclusion is that the technical benefits brought to the DSO by the e-carsharing company could be turned into services and advantages for both agents, increasing profit and mitigating negative impacts, such as higher operational costs.

Abstract
This work presents a risk-averse stochastic programming model for the optimal planning of hybrid electrical energy systems (HEES), considering the regulatory policy applied to distribution systems in Brazil. Uncertainties associated with variables related to photovoltaic (PV) generation, load demand, fuel price for diesel generation and electricity tariff are considered, through the definition of scenarios. The conditional value-at-risk (CVaR) metric is used in the optimization problem to consider the consumer's risk propensity. The model determines the number and type of PV panels, diesel generation, and battery storage capacities, in which the objective is to minimize investment and operating costs over the planning horizon. Case studies involving a large commercial consumer are carried out to evaluate the proposed model. Results showed that under normal conditions only the PV system is viable. The PV/diesel system tends to be viable in adverse hydrological conditions for risk-averse consumers. Under this condition, the PV/battery system is viable for a reduction of 87% in the battery investment cost. An important conclusion is that the risk analysis tool is essential to assist consumers in the decision-making process of investing in HEES.

Abstract
The increasing integration of Distributed Energy Resources (DER) in the distribution network has brought more importance to Peer-to-Peer (P2P) markets. However, energy traded in P2P markets can lead to voltage and congestion constraints in distribution networks operated by Distribution System Operators (DSOs). At the same time, Transmission System Operators (TSOs) may need to solve system problems, requesting the participation of DERs in frequency regulation services. To ensure competitive participation in P2P markets, as well as to ensure a correct operation of distribution networks and to contribute to mitigate problems at the system level, coordination mechanisms between the P2P market and the System Operators (SOs) are required. This paper introduces a set of mathematical models considering P2P flexibility trading at the distribution system, while assisting the DSO and TSO in solving the congestion, voltage and frequency problems, respectively. The models are assessed on an IEEE 37bus distribution network with high DER penetration. The first and second models are based on product differentiation to avoid violating the lines' thermal limits and the nodes' voltage limits, respectively. The second model also considers reactive power control in order to impact voltage constraints. The third model uses a virtual load, connected to the TSO network (before the power transformer), to model frequency regulation services. The last model proposes the integration of all methods. Results showed that each model was effective in solving its constraint. However, they do not dismiss the use of the peers' flexibility assets to assure an overall feasible techno-economic solution. The use of the methodology proposed in the present paper can significantly facilitate the adoption of full P2P markets as well as the confidence of the system operators in the integration of these markets.& COPY; 2023 Elsevier Ltd. All rights reserved.

Abstract
This work aims to present a thorough study of a district heating scenario in a Greek industrial park case. The work is supported by the EMB3Rs open-source platform, allowing to perform a feasibility analysis of the system. In particular, this work explores the market module of this platform to provide a detailed market analysis of energy exchange within the Greek industrial park. The results pinpoint the effectiveness of the platform in simulating different market designs like centralized and decentralized, making clear the potential benefit the sources in the test case may achieve by engaging in a market framework. Different options for market clearing are considered in the study, for instance, including CO2 signals to reach carbon neutrality or community preferences to increase community autonomy. One can conclude that excess heat from existing sources is enough to cover other industries/facilities' heat demand, leading to environmental benefits as well as a fairer financial profits allocation.

Abstract
Integration of prosumers in district heating networks brings new challenges to the market and the network operation since they can change the thermal flow and increase competition. Thus, it is mandatory to develop new market structures and network management mechanisms. In this scope, this work proposes the implementation of a coordination methodology based on a peer-to-peer market without a supervising entity. The goal is to achieve higher revenue by coping with the requirements of each agent. Furthermore, the model is validated through network nodal analysis inspired by the power sector. The results in a Nordic network point out that the coordination methodology can provide compromise solutions between market negotiation and network operation. This methodology succeeded in providing reliable network solutions, fixing 99.88% of network burdens just after one iteration, and encouraging prosumers' integration. This increases market competition which lowers the energy costs for consumers while avoiding the network's operating burdens.