Abstract
In this paper, a hybridization method based on Arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. The proposed AO-AOA algorithm follows two strategies to find a better optimal solution. The first strategy is to introduce an energy parameter (E) to balance the transition between the individuals' procedure of exploration and exploitation in AOAOA swarms. Next, a piecewise linear map is employed to reduce the energy parameter's (E) randomness. To evaluate the performance of the proposed AO-AOA algorithm, it is tested on two well-known power systems i.e., IEEE 30-bus test network, and IEEE 118-bus test system. Moreover, to validate the effectiveness of the proposed (AO-AOA), it is compared with a famous optimization technique as a competitor i.e., Teaching-learning-based optimization (TLBO), and recently published works on solving OPF problems. Furthermore, a robustness analysis was executed to determine the reliability of the AO-AOA solver. The obtained result confirms that not only the AO-AOA is efficient in optimization with significant convergence speed, but also denotes the dominance and potential of the AO-AOA in comparison with other works.

Abstract
The increasing number of Distributed Energy Resources (DERs) provides new opportunities for increased interactions between prosumers and local distribution companies. Aggregating large numbers of prosumers through Home Energy Management Systems (HEMS) allows for easier control and coordination of these interactions. With the contribution of the dedicated end-users in fulfilling the required flexibility during the day, the network operator can easily handle the power mismatches to avoid fluctuations in the load-generation side. The bi-level optimization allows for a more comprehensive and systematic assessment of flexibility procurement strategies. By considering both the network operator's objectives and the preferences and capabilities of end-users, this approach enables a more nuanced and informed decision-making process. Hence, this article presents a bi-level optimization model to examine the potential for several groups of prosumers to offer flexibility services to distribution companies. The model is applied to the IEEE 33 bus test system and solved through distributed optimization techniques. The model considers various DERs, including Battery Energy Storage Systems (BESS). Results show that the groups of aggregated consumers can provide between +/- 7 to +/- 29 kW flexibility in each interval, which is significant. Furthermore, the aggregators' flexibility capacity is closely linked to the demand at each node.

Abstract
The optimal power flow (OPF) is one of the most noticeable and integral tools in the power system operation and control and aims to obtain the most economical combination of power plants to exactly serve the total demand of the system without any load shedding or islanding through adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, the successful implementation of an expeditious and reliable optimization algorithm is crucial. To solve this issue, this research proposes an enhanced democratic political algorithm (DPA), which can effectively solve multi-objective optimum power flow problems. The proposed method is a version of the democratic political optimization algorithm in which the search capability of this method to cover the borders of the Pareto frontier is enhanced. For the sake of practicality, the objectives with innate differences such as total emission, active power loss, and fuel cost are selected. Due to the practical limitations in real power systems, additional restrictions including valve-point effect, multi-fuel characteristics, and forbidden operational zones, are also considered. The proposed approach is tested and validated on IEEE 57 bus and IEEE 118-bus systems with different case studies. Simulation results are analyzed and compared with two popular and commonly used multi-objective-evolutionary algorithms namely, non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) on the problem. The study results illustrate the effectiveness of the proposed method in handling different scales, non-convex, and multi-objective optimization problems.

Abstract
This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions.

Abstract
The growing adoption of microgrids necessitates efficient management of electrical energy storage units to ensure reliable and sustainable power supply. This paper investigates a thermal management system (TMS) for maintaining the longevity of large-scale batteries. To streamline the thermal modeling of batteries, the McCormick relaxation method is employed to linearize a nonlinear and interdependent heat generation model. The thermal model of the battery follows a nonlinear behavior where the generated heat makes the battery system temperature soar, thereby affecting the thermal performance of the battery. To showcase the efficacy of the proposed approach, four distinct case scenarios are studied, highlighting the critical importance of batteries within microgrid operations. A comparative analysis is conducted between linear and nonlinear models for TMS performance. A quantitative assessment based on simulation results demonstrates the precision of the linearized model, particularly in a multitemporal optimal power flow and day-ahead scheduling of microgrids incorporating energy storage units. Controlling the battery temperature within a permissible range (from 15 degrees C to 40 degrees C) is achieved by using a heating, ventilation, and air conditioning (HVAC) system. The paper explores the economic implications of energy storage units in microgrids by extracting and comparing daily operational costs with and without battery integration. The findings reveal that the inclusion of energy storage units yields substantial economic benefits, with potential profit margins of approximately 20 % during typical working days and 60 % on weekends.

Abstract
The transition towards a decentralized, decarbonized, and distributed energy infrastructure necessitates technoeconomic initiatives to empower local energy communities (LECs) to achieve self-reliance and evolve into selfsustained electricity networks. It is crucial to underscore the significance of network resilience, especially in the context of local power generation, battery storage, and the radial topology of low-voltage (LV) networks. While contemporary LV networks have made significant attempts to integrate distributed energy resources (DERs), the notable deficiency lies in their lack of network redundancy, posing a substantial challenge in the occurrence of high-impact, low-probability (HILP) events. Therefore, to enhance LV network resilience and leverage its capability to withstand unexpected disruptions, the network operator needs to unlock the potential contributions of end-users within the active distribution networks (ADNs). In this paper, a comprehensive model is developed based on multi-temporal optimal power flow (MTOPF) for unbalanced LV networks addressing the technical issues in islanded microgrid operational planning. The contributions of the grid-scale batteries in forming islanded microgrids and the flexibility that can be provided by the end-users in the LEC have been considered in this paper. To demonstrate the performance of the proposed model, the simulation studies have been carried out on a part of medium and low voltage networks, consisting of network reconfiguration and load transferring capability to reduce the service interruptions during HILP events. The energy-not-served (ENS) is chosen as one of the key performance indicators (KPIs) in this study. With the unlocking flexibility potentials and contribution of the DERs, including grid-scale energy storage (GES) units and Photovoltaic (PV) panels, the ENS has been reduced from 700.8 kWh to 447.5 kWh by activating the local resources, proper switching action, and contribution of the flexible loads, for one of the severe HILP events, i.e., the main grid outage. In this case, the full load curtailment index is reduced from 180 to 106 client hours.