Abstract
With the recent advancements in the development of hybrid offshore parks and the expected large-scale implementation of them in the near future, it becomes paramount to investigate proper energy management strategies to improve the integrability of these parks into the power systems. This paper addresses a multiobjective energy management approach using a hybrid energy storage system comprising batteries and hydrogen/fuel-cell systems applied to multi-source wind-wave and wind-solar offshore parks to maximize the delivered energy while minimizing the variations of the power output. To find the solution of the optimization problem defined for energy management, a strategy is proposed based on the examination of a set of weighting factors to form the Pareto front while the problem associated with each of them is assessed in a mixed-integer linear programming framework. Subsequently, fuzzy decision making is applied to select the final solution among the ones existing in the Pareto front. The studies are implemented in different locations considering scenarios for electrical system limitation and the place of the storage units. According to the results, applying the proposed multiobjective framework successfully addresses the enhancement of energy delivery and the decrease in power output fluctuations in the hybrid offshore parks across all scenarios of electrical system limitation and combinational storage locations. Based on the results, in addition to the increase in delivered energy, a decrease in power variations by around 40 % up to over 80 % is observed in the studied cases.

Abstract
Due to the complementarity of renewable energy sources, there has been a focus on technology hybridization in recent years. In the area of hybrid offshore power plants, the current research projects mostly focus on the combinational implementation of wind, solar, and wave energy technologies. Accordingly, considering the already existing offshore wind farms, there is the potential for the implementation of hybrid power plants by adding wave energy converters and floating photovoltaics. In this work, a stochastic sizing model is developed for the hybridization of existing offshore wind farms using wave energy converters and floating photovoltaics considering the export cable capacity limitation. The problem is modeled from an investor perspective to maximize the economic profits of the hybridization, while the costs and revenues regarding the existing units and the export cable are excluded. Furthermore, to tackle the uncertainties of renewable energy generation, as well as the energy price, a scenario generation method based on copula theory is proposed to consider the dependency structure between the different random variables. Altogether, the hybridization study is modeled in a mixed integer linear programming optimization framework considering the net present value of the project as the objective function. The results showed that hybrid-sources-based energy generation provided the highest economic profit in the studied cases in the different geographical locations. Furthermore, the technical specifications of the farms have also been considerably improved providing more stable energy generation, guaranteeing a minimum level of power in a high share of the time, and with a better utilization of the capacity of the cable while the curtailment of energy is maintained within the acceptable range.

Abstract
Regulatory changes associated with distributed generation have occurred in several countries (e.g., the USA, Germany, the UK, and Australia). However, there is a lack of robust and holistic analytical models that can be used to implement the best regulatory framework among possible options. In this context, the present paper proposes a cutting-edge regulatory framework for distributed generation based on multi-objective optimization, taking into account socioeconomic (socioeconomic welfare created by the regulated electricity market and electricity tariff affordability) and environmental (global warming potential) indicators. Such indicators are modeled primarily based on the optimized tariff model (socioeconomic regulated electricity market model), Bass diffusion model (forecasting model of distributed generation deployment), and life cycle assessment (environmental impact assessment method). The design variables are assumed to be the regulated electricity tariff and remuneration of the electricity injected into the grid over the years. First, the proposed methodology is applied to fifteen large-scale Brazilian concession areas with a significant deployment of distributed generation assuming two approaches, a multi-compensation scenario, where the compensation is set individually for each concession area, and a single-compensation scenario, where the compensation is set equally for all concession areas. Then, the optimal solutions are compared to Ordinary Law 14300, which is a recently implemented regulatory framework for distributed generation in Brazil. Results demonstrate that Ordinary Law 14300 is a dominated or non-optimal solution since it is not located on the optimal Pareto frontiers for any of the assessed concession areas. Assuming the Euclidian knee points, benefits averaging 33% and 15% were achieved in terms of electricity tariff affordability for the multi and single-compensation scenarios, respectively, with small losses of 8% and 3% in terms of socioeconomic welfare and global warming potential. Though the proposed methodology is applied in the Brazilian context, it can also be applied to other countries with regulated electricity markets; thus, it is expected to be valuable for researchers, government institutions, and regulatory agencies worldwide.

Abstract
Future electrical grids, particularly the distribution networks, may face more severe voltage rises/drops, and in general, more power quality problems in the presence of new loads such as electric vehicle chargers and renewable energy generation units like photovoltaic systems. This necessitates investing in additional high-cost infrastructure to increase the capability of the feeder in hosting higher levels of loads and generation units while the existing capacity is not utilized effectively. In the stated condition, effective voltage stabilization strategies in electrical distribution networks can contribute to hosting capacity improvement and the better utilization of the existing infrastructure. Accordingly, in this paper, the application of Open-UPQC in voltage profile improvement and hosting capacity enhancement is evaluated in low-voltage distribution networks. Furthermore, a dynamic reference voltage adjustment strategy is applied to the device to improve its capabilities in power quality improvement and hosting capacity enhancement. Simulation studies have been implemented to evaluate the capability of Open-UPQC either with static reference voltage or the dynamically-adjusted one in low-voltage networks with real measured data while different cases are assessed regarding the topology and the length of the feeder. The simulation results approved the capability of Open-UPQC especially with the dynamic reference voltage in hosting capacity enhancement while providing the highest level of voltage profile improvement among all the assessed custom power devices in the studied low-voltage networks.

Abstract
Hybrid storage systems that combine high energy density and high power density technologies can enhance the flexibility and stability of microgrids and local energy communities under high renewable energy shares. This work introduces a novel approach integrating rule-based (RB) methods with evolutionary strategies (ES)-based reinforcement learning. Unlike conventional RB methods, this approach involves encoding rules in a domain-specific language and leveraging ES to evolve the symbolic model via data-driven interactions between the control agent and the environment. The results of a case study with Liion and redox flow batteries show that the method effectively extracted rules that minimize the energy exchanged between the community and the grid.