Abstract
As prosumers and energy communities gain prominence in power systems, energy trading between prosumers in local P2P markets is paramount. Within this novel market design, peers can directly exchange energy with each other, leading to economic advantages while supporting the decarboniza-tion of the sector. To ensure that voltage and congestion issues are properly addressed, a thorough coordination between the P2P market and the Distribution System Operator is required. This paper presents and compares three mutual-benefit coordination methods. The first method entails applying product differentiation on an iterative basis to avoid exceeding the lines thermal limits, which is performed through penalties on P2P exchanges that may be overloading the network. The second method uses the P2P market with an AC-OPF, ensuring network operation through a flexibility market via upward and downward flexibility. The last one proposes an integrated operation of the P2P market with AC-OPF. All methods are assessed in a typical distribution network with high prosumers integration. The results show that the second method is the one that, fulfilling the network constraints, presents greater social welfare.& COPY; 2023 Elsevier Ltd. All rights reserved.

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
The power and energy sector transition into decentralized and distributed models brings a set of challenges that need to be overcome. Among them is consumer empowerment, which requires secure economic transactions and reliable system operation. Thus, new technologies and procedures such as blockchain have gained prominence, due to their reliability and privacy-preserving capabilities. These characteristics are essential for the proliferation of energy community markets. Therefore, this paper provides an overview of blockchain technology applicability to consumer-centric electricity markets, highlighting existing projects and initiatives. Additionally, key enablers and barriers to blockchain deployment in local electricity markets are discussed, followed by a roadmap for the comprehensive adoption of such technology.

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
Hydrogen is regarded as a flexible energy carrier with multiple applications across several sectors. For instance, it can be used in industrial processes, transports, heating, and electrical power generation. Green hydrogen, produced from renewable sources, can have a crucial role in the pathway towards global decarbonization. However, the success of green hydrogen production ultimately depends on its economic sustainability. In this context, this work evaluates the economic performance of a hydrogen power plant participating in the electricity market and supplying multiple hydrogen consumers. The analysis includes technical and economical details of the main components of the hydrogen power plant. Its operation is simulated using six different scenarios, which admit the production of either grey or green hydrogen. The scenarios used for the analysis include data from the Iberian electricity market for the Portuguese hub. An important conclusion is that the combination of multiple services in a hydrogen power plant has a positive effect on its economic performance. However, as of today, consumers who would wish to acquire green hydrogen would have to be willing to pay higher prices to compensate for the shorter periods of operation of hydrogen power plants and for their intrinsic losses. Nonetheless, an increase in green hydrogen demand based on a greater environmental awareness can lead to the need to not only build more of these facilities, but also to integrate more services into them. This could promote the investment in hydrogen-related technologies and result in changes in capital and operating costs of key components of these plants, which are necessary to bring down production costs.

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.