Abstract
Integrated transmission expansion planning (TEP) and generation expansion planning (GEP) with Wind Farms (WFs) is addressed in this paper. The optimal number of expanded lines, the optimal capacity of WFs installed capacity, and the optimal capacity of wind farms lines (WFLs) are determined through a new TEP optimization model. Furthermore, the optimum capacity additions including conventional generating units is obtained in the proposed model. The Benders decomposition approach is used for solving the optimization problem, including a master problem and two sub-problems with internal scenario analysis. In order to reduce the computational burden of the multi-year and multi-objective expansion planning problem, a multi-stage framework is presented in this paper. The uncertainties of wind speed and system demand along with contingency scenarios lead to a probabilistic optimization problem. Moreover, in the proposed model, the planning time horizon is divided into three predefined stages. This multi-stage approach is used to increase the proposed model accuracy in a power system with a high level of wind power penetration. Hence, in this paper a scenario-based probabilistic multistage model for transmission expansion planning is proposed, incorporating optimal WFs integration. It is recognized that high wind penetration increases the transmission expansion investment cost, but based on the reduction of the investment cost of conventional units, the total system cost will be smaller. This result emphasizes the main advantage of wind generating system over the conventional generating system. This planning methodology is applied to the modified IEEE 24-bus test system and simplified Iran 400-kV real system to show the feasibility of the proposed algorithm.

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
This paper presents an optimal bidding and offering strategy for a virtual power plant (VPP), which participates in day-ahead (DA) and balancing markets. The VPP comprises distributed energy resources, plug-in electric vehicles (PEVs) and flexible demands. The objective of the problem is maximizing the VPP's profit while demand response (DR) providers who aggregated the loads try to supply the required demand under their jurisdiction with minimum costs. The proposed optimization problem is formulated as a bi-level stochastic scheduling programming to address uncertainties in DA and balancing electricity prices, renewable energy source's (RES) and DR relationship. Simulation results demonstrate the applicability and effectiveness of the proposed model to any real markets. Also, numerical results show that the flexibility of responsive loads and PEVs can improve the VPP operator's energy management and increase its expected profit. © 2020 IEEE.

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
One of the most important aspects of the development of Electric Vehicles (EVs) is the optimal sizing and allocation of charging stations. Due to the interactions between the electricity and transportation systems, the key features of these systems (such as traffic network characteristics, charging demands and power system constraints) should be taken into account for the optimal planning. This paper addressed the optimal sizing and allocation of the fast-charging stations in a distribution network. The traffic flow of EVs is modeled using the User Equilibrium-based Traffic Assignment Model (UETAM). Moreover, a stochastic framework is developed based on the Queuing Theory (QT) to model the load levels (EVs' charging demand). The objective function of the problem is to minimize the annual investment cost, as well as the energy losses that are optimized through chance-constrained programming. The probabilistic aspects of the proposed problem are modeled by using the point estimation method and Gram-Charlier expansion. Furthermore, the probabilistic dominance criteria are employed in order to compare the uncertain alternatives. Finally, the simulation results are provided for both the distribution and traffic systems to illustrate the performance of the proposed problem.