Wind power operation capacity credit assessment considering energy storage

0 Introduction

With the continuous changes in global climate, many countries have put forward the strategic goal of energy transformation in recent years.The development of renewable energy generation technologies is of great significance.During 2013-2020, the global renewable energy power generation increased by 45%.It is expected to reach 40%of the total power generation by 2040 [1].In the power system, the integration of the a high proportion of renewable energy causes a series of problems such as intermittence and uncertainty, which creates significant changes in the system structure, and makes it challenging for the power system to maintain the real-time balance of generation and load [2].Because of the uncertainty of wind power, the load capacity of wind turbine is not the same as that of conventional generator under the same installed capacity [3].Wind power credible capacity or wind power capacity credit, which can reflect the replacement degree of wind power for conventional units so that wind power can be studied in the same sense as conventional units, is a hot research topic in recent years [4-8].

In previous studies, wind power capacity credit was generally used in the field of power system planning,which refers to the capacity of thermal power units that can be replaced by wind power without reducing the level of system reliability.The thermal power unit here refers to a conventional unit with a forced outage rate.In the late 1970s, Edward Kahn’s team first used the concept of credible capacity to solve related problems of wind power[9].Amelin expressed the system load loss probability and derived a calculation method for wind power capacity credit,but did not solve the time complexity problem caused by the increase in the number of units in the calculation process [10].In recent years, many novel methods have been proposed to study the capacity credit of wind power.In reference [11],an optimization model was adopted to approximate the wind power as a thermal power unit with a certain capacity.Based on the loss of load expectation (LOLE), a reliability analysis of the system before and after the wind power integration was analyzed.In references [12, 13], a statistical method was proposed to calculate the wind power capacity credit and fit the approximate calculation formula of the wind power credible capacity.Reference [14] proposed a maximum common factor step size algorithm, which can accurately calculate the capacity reliability of wind power while ensuring the calculation efficiency through the loadloss probability curve.Reference [15] proposed the use of a regular rattan copula function to model multiple wind farms under various prediction conditions to form a conditional prediction distribution and improve the quality of prediction.Reference [16] used the available electricity method to evaluate the wind power capacity credit by equating the wind farm to a multi-state generator unit and adding it to the stochastic production simulation.In terms of energy storage research, a joint control strategy for wind power storage based on spinning reserve and DC side energy storage was proposed in reference [17].In reference [18], a scheduling strategy was proposed to maximize the economic benefits of the combined system of wind, photovoltaic, thermal, and energy storage. [19, 20] referred to the reliability model of conventional units and wind farm, and by comparing different wind speed models and reliability indicators, the influence on the reliability of the power generation system and the reliability of the wind power capacity were studied.Based on the Gaussian mixture model, reference [21]constructed an analytical conditional distribution of prediction errors of multiple wind farms for different prediction values.In reference [22], based on the output correlation among multiple wind farms, the joint probability distribution model was used to study the annual average capacity credit of wind power.Reference [23] proposed a composite reliability model considering instantaneous response and uncertainty in the load recovery process, and studied the capacity reliability of distributed generation from the perspective of demand response.

However, most existed studies on the credibility of wind power capacity are focused at the level of power system planning and study the wind power credibility capacity from the perspective of system adequacy.With the increasing proportion of renewable energy in the system, it has brought a certain challenge to the system dispatching operation.Research on the annual capacity credit of wind power at the planning level cannot accurately reflect the impact of realtime changes in wind power on dispatching operations.In the dispatching plan, the conservative arrangement of wind power capacity may lead to the difficulty of wind power accommodation and the increase of system operation cost.At present, the estimation of wind power output in dispatching operations is relatively conservative, which causes curtailment of wind power resources in a certain period of time and has a significant impact on the wind power capacity of a high proportion of renewable energy power systems.

Therefore, it is necessary to study the reliability of wind power capacity credit from the perspective of power system operation, which employs a different research approach compared with the planning period.This paper puts forward the concept of wind power operation credible capacity,that is, the capacity of thermal power units that can be replaced by wind power per hour without changing the system operational reliability (Capacity credit is the ratio of credible capacity and wind power output); secondly, the available capacity models of different units are established,and the generation method of the system available capacity curve is given; then, the model of energy storage device is established, and the evaluation method of wind power operation capacity credit considering the energy storage device is given; finally, based on the case study, the influence of the energy storage device on wind power operation capacity credit is analyzed.

1 Available capacity model

Because the reliability of wind power capacity under the operation level is closely related to the operational reliability level of the system, and the reliability level fluctuates with time, this chapter studies the operational reliability of the system based on the available capacity distribution (ACD)method, to provide support for the following evaluation of the wind power operation capacity credit.

1.1 Thermal power units model

Because the thermal power unit has a certain forced outage rate, its actual available capacity is not completely equal to its installed capacity, and the available capacity model can reflect the impact of the forced outage rate on unit capacity.In the available capacity model, the conventional unit usually adopts a two-state model, which can be expressed as

where is the available capacity of thermal power unit i,is the installed capacity, andis the forced outage rate of thermal power unit i.

Once the available capacity of each thermal power unit is obtained, the distribution of the discrete available capacity of the system can be obtained by the convolution method.

whereis the available capacity of the system at time t,and n is the number of start-up thermal power units at time t.

1.2 Wind farm model

Wind power generation is affected by natural resources and has intermittent and fluctuating characteristics.The power generation state of the wind turbine and the output power depend on the wind speed.At present, research on short-term wind speed forecasting methods has made significant progress.The Karman filter, random time series,artificial neural network, fuzzy logic, and spatial correlation have been proposed.Forecast accuracy can meet the requirements of engineering applications.According to the wind speed forecast sequence, the output of wind power can be obtained from the relationship curve between the wind speed and wind power output.

The relationship curve in Fig.1 can be expressed by the following mathematical expression.

Fig.1 Relationship curve between wind speed and wind power output

where Pr is the rated power of the wind turbine, Vci, Vco, and Vr are the cut-in wind speed, cut-out wind speed, and rated wind speed, respectively; and A, B, and C are wind speed parameters.

Because the research problem is based on the operation level, the error distribution of the hourly forecasted power of wind power is very important.To better reflect the operational characteristics, this study considers the wind power forecast error distribution in the model.A large number of studies show that when a large number of wind turbines are geographically dispersed, it can be considered that the forecast error of wind power output follows the normal distribution [24-25].

In this study, wind power is available to multi-state units, and the forecast error is assumed to follow a normal distribution with a standard deviation of σ and a mean value of 0.Fig.2 shows the distribution of forecast error of wind power.

Fig.2 Probability density function of normal distribution of forecast error

According to the “3σ” principle of normal distribution,for a random variable obeying a normal distribution, most values lie within the interval (μ-3σ, μ+3σ).Based on the forecasted output of wind power at each moment and the normal distribution of prediction error, the available capacity model of wind power can be built.

The median values of the intervals in Fig.2 represent the forecast errors under different confidence levels.The multistate model of wind power can be obtained by calculating different probabilities corresponding to different forecast errors.

whereis the available capacity of the wind farm; Pfo,t is the forecast output; P1,t and P2,t are two states lower than the forecast; P3,t and P4,t are two states higher than the forecast;p1, p2, pfo, p3 and p4 are the corresponding probability values,corresponding to intervals (-3σ,-2σ), (-2σ,-σ), (-σ, σ),(σ,2σ), (2σ, 3σ) in Fig.2, respectively.

1.3 Available capacity model of system

After obtaining the available capacities of thermal power units and wind farms, these can be convoluted according to the start-up plan of the system at different times to obtain the available capacity distribution of the system at each time.At time t for example, if m thermal power units and n wind farms are arranged, the available capacity distribution is expressed as follows:

whereis the available capacity of system considering the wind farms are connected.

To obtain the wind power operation credible capacity,it is necessary to calculate the operational reliability of the system based on the system credible capacity distribution.According to the ACD method, when different numbers of units are loaded in the system, the available capacity distribution curve is different, as shown in Fig.3.

Fig.3 Available capacity distribution

According to the start-up plan of the system, and the forecast output of wind power in each hour, the available capacity distribution curve of the system at each time, and,thus, the operation reliability index of the system can be obtained.

For the loss of load probability (LOLP) of the system,when the system loads n units at a certain time t1 and the load level is L1, the probability that the generation capacity of the system is less than L1 is Pi(L1), and Pi(L1) is also the LOLP at that time.That is, when the load level is L1 at time t1, the operational reliability level of the system is

2 Operational capacity credit assessment considering energy storage

2.1 Energy storage model

The energy storage device has the characteristics of rapid response, which plays an obvious positive role in reducing the operation risk caused by wind power.

When the wind power output is surplus, the energy storage control mode is in the charging state to reduce the abandoned wind power.When the power difference is stored in an energy storage device, it can not exceed the capacity limit of the energy storage device.The charging state of the energy storage device is modeled as follows:

When the sum of the wind power output and the conventional unit output cannot meet the current load,the energy storage discharge is carried out.The discharge process is modeled as follows:

where and re the capacities of energy storage after charging and discharging, respectively, ES is the maximum capacity of the energy storage device, andare the remaining capacities of the previous period in the process of charging and discharging, respectively;ΔPt is the absolute value of the difference between the load and output of all generating units, which can be expressed as

where Pw,t, Pth,t, and PL,t are the wind power output,conventional unit output, and load value at time t.

Another important index indicating the energy storage charge and discharge state is the state of charge(SOC) of the system, which represents the percentage of remaining battery capacity in the rated capacity under the same conditions.After the capacity of energy storage at the current time is obtained from equation (7)and (8), the SOC of energy storage at that time can be calculated using equation (10).When SOC=1, the battery is fully charged, and when SOC=0, the battery is empty.The SOC of the energy storage system at time t can be expressed as

where Et is the remaining power of the energy storage.

2.2 Operation credible capacity assessment flow

After obtaining the operational reliability index of the system, this study evaluates the operational credible capacity of wind power using a binary search algorithm.According to the definition of operation capacity credit, the operational reliability level of the system must remain unchanged before and after the replacement of thermal power units by wind power, as shown in equation (11).

where Rt is the reliability level of the system at time t, Lt is the load level, is the forecast output for the wind farm,and s the thermal power unit capacity replaced by the wind farm.

The specific assessment process is as follows:

(1) According to the forecast output of wind power,calculate the available capacity of wind power for different hourly time-periods considering energy storage devices.

(2) Establish a two-state model for thermal power units and arrange a start-up plan for the conventional units based on the time sequence load information.Then, generate the available capacity distribution curve of conventional units in every hourly time-periods.

(3) Calculate the available capacity distribution curve of the system with wind power and calculate the reliability index of the system for each hourly time-period as the reference value.

(4) Remove wind power and use the binary search method to calculate capacity credit.

In step (4), after removing the wind power, it is necessary to add a conventional units with different capacities to the system at each time to maintain the reliability of the original system.The capacity of the conventional unit is the credible capacity of the wind power at that time.The specific process of the binary search algorithm is as follows:

(1) At a certain time, first, consider the capacity of the new conventional unit as 50% of the predicted output of the wind farm, and calculate the system operation reliability index according to the system available capacity distribution at that time.

(2) Compare the reliability index (LOLP) at this time with the index when the wind power is integrated(benchmark value).If it is higher than the benchmark value,the capacity of the new conventional unit is set as 75% of the predicted output of the wind power; otherwise, it is set as 25% of the predicted output.

(3) Repeat the binary search process until the difference between the reliability index and the benchmark value can be maintained within the error accuracy, and the calculation is stopped, and the hourly wind power operation credible capacity is obtained.

3 Case Study

3.1 Simulation system

In this study, the IEEE RTS-24 bus system (Fig.4)was used for the case study.Energy storage equipment and wind power were then added to the system.The total installed capacity of wind power is 120 MW, and the total installed capacity of the thermal power units is 3405 MW.The detailed parameters of the system are presented in the reference [26].

Fig.4 IEEE 24-Bus System

3.2 Analysis of wind power operation capacity credit

3.2.1 Calculation without energy storage devices

The load and wind power forecast data are listed in Tables 1 and 2, respectively.

Table 1 24-hour load level

Time Load/MW Time Load/MW Time Load/MW 1 2223 9 2280 17 2594 2 2172 10 2508 18 2850 3 1988 11 2565 19 2822 4 1891 12 2594 20 2765 5 1849 13 2565 21 2679 6 1826 14 2508 22 2622 7 1924 15 2480 23 2480 8 1995 16 2480 24 2309

Table 2 24-hour wind power forecast data

TimeOutput/MW Time Output/MW Time Output/MW 1 70 9 91 17 105 2 71 10 103 18 98 3 69 11 100 19 99 4 78 12 100 20 92 5 85 13 94 21 87 6 81 14 97 22 75 7 81 15 97 23 72 8 91 16 102 24 64

When the energy storage device is not considered, the operation capacity credit of wind power is completely determined by the hourly load level and the wind power output at that time.The wind power operation capacity credit for 24 hours on a certain day is calculated using the method proposed in this paper, and the results are shown in Fig.5.

Fig.5 Operation capacity credit and credible capacity without energy storage

As shown in Fig.5, owing to the uncertainty of wind power, a certain output of wind power cannot replace the same output of thermal power units.According to the predicted output of wind power in Table 2, the credible capacity of wind power depends on the output of wind power to a certain extent.However, for the capacity credit,the trend is not completely consistent with the credible capacity, which is explained at 4, 5, 10, 14, and other times.

3.2.2 Calculation with energy storage devices

The energy storage devices selected in this study are listed in Table 3, and the charging and discharging hours were 2 h.

Table 3 Energy storage devices

Number Charge and discharge time/h Charge and discharge power/MW 1 2 5 2 2 10 3 2 15

Fig.6 and Fig.7 show the calculation results of wind power operation credible capacity with different energystorage devices connected to the system.After the energy storage is connected to the system, the energy generated by wind power can be stored during the low-load period and released during the high-load period.

Fig.6 Operation credible capacity with energy storage

Fig.7 Operation capacity credit with energy storage

As seen in the figures owing to the addition of an energy storage system, the charging and discharging process of the wind power energy storage combined system improves the wind power operation credible capacity and capacity credit compared with that without the energy storage.With the increase in the capacity of energy storage devices, the operation credible capacity and capacity credit of wind power also increase.Therefore, the configuration of energy storage devices with larger capacity will be more conducive to improving the operational credible capacity of wind power, within a reasonable cost.

4 Conclusion

It is necessary to study the influence of energy storage devices on the wind power operation capacity credit.Firstly,the definition of the wind power credible capacity is given from the perspective of operation.Secondly, based on the system available capacity model and energy storage model,the evaluation method of the wind power operation credible capacity with an energy storage system is presented.Finally,through an analysis for typical days, the operational credible capacity of wind power and the impact of energy storage on the operational credible capacity of wind power are studied.

Through the case analysis, it can be seen that although wind power is associated with randomness and volatility,its output retains a certain degree of credibility.In addition,the energy storage can improve the wind power operation credible capacity and capacity credit, which is particularly obvious during the peak load at night, and the increase in energy storage capacity is also helpful to the wind power operation credible capacity.

The research of this paper is very important for the scheduling system to arrange the wind power output.In the dispatching operation of the power system, the arrangement of wind power can be considered according to its operational credible capacity and can be jointly configured with the energy storage system under certain conditions,which can have a far-reaching impact on reducing the startup of conventional units and promoting the consumption of wind power.

Acknowledgements

This work was supported by the Innovation Fund of China Electric Power Institute (Project of Research on Reliability of Renewable Energy Generation Capacity based on Probability Prediction and Probabilistic Production Simulation).

Declaration of Competing Interest

We declare that we have no conflict of interest.