1. Introduction
The large-scale grid connection of wind power and the improvement of electricity load have many impacts on the stability and reliability of the power system. To address this series of challenges, more refined scheduling strategies must be proposed. The strategy enhances the economic and operational flexibility in the power system [
1,
2].
At present, problems such as serious wind curtailment, excess coal and electricity, and the impact of wind power fluctuations on the power system still exist, which restrict the green, low-carbon, and sustainable development of China’s power industry. The Chinese government has provided clear instructions in some documents regarding the participation of the industrial loads’ in-demand response, accelerating the promotion of electricity management policies and improving electricity management strategies [
3,
4].
As environmental issues continue to worsen, the demand for wind power has also increased. However, the random, volatile, and anti-peak shaving nature of wind power generation can negatively impact the reliability and security of the power system [
5,
6]. In the traditional dispatching approach, thermal power units primarily serve as the main energy source. With the continuous expansion of new energy grid connections, the government has implemented a series of measures aimed at enhancing the integration of renewable energy sources [
7]. These measures ensure that adjustable resources can adapt their operational status according to the power system’s requirements [
8], thereby enhancing the reliability and economic efficiency of the power system.
For the sake of addressing the limited peak regulation capability of thermal power units and the volatile nature of wind power, substantial research efforts have been devoted to enhancing the flexibility of generation-side resources. In [
9], scholars propose a deep peak regulation transformation for traditional thermal power units to enhance their regulating capacity. In [
10], a plant-level operational domain model is established, and the dispatching interval is solved using the Particle Swarm Optimization (PSO) algorithm. This optimization technique improves the operational flexibility of cogeneration devices, maximizes the utilization of renewable energy under the same heat load, and significantly reduces CO
2 emissions. In [
11], both a non-cooperative pricing model and a cooperative pricing model are developed. While enhancing the flexibility of thermal power and promoting the integration of wind power, it is crucial to consider the impact of wind power fluctuations on the power system. In [
12], a two-level model for stabilizing the wind power fluctuations is established. Variable modulus decomposition is utilized to decompose the energy storage compensation power based on the frequency, enabling an accurate compensation power determination and enhancing the precision of wind power stabilization. In [
13,
14], an integrated Wind Energy Storage System (ESS) model is proposed to determine the appropriate scale of energy storage devices. The fluctuating power of wind power generation is calculated based on different energy storage device capacities, allowing for the effective adaptation to the wind power fluctuations.
The incorporation of load-side resources into the power system dispatching strategy and their interaction with the power generation side can effectively alleviate the peak load regulation pressure on thermal power units and enhance operational efficiency. Numerous studies have focused on the adjustable characteristics of load-side resources. In [
15], a load control algorithm is proposed for constant temperature buildings to optimize the utilization of wind power resources within a specific region. In [
16], a scheduling model for the joint operation of photovoltaic system, building system, and electric vehicle is proposed, which improves the load transfer from the daytime peak to night and improves the flexibility of the power system. However, small-capacity flexible loads have limitations in regulating large-scale and high-penetration wind power. Conversely, the EIL as a representative of flexible load resources exhibits significant power consumption, adjustable power output, and high flexibility [
17,
18], making it more manageable compared to other loads. This paper conducts research on improving the flexibility of the EIL, fully tapping into the regulatory capacity of the EIL while meeting the economic requirements of the EIL. Strengthening the interaction with the source side via the EIL is beneficial for compensating for the insufficient peak shaving capacity of thermal power units [
19]. In [
20], the EIL is introduced into the carbon emission market, along with the self-generation (SG) and load-scheduling models. The study also considers carbon emission permits and time-of-use (TOU) electricity tariffs to optimize the economic and environmental benefits. In [
21], according to the adjustable capacity of the virtual power plant, the author establishes the auxiliary service market optimization model, classifies the power of the EIL, and gives full play to the regulation ability of the EIL.
The above research focuses solely on enhancing the flexibility of either the source-side or the load-side without considering the combined source–load dispatching approach. A single flexibility on the source or load side alone is not conducive to improving the overall flexibility and changing economic efficiency. Moreover, they did not consider the impact and economic improvement of wind power grid connection under the combined action of thermal power, the EIL power, and energy storage devices. To expand the range of power system dispatching methods and improve wind power utilization, this paper proposes a two-stage regulation method for the source–load flexibility transformation. The paper aims to enhance the flexibility of thermal power units, the Energy Intensive Load (EIL), and wind plants across the day-ahead and intra-day time scales. By doing so, it stimulates the flexibility of dispatching resources within the power system, increases the incorporation of wind power, and mitigates the impact of unstable factors arising from the wind power fluctuations.
3. A Two-Stage Regulation Model for Source–Load
With the expanding connection of a great number of wind energy to the power grid, the issue of wind power prediction accuracy has become increasingly prominent. This paper establishes a two-stage optimization scheduling strategy considering the flexibility of the source–load based on the principle of “step-by-step coordination and multi-level optimization” [
34]. The two-stage regulation model includes two time scales: day-ahead and intra-day. Due to the many differences in response speed and the regulation methods among thermal power units, the units of the EIL and the energy storage batteries. The energy storage batteries are superior to the EIL and thermal power units in response speed. Different regulation methods are arranged based on the characteristics of thermal power units, the units of the EIL, and the energy storage batteries to reduce wind power obstruction while reducing the impact of wind power fluctuations on the power system and meeting the regulation needs of the power system, better leveraging the regulatory role of schedulable resources.
3.1. A Day-Ahead Two-Level Scheduling Model for Wind Power Incorporation
During the day-ahead stage, the two-level optimization model is established with the objective of optimizing wind power incorporation and achieving the optimal scheduling power allocation. The two-layer optimization model refers to the process where the raw data is input into an upper-level model to obtain the upper-level model results. These results are then input into a lower-level model for further optimization, and the final output result is obtained when both the upper-level and lower-level models are satisfied. The scheduling cycle spans 24 h with a time interval of 15 min. In the upper-level model, the focus is on maximizing wind power incorporation. Through the optimized model, the day-ahead total wind power, thermal power, and the EIL’s scheduling power are determined. The output data from the upper-level model serves as the input data for the lower-level model, which then allocates the scheduling power in a reasonable manner.
3.1.1. The Upper Level Model
Taking into account a series of constraints such as system power stability, thermal power, and wind power, the upper level model optimizes the scheduling power of wind power, formulates a wind power incorporation strategy for the EIL regulation, and achieves the goal of maximizing wind power incorporation.
The upper level model optimization objective is depicted by:
where
is the number of the specific wind plant, and
is the wind power of the
kth wind plant at time
t.
The optimization goal of the upper level model is to increase wind power consumption, and the output of the total power of thermal power, wind power, and the EIL’s for the next day.
3.1.2. The Lower Level Model
While improving wind power incorporation, it is also necessary to consider the operation plans of various thermal power units, wind plants, and the EIL and develop a scheduling power allocation plan for wind power incorporation. Based on the results of the upper level model, dispatch power is allocated to various thermal power units, wind plants, and the EILs to achieve the optimal dispatch power allocation.
The power allocation for thermal power units in the dispatching process aims to minimize operating cost, thereby reducing the frequency and duration of deep peak regulation, which leads to increasing operational expenses. The optimization objective of power allocation for thermal power units’ dispatch is depicted by:
The equation of coal consumption cost of thermal power units is depicted by:
where
Ng is the number of thermal power units, and
,
, and
are the coal consumption cost parameters of the thermal power units. The life loss of unit
and the fuel consumption cost
have been given in the previous text.
The day-ahead dispatching strategy obtains the day-ahead wind power dispatching output, and the wind power output distributed by each wind plant shall not exceed the capacity of each wind plant installations. The wind power output obtained from the upper level model is distributed to each wind plant in the area according to the ratio of the capacity of the wind plant installations, and the day-ahead dispatching plan of wind power is completed. The wind power distribution power equation is depicted by:
where
is total wind power in the day-ahead stage,
is the wind plant’s power,
is the ratio of capacity of the
wind plant installation to all the wind plants in the region,
is the capacity of the wind plant installations, and
is the total capacity of the wind plant installations in the region.
To avoid increasing the burden on the EIL, due to participating in power system scheduling, the allocation of power for the EIL scheduling is based on the principle of minimizing the adjustment cost for each EIL [
35,
36].
The EIL distribution regulation power optimization objectives are depicted by:
where
is the cost adjustment for the EIL,
is the increased wage cost,
is the increased storage cost,
is the initial operating state of the EIL,
t is the operating time of the EIL,
is the expected starting operating time, and
is the regulating state of the
EIL enterprise at time
t. In the case of 0, the EIL experiences a decline, while in the case of 1, the EIL undergoes an ascent.
is the daily production task volume of the
EIL enterprise,
,
are the cost parameters of the EIL, and
is the number of the EIL’s enterprises.
where
is the total load,
Pg (
i,
t) is the
ith thermal power unit in the day-ahead stage at time
t, and
is the power modification of the EIL.
and
represent the system load positive spinning reserve and the wind power positive spinning reserve.
and
represent the system load negative rotation reserve and the wind power negative rotation reserve.
The operational constraints of thermal power units [
37] are depicted by:
where
and
are the upper limit and the lower limit output of the thermal power unit,
is the start and stop condition of each thermal power unit at time
t,
is the operation duration of the
ith thermal power unit at time
t after the last startup,
is the continuous shutdown time of the
ith thermal power unit after the last shutdown,
is the lower limit startup duration of the
ith thermal power unit, and
is the lower limit shutdown duration of the thermal power unit.
and
represent the positive and negative ramp rates of the thermal power unit.
where
is the power of a single wind plant,
is the number of wind plants, and
is the day-ahead wind power forecast.
In the day-ahead phase, the goal is to maximize the wind power incorporation. According to the model constraints, the day-ahead scheduling model is solved. The day-ahead scheduling solution results are the output plans of each wind plant, the output plans of each thermal power unit, and the power regulation of each enterprise of the EIL.
3.2. Intra-Day Tracking Stage Based on Suppressing Wind Power Fluctuations
In the stage dedicated to mitigating the wind power fluctuations, there is a relatively high response speed in terms of scheduling resources. By utilizing the coordination mechanism between thermal power units and energy storage batteries, adjustments are made to the intra-day fluctuations of wind power, and the intra-day tracking strategy is formulated to enhance wind power stability. The intra-day stage is divided into 5 min intervals, and the wind power data for the next 4 h is obtained. The primary objective, during this stage, is to minimize the wind power fluctuations when connected to the power system. The intra-day regulation is carried out via the collaboration of thermal power units and energy storage devices. The intra-day tracking regulation model is solved based on the constraints specific to this stage. Drawing from the daily results, the planned output of wind power, the adjustment quantities for thermal power units, and the operational status of the energy storage devices are determined. The operational constraints of wind power and thermal power units have been discussed in the preceding text and will not be elaborated upon further. The subsequent section presents the intra-day stage model.
3.2.1. The Intra-Day Stage Optimization Objective
where
is the wind power wave rate,
is the wind power after the day-ahead regulation,
is the adjustment amount of thermal power in the intra-day stage, and
is the adjustment amount of the energy storage battery.
The constraint of power balance in the intra-day stage is depicted by:
where
is the load in the intra-day stage.
The constraints of
SOC [
38,
39] are depicted by:
where
SOC (state of charge) is the quantity of electricity stored,
is the charge efficiency,
is the discharge efficiency,
is the charge power,
is the discharge power, and
is the energy storage capacity.
and
represent the upper limit and lower limit values of
SOC.
where
and
are the upper limit values of the charge and discharge power of the energy storage, and
,
are the state variables of the energy storage charge and discharge.
3.2.2. Set Control Policy
According to the response speed and response capacity of thermal power units and energy storage devices, three control measures are set up in
Table 2 [
40].
is the stable fluctuation limit of wind power. When the fluctuation range of wind power is less than this limit, the wind power belongs to the stable fluctuation range. This stage is regulated by the thermal power unit. When the fluctuation range of wind power is greater than the stable fluctuation limit and less than the energy storage regulation limit , wind power belongs to the peak fluctuation range. This stage is regulated by the energy storage devices. When the wind power surpasses the energy storage regulation limit, the system opts to disregard wind energy.
3.2.3. The Intra-Day Dispatch Power Distribution
The regulation quantity of thermal power units shall be distributed according to the capacity ratio of each thermal power unit installations’ assembly unit. The formula for the adjustment allocation of thermal power units in the intra-day stage is depicted by:
where
is the power adjustment amount of the
ith thermal power unit,
is the ratio of the capacity of the
ith unit installations, and
is the adjustment of thermal power in the intra-day stage. The process diagram of the source–load two-stage scheduling strategy is shown in
Figure 6.
4. Case Study
The aforementioned two-stage scheduling model involves six thermal power units with a total capacity of 1012 MW. Among them, unit 1 is specifically designed for deep peak regulation.
Table 3 presents the detailed operating parameters of the thermal power unit. Additionally, there are four wind plant installations with a combined capacity of 850 MW, which is distributed as follows: 250 MW, 200 MW, 200 MW, and 250 MW, respectively. Due to the inherent uncertainty of wind power, two distinct wind power scenarios, characterized by strong volatility and large amplitude changes, have been selected for analysis. In order to fully consider the wind power uncertainty, two representative wind power scenarios, anti-peak shaving and positive peak shaving, are selected. The day-ahead and intra-day wind power forecasts for Scenario 1 and Scenario 2 are displayed in
Figure 7 and
Figure 8, respectively. In this paper, we only consider the impact of improving the source–load flexibility on wind power, thermal power, and the EIL, assuming that the impact on power losses is small and is not accounted for in this research program.
A lithium iron phosphate battery is utilized as the energy storage battery participating in the intra-day stage to stabilize the wind power fluctuations. The specific energy storage related coefficients are detailed in
Table 4.
The EILs are generally 24 h continuous operating loads. Considering the limitations of the DAL on the frequency of regulation adjustments, the frequency of regulation adjustments should not exceed six times based on the reference power to avoid affecting the quality of electrolytic aluminum load products. The parameters of the EIL are detailed in
Table 5.
The conventional load forecast power is shown in
Figure 9.
The scheduling results for Scenario 1 and Scenario 2 are presented in
Figure 10 and
Figure 11, respectively. The EIL adjustment diagrams for Scenario 1 and Scenario 2 are depicted in
Figure 12 and
Figure 13, respectively. The positive adjustment values indicate an upregulation state, while the negative values indicate a downregulation state. A comparison of wind power incorporation between Scenario 1 and Scenario 2 is also provided.
In the EIL adjustment diagram, the positive values indicate an upward state while the negative values indicate a downward state. Based on the dispatching results of thermal power units and the operational outcomes of the EIL, certain adjustments are made depending on the abundance or scarcity of wind power. When wind power generation is plentiful, the power output of thermal power units is reduced to prevent them from reaching a shutdown state and the instances of entering the deep peak regulation state is minimized. Simultaneously, the power output of the EIL is increased to ensure the smooth integration of wind power. The EIL chooses to increase the power between 0:00 and 8:30, and the wind power is absorbed due to the increase in the EIL reaching 680 MW/h. During the EIL upward stage, the maximum increase in the total load compared to the traditional dispatching mode is 137 MW. Conversely, when the wind power output is low, the power output of thermal power units is increased to avoid hindering the incorporation of wind power, while the power output of the EIL is reduced to alleviate the peak regulation pressure on thermal power units. The EIL reduced a total of 1185 MW/h of electricity during the downward stage between 9:30 and 22:30. During the EIL downward stage, the maximum decrease in the total load compared to the original scheduling method was 90 MW. Hence, it can be concluded that the EIL and thermal power units complement each other. The day-ahead dispatching strategy fully leverages the regulatory advantages of both the EIL and thermal power units, effectively meeting the requirements of wind power incorporation while reducing the peak regulation pressure on thermal power units and minimizing operation costs. The traditional dispatching mode energy-intensive loads are viewed as constant loads in the traditional dispatch models. We denote the EIL power consumption as the reference load. Energy storage is not involved in regulation. The traditional method relies only on the regulation of thermal power units for wind power consumption. When compared to the traditional dispatching mode, the operational cost of thermal power is reduced by 37,200 CNY in Scenario 1
In Scenario 2, the result analysis is similar to Scenario 1. The EIL increases its power consumption by 253 MW/h during the upregulation phase, with a maximum increase in the total load of 149 MW. During the EIL decrease phase, the power consumption decreased by 1778 MW/h, and the maximum total load decrease is 90 MW. When compared to the traditional dispatching mode, the operational cost of thermal power is reduced by 44,576 CNY in Scenario 2.
The wind power incorporation results in Scenario 1 is shown in
Figure 14.
The day-ahead wind power incorporation results in Scenario 1 is shown in
Figure 14. The conflicting nature of thermal power and wind power has resulted in a significant increase in wind power curtailment, which is a concerning phenomenon. However, the introduction of the EIL in the dispatching process has led to improvements in this issue.
Figure 14 clearly demonstrates that, compared to the traditional dispatching method, the involvement of the EIL in power system regulation has greatly reduced the duration of wind power curtailment. In Scenario 1, there are only 25 times of wind power curtailment. During these curtailment periods, when the wind power forecast reaches its upper limit, the maximum blocked wind power is 70 MW. This represents a reduction of 2630 MW·h in wind curtailment compared to the traditional dispatching mode.
Figure 15 is a comparison of wind power consumption in Scenario 2. In Scenario 2, there are only four times of wind power curtailment, with a maximum blocked power of 51.2 MW at the upper limit wind power forecast. Due to the less pronounced anti-peak regulation characteristics of wind power in Scenario 2, the wind curtailment is further reduced compared to Scenario 1, amounting to a decrease of 2478 MW·h compared to traditional dispatching. In summary, the traditional dispatching mode, which fails to consider the coordination capabilities of the EIL and thermal power units, not only leads to an increased air volume within the system but also exacerbates the peak regulation pressure and operational costs of thermal power units. On the other hand, the inclusion of the EIL in the day-ahead dispatching strategy effectively enhances wind power incorporation and alleviates the peak regulation pressure faced by thermal power units. The day-ahead scheduling results are shown in
Table 6.
In the intra-day stage, different adjustment strategies are implemented for thermal power units and energy storage devices to mitigate the impact of wind power fluctuations on the power system. The intraday dispatching cycle is set to 4 h with a time scale of 3 min. This approach aims to minimize the detrimental effects of wind power fluctuations on the power system. As the prediction accuracy improves, the intra-day range of the predicted wind power fluctuations is −30 MW and 30 MW. The results of smoothing the wind power fluctuations in Scenario 1 are illustrated in
Figure 16.
Furthermore,
Figure 16 demonstrates the impact of wind power incorporation after suppressing the wind power fluctuations in Scenario 1 via the thermal power units and energy storage. The regulation diagrams for thermal power and energy storage are presented in
Figure 17 and
Figure 18.
Figure 17 shows that for thermal power units in the 3 min regulation time period, the regulation power size is limited to only 20 MW, where it is difficult to meet the real-time volatility of wind power in order to make up for the defects of the thermal power units to regulate the power of the energy storage in the peak moments and act as a major means of suppressing wind power. When the wind power fluctuation rate is stabilized within the range of ±1%, it indicates a stable fluctuation stage of wind power, meeting the suppression requirements. In Scenario 1, the upper limit wind power fluctuation rate after wind power suppression is ±0.36%.
Scenario 2 is analyzed in a similar way to Scenario 1. The results of smoothing the wind power fluctuations in Scenario 2 are illustrated in
Figure 19. The regulation diagrams for thermal power and energy storage are presented in
Figure 20 and
Figure 21. In Scenario 2, the upper limit wind power fluctuation rate after wind power suppression is ± 0.25%. Both scenarios satisfy the operational requirements of the power system.
5. Conclusions
The main contribution of this paper lies in the joint transformation of source–load flexibility, utilizing the large dispatch power of the EIL to absorb more wind power and alleviate the pressure of thermal power peak shaving, where the flexibility of energy storage devices to adjust the frequency is utilized, and the fluctuations in wind power at small time scales are reduced. The harm of wind power to the power system is reduced. This paper presents the two-stage control strategy for the integration of large-scale wind power into the power system, taking into account the flexibility of both the source and the load. A coordinated optimization method is developed for the source-side and load-side. The feasibility and effectiveness of this method are demonstrated via a numerical example.
In the day-ahead and intra-day scales, the source and the load are coordinated and optimized, respectively. In the day-ahead stage, the two-level optimization model is used to improve wind power incorporation and optimize the dispatching power distribution of the power system. Compared with the traditional methods, the wind power consumption in Scenario 1 and Scenario 2 increases by 2741.1 MW/h and 2478.5 MW/h, respectively. The system operating costs have been reduced by 37,200 CNY and 44,576 CNY, respectively. In the real-time stage, via the coordinated optimization of thermal power units and energy storage, the fluctuation range of wind power is stable within ±1%. The reliability and economy of the power system have been improved.
This method and advanced forecasting technology can eliminate the deviation of other factors on wind power and load forecasting, improve the accuracy of power system dispatching, and better meet the demand of power system dispatching.