Energy storage rolling optimization control method and system considering new energy output uncertainty

Abstract

This invention discloses a rolling optimization control method and system for energy storage considering the uncertainty of new energy output. The method includes: acquiring the current state and forecast information of the new energy source; based on the current state and forecast information, constructing an optimization model including an objective function and constraints within the forecast time domain; the objective function includes a desired curtailment risk term to transform the reserve demand to cope with future uncertainties into a real-time quantifiable economic indicator, and allowing dynamic relaxation of energy storage reserve constraints; solving the optimization model to generate a sequence of energy storage charging and discharging plans for the next H time periods, executing only the energy storage charging and discharging power command for the first control period; repeating S1-S3 to achieve rolling optimization. This invention introduces a risk cost function to quantitatively compare the economics of "reserving reserves for future uncertainties" and "energy storage utilization in the current period," achieving dynamic optimal decision-making within the framework of rolling optimization.

Description

Technical Field

[0001] This invention mainly relates to the field of new energy technology, specifically to a rolling optimization control method and system for energy storage that takes into account the uncertainty of new energy output. Background Technology

[0002] With the increasing scale of grid-connected renewable energy sources such as wind and solar power, the randomness and volatility of these renewable energy sources pose challenges to the safe and stable operation of the power grid. Renewable energy power plants can typically consider configuring energy storage systems to smooth power fluctuations or track dispatch plans. However, in practical engineering, renewable energy forecasting is uncertain, meaning there are errors in the predicted power output, and the actual output may fluctuate within a certain range. To ensure grid connection reliability, energy storage needs to reserve capacity to cope with upward (requiring charging) or downward (requiring discharging) forecasts. However, energy storage capacity is limited by investment costs, and the energy storage configuration capacity within the power plant is relatively small (for example, for a 50MW wind farm, only 7.5MW / 15MWh of energy storage may be configured). This "small capacity" energy storage cannot simultaneously meet the dual requirements of "smoothing long-term fluctuations" and "reserving sufficient backup capacity." The shortcomings of existing technical solutions are as follows: If the traditional hard constraint method is adopted (that is, the available capacity of energy storage is always greater than the maximum fluctuation of the prediction confidence interval), in the case of small-capacity energy storage, the state of charge of the energy storage system will be artificially restricted to a low level, resulting in insufficient energy storage utilization, poor economic efficiency, and even, in extreme cases, active curtailment of electricity in order to reserve space, which violates the original intention of improving the absorption of energy.

[0003] In existing technologies, the optimized control of the combined operation of new energy sources and energy storage mainly includes the following methods: 1. Deterministic optimization method: This method treats the predicted value of new energy sources as a known quantity and optimizes the system with the goal of tracking the plan or maximizing profits. The drawback is that it does not consider prediction errors, resulting in poor control performance when the actual new energy sources deviate from the predicted values.

[0004] 2. Robust Optimization Method: This method considers the worst-case scenario of renewable energy output within the predicted confidence interval and formulates an energy storage strategy to ensure that the system does not exceed its limits under any circumstances. The drawback is that with small-capacity energy storage, the robust solution is often too conservative. To combat extremely low-probability fluctuations, it sacrifices the operational economy for most of the normal operating periods, resulting in the energy storage being idle or inefficient for extended periods.

[0005] 3. Chance-constrained programming: This method allows constraints to be violated with a certain probability, transforming deterministic constraints into probabilistic ones. While it alleviates conservatism to some extent, it typically requires precise probability distribution information and is complex to solve, making it difficult to apply in real-time in engineering practice.

[0006] None of the above methods have been able to adequately address the dynamic balance between "coping with uncertainty" and "improving utilization efficiency" in "small-capacity energy storage". Summary of the Invention

[0007] To address the technical problems existing in the prior art, this invention provides a rolling optimization control method and system for energy storage that takes into account the uncertainty of new energy output, making decision-making more flexible and economical.

[0008] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows: A rolling optimization control method for energy storage considering the uncertainty of new energy output includes the following steps: S1. Obtain the current status and forecast information of new energy sources; the current status includes the current time. Measured values ​​of energy storage state of charge; forecast information includes ultra-short-term predicted power sequences for new energy sources. The probability distribution of its prediction error (ω); S2. Based on the current status and forecast information of new energy sources, in the forecast time domain... Within this framework, an optimization model is constructed that includes an objective function and constraints. The objective function includes tracking deviation, energy storage loss, and expected curtailment risk. The expected curtailment risk is used to transform the reserve demand for addressing future uncertainties into a real-time quantifiable economic indicator, and to allow for dynamic relaxation of energy storage reserve constraints. S3. Solve the optimization model to generate a sequence of energy storage charging and discharging plans for the next H time periods. Only execute the energy storage charge and discharge plan sequence. Energy storage charging and discharging power command during the first control period After the control cycle ends, return to step S1 to update the current status and forecast information of new energy sources, and repeat steps S1-S3 to achieve rolling optimization.

[0009] Preferably, the objective function is as follows:

[0010] in, The active power actually injected into the power grid for the combined energy storage system of new energy power plants. The current day's planned values ​​for new energy power plants; λ1, λ2, and λ3 are weighting coefficients; For the cost of energy storage, The charging and discharging power for energy storage; This is the expected risk item for power curtailment.

[0011] Preferably, the expected curtailment risk term is based on the probability distribution of the new energy prediction error. and the available charging power of current energy storage The calculation yields the following formula:

[0012]

[0013] In the formula, For the predicted power of new energy sources, It is the error threshold at which the energy storage charging capacity is just fully utilized. w For prediction error, To effectively limit grid connection, For the installed capacity of the station, This is the planned value issued by the power grid.

[0014] Preferably, λ1 and λ3 are dynamically adjusted according to whether power grid dispatch instructions require power rationing: when power grid instructions require strict adherence to the plan, λ1 increases; when the power grid issues power rationing instructions, λ3 increases, encouraging energy storage charging and consumption.

[0015] Preferably, the constraints include: Energy storage SOC recursion:

[0016] In the formula, Let t be the state of charge of the stored energy. For charging and discharging efficiency, The duration is defined as the time period; for ease of modeling, the charging and discharging power of the energy storage is represented as... Split into two non-negative variables: , which is the discharge power; , which is the charging power; ; Energy storage SOC upper and lower limits constraints:

[0017] The upper and lower limits of energy storage SOC; Energy storage charging and discharging power constraints and mutual exclusion constraints:

[0018] in, , The maximum charging and discharging power of the energy storage; System power balance constraints:

[0019] in, To make actual contributions to new energy power stations This refers to the power output of the wind curtailment. Grid-connected power limit constraints: .

[0020] Preferably, the constraints also include dynamic reserve constraints: a dynamic guiding term that transforms reserve demand into a state of charge, calculated as follows:

[0021]

[0022] in, Let be the probability that the event is true. For the charging power available in the next moment, This is the upper limit of the state of charge. The state of charge at the next moment. For the rated capacity of energy storage, For charging efficiency, To control the duration, The upper skewness of the confidence interval for new energy prediction at the next moment; This is the dynamic confidence coefficient.

[0023] Preferably, Adjust according to grid status: If you need to charge more during periods of power rationing: Set =0.7, ensuring as much charging as possible at present; if currently in a normal tracking period: set =0.95.

[0024] The present invention also discloses a computer program product, comprising a computer program that, when executed by a processor, performs the steps of the method described above.

[0025] The present invention further discloses a computer-readable storage medium having a computer program stored thereon, the computer program executing the steps of the method described above when run by a processor.

[0026] The present invention also discloses an energy storage rolling optimization control system that takes into account the uncertainty of new energy output, including a memory and a processor connected to each other. The memory stores a computer program, and the computer program executes the steps of the method described above when run by the processor.

[0027] Compared with the prior art, the advantages of the present invention are as follows: This invention introduces a risk cost function to quantitatively compare the economics of "reserving reserves for future uncertainties" and "utilizing energy storage in the current period," achieving dynamic optimal decision-making within a rolling optimization framework. Specifically, the uncertainty of the prediction confidence interval is transformed into the expected curtailment risk cost, which is incorporated into the objective function. This allows the optimization algorithm to weigh the "benefits of charging more now / saved curtailment" against the "risk of future curtailment due to insufficient space," resulting in more flexible and economical decision-making. Simultaneously, the expected curtailment volume is calculated in real-time during the rolling optimization process. This value increases with rising State of Charge (SOC) (reduced available space) and varies with changes in the prediction error distribution. It provides a dynamic, quantitative risk indicator of whether energy storage should continue charging. Furthermore, a weighting factor λ3, linked to grid conditions (normal tracking / emergency power curtailment), is introduced, enabling the energy storage system to intelligently identify grid demand: under normal conditions, it primarily plays a role in "smoothing fluctuations"; during power curtailment, it actively switches to a "full absorption" mode, maximizing the use of limited energy storage capacity to absorb abandoned power. Through this mechanism, the pain point of small-capacity energy storage being hesitant to charge due to fear of risk is addressed. While ensuring a high probability of safety, a low probability of risk events (a small amount of abandoned power) is allowed to occur in exchange for improved overall energy storage utilization, thereby maximizing the comprehensive benefits of the wind-storage system. Attached Figure Description

[0028] Figure 1 This is a flowchart of an energy storage rolling optimization control method considering the uncertainty of new energy output, according to an embodiment of the present invention. Detailed Implementation

[0029] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0030] The energy storage rolling optimization control method considering the uncertainty of new energy output provided in this embodiment of the invention includes two parts: First, a long-term plan is formulated. Based on new energy forecasts, a rough energy storage charging and discharging plan is developed. At this stage, overly detailed confidence intervals can be disregarded, and only a general energy balance is required. Second, real-time control. Model predictive control (MPC) is employed in real-time operation. Each optimization round focuses only on the next 15-30 minutes. Within this short timeframe, the prediction accuracy for new energy sources is very high, with a confidence interval... It will become very small, therefore it requires Establishment becomes easier to achieve without unduly restricting energy storage. That is, in the short time domain, energy storage must be able to cope with fluctuations within that time domain; in the long time domain, SOC is allowed to vary over a wider range to pursue economic efficiency.

[0031] like Figure 1 As shown, the specific steps of the rolling time-domain method based on model predictive control (MPC) are as follows: S1: Data Acquisition and Prediction Get the current time Measured values ​​of energy storage state of charge (SOC) and ultra-short-term predicted power series of new energy sources The probability distribution of its prediction error (ω) (can be obtained through historical data statistics, or by dynamic fitting of confidence intervals); S2: Construction Optimization Problem Within the prediction time domain [t0, t0+H], construct an optimization model that includes the objective function and constraints; Specifically, a multi-objective function is adopted, which includes tracking deviation, energy storage loss, and expected curtailment risk. An expected curtailment risk term is introduced into the objective function, and the energy storage reserve constraint is allowed to relax dynamically based on the current state and forecast information.

[0032] Objective function:

[0033] in, The active power actually injected into the power grid for the combined energy storage system of new energy power plants. The current value represents the planned value for new energy power plants; λ1, λ2, and λ3 are weighting coefficients that can be dynamically adjusted according to grid dispatch instructions (whether power curtailment is required): λ1 increases when grid instructions require strict adherence to the plan; λ3 increases when the grid issues power curtailment instructions, encouraging energy storage charging and consumption. Simultaneously, a dynamic confidence coefficient is used. The setting is relatively low, such as 0.7. Costs associated with energy storage (such as depreciation and efficiency losses). The charging and discharging power for energy storage; It represents the expected amount of abandoned electricity, which is based on the probability distribution of the prediction error of new energy sources. and the available charging power of current energy storage The calculation yields the following formula:

[0034]

[0035] In the formula, For the predicted power of new energy sources, It is the error threshold at which the energy storage charging capacity is just fully utilized. , w The prediction error is an integral variable, and its fluctuation range is determined by the confidence interval. If the prediction gives a 90% confidence interval, then […]. δ, +δ], which means Pr{ δ≤ω≤+δ}=0.9, or in other words, ω in [ The probability within the range of δ,δ is 90%. To effectively limit grid connection, For the installed capacity of the station, The planned value issued by the power grid (when there is no power restriction). If there is a power outage, ).

[0036] Constraints: To facilitate modeling, the charging and discharging power of the energy storage is... Split into two non-negative variables: , which is the discharge power; , is the charging power. ; Energy storage SOC recursion:

[0037] In the formula, Let t be the state of charge of the stored energy. For charging and discharging efficiency, The duration is in hours (h).

[0038] Energy storage SOC upper and lower limits constraints:

[0039] Usually taken ; Energy storage charging and discharging power constraints and mutual exclusion constraints:

[0040] in, , The maximum charging and discharging power of the energy storage; System power balance constraints:

[0041] in, To make actual contributions to new energy power stations This refers to the power of wind curtailment.

[0042] Grid-connected power limit constraints:

[0043] Dynamic reserve constraint: A dynamic guide term that transforms reserve demand into state of charge, calculated as follows:

[0044]

[0045] in, Let be the probability that the event is true. For the charging power available in the next moment, This is the upper limit of the state of charge. The state of charge at the next moment (determined by the current decision) Decide), Rated energy storage capacity (MWh). For charging efficiency, To control the duration (h). The upper bias of the confidence interval for new energy prediction at the next moment (MW) is the reserve power demand that needs to be reserved. The dynamic confidence coefficient is not a fixed value, but a variable adjusted according to the power grid status. Its dynamic adjustment logic is as follows: If the current period is a power rationing period (requiring more charging): Set... =0.7 (Ensure maximum charging currently, allowing a 30% probability of insufficient space in the future); If currently in a normal tracking period: Set =0.95 (Energy storage will not be charged much, so the SOC must be kept relatively low to reserve space and ensure that energy storage can keep up with the plan).

[0046] This invention does not impose mandatory fixed reserve capacity hard constraints, but rather achieves this through the objective function. This will indirectly guide the reservation of reasonable space for energy storage.

[0047] S3: Solving and Execution Solving the above optimization problem yields the energy storage charging and discharging schedule sequence for the next H time periods. ; Only the energy storage charging and discharging power command for the first control period is executed. ; After the control cycle ends, return to step S1 to enter the next control cycle, update the SOC and prediction data, and repeat steps S1-S3 to achieve rolling optimization.

[0048] This invention introduces a risk cost function to quantitatively compare the economics of "reserving reserves for future uncertainties" and "utilizing energy storage in the current period," achieving dynamic optimal decision-making within a rolling optimization framework. Specifically, the uncertainty of the prediction confidence interval is transformed into the expected curtailment risk cost, which is incorporated into the objective function. This allows the optimization algorithm to weigh the "benefits of charging more now / saved curtailment" against the "risk of future curtailment due to insufficient space," resulting in more flexible and economical decision-making. Simultaneously, the expected curtailment volume is calculated in real-time during the rolling optimization process. This value increases with rising State of Charge (SOC) (reduced available space) and varies with changes in the prediction error distribution. It provides a dynamic, quantitative risk indicator of whether energy storage should continue charging. Furthermore, a weighting factor λ3, linked to grid conditions (normal tracking / emergency power curtailment), is introduced, enabling the energy storage system to intelligently identify grid demand: under normal conditions, it primarily plays a role in "smoothing fluctuations"; during power curtailment, it actively switches to a "full absorption" mode, maximizing the use of limited energy storage capacity to absorb abandoned power. Through this mechanism, the pain point of small-capacity energy storage being hesitant to charge due to fear of risk is addressed. While ensuring a high probability of safety, a low probability of risk events (a small amount of abandoned power) is allowed to occur in exchange for improved overall energy storage utilization, thereby maximizing the comprehensive benefits of the wind-storage system.

[0049] The present invention also discloses a computer program product, comprising a computer program that, when executed by a processor, performs the steps of the method described above.

[0050] The present invention further discloses a computer-readable storage medium having a computer program stored thereon, the computer program executing the steps of the method described above when run by a processor.

[0051] The present invention also discloses an energy storage rolling optimization control system that takes into account the uncertainty of new energy output, including a memory and a processor connected to each other. The memory stores a computer program, and the computer program executes the steps of the method described above when run by the processor.

[0052] The products, media, and systems of the present invention, corresponding to the methods described above, also possess the advantages described above.

[0053] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be implemented by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium includes: any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. The memory is used to store computer programs and / or modules. The processor implements various functions by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0054] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method of energy storage rolling optimization control considering new energy output uncertainty, characterized in that, Including the following steps: S1. Obtain current state and prediction information of new energy; the current state includes current time measured value of state of charge of energy storage; the prediction information includes ultra-short-term prediction power sequence of new energy and probability distribution of prediction error (ω) ​ S2. Based on the current status and forecast information of new energy sources, in the forecast time domain... Within this framework, an optimization model is constructed that includes an objective function and constraints. The objective function includes tracking deviation, energy storage loss, and expected curtailment risk. The expected curtailment risk is used to transform the reserve demand for addressing future uncertainties into a real-time quantifiable economic indicator, and to allow for dynamic relaxation of energy storage reserve constraints. S3. Solve the optimization model to generate a sequence of energy storage charging and discharging plans for the next H time periods. Only execute the energy storage charge and discharge plan sequence. Energy storage charging and discharging power command during the first control period After the control cycle ends, return to step S1 to update the current status and forecast information of new energy sources, and repeat steps S1-S3 to achieve rolling optimization.

2. The energy storage rolling optimization control method considering the uncertainty of new energy output according to claim 1, characterized in that, The objective function is as follows: in, The active power actually injected into the power grid for the combined energy storage system of new energy power plants. The current day's planned values ​​for new energy power plants; λ1, λ2, and λ3 are weighting coefficients; For the cost of energy storage, The charging and discharging power for energy storage; This is the expected risk item for power curtailment.

3. The energy storage rolling optimization control method considering the uncertainty of new energy output according to claim 2, characterized in that, The expected curtailment risk term is based on the probability distribution of new energy prediction errors. and the available charging power of current energy storage The calculation yields the following formula: In the formula, For the predicted power of new energy sources, It is the error threshold at which the energy storage charging capacity is just fully utilized. w For prediction error, To effectively limit grid connection, For the installed capacity of the station, This is the planned value issued by the power grid.

4. The energy storage rolling optimization control method considering the uncertainty of new energy output according to claim 2 or 3, characterized in that, λ1 and λ3 are dynamically adjusted according to whether power rationing is required by the power grid dispatch instructions: when the power grid instructions require strict adherence to the plan, λ1 increases; when the power grid issues power rationing instructions, λ3 increases to encourage energy storage charging and consumption.

5. The energy storage rolling optimization control method considering the uncertainty of new energy output according to claim 2 or 3, characterized in that, The constraints include: Energy storage SOC recursion: In the formula, Let t be the state of charge of the stored energy. For charging and discharging efficiency, The duration is defined as the time period; for ease of modeling, the charging and discharging power of the energy storage is represented as... Split into two non-negative variables: , which is the discharge power; , which is the charging power; ; Energy storage SOC upper and lower limits constraints: The upper and lower limits of energy storage SOC; Energy storage charging and discharging power constraints and mutual exclusion constraints: in, , The maximum charging and discharging power of the energy storage; System power balance constraints: in, To make actual contributions to new energy power stations This refers to the power output of the wind curtailment. Grid-connected power limit constraints: 。 6. The energy storage rolling optimization control method considering the uncertainty of new energy output according to claim 5, characterized in that, The constraints also include dynamic reserve constraints: dynamic steering terms that convert reserve requirements into state of charge, calculated as follows: in, Let be the probability that the event is true. For the charging power available in the next moment, This is the upper limit of the state of charge. The state of charge at the next moment. For the rated capacity of energy storage, For charging efficiency, To control the duration, The upper skewness of the confidence interval for new energy prediction at the next moment; This is the dynamic confidence coefficient.

7. The energy storage rolling optimization control method considering the uncertainty of new energy output according to claim 6, characterized in that, Adjust according to grid status: If you need to charge more during periods of power rationing: Set =0.7, ensuring as much charging as possible at present; if currently in a normal tracking period: set =0.

95.

8. A computer program product, comprising a computer program, characterized in that, The computer program is executed by the processor to perform the steps of the method as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when run by a processor, performs the steps of the method as described in any one of claims 1-7.

10. A rolling optimization control system for energy storage considering the uncertainty of new energy output, comprising an interconnected memory and a processor, wherein the memory stores a computer program, characterized in that, The computer program, when run by a processor, performs the steps of the method as described in any one of claims 1-7.

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