To reduce the influence of wind power random on system operation, energy storage systems (ESSs) and demand response (DR) are introduced to the traditional scheduling model of wind power and thermal power with carbon emission trading (CET). Firstly, a joint optimization scheduling model for wind power, thermal power, and ESSs is constructed. Secondly, DR and CET are integrated into the joint scheduling model. Finally, 10 thermal power units, a wind farm with 2800 MW of installed capacity, and
The implementation of China’s energy-saving and pollutant emission reduction strategies has prompted large-scale wind power development. In 2014, the installed capacity of wind power reached 115 million kW, ranking first in the world. However, influenced by the intermittency that is characteristic of wind power, the growth rate of wind power grid integration is smaller than the growth rate of the installed capacity. This phenomenon leads to a high rate of curtailed wind power in China. The average rate of curtailed wind power is approximately 12.8%. Especially in the “three north areas,” the rate of curtailed wind power has already reached 15.4%. In order to solve the problem of curtailed wind power, suitable backup service should be provided on the generation site for wind power connected to the grid. Energy storage systems (ESSs) could flexibly provide backup service by charging and discharging. This property gives ESSs the most potential as a means to provide backup service for wind power integration. Additionally, demand response (DR) could optimize customers’ power consumption behavior and incentivize customers to participate in system scheduling for wind power consumption.
Currently, China is implementing carbon emission trading (CET) pilot projects and plans to establish a CET market to prompt energy-saving and emissions reduction in the “thirteenth five-year plan” period. CET could affect the generation cost of thermal power units and make clean energy generation more advantageous. Hetzer et al. [
Selecting a better backup method is the most effective way to overcome the random nature of wind power. Currently, backup service for wind power grid integration consists of three main parts: thermal power, pumped storage power plants, and ESSs. Jiang et al. [
The operational theory of ESSs is similar to that of pumped storage power plants, but ESSs have more flexible installation requirements with better prospects for large-scale application. Wu et al. [
The researchers cited above have achieved good results in actual applications; however, the high initial investment cost limits the scale of application of energy storage systems. Therefore, other routes are needed to optimize the application of ESSs. DR can optimize customers’ power consumption behavior, smooth the demand load curve, and increase the consumption of wind power. Greening [
CET can highlight the environmental friendliness of wind power and increase the advantages of wind power generation [
The rest of this paper is organized as follows. Section
Power demand response refers to a situation in which customers dynamically adjust their power consumption behavior according to price, which should guarantee a balance between power supply and demand. From an economic point of view, when electricity price increases, the demand for power should decrease. Part of the power demand during the peak load period will be transferred to other periods, while the other part will be reduced. Thus, for the peak load period, the load reduction consists of three parts: one is load transfer due to an increased electricity price, the second is load reduction, and the third is load transfer due to a reduced electricity price during the valley load period. For the float load period, the load change consists of two parts: one part comes from the peak load period and the other part goes to the valley load period. For the valley load period, the load increase consists of three parts: one is load transfer due to a decreased electricity price, the second part comes from the peak load period, and the third part is new power demand due to the price reduction.
This study defines the power demand during the peak load period, float load period, and valley load period before implementing time-of-use (TOU) price as
The proportion of power demand reduction during the peak load period is given by
If the proportion of load change is the same at each point in time in the same period, the load at each time point is
The system load period will change from
ESSs can be regarded as both power resources and load demand. When wind power output is high at night, ESSs are regarded as load demand. In the daytime, they are regarded as power resources to meet load demand. Charge and discharge of ESSs are limited by the system capacity. Assuming the storage energy of ESSs at time
Charging and discharging power from ESSs is limited as follows:
In addition, the energy storage capacity of ESSs is limited:
Wind farm operators hope for higher consumption of wind power to gain more profits; however, this will cause more frequent adjustment of thermal power units for peak regulation, improving wind power grid integration but also increasing system coal consumption. To achieve the optimum energy efficiency, a joint optimization scheduling model for wind power and thermal power is built. The maximum total profit of ESSs, wind power, and thermal power is taken as the optimization objective:
The wind farm profit is calculated as follows:
The profit of thermal power units is calculated as follows:
The fuel cost of a thermal power unit is calculated by
In the joint optimization model, the constraints of demand and supply balance, thermal power unit operation, wind power operation, and ESS operation should be comprehensively considered.
After DR, the constraint is described by
Equation (
Therefore, if ESS operators hope to profit, the charging and discharging prices should obey
Equations (
Equations (
Currently, China is performing pilot construction and planning to establish a CET market in the “thirteenth five-year plan” period. CO2 emission from the thermal power industry accounts for approximately 40% of the total. Generation rights displacement and the CET mechanism are both market mechanisms to optimize the thermal industry structure and reduce energy consumption and emission, which are consistent in purpose and results.
The marginal generation cost of thermal power changes under a CET mechanism, and carbon emission parameters are different due to different unit technologies, so the generation scheduling plan also changes. To maximize system profit under a carbon trading mechanism, this study builds an optimization model with the objective of maximizing thermal and wind power profit:
Thermal power profit should meet the following conditions:
The actual carbon emission of thermal units is related to the power load rate. Generally speaking, the actual carbon emission of units can be expressed as a quadratic function, similar to (
Then, total system emissions are as follows:
Scheduling and operation constraint conditions for wind power and ESSs should be considered comprehensively in carbon emissions trading. The system demand and supply constraints, wind power unit operation constraints, and ESSs operation constraints are shown in (
In the mathematical model without CET, (
To analyze the impact of ESSs, DR, and CET on system operation, four cases are set up as follows.
Self-scheduling of the system without DR and CET: DR and CET are not considered, so the impact of ESSs on wind power grid integration is analyzed alone in this case. The ESS capacity is 3 × 80 MW, the charging and discharging power of a single ESS unit is 20 MW, and the charging and discharging loss coefficient is 15%.
Demand response is introduced into the joint scheduling. The demand load curve is divided into peak, valley, and float load periods according to the literature [
Period division of TOU.
Load | Valley load period | Float load period | Peak load period |
---|---|---|---|
Period | 0:00–6:00; 22:00–24:00 | 6:00–9:00; 14:00–19:00 | 9:00–14:00; 19:00–22:00 |
90% of CO2 emissions from Case
Collaborative optimization of CET and DR is analyzed with joint scheduling of wind power and ESSs.
This study uses 10 thermal power units and a wind farm with an installed capacity of 2800 MW as the simulation system. Coal consumption and carbon emission parameters of the thermal power units are listed in Table
Coal consumption and carbon emission parameters of thermal power units.
Unit |
|
|
|
|
|
|
---|---|---|---|---|---|---|
1# | 11.6 | 0.260 | 1.88 |
29.04 | 0.680 | 1.60 |
2# | 9.7 | 0.259 | 6.55 |
24.77 | 0.713 | 2.04 |
3# | 8.8 | 0.268 | 9.44 |
22.64 | 0.747 | 2.86 |
4# | 8.4 | 0.273 | 1.65 |
21.54 | 0.754 | 4.67 |
5# | 7.2 | 0.28 | 2.17 |
18.97 | 0.793 | 6.26 |
6# | 6.1 | 0.285 | 3.39 |
15.80 | 0.788 | 9.37 |
7# | 5.2 | 0.292 | 3.42 |
13.57 | 0.818 | 9.65 |
8# | 4.6 | 0.304 | 4.13 |
12.18 | 0.859 | 12.03 |
9# | 3.5 | 0.306 | 3.63 |
9.35 | 0.876 | 10.96 |
10# | 1.4 | 0.314 | 8.35 |
3.82 | 0.900 | 24.06 |
Operation coefficients of coal-fired power units.
Unit |
|
|
|
|
|
|
|
---|---|---|---|---|---|---|---|
1# | 250 | 600 | 280 | −280 | 8 | 8 | 4.9 |
2# | 200 | 500 | 240 | −240 | 8 | 8 | 5.3 |
3# | 200 | 450 | 210 | −210 | 7 | 7 | 5.2 |
4# | 180 | 400 | 180 | −180 | 7 | 7 | 5.7 |
5# | 150 | 350 | 150 | −150 | 6 | 6 | 6.1 |
6# | 150 | 300 | 150 | −150 | 5 | 5 | 6.8 |
7# | 120 | 300 | 120 | −120 | 4 | 4 | 6.9 |
8# | 100 | 250 | 100 | −100 | 4 | 4 | 7.3 |
9# | 70 | 150 | 70 | −70 | 3 | 3 | 8.3 |
10# | 30 | 100 | 50 | −50 | 2 | 2 | 8.7 |
Equivalent utilization of wind power units.
Period | Load/MW | Utilization rate/% | Period | Load/MW | Utilization rate/% | Period | Load | Utilization rate/% |
---|---|---|---|---|---|---|---|---|
1 | 1100 | 33 | 9 | 2300 | 28 | 17 | 1700 | 32 |
2 | 1200 | 55 | 10 | 2500 | 11 | 18 | 1900 | 29 |
3 | 1400 | 68 | 11 | 2600 | 26 | 19 | 2100 | 17 |
4 | 1600 | 76 | 12 | 2500 | 23 | 20 | 2500 | 13 |
5 | 1700 | 67 | 13 | 2400 | 12 | 21 | 2300 | 23 |
6 | 1900 | 51 | 14 | 2300 | 20 | 22 | 1900 | 38 |
7 | 2000 | 36 | 15 | 2100 | 9 | 23 | 1500 | 33 |
8 | 2100 | 32 | 16 | 1800 | 21 | 24 | 1300 | 38 |
The simulation was implemented in GAMS optimization software using the CPLEX 11.0 linear solver from ILOG_solver. The CPU time required for solving the problem for different case studies with an idea pad450 series laptop computer powered by a core T6500 processor and 4 GB of RAM was less than 10 s.
This case mainly analyzes the impact of ESSs on wind power grid integration. The scheduling result is shown in Figure
Output distribution of wind power and thermal power.
After introducing ESSs, the peak-valley ratio is 2, the coal consumption rate is reduced from 326 kg/MWh to 322.5 kg/MWh, the system profit is enhanced by 160000 yuan, the electricity from wind power delivered to the grid is increased from 16840.5 MWh to 18620.6 MWh, and the curtailed wind power rate is reduced by 8.1%. ESSs can smooth the demand load curve, provide backup service for wind power, and reduce the start-stop cost of thermal power units. The scheduling optimization results for the power system with and without ESSs are listed in Table
Scheduling optimization result of power system in different cases.
Wind power | Thermal power | System profit (104 yuan) | |||||
---|---|---|---|---|---|---|---|
Generating capacity (MWh) | Grid proportion (%) | Curtailed wind power rate (%) | Generating capacity (MWh) | Grid proportion (%) | Curtailed wind power rate (%) | ||
Without ESSs | 16840.5 | 31.2 | 24 | 37032.6 | 68.8 | 326 | 294 |
With ESSs | 18620.6 | 34.6 | 15.9 | 35252.5 | 65.4 | 322.5 | 290 |
If ESS operators hope to maximize their economic benefit in the optimization period, they should discharge all stored energy to gain more economic benefit. However, to reduce the impact of wind power output fluctuation, ESSs make charging and discharging decisions based on wind power output to reduce thermal power peak regulation, as shown in Figure
Charge and discharge optimization result for ESSs.
Total profit declines with addition of ESSs, due to their high investment cost and the lack of large-scale commercial production. China has gradually become concerned with large-scale ESS development; in the long term, it has great potential with the establishment of price mechanisms and mature technology.
The impact of DR on joint scheduling is analyzed in this case. DR can optimize customers’ power consumption behavior and smooth the demand load curve. With
System load under different cases.
According to Figure
System scheduling optimization results under different cases.
Scenario | Wind power | Thermal power | System profit (104 yuan) | ||||
---|---|---|---|---|---|---|---|
Generating capacity (MWh) | Grid proportion (%) | Curtailed wind power rate (%) | Generating capacity (MWh) | Grid proportion (%) | Curtailed wind power rate (%) | ||
Case |
18620.60 | 35.40 | 15.90 | 35252.50 | 64.60 | 322.50 | 290.00 |
Case |
19124.70 | 35.50 | 15.77 | 34748.40 | 64.50 | 318.72 | 301.54 |
Case |
19090.81 | 35.44 | 13.78 | 34782.29 | 64.56 | 320.60 | 294.45 |
Case |
19354.72 | 35.93 | 12.58 | 34518.38 | 64.07 | 314.82 | 312.72 |
The impact of CET on the joint scheduling problem is analyzed in this case. The CET price is 100 yuan/t. 98% of total emissions in Case
Comparison of thermal power generation under different carbon prices.
With a carbon trading price of 100 ¥/t, the output of wind power increased by 470.21 MWh, and the curtailed wind power rate decreased to 13.78%. CET would increase the cost of thermal power generation and change the system scheduling plan. The output of units with high carbon emission coefficient decreased, for example, unit 2 and unit 3. The output of units with low carbon emission coefficient rose, for example, unit 5. The optimization scheduling result in Case
Compared with Cases
The synergistic effects of DR and CET on the joint scheduling problem are analyzed in Case
In summary, DR leads to proper power consumption behavior, while system operation obtains an optimal result when introducing DR and CET at the same time. The comprehensive operation result of Case
The randomness of wind power output has become the primary impediment to wind power grid integration. To improve the system’s capability to consume wind power, proper backup service should be provided on the generation side. Demand response should also be introduced to incentivize customers to respond to system scheduling. This paper constructs a joint optimization scheduling model for wind power and energy storage systems with CET and DR. The simulation results show the following: ESSs can provide backup service for wind power and increase the system’s ability to consume wind power. However, total system generation profits would decrease due to the high fixed cost of ESSs. In the long term, China’s large-scale ESS market has great potential with the establishment of a reasonable price mechanism and the development of mature energy storage technology. Demand response can optimize customers’ power consumption behavior, smooth the demand load curve, and improve wind power grid integration. After introducing DR, wind power grid integration is enhanced, the system power structure becomes reasonable, thermal power costs are reduced, and the total system generation profit increases. Carbon emissions trading can increase the generation cost of thermal power units, enhance the advantages of integrating clean energy into the power grid, reduce the curtailed wind power rate, and optimize the system power structure. However, the total system generation profit is reduced due to the increased cost of thermal power generation. With the introduction of DR and CET, system operation and scheduling obtained an optimal result. The total system profit increased and the high cost of ESSs was reduced. In addition, the demand load curve was smoothed and the curtailed wind power rate and system coal consumption were reduced to the minimum. System profit increased to the maximum, which shows that a collaborative effect exists for DR and CET. Though the proposed model examined the optimization effect of ESSs, CET, and DR on wind power grid integration, some constraints related to practical applications are ignored for the sake of convenient analysis and reaching a conclusion.
Time index
Thermal power unit index
Wind farm index
Binary variable: 1 means in operation; 0 means not in operation
Charging power at time
Discharging power at time
Charging and discharging power loss coefficient
Upper limit for charging and discharging power
Maximum storage capacity of ESS
Wind farm profit
Thermal power unit profit
ESSs profit
Benchmark price of thermal power
Real-time generation power of unit
Power consumption rate of unit
Generation fuel cost
Operation and maintenance cost of unit
Depreciation cost of unit
Standard coal purchase price
Standard coal consumption of unit
Coal consumption parameters of unit
Start-up cost of unit
Shutdown cost of unit
Electricity price when charging ESSs
Electricity price when discharging ESSs
Fixed cost of ESSs
Running time of unit
Shutdown time of unit
Shortest running time of unit
Unit shortest shutdown time of unit
Equivalent utilization efficiency at time
Total installed capacity of wind farm
Maximum possible output of unit
Upward spinning reserve demand at time
Maximum possible energy generated by unit
Upward ramp rate of unit
Thermal power unit reserve coefficient
Power reserve coefficient for wind turbines
Minimum possible output of unit
Downward spinning reserve demand at time
Minimum possible generation capacity of unit
Downward ramp rate of unit
Carbon emission cost
Actual carbon emission of thermal unit during operation period
Total initial carbon emission right
Carbon trading price.
The authors declare that they have no competing interests.
This paper is supported by Beijing Union Cultivate Scientific Research Project and the Fundamental Research Funds of the Central Universities of China (2015XS29). Dr. Pan Ge helped in improving the language of the paper.