A multi-span scheduling method for energy storage power stations considering cross-day regulation requirements

Abstract

The application discloses a kind of multi-span scheduling methods of energy storage power station considering cross-day regulation demand.The method comprises: obtaining the operation state data of energy storage power station in the future for multiple days;Then, whether cross-day energy storage scheduling is triggered is judged according to the operation state data for multiple days in the future, if triggered, a cross-day scheduling period is generated, and a cross-period scheduling model considering cross-day additional cost is constructed based on the current cross-day scheduling period, after solving the cross-period scheduling model, the cross-day scheduling scheme of energy storage power station is obtained and issued to energy storage power station;If not triggered, the single-day scheduling scheme is issued to energy storage power station.The operation efficiency and comprehensive utilization level of energy storage power station can be further improved by reasonable cross-day energy storage scheduling.

Description

Technical Field

[0001] This invention belongs to the field of power storage power station optimization scheduling and energy management technology, specifically involving a multi-span scheduling method for energy storage power stations that considers cross-day control requirements. Background Technology

[0002] Currently, the temporal characteristics of the power grid load side and power supply side exhibit significant intraday and interday differences, with the intraday load peak-valley difference continuing to widen. Furthermore, influenced by factors such as fluctuations in renewable energy output and seasonal electricity consumption patterns, the differences in electricity consumption characteristics across days and weeks are becoming increasingly prominent. This places higher technical demands on energy storage power stations' ability to adapt, adjust, and flexibly across multiple time spans over long time scales. Long-duration energy storage, with its core advantages of controllable charging and discharging, rapid response, flexible configuration, and strong energy time-shifting capabilities, can effectively smooth intraday load fluctuations, absorb intermittent renewable energy output, and improve the overall operating efficiency of the power grid by implementing intermittent energy transfer across different time periods and dates. It has become an indispensable key regulation resource in high-proportion renewable energy power systems.

[0003] Currently, the mainstream energy storage dispatching models both domestically and internationally still use a single day as the dispatch boundary, requiring energy storage to complete a full charge-discharge cycle within 24 hours. While this intraday dispatching method can effectively meet the basic needs of the power grid for intraday peak shaving and smoothing short-term load fluctuations, ensuring the short-term stability of the power grid, its regulation range and capacity utilization efficiency are strictly limited by the single-day time boundary, making it difficult to adapt to the load and renewable energy fluctuation characteristics on a multi-day scale. To further explore the multi-day regulation potential of energy storage and improve the overall regulation efficiency of the system, some research has begun to explore multi-day energy storage dispatching models. By breaking the single-day time boundary and implementing multi-day scale power transfer, these models can adapt to the multi-day scale operation needs of the power grid. However, existing multi-day dispatching methods mostly adopt a simple design with a fixed number of days, failing to adapt to the actual multi-day operation characteristics of the power grid (such as electricity price fluctuations, load characteristics, and renewable energy output patterns) to determine the timing of multi-day dispatching and the optimal multi-day dispatching cycle. This leads to problems such as insufficient utilization of energy storage capacity, high multi-day cycle losses, and difficulty in fully realizing regulation efficiency in practical applications.

[0004] In terms of scheduling model construction, traditional energy storage control methods mostly rely on empirical rules or simple heuristic strategies, making it difficult to achieve globally optimal scheduling schemes under multiple physical constraints such as state of charge decay, charging and discharging power limitations, and energy storage lifetime losses. Furthermore, existing technologies have not systematically constructed a unified judgment and optimization model for multi-span, multi-day scheduling, and do not adequately consider key factors such as self-discharge losses during multi-day storage, long-cycle operation and maintenance costs, and energy coupling constraints at multi-day scales. This results in the energy storage's operational potential being difficult to fully realize in long-cycle, multi-span scenarios.

[0005] In summary, there is still a lack of systematic technical solutions for adaptive inter-day energy storage scheduling across multiple time spans. Summary of the Invention

[0006] To address the problems and needs existing in the background technology, this invention provides a multi-span scheduling method for energy storage power plants that considers inter-day regulation requirements. This invention establishes an inter-day energy storage scheduling trigger mechanism based on operational status data for the next few days and constructs a cross-cycle scheduling model that considers inter-day additional costs. This breaks down the single-day scheduling boundary, reduces idle energy storage capacity, achieves collaborative optimization of energy storage scheduling across multiple spans, and fully leverages the regulation potential of energy storage power plants.

[0007] The technical solution adopted in this invention is:

[0008] In a first aspect, the present invention proposes a multi-span scheduling method for energy storage power stations that considers inter-day control requirements, the method comprising the following steps:

[0009] The system acquires operational status data for the energy storage power station over the next few days. Based on this data, it determines whether cross-day energy storage scheduling is triggered. If triggered, a cross-day scheduling cycle is generated, and a cross-cycle scheduling model considering cross-day additional costs is constructed based on the current cross-day scheduling cycle. After solving the cross-cycle scheduling model, a cross-day scheduling plan for the energy storage power station is obtained and distributed to the energy storage power station. If not triggered, a single-day scheduling plan is distributed to the energy storage power station.

[0010] Optionally, determining whether to trigger cross-day energy storage dispatch based on operational status data over the next few days includes:

[0011] Based on the operational status data for the next few days, the daily operational characteristics of the first day are calculated and recorded as the baseline operational potential. The corresponding cross-day operational characteristics under different cross-day scheduling cycles are also calculated. The largest cross-day operational characteristic in different cross-day scheduling cycles is recorded as the optimal cross-day operational characteristic. The cross-day trigger coefficient is obtained by dividing the optimal cross-day operational characteristic by the baseline operational potential. If the cross-day trigger coefficient is greater than the preset trigger threshold, cross-day energy storage scheduling is triggered and the cross-day scheduling cycle corresponding to the optimal cross-day operational characteristic is output. Otherwise, cross-day energy storage scheduling is not triggered.

[0012] Furthermore, the daily operating characteristics of the first day in the future include a high-low load ratio factor, a high load duration factor, and a load fluctuation factor.

[0013] Furthermore, the cross-day operation characteristics corresponding to each cross-day scheduling cycle include cross-day correlation factor, high load duration factor, load fluctuation factor, and span factor.

[0014] Furthermore, the cross-day operation characteristics corresponding to each cross-day scheduling cycle are obtained by weighted summation of cross-day correlation factor, high load duration factor, load fluctuation factor and span factor.

[0015] Optionally, the constraints in solving the cross-cycle scheduling model that considers cross-day additional costs include span constraints constructed based on the cross-day scheduling cycle.

[0016] Furthermore, the constraints also include state of charge constraints and power constraints.

[0017] Secondly, this invention proposes a multi-span dispatching system for energy storage power stations that considers inter-day control requirements, the system comprising:

[0018] The data input unit for the energy storage power station is used to acquire the operating status data of the energy storage power station for the next several days.

[0019] The cross-day energy storage scheduling trigger judgment unit is used to determine whether to trigger cross-day energy storage scheduling based on the operating status data of the next few days and send the judgment result to the cross-cycle scheduling model construction unit and the single-day scheduling scheme storage unit;

[0020] The cross-cycle scheduling model construction unit is used to construct a cross-cycle scheduling model that takes into account cross-day additional costs based on the judgment result of the cross-day energy storage scheduling trigger judgment unit.

[0021] The model solver is used to solve the cross-cycle scheduling model, obtain the cross-day scheduling scheme of the energy storage power station, and distribute it.

[0022] The daily scheduling plan storage unit is used to issue daily scheduling plans based on the judgment result of the cross-day energy storage scheduling trigger judgment unit.

[0023] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the first aspect.

[0024] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0025] The present invention has the following beneficial effects:

[0026] Compared with existing energy storage dispatching methods, this invention breaks the single-day dispatching boundary, realizes cross-day energy storage determination and flexible multi-span dispatching, improves the flexibility and adaptability of energy storage dispatching, and fully leverages the regulation potential of energy storage power stations.

[0027] From the perspective of energy storage power station operation and management, the present invention proposes a global optimal scheduling based on the MILP model under multiple constraints, which takes into account cross-day storage loss and operation and maintenance costs, effectively improves the utilization efficiency of energy storage capacity and increases operating revenue.

[0028] The cross-day energy storage scheduling triggering mechanism proposed in this invention avoids the limitations of scheduling with a fixed number of cross-days, better tracks changes in multi-day operating characteristics, adapts to energy storage scheduling needs under different operating conditions, avoids waste of computing resources, and improves the stability and reliability of energy storage power station operation. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0030] Figure 1 This is a flowchart of the method of the present invention.

[0031] Figure 2 This is a line graph showing the typical periodic SOC changes across days.

[0032] Figure 3 A bar chart showing the monthly revenue of energy storage power stations under different maximum spans.

[0033] Figure 4 A bar chart is overlaid to show the cross-day movement characteristics under different span periods.

[0034] Figure 5 A bar chart showing the additional costs across days for different spans. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0036] like Figure 1 As shown, the multi-span scheduling method for energy storage power stations that considers inter-day regulation needs proposed in this invention includes the following steps:

[0037] The system acquires the operating status data of the energy storage power station and the basic parameters of the energy storage equipment for the next 31 days. Based on the operating status data for the next 31 days, it determines whether cross-day energy storage scheduling is triggered. If triggered, a cross-day scheduling cycle is generated. Then, based on the current cross-day scheduling cycle, a cross-cycle scheduling model considering the additional costs of cross-day scheduling is constructed using mixed integer linear programming (MILP). After solving the cross-cycle scheduling model with the basic parameters of the energy storage equipment, a cross-day scheduling plan for the energy storage power station is obtained and sent to the energy storage power station. If not triggered, the future single-day scheduling plan is sent to the energy storage power station.

[0038] In this embodiment, the energy storage power station is configured with a lithium battery energy storage power station. The operational status data for the next 31 days includes real-time electricity price data for each day, as well as operation and maintenance cost parameters and storage loss coefficients related to the time span. The real-time electricity price data for each day is sampled every 15 minutes (i.e., 15-minute resolution), totaling 96 points. The operation and maintenance cost parameters and storage loss coefficients related to the time span include a maximum charge / discharge power of 20MW, an energy storage capacity of 80MWh, a safe lower limit of 10% and a safe upper limit of 90% for the state of charge (SOC), and charging efficiency. The discharge efficiency is 0.85. The daily storage loss factor is 0.85. The daily maintenance cost is 0.005. The cost is 500 yuan per day, which is the unit cost of electricity loss. The price is 0.3 yuan / kWh. The collected raw data undergoes cleaning and preprocessing to remove outliers and fill in missing values, ensuring the validity and integrity of the data.

[0039] In one feasible implementation, determining whether to trigger cross-day energy storage dispatch based on operational status data for the next 31 days includes:

[0040] Based on the operational status data for the next 31 days, the daily operational characteristics of the first day are calculated and recorded as the baseline operational potential. The corresponding cross-day operational characteristics under different cross-day scheduling cycles (i.e., cross-day scheduling cycles of two days, three days, ..., and the preset maximum span) are also calculated. The largest cross-day operational characteristic among different cross-day scheduling cycles is recorded as the optimal cross-day operational characteristic. The optimal cross-day operational characteristic is then divided by the baseline operational potential to obtain the cross-day trigger coefficient. If the cross-day trigger coefficient is greater than the preset trigger threshold, cross-day energy storage scheduling is triggered and the cross-day scheduling cycle corresponding to the optimal cross-day operational characteristic is output. Otherwise, cross-day energy storage scheduling is not triggered.

[0041] In one feasible implementation, the daily operating characteristics of the first day in the future are obtained by weighted summation of the high-low load ratio factor, the high load duration factor, and the load fluctuation factor. The high-low load ratio factor is the ratio of the operating status parameters of the highest load operating period to the lowest load operating period on the first day in the future. The high load duration factor is the ratio of the duration of the highest load operating period to the duration of the fixed discharge demand on the first day in the future. The load fluctuation factor is the coefficient of variation of the ratio of the load standard deviation to the average load on the first day in the future, after exponential normalization.

[0042] The cross-day operation characteristics corresponding to each cross-day scheduling cycle are obtained by weighting and summing the cross-day correlation factor, high load duration factor, load fluctuation factor, and span factor, as shown in the following formula:

[0043]

[0044] in, These are the weights of each factor, and α is the inter-day correlation factor; β is the high load duration factor; γ is the span factor; δ is the load fluctuation factor.

[0045] For each cross-day scheduling cycle, the cross-day correlation factor is the difference between the operating status parameters of the highest load operating segment on the end date of the current cross-day scheduling cycle and the operating status parameters of the lowest load operating segment on the next day, and then normalized by dividing by the difference between the operating status parameters of the highest load operating segment and the lowest load operating segment on the next day; the high load duration factor is the ratio of the high load duration to the fixed discharge demand duration on the end date of the current cross-day scheduling cycle, and is truncated by the min function; the load fluctuation factor is the coefficient of variation of the ratio of the load standard deviation to the average load over all natural days, and is exponentially normalized; the span factor is the number of natural days between the natural day containing the highest load operating segment and the natural day containing the lowest load operating segment over all natural days in the current cross-day scheduling cycle, and is normalized by the exponential function.

[0046] Specifically as follows:

[0047] The cross-day correlation factor α is the ratio of the difference between the maximum electricity price on the last natural day of the span and the minimum electricity price on the first day of the span, divided by the peak-valley electricity price difference on the first day of the span. The calculation formula is as follows:

[0048]

[0049] Where d0 is the starting date of the span, d e Let λ(t,d) be the electricity price for the last natural day of the span, λ(t,d) be the electricity price for the t-th time period on the d-th day, and T be the number of time periods per day; These are the operating status parameters for the peak load operating period on the last natural day of the span; These are the operating status parameters for the lowest load operating period on the start date of the span (i.e., the first day in the future); These are the operating status parameters for the peak load period on the start date of the span.

[0050] The high-load duration factor β is the ratio of the high-load duration to the fixed discharge demand duration on the last natural day of the span, truncated by a min function. The calculation formula is as follows:

[0051]

[0052] in, For the last natural day of the span d e The set of high-load periods, where Δt is the time resolution. To fix the required discharge duration;

[0053] The span factor γ is the exponentially decaying normalized value of the span, calculated using the following formula:

[0054]

[0055] Where, γ = d e -d0 is the span, γ max To preset the maximum span, λ γ The attenuation coefficient;

[0056] The load fluctuation factor δ is the exponentially normalized value of the electricity price variation coefficient within the span period, and its calculation formula is as follows:

[0057]

[0058] in, The standard deviation of electricity prices over the span period. This represents the average electricity price over the spanning period.

[0059] Figure 4 This refers to the cross-day operational characteristics and single-day operational characteristics calculated using the above implementation scheme, with the first day as the starting day, under different span periods (i.e., cross-day scheduling periods). Figure 4 As can be seen, the calculated cross-day operational characteristics differ significantly under different span periods. Under the current circumstances, the cross-day operational characteristics corresponding to a two-day span period are the strongest. Therefore, a two-day span is chosen here, that is, the first period is from the start time of the first day to the end time of the second day.

[0060] In solving the cross-cycle scheduling model that considers cross-day additional costs, the constraints include span constraints, state of charge (SOC) constraints, and power constraints constructed based on the cross-day scheduling cycle. These constraints are used to ensure that the output charging and discharging periods, capacity, and span arrangements meet the physical operation requirements of energy storage equipment and cross-day scheduling needs.

[0061] The SOC constraint is that the SOC after charging the energy storage should not be higher than the upper safety limit, and the SOC after discharging should not be lower than the lower safety limit. During storage, the SOC will naturally decay according to the storage loss coefficient, and after decay, it will still not be lower than the lower safety limit.

[0062] The formula is as follows:

[0063]

[0064] Regular time:

[0065] Crossing the day:

[0066] in, Let t be the state of charge of the stored energy. SOC represents the state of charge at time t and the state of charge at time t. min As the lower limit of SOC security, SOC max As the upper limit of SOC security, This represents the daily storage loss factor. For charging efficiency, For discharge efficiency, The time resolution is 15 min, or 0.25 h. Let t be the energy storage charging power. Let t be the energy storage discharge power at time t.

[0067] Power constraint: The energy storage charging and discharging power shall not exceed the preset charging and discharging power limit, and the charging power and discharging power shall not be greater than zero at the same time to avoid charging and discharging behavior occurring simultaneously.

[0068] The calculation formula is:

[0069]

[0070]

[0071]

[0072] in, Maximum charging power, This represents the maximum discharge power.

[0073] In the cross-cycle scheduling model considering cross-day additional costs, these costs include operation and maintenance costs and storage loss costs related to the span duration. Storage loss costs are dynamically adjusted according to the span type, while operation and maintenance costs are calculated cumulatively over the span duration, achieving a comprehensive balance between cost and operational efficiency. The objective function of the cross-cycle scheduling model satisfies the following formula:

[0074]

[0075]

[0076]

[0077]

[0078] Where f is the total operating revenue of the energy storage power station; T is the total time step of the cycle, that is, the total time step within the current cross-day scheduling cycle; Let be the real-time electricity price at time t; D be the total number of days the energy storage power station has been in operation. For daily operation and maintenance costs, This represents the cross-day storage duration (in days) with day d as the start date of the cross-day scheduling cycle. When day d is not the start date of the cross-day scheduling cycle... =0, Store electrical energy for the transition between days d. Cost per unit of electrical energy loss; The cost of cross-day operation and maintenance for the current cycle, with day d as the starting date of the cross-day scheduling cycle; The cost of cross-day energy storage loss on day d.

[0079] Figure 5 The additional costs for cross-day operations under different span periods obtained using the above implementation scheme are as follows: Figure 5 It can be seen that the additional cost across days for different span periods is approximately linear. However, since the energy storage SOC value at the time of day is different for different span periods, the additional cost across days does not increase linearly with the span.

[0080] This invention employs a MILP solver to solve a cross-cycle scheduling model that considers cross-day additional costs. During the solution process, decision variables characterize the charging and discharging behavior and cross-day storage status of the energy storage power station at various times within 31 calendar days, achieving a globally optimal solution for the model. After the model is solved, a detailed cross-day scheduling scheme for the energy storage power station is output.

[0081] Figure 2 A line graph of SOC (State of Charge) for a typical cycle of trans-day characteristics obtained using the above implementation method is shown, and compared with daily energy storage. From Figure 2As can be seen from the data, due to the allowance of cross-day scheduling, energy storage can be charged during the cross-day period on the second and third days of this cycle by taking advantage of the low electricity price at the end of the second day, and more stored electricity can be released during the high electricity price on the third day, thus improving the energy utilization rate.

[0082] Figure 3 This section describes the monthly revenue of energy storage power stations under different maximum permissible cross-day dispatch cycles, obtained using the above implementation scheme. From... Figure 3 It can be seen that, due to the improved energy utilization rate, energy storage power stations can obtain higher returns through optimized scheduling.

[0083] This invention upgrades the scheduling mode of energy storage power stations from the traditional single-day fixed boundary, experience-based inter-day arrangement to a quantifiable and optimizable multi-span scheduling system by using inter-day energy storage scheduling trigger determination and multi-constraint mixed integer linear programming modeling. This allows for a more refined and verifiable trade-off between the safe operation of energy storage devices and the overall benefits of the power station. The method of this invention does not rely on specific energy storage device types or electricity market pricing mechanisms, exhibiting good versatility and providing a new technical path for the efficient operation and scheduling management of energy storage power stations in the context of high-proportion renewable energy integration.

[0084] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-span dispatching method for energy storage power stations considering inter-day control needs, characterized in that, Includes the following steps: The system acquires operational status data for the energy storage power station over the next few days. Based on this data, it determines whether cross-day energy storage scheduling is triggered. If triggered, a cross-day scheduling cycle is generated, and a cross-cycle scheduling model considering cross-day additional costs is constructed based on the current cross-day scheduling cycle. After solving the cross-cycle scheduling model, a cross-day scheduling plan for the energy storage power station is obtained and distributed to the energy storage power station. If not triggered, a single-day scheduling plan is distributed to the energy storage power station.

2. The multi-span dispatching method for energy storage power stations considering inter-day control needs according to claim 1, characterized in that, The step of determining whether to trigger cross-day energy storage dispatch based on operational status data over the next few days includes: Based on the operational status data for the next few days, the daily operational characteristics of the first day are calculated and recorded as the baseline operational potential. The corresponding cross-day operational characteristics under different cross-day scheduling cycles are also calculated. The largest cross-day operational characteristic in different cross-day scheduling cycles is recorded as the optimal cross-day operational characteristic. The cross-day trigger coefficient is obtained by dividing the optimal cross-day operational characteristic by the baseline operational potential. If the cross-day trigger coefficient is greater than the preset trigger threshold, cross-day energy storage scheduling is triggered and the cross-day scheduling cycle corresponding to the optimal cross-day operational characteristic is output. Otherwise, cross-day energy storage scheduling is not triggered.

3. The multi-span dispatching method for energy storage power stations considering inter-day control needs according to claim 1, characterized in that, The daily operating characteristics of the first day in the future include the high-low load ratio factor, the high load duration factor, and the load fluctuation factor.

4. The multi-span dispatching method for energy storage power stations considering inter-day control needs according to claim 1, characterized in that, The cross-day operation characteristics corresponding to each cross-day scheduling cycle include cross-day correlation factor, high load duration factor, load fluctuation factor, and span factor.

5. A multi-span dispatching method for energy storage power stations considering inter-day control needs according to claim 1, characterized in that, The cross-day operation characteristic corresponding to each cross-day scheduling cycle is the value obtained by weighted summation of cross-day correlation factor, high load duration factor, load fluctuation factor and span factor.

6. A multi-span dispatching method for energy storage power stations considering inter-day control needs according to claim 1, characterized in that, In solving the cross-cycle scheduling model that considers cross-day additional costs, the constraints include span constraints constructed based on the cross-day scheduling cycle.

7. A multi-span dispatching method for energy storage power stations considering inter-day control needs according to claim 6, characterized in that, The constraints also include state of charge constraints and power constraints.

8. A multi-span dispatching system for energy storage power stations considering inter-day control requirements, characterized in that, include: The data input unit for the energy storage power station is used to acquire the operating status data of the energy storage power station for the next several days. The cross-day energy storage scheduling trigger judgment unit is used to determine whether to trigger cross-day energy storage scheduling based on the operating status data of the next few days and send the judgment result to the cross-cycle scheduling model construction unit and the single-day scheduling scheme storage unit; The cross-cycle scheduling model construction unit is used to construct a cross-cycle scheduling model that takes into account cross-day additional costs based on the judgment result of the cross-day energy storage scheduling trigger judgment unit. The model solver is used to solve the cross-cycle scheduling model, obtain the cross-day scheduling scheme of the energy storage power station, and distribute it. The daily scheduling plan storage unit is used to issue daily scheduling plans based on the judgment result of the cross-day energy storage scheduling trigger judgment unit.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the multi-span scheduling method for energy storage power stations that takes into account inter-day control requirements as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-span scheduling method for energy storage power stations that takes into account the inter-day control requirements as described in any one of claims 1 to 7.

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