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The integration of energy efficiency programs and renewable resources in energy communities (EC) incorporating smart microgrids might be the future development of sustainable cities. Cooperation among EC with high penetration of renewable energy resources will support energy procurement. Mutual energy efficiency programs in EC associated with deploying renewable resources can guarantee sustainable development significantly. This paper proposes a new sustainability index (SI) influenced by the loss of load reduction while increasing the penetration of renewable energies. A multi‐objective optimization method is used to determine the optimal size and location of renewable resources incorporating energy efficiency programs. The objective functions, that is, the total cost and SI, are based upon social, economic, environmental, and technical (SEET) considerations. Since the outputs of renewable energies are uncertain, it may increase the loss of load probability. Therefore, this paper introduces a model that can handle the uncertainties of renewable energies. A new method so‐called adaptive multi‐objective crow search algorithm (AMCSA) is developed for the trade‐off between cost and sustainability in the optimization problem. The proposed methodology cannot only minimize the total cost in EC but also will maximize the SI. Simulation studies and results analysis indicates the effectiveness of the proposed methodology for the development of sustainability and renewable energies in the smart grid. [ABSTRACT FROM AUTHOR]

This research tries to assess the stochastic operation of the automated grids considering the reconfiguration strategy from social and economic points of view. To this end, a novel method based on the correlated approximation method, called unscented transform, is deployed to not only handle the independent uncertainty, but also model the correlated uncertainty. Different objective functions counting the power losses, total cost, the average system interruption frequency, and the average expected energy not supplied are modelled and analyzed to assess the economic and social aspects of the automation process. Considering the highly non‐linear nature of the problem, a heuristic search method with proper modification is proposed. The mixed integer non‐linear nature of the problem is well addressed and explained through the reconfiguration process. The suitable performance and reliable capability of the proposed stochastic framework are investigated on a standard IEEE test system. [ABSTRACT FROM AUTHOR]

Smart homes could have a significant impact on supplying the demand of both household consumers and the smart grid. Household consumers can trade energy via peer-to-peer (P2P) energy trading to reduce their costs. In other words, each of them can participate in the smart grid as a prosumer that can both produce and consume energy. On the other hand, more participation of smart homes in the demand-side management (DSM) program could help electricity decentralization. Also, energy storage systems (ESSs) and distributed energy resources (DERs) can lead to further decentralization of a smart microgrid. The production of renewable energy resources, such as photovoltaic (PV) systems, is associated with uncertainty. ESSs could be used as a reserve for PV systems. This paper presents a new risk-based model for P2P energy management of smart homes that consists of a PV system and EES and participates in the DSM program. The risk associated with the uncertainties of PVs' production and market prices has been modeled by conditional value-at-risk (CVaR). The mixed integer non-linear programming (MINLP) model of the problem has been solved by COUENNE in GAMS software. Numerical results show that the expected cost of all resources and the related risk is reduced by the proposed decision-making model for smart homes. [ABSTRACT FROM AUTHOR]

High impact–low probability (HILP) incidents, such as hurricanes, usually and gravely damage electric distribution networks. A resilient distribution network must have an ability to recover itself with a fast restoration methodology against the effects of HILP events. Due to improve the electric distribution network resiliency against HILP incidents, this paper suggests a probabilistic–proactive distribution network operation model based upon the chaos theory (P‐PDNOMC(. Here, a resilience index based upon operational cost and load shedding cost is employed to decrease the effects of a hurricane as an HILP incident. P‐PDNOMC is formulated as a framework that consists of a novel hurricane modelling and operation scheduling in normal and emergency conditions with uncertainties considering. It should be noted that a prediction approach based on the chaos theory and the least‐squares support vector machine (LS‐SVM) is also designed to consider the provisional and spatial behaviour of the hurricane. Furthermore, this paper proposes a novel optimization framework for damaged lines determination based upon the P‐PDNOM C (DLsP‐PDNOM C) by considering multi‐zone and multi‐period damaged equipment budget constraints. Here, the load shedding cost is applied to present the efficiency of the proposed model. The numerical results indicate the resiliency enhancement of the electric distribution network in the face of HILP incidents. [ABSTRACT FROM AUTHOR]