Abstract
This paper expresses the planning model of the backup distributed generation (DG) and lines hardening and tie lines in distribution networks according to resilient architecture (RA) strategy under natural disaster conditions such as earthquakes and floods. Indeed, the proposed deterministic problem of resilient distribution system planning considers the minimization of the daily investment, operation and resiliency (repair and load shedding) costs as objective functions subject to constraints of AC power flow equations, system operation limits, planning and operation model of backup DG and hardening and tie lines, as well as network reconfiguration formulation. The problem formulation is based on a mixed integer non-linear programming (MINLP) model, which is converted to a mixed integer linear programming (MILP) model on the basis of Benders decomposition (BD) approach using linearization approaches to achieve the optimal solution with the lower computational efforts and error. Besides, a hybrid stochastic/robust optimization (HSRO) based on the bounded uncertainty-based robust optimization (BURO) and a scenario-based stochastic optimization is used to model the uncertainties of load, energy price and availability of the network equipment under the extreme weather conditions. Finally, the proposed RA strategy is applied on 33-bus and 119-bus distribution test systems to investigate its capabilities in different case studies.

Abstract
Significant developments on semiconductor technology have captured the electronic industry and paved the way for dominating household appliances market. Typical loads in this market generally have nonlinear voltage current characteristics. Therefore, highly-integrated power-electronic based electrical equipment in the demand side has caused harmonic pollution, which is one of the most important power quality problems in distribution system operation. To address this issue, there have been significantly great attempts to keep total harmonic distortion (THD) and total demand distortion (TDD) levels within International standard limits defined by IEEE 519 and IEC 61000. On the other hand, load shifting has recently drawn special attention of power grid planners to improve system performance substantially in the smart grid paradigm. In this study, the real harmonic measurements of residential appliances (both linear and nonlinear) are carried out in the Smart Home Laboratory in Yildiz Technical University, Istanbul, Turkey. Different load profiles are then created with a high accuracy based on the measured voltage and current, active, reactive and apparent power. Also, three case studies are considered to investigate the impacts of load shifting strategies on power quality requirements in terms of satisfying the relevant standards. As a result, it is shown that the TDD value decreases below nearly 8% limitation by mitigating the harmonic distortion and the TDD index, which indicates the harmonic distortion effect on the system regarding the desired standard limits of IEEE.

Abstract
An efficient unit commitment planning must consider frequency regulation capacity in the model. Such models are more complicated under a high penetration level of renewable energy because of renewable ramping and uncertainty. This paper addresses these issues in the unit commitment. The proposed model for unit commitment considers uncertainty and ramping of wind power, frequency regulation capacity, spinning reserve, demand response, and pumped-storage hydroelectricity. Two reserve capacities including primary frequency regulation and spinning reserve are designed to handle the intermittency and ramping of renewable energies. In order to optimize the costs, the pumped-storage hydroelectricity and demand response program are also included to deal with ramping and uncertainty. The numerical results specify that the arrangement of frequency regulation capacity, pumped-storage system and demand response can effectively tackle both the ramping and uncertainty. The system includes 10-generator with total power equal to 1070 MW and one wind generator with 300 MW power. The initial wind integration level is about 28%. It is verified that decreasing the frequency regulation capacity by 10% reduces wind integration level by 94%. The demand response and pumped-storage increase wind integration level by 10% and 16%; while both together increase wind integration by 25% compared to the initial level. The wind integration level without large wind ramping can be increased up to 200%.

Abstract
This paper presents a self-scheduling model for home energy management systems (HEMS) in which a novel formulation of a linear discomfort index (DI) is proposed, incorporating the preferences of end-users in the daily operation of home appliances. The HEMS self-scheduling problem is modelled as a mixed-integer linear programming (MILP) multi-objective problem, aimed at minimizing the energy bill and DI. In this framework, the proposed DI determines the optimal time slots for the operation of home appliances while minimizing end-users? bills. The resulting multi-objective optimization problem has then been solved by using the epsilon-constraint technique and the VIKOR decision maker has been employed to select the most desired Pareto solution. The proposed model is tested considering tariffs in the presence of various price-based demand response programs (DRP), namely time-of-use (TOU) and real-time pricing (RTP). In addition, different scenarios considering the presence of electrical energy storage (EES) are investigated to study their impact on the optimal operation of HEMS. The simulation results show that the self-scheduling approach proposed in this paper yields significant reductions in the electricity bills for different electricity tariffs.

Abstract
Transportation electrification has become a prominent area of research and investment, especially in the last decades, regarding the increasing concerns for environmental sustainability. In this manner, there are different studies realized on electric vehicles (EVs), especially from the power system integration point of view to enable a more extensive penetration without causing adverse impacts on system operation. Different approaches for the direct and indirect management of EVs based charging demand in power systems have already been proposed in the literature as well as employed in the industry. In this study, from an indirect management point of view, a smart dynamic pricing approach based on a fuzzy logic controller based decision-making structure is proposed for EV charging in a distribution system. The proposed new decision-making method considers dynamically varying as well as static operational issues together with the social welfare of EV owners to provide real-time decisions compared to existing studies considering the wider EV charging service pricing topic from a different perspective. © 2020 IEEE.

Abstract
With the penetration of smart homes in distribution systems, and due to the effect of their schedulable load on reducing the peak load of the network as well as their comfort index, microgrid?s scheduling in the presence of smart homes has become an important issue. In this regard, this paper presents a tri-objective optimization framework for energy management of microgrids in the presence of smart homes and demand response (DR) program. The model is implemented on an 83-bus distribution system with 11 microgrids. The uncertainties of renewable energy resources (RESs) output power and load demand have been taken into account and the objective function is modeled in the form of bi-objective and tri-objective models using the max-min fuzzy method. The objectives include the operating cost, emissions, and peak-to-average ratio (PAR). The results indicate that an increase in DR penetration reduces the PAR and operating costs and leads to a decrease in the customers? comfort. Besides, the simulation results show that the best results are obtained from the tri-objective model, and in this model, three goals, including the operating costs, emissions, and PAR index are close to their optimal values, while the customers? comfort index is also satisfactory. Finally, the results show that considering smart homes in the network reduces the operation cost and emission by about 16 % and 17 %, respectively.