A wind-storage system scheduling method for multi-time-scale scenarios in the power market

Abstract

The present invention discloses a wind-storage system scheduling method for multi-timescale scenarios in the power market, comprising the following steps: obtaining target parameters, constructing an objective function of a day-ahead output scheduling model based on the target parameters and a pre-generated set of uncertain scenarios, and describing the correlation between the call time scales of three application scenarios in which the wind-storage system participates; establishing constraints for the day-ahead output scheduling model based on the target parameters and time scales; solving the day-ahead output scheduling model to obtain a charging and discharging plan for the wind-storage system's participation in smoothing wind power fluctuations and the first call time scale of the day-ahead electricity market, and a capacity reserve plan for the wind-storage system's participation in the standby auxiliary service market's second call time scale; and the wind-storage system executing the day-ahead output plan. The present invention achieves collaborative optimization among scenario applications in which the wind-storage system participates in different call time scales.

Description

Technical Field

[0001] The present invention relates to the field of energy management, and in particular to a wind-storage system scheduling method for multi-time-scale scenarios in the power market. Background Art

[0002] The proportion of clean energy sources such as wind power and photovoltaics in the power system is gradually increasing. However, while wind power reduces carbon emissions, it also has inherent issues such as susceptibility to environmental factors, large output fluctuations, and "anti-peaking" characteristics. As wind power continues to increase its share in new power systems, if its adverse effects are not addressed, it will not only exacerbate wind curtailment and reduce wind farm revenue, but also seriously threaten the safe and stable operation of the power system.

[0003] Energy storage, by temporarily storing electricity, can absorb renewable energy during periods of low load, effectively reducing wind and solar curtailment. It also has the ability to smooth the output of renewable energy, contributing to power system security. Therefore, with the large-scale integration of wind power into the grid, building wind-storage systems alongside wind farms has become an effective way to address the unfavorable characteristics of wind power.

[0004] However, existing research on wind-storage system operation and scheduling has mostly considered wind-storage systems in a single application scenario. However, current energy storage investment and operation and maintenance costs are relatively high, and single-scenario applications present wind-storage systems with a single profit scenario, limited economic returns, and a long payback period.

[0005] Patent CN111769602A discloses a multi-timescale wind-storage system optimization scheduling method. This method optimizes the revenue of a combined wind-storage system while taking into account grid channel congestion. This method significantly reduces wind curtailment, increases wind farm revenue, and ensures safe and stable grid operation. However, this method only analyzes the energy market, and the three revenue types considered are essentially revenue from selling electricity to the grid. It optimizes a single application scenario at different stages.

[0006] Overall, given the reality of the early stages of power market development, there is currently limited research on wind-storage systems participating in more than two application scenarios, and there is a lack of effective multi-scenario profit-making strategies. Furthermore, current research on multi-scenario applications mostly focuses on scenarios within the same call time scale, and research on collaborative optimization strategies for scenarios coupled with different call time scales, such as those at the hourly and minute levels, is still insufficient. Furthermore, existing multi-time scale research mostly refers to the different optimization stages of a single application scenario, day-ahead and intraday, rather than compatibility and synergy between different scenarios with different call time scales. These shortcomings in research hinder the flexibility of wind-storage system revenue scenario selection and hinder the expansion of revenue channels by participating in diverse combination scenarios. Therefore, there is an urgent need to study collaborative operation optimization scheduling strategies for wind-storage systems across multiple scenarios with different call time scales in the power market environment, increasing revenue scenarios while enhancing operational flexibility and maximizing the economic benefits of wind-storage systems in the power market environment. Summary of the Invention

[0007] The technical problem to be solved by the present invention is: in response to the technical problems existing in the existing technology, the present invention provides a wind storage system scheduling method for multi-time scale scenarios in the power market, which realizes the collaborative optimization of wind storage systems participating in scenario applications with different calling time scales, and smoothes short-term wind power fluctuations, and further participates in the electric energy market and the hourly calling scale standby auxiliary service market to obtain economic benefits.

[0008] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0009] A wind-storage system scheduling method for a multi-timescale scenario in a power market includes the following steps:

[0010] Obtain target parameters, and construct an objective function for the day-ahead output dispatch model based on the target parameters and a pre-generated set of wind power forecast output scenarios, a set of electricity market forecast price scenarios, and a set of backup ancillary service market forecast price scenarios. The model also describes the relationship between the first call time scale for the wind power fluctuation smoothing scenario and the electricity market scenario, and the second call time scale for the backup ancillary service market scenario.

[0011] Establishing constraints for the day-ahead output dispatch model based on the target parameters, the first call time scale, and the second call time scale. The constraints include multi-call time scale coupling constraints and constraints for the wind-storage system to participate in smoothing wind power fluctuations, in the electric energy market, and in the reserve ancillary service market.

[0012] Solve the day-ahead output dispatch model to obtain the energy storage charging and discharging power sequence of the wind-storage system participating in smoothing wind power fluctuations and the first call time scale of the electricity energy market, and the energy storage backup capacity reserve sequence of the wind-storage system participating in the second call time scale of the backup ancillary service market. The wind-storage system executes the day-ahead output plan based on the energy storage charging and discharging power sequence and the backup capacity reserve sequence.

[0013] Furthermore, the objective function expression is as follows:

[0014]

[0015] Among them, the first item is the economic benefit obtained by the wind storage system participating in the electric energy market; the second item is the economic benefit obtained by the wind storage system participating in the reserve ancillary service market; I and J are the electric energy market forecast electricity price scenario set and the reserve ancillary service market forecast electricity price scenario set respectively; t is any time period of the first call time scale within the dispatching cycle when the wind storage system participates in the wind power fluctuation smoothing scenario and the electric energy market scenario; t′ is any time period of the second call time scale within the dispatching cycle when the wind storage system participates in the reserve ancillary service market scenario; T is the time period set of the first call time scale within the dispatching cycle; T′ is the time period set of the second call time scale within the dispatching cycle; Δt is the first time interval; Δt′ is the second time interval; W is the wind power forecast output scenario set; π i and π j are the probability parameters of electricity price scenario i in the energy market and electricity price scenario j in the backup ancillary service market respectively; π w is the probability parameter of wind power prediction output scenario w; is the predicted grid-connected power variable of the wind-storage system at the first call time scale period t under the wind power forecast output scenario w; is the predicted electricity price parameter at the first call time scale period t in the day-ahead electricity energy market; and are the predicted electricity price parameters for upward and downward reserve in the second call time period t′ of the day-ahead reserve ancillary service market; and They are the upward reserve capacity variable and downward reserve capacity variable of the wind-storage system participating in the reserve ancillary service market in the second call time scale period t′.

[0016] Furthermore, the relationship between the first call time scale of the wind-storage system participating in the wind power fluctuation smoothing scenario and the electric energy market scenario and the second call time scale of the wind-storage system participating in the backup ancillary service market scenario is described as follows:

[0017] 6t′-5≤t≤6t′

[0018] Among them, t is any period of the first call time scale within the dispatching cycle when the wind-storage system participates in the wind power fluctuation smoothing scenario and the electric energy market scenario; t′ is any period of the second call time scale within the dispatching cycle when the wind-storage system participates in the standby auxiliary service market scenario.

[0019] Furthermore, the first call time scale is 10 minutes, the second call time scale is 1 hour, and the multi-call time scale coupling constraint includes: the maximum value of the reserved spare capacity within the 1-hour time scale is the minimum value of the available spare capacity in the six 10-minute time scale periods within the 1-hour, as described below:

[0020]

[0021]

[0022] in, They are the maximum value variables of the reserve capacity available in the wind-storage system for the next 10 minutes, 20 minutes, 30 minutes, 40 minutes, and 50 minutes at the first call time scale period t; They are the maximum value variables of the reserve capacity available in the wind-storage system for the next 10 minutes, 20 minutes, 30 minutes, 40 minutes, and 50 minutes at the first call time period t; and They are the upward reserve capacity variable and downward reserve capacity variable at the second call time scale period t′ when the wind-storage system participates in the reserve ancillary service market.

[0023] Furthermore, the constraints for the wind-storage system to participate in smoothing wind power fluctuations in scenarios, electric energy market scenarios, and standby auxiliary service market scenarios include constraints for the wind-storage system to participate in smoothing wind power fluctuations, including: determining whether wind power output power fluctuations exceed a limit to determine whether energy storage performs a smoothing action; wind power output power should be a non-negative value; the predicted grid-connected power of the wind-storage system is equal to the sum of the predicted wind power output and the planned energy storage output; and if a smoothing action is performed, the grid-connected power of the wind-storage system after smoothing is limited to within the grid connection standard, as described below:

[0024]

[0025] p w,g,t ≥0

[0026]

[0027]

[0028] in, and are the charge and discharge state 0-1 variables of the energy storage at the first call time scale period t, respectively; and are the charging and discharging power variables of the energy storage at the first call time scale period t; M is a maximum parameter; P w,g,t P is the wind power forecast output power variable at the first call time scale period t under the wind power forecast output scenario w; w,g,t-Δt is the wind power predicted output power variable in the first time interval before the first call time scale period t under the wind power predicted output scenario w; is the predicted grid-connected power variable of the wind-storage system at the first call time scale period t under the wind power forecast output scenario w; P is the predicted grid-connected power variable of the wind-storage system in the first time interval before the first call time scale period t under the wind power forecast output scenario w; δ It is the maximum fluctuation amplitude parameter allowed for wind power within the first call time scale.

[0029] Furthermore, the constraints for the wind-storage system to participate in the wind power fluctuation smoothing scenario, the electric energy market scenario, and the backup ancillary service market scenario include energy storage power constraints when the wind-storage system participates in the wind power fluctuation smoothing and the electric energy market, including: the energy storage charging and discharging power is not allowed to exceed the rated value, and simultaneous charging and discharging are not allowed, as described below:

[0030]

[0031]

[0032]

[0033] in, and are the charge and discharge state 0-1 variables of the energy storage at the first call time scale period t, respectively; and are the charging and discharging power variables of the energy storage at the first call time scale period t respectively; is the rated charge and discharge power parameter of energy storage.

[0034] Furthermore, the constraints of the wind-storage system participating in the wind power fluctuation smoothing scenario, the electric energy market scenario, and the backup ancillary service market scenario include energy storage energy constraints when the wind-storage system participates in the wind power fluctuation smoothing and the electric energy market, including: the energy value of the energy storage at any time should not exceed its rated value, the energy value of the energy storage at a certain moment is equal to the energy value at the previous moment plus the energy change in the interval time period, and to ensure that the energy storage is smoothly scheduled in the next scheduling cycle, the energy value of the energy storage must return to the set initial energy value at the end of each scheduling cycle, as described below:

[0035]

[0036]

[0037] E e,144 =E e,0

[0038] Among them, E e,t E is the energy value variable of the energy storage at the first call time scale period t; e,t-1 The energy value variable of the energy storage in the first calling time scale period t before the first time interval; is the rated capacity parameter of energy storage; η ch and η dis are the energy storage charging and discharging efficiency parameters respectively; E e,0 E is the initial energy value parameter of each scheduling cycle of energy storage; e,144 It is the energy value variable at the end of each scheduling cycle of energy storage.

[0039] Furthermore, the constraints on the wind-storage system's participation in wind power fluctuation smoothing scenarios, electric energy market scenarios, and reserve ancillary service market scenarios include available capacity constraints when the wind-storage system participates in the reserve ancillary service market, including: increasing discharge power in the discharge state, reducing charging power in the charging state, or switching to the discharge state to provide upward reserve; increasing charging power in the charging state, reducing output in the discharge state, or switching to the charging state to provide downward reserve, as described below:

[0040]

[0041]

[0042] in, and are the maximum variable of the upward reserve capacity and the maximum variable of the downward reserve capacity available in the wind-storage system at the first call time scale period t, respectively; and are the charge and discharge state 0-1 variables of the energy storage at the first call time scale period t, respectively; and are the charging and discharging power variables of the energy storage at the first call time scale period t respectively; is the rated charge and discharge power parameter of energy storage.

[0043] Furthermore, the constraints on the wind-storage system's participation in wind power fluctuation smoothing scenarios, electricity market scenarios, and reserve ancillary service market scenarios include energy constraints when the wind-storage system participates in the reserve ancillary service market, including: the energy storage in the wind-storage system must meet 4 hours of continuous callability, the energy available for upward reserve adjustment at a certain moment must not exceed the energy value stored in the energy storage at the current moment, and the sum of the energy space available for downward reserve adjustment and the energy stored in the energy storage at the current moment must not exceed the rated energy value of the energy storage, as described below:

[0044]

[0045]

[0046]

[0047]

[0048] in, and are respectively the maximum value variable of the energy storage energy space when increasing and the maximum value variable of the energy storage energy space when decreasing, which are reserved in the second calling time scale period t′ to ensure that the reserve capacity is feasible when being called continuously; is the upward reserve capacity variable of the i-th period after the second call time scale period t′ when the wind-storage system participates in the reserve ancillary service market; is the reserve capacity variable for the i-th period after the second call time scale period t′ when the wind-storage system participates in the reserve ancillary service market; i∈{0,1,2,3} is used to index the reserve capacity of the four consecutive second time intervals starting from the second call time scale period t′; E e,t is the energy value variable of the energy storage at the first call time scale period t; Δt′ is the second time interval.

[0049] Furthermore, before obtaining the target parameters, the step of generating uncertainty scenario sets for wind power forecast output, electric energy market forecast electricity price and standby ancillary service market forecast electricity price is also included, including: using historical data of wind power output, electric energy market electricity price and standby ancillary service market electricity price to generate scenario sets that describe the uncertainty of wind power forecast output data, electric energy market forecast electricity price data and standby ancillary service market forecast electricity price data through the Monte Carlo method, and then obtaining the wind power forecast output scenario set, electric energy market forecast electricity price scenario set, standby ancillary service market forecast electricity price scenario set and the probability of occurrence of corresponding scenarios in the reduction results through the distance reduction method.

[0050] Compared with the prior art, the advantages of the present invention are:

[0051] The present invention aims at the problem of multi-call time scale coupling in different scenarios, considers the synergy between application scenarios coupled with different call time scales, synchronously optimizes scenarios coupled with different call time scales, and constructs a day-ahead output scheduling model that comprehensively considers the characteristics of the wind-storage system and scenario operation constraints. It can form a day-ahead scheduling plan for the wind-storage system under the premise of meeting the operation characteristics of the wind-storage system, guide the wind-storage system to participate in multiple application scenarios under multiple call time scales in the power market environment, increase revenue scenarios while enhancing operational flexibility, and maximize the economic benefits of the wind-storage system in the power market environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic diagram of the structure of the wind storage system according to an embodiment of the present invention.

[0053] Figure 2 4 is a basic flow chart of the method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be further described below in conjunction with the accompanying drawings and specific preferred embodiments, but the scope of protection of the present invention is not limited thereby.

[0055] like Figure 1 As shown in Figure 1, the wind-storage system structure consists of a wind farm, energy storage, and a collection busbar. The wind-storage system is connected to the external grid via transmission lines for energy transfer. Both the wind farm and energy storage are connected to the collection busbar for energy transfer, with the energy storage enabling bidirectional energy transfer with the external grid.

[0056] We propose two time scales for dispatching wind-storage systems. The first time scale applies to wind-storage systems used for smoothing wind power fluctuations and participating in the electricity market, both with 10-minute intervals. The second time scale applies to wind-storage systems used for participating in the reserve ancillary services market, with 1-hour intervals. Therefore, a typical day under these two time scales consists of 144 and 24 periods, respectively.

[0057] Based on the above two time scales, this embodiment proposes a wind-storage system scheduling method for the multi-time scale scenario of the power market, such as Figure 2 As shown, the following steps are included:

[0058] Step 1: Construct the objective function of the day-ahead output dispatch model, obtain the target parameters, and construct the objective function of the day-ahead output dispatch model based on the target parameters and the pre-generated uncertainty scenario set (including the wind power forecast output scenario set, the electric energy market forecast electricity price scenario set, and the standby ancillary service market forecast electricity price scenario set). In this embodiment, the objective function is to maximize the economic benefits of the wind-storage system participating in multiple application scenarios coupled with multiple call time scales, and describe the correlation between the first call time scale of the wind-storage system participating in the wind power fluctuation smoothing scenario and the electric energy market scenario, and the second call time scale of the wind-storage system participating in the standby ancillary service market scenario, thereby providing the correlation between the call time scales of the wind power fluctuation smoothing, electric energy market, and standby ancillary service market scenarios with different call time scales;

[0059] Step 2: Establish the constraints of the day-ahead output dispatch model. Based on the target parameters, the first call time scale, and the second call time scale, establish the constraints of the day-ahead output dispatch model. The constraints of the day-ahead output dispatch model include multi-call time scale coupling constraints, as well as constraints for wind-storage system participation in smoothing wind power fluctuations, electricity market scenarios, and reserve ancillary service market scenarios. These constraints comprehensively consider the operational characteristics of the wind-storage system and the characteristics of the scenarios.

[0060] Step 3: Optimize and solve the day-ahead output scheduling model to obtain the charge and discharge power sequence for the wind-storage system participating in wind power fluctuation smoothing and the first call time scale of the electric energy market, the reserve capacity sequence for the second call time scale of the backup ancillary service market, and the expected total revenue for the three application scenarios of participating in wind power fluctuation smoothing, the electric energy market, and the backup ancillary service market. The charge and discharge power sequence and the reserve capacity sequence constitute the day-ahead output plan of the wind-storage system. At this point, a 24-hour energy storage charge and discharge power plan for the first call time scale (a 10-minute time scale is used in this embodiment) has been solved for the electric energy market. This plan is derived by comprehensively considering load demand and predicted wind power fluctuations. Therefore, it is sufficient to implement the optimized energy storage output plan for each time period. For the backup ancillary service market, the optimized solution is a 24-hour energy storage reserve capacity plan for the second call time scale (a 1-hour time scale is used in this embodiment). Energy storage power can be reserved according to this plan. The day-ahead output plan of the wind-storage system established at this time is the optimal scheduling strategy.

[0061] In order to reflect the randomness of wind power forecast output, electric energy market forecast electricity price and standby auxiliary service market forecast electricity price, before step one of this embodiment, it also includes the step of generating a set of uncertainty scenarios of wind power forecast output, electric energy market forecast electricity price and standby auxiliary service market forecast electricity price: using the historical data of wind power output, electric energy market electricity price and standby auxiliary service market electricity price to generate a large number of scenario sets describing the uncertainty of wind power forecast output data, electric energy market forecast electricity price data and standby auxiliary service market forecast electricity price data through the Monte Carlo method, and then obtain the probability of occurrence of several wind power forecast output scenarios, electric energy market forecast electricity price scenarios, standby auxiliary service market forecast electricity price scenarios and corresponding scenarios in the reduction results through the distance reduction method. These probabilities are expressed by probability parameters. The specific implementation process of generating and determining the relevant scenarios is a conventional technical solution in this field and is not the focus of this solution, so it will not be repeated here.

[0062] It should be noted that in the following description of the parameters of each formula, the parameters are known quantities, that is, the target parameters obtained in step 1; the variables are decision variables, which need to be optimized and solved.

[0063] In step 1 of this embodiment, the objective function is specifically constructed with the objective function of maximizing the economic benefits of the wind-storage system participating in multiple application scenarios with multiple call time scales coupled as the objective function of the day-ahead output scheduling model. In the day-ahead optimal scheduling, the wind-storage system maximizes the system economic benefits based on the day-ahead wind power forecast, the energy state and charging and discharging characteristics of the energy storage, the predicted electricity price in the electricity energy market, and the predicted electricity price in the standby ancillary service market. Correspondingly, the constructed objective function expression is as follows:

[0064]

[0065] Among them, the first item is the economic benefits obtained by the wind storage system participating in the electric energy market. The economic benefits of the electric energy market include both the economic benefits obtained through the price difference of low storage and high generation, and the economic benefits obtained by directly selling wind power to the grid; the second item is the economic benefits obtained by the wind storage system participating in the reserve auxiliary service market; I and J are the predicted electricity price scenario sets of the electric energy market and the predicted electricity price scenario sets of the reserve auxiliary service market respectively; t is the arbitrary time scale of the first call (i.e., 10 minutes) in the dispatch cycle when the wind storage system participates in the wind power fluctuation smoothing scenario and the electric energy market scenario. Time period, with values of 1, 2, 3…144; t′ is any time period of the second call time scale (i.e., 1 hour) in the dispatching cycle when the wind-storage system participates in the standby auxiliary service market scenario, with values of 1, 2, 3…24; T is a set of time periods with a time scale of 10 minutes in the dispatching cycle; T′ is a set of time periods with a time scale of 1 hour in the dispatching cycle; Δt is the first time interval, which is 10 minutes in this embodiment; Δt′ is the second time interval, which is 1 hour in this embodiment; W is the set of wind power predicted output scenarios; π i and π j are the probability parameters for the electricity price scenario i in the energy market and the electricity price scenario j in the backup ancillary service market; π w is the probability parameter of wind power prediction output scenario w; is the predicted grid-connected power variable of the wind-storage system at the first call time scale period t under the wind power forecast output scenario w; is the predicted electricity price parameter at the first call time scale period t in the day-ahead electricity energy market; and are the predicted electricity price parameters for the upward and downward reserve in the second call time period t′ of the day-ahead reserve ancillary service market; and They are the upward reserve capacity variable and downward reserve capacity variable of the wind-storage system participating in the reserve ancillary service market in the second call time scale period t′.

[0066] In step 1, the call time scale for the wind-storage system in the wind power fluctuation smoothing scenario and the electric energy market scenario is 10 minutes, and the call time scale for the reserve ancillary service market scenario is 1 hour. The correlation between the call time scales in different scenarios is described as follows:

[0067] 6t′-5≤t≤6t′ (2)

[0068] Among them, t is any time period with a time scale of 10 minutes within the dispatch cycle when the wind-storage system participates in the wind power fluctuation smoothing scenario and the electric energy market scenario, and its value is 1, 2, 3…144; t′ is any time period with a time scale of 1 hour within the dispatch cycle when the wind-storage system participates in the standby auxiliary service market scenario, and its value is 1, 2, 3…24.

[0069] In step 2 of this embodiment, the constraints of the day-ahead output scheduling model include:

[0070] (1) The wind-storage system participates in smoothing the wind power fluctuation constraint and judges whether the wind power fluctuation exceeds the limit. When the wind power output power fluctuation exceeds the limit, the energy storage system will perform a smoothing action, and the wind power output power should be non-negative. The predicted grid-connected power of the wind-storage system is equal to the sum of the predicted wind power output and the planned energy storage output. If the energy storage system performs a smoothing action, the grid-connected power of the wind-storage system after smoothing should also be limited to meet the grid connection standard. The description is as follows:

[0071]

[0072] p w,g,t ≥0 (4)

[0073]

[0074]

[0075] in, and are the charge and discharge state 0-1 variables of the energy storage at the first call time scale period t, respectively; and are the charging and discharging power variables of the energy storage at the first call time scale period t; M is a maximum parameter; P w,g,t P is the wind power forecast output power variable at the first call time scale period t under the wind power forecast output scenario w; w,g,t-Δt is the wind power forecast output power variable in the first 10-minute period before the first call time scale period t under the wind power forecast output scenario w; is the predicted grid-connected power variable of the wind-storage system at the first call time scale period t under the wind power forecast output scenario w; P is the predicted grid-connected power variable of the wind-storage system in the first 10 minutes under the wind power forecast output scenario w; δ It is the maximum fluctuation amplitude parameter allowed for wind power within a 10-minute time scale.

[0076] (2) Energy storage power constraints when wind-storage systems participate in smoothing wind power fluctuations and in the electricity market. When wind-storage systems participate in smoothing wind power fluctuations and in the electricity market, energy storage charging and discharging actions are involved. The energy storage charging and discharging actions in the two scenarios can be treated as a whole, and the energy storage charging and discharging power is not allowed to exceed the rated value, and simultaneous charging and discharging are not allowed. The overall description is as follows:

[0077]

[0078]

[0079]

[0080] in, and are the charge and discharge state 0-1 variables of the energy storage at the first call time scale period t, respectively; and are the charging and discharging power variables of the energy storage at the first call time scale period t respectively; is the rated charge and discharge power parameter of energy storage.

[0081] (3) When the wind-storage system participates in smoothing wind power fluctuations and the electricity market, the energy storage energy constraint is that the energy value of the energy storage at any time should not exceed its rated value. The energy value of the energy storage at a certain moment is equal to the energy value at the previous moment plus the energy change in the interval time period. In order to ensure that the energy storage is smoothly dispatched in the next dispatch cycle, the energy value of the energy storage should also be returned to the set initial energy value at the end of each dispatch cycle, as described below:

[0082]

[0083]

[0084] E e,144 =E e,0 (12)

[0085] Among them, E e,t E is the energy value variable of the energy storage at the first call time scale period t; e,t-1 The energy value variable of the energy storage in the first call time scale period t before the 10-minute time interval; is the rated capacity parameter of energy storage; η ch and η dis are the energy storage charging and discharging efficiency parameters respectively; E e,0 E is the initial energy value parameter of each scheduling cycle of energy storage; e,144 The energy value variable at the end of each scheduling cycle of energy storage is e,0The purpose is to ensure that at the end of each scheduling cycle, the energy storage returns to the set initial energy value to ensure the smooth progress of the next scheduling cycle.

[0086] (4) Available capacity constraints when the wind-storage system participates in the reserve auxiliary service market. When the wind-storage system participates in the reserve auxiliary service market, it can provide reserve capacity by adjusting the energy storage operation state. Increasing the discharge power in the discharge state, reducing the charging power in the charging state, or switching to the discharge state can provide upward reserve. Increasing the charging power in the charging state, reducing the output in the discharge state, or switching to the charging state can provide downward reserve. The constraint derivation results are described as follows:

[0087]

[0088]

[0089] in, and are the maximum variable of the upward reserve capacity and the maximum variable of the downward reserve capacity available in the wind-storage system at the first call time scale period t, respectively; and are the charge and discharge state 0-1 variables of the energy storage at the first call time scale period t, respectively; and are the charging and discharging power variables of the energy storage at the first call time scale period t respectively; is the rated charge and discharge power parameter of energy storage.

[0090] (5) Multi-call time scale coupling constraint, in order to solve the multi-call time scale coupling problem in the synchronous optimization scheduling of different application scenarios, it is ensured that the energy call of the 10-minute call time scale is not affected, and the capacity reserve of the 1-hour call time scale can be provided. The maximum reserve capacity reserved within the 1-hour time scale is and It should be the minimum value of the available reserve capacity in the six 10-minute time periods within the 1 hour, described as follows:

[0091]

[0092]

[0093] in, They are the maximum value variables of the reserve capacity available in the wind-storage system for the next 10 minutes, 20 minutes, 30 minutes, 40 minutes, and 50 minutes at the first call time scale period t; They are the maximum value variables of the reserve capacity available in the wind-storage system for the next 10 minutes, 20 minutes, 30 minutes, 40 minutes, and 50 minutes at the first call time period t; and They are the upward reserve capacity variable and downward reserve capacity variable at the second call time scale period t′ when the wind-storage system participates in the reserve ancillary service market.

[0094] (6) Energy constraints when wind-storage systems participate in the reserve auxiliary service market. According to relevant regulations of some provinces and cities, the energy storage in the wind-storage system should meet the requirements of 4 hours of continuous call, and the energy available for upward reserve at a certain moment should not exceed the energy value stored in the energy storage at the current moment. The sum of the energy space available for downward reserve and the energy stored in the energy storage at the current moment should not exceed the rated energy value of the energy storage, as described below:

[0095]

[0096]

[0097]

[0098]

[0099] in, and are respectively the maximum value variable of the energy storage energy space when increasing and the maximum value variable of the energy storage energy space when decreasing, which are reserved in the second calling time scale period t′ to ensure that the reserve capacity is feasible when being called continuously; is the upward reserve capacity variable of the i-th period after the second call time scale period t′ when the wind-storage system participates in the reserve ancillary service market; is the reserve capacity variable for the i-th period after the second call time scale period t′ when the wind-storage system participates in the reserve ancillary service market; i∈{0,1,2,3} is used to index the reserve capacity for 4 consecutive hours starting from the second call time scale period t′; E e,t is the energy value variable of the energy storage at the first call time scale period t; Δt′ is a 1h time interval.

[0100] Among the above constraints, the constraints on wind-storage systems participating in smoothing wind power fluctuations, the energy storage power constraints when wind-storage systems participate in smoothing wind power fluctuations and the electricity energy market, the energy storage energy constraints when wind-storage systems participate in smoothing wind power fluctuations and the electricity energy market, the available capacity constraints when wind-storage systems participate in the standby ancillary service market, and the energy constraints when wind-storage systems participate in the standby ancillary service market all belong to the constraints of wind-storage systems participating in smoothing wind power fluctuations, electricity energy market scenarios, and standby ancillary service market scenarios.

[0101] In step 3 of this embodiment, a solver is used to solve the day-ahead output scheduling model. The solver used can be mature commercial software such as GUROBI. The use of relevant solvers is a well-known technical means in the art and is not the focus of this solution. It will not be detailed here.

[0102] Through the above steps, the wind-storage system scheduling method for multi-timescale scenarios in the power market proposed in this embodiment achieves collaborative optimization between wind-storage system applications in scenarios with different call time scales, smoothing short-term wind power fluctuations while further participating in the electricity energy market and the hourly call time scale standby ancillary service market to obtain economic benefits. To address the multi-call time scale coupling problem in different scenarios, a day-ahead output scheduling model is constructed that comprehensively considers the characteristics of the wind-storage system and the scenario operation constraints. This model can form a day-ahead scheduling plan for the wind-storage system while satisfying the operational characteristics of the wind-storage system, guiding the wind-storage system to participate in multiple application scenarios with multiple call time scales in the power market environment, increasing revenue scenarios while enhancing operational flexibility and maximizing the economic benefits of the wind-storage system in the power market environment.

[0103] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed above with reference to the preferred embodiment, it is not intended to limit the present invention. Therefore, any simple modifications, equivalent variations, and modifications to the above embodiment that do not depart from the technical solution of the present invention and are based on the technical essence of the present invention shall fall within the scope of protection of the technical solution of the present invention.

Claims

1. A wind-storage system scheduling method for multi-time-scale scenarios in the power market, characterized by: The following steps are involved: Obtain target parameters, and construct the objective function of the day-ahead output dispatch model based on the target parameters and the pre-generated wind power forecast output scenario set, the electric energy market forecast price scenario set, and the standby ancillary service market forecast price scenario set. The objective function expression is as follows: Among them, the first item is the economic benefit obtained by the wind storage system participating in the electric energy market; the second item is the economic benefit obtained by the wind storage system participating in the reserve ancillary service market; I and J are the electric energy market forecast electricity price scenario set and the reserve ancillary service market forecast electricity price scenario set respectively; t is any time period of the first call time scale within the dispatching cycle when the wind storage system participates in the wind power fluctuation smoothing scenario and the electric energy market scenario; t′ is any time period of the second call time scale within the dispatching cycle when the wind storage system participates in the reserve ancillary service market scenario; T is the time period set of the first call time scale within the dispatching cycle; T′ is the time period set of the second call time scale within the dispatching cycle; Δt is the first time interval; Δt′ is the second time interval; W is the wind power forecast output scenario set; π i and π j are the probability parameters of electricity price scenario i in the energy market and electricity price scenario j in the backup ancillary service market respectively; π w is the probability parameter of wind power prediction output scenario w; is the predicted grid-connected power variable of the wind-storage system at the first call time scale period t under the wind power forecast output scenario w; is the predicted electricity price parameter at the first call time scale period t in the day-ahead electricity energy market; and are the predicted electricity price parameters for upward and downward reserve in the second call time period t′ of the day-ahead reserve ancillary service market; and They are the upward reserve capacity variable and downward reserve capacity variable of the wind-storage system participating in the reserve ancillary service market during the second call time period t′; The first call time scale for the wind-storage system to participate in the wind power fluctuation smoothing scenario and the electric energy market scenario, and the association relationship between the second call time scale for the wind-storage system to participate in the backup ancillary service market scenario are described as follows: 6t′-5≤t≤6t′ Wherein, t is any period of the first call time scale within the dispatch cycle when the wind-storage system participates in the wind power fluctuation smoothing scenario and the electric energy market scenario; t′ is any period of the second call time scale within the dispatch cycle when the wind-storage system participates in the standby ancillary service market scenario; Establishing constraints for the day-ahead output dispatch model based on the target parameters, the first call time scale, and the second call time scale. The constraints include multi-call time scale coupling constraints and constraints for the wind-storage system to participate in smoothing wind power fluctuations, in the electric energy market, and in the reserve ancillary service market. Solve the day-ahead output dispatch model to obtain the energy storage charging and discharging power sequence of the wind-storage system participating in smoothing wind power fluctuations and the first call time scale of the electricity energy market, and the energy storage backup capacity reserve sequence of the wind-storage system participating in the second call time scale of the backup ancillary service market. The wind-storage system executes the day-ahead output plan based on the energy storage charging and discharging power sequence and the backup capacity reserve sequence.

2. The wind-storage system scheduling method for multi-time-scale scenarios in the power market according to claim 1 is characterized in that: The first call time scale is 10 minutes, the second call time scale is 1 hour, and the multi-call time scale coupling constraint includes: the maximum value of the reserved spare capacity within the 1 hour time scale is the minimum value of the available spare capacity in the six 10-minute time scale periods within the 1 hour.

3. The wind-storage system scheduling method for multi-time-scale scenarios in the power market according to claim 2 is characterized in that: The call time scale coupling constraint is described as follows: in, They are the maximum value variables of the reserve capacity available in the wind-storage system for the next 10 minutes, 20 minutes, 30 minutes, 40 minutes, and 50 minutes at the first call time scale period t; They are the maximum value variables of the reserve capacity available in the wind-storage system for the next 10 minutes, 20 minutes, 30 minutes, 40 minutes, and 50 minutes at the first call time period t; and They are the upward reserve capacity variable and downward reserve capacity variable in the second call time scale period t′ when the wind-storage system participates in the reserve ancillary service market.

4. The wind-storage system scheduling method for multi-time-scale scenarios in the power market according to claim 1 is characterized in that: The constraints on the wind-storage system's participation in smoothing wind power fluctuations in scenarios, electric energy market scenarios, and standby auxiliary service market scenarios include constraints on the wind-storage system's participation in smoothing wind power fluctuations, including: judging whether the wind power output power fluctuation exceeds the limit to determine whether the energy storage performs a smoothing action; if a smoothing action is performed, the grid-connected power of the wind-storage system after smoothing is limited to the grid-connected standard.

5. The wind-storage system scheduling method for multi-time-scale scenarios in the power market according to claim 4 is characterized in that: The constraints of the wind-storage system participating in smoothing wind power fluctuations are described as follows: in, and are the charge and discharge state 0-1 variables of the energy storage at the first call time scale period t; M is a maximum value parameter; P w,g,t P is the wind power forecast output power variable at the first call time scale period t under the wind power forecast output scenario w; w,g,t-Δt is the wind power predicted output power variable in the first time interval before the first call time scale period t under the wind power predicted output scenario w; is the predicted grid-connected power variable of the wind-storage system at the first call time scale period t under the wind power forecast output scenario w; P is the predicted grid-connected power variable of the wind-storage system in the first time interval before the first call time scale period t under the wind power forecast output scenario w; δ It is the maximum fluctuation amplitude parameter allowed for wind power within the first call time scale.

6. The wind-storage system scheduling method for multi-time-scale scenarios in the power market according to claim 1 is characterized in that: The constraints on the wind-storage system's participation in scenarios for smoothing wind power fluctuations, electric energy market scenarios, and standby auxiliary service market scenarios include available capacity constraints when the wind-storage system participates in the standby auxiliary service market, including: increasing discharge power in the discharge state, reducing charging power in the charging state, or switching to the discharge state to provide upward reserve; increasing charging power in the charging state, reducing output in the discharge state, or switching to the charging state to provide downward reserve.

7. The wind-storage system scheduling method for multi-time-scale scenarios in the power market according to claim 6 is characterized in that: The available capacity constraints of the wind-storage system when participating in the reserve ancillary service market are described as follows: in, and are the maximum variable of the upward reserve capacity and the maximum variable of the downward reserve capacity available in the wind-storage system at the first call time scale period t, respectively; and are the charge and discharge state 0-1 variables of the energy storage at the first call time scale period t, respectively; and are the charging and discharging power variables of the energy storage at the first call time scale period t respectively; is the rated charge and discharge power parameter of energy storage.

8. The wind-storage system scheduling method for multi-time-scale scenarios in the power market according to claim 1 is characterized in that: The constraints on the wind-storage system's participation in smoothing wind power fluctuations, the electric energy market, and the backup ancillary service market also include: Energy storage power constraints when wind-storage systems participate in smoothing wind power fluctuations and in the electricity market include: energy storage charging and discharging power is not allowed to exceed the rated value, and simultaneous charging and discharging are not allowed; Energy storage constraints when wind-storage systems participate in smoothing wind power fluctuations and the electricity market include: the energy value of energy storage at any time should not exceed its rated value; the energy value of energy storage at a certain moment is equal to the energy value at the previous moment plus the energy change during the interval; to ensure that energy storage is smoothly dispatched in the next dispatch cycle, the energy value of energy storage must return to the set initial energy value at the end of each dispatch cycle; Energy constraints for wind-storage systems participating in the reserve ancillary service market include: the energy storage in the wind-storage system should be able to meet 4 hours of continuous callability, the energy available for upward reserve adjustment at a certain moment should not exceed the energy value stored in the energy storage at the current moment, and the sum of the energy space available for downward reserve adjustment and the energy stored in the energy storage at the current moment should not exceed the rated energy value of the energy storage.

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