Multi-time-scale energy storage peak regulation frequency modulation time division multiplexing method

Abstract

The invention discloses a multi-time-scale energy storage peak regulation frequency modulation time division multiplexing method, which comprises the steps of dynamically dividing a peak regulation frequency modulation working area day ahead based on an energy storage cluster available capacity and a load curve; peak valley arbitrage, peak regulation compensation and frequency modulation benefits are considered, an energy storage cluster peak regulation and frequency modulation optimization model is established by taking maximization of the economic benefits of energy storage cluster peak regulation and frequency modulation as a target, and the overall optimal charging and discharging power of the energy storage cluster is obtained; based on this, the differential adjustment requirements of long-time scale peak regulation and short-time scale frequency modulation are considered, and the power constraint, capacity constraint, charging and discharging power range constraint and virtual queue backlog variable constraint capable of tracking the energy storage capacity change of each energy storage power station in the energy storage cluster are obtained. And constructing a peak regulation and frequency modulation distribution model of the energy storage cluster meeting the constraint condition. According to the invention, the performance of the energy storage cluster in the peak regulation and frequency modulation task is ensured not to be lost while multi-task coordination is realized, and the utilization rate of the energy storage power station is improved.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering, and in particular to a multi-time-scale energy storage peak-shaving and frequency-modulating time-sharing multiplexing method. Background Art

[0002] With the rapid development of renewable energy sources such as wind power and photovoltaics, energy shortages and environmental pollution have been alleviated to a certain extent. However, the volatility and uncontrollability of renewable energy generation pose significant challenges to the safe and stable operation of the power grid, particularly in peak and frequency regulation. Energy storage, as a flexible regulation resource, plays a vital role in peak and frequency regulation due to its energy time-shifting and rapid power response. Currently, most research on energy storage focuses on single-function applications. In these scenarios, energy storage plants remain idle most of the time, resulting in significant resource waste. In reality, energy storage plants, with their dual advantages of high power and large capacity, should be managed in a clustered manner to leverage their value in multiple scenarios, such as peak and frequency regulation, thereby comprehensively improving the flexibility and stability of power grid operation. By leveraging the complementary characteristics of different application scenarios and different energy storage plants, energy storage plant clusters can further tap into regulation potential and achieve multiple benefits.

[0003] On the other hand, as the proportion of renewable energy sources such as wind power and photovoltaics in the power grid continues to increase, the low inertia characteristics of these energy sources make it more challenging for power systems to maintain a real-time balance between power output and load, leading to increased frequency fluctuations. This situation urgently requires the introduction of more flexible and responsive regulation resources. Energy storage power station clusters, with their superior fast response characteristics, have become an important means of grid frequency regulation.

[0004] However, due to the significant differences in the timescales between peak shaving and frequency regulation, energy storage clusters face complex optimization and allocation challenges in simultaneously meeting both requirements. Peak shaving primarily addresses electricity supply and demand imbalances over longer timescales, typically involving the storage and release of energy on timescales of hours or even longer. Frequency regulation, on the other hand, addresses short-term grid frequency fluctuations, requiring energy storage to respond rapidly within seconds or even shorter timescales. Furthermore, each energy storage plant in an energy storage cluster must have a reasonable allocation strategy for the coordinated optimization of peak and frequency regulation to leverage their complementary strengths. Existing research on the coordinated optimization of peak and frequency regulation fails to fully balance economic efficiency and flexibility, and fails to fully exploit the regulatory potential of energy storage clusters. Furthermore, few studies explore how to achieve multiplexing of peak shaving and frequency regulation within energy storage clusters within the same timeframe. Therefore, how to coordinate the differentiated regulatory requirements of peak shaving and frequency regulation on different timescales while fully tapping the regulatory potential of energy storage clusters remains a key challenge facing the current large-scale deployment of energy storage plants. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention proposes a multi-time-scale energy storage peak-shaving and frequency-regulation time-sharing multiplexing method, which enables the energy storage power station to participate in the grid frequency regulation during the full cycle period and participate in the grid peak-shaving during the peak-shaving and valley-filling period. In order to effectively deal with the differences in time scales, the optimization problem is divided into two stages: the first stage is the day-ahead stage, which conducts a multi-dimensional evaluation of the energy storage power station in the peak-shaving and frequency-regulation scenario, determines the peak-shaving and frequency-regulation working area, and ensures the accurate switching of the energy storage peak-shaving and frequency-regulation working state; the second stage is the intraday stage, which realizes the coordinated optimization of energy storage output in the peak-shaving and frequency-regulation scenario by coordinating long-time-scale peak-shaving and short-time-scale frequency-regulation. Through reasonable task allocation and regional division, this method ensures that the performance of the energy storage cluster in the peak-shaving and frequency-regulation tasks is not lost while achieving multi-task collaboration, and fully taps the regulation potential of the energy storage cluster.

[0006] The specific technical solutions are as follows:

[0007] A multi-time-scale energy storage peak-shaving and frequency-modulating time-division multiplexing method comprises the following steps:

[0008] S1: Based on the available capacity of the energy storage cluster and the load curve, the peak and frequency regulation working areas are dynamically divided on the day before.

[0009] S2: Considering peak-valley arbitrage, peak-shaving compensation, and frequency regulation benefits, with the goal of maximizing the economic benefits of peak-shaving and frequency regulation of the energy storage cluster, an energy storage cluster peak-shaving and frequency regulation optimization model is established to obtain the optimal charging and discharging power of the entire energy storage cluster.

[0010] S3: Based on the optimal charging and discharging power of the energy storage cluster as a whole, and considering the differentiated regulation requirements of long-time-scale peak regulation and short-time-scale frequency regulation, the power constraints, capacity constraints, energy state range constraints, charging and discharging power range constraints, and virtual queue backlog variable constraints that can track changes in energy storage capacity of each energy storage power station in the energy storage cluster are obtained. A peak-shaving and frequency regulation allocation model for the energy storage cluster that meets the constraints is then constructed.

[0011] Furthermore, the S1 is specifically implemented through the following sub-steps:

[0012] S101: Sum the maximum available capacity of each energy storage power station in the energy storage cluster to obtain the maximum available capacity E of the energy storage cluster. agg ;

[0013] S102: Initialize the load curve and calculate its maximum load and minimum load, and initialize the number of iterations; use the maximum load as the initial value of the peak shaving amount, and the minimum load as the initial value of the valley filling amount;

[0014] S103: The horizontal coordinate times of the two intersections of the peak shaving line and the load curve corresponding to the current peak shaving amount are recorded as t1 and t2, and the released power E of the energy storage cluster at the current moment is calculated. p for:

[0015]

[0016] Where, P max is the current peak shaving amount, P t is the load output at time t;

[0017] If E p <E agg , then increase the number of iterations by 1, and move the peak clipping line downward with a step size of ΔP, and calculate E again p , until E p =E agg , the peak clipping line at this time is the final peak clipping line P p,t ;

[0018] S104: The horizontal coordinate times of the two intersection points of the valley filling line and the load curve corresponding to the current valley filling amount are recorded as t3 and t4, and the energy storage cluster's absorbed electricity E at the current moment is calculated. v for:

[0019]

[0020] Where, P min is the current valley filling amount;

[0021] If E v <E agg , then increase the number of iterations by 1, and move the valley filling line upward with a step size of ΔP, and calculate E again v , until E v =E agg , the valley filling line at this time is the final valley filling line P v,t ;

[0022] S105: Record the peak clipping line P p,t The horizontal coordinates of the two intersection points of the load curve are t1 and t2, and the valley line P v,t The horizontal coordinates of the two intersection points with the load curve are t3 and t4, and the peak and frequency regulation working area is obtained.

[0023] Furthermore, the S2 is specifically implemented through the following sub-steps:

[0024] S201: Considering the peak-valley arbitrage benefit, charging at a low price during low load periods and discharging at a high price during peak load periods, we can obtain the peak-valley arbitrage benefit B of the energy storage cluster. pr1 The expression is as follows:

[0025]

[0026] Where n i is the charging and discharging efficiency of energy storage station i, N is the number of energy storage stations; is the charging power of energy storage station i at time t, is the discharge power of energy storage station i at time t; P price,t is the electricity price at time t, T is the peak-shaving scheduling period;

[0027] S202: Considering the peak load compensation of the energy storage cluster, its benefit is B pr2 The expression is as follows:

[0028] B pr2 =C peak ·p co

[0029] Where C peak is the peak-shaving capacity of the energy storage cluster, p co The unit price for peak load compensation;

[0030] S203: Considering the frequency modulation benefit, its benefit is B fr The expression is as follows:

[0031]

[0032] In the formula, C represents the backup power capacity of the energy storage station, R c represents the hourly fee per MW paid by the grid operator to the resource; R mis The regulation mismatch penalty per MWh imposed on the energy storage power station is used to indicate the absolute error between the frequency modulation signal and the actual response of the energy storage. P represents the rated power of the energy storage cluster, and r(t) is the normalized frequency modulation signal.

[0033] S204: Taking maximizing the economic benefits of peak-shaving and frequency regulation of the energy storage cluster as the objective function, an energy storage cluster peak-shaving and frequency regulation optimization model is constructed to solve the optimal charging power and discharging power of the entire energy storage cluster. The model expression is as follows:

[0034] min[-(μ f B fr +B pr1 +B pr2 )]

[0035] Where μ f is a binary integer variable, when μ f =1, it indicates that it is in the peak shaving and valley filling period, and the energy storage cluster coordinates and optimizes the peak and frequency regulation; when μ f When =0, it means that the energy storage cluster only participates in grid frequency regulation.

[0036] Furthermore, in S3, the power constraint of each energy storage power station in the energy storage cluster is expressed as follows:

[0037]

[0038] Where, is the peak load power of energy storage station i at time t, is the peak-shaving charging power of energy storage station i at time t, is the peak-shaving discharge power of energy storage station i at time t; is the frequency modulation power of energy storage station i at time t, is the frequency regulation charging power of energy storage station i at time t, is the frequency modulation discharge power of energy storage station i at time t; p i,max is the maximum output of energy storage station i, which is the rated value; is the charging power of energy storage station i at time t, is the discharge power of energy storage station i at time t.

[0039] Furthermore, in S3, the capacity constraints of each energy storage power station in the energy storage cluster are as follows: if the state of charge range of the energy storage power station in the peak shaving scenario is set to [a1, a2], and a certain capacity is reserved for frequency regulation, then the range of energy states required for peak shaving is:

[0040] a1E i,max ≤E i,t ≤a2E i,max

[0041] Where, E i,t represents the energy state of energy storage station i at time t; E i,max represents the maximum capacity of energy storage station i;

[0042] Regarding the energy state range constraints of each energy storage power station in the energy storage cluster, in the peak-shaving and frequency-regulation coordinated optimization scenario, the energy state of peak regulation on a slow time scale is used as the boundary condition, and the state of charge range in the energy storage frequency regulation scenario is set to [a3, a4], 0≤a3≤a1≤a2≤a4. The energy state range required for frequency regulation is obtained as follows:

[0043]

[0044] Where, represents the energy state of energy storage station i at time t after being updated based on the peak-shaving power;

[0045] During the peak and frequency regulation process of the energy storage power station, the initial state of charge is proportional to the initial state of charge of the last time slot, and the difference cannot exceed the set threshold range. The expression is as follows:

[0046] Ei,0 =wE i,T-1

[0047] Where, E i,0 represents the energy state of energy storage station i at the initial moment, E i,T-1 It represents the energy state of energy storage station i at the last moment of the scheduling period, and w is a constant within the set threshold range.

[0048] Furthermore, in S3, the virtual queue backlog variable Q that can track the change of energy storage capacity t The expression is as follows:

[0049] Q t =E t -D

[0050] Where, E t represents the energy state of the energy storage cluster at time t; D represents the drift, which is a finite constant;

[0051] The virtual queue backlog variables at adjacent moments dynamically satisfy the following conditions:

[0052]

[0053] Where n i is the charging and discharging efficiency of energy storage station i, N is the number of energy storage stations, is the charging power of energy storage station i at time t, is the discharge power of energy storage station i at time t; Δt is the time difference between time t and time t+1.

[0054] Furthermore, in S3, the charge and discharge power range constraints of each energy storage power station in the energy storage cluster include:

[0055]

[0056] Where N is the number of energy storage power stations, is the charging power of energy storage station i at time t, is the discharge power of energy storage station i at time t; For charging instructions, is the discharge instruction; p i,max is the maximum output of energy storage station i, which is the rated value.

[0057] Furthermore, in S3, the objective function of the peak-shaving and frequency-regulating allocation model of the energy storage cluster is as follows:

[0058]

[0059] Where Q trepresents the virtual queue backlog variable that can track the change of energy storage capacity, Δt is the time difference between time t and time t+1; n i is the charging and discharging efficiency of energy storage station i, N is the number of energy storage stations, is the charging power of energy storage station i at time t, is the discharge power of energy storage station i at time t; V a is the weight coefficient, which is used to control the queue size to ensure that the state of charge of the energy storage station meets the constraints; C represents the backup power capacity of the energy storage station, R c represents the hourly fee per MW paid by the grid operator to the resource; R mis The regulation mismatch penalty per MWh imposed on the energy storage plant is used to indicate the absolute error between the frequency regulation signal and the actual response of the energy storage; For charging instructions, It is the discharge instruction.

[0060] The beneficial effects of the present invention are:

[0061] (1) The multi-time-scale energy storage peak-shaving and frequency-regulating time-sharing multiplexing method proposed in the present invention can ensure that the performance of the energy storage cluster in the peak-shaving and frequency-regulating tasks is not lost while achieving multi-task collaboration, and improve the utilization rate of the energy storage power station.

[0062] (2) The peak-shaving and frequency-regulating allocation model of the energy storage cluster proposed in the present invention enables other energy storage stations to supplement the charge state of a certain energy storage station by increasing their output power when the charge state reaches the boundary value, ensuring that the total output power of the energy storage cluster meets the grid demand as much as possible. This peak-shaving and complementary mechanism greatly enhances the flexibility and reliability of the energy storage station cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of a multi-time-scale energy storage peak-shaving and frequency-modulating time-sharing multiplexing method in an embodiment of the present invention.

[0064] Figure 2 This is a flow chart of the dynamic division of peak-shaving and frequency-regulating working areas in an embodiment of the present invention.

[0065] Figure 3 Schematic diagram of the coordinated optimization of long time scale and short time scale in an embodiment of the present invention.

[0066] Figure 4 This is a flow chart of establishing a peak-shaving and frequency-regulating allocation model for an energy storage cluster in an embodiment of the present invention to obtain the charging and discharging power of each energy storage power station. DETAILED DESCRIPTION

[0067] The present invention will be described in detail below based on the accompanying drawings and preferred embodiments. The purpose and effects of the present invention will become more apparent. The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0068] like Figure 1 As shown, a multi-time-scale energy storage peak-shaving and frequency-modulating time-sharing multiplexing method includes the following steps:

[0069] S1: Based on the available capacity of the energy storage cluster and the load curve, the peak-shaving and frequency-regulating working areas are dynamically divided (i.e., the peak-shaving line and valley-filling line, as well as the intersection time of the two with the load curve, are determined) to ensure the accurate switching of the energy storage peak-shaving and frequency-regulating working states. Figure 2 As shown, S1 is implemented through the following sub-steps:

[0070] S101: Filter and obtain peak-shaving and frequency-regulating energy storage clusters based on actual needs, and sum the maximum available capacities of each energy storage power station in the energy storage cluster to obtain the maximum available capacity E of the energy storage cluster. agg .

[0071] S102: Initialize the load curve and calculate its maximum load P l,max and minimum load P l,min , initialize the number of iterations k = 0. With the maximum load P l,max As the initial value of the peak shaving amount, the minimum load P l,min As the initial value of the valley filling amount.

[0072] S103: The horizontal coordinate times of the two intersections of the peak shaving line and the load curve corresponding to the current peak shaving amount are recorded as t1 and t2, and the released power E of the energy storage cluster at the current moment is calculated. p for:

[0073]

[0074] Where, P max is the current peak shaving amount, P t is the load output at time t.

[0075] If E p <E agg , then the number of iterations is increased by 1 (i.e. k = k + 1), and the peak clipping line is moved downward with a step size of ΔP (i.e. P max =P max -ΔP), calculate E again p , until E p =E agg , determine the peak clipping line corresponding to the current peak clipping amount as the final peak clipping line P at the current moment p,t .

[0076] S104: The horizontal coordinate times of the two intersection points of the valley filling line and the load curve corresponding to the current valley filling amount are recorded as t3 and t4, and the energy storage cluster's absorbed electricity E at the current moment is calculated. v for:

[0077]

[0078] Where, P min is the current valley filling amount.

[0079] If E v <E agg , then the number of iterations is increased by 1 (i.e. k=k+1), and the valley filling line is moved upward with a step size of ΔP (i.e. P min =P min +ΔP), calculate E again v , until E v =E agg , determine the valley filling line corresponding to the current valley filling amount as the final valley filling line P at the current moment v,t .

[0080] S105: Record the current peak clipping line P p,t The horizontal coordinates of the two intersection points of the load curve are t1 and t2, and the valley line P at the current moment v,t The horizontal coordinates of the two intersection points with the load curve, time t3 and t4, give the peak and frequency regulation working area.

[0081] S2: Considering peak-valley arbitrage, peak-shaving compensation, and frequency regulation benefits, with the goal of maximizing the economic benefits of peak-shaving and frequency regulation of the energy storage cluster, an energy storage cluster peak-shaving and frequency regulation optimization model is established to obtain the optimal charging and discharging power of the energy storage cluster as a whole. S2 is specifically implemented through the following sub-steps:

[0082] S201. Considering the peak-valley arbitrage benefits: The energy storage cluster fully utilizes the difference in electricity prices for arbitrage, charging at low prices during low load periods and discharging at high prices during peak load periods, obtaining the peak-valley arbitrage benefit B of the energy storage cluster. pr1 The expression is as follows:

[0083]

[0084] Where n i is the charging and discharging efficiency of energy storage station i, N is the number of energy storage stations; is the charging power of energy storage station i at time t, is the discharge power of energy storage station i at time t; P price,t is the electricity price at time t, and T is the peak-shaving scheduling period.

[0085] S202. Consider peak load compensation: The energy storage cluster responds to the peak load demand of the power grid and will receive peak load compensation. The peak load compensation income of the energy storage cluster is B pr2 The expression is as follows:

[0086] B pr2 =C peak ·p co

[0087] Where C peak is the peak-shaving capacity of the energy storage cluster, p co The unit price for peak load compensation.

[0088] S203. Considering frequency regulation benefits: The frequency regulation benefits of energy storage power stations are calculated based on capacity compensation and mileage compensation. To provide frequency regulation services, the grid operator pays a fee of R per MW to the resource with backup power capacity C (i.e., energy storage power station) every hour. c During frequency regulation, resources are penalized with a mismatch penalty of R per MWh. mis , which indicates the absolute error between the frequency regulation signal and the actual response of the energy storage. Frequency regulation benefit of the energy storage cluster B fr The expression is as follows:

[0089]

[0090] Where P represents the rated power of the energy storage cluster, and r(t) is the normalized frequency modulation signal.

[0091] S204: Maximizing the economic benefits of the energy storage cluster's peak-shaving and frequency-regulation, that is, minimizing the negative value of the economic benefits, is used as the objective function of the energy storage cluster's peak-shaving and frequency-regulation optimization model. The expression is as follows:

[0092] min[-(μ f B fr +B pr1 +B pr2 )]

[0093] Where μ f is a binary integer variable, when μ f =1, it indicates that it is in the peak shaving and valley filling period, at this time the energy storage cluster needs to coordinate and optimize the peak and frequency regulation; when μ f When =0, it means that the energy storage cluster only participates in grid frequency regulation.

[0094] Since only charging or discharging is possible at the same time, and One of them must be zero. Therefore, the charging power and discharging power of the energy storage cluster as a whole can be obtained when the objective function is minimized, that is, the optimal charging and discharging power of the energy storage cluster as a whole can be obtained.

[0095] S3: Based on the optimal charging and discharging power of the energy storage cluster as a whole, considering the differentiated regulation requirements of long-time scale peak regulation and short-time scale frequency regulation, a peak regulation and frequency regulation allocation model of the energy storage cluster is established, such as Figure 3 As shown in the figure, the peak-shaving and frequency-regulating allocation model is divided into two stages: fast time scale and slow time scale. The peak-shaving problem optimizes the energy distribution of the energy storage cluster on the slow time scale, and the frequency regulation problem adjusts the power output in real time on the fast time scale to obtain the charging and discharging power of each energy storage power station. Figure 4 As shown, S3 is implemented through the following sub-steps:

[0096] S301: Consider the power constraints of each energy storage power station in the energy storage cluster, which are expressed as follows:

[0097]

[0098] Where, is the peak load power of energy storage station i at time t, is the peak-shaving charging power of energy storage station i at time t, is the peak-shaving discharge power of energy storage station i at time t; is the frequency modulation power of energy storage station i at time t, is the frequency regulation charging power of energy storage station i at time t, is the frequency modulation discharge power of energy storage station i at time t; p i,max is the maximum output of energy storage station i, which is the rated value.

[0099] S302: Considering the capacity constraints of each energy storage station in the energy storage cluster, the state of charge (SOC) of the energy storage station in the peak shaving scenario is set to 0.2-0.8, and a certain capacity is reserved for frequency regulation. The energy state range required for peak shaving is:

[0100] 0.2E i,max ≤E i,t ≤0.8E i,max

[0101] Where, E i,t represents the energy state of energy storage station i at time t; E i,max Represents the maximum capacity of energy storage station i.

[0102] Then, in the peak-shaving and frequency-regulation coordinated optimization scenario, the energy state of peak shaving on the slow time scale is used as the boundary condition, and the SOC in the energy storage frequency regulation scenario is set to 0.1-0.9. The energy state range required for frequency regulation is obtained as follows:

[0103]

[0104] Where, It represents the energy state of energy storage station i at time t after being updated based on the peak-shaving power.

[0105] The SOC range for peak shaving and frequency regulation in energy storage power plants is optimized, ensuring that the SOC range in frequency regulation is greater than the SOC range in peak shaving. When the SOC is within the 0.2-0.8 range, the energy storage power plant can coordinate peak shaving and frequency regulation. Within the 0.1-0.2 and 0.8-0.9 ranges, the energy storage power plant only performs frequency regulation. Outside the 0.1-0.9 range, the energy storage power plant does not perform peak shaving or frequency regulation.

[0106] During the peak and frequency regulation process of the energy storage power station, the difference between the initial SOC and the SOC of the last time slot must not be too large to ensure the sustainability of the scheduling strategy. Therefore, the expression for setting the energy state is as follows:

[0107] E i,0 =wE i,T-1

[0108] Where, E i,0 represents the energy state of energy storage station i at the initial moment, E i,T-1 represents the energy state of energy storage station i at the last moment of the scheduling period; w is a constant within a specified range, which is 0.8-1.2 in this embodiment.

[0109] In summary, the boundary constraint conditions in the peak-shaving and frequency-regulating allocation model of the energy storage cluster have been set. The boundary constraint conditions are integrated as follows:

[0110]

[0111] S303: Define a virtual queue backlog variable Q that can track changes in energy storage capacity t , which is expressed as follows:

[0112] Q t =E t -D

[0113] Where, E t It represents the energy state of the energy storage cluster at time t; D is the drift, which is a finite constant and its value range is determined according to actual conditions.

[0114] The virtual queue backlog variables at adjacent moments dynamically satisfy the following conditions:

[0115]

[0116] Where Q t+1 is the virtual queue backlog variable at time t+1, and the time difference between time t and time t+1 is Δt.

[0117] S304: Considering the three constraints of S301-S303 and the charging and discharging power range requirements of energy storage station i, the following energy storage cluster allocation model is established:

[0118]

[0119] Where V a is the weight coefficient, which is used to control the queue size and, together with the drift D, ensures that the SOC of the energy storage power station meets the constraints; For charging instructions, It is the discharge instruction.

[0120] S308: Solve the energy storage cluster allocation model to obtain the charging and discharging power of each energy storage power station.

[0121] The method of dynamically dividing the peak-shaving and frequency-regulating working areas proposed in the present invention can better reflect the mechanism of matching peak-shaving demand with the capacity of the energy storage cluster. It is also highly consistent with the period characteristics of time-of-use electricity prices, fully reflecting the advantages of the energy storage cluster in terms of economy and flexibility. Combining the peak-shaving and frequency-regulating demand and the cost structure of the energy storage cluster itself, the operating cost and benefits of energy storage in integrated applications are comprehensively calculated to form an energy storage cluster peak-shaving and frequency-regulating optimization model. The method of the present invention enables the energy storage power station to participate in the grid frequency regulation during the full cycle period and participate in the grid peak-shaving during the peak-shaving and valley-filling period. It can improve the utilization rate of the energy storage power station without weakening the peak-shaving and frequency-regulating performance. In addition, by utilizing the peak-shaving and complementary mechanism of the energy storage power station in the peak-shaving and frequency-regulating scenario, the flexibility and reliability of the energy storage power station cluster are enhanced.

[0122] Those skilled in the art will understand that the foregoing descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. Although the invention has been described in detail with reference to the foregoing examples, those skilled in the art will still be able to modify the technical solutions described in the foregoing examples or substitute equivalents for some of the technical features therein. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the invention shall be included within the scope of protection of the invention.

Claims

1. A multi-time-scale energy storage peak-shaving and frequency-modulation time-division multiplexing method, characterized in that: The following steps are involved: S1: Based on the available capacity of the energy storage cluster and the load curve, the peak and frequency regulation working areas are dynamically divided on the day before. S2: Considering peak-valley arbitrage, peak-shaving compensation, and frequency regulation benefits, with the goal of maximizing the economic benefits of peak-shaving and frequency regulation of the energy storage cluster, an energy storage cluster peak-shaving and frequency regulation optimization model is established to obtain the optimal charging and discharging power of the entire energy storage cluster. S3: Based on the optimal charging and discharging power of the energy storage cluster as a whole, and considering the differentiated regulation requirements of long-time-scale peak regulation and short-time-scale frequency regulation, the power constraints, capacity constraints, energy state range constraints, charging and discharging power range constraints, and virtual queue backlog variable constraints that can track changes in energy storage capacity of each energy storage power station in the energy storage cluster are obtained. A peak-shaving and frequency regulation allocation model for the energy storage cluster that meets the constraints is then constructed.

2. The multi-time-scale energy storage peak-shaving and frequency-modulation time-division multiplexing method according to claim 1 is characterized in that: The S1 is specifically implemented through the following sub-steps: S101: Sum the maximum available capacity of each energy storage power station in the energy storage cluster to obtain the maximum available capacity E of the energy storage cluster. agg ; S102: Initialize the load curve and calculate its maximum load and minimum load, and initialize the number of iterations; The maximum load is used as the initial value of the peak shaving amount, and the minimum load is used as the initial value of the valley filling amount; S103: The horizontal coordinate times of the two intersections of the peak shaving line and the load curve corresponding to the current peak shaving amount are recorded as t1 and t2, and the released power E of the energy storage cluster at the current moment is calculated. p for: Where, P max is the current peak shaving amount, P t is the load output at time t; If E p <E agg , then increase the number of iterations by 1, and move the peak clipping line downward with a step size of ΔP, and calculate E again p , until E p =E agg , the peak clipping line at this time is the final peak clipping line P p,t ; S104: The horizontal coordinate times of the two intersection points of the valley filling line and the load curve corresponding to the current valley filling amount are recorded as t3 and t4, and the energy storage cluster's absorbed power E at the current moment is calculated. v for: Where, P min is the current valley filling amount; If E v <E agg , then increase the number of iterations by 1, and move the valley filling line upward with a step size of ΔP, and calculate E again v , until E v =E agg , the valley filling line at this time is the final valley filling line P v,t ; S105: Record the peak clipping line P p,t The horizontal coordinates of the two intersection points of the load curve are t1 and t2, and the valley line P v,t The horizontal coordinates of the two intersection points with the load curve are t3 and t4, and the peak and frequency regulation working area is obtained.

3. The multi-time-scale energy storage peak-shaving and frequency-modulation time-division multiplexing method according to claim 1, characterized in that: The S2 is specifically implemented through the following sub-steps: S201: Considering the peak-valley arbitrage benefit, charging at a low price during low load periods and discharging at a high price during peak load periods, we can obtain the peak-valley arbitrage benefit B of the energy storage cluster. pr1 The expression is as follows: Where n i is the charging and discharging efficiency of energy storage station i, N is the number of energy storage stations; is the charging power of energy storage station i at time t, is the discharge power of energy storage station i at time t; P price,t is the electricity price at time t, T is the peak-shaving scheduling period; S202: Considering the peak load compensation of the energy storage cluster, its benefit is B pr2 The expression is as follows: B pr2 =C peak ·p co Where C peak is the peak-shaving capacity of the energy storage cluster, p co The unit price for peak load compensation; S203: Considering the frequency modulation benefit, its benefit is B fr The expression is as follows: In the formula, C represents the backup power capacity of the energy storage station, R c represents the hourly fee per MW paid by the grid operator to the resource; R mis The regulation mismatch penalty per MWh imposed on the energy storage power station is used to indicate the absolute error between the frequency modulation signal and the actual response of the energy storage. P represents the rated power of the energy storage cluster, and r(t) is the normalized frequency modulation signal. S204: Taking maximizing the economic benefits of peak-shaving and frequency regulation of the energy storage cluster as the objective function, an energy storage cluster peak-shaving and frequency regulation optimization model is constructed to solve the optimal charging power and discharging power of the entire energy storage cluster. The model expression is as follows: min[-(μ f B fr +B pr1 +B pr2 )] Where μ f is a binary integer variable, when μ f =1, it indicates that it is in the peak shaving and valley filling period, and the energy storage cluster coordinates and optimizes the peak and frequency regulation; when μ f When =0, it means that the energy storage cluster only participates in grid frequency regulation.

4. The multi-time-scale energy storage peak-shaving and frequency-modulation time-division multiplexing method according to claim 1, characterized in that: In S3, the power constraint of each energy storage power station in the energy storage cluster is expressed as follows: Where, is the peak load power of energy storage station i at time t, is the peak-shaving charging power of energy storage station i at time t, is the peak-shaving discharge power of energy storage station i at time t; is the frequency modulation power of energy storage station i at time t, is the frequency regulation charging power of energy storage station i at time t, is the frequency modulation discharge power of energy storage station i at time t; p i,max is the maximum output of energy storage station i, which is the rated value; is the charging power of energy storage station i at time t, is the discharge power of energy storage station i at time t.

5. The multi-time-scale energy storage peak-shaving and frequency-modulation time-division multiplexing method according to claim 1, characterized in that: In S3, the capacity constraints of each energy storage power station in the energy storage cluster are as follows: If the state of charge range of the energy storage power station in the peak shaving scenario is set to [a1, a2], and a certain capacity is reserved for frequency regulation, the range of energy states required for peak shaving is: a1E i,max ≤E i,t ≤a2E i,max Where, E i,t represents the energy state of energy storage station i at time t; E i,max represents the maximum capacity of energy storage station i; Regarding the energy state range constraints of each energy storage power station in the energy storage cluster, in the peak-shaving and frequency-regulation coordinated optimization scenario, the energy state of peak regulation on a slow time scale is used as the boundary condition, and the state of charge range in the energy storage frequency regulation scenario is set to [a3, a4], 0≤a3≤a1≤a2≤a4. The energy state range required for frequency regulation is obtained as follows: Where, represents the energy state of energy storage station i at time t after being updated based on the peak-shaving power; During the peak and frequency regulation process of the energy storage power station, the initial state of charge is proportional to the initial state of charge of the last time slot, and the difference cannot exceed the set threshold range. The expression is as follows: E i,0 =wE i,T-1 Where, E i,0 represents the energy state of energy storage station i at the initial moment, E i,T-1 It represents the energy state of energy storage station i at the last moment of the scheduling period, and w is a constant within the set threshold range.

6. The multi-time-scale energy storage peak-shaving and frequency-modulation time-division multiplexing method according to claim 1, characterized in that: In S3, the virtual queue backlog variable Q that can track the change of energy storage capacity t The expression is as follows: Q t =E t -D Where, E t represents the energy state of the energy storage cluster at time t; D represents the drift, which is a finite constant; The virtual queue backlog variables at adjacent moments dynamically satisfy the following conditions: Where n i is the charging and discharging efficiency of energy storage station i, N is the number of energy storage stations, is the charging power of energy storage station i at time t, is the discharge power of energy storage station i at time t; Δt is the time difference between time t and time t+1.

7. The multi-time-scale energy storage peak-shaving and frequency-modulation time-division multiplexing method according to claim 1, characterized in that: In S3, the charging and discharging power range constraints of each energy storage power station in the energy storage cluster include: Where N is the number of energy storage power stations, is the charging power of energy storage station i at time t, is the discharge power of energy storage station i at time t; For charging instructions, is the discharge instruction; p i,max is the maximum output of energy storage station i, which is the rated value.

8. The multi-time-scale energy storage peak-shaving and frequency-modulation time-division multiplexing method according to claim 1, characterized in that: In S3, the objective function of the peak-shaving and frequency-regulating allocation model of the energy storage cluster is as follows: Where Q t represents the virtual queue backlog variable that can track the change of energy storage capacity, Δt is the time difference between time t and time t+1; n i is the charging and discharging efficiency of energy storage station i, N is the number of energy storage stations, is the charging power of energy storage station i at time t, is the discharge power of energy storage station i at time t; V a is the weight coefficient, which is used to control the queue size to ensure that the state of charge of the energy storage station meets the constraints; C represents the backup power capacity of the energy storage station, R c represents the hourly fee per MW paid by the grid operator to the resource; R mis The regulation mismatch penalty per MWh imposed on the energy storage plant is used to indicate the absolute error between the frequency regulation signal and the actual response of the energy storage; For charging instructions, It is the discharge instruction.

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