In order to promote grid’s wind power absorptive capacity and to overcome the adverse impacts of wind power on the stable operation of power system, this paper establishes benefit contrastive analysis models of wind power and plug-in hybrid electric vehicles (PHEVs) under the optimization goal of minimum coal consumption and pollutant emission considering multigrid connected modes. Then, a two-step adaptive solving algorithm is put forward to get the optimal system operation scheme with the highest membership degree based on the improved
With the promotion of the national energy conservation policy, implementing energy-saving generation scheduling in power system has been already put in the process. This means renewable energies are facing excellent development opportunities. With wind power’s scale increasing, in 2013, wind power newly increased installed capacity was 16093.3 MW and the gross installed capacity reached 92038.49 MW. Both of the two indicator values rank first in the world. Currently, China is planning to build 8 million level wind power bases. The gross installed capacity of wind power will reach one hundred million in 2020 MW [
However, because of wind power output volatility, power sources away from the load centers, uncoordinated grid construction, and other factors, wind power absorptive problem has become the bottleneck of wind power in China. According to the statistical data released by The National Energy Board, in 2012, the total abandoned wind power reaches 20TW
There are already many researches focused on improving the wind power absorptive problem. Optimization methods are mainly aimed at optimizing wind power’s backup service. They could be divided into four categories, namely, wind power-AGC collaborative grid connection [
Plug-in hybrid electric vehicles (PHEVs) have chargeable and dischargeable capacity, which makes PHEV have the potential to be the backup service for wind power grid connection. PHEV’s orderly grid connection can bring multiple benefits such as achieving energy conservation by using electricity rather than oil, smoothing the load curve by controlling charging and discharging behavior, and promoting wind power grid connection. However, if PHEV’s grid connection behavior is not under control, its arbitrary charging and discharging behavior would exacerbate the volatility of system load, which would decrease wind power consumption capacity. To solve these problems, wind power-PHEV synergistic scheduling optimization method needs to be studied.
Researches on wind power-PHEV synergistic scheduling optimization are mainly focused on two aspects, namely, model building and solution algorithm. As for model building, literature [
In terms of solution algorithm, minimizing pollution emission becomes another optimization objective of wind power-PHEV synergistic scheduling under energy conservation requirements. This makes the optimization problems become multiobjective problems. Multiobjective problems need suitable solution algorithms to get the optimal solution sets. Literature [
Based on the analysis above, to overcome the deficiencies of the study on wind power-PHEV synergistic scheduling, this paper mainly contains 7 sections. Section
In terms of charging model, there are some research results. PHEV’s charging modes can be divided from two aspects, namely, ordered or disordered charging, distinguish time periods, or continue charging. In this way, its charging modes can be classified into 3 kinds as follows: the no-control charging mode, the continuous charging mode, and the delayed charging mode [
The PHEV charging load profile in the uncontrolled charging mode [
The PHEV charging load profile in the continuous charging mode [
The PHEV charging load profile in the delayed charging mode [
The no-control charging mode: in this mode users’ charging behaviors are not influenced by anything. They just follow their own needs and habits. So PHEV’s charging load would have strong volatility. Therefore PHEV’s grid connection would bring some bad effects to electric system’s short term operation stability. However, in the long term operation aspect, electric system could grasp users’ charging habit and formulate corresponding measures to overcome the bad effects.
The delayed charging mode: being different from the no-control charging mode, this charging mode applies time of use (TOU) and other control means to lead users’ charging behaviors. Users are trend to adjust their charging behavior according to the load curve. This charging mode is usually considered to be a practical scenario, because in this mode PHEV mainly charge in the valley period and discharge in the peak period. So PHEV could play an important role in load shifting, smoothing the load curve.
The continuous charging mode: in the no-control charging mode or delayed charging mode, PHEV is supposed to charge and discharge only once a day. That can be true for individual PHEV, but it is not so practical for public PHEV. In the continuous charging mode, as long as PHEV is not in operation, it can charge or discharge. So in this mode user’s charging and discharging behavior would not be limited. PHEV’s grid connection would increase, which means gasoline consumption and CO2 emission would decrease. However, users’ casual charging and discharging behavior would bring some uncontrolled effects to electric system’s operation. For example, the volatility of load curve may increase. This is not conducive to wind power grid connection.
The fully optimal charging mode: based on the delayed charging mode, the fully optimal charging mode takes measure to control users’ charging behavior directly. In this mode, PHEV’s charging or discharging would absolutely be in accordance with power system’s supply and demand situation. If TOU is rational in the continuous charging mode, its optimization result would be almost the same with the fully optimal mode. So the load curve of the fully optimal charging mode is similar to the continuous mode.
PHEV’s discharging mode is influenced by battery type, capacity, related parameters, discharging cycle, discharging loop power, and other factors. Because it is hardly to decide discharging parameters, this paper uses existing research results [
PHEV is used as a kind of backup service for wind power grid connection, in a certain extent; it is determined by controlling and estimating PHEV’s discharging capacity. So the uncertainty of PHEV’s discharging time and number brings considerable difficulties to accurately estimate the discharge capacity. Literature [
Different types of PHEV discharging characteristic curves [
PHEVs need storage battery as their energy storage component to achieve charging and discharging behavior. Load change of storage battery can be tracked by energy storage controller [ When PHEV is in the charging status,
When PHEV is in the discharging status,
where
Generally, PHEV’s charging or discharging power should not exceed 20% of the maximum capacity of the storage battery [
Wind power output is limited by the income-wind velocity. However, if the income-wind velocity is lower than the cut-in wind velocity or is higher than the cut-out wind velocity, the wind farm will not generate power. The relationship between output power and the income-wind velocity could be expressed as
In the actual scheduling progress, wind power output can be determined through two ways, namely, forecast and simulation. In terms of wind power forecast method, there are classical forecast method [
In order to gain the wind power scenarios, interval method is used to simulate wind power output. Divide wind power output into several intervals. Set the value of a point in the interval as wind power output expectation. When the number of intervals is sufficient, the forecast value could be regarded as the real output. The details are shown in Figure
According to Figure
Discrete method for wind forecasting power.
Wind scene tree.
The basic concept of scenario reduction is comparing a scenario with other scenarios and removing the closest one. And the bigger the scenario number, the bigger the workload of scenario reduction. To overcome this problem, this paper introduces the Kantorovich distance [
Assume
Then the Kantorovich distance would be
Define
Then build wind farm scenario reduction optimization method based on (
According to (
For thermal units, coal consumption cost mainly includes power generation coal consumption cost and startup-shutdown coal consumption cost; the objective function is
Pollutant emission cost of power generation can be calculated by the least squares method using historical pollutant emission data. The objective function is
Consider
Thermal power output constraints include power generation upper and lower constraints, power climbing constraints, and the minimum startup and downtime constraints, as described in formulas (
Consider
Consider
Consider the following: where where
To simplify the solving process, we need to do a linearization for the quadratic objective function. Here we divide unit
To facilitate solving, we need to do linearization of formula (
When solving multiobjective models, if a solution
This paper selects the improved constraints method to calculate multiobjective optimization problems. And then it uses the determination method in literature [
However, the enhanced
To meet decision-makers’ demand, this paper chooses fuzzy decision method to calculate membership degrees of the Pareto optimal solution set. Define membership function
The corresponding membership function of optimization objective.
Based on the two-step adaptive solving algorithm, we can analyze different wind power-PHEV effectiveness in multigrid connected mode. Its solving steps are as follows. Input the original data of the model. The objectives Based on step Based on step Based on step
The input-output tables of objective function.
Objective function |
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Remark: * indicates that the objective function is the target to solve.
Flowchart of the solving progress.
This paper uses IEEE36 nodes 10-unit system as a simulation base. Add in wind farms at nodes 10, 15, and 24, respectively. The installed capacities of those wind farms are 200 MW, 300 MW, and 150 MW. Power generation coal consumption and pollutant emission parameters are listed in Table
Wind power output and power load demand.
Period | Wind power |
Power demand/MW·h |
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1 | 286 | 900 |
2 | 513.5 | 1000 |
3 | 533 | 1100 |
4 | 559 | 1200 |
5 | 520 | 1500 |
6 | 435.5 | 1700 |
7 | 383.5 | 1900 |
8 | 305.5 | 2100 |
9 | 182 | 2300 |
10 | 188.5 | 2500 |
11 | 169 | 2600 |
12 | 195 | 2700 |
13 | 169 | 2500 |
14 | 143 | 2300 |
15 | 214.5 | 2100 |
16 | 260 | 1800 |
17 | 240.5 | 1700 |
18 | 188.5 | 1900 |
19 | 136.5 | 2100 |
20 | 130 | 2500 |
21 | 214.5 | 2300 |
22 | 292.5 | 1900 |
23 | 423.8 | 1300 |
24 | 370.5 | 1000 |
Coefficients of coal-fired power units.
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1# | 11.6 | 0.260 |
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8 | 8 | 25.6 | 250 | 600 | 280 | −280 |
2# | 9.7 | 0.259 |
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8 | 8 | 23.1 | 200 | 500 | 240 | −240 |
3# | 8.8 | 0.268 |
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7 | 7 | 22.3 | 200 | 450 | 210 | −210 |
4# | 8.4 | 0.273 |
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7 | 7 | 19.6 | 180 | 400 | 180 | −180 |
5# | 7.2 | 0.28 |
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6 | 6 | 16.2 | 150 | 350 | 150 | −150 |
6# | 6.1 | 0.285 |
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5 | 5 | 15.4 | 150 | 300 | 150 | −150 |
7# | 5.2 | 0.292 |
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4 | 4 | 12.3 | 120 | 300 | 120 | −120 |
8# | 4.6 | 0.304 |
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4 | 4 | 8.1 | 100 | 250 | 100 | −100 |
9# | 3.5 | 0.306 |
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3 | 3 | 4.3 | 70 | 150 | 70 | −70 |
10# | 1.4 | 0.314 |
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2 | 2 | 2.1 | 30 | 100 | 50 | −50 |
Currently, there are three main types of PHEV, namely, BEVs, V2G, Triple-VG2 (equivalent to three V2G), and PCEV. Compared with other types, V2G has the advantage of being rechargeable and dischargeable, which makes it have better prospects. Therefore, this paper chooses 50000 V2G cars and studies V2G cars grid connection influence on wind power consumption. Assume that their average charging power is 1.8 kW and the maximum charging power is 2.4 kW. Charging period lasts for 6 hours and total charged electricity is 10.8 kWh [
The simulation has been implemented in GAMS optimization software using CPLEX 11.0 linear solver from ILOG solver. The CPU time required for solving the problem for different case studies with a VAIO E series laptop computer powered by core i3 processor and 2 GB of RAM was less than 10 s. Firstly, we verified the validity and applicability of the solving algorithm in Section
This paper sets the no-control charging mode as the basic simulation scenario and chooses improved multiobjective nondominated sorting genetic algorithm-II (NSGA-II) [
Results comparing the two algorithms.
Algorithms | Coal consumption/tce | Pollutant emission/t | Grid-connected electricity/MW·h | Iteration number | Solving time | |
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Thermal power | Wind power | |||||
The proposed | 9341.2 | 40016.13 | 40425.32 | 5728.66 | 485 | 5.2 |
The NSGA-II | 9438.5 | 40298.24 | 40921.47 | 5432.51 | 642 | 7.1 |
According to Table
Analyzing the solution of the algorithm put forward by this paper can verify its applicability of solving wind power-PHEV synergistic scheduling optimization model under multiobjective functions. Figure
Electric vehicle charging and discharging time distributions in the no-control charging mode.
In this section, we will compare thermal units’ output structure, wind power grid connection statue, power generation cost, and pollutant emission after PHEV’s grid connection under 4 charging modes. Table
Best Pareto-optimal solution in different modes.
Number | No-control charging | Continuous charging | Delayed charging | Fully optimal charging | ||||
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1 | 9341.32 | 40016.13 | 8622.48 | 38576.44 | 8565.85 | 38803.84 | 8492.16 | 38480.11 |
2 | 9424.81 | 33955.16 | 8719.04 | 32034.96 | 8652.33 | 32776.17 | 8585.55 | 31897.44 |
3 | 9653.03 | 27894.19 | 9000.33 | 25493.48 | 8885.29 | 26748.50 | 8871.93 | 25314.62 |
4 | 10074.93 | 21833.21 | 9541.44 | 18951.98 | 9321.98 | 20720.82 | 9429.48 | 18731.95 |
5 | 11099.96 | 15772.26 | 11553.41 | 12410.50 | 10414.55 | 14693.17 | 11435.41 | 12149.31 |
Compared with the no-control charging mode, in the other 3 charging modes, 1# unit achieved full capacity operation and the large capacity generators’ output increased, namely, 2# and 3# units. In the continuous charging mode, charge and discharge times are not limited. Therefore, PHEV’s grid connection is the maximum, which makes thermal units’ output decrease. But for units with small installed capacity, like 4# and 5# units, their output would be more than that in the delayed charging mode or the fully optimal charging mode. In the fully optimal charging mode, thermal units’ output allocation is determined by their installed capacity. That means units with big installed capacity would output much more than the small ones. In this way, the output structure comes to the optimal. Table
According to Table Thermal units’ output decreased obviously and the output curve is relatively smooth. In section of the base load, unit 1# and unit 2# supply the basis load demand. In section of the waist load, units 3# and 6# supply the waist load demand before PHEV grid connection and that changed to be unit 3# and unit 4# in the fully optimal charging mode. In section of the peak load, units 7# and 8# supply the peak load demand before PHEV grid connection.
Output allocation of thermal power unit in four modes (MW).
Unit | Before PHEV grid connection | After PHEV grid connection | |||
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No-control charging | Continuous charging | Delayed charging | Fully optimal charging | ||
1# | 14 388 | 14 380 | 14 400 | 14 400 | 14 400 |
2# | 10 105 | 10 877 | 11 563 | 11 424 | 11 512 |
3# | 6052 | 6539 | 14400 | 14400 | 14400 |
4# | 4626 | 4372 | 11324 | 11463 | 11512 |
5# | 3197 | 2090 | 7228 | 7621 | 7891 |
6# | 2200 | 2167 | 0 | 0 | 0 |
7# | 480 | 0 | 0 | 0 | 0 |
8# | 400 | 0 | 0 | 0 | 0 |
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Total output | 41 448 | 40 425 | 38796 | 39070 | 38967 |
Thermal units’ output allocation in the fully optimal charging mode.
Thermal units’ output allocation before PHEV grid connection.
And that changed to be unit 5# in the fully optimal charging mode.
The output of wind power in four modes.
System optimization results contrast in different modes.
Startup and shutdown cost/tce | Coal consumption cost/tce | Abandoned wind/MW·h | Emission/t | |||
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CO2 | SO2 |
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Before connection | 177.29 | 9783.80 | 1511.40 | 40743.09 | 132.63 | 155.43 |
Uncontrolled | 153.44 | 9187.88 | 1325.14 | 39735.20 | 129.35 | 151.58 |
Delayed | 103.36 | 8519.12 | 232.86 | 38305.61 | 124.70 | 146.13 |
Continuous | 153.36 | 8412.49 | 545.40 | 38531.42 | 125.43 | 146.99 |
Fully optimal | 98.65 | 8393.51 | 25.64 | 38209.95 | 124.39 | 145.77 |
As government’s support to develop PHEV is increasing, PHEV’s number grows. This means the charging and discharging behaviors of PHEV would influence power system’s load curve directly. To verify the influence of PHEV’s grid connection on power system’s load curve economic benefit and environmental benefit, this paper sets 25, 50, 75, and 1000 thousand PHEVs in succession to do system simulation. The results are shown in Figures
Sensitive analysis result on PHEV number.
PHEV number | Peak load | Valley load | Peak and valley difference |
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25000 | 2700.5 | 1700 | 1000.5 |
50000 | 2615 | 1790 | 825 |
75000 | 2580 | 1772.5 | 807.5 |
10000 | 2545 | 1830 | 715 |
Demand load curve comparison.
Economic and environmental benefits comparison.
As the number of PHEVs increases, system’s load curve becomes gentler, which means more obvious load shifting effect. When PHEV number is 25 thousand, the peak load is 2700 MW and the valley load is 1700 MW. And when PHEV number is 100 thousand, the peak and valley loads changed to 2545 MW and 1830 MW. Their difference decreased by 29%.
According to Figure
To analyze PHEV’s grid connection benefits, this paper established a wind power-PHEV synergistic scheduling optimization model and put forward a two-step adaptive solving algorithm based on improved This paper uses the improved PHEV’s grid connection can help in load shifting, decrease abandoned wind and power generation cost, and obviously bring economic and environmental benefits. And in delayed charging pattern and fully optimal charging pattern, system would get the optimal benefits. Compared with the no-control charging mode, the delayed charging mode has the maximum grid connection electricity amount, the least thermal power generation, and little abandoned wind. However, since users’ casually charging behavior, PHEV grid connection can bring big impact on system’s safe and stable operation. So the abandoned wind in this mode is higher than that in the delayed charging mode or fully optimal charging mode. In the fully optimal charging mode, abandoned wind comes to be the least. That is because in this mode users would adjust their charging behavior according to the load curve. Then the load shifting effect would be increased and PHEV grid connection can help wind power grid connection, increasing economic and environment benefits. Based on the conclusions above, to make full use of PHEV grid connection, we need to commence from two aspects. On one hand, use incentive policies to promote the development of electric vehicles. And on the other hand, use reasonable demand side electricity to guide users’ charging behavior, which would be an important research direction on wind power-PHEV synergistic scheduling.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This paper is supported by the National Science Foundation of China (Grant nos. 71071053 and 71273090).