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Microgrids are a new concept in electrical systems that can independently supply electricity and heat (CHP) to their residents. These microgrids are capable of integrating renewable energy units with a probabilistic nature, which creates significant challenges for them. This paper addresses the timing of microgrid production, considering the risk of power supply from renewable units in the presence of electric vehicles. The aim of this study is to reduce the risk of power outage using electric vehicles, increase microgrid independence and stability, and intelligently distribute power with minimal environmental and economic costs. To this end, a probabilistic model structure is first introduced, and then the optimization problem on this model is simulated using three optimization algorithms: Gray Wolf Optimization (GWO), Firefly Algorithm (FA), and Particle Swarm Optimization (PSO). Ultimately, microgrid indices such as microgrid independence and stability, Risk level, power distribution index, and optimal environmental and economic costs are calculated. By simulating the proposed problem, in addition to reducing the risk of power outages using electric vehicles, the objectives of the problem are optimized.

While the market for new electric vehicles is growing significantly, some environmental issues, such as carbon emissions, are often attributed to the production of electric cars. In this research, a supply chain including a manufacturer that sells electric and gasoline vehicles through a retailer was investigated. A game theory approach has been used for modeling and problem solving. The assumption was made that the government, as a leader or pioneer, implements various policies based on the concept of sustainability. Consequently, the manufacturer and retailer seek to maximize their profits in the Stackelberg competition based on these policies. As a result of this research, managerial insights for real-world application were obtained. For instance, thresholds for coefficients and parameters, such as the conversion rate of environmental value to financial value, were identified. These show that if green investment in production lines depends on product demand, the market (or consumer) price and the factory price of electric cars will be higher compared to a scenario where the importance of green investment for the government is independent of demand. However, in the first scenario explained, the government provides more support for electric cars. To provide practical managerial insights in the real world, this research focused on parametric analyses.

In recent years, with increasing application of electric vehicles, studies about fast-charging stations have received attention. One of the most important factors in the development of electric transportation is the availability of fast-charging stations. This paper examines the sizing and siting of fast-charging stations while taking into account how they will affect the traffic flow of urban transportation networks and the power quality indexes of the distribution network. On the other hand, the optimal usage of the reactive power compensation and active filtering capabilities of fast-charging station have been considered. In the proposed method, the harmonic power flow, staircase cost facility location model (SCFLM), and user-equilibrium traffic assignment for modeling constraints of distribution and transportation networks are used. The proposed model aims to reduce traveling time in the transportation network by considering a penalty cost for incremental road traffic congestion in the objective function. Also, the proposed model aims to improve the power quality of the distribution network. The proposed integer linear programming model is implemented in GAMS software and finally, the effectiveness of the proposed method is shown using numerical results.

In this paper, in order to intelligently manage the demand for smart home electrical networks, it includes electrical loads such as refrigerators, air conditioning systems, washing machines, dishwashers, electric ovens, and PHEV that can be charged and discharged by connecting to the city electricity. In this research, the DE algorithm has been used in order to intelligently optimize household load management with the presence of renewable elements and controllable loads. Also, wind and solar power generation sources have been considered, and the uncertainty related to power generation by their annual power generation regime has also been considered. The proposed goal is to reduce electricity costs and also improve the voltage profile of household loads. The load profile considered in this article is real and without averaging, which is adapted from authentic articles. The simulation was implemented by MATLAB software and the results were compared with the basic state in order to validate the results.

In this article, a comprehensive two-stage framework for conducting competitive energy and ancillary services markets in transmission and distribution networks is presented. In the first and second stages of the proposed framework, energy and ancillary services markets are held, respectively. In the proposed framework, the suppliers of spinning reserve market capacities are conventional thermal units, while the suppliers of regulation market capacities are fast response generators, energy storage systems, electric vehicles, and demand response aggregators. A linear AC power flow program is included in the proposed framework to verify the applicability of the simulation results in real operating conditions. The introduced framework is modeled as a linear optimization problem in which the objective function of each stage is solved separately. This framework is implemented on a test system that includes a 30-bus transmission network connected to four 8-bus distribution networks, and the CPLEX solver in GAMS software is used to simulate it. The simulation outputs clearly confirm that the participation of resources within the distribution networks in providing spinning reserve capacities significantly reduces the share of expensive thermal units in the market and thereby lowers the daily costs of the system. Moreover, the simulation outputs indicate that the participation of demand response aggregators, energy storage systems and electric vehicles in providing regulation market capacities, not only lowers the costs of this market but also significantly improves technical indicators such as voltage characteristics.

This paper presents a tri-level model for the simultaneous management of energy and ancillary services markets between transmission and distribution networks integrated with renewable energy sources, smart homes based on the Internet of Things, and electric vehicles. In the first level of the proposed model, smart homes plan their participation in the energy and regulation markets and send it to the distribution network operator. In the second level, the operators of the distribution networks plan their area according to the programs received from the smart homes and determine their strategy for participation in the energy, reservation and adjustment markets. In the third level, the strategy of distribution networks is sent to the operator of the transmission system so that the final planning of the energy, reservation and adjustment markets can be done according to them. The proposed model is formulated as a mixed integer linear programming problem and solved by GUROBI solver in GAMS. The implementation of the proposed model showed that this model was able to significantly use the potential of subscribers based on the Internet of Things, electric vehicles, storage systems and demand response programs to improve the technical aspects of transmission and distribution networks as well as to improve the economic aspects of energy markets and ancillary services.

Today, electric vehicles have been significantly considered due to the lack of air pollution. However, due to the large volume of vehicles in the city, connecting them to the distribution network without a control program may cause congestion in the network lines and substations. Therefore, a bi-level hierarchical method for connecting electric vehicles to the distribution network to coordinate services and performance constraints of the three main elements, namely fleet operator, distribution system operator, and capacity market operator in the presence of vehicle owners and taking into account driving requirements, charging and compaction costs of power lines and transformers are recommended. In this method, first car owners and fleet operators present their charging program to the distribution system operator according to the predicted market prices. In the next step, if this charging program leads to congestion in the lines or substations and power transformers, it is returned to the fleet operators and the shadow price is determined by the capacity market operator based on the congestion status. Then the new charging program is dispatch according to the new market prices. The proposed method is implemented in a sample distribution network and the simulation results show the efficiency of the proposed method.

In this paper, a three-level scenario-based framework for determining the optimal strategy and planning of microgrids located in a 118-bus distribution system is presented. This paper considers the uncertai nties of renewable energy resources, load demand, and the charge / discharge schedule of electric vehicles. In order to increase planning flexibility, the operator will be able to change the flow through the distribution feeder reconfiguration. Also in the proposed model, customers will be able to reduce their costs by participating in a demand response program. In the first level of the proposed model, the bidding strategy of microgrids is determined. In the second level, the market clearing price is determined by the independent system operator and according to the submitted bids. Finally, in the third stage, the problem of final microgrid programming is solved by a participatory game theory method. The proposed model is solved by the CPLEX solver in GAMS software and the results show that the dynamic topology improves the planning flexibility and thus reduces the total operating cost by about 10%. The results also show that the coordination of electric vehicles with scheduling, the presence of storage systems and the implementation of the demand response program leads to a significant reduction in the level of market-clearing price and thus reduce operating costs.

Many factors affect the performance of lithium-ion battery-based systems (such as satellites and electric vehicles). One of the most important ones is the imbalance of battery cells due to continuous charging and discharging, the temperature of the battery environment and the process of making batteries. With the imbalance of the lithium-ion battery pack cells, their battery capacity and useful life are reduced, and in some cases, it even causes the battery pack to explode and explode. So far, various methods have been proposed to balance lithium-ion battery cells. This paper presents a new configuration for the lithium-ion battery balancing circuit. The proposed circuit has an automatic balancing process and the energy transfer in it occurs depending on the cell in both switching cycles. As a result, its balancing speed has increased significantly compared to similar designs. The proposed circuit for 42 watts in the simulated Orcad Capture software, which has an efficiency of 81% and an equalization time of 1.5 milliseconds (according to the modeled conditions of the battery pack in the simulation) at a voltage difference of 0.5 volts between weak and strong cells, and the results The result indicates the proper operation of the proposed circuit.

In the event of a severe incident with a high impact and low probability of occurrence, distribution networks may be separated from upstream networks and several feeders may be disconnected simultaneously within the distribution networks. In such circumstances, to maximize the resilience of the distribution networks and to prevent long-term global outages, they are reconfigured and islanded to allow restoring the loads as much as possible. On the other hand, because of the increasing penetration of electric vehicles in todaychr('39')s advanced societies for environmental reasons and fossil fuel prices, charging and discharging management of plug-in electric vehicles (PEVs) into parking lots can have a significant impact on improving the resilience of distribution networks. Technically, electric vehicles can be connected to the electrical grid in two modes of charging or grid-to-vehicle (G2V) mode as an electrical load and discharging or vehicle-to-grid (V2G) mode as energy storage units. Therefore, this paper proposes a mixed integer linear stochastic programming model to improve the resilience of distribution networks equipped with distributed generation (DG) resources. The proposed model optimizes the distribution network reconfiguration with the aim of maximizing load restoration criteria by using optimal charging and discharging planning of PEVs in parking lots. In the proposed model, the master and slave DGs in each island and the charging and discharge planning of PEVs are optimally determined considering the movement of PEVs between parking lots and the formation and boundaries of the islands. To present a realistic model, the uncertainty of the generation capacity of renewable wind resources is also taken into account. The proposed model has been implemented and validated by applying several concurrent faults on a 118-bus distribution network in GAMS software. The results show the positive impact of PEVs parking lots on improving the resilience and increasing the load recovery of the distribution network.

Environmental concerns and recent developments in electric vehicle (EV) technology have attracted the attention of the international community to use EVs. In this paper, by considering an urban transportation network, a design strategy is presented to maximize drivers' travel comfort in urban trips. Further, the presented strategy meets various constraints, such as properly locating charging stations in the city, considering traffic volume, taking the shortest route per trip, reducing charging waiting time, etc. The travel comfort index in this paper corresponds to a situation in which the driver does not experience a depleted battery during a trip and successfully finish the trip. Therefore, charging stations should be located throughout the city so that the drivers can access them. Since the movement of vehicles over the course of a day does not follow any particular pattern, in this paper, we use unscented transformation (UT) to investigate the uncertainty in different probabilistic parameters of EVs. Moreover, by clustering the locations of EVs within the urban area, the optimal locations of EV charging stations are determined over the course of the day using an objective function based on a genetic algorithm. The simulation results of the urban transportation confirm the efficacy of the proposed method.

The very low cost of renewable energy resources and the increase of the greenhouse gas emissions and fuel cost have led to a simultaneous increase in utilizing the renewable energy resources (RESs) and electric vehicles (EVs). In this paper, a mixed-integer linear programming (MILP) model is proposed for the stochastic unit commitment problem with the aim of minimizing the operation cost and the emission in the presence of EVs and RESs. EVs with the capability of vehicle-to-grid (V2G) can operate as energy storage units in the smart grid and, if necessary, be connected to the network as generation resources. In this paper, an aggregator is responsible for coordinating the charging and discharging of EVs. The RESs uncertainties has complicated the management of electric vehicles and the unit commitment problem. Therefore, in this paper, Monte Carlo simulation method is used for modeling the uncertainties of the wind and solar power and the load demand. The simulation results show that the simultaneous utilization of the proposed MILP model and the probability distance method for reducing the number of scenarios, can minimize the operation cost of thermal units and pollutant emissions while reduces the solution time, significantly

Microgrid and smart electrical grids are among the new concepts in power systems that support new technologies within themselves. Electric cars are some advanced technologies that their optimized use can increase grid efficiency. The modern electric cars sometimes, through the necessary infrastructure and proper management, can serve as an energy source to supply grid loads. This study was conducted to investigate the energy management for production and storage resources. For this purpose, we considered the market price of energy, the prices quoted by distributed generation sources, and electric vehicles in the grid and responsive loads. The load response programs used include the time of use and direct load control. The problem has a linear mixed-integer planning structure that was simulated using the GAMS software. The results show that with this planning, the proposed load response programs have a positive impact on cost reduction.

In recent years, electric vehicles have attracted significant attention due to environmental issues. Charging stations installation requires a systematic consideration of relevant issues such as determination of the location and size of charging stations. On the other hand, it is necessary to encourage private investors to invest in charging stations installations and to provide proper conditions for them so that they can profit from their investment. In this paper, the fast charging station (FCS) planning problem is modeled as a mixed integer nonlinear programming (MINLP), in which the objective function of distribution company (DISCO) and FCS owner (FCSO) have been considered, separately. In the proposed model, the location and size of FCSs as well as the price of transacted energy between DISCO and FCSO are determined, such that the objective functions of DISCO and FCSO are optimized. In the proposed method, queuing theory and user equilibrium based traffic assignment model are used to determine the size of FCSs. Then, the problem of multi-objective planning of FCSs has been investigated, considering the objectives of DISCO and FCSO. In addition, the final solution is chosen from the Pareto front solutions based on the economic and operational indices. Finally, the efficiency of the proposed method is demonstrated by numerical results.

Considering economic and environmental factors, it is expected that the number of plug-in electric vehicles (PEVs) will be increased, rapidly. The high penetration of EVs, can affect the power system. Therefore, in recent years, various studies have paid their attention to the impacts of PEVs charging on the network. In this paper, a probabilistic model based on the queueing theory is extracted using Monte Carlo simulation for modeling EV charging station load. It is assumed that the vehicles are the taxis of Amol city in Mazandaran province. Required data such as the time of arrival and the state of charge of the battery before charging, were collected and extracted using three methods from intra-city taxis in the city of Amol. To obtain the demand load of EV charging, the traffic-based behavior of drivers is needed. This behavior is stochastic. Therefore, its related variables will not be deterministic and must be evaluated using probabilistic methods.

Electric vehicles(EV) are the most important technology that can be used in a Smart Grid environment. These vehicles can have many benefits to the network. In order to engage EV owners in the proposed plans of the utility, there must be sufficient and attractiveness conditions such as economic profitability. On the other hand, due to the transfer of vehicles to special parking lot, the mileage of EVs to these parking lot should be reduced as much as possible. On the side of the utility, charging and discharging planning should be such that the system objectives are improve. This scheduling depends on to the number of EV in each parking lot. Therefore, in this paper, using the optimal area of EV lot, as well as scheduling of charging and discharging EV, will increase the profitability of the EV owners and utility at the lowest possible losses, and reducing the mileage of vehicles to the parking lot. Then, due to importance of such load as economic, medical or security center, the effect of the location of these loads is examined on parking area and scheduling of charging and discharging EV.

With appearance and development of Microgrids (MGs), the number of MGs is continuously increased in smart distribution networks. Hence the future distribution network operation and planning is encountered with new challenges. In this paper, the problem of networked-microgrids based distribution network planning is considered. The parking lot for electric vehicles as storage resources to reduce the impacts of uncertainties on networked-MGs design is investigated. The boundary of MGs is determined and segmented considering techno-economical constraints such as total network planning and operation costs, reliability improvement of MGs, optimal sizing and locating of energy resources within each MGs and the voltage profile improvement in the presence of EV’s parking lot as storages. For each objective function, optimal border of each MG, its relevant energy resources and EV parking lot capacity and location as well as optimal layout of smart distribution network is obtained. The results are represented both tabular form and graphically.

In this paper, the impact of electric vehicles (EVs) and a price-based and an incentive-based demand response programs and the combination of both programs, has been investigated on the optimal operation of the distribution system, which owns a wind unit, in the framework of a new bi-level model. In this model, simultaneously, uncertainty of wind unit and electric vehicles are also considered. The aim of both levels is to maximization the profits. In the upper-level, the distribution system, due to the existence of a wind unit and the vehicle to grid capability of EVs, gains more profit by not purchasing the electrical energy from the upstream network. At the lower-level, owner of the EVs parking lots is obtained more benefit, due to the selling of energy to EVs owners and distribution system. Forasmuch as this model is converted to single-level mix-integer linear problem by using of Karush–Kuhn–Tucker (KKT) conditions and auxiliary binary variables and is solved by GAMS software. The presented model is tested on the IEEE 15-bus distribution system over a 24-h period and results prove the effectiveness of the model.

By increasing the number of electric vehicles (EVs) in the power system, it is necessary to manage their interaction with the grid such that to minimize the costs of the owners as well as to maximize the profit of their serving entities. Therefore, in this paper, a stochastic bi-level decision making problem for the participation of EV aggregator in a competitive environment is presented, with considering various sources of uncertainty. The sources of uncertainty for the participation of aggregators in the electricity market include day-ahead (DA) and balancing market prices as well as rival's suggested prices and charging and discharging EVs demand that are modeled with time series. In the proposed bi-level program, the goal of the first level is to maximize the aggregator's profit in interaction with the network, and the goal of the second level is to minimize the payments of the owners. Since the objective function of the first and second levels are in contradiction with each other, with using KKT optimization conditions and the duality theory, the proposed two-level problem has become a linear single-level problem. Finally, the proposed program is implemented in typical test system and the results show that by using this model for the aggregator's decision making, it can offer proper charge and discharge price signals to the EV owners to attract the them in a competitive market and also to maximize its profit.