Abstract
In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards of robustness and the quality of power grids. One of the main challenges hindering this progress are uncertainties and stochasticity associated with the electricity sector and especially renewable generation. In this paradigm shift, forecasting tools are indispensable, and their utilization can significantly improve system operation and minimize costs associated with all related activities. Thus, forecasting tools have an essential key role in all decision-making stages. In this work, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP) combining wavelet transforms (WT), hybrid particle swarm optimization (DEEPSO), adaptive neuro-fuzzy inference system (ANFIS), and Monte Carlo simulation (MCS). The proposed hybrid probabilistic forecasting model (HPFM) was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets. The proposed model exhibited favorable results and performance in comparison with previously published work considering electricity market prices (EMP) data, which is notable.

Abstract
In this paper, a novel direct load control (DLC) planning based on providing free energy credits to residential end-users for their heating, ventilation, and air conditioning load during demand response (DR) events is proposed. The obtained credit can then be used by the end-users during relatively higher price periods free-of-cost to enable them lowering their energy procurement costs. Furthermore, the resulting reduction in the total household energy consumption considerably decreases the critical load demands in power systems, which is of vital importance for load-serving entities in maintaining the balance between supply and demand during peak load periods. In this regard, the aforementioned energy credits-based incentive mechanism is proposed for end-users enrolled in the DLC-based DR program, as a new contribution to the existing literature, testing it in a stochastic day-ahead planning context.

Abstract
In this paper, a new bi-level framework is presented for operational scheduling of a smart distribution company (SDISCO) with electric vehicle (EV) parking lot (PL) and renewable energy sources (RES), i.e., wind and photovoltaic (PV) units. In the proposed bi-level model, maximization of the profit of SDISCO is obtained in the upper-level (leader) problem by minimizing the cost of power purchased from the wholesale market due to the EV PL unique capability, i.e., PL-to-grid. The lower-level (follower) problem aims to maximize the profit of the PL owner. This model is converted to a non-linear single-level problem by using Karush-Kuhn-Tucker (KKT) conditions. Fortuny-Amat and McCarl method is used for linearization based on auxiliary binary variables and sufficiently large constants. Moreover, uncertainties such as duration of the presence of EVs in PL, the initial state of the charge (SOC) of EVs and output power generation of wind and PV units are simultaneously considered through a set of scenarios. The SDISCO's profit is investigated in four modes: (1) without RES and with the controlled charging of EVs; (2) without RES and with smart charging/discharging of EVs; (3) with RES and with the controlled charging of EVs; (4) with RES and with smart charging/discharging of EVs. In all these modes, a price-based demand response (DR) program is considered, as well as incentive-based DR, and combined price-based DR and incentive-based DR. The presented model is tested on the IEEE 15-bus distribution system over a 24-h period. The results show that SDISCO gains more profit by using a suitable charging/discharging schedule and employing a critical peak pricing (CPP) program. Furthermore, by comparing this bi-level model with the centralized model, the effectiveness of the bi-level model is demonstrated. Also, sensitivity analyses on the number of EVs, size of RES and the percentage of customer participation in the DR program are evaluated on the optimal operation of the SDISCO.

Abstract
This paper addresses an optimized management of a storage energy battery which is part of a microgrid with a connection to the main grid and is supplied by a photovoltaic (PV) power plant. The main contribution of this paper is to consider uncertainty in electricity price while managing the battery storage. The forecasted value for demand and PV unit are predicted by a seasonal autoregressive integrated moving average model (SARIMA)-capable of accurately characterizing both seasonality effects and tail fatness. The optimal operation of the battery is determined by resolving a linear optimization program in which the objective function comprises the conditional value at risk (CVaR). Using CVaR ensures that the demand is fully supplied while minimizing the risk and operational cost. The cost function is the difference between power sold and bought subject to the charging and discharging rates for the battery and defining upper and lower bounds for the level of battery charge. The simulation results confirm that the risk consideration has a significant effect on the optimized management of a storage energy battery in a photovoltaic grid-connected microgrid.

Abstract
The hybrid AC/DC microgrids have become considerably popular as they are reliable, accessible and robust. They are utilized for solving environmental, economic, operational and power-related political issues. Having this increased necessity taken into consideration, this paper performs a comprehensive review of the fundamentals of hybrid AC/DC microgrids and describes their components. Mathematical models and valid comparisons among different renewable energy sources' generations are discussed. Subsequently, various operational zones, control and optimization methods, power flow calculations in the presence of uncertainties related to renewable energy sources are reviewed.

Abstract
This paper proposes a new strategy for an independent system operator (ISO) to trade demand response (DR) with different DR aggregators while considering various operational constraints. The ISO determines the energy scheduling and reserve deployment in a pre-emptive market while setting DR contracts with the DR aggregators. The ISO applies a two-stage stochastic programming to cope with the uncertainty associated with wind power production. DR aggregators' behavior is modeled through a profit maximization function. Aggregators determine their DR trading shares with ISO and customers through three DR options, including load curtailment, load shifting, and load recovery. A stochastic bilevel problem is formulated, in which in the upper level, the ISO minimizes the total operation cost, and in the lower level, the DR aggregator maximizes the profit. Afterwards, the problem is transferred to a single-level mathematical problem with equilibrium constraints by replacing the lower level program with its Karush-Kuhn-Tucker (KKT) conditions. As a result, the total operation cost is reduced using the proposed method comparatively to run the program without considering the lower level.