Abstract
In this paper, a post-event restoration framework for distribution system in response to high impact low probable events is presented. The mobile emergency generators and mobile energy storage systems are considered for grid-support services in order to boost the system performance by supplying load demands, weighted by their priority. The restoration strategy of repair crew is dispatched in a deterministic way and the coordinated operation is performed with mobile power sources for picking up lost loads as soon as possible. Also, the transportation system constraints are integrated into the restoration tools for obtaining more realistic results while directing mobile power sources from staging locations to the damaged areas. A mixed integer linear programing (MILP)based scheme is performed on a modified version of the 10-bus test system with different case studies for illustrating the capabilities of the model implemented in the PuLP 1.6.8 open source library of Python 2.7 version, solved by CPLEX solver.

Abstract
Phasor measurement unit (PMU) provides beneficial information for dynamic power system stability, analysis, and control. One main application of such useful information is data-driven analysis and control. This article presents an approach for optimal signal selection and controller structure determination in PMU-based power system stabilizer (PSS) design. An algorithm is suggested for selecting the optimal input and output signals for PSS, in which a combination of system clustering, modal analysis, and principal component analysis techniques is used. The solution for the optimal PSS input-output selection is determined to increase the observability and damping of the power system. The approach can efficiently reduce the number of input-output signals, while the overall performance is not deteriorated. Then, a linear matrix inequality-based technique is elaborated to design the PMU-based PSS parameters. The stabilizer design approach is formulated as a convex optimization problem and the appropriate stabilizer for pole allocation of the closed-loop model is designed. This method is simulated on two sample power systems. Also, to compare the results with the previous methods, the system is simulated and the results of two previously developed algorithms are compared with the proposed approach. The results show the benefit of the suggested method in reducing the required signals, which decreases the number of required PMUs, while the system damping is not affected.

Abstract
Electric vehicles (EVs) are seen as a crucial tool to reduce the polluting emissions caused by the transport and power systems (PS) sector and the associated shift to a cleaner and more sustainable energy sector. The com-bination of EVs and solar photovoltaics (PV) in PS, specifically through the aggregation of EVs in parking lots (PLs), may improve the reliability and flexibility of the PS, assisting the power network in critical moments. This work proposes a novel aggregator agent in the energy system which is an EV charging station with an installed PV system. In this work, an optimal operation strategy for the solar-powered EV PL (EVSPL) operation is pre-sented. The model optimizes the EVSPL's participation in various energy and ancillary services markets, including the effects of capacity payments. The results show that the EVSPL leads to higher profits. The EVSPL's participation in ancillary services is highly influenced by the prices. The results of this work show that this novel agent can actively participate in the energy system in an economically viable manner while respecting the technical constraints of the network and providing important ancillary services to the system operator. Superscript/Subscript Available

Abstract
This paper presents a new stochastic mixed-integer second-order cone programming model to solve the problem of optimal operation of distribution systems considering network reconfiguration, voltage control devices, dispatchable and nondispatchable distributed generators (DGs), and the possibility of closed-loop topology operation. The decision variables are the active and reactive power generation of DGs, the tap position of substations' (SS) on-load tap changers and voltage regulators, the number of switchable capacitor banks in operation, and the operational statuses of sectionalizing and tie switches. The proposed formulation considers the minimization of (i) the cost of the energy purchased from the distribution SSs and dispatchable DGs, (ii) greenhouse gas emissions, (iii) technical energy losses, and (iv) the number of basic loops formed in the network. Tests are carried out using the 33-node system and the results demonstrate the effectiveness of the proposed formulation. The benefits provided by the presented approach include reduced operational costs and greenhouse gas emissions mitigation.

Abstract
The trend towards a decentralized, decarbonized, and digital energy system is gaining momentum. A key driver of this change is the rapid penetration increase of Distributed Energy Resources (DER). Commercial consumers can offer significant contributions to future energy systems, especially by engaging in demand response services. Virtual Power Plants (VPP) can aggregate and operate DERs to provide the required energy to the local grid and allowing for the participation in wholesale energy markets. This work considers both the technical constraints of the distribution system as well as the commercial consumer's comfort preferences. A stochastic mixed-integer linear programming (MILP) optimization model is developed to optimize the scheduling of various DERs owned by commercial consumers to maximize the profit of the TVPP. A case study on the IEEE 119-bus test system is carried out. Results from the case study show that the TVPP provides optimal DER scheduling, improved system reliability and increase in demand response engagement, while maintaining commercial consumer comfort levels. In addition, the profit of the TVPP increases by 49.23% relative to the baseline scenario.

Abstract
This work addresses a stochastic framework for optimal coordination of a microgrid-based virtual power plant (VPP) that participates in day-ahead energy and ancillary service markets. The microgrids are equipped with different types of distributed energy resources. A two-stage optimization formulation is proposed to maximize the benefit of the virtual power plant and minimize the energy procurement costs of the Distribution System Operator (DSO). The proposed model determines the optimal commitment scheduling of energy resources, considering the capacity withholding opportunities of the VPP that should be detected by the DSO. To evaluate the effectiveness of the proposed model, the algorithm is assessed for the 123-bus IEEE test system. The results show that the proposed method successfully maximizes the virtual power plant profit considering capacity withholding penalties.