Intra-day economic dispatching method of wind storage combined system considering multi-scene collaboration

Abstract

The invention relates to the technical field of energy storage auxiliary wind power plant grid connection, in particular to a wind storage combined system intraday economic dispatching method considering multi-scene collaboration, which comprises the following steps: establishing an energy storage economic dispatching model and an energy storage power revision model based on rolling optimization, and taking energy storage to reduce wind power plant assessment pressure and peak-valley arbitrage income as a multi-objective optimization function; and due to the timeliness of the ultra-short-term prediction, the energy participating in the peak-valley arbitrage is taken as a target when the peak-valley arbitrage is carried out on the stored energy. In addition, in the optimization scheduling model, only the electric quantity of the system is optimized, so that an energy storage power revision model is established, and the energy storage power is revised to obtain the optimal output of the energy storage time sequence. The energy storage utilization rate can be increased, the wind power adjusting capacity of energy storage is improved, and the system operation economic benefits are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage assisted wind farm grid connection, and particularly to an intraday economic dispatch method for a wind-storage integrated system considering multi-scenario collaboration. Background Technique

[0002] Since the "dual carbon" goal was put forward, the wind power industry in China has developed rapidly. As of the end of 2023, the total installed capacity of wind power has exceeded 440 million kilowatts, and the proportion of wind power generation in the power system has been continuously increasing. However, affected by natural resources, wind power has strong randomness and volatility, with unstable output power and weak dispatchability.

[0003] An energy storage system (ESS) is a flexible regulation resource that can effectively make up for the disadvantages of wind power generation and enhance the balancing dispatch ability of a high-proportion new energy power grid. As of the end of 2023, the proportion of the newly added installed capacity of energy storage for new energy power generation in the power source side reached more than 90%, among which the scale of wind-storage projects was close to 3.5 GW, and the electrochemical energy storage type dominated.

[0004] However, energy storage usually only considers tracking the planned output of wind power and suppressing the power fluctuation of wind power grid connection, resulting in low energy storage utilization rate and poor economic benefits. Therefore, exploring different operation modes of ESS, improving the energy storage utilization rate, and improving the operation income of the energy storage system while taking into account the regulation ability of the energy storage system for wind power have become urgent problems to be solved. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects of the existing technology and propose an intraday economic dispatch method for a wind-storage integrated system considering multi-scenario collaboration, which can enable energy storage to assist wind farm grid connection to reduce the assessment loss and participate in peak-valley arbitrage in the electricity energy market, thereby improving the operation economy of the energy storage system.

[0006] To achieve the above purpose, the present invention adopts the following specific technical solutions:

[0007] The intraday economic dispatch method for a wind-storage integrated system considering multi-scenario collaboration provided by the present 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 constraint conditions;

[0009] The basic scenario processing is used to make the energy storage perform prediction error compensation and fluctuation suppression to reduce the grid connection assessment pressure of the wind farm, so as to reduce the prediction accuracy assessment and fluctuation assessment power of the wind farm grid connection;

[0010] Peak-valley arbitrage scenario processing is used to optimize the electricity quantity during peak and valley periods, store wind power electricity, increase the electricity quantity purchased from the power grid during valley electricity price periods, and increase the discharge electricity quantity during peak electricity price periods;

[0011] The objective function is used to release or absorb electric energy through market transactions by the energy storage when the energy storage regulation resources are insufficient, so that the energy storage can respond to the charging or discharging demands for compensating prediction errors and suppressing wind power fluctuations;

[0012] Step 2: Establish an energy storage power revision model, which is used to revise the time-series power of the energy storage based on the energy storage economic dispatch model to obtain the optimal time-series output of the energy storage, so as to improve the energy storage utilization rate and the energy storage's regulation ability for wind power; solve the energy storage economic dispatch model and the energy storage power revision model.

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

[0014] The objective function E of the basic scenario exa is to minimize the assessment electricity quantity:

[0015] E exa = min(E err + E δ );

[0016] In the formula, E err is the assessment electricity quantity for wind power prediction error, and E δ is the assessment electricity quantity for wind power grid connection fluctuation;

[0017] After the energy storage regulation, if the wind power prediction error still exceeds the allowable error range, the assessment electricity quantity expression is:

[0018]

[0019] In the formula, P err,t is the prediction error at time t, P spre,t is the ultra-short-term wind power prediction value at time t, is the regulation power of the energy storage for wind power, P pre,t is the wind power prediction value for time t in the day-ahead, is the allowable limit of the prediction error

[0020] After the energy storage regulation, if the actual grid connection power volatility of the wind power still does not meet the requirements, the assessment electricity quantity expression is:

[0021]

[0022] In the formula, P δ,t is the combined grid connection fluctuation power of wind and energy storage at time t, is the allowable limit of the grid connection power fluctuation;

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

[0024]

[0025] Where P batn is the energy storage rated power.

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

[0027] The optimization goal of the peak-valley arbitrage scenario is to store wind power and purchase electricity at a low price from the grid during the valley electricity price period. in,ch At most, the discharge amount during the peak electricity price period is E in,dis most;

[0028]

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

[0030]

[0031] is the charging resistance coefficient, is the discharge resistance coefficient, which respectively indicates the resistance degree of the energy storage power station to charging and discharging; soc H and soc L are the upper and lower limits of energy storage peak-valley arbitrage respectively; sign() is the sign function; when When negative, it resists charging. When negative, it resists discharge, soc t is the soc size at time t;

[0032] The goals of peak-valley arbitrage for energy storage are:

[0033]

[0034] Considering the scheduling plan for the next day, the initial SOC of 0.5 is conducive to meeting the charging and discharging needs of the next day. After analysis, the last two electricity price periods in a day are "peak-flat electricity prices" in sequence, so the soc of the flat electricity price period can be adjusted. H =0.5, when the energy storage SOC state is lower than 0.5, it is charged, and when it is higher than 0.5, it will not be charged more for energy storage, and a certain degree of SOC recovery will be performed:

[0035]

[0036] In the peak-valley arbitrage scenario, the operating range of energy storage can be expressed as:

[0037]

[0038] is the peak-valley electricity price coefficient, which is 1 at the flat electricity price moment and 0 at other times.

[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, a >> b > c, and E b is the energy storage operation function. When the energy storage conducts multi-scenario coordinated control, to avoid meaningless actions of the energy storage device, the energy storage operation is minimized.

[0043] Furthermore, the constraint conditions are as follows:

[0044]

[0045] In the formula, is the wind power grid-connected power value after energy storage regulation at time t, and P wn is the rated installed capacity of the wind farm;

[0046] Power constraint of the connection line between the wind farm and the energy storage. The exchange power between the wind farm and the energy storage system does not exceed the rated power of the energy storage device;

[0047]

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

[0049] Total power constraint of the energy storage. The output / absorption power of the energy storage at the same moment does not exceed the rated power of the energy storage device;

[0050]

[0051] Constraint that the energy storage cannot charge and discharge simultaneously;

[0052]

[0053] To ensure the safe operation of the energy storage, the SOC state of the energy storage cannot exceed the limit at each moment;

[0054] soc min ≤soct ≤soc max ;

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

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

[0057]

[0058] Where: η ch , η dis are the charging and discharging efficiencies of the energy storage device; Δt is the time step; E batn is the rated capacity of the energy storage device.

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

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

[0061]

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

[0063]

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

[0065] The objective function of the energy storage power revision model is:

[0066]

[0067] In the formula, E′ exa is the expected total test power after revision; D(x) represents the variance of the series x;

[0068] The nonlinear parts 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] The intra-day economic dispatch method of the wind-storage integrated system considering multi-scenario collaboration provided by the present invention uses the reduction of the assessment pressure of the wind farm and the peak-valley arbitrage income of the energy storage as a multi-objective optimization function to obtain an optimal control scheme for the energy storage system that takes into account both economy and technology. And due to the timeliness of the ultra-short-term prediction, when the energy storage conducts peak-valley arbitrage, the energy participating in the peak-valley arbitrage is taken as the target. In addition, since only the system power is optimized in the optimization dispatch model, an energy storage power revision model is established to revise the energy storage power so as to obtain the optimal hourly output of the energy storage. The present invention can improve the utilization rate of the energy storage, improve the regulation ability of the energy storage for wind power, and enhance the economic benefits of the system operation. Description of the Drawings

[0071] Figure 1 is a flowchart for solving the intra-day economic dispatch method of the wind-storage integrated system considering multi-scenario collaboration provided by an embodiment of the present invention;

[0072] Figure 2 is a schematic diagram of time-of-use electricity price provided by an embodiment of the present invention;

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

[0074] Figure 4 is an effect diagram of the energy storage regulating wind power provided by an embodiment of the present invention;

[0075] Figure 5 is a diagram of the hourly power of the energy storage provided by an embodiment of the present invention;

[0076] Figure 6 is a diagram of the technical and economic indexes of the energy storage dispatch results for four schemes provided by an embodiment of the present invention. Detailed Embodiments

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

[0078] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0079] The day-ahead economic dispatch method for a wind-storage integrated system considering multi-scenario collaboration provided by the embodiments of the present invention aims to reduce the assessment loss by enabling energy storage to assist the grid connection of a wind farm and participate in the peak-valley arbitrage in the electricity energy market, thereby improving the operating economy of the energy storage system. It includes the following contents:

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

[0081] First, a multi-scenario collaborative control method for energy storage is proposed to process the energy storage action target and output range of a single scenario under the multi-scenario collaborative mode. Then, a weighted multi-objective optimization method is used to consider the grid and ESS constraints, and an energy storage economic dispatch model based on rolling optimization is established to optimize the multi-application scenarios of ESS, obtaining the energy storage output plan for the energy storage power under multiple scenarios and realizing the collaborative operation of multiple scenarios. Finally, the energy storage time-series power is revised with "the minimum assessment power, the minimum prediction error, and the unchanged arbitrage energy".

[0082] (1) Basic scenario processing

[0083] In the basic application scenario, the energy storage can reduce the grid connection assessment pressure of the wind farm by compensating for the prediction error and suppressing the fluctuation, making the grid connection prediction accuracy assessment and the fluctuation assessment power of the wind farm the smallest. Therefore, the objective function E exa is the minimum assessment power:

[0084] E exa = min(E err + E δ ) (1);

[0085] E err is the assessment power for the wind power prediction error, and E δ is the assessment power for the wind power grid connection fluctuation.

[0086] After the energy storage regulation, if the wind power prediction error still exceeds the allowable error range, the assessment power expression is:

[0087]

[0088] In the formula, P err,t is the prediction error at time t, P spre,t is the ultra-short-term prediction value of the wind power at time t, is the regulation power of the energy storage for the wind power, P pre,t is the predicted wind power value at time t for the day-ahead, is the allowable limit of the prediction error; is the regulation power of the energy storage for the wind power.

[0089] When the actual grid connection power volatility of the wind power still does not meet the requirements after the energy storage regulation, the assessment power expression is:

[0090]

[0091]

[0092] In the formula, P δ,t is the combined grid-connected fluctuation power of wind power and energy storage at time t, is the allowable limit of grid-connected power fluctuation.

[0093] In the multi-scenario collaborative mode, the wind power fluctuation refers to the combined grid-connected power fluctuation of wind power and energy storage after energy storage regulation within a certain period of time. When is satisfied, the energy storage can both release power at time t and absorb power at time t - 1 to make P δ,t decrease. Based on the flexibility of suppressing fluctuations, no additional requirements are imposed on the operation range of the energy storage in this scenario.

[0094] As a single entity of the same interest, in the multi-scenario collaborative mode, the energy storage can sell the stored wind power during high electricity price periods to obtain the electricity price difference. To ensure the regulation effect on wind power in the basic application scenario, the ESS cannot obtain energy from the wind farm infinitely for peak-valley arbitrage. The energy storage should charge from the wind farm with the limit of not increasing the prediction error. To sum up, the operation range of the energy storage in the basic application scenario of the multi-scenario collaborative mode can be expressed as:

[0095]

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

[0097] (2) Peak-valley arbitrage scenario

[0098] Since the ultra-short-term prediction only has the prediction information for the next 4 hours, and the peak-valley electricity price changes intermittently throughout the day, that is, the peak-valley electricity price may not exist simultaneously within an optimization cycle relying on ultra-short-term prediction. When the optimization cycle rolls to a period where the whole cycle is in "valley electricity price" or "valley-flat electricity price", due to the lack of peak electricity price periods for the energy storage to sell electricity, the charging income of the energy storage cannot be reflected. Therefore, the electricity quantity during peak-valley periods is optimized.

[0099] At the same time, because the wind farm and the energy storage belong to the same entity of the same interest, the energy storage can not only purchase electricity from the grid at a low price, but also conduct peak-valley arbitrage by absorbing the excess wind power. Therefore, the optimization goal of the energy storage considering peak-valley arbitrage during multi-scenario coordination is: storing the maximum amount of wind power and the electricity quantity purchased from the grid at a low price E in,ch during valley electricity price periods, and discharging the maximum amount of electricity E in,dis during peak electricity price periods:

[0100]

[0101] In the formula, is the charging power for energy storage to regulate wind power; are the charging and discharging powers for energy storage to conduct peak-valley arbitrage in the electricity energy market at time t respectively; is the peak-valley electricity price coefficient, is 1 at high electricity price times and 0 at other times; is 1 at low electricity price times and 0 at other times.

[0102] In the multi-scenario collaborative mode, when energy storage conducts peak-valley arbitrage, it needs to consider the power / capacity requirements in the basic application scenario, and the power demand in the basic application scenario is related to the random wind power. If it is planned to fully charge or fully discharge the energy storage within a scheduling period, the energy storage cost will increase when there is a charging or discharging demand in the future, affecting the regulation effect of the energy storage on the basic application scenario. Therefore, when the energy storage conducts peak-valley arbitrage, it needs to reserve a part of the charging and discharging space and only conduct limited energy arbitrage. Therefore, the charging resistance coefficient discharge resistance coefficient are introduced to represent the resistance degrees of the energy storage power station to charging and discharging respectively:

[0103]

[0104] In the formula, soc H and soc L are the upper and lower limits of energy storage peak-valley arbitrage respectively; sign() is the sign function; when is negative, it resists charging, and when is negative, it resists discharging, and soc t is the soc value at time t.

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

[0106]

[0107] Considering the scheduling plan for the next day, an initial SOC of 0.5 is beneficial for coping with the charging and discharging demands of the next day. After analysis, the last two electricity price periods in a day are "peak-flat electricity price" in sequence, and it can be adjusted to make the soc H = 0.5 at this flat electricity price period. When the SOC state of the energy storage is lower than 0.5, it charges, and when it is higher than 0.5, it no longer charges the energy storage more, and a certain degree of SOC recovery is carried out:

[0108]

[0109] From the perspective of the economic operation of energy storage, it is required that energy storage can generally only purchase electricity from the power grid at a low price and sell electricity to the power grid at a high price. From the perspective of realizing the coordinated operation of energy storage, when the charging capacity is insufficient in the basic application scenario, energy storage can release a part of the electricity in advance through market transactions, that is, the energy storage system can also discharge electricity during the flat electricity price period, so as to improve the regulation ability for the basic application scenario. To sum up, the action range of energy storage in the peak-valley arbitrage scenario can be expressed as:

[0110]

[0111] In the formula, is the peak-valley electricity price coefficient, which is 1 at the flat electricity price moment and 0 at other times.

[0112] (3) Objective function

[0113] After processing each single scenario in the multi-scenario mode, the energy storage device operates according to the following rules, so as to optimize the coordination by connecting the single scenarios in series.

[0114] When the energy storage regulation resources are sufficient

[0115] 1) If the power demands of the basic scenario and the peak-valley arbitrage scenario are the same at the same moment, the energy storage satisfies the power demands of both. If the power demands of the basic scenario and the peak-valley arbitrage scenario conflict at the same moment, the energy storage preferentially satisfies the power demand in the basic scenario. At this time, because the energy storage cannot charge and discharge simultaneously, the response of the energy storage to the power demand in the peak-valley arbitrage scenario is 0.

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

[0117] If the charging capacity of the energy storage is insufficient at this time, that is, the SOC of the energy storage is in a high state, and the energy storage needs to absorb the excess electricity to make up for the prediction error and suppress the wind power fluctuation. If the current period is in the "flat-peak electricity price period", the energy storage releases a part of the electric energy through market transactions, so that the energy storage can respond to more charging demands for making up the prediction error and suppressing the wind power fluctuation.

[0118] If the discharging capacity of the energy storage is insufficient at this time, that is, the SOC of the energy storage is in a low state, and the energy storage needs to release electric energy to make up for the prediction error and suppress the wind power fluctuation. If the current period is in the "valley electricity price period", the energy storage absorbs a part of the electric energy through market transactions, so that the energy storage can respond to more discharging demands for making up the prediction error and suppressing the wind power fluctuation.

[0119] In multi-scenario coordinated operation, especially when there are basic application scenarios in this paper, multiple objectives should be optimized uniformly in a single objective function to deeply explore the value of their synergy. In this paper, the multi-objective weighting method is adopted to link different objectives to achieve the above action goals. The objective function includes the electricity quantity for evaluating the wind power prediction error, the electricity quantity for evaluating the fluctuation of wind power grid connection, the electricity quantity for the energy storage to participate in peak-valley arbitrage, and the energy storage operation cost, as shown 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 the objectives of this paper, a >> b > c. E b is the energy storage operation function. When the energy storage conducts multi-scenario coordinated control, to avoid meaningless actions of the energy storage device, the energy storage operation is minimized.

[0123] (4) Constraint conditions

[0124] For the proposed model, it mainly includes two aspects of constraints on the operating states of the wind farm and the energy storage system.

[0125] Power constraint of the wind farm and the power grid connection line. After the energy storage compensates for the error, the wind power grid connection power shall not exceed the rated power of the wind farm;

[0126]

[0127] In the formula, P wn is the rated installed capacity of the wind farm, is the wind power grid connection power after energy storage regulation.

[0128] Power constraint of the wind farm and the energy storage connection line. The exchange power between the wind farm and the energy storage system shall not exceed the rated power of the energy storage device;

[0129]

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

[0131] Total power constraint of the energy storage. At the same moment, the output / absorption power of the energy storage shall not exceed the rated power of the energy storage device;

[0132]

[0133] The energy storage cannot charge and discharge simultaneously constraint;

[0134]

[0135] To ensure the safe operation of energy storage, the SOC state of energy storage at each moment shall not exceed the limit;

[0136] soc min ≤soc t ≤soc max (20);

[0137] In the formula, soc max and soc min are the upper and lower limits of the SOC state for the safe operation of energy storage respectively.

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

[0139]

[0140]

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

[0142] 2. Energy Storage Power Revision Model

[0143] Due to the large allowable action range of energy storage in the basic application scenario and the existence of the peak-valley arbitrage method that requires energy storage to charge and discharge more, the optimal result of the above collaborative control method is only the overall optimal in terms of total power, and there is still room for revision. Therefore, on the basis of the optimization result of the collaborative optimization method, the energy storage time-series power is revised, and the basic considerations are as follows:

[0144] 1) The evaluated power E exa in each scenario before and after revision is still the smallest;

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

[0146] 3) The charging power of energy storage for regulating the actual power of the wind farm at time t after revision is equal; the charge and discharge power of energy storage for peak-valley arbitrage in the electricity energy market at time t after revision is equal;

[0147]

[0148] Among them, is the charging power for the energy storage to regulate the actual power of the wind farm at time t after revision; are the charge and discharge powers for the energy storage to conduct peak-valley arbitrage in the electricity energy market at time t after revision respectively;

[0149] 4) To serve the basic application scenarios, the sum of the electricity discharged to the wind farm and the electricity directly discharged to the grid during the flat electricity price period is equal before and after the revision for each scenario. Combining with the objective function, when directly discharging electricity to the grid in response to the error regulation demand during the flat electricity price period, priority is given to minimizing the prediction error.

[0150]

[0151] Among them, is the discharge power for the energy storage to adjust the actual power of the wind farm at time t after the revision;

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

[0153]

[0154] In the formula, E′ exa is the expected total assessment electricity after the revision; D(x) represents the variance of the sequence x

[0155] Revision model constraints:

[0156]

[0157] Solution method:

[0158] Both the above energy storage economic dispatch model and the energy storage power revision model contain a large number of non-linear constraints. To avoid the intelligent algorithm being easily trapped in the local optimal solution, the non-linear part in the proposed model is linearized by the big M method and then the CPLEX solver is used to solve the optimization model. The solution process is as Figure 1 shown.

[0159] The total installed capacity of the wind farm is 100 MW, and a lithium iron phosphate battery energy storage device with a capacity of 20 MW / 40 MWh is configured to form a wind-storage combined system. The time-of-use electricity price is as Figure 2 shown, and the operating parameters of the wind-storage system are as Figure 3 shown.

[0160] The energy storage collaborative dispatch results are as Figure 4 shown. It can be seen from the figure that during the period with a large error duration from 7 to 13 h, due to the insufficient energy storage capacity, the ESS cannot completely compensate for the prediction error, but discharges electricity to the grid first to release the capacity, so that most of the errors can finally be controlled within the allowable error range. During the periods with small errors from 2 to 5 h and 18 to 22 h, the ESS can effectively respond to the power demand of the basic application scenarios and compensate for the prediction error and power fluctuation. After the energy storage regulation, the assessment electricity has decreased from 92.1 MWh to 7.4 MWh, and the root mean square value of the wind power prediction error has decreased from 16.78% to 10.91%. The multi-scenario collaborative strategy has enhanced the regulation ability of the ESS in the basic application scenarios.

[0161] The time series output of energy storage for regulating a wind farm and participating in peak-valley arbitrage under multiple scenarios is as follows Figure 5 shown. It can be seen that from 0 to 5h and from 7 to 13h, the ESS stores / releases energy through peak-valley arbitrage under the multi-scenario collaborative strategy, greatly enhancing the regulation ability for the basic application scenario, and being able to release electric energy to the power grid during high electricity price periods, with obvious economic benefits. At the 22nd hour, the energy storage is still charging when the SOC is greater than 0.5, causing a small increase in the SOC. This is because under the action of the energy storage power revision model, a part of the energy in the 20 - 22 time period is moved to the 22nd hour for the goal of minimizing the error, resulting in an increase in the SOC curve. Under the multi-scenario collaborative strategy, the energy storage does not operate from 22 to 24h, and the overall energy relationship remains unchanged.

[0162] To further verify the effectiveness and economy of the energy storage economic dispatch scheme proposed in this application, and the necessity of energy storage power revision, the following schemes are defined for comparative analysis as a control.

[0163] Scheme 1: The wind farm is not equipped with energy storage;

[0164] Scheme 2: The wind farm is equipped with energy storage, and the energy storage does not perform peak-valley arbitrage, only considering the basic application scenario of compensating for wind power prediction errors and suppressing fluctuations;

[0165] Scheme 3: The wind farm is equipped with energy storage, and the energy storage performs peak-valley arbitrage but does not perform power revision;

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

[0167] The energy storage is scheduled respectively according to the above four schemes, and the technical and economic indicators of the scheduling results are as follows Figure 6 shown. By comparing the scheduling results of different schemes, it can be seen that under the proposed energy storage collaborative control strategy, the energy storage assessment cost is reduced from 73,639 yuan to 5,943 yuan, and the root mean square error is reduced from 16.78% to 10.91%. The regulation ability of the energy storage for the basic application scenario has been significantly improved, verifying the collaborative control effect of multiple scenarios. Compared with the scheme without energy storage, the net income of the energy storage system is about 65,000 yuan; compared with the energy storage control scheme considering only a single application scenario, the net income of the energy storage system is about 46,000 yuan. The multi-scenario collaborative control strategy of energy storage can effectively improve the regulation ability for the basic application scenario, and can effectively obtain the price difference income through peak-valley arbitrage, with obvious economic benefits.

[0168] From the above analysis, it can be seen that energy storage assisting wind power grid connection has certain advantages in reducing the assessment pressure, improving the energy storage utilization rate, and the economic operation of the system.

[0169] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

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

[0171] The above specific implementation manners of the present invention do not constitute a limitation to the protection scope of the present invention. Any other corresponding changes and deformations made according to the technical concept of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for intraday economic dispatch of a wind-storage joint system considering multi-scenario collaboration, characterized in that: The steps include: 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, so that the wind farm grid connection prediction accuracy assessment and fluctuation assessment power are reduced; The peak-valley arbitrage scenario processing is used to optimize the amount of electricity during peak and valley periods, store wind power and increase the amount of electricity purchased from the power grid during valley electricity price periods, and increase the amount of electricity discharged during peak electricity price periods; The objective function is used to adjust the energy storage resources according to whether they are sufficient, and to enable the energy storage to release or absorb electric energy through market transactions when the energy storage adjustment resources are insufficient, so that the energy storage can respond to the charging or discharging needs of compensating for the forecast error and smoothing the fluctuation of wind power; Step 2: Establish an energy storage power revision model to revise the energy storage time sequence power based on the energy storage economic dispatch model to obtain the optimal energy storage time sequence output, so as to improve the energy storage utilization rate and the energy storage regulation ability for wind power; solve the energy storage economic dispatch model and the energy storage power revision model.

2. The intraday economic dispatch method for wind-storage combined system considering multi-scenario collaboration according to claim 1 is characterized in that: The basic scenario is processed as follows: Objective function E of the basic scenario exa To test the minimum power: The exa =min(E err +E δ ); In the formula, E err is the wind power forecast error assessment quantity, E δ To assess the power consumption of wind power grid-connected power; After energy storage adjustment, the wind power prediction error still exceeds the allowable error range, and the evaluation power expression is: Where P err,t is the prediction error at time t, P spre,t is the ultra-short-term forecast value of wind power at time t, is the regulating power of energy storage to wind power, P pre,t is the wind power forecast value at time t the day before, is the allowable limit of prediction error; After energy storage adjustment, when the actual grid power fluctuation rate of wind power still does not meet the requirements, the assessment power expression is: Where P δ,t is the wind-storage combined grid-connected fluctuating power at time t, It is the permissible limit of grid-connected power fluctuation; The energy storage action range in the basic scenario is expressed as: Where P batn is the energy storage rated power.

3. The intraday economic dispatch method for wind-storage joint system considering multi-scenario collaboration according to claim 2 is characterized in that: The peak-to-valley arbitrage scenario is handled as follows: The optimization goal of the peak-valley arbitrage scenario is to store wind power and purchase electricity at a low price from the grid during the valley electricity price period. in,ch At most, the discharge amount during the peak electricity price period is E in,dis most; In the formula, Adjust the charging power of wind power for energy storage; and The charging and discharging power of the energy storage for peak-valley arbitrage in the electric energy market at time t respectively; and is the peak-valley electricity price coefficient, The value is 1 during high electricity price periods and 0 during other periods; The value is 1 during low electricity price hours and 0 during other periods; is the charging resistance coefficient, is the discharge resistance coefficient, which respectively indicates the resistance degree of the energy storage power station to charging and discharging; soc H and soc L are the upper and lower limits of energy storage peak-valley arbitrage respectively; sign() is the sign function; when When negative, it resists charging. When negative, it resists discharge, soc t is the soc size at time t; The goals of peak-valley arbitrage for energy storage are: Considering the scheduling plan for the next day, the initial SOC of 0.5 is conducive to meeting the charging and discharging needs of the next day. After analysis, the last two electricity price periods in a day are "peak-flat electricity prices" in sequence, so the SOC of the flat electricity price period can be adjusted. H =0.5, when the energy storage SOC state is lower than 0.5, it is charged, and when it is higher than 0.5, it will not be charged more for energy storage, and a certain degree of SOC recovery will be performed: The energy storage action range in the peak-valley arbitrage scenario can be expressed as: It is the peak-valley electricity price coefficient, which is 1 at the flat electricity price time and 0 at other times.

4. The intraday economic dispatch method for wind-storage combined system considering multi-scenario collaboration according to claim 3 is characterized in that: The objective function is as follows: minE=aE exa +bE in +cE b ; In the formula, a, b, and c represent the weight coefficients of different objectives, a>>b>c, E b It is the energy storage action function. When the energy storage is coordinated and controlled in multiple scenarios, in order to avoid meaningless actions of the energy storage equipment, the energy storage action is minimized.

5. The intraday economic dispatch method for wind-storage combined system considering multi-scenario collaboration according to claim 4 is characterized in that: The constraints are as follows: In the formula, is the wind power grid-connected power after energy storage regulation, P wn is the rated installed capacity of the wind farm; The power constraint of the interconnection line between the wind farm and the energy storage system is that the exchange power between the wind farm and the energy storage system shall not exceed the rated power of the energy storage equipment; Where P batn is the energy storage rated power; The total power constraint of energy storage: the output / absorption power of energy storage at the same time shall not exceed the rated power of the energy storage equipment; The storage capacity cannot be charged and discharged simultaneously; To ensure the safe operation of energy storage, the energy storage SOC state at each moment cannot exceed the limit; soc min ≤soc t ≤soc max ; In the formula, soc max and soc min They are the upper and lower limits of the SOC state for safe operation of energy storage respectively; The energy storage SOC state at each moment can be expressed as: Where: η ch , η dis are the charging and discharging efficiencies of the energy storage device; Δt is the time step; E batn is the rated capacity of the energy storage device.

6. The intraday economic dispatch method for wind-storage combined system considering multi-scenario collaboration according to claim 5 is characterized in that: The energy storage power revision model is as follows: The energy storage power revision model revises the energy storage time series power, and the goal is to assess the power E in each scenario before and after the revision. exa It is still the smallest; after the revision, the overall prediction error in each scenario should be the smallest, and the variance between the actual wind power after energy storage adjustment and the day-ahead predicted power is the smallest; the energy storage charging power in the optimization cycle in each scenario before and after the revision is equal, and the power directly discharged to the grid during the peak electricity price period is equal; In the formula, The charging power used by the energy storage to adjust the actual power of the wind farm at time t after revision; and They are respectively the charging and discharging power of energy storage for peak-valley arbitrage in the electric energy market at time t after the revision; In the formula, The discharge power of the energy storage used to adjust the actual power of the wind farm at time t after revision; The objective function of the energy storage power revision model is: In the formula, E′ exa is the expected total test power after revision; D(x) represents the variance of the series x; The nonlinear parts 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.

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