Abstract
This paper presents the optimal operation strategy for home energy management system (HEMS) in the presence of the inverter-based heating, ventilation and air conditioning (HVAC) system. The main target of this paper is to find the optimal scheduling of the home appliances in line with the optimal operation of the air conditioner system to reduce the daily bills while the end-users discomfort index would be minimized. In this paper, the mathematical formulation is represented in mixed-integer linear programming (MILP) framework to reduce the computational burden and easily be adapted by hardware for implementation. The HEMS is the main responsible for optimal scheduling of controllable and interruptible loads as well as serving the fixed loads. The electricity tariff is based on time-of-use (TOU) mechanism and three different tariffs have been considered during the daily consumptions. The simulation results for the daily operation of a residential home confirms that the proposed model can effectively reduce the electricity bill while the consumer predefined comfort level is appropriately maintained.

Abstract
Today, the fact that consumers are becoming more active in electrical power systems, along with the development in electronic and control devices, makes the design of Home Energy Management Systems (HEMSs) an expedient approach to mitigate their costs. The added costs incurred by consumers are mainly paying for the peak-load demand and the system's operation and maintenance. Thus, developing and utilizing an efficient HEMS would provide an opportunity both to the end-users and system operators to reduce their costs. Accordingly, this paper proposes an effective HEMS design for the self-scheduling of assets of a residential end-user. The suggested model considers the existence of a dynamic pricing scheme such as Real-Time Pricing (RTP), Time-of-Use (TOU), and Inclining Block Rate (IBR), which are effective Demand Response Programs (DRPs) put in place to alleviate the energy bill of consumers and incentivize demand-side participation in power systems. In this respect, the self-scheduling problem is modeled using a stochastic Mixed-Integer Linear Programming (MILP) framework, which allows optimal determination of the status of the home appliances throughout the day, obtaining the global optimal solution with a fast convergence rate. It is noted that the consumer is equipped with self-generation assets through a Photovoltaic (PV) panel and a battery. This system would make the consumers have energy arbitrage and transact energy with the utility grid. Consequently, the proposed model is demonstrated by determining the best operation schedule for different case studies, highlighting the impact each different DRP has on designing and utilizing the HEMS system for best results.

Abstract
The stability and monitoring of AC microgrids (AC MG) are greatly influenced by gathering sufficient and precise information. Since installing several sensors on AC MGs is costly and increases AC MG ripple, integrating a minimum number of cost-effective sensors is preferred. In this paper, a joint-estimating advanced augmented-Kalman filter (KF) to estimate the current of the AC MG and unknown time-varying loads from the noisy measurement of the AC bus voltage is developed. The proposed approach also provides smooth and noise-less information from the measured voltage. The presented method has less complexity to handle and as a robust approach, it would be capable of dealing with uncertainties due to the load, which can be linear, nonlinear, or unbalanced. The joint-estimating augmented-KF outputs can be then utilized in the monitoring, fault detection, and control design purposes. The developed framework is tested on an AC MG supplying time-varying load and numerical results verify the applicability and accuracy of the developed technique to estimate the load and filter currents.

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.