Abstract
The scarcity and price volatility of fossil fuels as well as environmental concerns has motivated the replacement of fossil fuel-powered vehicles by electric vehicles (EVs). Long charging time in battery charging stations is a serious barrier for large-scale adoption of EVs, so battery swap stations (BSSs) were developed wherein the near-empty batteries are exchanged with fully charged batteries and EV refilling is done in only a couple of minutes. Nowadays, BSSs are typically connected to a microgrid (MG) in their neighborhood. In this research, the optimal scheduling of MG resources and BSS is done for a grid-connected MG with dispatchable, photovoltaic and wind distributed generation (DG) units and operation cost of MG is minimised. It is assumed that the BSS services Tesla 3 EVs with 75 kWh batteries and a driving range of 496 km. A var compensator (VC) is connected to the MG that can purchase reactive power from var compensator. AC optimal power flow is done for the MG, while all network constraints, power loss and reactive power dispatch are taken into account and the cost of provision of reactive power is included in the operation cost of the MG. Generalized reduced-gradient (GRG) algorithm is used for the optimisation process. The effects of VC, optimal BSS scheduling and reactive power costs on active/ reactive power dispatch and MG operation cost are duly investigated.

Abstract
The advantages of wind power integration over other renewable resources are well-known information and the natural results are the massive worldwide integration. Such massive integration, without the correct management together with conventional generation leads to an augmented complexity and the inflexibility of conventional power systems. For several reasons, forecasting tools are one of the most valuable tools in the power systems field, because they helps to decide in advance the way to manage correctly and with profits the electrical mix production. In this work, an extended hybrid wind power forecasting approach, with probabilistic features, is proposed to forecast twenty-four hours-ahead, considering only real historical wind power data. To validate the proposed forecasting approach, a comparison with other validated models is performed to offer a fair and proportional analysis. The outcomes show that the suggested forecasting approach performs adequately even considering the reduced data available as input.

Abstract
Decentralization of power systems is creating a need for tools which can provide fast and accurate optimal power flow (OPF) solutions, without being dependent on the availability of all system information and/or uncertain variables. In this study, a hybrid probabilistic algorithm is proposed to accurately and efficiently predict ideal generation levels of individual generators to minimize the total system cost (as per AC-OPF), while having no information on the grid structure and with limited information on system variables. The proposed hybrid algorithm combines the use of correlation analysis, k-means clusters, and kernel density estimation (KDE), to predict ideal generation levels of each generator based only on historical datasets of local information (i.e. adjacent load centers). By simulating the AC-OPF problem on the IEEE 9-bus test system, a historical dataset of 1000 samples is synthetically generated and randomized local information is given as input for each agent. Quasi-deterministic Monte-Carlo simulations with 100000 samples were used for validation. In the most uncertain operating conditions, the proposed algorithm was capable of predicting the ideal generation level of the most expensive generator with a 1.65% error, while being three times faster than a Neural Network (NN), taking only 0.39 seconds to run on a standard laptop computer.

Abstract
Continuous-time optimization models have successfully been used to capture the impact of ramping limitations in power systems. In this paper, the continuous-time framework is adapted to model flexible hydropower resources interacting with slow-ramping thermal generators to minimize the hydrothermal system cost of operation. To accurately represent the non-linear hydropower production function with forbidden production zones, binary variables must be used when linearizing the discharge variables and the continuity constraints on individual hydropower units must be relaxed. To demonstrate the performance of the proposed continuous-time hydrothermal model, a small-scale case study of a hydropower area connected to a thermal area through a controllable high-voltage direct current (HVDC) cable is presented. Results show how the flexibility of the hydropower can reduce the need for ramping by thermal units triggered by intermittent renewable power generation. A reduction of 34% of the structural imbalances in the system is achieved by using the continuous-time model.

Abstract
Nowadays, the operation of the smart distribution system (SDS) is more complicated with the penetration of electric vehicles (EVs), due to EVs' uncertainties as well as the capability of vehicle-to-grid (V2G). On the other hand, distribution transformers (DTs) which have to meet the demand of EVs are one of the essential components of SDS; indeed, their failure can lead to irreparable damage. The cause of most of these failures is overloading and high ambient temperature. The overloading increases the temperature of the various parts of the DTs, especially hot spot temperature (HST). Increasing this temperature reduces the nominal life of the DTs. With a high number of EVs in the future, and as a consequence high energy demand which has not been taken into account in proper operating program, it could lead to the overloading of DTs. So, in this paper, the loss of life (LOL) of a DT that feeds the residential loads and an EV parking lot (EV PL) is investigated. The maximization of the profit of the distribution system operator (DSO) is considered in two different parts i.e. with/without the appropriate operation coefficient (OC) of DT. Also, two different scenarios are applied i.e. charging mode (CM) of EVs and charging/discharging mode (CDM) of EVs. The results show that if the OC is not properly considered, the LOL of the transformers will be significantly high, implying a higher total ownership cost.

Abstract
Self-scheduling of Home Energy Management Systems (HEMS) is one of the most interesting problems for active end-users to reduce their electricity bills. The electricity bill reduction by adopting Demand Response Programs (DRP) considering the flexibility of the end-users is addressed in this paper. The problem is addressed as a multi-objective optimization problem. The first objective function is the minimization of the daily bill, while the second objective aims to minimize the Discomfort Index (DI) regarding shifting the home appliances plugging-in time. The Time-of-Use (ToU) tariff is adopted in this paper and therefore, the end-users can benefit from shifting their flexible loads from peak hours to the off-peak hours and this reduces their bills, accordingly. In this case, the end-users have to change their energy consumption which imposes a level of discomfort on the end-users. Therefore, a two-stage model is proposed in this paper to deal with the mentioned objective functions. The proposed model is represented as standard mixed-integer linear programming (MILP) and for solving this problem the epsilon-constraint method is adopted in this study. The obtained Pareto front from the epsilon-constraint multi-objective framework is fed to the fuzzy satisfying method for final plan selection. These results show that by providing the Pareto set of optimal solutions to the user, they are more informed and can make decisions that better suit their preferences.