Abstract
The optimal management of distributed energy resources (DERs) and renewable-based generation in multi -energy systems (MESs) is crucial as it is expected that these entities will be the backbone of future energy sys-tems. To optimally manage these numerous and diverse entities, an aggregator is required. This paper proposes the self-scheduling of a DER aggregator through a hybrid Info-gap Decision Theory (IGDT)-stochastic approach in an MES. In this approach, there are several renewable energy resources such as wind and photovoltaic (PV) units as well as multiple DERs, including combined heat and power (CHP) units, and auxiliary boilers (ABs). The approach also considers an EV parking lot and thermal energy storage systems (TESs). Moreover, two demand response (DR) programs from both price-based and incentive-based categories are employed in the microgrid to provide flexibility for the participants. The uncertainty in the generation is addressed through stochastic pro-gramming. At the same time, the uncertainty posed by the energy market prices is managed through the application of the IGDT method. A major goal of this model is to choose the risk measure based on the nature and characteristics of the uncertain parameters in the MES. Additionally, the behavior of the risk-averse and risk -seeking decision-makers is also studied. In the first stage, the sole-stochastic results are presented and then, the hybrid stochastic-IGDT results for both risk-averse and risk-seeker decision-makers are discussed. The pro-posed problem is simulated on the modified IEEE 15-bus system to demonstrate the effectiveness and usefulness of the technique.

Abstract
In this paper, we consider a demand response (DR) aggregator responsible for participating in the wholesale electricity market on behalf of the end-users who participated in the DR programs. Thus, the DR aggregator can trade its acquired DR within the short-term electricity markets, i.e., the day-ahead and the balancing (real-time) markets. In the proposed framework, the electricity market prices are considered uncertain, and a robust optimization approach is applied to address the uncertainties to maximize the profit of the DR aggregator. A model for analyzing the impact of the energy storage system (ESS) unit on a DR aggregator's performance is developed to provide more flexibility for the consumers. The direct interactions of a DR aggregator with an ESS are neglected in many models. However, this consideration can lead to improvement in the flexibility of the aggregator and also increase the profit of the entity by trading energy in the short-term markets to charge the ESS during the low-price periods and discharge it to the market while the electricity market prices are high. Hence, it is assumed that the DR aggregator owns an ESS unit and can cover a percentage of its traded power through the ESS. An analysis of the impact of the ESS unit on the DR aggregator's performance is applied to study the most appropriate size of the ESS that can maximize the profit of the aggregator. In addition, renewable energy production is employed for end-users through the installation of rooftop photovoltaic (PV) panels. This demand-side renewable generation can provide more flexibility for the participants in DR programs. Various feasible case studies have been applied to demonstrate the model's effectiveness and usefulness, and conclusions are duly drawn. The numerical results indicate that having an ESS seems necessary when the decision-maker desires to protect its profit from the worst-case scenarios and reduces the negative effect of the uncertain parameter, i.e., the wholesale electricity market prices. Thus, it can be shown that having a greater capacity for the ESS has a significant and direct impact on increasing the profit of the aggregator even in the worst-case scenarios, where the profit rises 20 % when the budget of uncertainty in the robust optimization is equal to 12.

Abstract
This paper presents a centralized model for operating multi-energy microgrids. The proposed model is based on a linearized optimal power flow (OPF) model for handling the network constraints in the distribution networks. It is assumed that each local microgrid is self-sustaining and can be operated independently from the other microgrids. However, the network access provides more flexibility to the multi-energy microgrid operators to supply their loads. The network-based electrical energy transactions are accepted in this study, while energy transformation from electricity to the other carriers is an asset to minimize the overall operating cost of the centralized multi-energy microgrid operation. The proposed model is tested and verified on the modified IEEE 33-bus test system. © 2023 IEEE.

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.