Abstract
In recent years, virtual power plants (VPPs) rose as an effective framework to aggregate the collective potential of distributed energy resources (DERs), including distributed generation (DG) and energy storage systems (ESS), through demand response (DR) program implementation. In this work, the operation of two indispensable DER assets, electric vehicles (EVs) and photovoltaic-equipped parking lots (PVPLs), is coordinated in an optimal energy management framework, in order to study their possible aggregation as a VPP. The proposed energy management system (EMS) was developed using the optimization and simulation tools, namely GAMS and MATLAB, and is intended for use by grid operators to coordinate the operation of PVPLs and home energy management systems (HEMSs) in the context of smart cities. The developed model was validated and tested by considering real-life case studies in the city of Porto, Portugal.

Abstract
Grid-connected photovoltaic (PV) systems are considered as the best options for home solar electric system applications. Compared to other alternatives, grid-connected PV systems offer the least expensive and lowest-maintenance choice for residential usage. The PV systems are constructed using some solar cells to extract the energy from sun radiations and power converters to convert the output DC voltage into AC one. In the present study a hybrid scheme is suggested to control the grid-connected PV converters. The developed scheme is able to inject the power created by solar arrays into the network. Also, it can rectify the problems associated with the reactive power and load harmonics in the system. Moreover, the suggested technique can secure the load active and reactive power during the network failure conditions. One of the contributions of the proposed method is that it can manage the power of solar arrays to supply the load, filter the harmonics and compensate the reactive power in a situation where the power produced by solar arrays is less than the power of converters. In this condition, the proposed technique can make the current drawn from the grid completely sine at unity power factor.

Abstract
Two increasingly popular distributed energy resources (DERs), especially within the European context, are photovoltaic (PV) installations and electric vehicles (EVs). Numerous models have been proposed for optimal management thereof, such as Home Energy Management Systems (HEMSs) and EV parking lot management systems (EVPLMS). However, these approaches are often designed to benefit only one party without taking into account the effect of any other management systems. I.e., HEMSs are designed to only maximize the economic benefit of home owners, while EVPLMSs are designed to only maximize the profit of parking lot owners. In this study, the coordinated use of these systems is modeled and simulated to investigate whether a synergistic relationship exists in which consumers (EV owners) have an added economic benefit by the simultaneous operation of HEMSs and EVPLMSs. As such, a cost-benefit analysis is conducted from the point of view of the EV owners, utilizing a HEMS at home and an EVPLMS at work. The analysis was performed on case studies that are based on real facilities, locations, meteorological data, and electricity market prices in Porto, Portugal.

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
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
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.