Abstract
Electricity generation from wind energy is one of the main drivers of decarbonization in energy systems. However, installing wind farm facilities may have beneficial and harmful impacts on the habitat of living beings. This study reviews the literature based on economic analysis to identify the main externalities related to the installation of wind farms and the economic methodologies used to assess these externalities, filling an existent literature gap. A systematic literature review followed the Preferred Reporting Items on Systematic Reviews and Meta-analysis standards. A total of 33 studies were identified, most of them carried out in Europe. The studies cover 24 years, between 1998 and 2022. The externalities associated with wind electricity generation are classified into three categories: the impact on well-being, the impact of wind turbines, and the impacts of avoided externalities. Most studies (24 out of 33) determine economic values by stated preference methods through choice experiments, discrete choice experiments, and contingent valuation. Revealed preference methods were identified in 5 studies using hedonic pricing and travel cost techniques. The challenges and limitations of this analysis in terms of externalities identification and their assessment are also discussed, concluding that additional updated review studies are needed since the latest ones were published in 2016 and 2017. Moreover, it gives insights to policymakers and academics on a more complete approach they can use to evaluate the impacts of decarbonization, which, apart from the technological view, also considers and estimates the socio-economic and environmental perspectives.

Abstract

Abstract
The Renewable Energy Communities (RECs) and self-consumption frameworks defined in Directive (EU) 2023/2413 and Directive (EU) 2024/1711 are currently being integrated into national regulations across EU member states, adapting legislation to incorporate these new entities. These regulations establish key principles for individual and collective self-consumption, outlining operational rules such as proximity constraints, electricity sharing mechanisms, surplus electricity management, grid tariffs, and various organizational aspects, including asset sizing, licensing, metering, data exchange, and role definitions. This study introduces a model tailored to optimize investment and energy-sharing decisions within RECs, enabling multiple members to invest in solar photovoltaic (PV) and wind generation assets. The model determines the optimal generation capacity each REC member should install for each technology and calculates the energy shared between members in each period, considering site-specific constraints on renewable deployment. A case study with a four-member REC is used to showcase the model’s functionality, with simulation results underscoring the benefits of CSC over ISC. © 2025 by the authors.

Abstract

Abstract
This paper presents a fast and accurate optimization technique for optimal power flow (OPF) that can be conveniently applied to transmission and distribution systems. The method is based on the branch flow and DC optimal power flow (DCOPF) models. As the branch flow model is independent of the bus voltage angle, the model needs further development to enable use in meshed transmission systems. Thus, this paper adds the bus voltage angle constraint as a key constraint to the branch flow model so that the voltage angle can also be used in the power flow model in addition to the voltage magnitude control. The problem is based on second-order programming and modeled as a quadratically-constrained programming (QCP) problem solved using the CPLEX solver in GAMS. The functionality of the proposed model is tested utilizing four standard distribution systems, three transmission systems, a combined transmission-distribution network. The studied distribution systems include the 33-bus, 69-bus, 118-bus distribution (118-D) test systems, and 730-bus distribution system (730-D). Additionally, the studied transmission systems include 9-bus, 30-bus, and 118-bus transmission (118-T) test systems. The combined transmission-distribution system included the 9-bus transmission system with three connected distribution systems. The simulation results obtained from the developed technique are compared to those obtained from a conventional optimal flow model. The power losses and the absolute error of the solution are used as the two metrics to compare the methods' performance for distribution networks. The absolute error of the solution derived from the proposed hybrid OPF compared to MATPOWER for the 33-bus system is 0.00198 %. For the 69-bus system, the error is 0.00044 %. In addition, for the 118-D and 730-D systems, the absolute errors are 0.0026 %, and 0.05 %, respectively. For the transmission network, the operating costs and the solution absolute error are the two metrics used for comparing the proposed hybrid OPF model and MATPOWER. The results indicate the superior performance of the hybrid OPF model to the Newton-Raphson method in MATPOWER in terms of operating cost. In this regard, cost reductions relative to values given by MATPOWER are 0.0005 %, 0.838 %, and 0.015 %, for the 9-bus, 30-bus, and 118-T systems, respectively. The simulation studies demonstrate the performance of the presented branch flow-based model in solving the OPF problem with accurate results.

Abstract
The increasing penetration of electric vehicles will be accompanied for a wide deployment of charging infrastructures. Large charging demand brings formidable challenges to existing power networks, driving them near to their operational limits. In this regard, it becomes pivotal developing novel energy management strategies for active distribution networks that take into account the strategic behaviour of parking lots. This paper focuses on this issue, developing a novel energy management tool for distribution networks encompassing distributed generators and parking lots. The new proposal casts as a tri-level game equilibrium framework where the profit maximization of lots is implicitly considered, thus ensuring that network-level decisions do not detract the profit of parking owners. The original tri-level model is reduced into a tractable single-level mixed-integer-linear programming by combining equivalent primal-dual and first-order optimality conditions of the distribution network and parking operational models. This way, the model can be solved using off-the-shelf solvers, with superiority against other approaches like metaheuristics. The developed model is validated in well-known 33-, and 85-bus radial distribution systems. Results show that, even under unfavourable conditions with limited distributed generation, charging demand is maximized, thus preserving the interests of parking owners. Moreover, the model is further validated through a number of simulations, showing its effectiveness. Finally, it is demonstrated that the developed tool scales well with the size of the system, easing its implementation in real-life applications.