Jamal Faraji

Increasing electrification and distributed energy integration exacerbate congestion and voltage deviations in distribution networks. Renewable energy communities (RECs) have emerged as a promising bottom-up solution, enabling local coordination and grid support through participation in local energy and flexibility markets. However, market inefficiencies, such as strategic manipulation of the baseline, and the heterogeneity of user preferences introduce substantial complexity to their design and operation. Motivated by these challenges, this paper proposes a novel three-stage framework that jointly considers user behavior and system-level needs. In the first stage, RECs engage in local energy markets (LEMs), where electricity prices are competitive and consider collective users' diverse preferences to ensure efficient energy trading. In the second stage, RECs offer baseline-constrained flexibility services to the distribution system operator (DSO) through the local flexibility market (LFM), which allocates rewards based on actual grid support delivered. In the third stage, internal revenue sharing among REC members is determined through a fairness-guaranteeing method that accounts for marginal flexibility contributions. Simulation results show that, beyond users' preferences, all RECs benefit from participation in the framework; however, these benefits vary according to the preferences and seasonal conditions. These factors also influence REC performance as well as DSO operation and associated costs. The LFM results for a representative winter day indicate the financially-driven member achieves the highest total flexibility revenue (€140.88), while the comfort-driven member earns lower revenue (€35.27). Furthermore, DSO congestion management costs decrease from 2738.83 €/day in winter to 795.17 €/day in summer, which shows seasonal dependence and user behavior in grid operation.

Local energy communities play a pivotal role in facilitating the transition towards a sustainable and environmentally-friendly energy infrastructure. This paper constructs a novel energy sharing mechanism for a centralized energy community. The proposal includes a product differentiation approach to facilitate green, local, and gray electricity distribution among the community members, ensuring a centralized preference-based sharing and promotion of sustainable practices, and an internal pricing scheme for the community market where members indirectly encounter grid and commodity costs. Initially, a compositional model is employed to measure the socio-economic preferences of members toward various energy products. Then, a product differentiation strategy is proposed based on the part-worth utilities of the preferred energy supply option. Afterward, a robust Stackelberg game is introduced between the community manager (CM) and the members to determine the community's internal electricity prices and energy exchanges. Simulation results, including the robust optimization approach and comparison with non-preference-based energy sharing optimization, demonstrate the efficacy of the proposed framework in terms of both benefits and energy sharing performance.

In this paper, the reliability of cyber-physical energy hubs (CPEHs) is evaluated based on the Monte Carlo simulation (MCS) method. Previous works in the related literature are generally focused on the physical layer of energy hubs (EHs). However, the security of EHs heavily relies on the reliable and safe performance of the cyber system. Therefore, this study considers the failure of cyber-physical components, data packet errors, and information transmission delay to improve the reliability assessment accuracy. The physical layer of the proposed CPEH is divided into two sections, namely heat and power sections, and reliability indices are calculated for both sections. Various scenarios are suggested to precisely analyze the effects of the cyber layer consideration on the CPEH reliability results. Sensitivity analysis on the failure rate of the components is also conducted to understand better the performance of the CPEH in situations with a higher risk of heat components’ failure. The notable impacts of the cyber layer disturbances on the CPEH reliability evaluation are demonstrated through the simulation results.

This paper studies the risk management of a battery bidding in both day-ahead and intraday markets arising from the uncertain nature of electricity prices. To this end, a coherent risk measure, Second-order Stochastic Dominance (SSD), which is capable of expressing battery preferences in the form of a preset fixed benchmark (profit), is incorporated into the bidding model. The SSD serves the decision-maker as a risk-averse optimizer exploring for profit distribution members greater than a preset fixed benchmark. The most challenging facet of SSD-constrained methodologies is how to effectually define the preset fixed benchmark. In this regard, first, a generic approach is offered to find the feasible region for benchmark selection in SSD-constrained optimization problems. Then, a novel benchmark selection technique considering both the decision-maker's regret and out-of-sample profit, leverages the VIKOR method to get the ranking of different solutions and find the compromise benchmark in the risk-aware environment. Consequently, two decisive criteria from both ex- ante and ex- post tests are involved in the benchmark selection procedure, making the bidding problem regret- and consequence-aware. The numerical results of the developed methodology against risk-neutral and deterministic approaches show the efficiency of the proposed model.

Optimal planning of hybrid energy systems has always been a considerable task. While several studies have conducted techno‐economic analysis on hybrid energy systems, most of them have neglected to consider appropriate electric and thermal load growth rates. This paper proposes an efficient strategy for optimizing a grid‐connected hybrid energy system considering electric and thermal load growth rates. HOMER software is used for optimizing hybrid energy systems. It uses a nonderivative optimization to recognize the system with the minimum net present cost (NPC) among hundreds of configurations. The exponential smoothing method is also used for predicting upcoming load peak values based on historical data. The proposed hybrid system includes solar photovoltaic (SPV) system, wind turbine (WT) system, converter, battery storage system (BSS), gas generator (GasGen), fuel cell (FC), fuel‐fired boiler, and resistive boiler. Unlike previous studies, electricity price growth and SPV degradation rates are considered in this study. The grid‐connected system has also been compared to the stand‐alone system to understand grid connection impacts on the optimization results. Environmental analysis has been performed to analyze the greenhouse gas emissions of each system. The effects of growth rates in thermal and electric loads and electricity price are comprehensively investigated. Neglecting load growth rates affected the financial results severely. In this condition, the NPC and COE are achieved −11 383.4 US$ and −0.02376 US$/kWh. Results indicate that the combination of WT system (15 kW), converter (10 kW), resistive, and fuel‐fired (10 kW) boilers is the most economical configuration in grid‐connected mode. The WT system plays an essential role in the electric power provision and revenues of the hybrid system. The net present cost (NPC) and the cost of energy (COE) for grid‐connected operating mode are achieved −0.0162272 US$/kWh and −5243.956 US$, respectively. For off‐grid operating mode, the NPC and COE are obtained 47 024.19 US$ and 0.1983879 US$/kWh, respectively. Thanks to the feed‐in‐tariff (FiT) policy, the proposed grid‐connected system is more economical and environmentally friendly than the stand‐alone system. In off‐grid operating mode, 63 575.45285 kg CO₂ is produced, which is higher than CO₂ production in the grid‐connected mode (more than 8262 kg).

Renewable energy sources (RESs) are playing a significant role in the optimal operation of residential prosumers. Uncertainty of weather parameters such as solar irradiance and wind speed can affect the output power of RESs and consequently day-ahead operation of prosumers. Therefore, in this study, a new energy management system (EMS) has been proposed to mitigate fluctuations of RESs by proposing a 2-Level corrective weather forecasting method based on multilayer perceptron artificial neural networks (MLP-ANN). Forecasted data of the proposed method is applied to a peer-to-peer (P2P) prosumer environment. And the effects of weather uncertainty on operation cost and operational decisions of the residential prosumer are investigated. Simulation results indicate the effectiveness of the suggested data-driven method for weather prediction in the optimization of the prosumer.