Abstract
This paper presents a two-layer framework for improving the resilience of a 118-bus active distribution network consisting of four microgrids, which includes hybrid storage systems, electric buses (EBs), and the direct load control (DLC) program. In the proposed model, the uncertainties of RESs generation, demand, and EBs’ mobility are considered, and the robust optimization approach is used to tackle them. In the first layer, the planning of each microgrid is done separately and the energy purchase/sale request is sent to the control center. Then in the second layer, the control center performs the planning of the main network according to the requested program of the microgrids. Note that in this layer, the control center is able to rearrange the distribution feeder and send EBs to vital points of the network. Finally, the validity of the proposed model is evaluated through the implementation on seven case studies and the results show that the presence of hydrogen and electrical storage devices reduces forced load shedding (FLS) by 45.03% and 12.19%, respectively, during emergency situations. In addition, the results indicate that robust planning and the use of EBs for network recovery increase the resilience index by 3.35% and 3.98%, respectively.

Abstract
The increasing occurrence of extreme weather events has severely compromised the resilience of power distribution systems, resulting in widespread outages and substantial economic losses. This paper proposes a novel solution to enhance the resilience of distribution networks without the need for significant infrastructure upgrades. We introduce a bilevel optimization framework that integrates Demand Response Programs (DRPs) to strategically manage electricity consumption and mitigate the impact of system disruptions. The approach fosters collaboration between Distribution System Operators (DSOs) and Demand Response Aggregators (DRAs), optimizing both operational resilience and economic efficiency. To solve the bilevel problem, we employ a Mathematical Program with Equilibrium Constraints (MPEC), transforming the bilevel model into a single- level problem by utilizing the Karush-Kuhn-Tucker (KKT) conditions. This method is applicable when the lower-level problem is convex with linear constraints. The model also incorporates Long Short-Term Memory (LSTM) neural networks for wind generation forecasting, enhancing decision-making precision. Furthermore, we conduct multiple case studies under varying severities of incidents to evaluate the method's effectiveness. Simulations performed on the IEEE 33-bus test system using GAMS and Python validate that the proposed method not only improves system resilience but also encourages active consumer participation, making it a robust solution for modern smart grid applications. The simulation results show that by performing DRP to handle the contingencies in a high-impact incident, the resilience of the system can be improved by 5.3%.

Abstract

Abstract
This paper proposes a centralized energy management system for low voltage (LV) distribution networks. The main contribution of this model is to manage the energy serving at the local energy communities in the presence of electric vehicle supply equipment (EVSE). Unlocking the demand response potential by the EVSE at the distribution network with the contribution of the active residential prosumers has been investigated in this study under different operational planning scenarios. The developed model is based on the multi-temporal optimal power flow (MTOPF) concept while the unbalanced nature of LV networks has been addressed using unbalanced power flow equations. The aggregator can effectively manage the optimal charging of electric vehicles (EVs) by home and public chargers available at the distribution network. Simulation results on a modified unbalanced LV network illustrate that the optimal operation of EVSE minimizes the electricity costs of end-users. The simulation results show that the operating costs and systems losses reduce by 9.22% and 43.45%, respectively. These results have been obtained considering the switching actions and 100% PV power generation index using the presented MV-LV coordinated operational model. Besides, the energy storage systems improve the peak-to-average (PAR) ratio by 9.87%.

Abstract
This paper deals with cost-effective strategies for controlling indoor temperature using different technologies, including inverter-based and thermostatic control systems. In this regard, the indoor temperature control model incorporates instant heat loss coefficient, heat transfer capability, and heat energy conversion coefficient. The decision variable is the power setpoint of the energy conversion system, which can be operated in both cooling and heating modes. The thermal system coefficients have been estimated based on historical data for energy consumption, indoor, and outdoor temperatures of the case study presented, which are the minimal datasets required for the coefficient estimation. The inverter-based model benefits from the quasi-continuous power consumption model, while the thermostatic model has a hysteresis functionality resulting in discrete power consumption with several turn-on and turn-off modes, which can be controlled by changing the thresholds. The flexible thermal range resulted in 4.715% and 6.235% cost reductions for thermostat-based and inverter-driven heat pumps, respectively. © 2025 Elsevier B.V., All rights reserved.