A method for day-ahead economic dispatch of a wind storage combined system considering multi-scenario cooperation

Abstract

This invention relates to the field of energy storage-assisted wind farm grid connection technology, and particularly to an intraday economic dispatch method for a wind-storage integrated system considering multi-scenario collaboration. It establishes an energy storage economic dispatch model and an energy storage power revision model based on rolling optimization. Using the reduction of wind farm performance pressure and peak-valley arbitrage revenue through energy storage as multi-objective optimization functions, it obtains an optimal control scheme for the energy storage system that balances economics and technicality. Furthermore, due to the timeliness of ultra-short-term forecasting, the energy participating in peak-valley arbitrage is targeted. In addition, since the optimized dispatch model only optimizes the system's power output, an energy storage power revision model is established to revise the energy storage power, thereby obtaining the optimal energy storage output in the time sequence. This invention can improve energy storage utilization, enhance the regulation capability of energy storage for wind power, and improve the economic efficiency of system operation.

Description

Technical Field

[0001] This invention relates to the field of energy storage-assisted wind farm grid connection technology, and in particular to an intraday economic dispatch method for a wind-storage integrated system that considers multi-scenario collaboration. Background Technology

[0002] Since the "dual carbon" target was proposed, China's wind power industry has developed rapidly. By the end of 2023, the total installed capacity of wind power exceeded 440 million kilowatts, and the proportion of wind power generation in the power system has continued to increase. However, due to the influence of natural resources, wind power is highly random and volatile, and its output power is unstable and has weak dispatchability.

[0003] Energy storage systems (ESS) are flexible resource adjustments that can effectively compensate for the drawbacks of wind power generation and enhance the balance and dispatch capabilities of power grids with a high proportion of renewable energy. By the end of 2023, the newly added renewable energy power generation and energy storage capacity accounted for more than 90% of the power supply side, with wind-storage projects reaching nearly 3.5 GW, and electrochemical energy storage dominating the market.

[0004] However, energy storage typically only considers tracking planned wind power output and mitigating grid-connected wind power fluctuations, resulting in low energy storage utilization and poor economic benefits. Therefore, exploring different operating modes of ESS (Energy Storage System) to improve energy storage utilization and enhance the operating profitability of the energy storage system while also considering its ability to regulate wind power has become an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an intraday economic dispatch method for wind-storage integrated systems that considers multi-scenario collaboration. This method enables energy storage to assist wind farms in grid connection and reduce assessment losses, while also participating in the electricity market for peak-valley arbitrage, thereby improving the economic efficiency of energy storage system operation.

[0006] To achieve the above objectives, the present invention adopts the following specific technical solution:

[0007] The intraday economic dispatch method for a wind-storage integrated system considering multi-scenario collaboration provided by this invention includes the following steps:

[0008] Step 1: Establish an energy storage economic dispatch model based on rolling optimization, including basic scenario processing, peak-valley arbitrage scenario processing, objective function, and constraints;

[0009] Basic scenario processing is used to enable energy storage to compensate for prediction errors and smooth fluctuations, thereby reducing the pressure on wind farm grid connection assessment and reducing the amount of electricity required for wind farm grid connection prediction accuracy assessment and fluctuation assessment.

[0010] Peak-valley arbitrage scenario processing is used to optimize electricity consumption during peak and valley periods, store wind power, increase the amount of electricity purchased from the grid during valley pricing periods, and increase the amount of electricity discharged during peak pricing periods;

[0011] The objective function is used to determine whether energy storage regulation resources are sufficient, and to enable energy storage to release or absorb electricity through market transactions when energy storage regulation resources are insufficient, so that energy storage can respond to charging or discharging demands to compensate for prediction errors and smooth out wind power fluctuations.

[0012] Step 2: Establish an energy storage power revision model to revise the time-series power of energy storage based on the energy storage economic dispatch model, so as to obtain the optimal output of energy storage in time series, thereby improving the utilization rate of energy storage and the regulation capability of energy storage on wind power; solve the energy storage economic dispatch model and the energy storage power revision model.

[0013] Furthermore, the basic scene processing is as follows:

[0014] The objective function E of the basic scenario exa To minimize the amount of electricity used in the assessment:

[0015] E exa =min(E err +E δ );

[0016] In the formula, E err E is used to assess the amount of electricity generated by wind power forecasting errors. δ The power volume for assessing fluctuations in wind power grid connection;

[0017] Even after energy storage adjustment, the wind power prediction error still exceeds the allowable error range. The assessment power expression is as follows:

[0018]

[0019] In the formula, P err,t Let P be the prediction error at time t. spre,t Let be the ultra-short-term wind power forecast value at time t. For energy storage to regulate the power of wind power, P pre,t This represents the predicted wind power output at time t. For the allowable limit of prediction error

[0020] When the actual grid-connected power fluctuation rate of wind power still does not meet the requirements after energy storage regulation, the expression for the assessment power volume is:

[0021]

[0022] In the formula, P δ,t Let t be the combined wind and energy storage grid-connected power fluctuation. The allowable limit for grid-connected power fluctuation;

[0023] The energy storage action range in the basic scenario is represented as follows:

[0024]

[0025] In the formula, P batn This is the rated power of the energy storage.

[0026] Furthermore, the peak-valley arbitrage scenario is handled as follows:

[0027] The optimization objective of the peak-valley arbitrage scenario is to store wind power and purchase electricity (E) from the grid at a lower price during off-peak hours. in,ch At most, the discharge volume E during peak electricity price periods in,dis most;

[0028]

[0029] In the formula, To regulate the charging power of wind power for energy storage; The charging and discharging power of energy storage at time t for peak-valley arbitrage in the electric energy market; This refers to the peak-valley electricity price coefficient. The value is 1 during periods of high electricity prices and 0 during other periods. The value is 1 during periods of low electricity price and 0 during other periods.

[0030]

[0031] For charging resistance coefficient, Here, SOC represents the discharge immunity coefficient, indicating the degree of resistance of the energy storage power station to charging and discharging. H and soc L These are the upper and lower limits for peak-valley arbitrage in energy storage, respectively; sign() is the sign function; when Resist charging when the time is negative. When the value is negative, it resists discharge; soc t Let be the size of soc at time t;

[0032] The goal of energy storage for peak-valley arbitrage is:

[0033]

[0034] Considering the scheduling plan for the second day, an initial SOC of 0.5 is beneficial for handling the charging and discharging demand of the coming day. Analysis shows that the last two electricity price periods of the day are "peak-flat price" periods, so the SOC can be adjusted to be lowered during the flat price period. H =0.5. When the energy storage SOC is below 0.5, charging is performed; when it is above 0.5, no further charging is performed, and a certain degree of SOC recovery is carried out.

[0035]

[0036] The operational range of energy storage in peak-valley arbitrage scenarios can be expressed as:

[0037]

[0038] This represents the peak-valley electricity price coefficient, which is 1 during peak electricity price periods and 0 during other periods.

[0039] Furthermore, the objective function is as follows:

[0040] min E=aE exa +bE in +cE b ;

[0041]

[0042] In the formula, a, b, and c represent the weight coefficients of different objectives, where a >> b > c, and E b This is the energy storage action function, designed to minimize the energy storage action in order to avoid meaningless actions by the energy storage device during multi-scenario coordinated control of energy storage.

[0043] Furthermore, the constraints are as follows:

[0044]

[0045] In the formula, Let P be the grid-connected wind power value after energy storage regulation at time t. wn This refers to the rated installed capacity of the wind farm.

[0046] Power constraints on the wind farm-energy storage interconnection line: the exchange power between the wind farm and the energy storage system shall not exceed the rated power of the energy storage equipment.

[0047]

[0048] In the formula, P batn Rated power of energy storage;

[0049] The total energy storage power limit is that the energy storage output / absorption power must not exceed the rated power of the energy storage device at any given time.

[0050]

[0051] The ability of energy storage to charge and discharge simultaneously is constrained.

[0052]

[0053] To ensure the safe operation of energy storage, the SOC state of energy storage must not exceed the limit at any time;

[0054] soc min ≤soct ≤soc max ;

[0055] In the formula, soc max and soc min These are the upper and lower limits of the SOC state for safe operation of energy storage;

[0056] The SOC state of energy storage at each moment can be expressed as:

[0057]

[0058] In the formula: η ch η dis These represent the charging and discharging efficiencies of the energy storage device, respectively; Δt is the time step; E batn This refers to the rated capacity of the energy storage device.

[0059] Furthermore, the revised energy storage power model is as follows:

[0060] The energy storage power revision model revises the energy storage time-series power, with the goal of assessing the energy consumption E under various scenarios before and after the revision. exa It remains the smallest; the overall prediction error in each scenario after revision should be the smallest, and the variance between the actual wind power and the day-ahead predicted power after energy storage adjustment should be the smallest; the amount of energy storage charging within the optimization cycle is equal before and after revision, and the amount of energy directly discharged to the grid during peak electricity price periods is equal.

[0061]

[0062] In the formula, The charging power used by the energy storage system at time t after revision to adjust the actual power of the wind farm; These represent the charging and discharging power of energy storage at time t after the revision for peak-valley arbitrage in the electric energy market;

[0063]

[0064] In the formula, The discharge power at time t after revision is the energy storage used to adjust the actual power of the wind farm.

[0065] Objective function of the revised energy storage power model:

[0066]

[0067] In the formula, E′ exa The revised expected total assessment power consumption; D(x) represents the variance of the sequence x;

[0068] The nonlinear components in the energy storage economic dispatch model and the energy storage power revision model are linearized using the Big M method and then solved using the CPLEX solver.

[0069] The present invention can achieve the following technical effects:

[0070] This invention provides a method for intraday economic dispatch of a wind-storage integrated system considering multi-scenario collaboration. Using the reduction of wind farm performance pressure and peak-valley arbitrage revenue through energy storage as a multi-objective optimization function, it obtains an optimal control scheme for the energy storage system that balances economics and technical feasibility. Furthermore, due to the timeliness of ultra-short-term forecasting, the energy participating in peak-valley arbitrage is targeted. In addition, since the optimization dispatch model only optimizes system power, an energy storage power revision model is established to revise the energy storage power and obtain the optimal energy storage output in the time sequence. This invention can improve energy storage utilization, enhance the regulation capability of energy storage for wind power, and improve the economic efficiency of system operation. Attached Figure Description

[0071] Figure 1 This is a flowchart of the solution for the intraday economic scheduling method of the wind-storage joint system considering multi-scenario collaboration, provided by an embodiment of the present invention.

[0072] Figure 2 This is a time-of-use electricity pricing diagram provided according to an embodiment of the present invention;

[0073] Figure 3 This is a schematic diagram of the operating parameters of a wind-storage system provided according to an embodiment of the present invention;

[0074] Figure 4 This is a diagram illustrating the effect of energy storage on wind power regulation according to an embodiment of the present invention;

[0075] Figure 5 This is a time-series power diagram for energy storage provided according to an embodiment of the present invention;

[0076] Figure 6 This is a graph showing the technical and economic indicators of the energy storage scheduling results for the four schemes provided in the embodiments of the present invention. Detailed Implementation

[0077] In the following description, embodiments of the invention will be described with reference to the accompanying drawings. In the description below, the same modules are denoted by the same reference numerals. Where the same reference numerals are used, their names and functions are also the same. Therefore, their detailed description will not be repeated.

[0078] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not constitute a limitation thereof.

[0079] The intraday economic dispatch method for a wind-storage integrated system considering multi-scenario collaboration provided in this invention aims to improve the economic operation of the energy storage system by enabling energy storage to assist wind farm grid connection in reducing performance losses while participating in the electricity market for peak-valley arbitrage. It includes the following:

[0080] 1. Establish an economic dispatch model for energy storage based on rolling optimization.

[0081] First, a multi-scenario collaborative control method for energy storage is proposed. The energy storage action objectives and output range of a single scenario under the multi-scenario collaborative mode are processed. Then, a weighted multi-objective optimization method is adopted to consider grid and ESS constraints, and an energy storage economic dispatch model based on rolling optimization is established to perform collaborative optimization of ESS in multiple application scenarios. This yields the energy storage output plan for energy storage power in multiple scenarios, realizing the collaborative operation of multiple scenarios. Finally, the energy storage time-series power is revised based on the criteria of "minimum assessment power, minimum prediction error, and unchanged arbitrage energy".

[0082] (1) Basic scene processing

[0083] In basic application scenarios, energy storage can reduce the pressure on wind farm grid connection assessments by compensating for prediction errors and mitigating fluctuations, thus minimizing the amount of electricity required for wind farm grid connection prediction accuracy and fluctuation assessments. Therefore, the objective function E in the basic scenario is... exa To minimize the amount of electricity used in the assessment:

[0084] E exa =min(E err +E δ (1);

[0085] E err E is used to assess the amount of electricity generated by wind power forecasting errors. δ The fluctuation in wind power grid connection is assessed based on the amount of electricity generated.

[0086] Even after energy storage adjustment, the wind power prediction error still exceeds the allowable error range. The assessment power expression is as follows:

[0087]

[0088] In the formula, P err,t Let P be the prediction error at time t. spre,t Let be the ultra-short-term wind power forecast value at time t. For energy storage to regulate the power of wind power, P pre,t This represents the predicted wind power output at time t. This refers to the allowable limit for prediction error; This refers to the regulation of wind power by energy storage.

[0089] When the actual grid-connected power fluctuation rate of wind power still does not meet the requirements after energy storage regulation, the expression for the assessment power volume is:

[0090]

[0091]

[0092] In the formula, P δ,t Let t be the combined wind and energy storage grid-connected power fluctuation. This refers to the allowable limit for grid-connected power fluctuations.

[0093] In a multi-scenario collaborative mode, wind power fluctuation refers to the fluctuation in combined wind and storage grid-connected power after energy storage adjustment within a certain period. At that time, energy storage can both release power at time t and absorb power at time t-1, thus making P δ,t Reduced. Based on the flexibility of smoothing fluctuations, no additional requirements are placed on the operational range of energy storage in this scenario.

[0094] As a single entity with shared interests, wind-storage power plants in multi-scenario collaborative models can profit from the price difference by storing wind power and selling it during periods of high electricity prices. To ensure the effective regulation of wind power in basic application scenarios and prevent energy storage systems (ESS) from indefinitely obtaining energy from wind farms for peak-valley arbitrage, the energy storage's charging from wind farms should be limited to a level that does not increase prediction errors. In summary, the operational range of energy storage in the basic application scenarios within the multi-scenario collaborative model can be expressed as:

[0095]

[0096] In the formula, P batn This is the rated power of the energy storage.

[0097] (2) Peak-valley arbitrage scenario

[0098] Since ultra-short-term forecasts only provide information for the next 4 hours, and peak and off-peak electricity prices change intermittently throughout the day, peak and off-peak electricity prices may not exist simultaneously within an optimization cycle that relies on ultra-short-term forecasts. When the optimization cycle rolls over to the point where the entire cycle is in "off-peak electricity price" or "off-peak flat electricity price", the revenue from energy storage charging cannot be reflected due to the sale of electricity during periods without peak electricity prices. Therefore, the electricity volume during peak and off-peak periods needs to be optimized.

[0099] Meanwhile, since wind farms and energy storage belong to the same stakeholders, energy storage can not only purchase electricity from the grid at a low price, but also profit from peak-valley arbitrage by absorbing surplus wind power. Therefore, when coordinating energy storage across multiple scenarios, its optimization objective for peak-valley arbitrage is: storing wind power and purchasing electricity from the grid at a low price during off-peak hours, E. in,ch At most, the discharge volume E during peak electricity price periods in,dis most:

[0100]

[0101] In the formula, To regulate the charging power of wind power for energy storage; The charging and discharging power of energy storage at time t for peak-valley arbitrage in the electric energy market; This refers to the peak-valley electricity price coefficient. The value is 1 during periods of high electricity prices and 0 during other periods. The value is 1 during periods of low electricity price and 0 during other periods.

[0102] In a multi-scenario collaborative mode, energy storage for peak-valley arbitrage needs to consider the power / capacity requirements of the basic application scenario, which is related to the randomness of wind power. If energy storage is planned to be fully charged or fully discharged within a scheduling cycle, future charging or discharging needs will increase energy storage costs and affect the energy storage's regulation effect on the basic application scenario. Therefore, when energy storage engages in peak-valley arbitrage, it needs to reserve a portion of charging and discharging space and only perform limited energy arbitrage. This necessitates the introduction of a charging resistance coefficient. Discharge resistance coefficient These represent the degree to which the energy storage power station resists charging and discharging:

[0103]

[0104] In the formula, soc H and soc L These are the upper and lower limits for peak-valley arbitrage in energy storage, respectively; sign() is the sign function; when Resist charging when the time is negative. When the value is negative, it resists discharge; soc t Let be the size of soc at time t.

[0105] Therefore, in a multi-scenario collaborative model, the goal of energy storage for peak-valley arbitrage should be:

[0106]

[0107] Considering the scheduling plan for the second day, an initial SOC of 0.5 is beneficial for handling the charging and discharging demand of the following day. Analysis shows that the last two electricity price periods of the day are "peak-flat price" periods, so the SOC can be adjusted to be lowered during the flat price period. H =0.5. When the energy storage SOC is below 0.5, charging is performed; when it is above 0.5, no further charging is performed, and a certain degree of SOC recovery is carried out.

[0108]

[0109] From an economic perspective, energy storage systems are generally required to purchase electricity from the grid at a low price and sell it back at a high price. From the perspective of achieving coordinated operation of energy storage, when charging capacity is insufficient in basic application scenarios, energy storage can release a portion of its electricity in the market in advance. This means the energy storage system can discharge even during periods of flat electricity prices, thereby improving its ability to regulate basic application scenarios. In summary, the operational range of energy storage in peak-valley arbitrage scenarios can be expressed as:

[0110]

[0111] In the formula, This represents the peak-valley electricity price coefficient, which is 1 during peak electricity price periods and 0 during other periods.

[0112] (3) Objective function

[0113] After processing each individual scenario separately in the multi-scenario mode, the energy storage device operates according to the following rules, thereby connecting the individual scenarios for collaborative optimization.

[0114] When energy storage regulation resources are sufficient

[0115] 1) If the power demand of the basic scenario and the peak-valley arbitrage scenario are the same at the same time, the energy storage will meet the power demand of both scenarios simultaneously. If the power demand of the basic scenario and the peak-valley arbitrage scenario conflict at the same time, the energy storage will prioritize meeting the power demand of the basic scenario. In this case, since the energy storage cannot charge and discharge simultaneously, the energy storage response to the power demand of the peak-valley arbitrage scenario is 0.

[0116] 2) When energy storage regulation resources are insufficient (because participating in peak-valley arbitrage is an active behavior that makes full use of idle capacity, there is no shortage of charging and discharging space for energy storage to carry out peak-valley arbitrage)

[0117] If the energy storage charging capacity is insufficient at this time, meaning the energy storage SOC is high, and the energy storage needs to absorb excess electricity to compensate for prediction errors and smooth out wind power fluctuations, then if the current period is during the "peak-off-peak electricity price period," the energy storage can release some electricity through market transactions, enabling it to respond to more charging demands to compensate for prediction errors and smooth out wind power fluctuations.

[0118] If the energy storage discharge capacity is insufficient at this time, that is, the energy storage SOC is in a low state, and the energy storage needs to release electricity to make up for prediction errors and smooth out wind power fluctuations. If the current period is during the "off-peak electricity price period", the energy storage can absorb some electricity through market transactions, so that the energy storage can respond to more of the discharge demand to make up for prediction errors and smooth out wind power fluctuations.

[0119] In multi-scenario collaborative operation, especially in the case of the basic application scenario presented in this paper, multiple objectives should be uniformly optimized within a single objective function to fully explore their synergistic value. This paper adopts a multi-objective weighting method to link different objectives together to achieve the aforementioned action objectives. The objective function includes the wind power prediction error assessment electricity, the wind power grid connection fluctuation assessment electricity, the energy storage participation in peak-valley arbitrage, and the energy storage operation cost, as detailed below;

[0120] min E=aE exa +bE in +cE b (13)

[0121]

[0122] In the formula, a, b, and c represent the weight coefficients of different objectives; in this paper, the objectives are a >> b > c. E b This is the energy storage action function, designed to minimize the energy storage action in order to avoid meaningless actions by the energy storage device during multi-scenario coordinated control of energy storage.

[0123] (4) Constraints

[0124] The proposed model mainly includes two aspects of constraints: the operating status of the wind farm and the energy storage system.

[0125] Power constraints on the wind farm-grid interconnection line; after energy storage compensates for errors, the wind power connected to the grid must not exceed the wind farm's rated power.

[0126]

[0127] In the formula, P wn The rated installed capacity of the wind farm This refers to the grid-connected power of wind power after energy storage regulation.

[0128] Power constraints on the wind farm-energy storage interconnection line: the exchange power between the wind farm and the energy storage system shall not exceed the rated power of the energy storage equipment.

[0129]

[0130] In the formula, P batn This is the rated power of the energy storage.

[0131] The total energy storage power limit is that the energy storage output / absorption power must not exceed the rated power of the energy storage device at any given time.

[0132]

[0133] The ability of energy storage to charge and discharge simultaneously is constrained.

[0134]

[0135] To ensure the safe operation of energy storage, the SOC state of energy storage must not exceed the limit at any time;

[0136] soc min ≤soc t ≤soc max (20);

[0137] In the formula, soc max and soc min These represent the upper and lower limits of the State of Charge (SOC) for safe operation of energy storage.

[0138] The SOC state of energy storage at each moment can be expressed as:

[0139]

[0140]

[0141] In the formula, η ch η dis These represent the charge and discharge efficiencies of the energy storage device; Δt is the time step; E batn This refers to the rated capacity of the energy storage device.

[0142] 2. Energy Storage Power Revision Model

[0143] Because basic application scenarios require a wide range of permissible actions for energy storage and the existence of peak-valley arbitrage methods that involve frequent charging and discharging, the optimal result of the above-mentioned collaborative control method is only optimal in terms of overall power output and there is still room for revision. Therefore, based on the optimization results of the collaborative optimization method, the energy storage time-series power is revised, with the following basic considerations:

[0144] 1) Assessment power consumption E under various scenarios before and after revision exa Still the smallest;

[0145] 2) The overall prediction error under each scenario after revision should be minimized, and the variance between the actual wind power and the day-ahead predicted power after energy storage regulation should be minimized;

[0146] 3) The amount of energy storage charging is the same in each scenario before and after the revision; the amount of energy discharged directly to the grid during peak electricity price periods is the same.

[0147]

[0148] in, The charging power used by the energy storage system at time t after revision to adjust the actual power of the wind farm; These represent the charging and discharging power of energy storage at time t after the revision for peak-valley arbitrage in the electric energy market;

[0149] 4) To serve basic application scenarios, the sum of the amount of electricity discharged to wind farms and the amount of electricity discharged directly to the grid during the flat electricity price period is equal in all scenarios before and after the revision. Combined with the objective function, when the grid is directly discharged in response to the error adjustment needs during the flat electricity price period, priority is given to making up for the prediction error to the minimum value.

[0150]

[0151] in, The discharge power at time t after revision is the energy storage used to adjust the actual power of the wind farm.

[0152] Therefore, the objective function of the energy storage power revision model is:

[0153]

[0154] In the formula, E′ exa The revised expected total assessment power consumption; D(x) represents the variance of the sequence x.

[0155] Revise model constraints:

[0156]

[0157] Solution method:

[0158] Both the above energy storage economic dispatch model and energy storage power revision model contain a large number of nonlinear constraints. To avoid the intelligent algorithm from easily getting trapped in local optima, the nonlinear parts of the proposed model are linearized using the Big M method, and then the CPLEX solver is used to solve the optimization model. The solution process is as follows: Figure 1 As shown.

[0159] The wind farm has a total installed capacity of 100MW, equipped with 20MW / 40MWh lithium iron phosphate battery energy storage devices to form a wind-storage integrated system. Time-of-use pricing is as follows: Figure 2 As shown, the operating parameters of the wind-storage system are as follows: Figure 3 As shown.

[0160] The results of energy storage collaborative scheduling are as follows Figure 4 As shown in the figure, during the period of large error duration (7-13 hours), due to insufficient energy storage capacity, the ESS cannot fully compensate for the prediction error. Instead, it first discharges capacity to the grid, thus controlling most of the error within the allowable range. During the periods of smaller error (2-5 hours and 18-22 hours), the ESS can effectively respond to the power demand of basic application scenarios, compensating for prediction errors and power fluctuations. After energy storage regulation, the assessed power consumption decreased from 92.1 MWh to 7.4 MWh, and the root mean square value of wind power prediction error decreased from 16.78% to 10.91%. The multi-scenario collaborative strategy enhanced the ESS's regulation capability in basic application scenarios.

[0161] Energy storage regulates wind farm output and participates in peak-valley arbitrage in various scenarios, such as Figure 5 As shown in the diagram, during the 0-5h and 7-13h periods, the ESS, under the multi-scenario collaborative strategy, significantly enhances its ability to regulate basic application scenarios by storing / releasing energy through peak-valley arbitrage. It can also release energy back to the grid during periods of high electricity prices, resulting in significant economic benefits. At the 22h time, the energy storage continues charging even when the SOC is greater than 0.5, causing a slight increase in SOC. This is because, under the energy storage power revision model, to minimize error, some energy from the 20-22h period is shifted to the 22h time, causing the SOC curve to rise. However, under the multi-scenario collaborative strategy, energy storage remains inactive from 22-24h, and the overall energy relationship does not change.

[0162] To further verify the effectiveness and economy of the proposed energy storage economic dispatch scheme and the necessity of revising the energy storage capacity, the following schemes are defined as controls for comparative analysis.

[0163] Option 1: Wind farms will not be equipped with energy storage;

[0164] Option 2: Configure energy storage in wind farms. The energy storage will not be used for peak-valley arbitrage, but will only be considered for basic application scenarios such as compensating for wind power forecasting errors and smoothing out fluctuations.

[0165] Option 3: Configure energy storage in wind farms to perform peak-valley arbitrage, but do not adjust power output.

[0166] Option 4: The energy storage economic dispatch method proposed in this paper.

[0167] The energy storage was dispatched using the four schemes described above, and the technical and economic indicators of the dispatch results are as follows: Figure 6 As shown in the figure, comparing the scheduling results of different schemes reveals that under the proposed energy storage collaborative control strategy, the energy storage assessment cost decreased from 73,639 yuan to 5,943 yuan, and the root mean square error decreased from 16.78% to 10.91%. The energy storage's ability to regulate basic application scenarios has been significantly improved, validating the collaborative control effect across multiple scenarios. Compared to the scheme without energy storage, the net profit of the energy storage system is approximately 65,000 yuan; compared to the energy storage control scheme considering only a single application scenario, the net profit of the energy storage system is approximately 46,000 yuan. The multi-scenario collaborative control strategy for energy storage can effectively improve the regulation capability for basic application scenarios and effectively obtain electricity price difference revenue through peak-valley arbitrage, demonstrating significant economic benefits.

[0168] The above analysis shows that energy storage-assisted wind power grid connection has certain advantages in reducing assessment pressure, improving energy storage utilization, and enhancing system operation economy.

[0169] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0170] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

[0171] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for intraday economic dispatch of a wind-storage integrated system considering multi-scenario collaboration, characterized in that, Includes the following steps: Step 1: Establish an energy storage economic dispatch model based on rolling optimization, including basic scenario processing, peak-valley arbitrage scenario processing, objective function, and constraints; The basic scenario processing is used to enable energy storage to perform prediction error compensation and fluctuation smoothing, so as to reduce the pressure of wind farm grid connection assessment and reduce the amount of electricity required for wind farm grid connection prediction accuracy assessment and fluctuation assessment. The basic scenario processing is as follows: Objective function of the basic scenario To minimize the amount of electricity used in the assessment: ； In the formula, To assess the power generation based on wind power forecasting errors, The power volume for assessing fluctuations in wind power grid connection; Even after energy storage adjustment, the wind power prediction error still exceeds the allowable error range. The assessment power expression is as follows: ； ； In the formula, Let be the prediction error at time t. Let be the ultra-short-term wind power forecast value at time t. Let t be the regulating power of energy storage on wind power. This represents the predicted wind power output at time t. This refers to the allowable limit for prediction error; When the actual grid-connected power fluctuation rate of wind power still does not meet the requirements after energy storage regulation, the expression for the assessment power volume is: ； ； In the formula, Let t be the combined wind and energy storage grid-connected power fluctuation. The allowable limit for grid-connected power fluctuation; The energy storage action range in the basic scenario is represented as follows: ； In the formula, Rated power of energy storage; The peak-valley arbitrage scenario processing is used to optimize the power consumption during peak and valley periods, store wind power, increase the amount of electricity purchased from the grid during valley electricity price periods, and increase the amount of electricity discharged during peak electricity price periods; The objective function is used to determine whether energy storage regulation resources are sufficient, and to enable energy storage to release or absorb electricity through market transactions when energy storage regulation resources are insufficient, so that energy storage can respond to charging or discharging demands to compensate for prediction errors and smooth wind power fluctuations. Step 2: Establish an energy storage power revision model to revise the time-series power of energy storage based on the energy storage economic dispatch model, so as to obtain the optimal time-series output of energy storage, thereby improving the energy storage utilization rate and the energy storage regulation capability of wind power; solve the energy storage economic dispatch model and the energy storage power revision model; The revised energy storage power model is as follows: The energy storage power revision model revises the energy storage time-series power, with the goal of assessing the power consumption in various scenarios before and after the revision. It remains the smallest; the overall prediction error in each scenario after revision should be the smallest, and the variance between the actual wind power and the day-ahead predicted power after energy storage adjustment should be the smallest; the energy storage charging amount is the same in each scenario before and after revision, and the amount of energy discharged directly to the grid during peak electricity price periods is the same. ； In the formula, The charging power used by the energy storage system at time t after revision to adjust the actual power of the wind farm; and These represent the charging and discharging power of energy storage at time t after the revision for peak-valley arbitrage in the electric energy market; ； In the formula, The discharge power at time t after revision is the energy storage used to adjust the actual power of the wind farm. Objective function of the revised energy storage power model: ； In the formula, The revised expected total assessment power consumption; Indicates taking a sequence x The variance; The nonlinear components in the energy storage economic dispatch model and the energy storage power revision model are linearized using the Big M method and then solved using the CPLEX solver.

2. The intraday economic dispatch method for a wind-storage integrated system considering multi-scenario collaboration as described in claim 1, characterized in that, The peak-valley arbitrage scenario is handled as follows: The optimization objective of peak-valley arbitrage scenarios is to store wind power and purchase electricity from the grid at lower prices during off-peak hours. At most, the amount of electricity discharged during peak electricity price periods most; ； ； In the formula, To regulate the charging power of wind power for energy storage; and The charging and discharging power of energy storage at time t for peak-valley arbitrage in the electric energy market; and This refers to the peak-valley electricity price coefficient. The value is 1 during periods of high electricity prices and 0 during other periods. The value is 1 during periods of low electricity price and 0 during other periods. ； For charging resistance coefficient, , where represents the discharge resistance coefficient, indicating the degree of resistance of the energy storage power station to charging and discharging; and These are the upper and lower limits for peak-valley arbitrage in energy storage, respectively. For sign functions; when Resist charging when the time is negative. When it is negative, it resists discharge. Let be the size of soc at time t; The goal of energy storage for peak-valley arbitrage is: ； Considering the scheduling plan for the second day, an initial SOC of 0.5 is beneficial for handling the charging and discharging demand of the coming day. Analysis shows that the last two electricity price periods of the day are "peak-flat price" periods, and adjustments will be made to the flat price period. When the energy storage SOC is below 0.5, it will charge; when it is above 0.5, it will stop charging the energy storage and perform SOC recovery. The energy storage operation range in the peak-valley arbitrage scenario is represented as follows: ； This represents the peak-valley electricity price coefficient, which is 1 during peak electricity price periods and 0 during other periods.

3. The intraday economic dispatch method for a wind-storage integrated system considering multi-scenario collaboration as described in claim 2, characterized in that, The objective function is as follows: ； ； In the formula, a, b, and c represent the weight coefficients for different objectives. , This is the energy storage action function, designed to minimize the energy storage action in order to avoid meaningless actions by the energy storage device during multi-scenario coordinated control of energy storage.

4. The intraday economic dispatch method for a wind-storage integrated system considering multi-scenario collaboration as described in claim 3, characterized in that, The constraints are as follows: ； ； In the formula, The grid-connected power of wind power is regulated by energy storage. This refers to the rated installed capacity of the wind farm. Power constraints on the wind farm-energy storage interconnection line: the exchange power between the wind farm and the energy storage system shall not exceed the rated power of the energy storage equipment. ； In the formula, Rated power of energy storage; The total energy storage power limit is that the energy storage output / absorption power must not exceed the rated power of the energy storage device at any given time. ； The ability of energy storage to charge and discharge simultaneously is constrained. ； To ensure the safe operation of energy storage, the SOC state of energy storage must not exceed the limit at any time; ； In the formula, and These are the upper and lower limits of the SOC state for safe operation of energy storage; The SOC state of energy storage at each time point is represented as follows: ； In the formula: , These refer to the charging and discharging efficiencies of energy storage devices, respectively. For time step; This refers to the rated capacity of the energy storage device.

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