Abstract
This paper presents a robust optimisation framework for long-term composite generation and transmission expansion planning problem which considers inherent uncertainties such as load growth, fuel cost and renewable energy output uncertainties. In this paper, a bi-level robust optimisation framework is proposed to accommodate wind output uncertainty in line with the uncertain demanded loads and uncertain fuel cost. The addressed optimisation problem is modelled as a mixed-integer optimisation framework with the objective of providing a robust expansion plan while maintaining the minimum cost expansion. In order to evaluate the robustness of each plan, an off-line Lattice Monte Carlo simulation technique is adopted in this study. The validity of the proposed method is examined on a simple six-bus and modified IEEE 118-bus test system as a large-scale case study. The simulation results show that the presented method is both satisfactory and consistent with expectation. © 2016 Informa UK Limited, trading as Taylor & Francis Group.

Abstract
This paper proposes a robust optimization framework for energy hub management. It is well known that the operation of energy systems can be negatively affected by uncertain parameters, such as stochastic load demand or generation. In this regard, it is of high significance to propose efficient tools in order to deal with uncertainties and to provide reliable operating conditions. On a broader scale, an energy hub includes diverse energy sources for supplying both electrical load and heating/cooling demands with stochastic behaviors. Therefore, this paper utilizes the Information Gap Decision Theory (IGDT) to tackle this uncertainty as an efficient robust optimization tool with low complexity to ensure the optimal operation of the system according to the priorities of the decision maker entity. The proposed optimization framework is also implemented on a benchmark energy hub which includes different energy sources and evaluated under different working conditions. © 2017 IEEE.

Abstract
Microgrids are able to improve several features of power systems, such as energy efficiencies, operating costs and environmental impacts. Nevertheless, microgrids' protection must work congruently with power distribution protection to safely take all advantages. This research contributes to enable their protection by proposing a bilevel method to simultaneously solve the allocation and coordination problems, where the proposed scheme also includes local protections of distributed energy resources. The uncertainties associated with generation and loads are categorized by the k-means method, as well. The non-dominated sorting genetic algorithm II is employed in the upper-level task to solve the protection and control devices allocation problem with two opposing objectives. In the lower-level task, a genetic algorithm ensures their coordination. Protection devices include reclosers and fuses from the network, and directional relays for the point of common coupling of microgrids, while control devices consist of remote-controlled switches. In contrast to related works, local devices installed at the point of coupling of distributed generation units are considered as well, such as voltage-restrained overcurrent relays and frequency relays. The optimal solution for the decision-maker is achieved by utilizing the compromise programming technique. Results show the importance of solving the allocation and coordination problems simultaneously, achieving up to $25,000 cost savings compared to cases that solve these problems separately. The integrated strategy allows the network operator to select the optimum solution for the protective system and avoid corrective actions afterward. The results also show the viability of the islanding operation depending on the decision maker's criteria.

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
An energy community equipped with Home Energy Management Systems (HEMSs) is considered in this paper. A local energy controller in the energy community makes it possible to transact energy between houses to support the different consumption patterns of each end-user. Price-based voluntary Demand Response (DR) programs are applied to each house to motivate end-users to alter their consumption patterns, allowing the necessary flexibility of the electrical grid. Also, the existence of Renewable Energy Sources (RES) micro-generation and an Energy Storage System (ESS) are taken into account. The results demonstrate that the proposed model based on Mixed-Integer Linear Programming (MILP) is fully capable of reducing daily electricity costs while considering end-users' comfort and respecting the different technical constraints. © 2023 IEEE.

Abstract
The growth and modernization of the power system are the keys to enabling economic progress. The deregulation, added to the new emerging production technologies, conversion, and storage, triggered a change in the way of managing the power system worldwide. This work analyses the optimal dispatch of a virtual power plant (VPP) with active participation in the electricity market, considering multi-energy systems. The objective is to minimize the total operating cost of the power plant. The power plant is fed by two external networks: electrical and natural gas. The VPP is composed of energy production, conversion, and storage technologies, also considering the integration of a wind turbine and a set of electric vehicles (EVs). In addition to the Grid-to-Vehicle (G2V) charging, the advantage of Vehicle-to-Grid (V2G) technology is also verified, which allows the injection of power into the grid through the vehicles and Vehicle-to-Load (V2L) technology, enabling EVs to contribute to the satisfaction of the electrical load, reducing the costs, showing the advantages as well of EVs' integration in the VPP under analysis.