Optimal Operation of Virtual Power Plant with Considering the Demand Response and Electric Vehicles

Abstract

The needs of human communities for electrical energy is increasing every day, and as a result, the price of fossil fuels is steadily increasing. Considering the trend of advances in renewable energy technologies and the support of governments and energy policymakers to make more use of these clean and inexpensive resources. Limitations such as low capacity, the uncertainty of output power and stability issues have made it costly and difficult to use Distributed Energy Resources (DERs). To solve these problems, a new concept known as Virtual Power Plant (VPP) has been proposed to facilitate the exploitation of DERs. The VPP is a set of DERs that are put together for market participation. Also, efforts to reduce the amount of environmental pollution have replaced the use of Electric Vehicles (EV) instead of internal combustion engines. Regarding this paper, a new model for optimizing virtual power plant is presented. In this model, the Demand response (DR) has been used, whose modeling is based on Price-Based Demand Response for ordinary loads and incentive. To solve the problem a mixed integer linear programming model is proposed to maximize the profit of the virtual power plant. In order to see the effectiveness and satisfying performance of proposed model, a case study including DERs, EV, and loads is studied as test system. The simulation results using Matlab and GAMS show the efficiency of the proposed model and proves that simultaneous use of the price-based demand response program, smart charging, and the participation of electric vehicles in demand responses reduces operating costs. KeywordsVirtual power plant • Electric vehicles • Distributed generation resources • Demand response • Mixed integer linear programming * Javad Nikoukar

Coordinated multi‐objective scheduling of a multi‐energy virtual power plant considering storages and demand response

“…Technical and economic specifications of all units are given in Table 3 [21, 30, 44, 45]. Electric vehicle requirements, such as distance travelled, arrival time, and departure time from the parking lot, are handled using the scenario generation method mentioned in Section 3.1, and specifications for electric vehicle batteries are taken from reference [32]. Consumer participation in DR programs is considered 10%.…”

“…EVs are new technologies that are also known as mobile storage. The battery of these vehicles is rechargeable, so the MEVPP operator can use them to increase network flexibility [32]. Equation (35) shows the cost of charging and discharging EVs.…”

“…After deleting these scenarios, 109 scenarios remain. Finally, in the last part, using the information of reference [32], values of battery capacity, charge rate, and the electrical energy consumption per mile are randomly assigned to each of the EVs. A schematic of the scenario generation process for EVs is shown in Figure 5.…”

Capacity sizing method of virtual power plants based on game theory

“…Assuming that the initial operation of the DG is at (0, P DG , n ), when t = τ 1 , the cumulative upregulation amount of electricity in DG is E DG + . when t = τ 2 , the cumulative downregulation amount of electricity in DG is E DG − , as shown in Equation (3).…”

“…Compared with microgrid, VPP has smaller geographical limitations and larger scale of gridconnected distributed generators. Therefore, VPP can more effectively connect distributed generators and power systems to realize resource integration and distribution, which is an important way for smart grid to realize interaction and intelligence on the energy supply side in many studies, [2][3][4][5][6] and the ultimate configuration of energy Internet in Qian. 7 However, to make the best use of VPP, it is very important to make reasonable capacity sizing for a variety of distributed generators in the planning stage.…”