Abstract
Natural disasters such as earthquakes, hurricanes, and other extreme weather events along with human sabotage attacks pose serious risks to critical infrastructures especially electrical energy systems. Hardening and operational actions are the measures to improve the resiliency of the power systems against extreme events. The long-term hardening actions strive to organize the reinforcement of power system infrastructures which accomplished at the pre-events stage. Besides, the short-term operational measures such as network reconfiguration and generation scheduling are applied to form the multiple microgrids aimed at increasing the flexibility of the power system to cope with the severe events. These measures are taken during and after the occurrence of the disasters. In this paper, an integrated framework has been proposed to increase the resiliency of distribution system. In the proposed framework, there are two models so called defender-attacker-defender which are made to find the best possible solution in order to reduce the load-shedding of the system during extreme events. In the first model, the hardening measures are examined at the first level to increase the robustness of the system. The worst scenarios with the highest load-shedding are calculated in the second level and subsequently reconfiguration is performed in the third level to decrease the load-shedding. In the second model, the first and second levels specify the best reinforcement plan and the worst attack scenario respectively, and in the third level, optimal distributed generation placement is accomplished to supply the demand during islanding mode of microgrids. The proposed models are organized as tri-level mixed integer optimization problem and column constraint generation algorithm is utilized to make them computationally obedient. At the end, we have implemented the suggested models on the well-known IEEE 33-bus and 69-bus systems to prove their effectiveness and applicability at improving the resiliency of the distribution systems.

Abstract
The precise minute time scale forecasting of an individual PV power station output relies on accurate prediction of cloud distribution, which can lead to dramatic fluctuation of PV power generation. Precise cloud distribution information is mainly achieved by ground-based total sky imager, then the future cloud distribution can also be achieved by sky image prediction. In previous studies, traditional digital image processing technology (DIPT) has been widely used in predicting sky images. However, DIPT has two deficiencies: relatively limited input spatiotemporal information and linear extrapolation of images. The first deficiency makes the input spatiotemporal information not rich enough, while the second creates the prediction error from the beginning. To avoid these two deficiencies, convolutional autoencoder (CAE) based sky image prediction models are proposed due to the spatiotemporal feature extraction ability of two-dimensional (2-D) CAEs and 3-D CAEs. For 2-D CAEs and 3-D CAEs, four architectures are given respectively. To verify the effectiveness of the proposed models, two typical DIPT methods, including particle image velocimetry and Fourier phase correlation theory are introduced to build the benchmark models. Besides, five different scenarios are also set and the results show that the proposed models outperform the benchmark models in all scenarios.

Abstract
This paper presents a stochastic planning algorithm to plan an operation of a multi-microgrid (MMG) in an electricity market considering the integration of stochastic renewable energy resources (RERs). The proposed planning algorithm investigates the optimal operation of resources (i.e., wind turbine (WT), fuel cell (FC), Electrolyzer, photovoltaic (PV) panel, and microturbine (MT)) and energy storage (ES). Various uncertainties (e.g., the power production of WT, the power production of PV, the departure time of electric vehicle (EV), the arrival time of EV, and the traveled distance of EV) are initially forecasted according to the observed data. The prediction error is estimated by fitting the forecasted data and observed data using a Copula method. A Cournot equilibrium and game theory (GT) are applied to model the real-time electricity market and its interactions with the MMG. The proposed algorithm is examined in a sample MMG to determine the operation of uncertain resources and ES. The obtained results are compared with a baseline and the other conventional optimization methods to verify the effectiveness of the proposed algorithm. The obtained results authenticate the importance of modeling the interaction between the MMG and electricity market, especially under the high integration of uncertain RERs, resulting in above 8% cost reduction in the MMG.

Abstract
Deploying distributed renewable energy at the demand side is an important measure to implement a sustainable society. However, the massive small solar and wind generation units are beyond the control of a central operator. To encourage users to participate in energy management and reduce the dependence on dispatchable resources, a peer-to-peer energy sharing scheme is proposed which releases the flexibility at the demand side. Every user makes decision individually considering only local constraints; the microgrid operator announces the sharing prices subjective to the coupling constraints without knowing users' local constraints. This can help protect privacy. We prove that the proposed mechanism can achieve the same disutility and flexibility as centralized dispatch, and develop an effective modified best-response based algorithm for reaching the market equilibrium. The concept of "absorbable region" is presented to measure the operating flexibility under the proposed energy sharing mechanism. A linear programming based polyhedral projection algorithm is developed to compute that region. Case studies validate the theoretical results and show that the proposed method is scalable.

Abstract
Providing efficient support mechanisms for renewable energy promotion has drawn much attention from researchers in the recent years. The connection of a new renewable power plant to the transmission system has impacts on different electricity market indices since the other strategic generation units change their behaviour in the new multi-agent environment. In this paper, as the main contribution to the previous literature, a combination of multi-criteria decision-making approach and multi-agent modelling technique is developed to obtain the maximum possible profits for an intended renewable generation plan and also direct the investment to be located in a way to improve electricity market indices besides supporting renewable energy promotion. Fuzzy Q-learning electricity market modelling approach in combination with the technique for order preference by similarity (TOPSIS) is used as a new decision support system for promotion of renewable energy for the first time in the literature. The proposed interactive multi-criteria decision-making framework between the independent system operator (ISO) and the renewable power plant planner provides a win win situation that improve market indices while help the renewable power plant planning. The effectiveness of the proposed method is examined on the IEEE 30-bus test system and the results are discussed.

Abstract
The need for the operational reserve is growing due to the increase of variability and intermittency in both generation and demand sides. Hence, energy storage systems (ESSs) are considered as an alternative source of the reserve, while conventional generators are not efficient based on economic and environmental perspectives. This paper studies an enhanced model for ESSs? participation as a fast reserve provider. The day-ahead scheduling of ESSs within scenarios disturbs their stored energy in the sequence of hours. This issue can dramatically increase or decrease the stored energy of ESSs and threatens the safety of operational planning. The proposed model of this paper introduces coordination strategies for the deployment of fast reserves of ESSs. The stochastic model of this paper considers the fluctuations of wind speed and also the load forecasting errors as the source of uncertainties. A decomposition-based method is employed to reduce the complexity of the model dealing with a large number of variables. A modified version of the IEEE RTS-24 test system is used to evaluate different strategies for managing of ESSs? reservoir. The result shows that large deviations of the reservoir can make the operation of ESSs infeasible in uncoordinated strategies. Also, two proposed strategies for performance under normal and conservative criteria provide choices for system operators based on the desired level of security. Besides, the deployment of fast reserves of ESSs improves operation quality by the money-saving and increasing the quality of power delivery.