Abstract
This paper discusses the optimal deployment of a cluster consisting of connected AC-coupled, low voltage (48 V) multi-carrier microgrids within an integrated framework. The utilization of this integrated framework proves to be an effective approach for enhancing the reliability, resiliency, and operational quality of the clustered multi-carrier microgrids. Furthermore, it enables improved utilization of distributed energy resources in both grid-connected and stand-alone scenarios. In order to address local objectives, this paper presents a hybrid approach to determine the optimal integration and size of distributed energy resources in autonomous multi-carrier microgrids. Additionally, the proposed model identifies the ideal demand response intensity for each multi-carrier microgrid, which can result in energy savings and financial profits by modifying energy demands during peak hours. The primary objective is to minimize the development cost of clustered multi-carrier microgrids while ensuring a desired level of local reliability and online reserve. To address the planning problem of the proposed integrated parallel multi-carrier microgrid network, a mixed-integer programming model is formulated. Numerical results obtained from a three-microgrid system demonstrate the effectiveness of the proposed integrated planning model, validating the economic viability of the expansion project from various financial perspectives. Finally, a practical financing strategy is proposed to facilitate the successful implementation and deployment of parallel multi-carrier microgrids, thereby contributing to the achievement of sustainable development goals. The study examines the role of governments in facilitating capital investments for clustered multi-carrier microgrid projects, aligning with sustainable development goals. It proposes a feasible financing strategy through settled billing tax rates ranging from 4% to 26% for multi-carrier microgrid customers over ten years. This strategy can assist policymakers in formulating supportive policy programs to effectively implement and promote multi-carrier microgrids in diverse premises.

Abstract
This article addresses the topic of frequency regulation of a single-area power system connected to an electric vehicle (EV) aggregator over a non-ideal communication network. It is considered that the command control action is received by the EV aggregator with constant delay and the power system includes uncertain parameters. Due to the presence of uncertainties and the delay term, the frequency regulation problem is non-convex and hard to solve. The present approaches in the literature convert the non-convex control design problem into a convex problem with a set of Linear Matrix Inequalities (LMIs), which is conservative and in many cases results infeasibility. In this paper, an innovative iterative algorithm, called direct search, is employed for the time-delayed system to design the unknown parameters of a pre-assumed controller. The controller choice is not limited and various controllers' structures can be assumed. Without loss of generality, a proportional-integral (PI) controller is designed. The novel direct search algorithm can determine a feasible solution whenever at least one solution lays in the design space. Hence, by selecting a wide design space, we can anticipate that the PI controller guarantees closed-loop stability. Numerical simulations are carried out to demonstrate the performance of the developed controller compared to the state-of-the-art approach.

Abstract
The ongoing transition of the energy system towards being low-carbon, digitized and distributed is accelerating. Distributed Energy Resources (DERs) are playing a major role in this transition. These DERs can be aggregated and controlled by Virtual Power Plants (VPPs) to participate in energy markets and make full use of the potential of DERs. Many existing VPP models solely focus on the financial impact of aggregating DERs and do not consider the technical limitations of the distribution system. This may result in technically unfeasible solutions to DERs operations. This paper presents an expanded VPP model, termed the Technical Virtual Power Plant (TVPP), which explicitly considers the technical constraints of the network to provide operating schedules that are both economically beneficial to the DERs and technically feasible. The TVPP model is formulated as a bi-level sto-chastic mixed-integer linear programming (MILP) optimization model. Two objective functions are used, the upper level focuses on minimizing the amount of power imported into the TVPP from the external grid, while the lower level is concerned with optimally scheduling a mixture of DERs to increase the profit of the TVPP operator. The model considers three TVPPs and allows for energy trading among the TVPPs. The model is applied to several case studies based on the IEEE 119-node test system. Results show improved DERs operating schedules, improved system reliability and an increase in demand response engagement. Finally, energy trading among the TVPP is shown to further reduce the costs of the TVPP and power imported from the upstream electrical network.

Abstract
This study presents a planning model for utilizing emergency transmission capacity in the power system reserve market with renewable energy sources. To this end, first, the effects of the operation of a transmission line at higher power than rated power are described. The lifetime reduction of transmission lines caused by operation under these conditions is then measured, and finally, the price is determined based on the rate of lifetime reduction. This surplus capacity is then entered into a two-stage model of the energy and reserve market as a function of price offer, while also taking renewable energy sources into account. The numerical results of a 6-bus network indicates that the introduction of renewable energy sources reduced energy costs while increasing reserve market costs due to uncertainty. Despite the emergency capacity, such costs are reduced due to the network's utilization of low-cost resources.

Abstract
This article provides a framework for coordinating the operation of multiple microgrids with hydrogen systems in a distribution network considering the uncertainties of wind and solar power generation as well as load demands. The model is based upon a bilevel stochastic programming problem. On the upper level, the distribution system is the leader with a profit-maximization goal, and the microgrids are followers with cost-minimization goals on the lower level. The problem is solved by transforming the model to a single-level model using Karush-Kuhn-Tucker (KKT) conditions and linearized using McCormick's relaxation and Fortuny-Amat techniques. Unlike previous studies, both levels are modeled as scenario-based stochastic problems. Moreover, the scenarios associated with uncertain variables are obtained from a real data set. After preparing the data set, scenarios are reduced using a machine learning-based clustering approach. An application of the coordinated operation model is developed for a distribution network containing several microgrids. By solving the problem, the optimal amount of power exchange and the clearing price between microgrids and distribution systems are determined. Moreover, the proposed bilevel model made 13% more profit for the distribution system than the centralized model. Also, the effects of integrating hydrogen systems with microgrids on increasing the flexibility of operators are investigated.

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.