Abstract
This paper presents a novel method for resiliency assessment of the distribution system considering smart homes' arbitrage strategies in the day-ahead and real-time markets. The main contribution of this paper is that the impacts of smart homes' arbitrage strategy on the resilient operation of the distribution system are explored. The optimal commitment of smart homes in external shock conditions is another contribution of this paper. An arbitrage index is proposed to explore the impacts of this process on the system costs and resiliency of the system. A two-level optimization process is proposed for day-ahead and real-time markets. At the first stage of the first level, the optimal bidding strategies of smart homes are estimated for the day-ahead market. Then, the database is updated and the optimal bidding strategies of smart homes for real-time horizon are assessed in the second stage of the first level problem. At the first stage of the second level problem, the optimal day-ahead scheduling of the distribution system is performed considering the arbitrage and resiliency indices. At the second stage of the second level, the distribution system optimal scheduling is carried out for the real-time horizon. Finally, at the third stage of the second level, if an external shock is detected, the optimization process determines the optimal dispatch of system resources. The proposed method is assessed for the 33-bus and 123-bus IEEE test systems. The proposed framework reduced the expected values of aggregated costs of 33-bus and 123-bus systems by about 62.14 % and 32.06 % for the real-time horizon concerning the cases in which the smart homes performed arbitrage strategies. Furthermore, the average values of the locational marginal price of 33-bus and 123-bus systems were reduced by about 59.38 % and 63.98 % concerning the case that the proposed method was not implemented.

Abstract
The concept of smart cities has emerged as an ongoing research in recent years. In this case, there is a proven association between the smart cities and the smart devices, which have caused the power systems to become more flexible, controllable and detectable. Along with these promising results, many disputes have been generated over the cyber-attacks as unpredictable destructive threats, if not properly repelled, which could seriously endanger the power system. With this in mind, this paper explores a novel stochastic virtual assignment (SVA) method based on a directed acyclic graph (DAG) approach, where the essential data of the system sections are broadcasted decentralized through the data blocks, as a worthwhile step to deal with the cyber attacks' risk. To do so, an additional security layer is added to the data blocks aiming to enhance the security of the data against the long lasting data sampling by virtually assigning the hash addresses (HAs) to the data blocks, which are randomly changed based on a stochastic process. The basic network architecture is based on a Provchain structure as a new framework to constantly monitor data operation. Two pivotal strategies also represented to deal with the energy and time needed for the HAs generation process, which have improved the proposed method. In this paper, the proposed security framework is implemented in a smart city environment to provide a secure energy transaction platform. Results show the authenticity of this model and demonstrate the effectiveness of the SVA method in decreasing the successful probability of cyber threat, increasing the time needed for the cyber attacker to decrypt and manipulate the data block.

Abstract
Probabilistic load forecasting (PLF) is necessary for power system operations and control as it assists in proper scheduling and dispatch. Moreover, PLF adequately captures the uncertainty whether that uncertainty is related to load data or the forecasting model. And there are not many PLF models, and those which exist are very complex or difficult to interpret. This paper proposes a novel neuroevolution algorithm for handling the uncertainty associated with load forecasting. In this paper, a new modified evolutionary algorithm is proposed which is used to find the optimal hyperparameters of 1D-Convolutional neural network (CNN). The probabilistic forecasts are produced by minimizing the mean scaled interval score loss function at 50%, 90% and 95% prediction intervals. The proposed neuroevolution algorithm is tested on a global energy forecasting competition (GEFCom-2014) load dataset, and two different experiments are conducted considering load only and one with load and temperature. Strong conclusions are drawn from these experiments. Also, the proposed model is compared with other benchmark models, and it has been shown that it outperforms the other models.

Abstract
This paper presents an adaptive robust optimization approach to optimal operation of multi-layout energy hubs under uncertainty. In the first step, the multi-layout energy hub concept is presented and discussed comprehensively followed by its required energy management model, but in the deterministic form. In the next step, an adaptive robust optimization approach is developed for the energy management model of multi-layout energy hubs. The uncertainties of energy hub load as well as upstream energy market prices are considered through bounded intervals using polyhedral uncertainty sets. The proposed adaptive-robust multi-layout EHS optimizer (ARMEO) is developed as a tri-level min-max-min optimization problem which cannot be solved directly. To do so, column-and-constraint (C&C) technique is used to recast the tri-level model into a min master problem and a max-min sub-problem. However, the max-min sub-problem is still a bi-level model and cannot be solved directly. To cope, block coordinate descent (BCD) methodology is applied to the sub-problem to iteratively solve the max-min sub-problem. An industrial-based case study is conducted to show the effectiveness of the proposed model in 1) managing multi-layout energy hubs, and 2) provide immunized operational solutions against uncertainties. Based on the results, it is observed that the ARMEO model is subject to a higher operation cost (compared to deterministic model), however, the obtained operating solutions are immunized against the uncertainties. Moreover, it has been shown that the proposed multi-layout EHS model can provide reasonable operating solutions for all layouts of the system as a whole.

Abstract
With the rising uncontrolled growth of energy demand, smart multi-energy systems (MESs) have emerged as a promising energy-efficient concept that provides the opportunity of supplying multiple energy services (electricity, heating, and cooling) to end-users simultaneously. To this end, a smart multi-energy system consisting of renewable energy sources (RESs), combined heat and power units (CHPs), heat pumps (HPs), community energy storage (CES), electric vehicles (EVs) and multi-energy demands are considered in this study, with the objective of maximizing the total system efficiency and minimizing the total operating costs, while meeting the required demands. The optimal operation of the smart MES is also evaluated by considering demand response (DR) based on the time-of-use (TOU) tariff for electricity, and the associated constraints throughout the whole operational horizons with the interconnected relationship between heating, cooling and power are taken into account. © 2022 IEEE.

Abstract
This paper presents an innovative strategy to assess the photovoltaic (PV)-based distributed generation (DG) hosting capacity considering the operation under normal and emergency conditions of electrical distribution systems (EDSs). In the emergency condition, the proposed strategy aims to improve the recoverability of EDSs against a set of high-impact fault scenarios. This recoverability process is achieved by the optimal coordination of topology reconfiguration, islanding operation of dispatchable DG units, and pre-positioning and displacement of mobile DG (MDG) units. This problem is formulated as a two-stage stochastic formulation, where the first one defines the DG hosting capacity and the amount of MDG units to be positioned in staging locations. Meanwhile, the second stage simulates high-impact fault events and, by applying resilience alternatives, the EDS recoverability can be improved. Inherently, the two-stage stochastic formulation is represented by a mixed-integer linear programming (MILP) model. The objective function of this MILP model maximizes the installed PVbased DG capacity while the amount of energy load shedding after fault events is minimized. To validate and show the scalability of the proposed strategy, two EDS are studied under different high-impact fault events and considering the application of multiple resilience alternatives. Results show that by estimating the capacity of PV-based DG simultaneously with the restoration process, the number of pre-positioned and dispatched MDG units can be reduced. On the other hand, when this PV capacity is determined disregarding fault scenarios, this solution could lead to unviable conditions and, thus, generation curtailment of up to 80% could be required.