Abstract
The integration of new technologies at the residential level such as energy storage systems, electric vehicles, solar photovoltaic generation and mini wind turbines triggered the appearance of a new agent in the power systems called prosumers. This agent has the potential to provide new forms of flexibility and cost-effective solutions. However, associated with these new solutions there are also a number of problems that affect these solutions, particularly network constraints. This work presents an analysis not only on the benefits of utilizing the prosumer's flexibility but also to the problems associated with the operation and optimization of the network. A new model is presented that considers energy transactions between prosumers in the neighborhood and between them and the network using on a stochastic framework, in order to account for a set of uncertainties in the form of scenarios associated with the availability of various resources and technologies. The results show the economic benefit of energy transactions between prosumers resulting in more flexibility for the system while highlighting the effect of network restrictions and potential problems associated with them.

Abstract
Electrification of public transport systems has become a strategic priority due to the highly versatile potential of electric vehicles (EVs) as distributed energy resources (DERs) Despite the logistic ease of transitioning conventional public transport systems to fully-electric ones, the latter must be properly designed to adhere to technical constraints and minimize their total ownership cost (TOC). Most published work addresses this issue with a narrow scope, such as modeling a very specific case study, leading to non-generic models which are difficult to apply for a universal case. In this study, a mixed-integer linear programming (MILP) model is formulated to model a generic public transport network, comprising the routes, electric bus models, and charging infrastructures. The objective function minimizes the TOC of a generic network, which can be applied to any universal case study. The constructed model is tested by simulating a system with different routes, bus models, and types of chargers to obtain the optimal configuration of the charging infrastructure.

Abstract
There is an ongoing paradigm shift occurring in the electricity sector. In particular, previously passive consumers are now becoming active prosumers and they can now offer important and cost-effective new forms of flexibility and demand response potential to the electricity sector and this can translate into system-wide operational and economic benefits. This work focuses on developing a model where prosumers participate in demand response programs through varying tariff schemes, and the model also quantifies the benefits of this flexibility and cost-reductions. This work includes transactive energy trading between various prosumers, the grid and the neighborhood. A stochastic tool is developed for this analysis, which also allows the quantification of the collective behavior so that the periods with the greatest demand response potential can be identified. Numerical results indicate that the optimization of energy transactions amongst the prosumers, and including the grid, leads to considerable cost reductions as well as introducing additional flexibility in the presence of demand response mechanisms.

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
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.