Energy storage rolling optimization calculation method and system considering prediction deviation

Abstract

The invention discloses an energy storage rolling optimization calculation method and system considering prediction deviation, and the method comprises the steps: constructing a day-ahead time scale unit power generation plan making model, and carrying out the day-ahead power generation plan making; the method comprises the following steps: constructing an intra-day time scale unit power generation planning model, and constructing a segmentation cost function considering new energy consumption as an intra-day energy storage unit SOC expected value to adjust bandwidth by taking a day-ahead energy storage unit SOC planned value calculated by a day-ahead power generation planning in a fixed time period as an expected value; the ultra-short-term prediction deviation of the intra-day new energy is apportioned and consumed according to the rated capacity proportion of an energy storage unit, and pre-compiling of an intra-day power generation plan is carried out; forming an adjusting bandwidth according to the deviation between the SOC of the day-ahead energy storage unit and the SOC of the intra-day energy storage unit, constructing a real-time time scale unit power generation plan making model, and carrying out real-time unit power generation plan making; according to the method, the situation that the energy storage unit is fully charged in advance and cannot deal with the noon new energy peak due to multiple new energy generation when the energy storage unit plan is optimized in the day level is avoided.

Description

Technical Field

[0001] The present invention relates to a method and system for calculating rolling optimization of energy storage, and in particular to a method and system for calculating rolling optimization of energy storage taking into account prediction deviation, and belongs to the technical field of power system and automation thereof. Background Art

[0002] At present, the construction of power markets in various provinces and cities across the country is in full swing. The power markets in Guangdong, Gansu and other provinces have entered the year-round settlement operation mode, and the second batch of power market pilot provinces have also successively carried out simulation test runs and settlement test runs. With the increasing scale of domestic energy storage construction, the grid-connected installed capacity of energy storage units has increased year by year, becoming an important factor affecting the safe dispatch of the power grid.

[0003] In provinces where new energy accounts for a relatively large proportion, the deviations in the short-term new energy power forecast and the intraday ultra-short-term new energy forecast seriously interfere with the dispatching operation and also bring difficulties to the reasonable arrangement of energy storage charging and discharging plans. When it is required to arrange the energy storage charging and discharging plan on the real-time dispatching time scale, it is necessary to correct its own charging and discharging plan according to the new energy forecast deviation. However, the real-time dispatching time scale is generally two hours, and it is impossible to consider the energy storage plan arrangement for the whole day. There is a risk that the energy storage unit will be fully charged before noon and cannot cope with the peak of new energy at noon. Therefore, it is necessary to use the energy storage unit charging and discharging plan on the day-ahead time scale as a guide. It is necessary to establish a method for compiling energy storage unit power generation plans with coordinated optimization of multiple time scales, day-ahead, intraday, and real-time. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a method and system for calculating energy storage rolling optimization that takes into account prediction deviations and can cope with the noon new energy peak.

[0005] Technical solution: The energy storage rolling optimization calculation method considering prediction deviation described in the present invention includes:

[0006] Based on the day-ahead time scale, a unit power generation plan compilation model that takes into account the operation constraints of conventional thermal power and energy storage units is constructed to carry out day-ahead power generation plan compilation;

[0007] On the intraday time scale, according to the deviation between the intraday ultra-short-term new energy forecast value and the day-ahead short-term new energy forecast value, taking into account the system reserve reserve, the system new energy absorption capacity, and the transmission line delivery capacity factors, a model for the preparation of unit power generation plans on the intraday time scale is constructed. The day-ahead energy storage unit SOC plan value of a fixed time period calculated by the day-ahead power generation plan preparation is used as the expected value, and a piecewise cost function considering the absorption of new energy is constructed as the adjustment bandwidth of the SOC expected value of the intraday energy storage unit, so that the deviation of the intraday ultra-short-term new energy forecast is shared and absorbed according to the rated capacity ratio of the energy storage unit, and the pre-preparation of the intraday power generation plan is carried out to form the output plan curve of the intraday energy storage unit and the SOC plan curve of the intraday energy storage unit;

[0008] In the real-time time scale, the SOC planning value of the day-ahead energy storage unit in a fixed time period of the day-ahead power generation plan preparation result is taken as the expected value, and the adjustment bandwidth is formed according to the SOC deviation of the day-ahead energy storage unit and the SOC of the intra-day energy storage unit. The real-time time scale unit power generation plan preparation model is constructed to carry out the real-time unit power generation plan preparation.

[0009] Furthermore, the day-ahead time-scale unit power generation plan compilation model includes the following objective function:

[0010]

[0011] Where: N represents the total number of units; T represents the total number of time scale moments considered before the day; P i,t represents the output of unit i at time t; C i,t (P i,t )and are the calling cost and startup cost of unit i at time t, M w is the penalty factor for abandonment of new energy, The difference between the predicted value and the planned value of the new energy unit w at time t; M s is the network power constraint relaxation penalty factor for market clearing optimization, and are the forward flow relaxation variable and reverse flow relaxation variable of section s respectively; NS is the total number of sections.

[0012] Furthermore, the day-ahead time-scale unit power generation plan compilation model includes system operation constraints, conventional unit operation constraints, network security constraints, and energy storage unit operation characteristic constraints;

[0013] The system operation constraints include: load balancing constraints and system standby constraints;

[0014] The load balancing constraints are:

[0015]

[0016] Among them, P i,t represents the output of unit i at time t, L j,t represents the planned power of tie line j at time t, NT is the total number of tie lines, D t is the system load at time t, and N represents the total number of units;

[0017] The system standby constraints are:

[0018]

[0019] Among them, ΔP i U is the maximum ramp rate of unit i, is the maximum down-slope rate of unit i; and are the maximum and minimum output of unit i at time t respectively; and They are the upward adjustment of spinning reserve requirement and the downward adjustment of spinning reserve requirement at time t respectively;

[0020] The conventional unit operation constraints include: unit output limit constraints, unit climbing constraints, unit minimum continuous start and stop time constraints, and unit start and stop switching variable constraints;

[0021] The unit output limit constraint is:

[0022]

[0023] Among them, if the unit is shut down, then α i,t =0, through this constraint condition, the unit output is limited to 0; when the unit is turned on, then α i,t =1, the constraint condition is the conventional upper and lower limit constraints of the unit output; P i,t represents the output of unit i at time t, is the upper limit of the output of unit i at time t, is the lower limit of the output of unit i at time t;

[0024] The unit climbing constraint is:

[0025]

[0026] In the formula, is the maximum ramp rate of unit i, is the maximum down-slope rate of unit i;

[0027] When the unit is in normal operation, the unit's lifting output range is and Decide;

[0028]

[0029] In the formula, R i is the ramp rate of the unit, D i is the unit's landslide rate, PD t To calculate the particle size;

[0030] The minimum continuous start and stop time constraint of the unit is:

[0031]

[0032] Where: T U and T D The minimum continuous start time and minimum continuous shutdown time of the unit; and is the continuous start-up time and continuous shutdown time of unit i at time t, through the state variable U i,t express:

[0033]

[0034] The unit startup and shutdown switching variable constraints are:

[0035]

[0036] Where: η i,t is the unit startup integer variable, η i,t =1 means that unit i is turned on at time t; when the unit is shut down, γ i,t is the unit startup integer variable, γ i,t =1 means that unit i is shut down at time t and meets the following conditions:

[0037]

[0038] The network security constraints are the power flow constraints of the key sections, specifically:

[0039]

[0040] in, and are the minimum and maximum values ​​of the power flow transmission capacity of section s respectively; P i,t represents the output of unit i at time t, B k,t is the predicted value of bus load k at time t; L j,t is the planned value of j at time t when the tie line is equal; G s-i is the generator output power transfer distribution factor of unit i to section s; G s-k is the output power transfer distribution factor of bus load k to section s; G s-jis the output power transfer distribution factor of tie line equivalent machine j to section s; and are the forward flow relaxation variables and reverse flow relaxation variables of section s respectively;

[0041] The energy storage unit operation characteristic constraints include: energy storage unit maximum and minimum charge and discharge power constraints, energy storage unit SOC constraints, energy storage unit SOC same constraint, energy storage unit charge and discharge state transfer constraint, energy storage unit charge and discharge state transfer number constraint, energy storage unit charge and discharge state retention time constraint;

[0042] The maximum and minimum charging and discharging power constraints of the energy storage unit are:

[0043]

[0044] Where: and are the maximum charging power and the maximum discharging power allowed by the energy storage unit b respectively; and are the minimum charging power and the minimum discharging power allowed by the energy storage unit b respectively; is the charging power of energy storage unit b at time t; is a 0-1 variable, indicating the charging state of the energy storage unit b at time t. Indicates charging status; is the discharge power of energy storage unit b at time t; is a 0-1 variable, indicating the discharge state of the energy storage unit b at time t. Indicates the discharge status;

[0045] The energy storage unit SOC constraint includes:

[0046] Assume that the charging capacity of the energy storage unit at each moment is:

[0047]

[0048] Where: is the charge amount of the energy storage unit b at time t; α is the charging efficiency coefficient of the energy storage device; Δ t is the time period length; the discharge amount of the energy storage unit at each moment is:

[0049]

[0050] Where: is the discharge amount of the energy storage unit at time b; β is the discharge efficiency coefficient of the energy storage unit;

[0051] The expression of the amount of electricity stored in the energy storage device at each moment is:

[0052]

[0053] The SOC constraint expression of the energy storage unit at each moment is:

[0054]

[0055] SOC b,min ≤SOC b,t ≤SOC b,max

[0056] Where: SOC b,max and SOC b,min They represent the maximum SOC limit and minimum SOC limit of energy storage unit b respectively; SOC b,t represents the SOC value of energy storage unit b at time t; Indicates the rated capacity of energy storage unit b;

[0057] The energy storage unit SOC is always the same constraint:

[0058] SOC b,end =SOC b,init

[0059] Where: SOC b,init is the initial storage energy of energy storage unit b at the beginning of the calculation; SOC b,end is the target expected SOC of energy storage unit b at the end of the calculation example;

[0060] The energy storage unit charge and discharge state transfer constraints include:

[0061] The charging state transfer constraints of the energy storage unit are as follows:

[0062]

[0063] Where: is a 0-1 variable, indicating whether the energy storage unit b enters the charging state at time t. Indicates that the energy storage unit b enters the charging state at time t; It is a 0-1 variable, indicating whether the energy storage unit b exits the charging state at time t. Indicates that energy storage unit b exits the charging state at time t;

[0064] The discharging state transfer constraints of the energy storage unit are as follows:

[0065]

[0066] Where: is a 0-1 variable, indicating whether the energy storage unit b enters the discharge state at time t. It indicates that the energy storage unit b enters the discharge state at time t; is a 0-1 variable, indicating whether the energy storage unit b exits the discharge state at time t. Indicates that the energy storage unit b exits the discharge state at time t;

[0067] The energy storage unit charge and discharge state transition times are constrained as follows:

[0068]

[0069] Where: D b Indicates the maximum number of times that energy storage unit b enters the discharge state; C b Indicates the maximum number of times that energy storage unit b enters the charging state;

[0070] The energy storage unit charge and discharge state maintenance time constraint is:

[0071]

[0072] Among them, T c and T d They are the minimum continuous charging time and the minimum continuous discharging time of the unit respectively; is a 0-1 variable, indicating the discharge state of the energy storage unit b at time t. Indicates the discharge status; and are the continuous startup time and continuous shutdown time of energy storage unit b at time t, respectively, expressed by state variables:

[0073]

[0074] Furthermore, the objective function of the intra-day time scale unit power generation plan compilation model is as follows:

[0075]

[0076] Where: N represents the total number of units; T represents the total number of time scale moments considered before the day; P i,t represents the output of unit i at time t; C i,t (P i,t )and are the calling cost and startup cost of unit i at time t, M w is the penalty factor for abandonment of new energy, The difference between the predicted value and the planned value of the new energy unit w at time t; M s is the network power constraint relaxation penalty factor for market clearing optimization, and are the forward flow relaxation variables and reverse flow relaxation variables of section s respectively; NS is the total number of sections; and are the upper adjustment cost and lower adjustment cost of the energy storage unit b in the sg segment at time t, and are the upper and lower adjustment amounts of the energy storage unit b at the sgth segment at time t, B is the total number of energy storage units, and SG is the total number of segments;

[0077] Among them, the segmented adjustment cost of the energy storage unit is a monotonically increasing cost function, and the construction method is as follows:

[0078]

[0079] In the formula, and is the new energy deviation direction indicator on the day-ahead time scale and the intraday time scale. If the short-term new energy forecast value of the example is greater than the ultra-short-term new energy forecast value within the day, then If the short-term new energy forecast value of the previous day is less than the ultra-short-term new energy forecast value within the day, then

[0080] To adjust the cost base, it is a negative number that satisfies:

[0081]

[0082] and The segment step range requirements must be met:

[0083]

[0084] Among them, SOC b,max and SOC b,min They represent the maximum SOC limit and minimum SOC limit of energy storage unit b respectively, The day-ahead SOC planned value of energy storage unit b at time t is taken as the target expected value.

[0085] Furthermore, the constraints of the intraday time scale unit power generation plan compilation model include: energy storage maximum and minimum charge and discharge power constraints, energy storage unit SOC constraints, energy storage unit expected SOC constraints, energy storage unit charge and discharge state transfer constraints and energy storage unit charge and discharge state retention time constraints;

[0086] The maximum and minimum charging and discharging power constraints of energy storage are:

[0087]

[0088] Where: and are the maximum charging power and the maximum discharging power allowed by the energy storage unit b respectively; and are the minimum charging power and the minimum discharging power allowed by the energy storage unit b respectively; is the charging power of energy storage unit b at time t; is a 0-1 variable, indicating the charging state of the energy storage unit b at time t. Indicates charging status; is the discharge power of energy storage unit b at time t; is a 0-1 variable, indicating the discharge state of the energy storage unit b at time t. Indicates the discharge status;

[0089] The energy storage unit SOC constraint includes:

[0090] Assume that the charging capacity of the energy storage unit at each moment is:

[0091]

[0092] Where: is the charging capacity of the energy storage unit at time b; α is the charging efficiency coefficient of the energy storage unit; Δ t is the segment length; the discharge amount of the energy storage unit at each moment is:

[0093]

[0094] Where: is the discharge amount of the energy storage unit at time b; β is the discharge efficiency coefficient of the energy storage unit;

[0095] The expression of the energy storage unit storage capacity at each moment is:

[0096]

[0097] The SOC constraint expression of the energy storage unit at each moment is:

[0098]

[0099] SOC b,min ≤SOC b,t ≤SOC b,max

[0100] Where: SOC b,max and SOC b,min They represent the maximum SOC limit and minimum SOC limit of energy storage unit b respectively; SOC b,t represents the SOC value of energy storage unit b at time t; Indicates the rated capacity of energy storage unit b;

[0101] The energy storage unit charge and discharge state maintenance time constraint is:

[0102]

[0103] Among them, T c and T d They are the minimum continuous charging time and the minimum continuous discharging time of the unit respectively; is a 0-1 variable, indicating the discharge state of the energy storage unit b at time t. Indicates the discharge status; and are the continuous startup time and continuous shutdown time of energy storage unit b at time t, respectively, expressed by state variables:

[0104]

[0105] The energy storage unit's desired SOC constraint is:

[0106]

[0107] Where: SOC b,t is the planned SOC value of energy storage unit b at time t; The day-ahead SOC planned value of energy storage unit b at time t is taken as the target expected value; is the upward adjustment amount of energy storage unit b at time t; is the regulation amount of energy storage unit b at time t;

[0108] in, It should be the accumulation of segment adjustment, described as:

[0109]

[0110] Furthermore, the objective function of the real-time time scale unit power generation planning model is as follows:

[0111]

[0112] Where: N represents the total number of units; T represents the total number of time scale moments considered before the day; P i,t represents the output of unit i at time t; C i,t (P i,t )and are the calling cost and startup cost of unit i at time t, M w is the penalty factor for abandonment of new energy, The difference between the predicted value and the planned value of the new energy unit w at time t; M s is the network power constraint relaxation penalty factor for market clearing optimization, and are the forward flow relaxation variable and reverse flow relaxation variable of section s respectively; NS is the total number of sections.

[0113] Furthermore, the constraints of the real-time time scale unit power generation plan compilation model include: maximum and minimum charge and discharge power constraints of the energy storage unit, SOC constraints of the energy storage unit, and expected SOC constraints of the energy storage unit;

[0114] The maximum and minimum charging and discharging power constraints of the energy storage unit are:

[0115]

[0116] Where: and are the maximum charging power and the maximum discharging power allowed by the energy storage unit b respectively; and are the minimum charging power and the minimum discharging power allowed by the energy storage unit b respectively; is the charging power of energy storage unit b at time t; is a 0-1 variable, indicating the charging state of the energy storage unit b at time t. Indicates charging status; is the discharge power of energy storage unit b at time t; is a 0-1 variable, indicating the discharge state of the energy storage unit b at time t. Indicates the discharge status;

[0117] The energy storage unit SOC constraint includes:

[0118] Assume that the charging capacity of the energy storage unit at each moment is:

[0119]

[0120] Where: is the charging capacity of the energy storage unit at time b; α is the charging efficiency coefficient of the energy storage unit; Δ t is the segment length; the discharge amount of the energy storage unit at each moment is:

[0121]

[0122] Where: is the discharge amount of the energy storage unit at time b; β is the discharge efficiency coefficient of the energy storage unit;

[0123] The expression of the energy storage unit storage capacity at each moment is:

[0124]

[0125] The SOC constraint expression of the energy storage unit at each moment is:

[0126]

[0127] SOCb,min ≤SOC b,t ≤SOC b,max

[0128] Where: SOC b,max and SOC b,min They represent the maximum SOC limit and minimum SOC limit of energy storage unit b respectively; SOC b,t represents the SOC value of energy storage unit b at time t; Indicates the rated capacity of energy storage unit b

[0129] The expected SOC constraint of the energy storage unit is:

[0130]

[0131] Where: is the planned SOC value of energy storage unit b at time t.

[0132] Based on the same inventive concept, the present invention also provides an energy storage rolling optimization calculation system considering prediction deviation, comprising:

[0133] The day-ahead module is used to build a day-ahead time scale unit power generation plan compilation model that takes into account the operation constraints of conventional thermal power and energy storage units, and to carry out day-ahead power generation plan compilation;

[0134] The intraday module is used to construct a generation plan compilation model for the unit on the intraday time scale, based on the intraday ultra-short-term new energy forecast value and the day-ahead short-term new energy forecast value deviation, taking into account the system reserve reserve, the system new energy absorption capacity, and the transmission line delivery capacity factors. The day-ahead energy storage unit SOC plan value of the fixed time period calculated by the day-ahead power generation plan compilation is used as the expected value, and a piecewise cost function considering the absorption of new energy is constructed as the SOC expected value adjustment bandwidth of the intraday energy storage unit, so as to realize the absorption of the intraday ultra-short-term new energy forecast deviation according to the rated capacity ratio of the energy storage unit, carry out the pre-compilation of the intraday power generation plan, and form the output plan curve of the intraday energy storage unit and the SOC plan curve of the intraday energy storage unit;

[0135] The real-time module is used to form an adjustment bandwidth based on the SOC planning value of the day-ahead energy storage unit in a fixed time period of the day-ahead power generation plan preparation result in real-time time scale, and to build a real-time time scale unit power generation plan preparation model to carry out real-time unit power generation plan preparation.

[0136] Based on the same inventive concept, the present invention also provides a computing device, comprising: one or more processors, one or more memories and one or more programs, wherein the programs are stored in the memories and configured to be executed by the processors, and when the programs are loaded into the processors, the steps of the energy storage rolling optimization calculation method considering the prediction deviation as described in any one of the above items are implemented.

[0137] Based on the same inventive concept, the present invention also provides a storage medium, which stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the steps of the energy storage rolling optimization calculation method considering the prediction deviation according to any one of the above items.

[0138] Beneficial effect: Compared with the prior art, the present invention considers factors such as system standby reservation, system new energy consumption capacity, and transmission line delivery capacity, and constructs a day-ahead, intraday, and real-time multi-time scale energy storage unit planning calculation coordination optimization model; considering the deviation of day-ahead short-term new energy forecast and intraday ultra-short-term new energy forecast, constructs an intraday energy storage unit planning optimization model that considers the new energy consumption space reservation, so as to avoid the situation in which the energy storage unit is fully charged in advance and cannot cope with the new energy peak at noon due to the frequent generation of new energy when optimizing the energy storage unit plan at the intraday level. BRIEF DESCRIPTION OF THE DRAWINGS

[0139] Figure 1 The figure is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0140] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0141] As attached Figure 1 As shown, the energy storage rolling optimization calculation method considering the prediction deviation of this embodiment includes:

[0142] Based on the day-ahead time scale, a unit power generation plan compilation model that takes into account the operation constraints of conventional thermal power and energy storage units is constructed to carry out day-ahead power generation plan compilation;

[0143] On the intraday time scale, according to the deviation between the intraday ultra-short-term new energy forecast value and the day-ahead short-term new energy forecast value, taking into account the system reserve reserve, the system new energy absorption capacity, and the transmission line delivery capacity factors, a model for the preparation of unit power generation plans on the intraday time scale is constructed. The day-ahead energy storage unit SOC plan value of a fixed time period calculated by the day-ahead power generation plan preparation is used as the expected value, and a piecewise cost function considering the absorption of new energy is constructed as the adjustment bandwidth of the SOC expected value of the intraday energy storage unit, so that the deviation of the intraday ultra-short-term new energy forecast is shared and absorbed according to the rated capacity ratio of the energy storage unit, and the pre-preparation of the intraday power generation plan is carried out to form the output plan curve of the intraday energy storage unit and the SOC plan curve of the intraday energy storage unit;

[0144] In the real-time time scale, the SOC planning value of the day-ahead energy storage unit in a fixed time period of the day-ahead power generation plan preparation result is taken as the expected value, and the adjustment bandwidth is formed according to the SOC deviation of the day-ahead energy storage unit and the SOC of the intra-day energy storage unit. The real-time time scale unit power generation plan preparation model is constructed to carry out the real-time unit power generation plan preparation.

[0145] Specifically, the objective function of the generation plan compilation model for the unit on the day-ahead time scale is constructed:

[0146]

[0147] in:

[0148] N represents the total number of units;

[0149] T represents the number of moments of the day-ahead time scale considered;

[0150] P i,t represents the output of unit i at time t;

[0151] C i,t (P i,t ), are the calling cost and startup cost of unit i at time t, respectively, where the calling cost of unit C i,t (P i,t ) is a multi-segment linear function related to the output range of each section of the unit and the corresponding coal consumption cost, including the call-up cost of the energy storage unit.

[0152] M w It is the penalty factor for abandoning electricity from new energy sources.

[0153] is the amount of power abandoned by the new energy unit, and is the difference between the predicted value and the planned value of the new energy unit w at time t.

[0154] M s is the penalty factor for relaxing the network power flow constraints for market clearing optimization.

[0155] are the positive and negative flow relaxation variables of section s respectively; NS is the total number of sections.

[0156] The system operation constraints, conventional unit operation constraints, network security constraints, and energy storage unit operation characteristic constraints for constructing the day-ahead time scale unit power generation plan compilation model are as follows:

[0157] System operation constraints include: load balance constraints and system backup constraints.

[0158] The load balance constraint requires that the power generation and consumption be balanced at every moment, which can be described as:

[0159]

[0160] Among them, P i,t represents the output of unit i at time t, L j,t represents the planned power of tie line j at time t (input is positive, output is negative), NT is the total number of tie lines, D t is the system load at time t.

[0161] Conventional unit operation constraints include: unit output limit constraints, unit climbing constraints, unit minimum continuous start and stop time constraints, and unit start and stop switching variable constraints.

[0162] For each time t, the unit output limit constraint can be described as:

[0163]

[0164] If the unit is shut down, α i,t =0, then the unit output can be limited to 0 through this constraint; when the unit is turned on, α i,t =1, this constraint is the conventional upper and lower output limit constraint. is the upper limit of the output of unit i at time t, is the lower limit of the output of unit i at time t.

[0165] When the unit is climbing up or down a slope, it should meet the climbing rate requirements. The climbing constraint can be described as:

[0166]

[0167] In the formula, is the maximum ramp rate of unit i, is the maximum ramp down rate of unit i.

[0168] When the unit is in normal operation, the unit's lifting output range is Decide;

[0169]

[0170] In the formula, R i is the ramp rate of the unit, D i is the unit's landslide rate, PD t To calculate the particle size.

[0171] The minimum continuous start and stop time constraint of the unit can be described as:

[0172]

[0173] Where: T U , T D The minimum continuous start time and minimum continuous shutdown time of the unit; is the continuous startup time and continuous shutdown time of unit i at time t, which can be expressed as the state variable U i,t To express:

[0174]

[0175] Unit startup and shutdown switching variable constraints

[0176]

[0177] Where: η i,t is the unit startup integer variable, η i,t =1 means that unit i is started at time t; when the unit is shut down, γ i,t is the unit startup integer variable, γ i,t =1 means that unit i is shut down at time t and meets the following conditions:

[0178]

[0179] The network security constraint is the flow constraint of the key section, and the flow of the key section should not exceed the limit value.

[0180] The flow constraint of the critical section can be described as:

[0181]

[0182] in, and are the minimum and maximum values ​​of the power flow transmission capacity of section s respectively; P i,t represents the output of unit i at time t, B k,t is the predicted value of bus load k at time t; L j,t is the planned value of j at time t when the tie line is equal; G s-i is the generator output power transfer distribution factor of unit i to section s; G s-k is the output power transfer distribution factor of bus load k to section s; G s-jis the output power transfer distribution factor of tie-line equivalent machine j to section s. The tie-line equivalent machine is equivalent to the equivalent unit established for the tie-line landing point outside the area, which is equivalent to a virtual generator; and are the forward flow relaxation variables and reverse flow relaxation variables of section s respectively.

[0183] The operating characteristic constraints of the energy storage unit include: maximum and minimum charging and discharging power constraints of energy storage, SOC constraints of the energy storage unit, SOC consistency constraints of the energy storage unit, charging and discharging state transfer constraints of the energy storage unit, number of charging and discharging state transfer constraints of the energy storage unit, and charging and discharging state retention time constraints of the energy storage unit.

[0184] Maximum and minimum charging and discharging power constraints of energy storage units:

[0185]

[0186] Where: and are the maximum charging power and the maximum discharging power allowed by the energy storage unit b respectively; and are the minimum charging power and the minimum discharging power allowed by the energy storage unit b respectively; is the charging power of energy storage unit b at time t; is a 0-1 variable, indicating the charging state of the energy storage unit b at time t. Indicates charging status; is the discharge power of energy storage unit b at time t; is a 0-1 variable, indicating the discharge state of the energy storage unit b at time t. Indicates the discharge status;

[0187] The energy storage unit SOC constraint includes:

[0188] The charging capacity of the energy storage unit at each moment is:

[0189]

[0190] Where: is the charge amount of the energy storage unit b at time t; α is the charging efficiency coefficient of the energy storage device unit; Δ t The length of the period.

[0191] The discharge amount of the energy storage device at each moment is:

[0192]

[0193] Where: is the discharge amount of the energy storage unit at time b; β is the discharge efficiency coefficient of the energy storage unit.

[0194] The expression of the energy storage unit storage capacity at each moment is:

[0195]

[0196] The SOC constraint expression of the energy storage unit at each moment is:

[0197]

[0198] SOC b,min ≤SOC b,t ≤SOC b,max

[0199] Where: SOC b,max , SOC b,min Respectively represent the maximum and minimum SOC limits of energy storage unit b; SOC b,t Represents the SOC value of the energy storage unit b at time t; Indicates the rated capacity of energy storage unit b.

[0200] The SOC of the energy storage unit is always the same

[0201] For the day-ahead power generation plan, the energy storage unit is generally required to have the same SOC at the beginning and end, and the constraint expression is:

[0202] SOC b,end =SOC b,init

[0203] Where: SOC b,init is the initial storage energy of energy storage unit b at the beginning of the calculation; SOC b,end is the target expected SOC of energy storage unit b at the end of the calculation example.

[0204] Energy storage unit charging and discharging state transfer constraints:

[0205] The charging state transfer constraints of the energy storage unit are as follows:

[0206]

[0207] Where: Indicates whether the energy storage unit b enters the charging state at time t, a 0-1 variable, It indicates that the energy storage unit b enters the charging state at time t; Indicates whether the energy storage unit b exits the charging state at time t, a 0-1 variable, It indicates that the energy storage unit b exits the charging state at time t.

[0208] The discharging state transfer constraints of the energy storage device are as follows:

[0209]

[0210] Where: Indicates whether the energy storage unit b enters the discharge state at time t, a 0-1 variable, It indicates that the energy storage unit b enters the discharge state at time t; Indicates whether the energy storage unit b exits the discharge state at time t, a 0-1 variable, It indicates that the energy storage unit b exits the discharge state at time t.

[0211] Constraints on the number of charge and discharge state transitions of energy storage units

[0212] In order to ensure the smooth operation of the energy storage unit, the energy storage unit charge and discharge state transfer times constraint is introduced:

[0213]

[0214] Where: D b Indicates the maximum number of times that energy storage unit b enters the discharge state; C b Indicates the maximum number of times that energy storage unit b enters the charging state.

[0215] Energy storage unit charging and discharging state maintenance time constraints

[0216] In order to avoid frequent switching of the charging and discharging states of the energy storage unit, the charging and discharging state holding time constraint of the energy storage unit is introduced. The specific expression is:

[0217]

[0218] Among them, T c , T d They are the minimum continuous charging time and the minimum continuous discharging time of the unit respectively; are the continuous startup time and continuous shutdown time of energy storage unit b at time t, which can be expressed by state variables:

[0219]

[0220] The day-ahead time scale unit power generation plan compilation model is used to calculate the day-ahead charge and discharge plan curve of the energy storage unit and the day-ahead energy storage unit SOC plan value, and the day-ahead energy storage unit SOC plan curve and new energy short-term forecast plan value are sent to the intraday time scale unit power generation plan compilation model to carry out intraday power generation plan compilation.

[0221] The system operation constraints, conventional unit operation constraints, network security constraints, and energy storage unit operation characteristic constraints of the intraday time scale unit power generation plan compilation model are constructed. The parts that are repeated with the constraints of the day-ahead time scale unit power generation plan compilation model are not repeated. Compared with the day-ahead time scale, the changes of the intraday time scale unit power generation plan compilation model are as follows:

[0222] The objective function increases the expected adjustment cost of the energy storage unit SOC, which can be described as:

[0223]

[0224] in: is the adjustment cost of the energy storage unit b in the sg segment at time t, is the upper and lower adjustment amount of the energy storage unit b at the sgth segment at time t. SG is the total number of segments, which is set to 5.

[0225] Among them, the segment adjustment cost of the energy storage unit is a monotonically increasing cost function, and the construction method is described as follows:

[0226]

[0227] In the formula, is the new energy deviation direction indicator on the day-ahead time scale and the intraday time scale. If the short-term new energy forecast value of the example is greater than the ultra-short-term new energy forecast value within the day, then If the short-term new energy forecast value of the previous day is less than the ultra-short-term new energy forecast value within the day, then

[0228] To adjust the cost base to a negative number, the following conditions must be met:

[0229]

[0230] It should meet the segment step range requirements, which can be described as:

[0231]

[0232] Delete the constraint on the number of charge and discharge state transfers of the energy storage unit, and modify the constraint on the energy storage unit's SOC being the same from beginning to end;

[0233] Added expected SOC constraints for energy storage units:

[0234]

[0235] Where: SOC b,t is the planned SOC value of energy storage unit b at time t; The day-ahead SOC planned value of energy storage unit b at time t is taken as the target expected value; is the upward adjustment amount of energy storage unit b at time t; is the regulation value of energy storage unit b at time t.

[0236] It should be the accumulation of segmented adjustment, which can be described as:

[0237]

[0238] By constructing a daily time scale unit power generation plan compilation model that takes into account the deviation between the day-ahead and intraday new energy forecasts, it is possible to prioritize the use of thermal power units to consume new energy when the intraday new energy forecast is higher than the day-ahead, and replace the power generation plans of thermal power units and energy storage units to reserve charging space in advance for the noon new energy peak; the SOC plan curve of the intraday energy storage unit is transmitted to the real-time time scale to carry out real-time power generation plan compilation.

[0239] Construct a real-time time scale unit power generation planning model, as well as the corresponding system operation constraints, conventional unit operation constraints, network security constraints, and energy storage unit operation characteristic constraints. The parts that are repeated with the constraints of the day-ahead and intraday scale unit planning model are not repeated here. Compared with the intraday scale, the changes of the real-time scale unit planning model are as follows:

[0240] The objective function of the real-time time scale unit power generation planning model is as follows:

[0241]

[0242] Where: N represents the total number of units; T represents the total number of time scale moments considered before the day; P i,t represents the output of unit i at time t; C i,t (P i,t )and are the calling cost and startup cost of unit i at time t, M w is the penalty factor for abandonment of new energy, The difference between the predicted value and the planned value of the new energy unit w at time t; M s is the network power constraint relaxation penalty factor for market clearing optimization, and are the forward flow relaxation variables and reverse flow relaxation variables of section s respectively; NS is the total number of sections

[0243] The expected SOC constraint of the energy storage unit is modified to:

[0244]

[0245] Where: is the planned SOC value of energy storage unit b at time t. The expected bandwidth of real-time SOC has no piecewise penalty function.

[0246] Based on the same inventive concept, this embodiment also provides an energy storage rolling optimization calculation system considering prediction deviation, including:

[0247] The day-ahead module is used to build a day-ahead time scale unit power generation plan compilation model that takes into account the operation constraints of conventional thermal power and energy storage units, and to carry out day-ahead power generation plan compilation;

[0248] The intraday module is used to construct a generation plan compilation model for the unit on the intraday time scale, based on the intraday ultra-short-term new energy forecast value and the day-ahead short-term new energy forecast value deviation, taking into account the system reserve reserve, the system new energy absorption capacity, and the transmission line delivery capacity factors. The day-ahead energy storage unit SOC plan value of the fixed time period calculated by the day-ahead power generation plan compilation is used as the expected value, and a piecewise cost function considering the absorption of new energy is constructed as the SOC expected value adjustment bandwidth of the intraday energy storage unit, so as to realize the absorption of the intraday ultra-short-term new energy forecast deviation according to the rated capacity ratio of the energy storage unit, carry out the pre-compilation of the intraday power generation plan, and form the output plan curve of the intraday energy storage unit and the SOC plan curve of the intraday energy storage unit;

[0249] The real-time module is used to form an adjustment bandwidth based on the SOC planning value of the day-ahead energy storage unit in a fixed time period of the day-ahead power generation plan preparation result in real-time time scale, and to build a real-time time scale unit power generation plan preparation model to carry out real-time unit power generation plan preparation.

[0250] Based on the same inventive concept, this embodiment also provides a computing device, including: one or more processors, one or more memories and one or more programs, wherein the programs are stored in the memories and are configured to be executed by the processors, and when the programs are loaded into the processors, the steps of the energy storage rolling optimization calculation method considering the prediction deviation as described in any one of the above items are implemented.

[0251] Based on the same inventive concept, this embodiment also provides a storage medium, which stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the steps of the energy storage rolling optimization calculation method considering the prediction deviation according to any one of the above items.

[0252] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0253] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0254] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0255] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0256] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for calculating energy storage rolling optimization considering prediction deviation, characterized in that: include: Based on the day-ahead time scale, a unit power generation plan compilation model that takes into account the operation constraints of conventional thermal power and energy storage units is constructed to carry out day-ahead power generation plan compilation; On the intraday time scale, according to the deviation between the intraday ultra-short-term new energy forecast value and the day-ahead short-term new energy forecast value, taking into account the system reserve reserve, the system new energy absorption capacity, and the transmission line delivery capacity factors, a model for the preparation of unit power generation plans on the intraday time scale is constructed. The day-ahead energy storage unit SOC plan value of a fixed time period calculated by the day-ahead power generation plan preparation is used as the expected value, and a piecewise cost function considering the absorption of new energy is constructed as the adjustment bandwidth of the SOC expected value of the intraday energy storage unit, so that the deviation of the intraday ultra-short-term new energy forecast is shared and absorbed according to the rated capacity ratio of the energy storage unit, and the pre-preparation of the intraday power generation plan is carried out to form the output plan curve of the intraday energy storage unit and the SOC plan curve of the intraday energy storage unit; In the real-time time scale, the SOC planning value of the day-ahead energy storage unit in a fixed time period of the day-ahead power generation plan preparation result is taken as the expected value, and the adjustment bandwidth is formed according to the SOC deviation of the day-ahead energy storage unit and the SOC of the intra-day energy storage unit. The real-time time scale unit power generation plan preparation model is constructed to carry out the real-time unit power generation plan preparation.

2. The energy storage rolling optimization calculation method considering prediction deviation according to claim 1 is characterized in that: The day-ahead time-scale unit power generation planning model includes the following objective functions: Where: N represents the total number of units; T represents the total number of time scale moments considered before the day; P i,t represents the output of unit i at time t; C i,t (P i,t )and are the calling cost and startup cost of unit i at time t, M w is the penalty factor for abandonment of new energy, The difference between the predicted value and the planned value of the new energy unit w at time t; M s is the network power constraint relaxation penalty factor for market clearing optimization, and are the forward flow relaxation variable and reverse flow relaxation variable of section s respectively; NS is the total number of sections.

3. The energy storage rolling optimization calculation method considering prediction deviation according to claim 1 is characterized in that: The day-ahead time-scale unit power generation plan compilation model includes system operation constraints, conventional unit operation constraints, network security constraints, and energy storage unit operation characteristic constraints; The system operation constraints include: load balancing constraints and system standby constraints; The load balancing constraints are: Among them, P i,t represents the output of unit i at time t, L j,t represents the planned power of tie line j at time t, NT is the total number of tie lines, D t is the system load at time t, and N represents the total number of units; The system standby constraints are: in, is the maximum ramp rate of unit i, is the maximum down-slope rate of unit i; and are the maximum and minimum output of unit i at time t respectively; and They are the upward adjustment of spinning reserve requirement and the downward adjustment of spinning reserve requirement at time t respectively; The conventional unit operation constraints include: unit output limit constraints, unit climbing constraints, unit minimum continuous start and stop time constraints, and unit start and stop switching variable constraints; The unit output limit constraint is: Among them, if the unit is shut down, then α i,t =0, through this constraint condition, the unit output is limited to 0; when the unit is turned on, then α i,t =1, the constraint condition is the conventional upper and lower limit constraints of the unit output; P i,t represents the output of unit i at time t, is the upper limit of the output of unit i at time t, is the lower limit of the output of unit i at time t; The unit climbing constraint is: In the formula, is the maximum ramp rate of unit i, is the maximum down-slope rate of unit i; When the unit is in normal operation, the unit's lifting output range is and Decide; In the formula, R i is the ramp rate of the unit, D i is the unit's landslide rate, PD t To calculate the particle size; The minimum continuous start and stop time constraint of the unit is: Where: T U and T D The minimum continuous start time and minimum continuous shutdown time of the unit; and is the continuous start-up time and continuous shutdown time of unit i at time t, through the state variable U i,t express: The unit startup and shutdown switching variable constraints are: Where: η i,t is the unit startup integer variable, η i,t =1 means that unit i is turned on at time t; when the unit is shut down, γ i,t is the unit startup integer variable, γ i,t =1 means that unit i is shut down at time t and meets the following conditions: The network security constraints are the power flow constraints of the key sections, specifically: in, and are the minimum and maximum values ​​of the power flow transmission capacity of section s respectively; P i,t represents the output of unit i at time t, B k,t is the predicted value of bus load k at time t; L j,t is the planned value of j at time t when the tie line is equal; G s-i is the generator output power transfer distribution factor of unit i to section s; G s-k is the output power transfer distribution factor of bus load k to section s; G s-j is the output power transfer distribution factor of tie line equivalent machine j to section s; and are the forward flow relaxation variables and reverse flow relaxation variables of section s respectively; The energy storage unit operation characteristic constraints include: energy storage unit maximum and minimum charge and discharge power constraints, energy storage unit SOC constraints, energy storage unit SOC same constraint, energy storage unit charge and discharge state transfer constraint, energy storage unit charge and discharge state transfer number constraint, energy storage unit charge and discharge state retention time constraint; The maximum and minimum charging and discharging power constraints of the energy storage unit are: Where: and are the maximum charging power and the maximum discharging power allowed by the energy storage unit b respectively; and are the minimum charging power and the minimum discharging power allowed by the energy storage unit b respectively; is the charging power of energy storage unit b at time t is a 0-1 variable, indicating the charging state of the energy storage unit b at time t. Indicates charging status; is the discharge power of energy storage unit b at time t; is a 0-1 variable, indicating the discharge state of the energy storage unit b at time t. Indicates the discharge status; The energy storage unit SOC constraint includes: Assume that the charging capacity of the energy storage unit at each moment is: Where: is the charge amount of the energy storage unit b at time t; α is the charging efficiency coefficient of the energy storage device; Δ t is the time period length; The discharge amount of the energy storage unit at each moment is: Where: is the discharge amount of the energy storage unit at time b; β is the discharge efficiency coefficient of the energy storage unit; The expression of the energy storage unit storage capacity at each moment is: The SOC constraint expression of the energy storage unit at each moment is: SOC b,min ≤SOC b,t ≤SOC b,max Where: SOC b,max and SOC b,min They represent the maximum SOC limit and minimum SOC limit of energy storage unit b respectively; SOC b,t represents the SOC value of energy storage unit b at time t; Indicates the rated capacity of energy storage unit b; The energy storage unit SOC is always the same constraint: SOCIETY b,end =SOC b,init Where: SOC b,init is the initial storage energy of energy storage unit b at the beginning of the calculation; SOC b,end is the target expected SOC of energy storage unit b at the end of the calculation example; The energy storage unit charge and discharge state transfer constraints include: The charging state transfer constraints of the energy storage unit are as follows: Where: is a 0-1 variable, indicating whether the energy storage unit b enters the charging state at time t. Indicates that the energy storage unit b enters the charging state at time t; It is a 0-1 variable, indicating whether the energy storage unit b exits the charging state at time t. Indicates that energy storage unit b exits the charging state at time t; The discharging state transfer constraints of the energy storage unit are as follows: Where: is a 0-1 variable, indicating whether the energy storage unit b enters the discharge state at time t. It indicates that the energy storage unit b enters the discharge state at time t; is a 0-1 variable, indicating whether the energy storage unit b exits the discharge state at time t. Indicates that the energy storage unit b exits the discharge state at time t; The energy storage unit charge and discharge state transition times are constrained as follows: Where: D b Indicates the maximum number of times that energy storage unit b enters the discharge state; C b Indicates the maximum number of times that energy storage unit b enters the charging state; The energy storage unit charge and discharge state maintenance time constraint is: Among them, T c and T d They are the minimum continuous charging time and the minimum continuous discharging time of the unit respectively; is a 0-1 variable, indicating the discharge state of the energy storage unit b at time t. Indicates the discharge status; and are the continuous startup time and continuous shutdown time of energy storage unit b at time t, respectively, expressed by state variables:

4. The energy storage rolling optimization calculation method considering prediction deviation according to claim 1 is characterized in that: The objective function of the intraday time scale unit power generation planning model is as follows: Where: N represents the total number of units; T represents the total number of time scale moments considered before the day; P i,t represents the output of unit i at time t; C i,t (P i,t )and are the calling cost and startup cost of unit i at time t, M w is the penalty factor for abandonment of new energy, The difference between the predicted value and the planned value of the new energy unit w at time t; M s is the network power constraint relaxation penalty factor for market clearing optimization, and are the forward flow relaxation variables and reverse flow relaxation variables of section s respectively; NS is the total number of sections; and are the upper adjustment cost and lower adjustment cost of the energy storage unit b in the sg segment at time t, and are the upper and lower adjustment amounts of the energy storage unit b at the sgth segment at time t, B is the total number of energy storage units, and SG is the total number of segments; Among them, the segmented adjustment cost of the energy storage unit is a monotonically increasing cost function, and the construction method is as follows: In the formula, and is the new energy deviation direction indicator on the day-ahead time scale and the intraday time scale. If the short-term new energy forecast value of the example is greater than the ultra-short-term new energy forecast value within the day, then If the short-term new energy forecast value of the previous day is less than the ultra-short-term new energy forecast value within the day, then To adjust the cost base, it is a negative number that satisfies: and The segment step range requirements must be met: Among them, SOC b,max and SOC b,min They represent the maximum SOC limit and minimum SOC limit of energy storage unit b respectively, The day-ahead SOC planned value of energy storage unit b at time t is taken as the target expected value.

5. The energy storage rolling optimization calculation method considering prediction deviation according to claim 1 is characterized in that: The constraints of the intraday time scale unit power generation plan compilation model include: energy storage maximum and minimum charging and discharging power constraints, energy storage unit SOC constraints, energy storage unit expected SOC constraints, energy storage unit charging and discharging state transfer constraints and energy storage unit charging and discharging state retention time constraints; The maximum and minimum charging and discharging power constraints of energy storage are: Where: and are the maximum charging power and the maximum discharging power allowed by the energy storage unit b respectively; and are the minimum charging power and the minimum discharging power allowed by the energy storage unit b respectively; is the charging power of energy storage unit b at time t; is a 0-1 variable, indicating the charging state of the energy storage unit b at time t. Indicates charging status; is the discharge power of energy storage unit b at time t; is a 0-1 variable, indicating the discharge state of the energy storage unit b at time t. Indicates the discharge status; The energy storage unit SOC constraint includes: Assume that the charging capacity of the energy storage unit at each moment is: Where: is the charging capacity of the energy storage unit at time b; α is the charging efficiency coefficient of the energy storage unit; Δ t is the segment length; The discharge amount of the energy storage unit at each moment is: Where: is the discharge amount of the energy storage unit at time b; β is the discharge efficiency coefficient of the energy storage unit; The expression of the energy storage unit storage capacity at each moment is: The SOC constraint expression of the energy storage unit at each moment is: SOC b,min ≤SOC b,t ≤SOC b,max Where: SOC b,max and SOC b,min They represent the maximum SOC limit and minimum SOC limit of energy storage unit b respectively; SOC b,t represents the SOC value of energy storage unit b at time t; Indicates the rated capacity of energy storage unit b; The energy storage unit charge and discharge state maintenance time constraint is: Among them, T c and T d They are the minimum continuous charging time and the minimum continuous discharging time of the unit respectively; is a 0-1 variable, indicating the discharge state of the energy storage unit b at time t. Indicates the discharge status; and are the continuous startup time and continuous shutdown time of energy storage unit b at time t, respectively, expressed by state variables: The energy storage unit's desired SOC constraint: Where: SOC b,t is the planned SOC value of energy storage unit b at time t; The day-ahead SOC planned value of energy storage unit b at time t is taken as the target expected value; is the upward adjustment amount of energy storage unit b at time t; is the regulation amount of energy storage unit b at time t; in, It should be the accumulation of segment adjustment, described as:

6. The energy storage rolling optimization calculation method considering prediction deviation according to claim 1 is characterized in that: The objective function of the real-time time scale unit power generation planning model is as follows: Where: N represents the total number of units; T represents the total number of time scale moments considered before the day; P i,t represents the output of unit i at time t; C i,t (P i,t )and are the calling cost and startup cost of unit i at time t, M w is the penalty factor for abandonment of new energy, The difference between the predicted value and the planned value of the new energy unit w at time t; M s is the network power constraint relaxation penalty factor for market clearing optimization, and are the forward flow relaxation variable and reverse flow relaxation variable of section s respectively; NS is the total number of sections.

7. The energy storage rolling optimization calculation method considering prediction deviation according to claim 1 is characterized in that: The constraints of the real-time time scale unit power generation plan compilation model include: maximum and minimum charge and discharge power constraints of the energy storage unit, SOC constraints of the energy storage unit, and expected SOC constraints of the energy storage unit; The maximum and minimum charging and discharging power constraints of the energy storage unit are: Where: and are the maximum charging power and the maximum discharging power allowed by the energy storage unit b respectively; and are the minimum charging power and the minimum discharging power allowed by the energy storage unit b respectively; is the charging power of energy storage unit b at time t; is a 0-1 variable, indicating the charging state of the energy storage unit b at time t. Indicates charging status; is the discharge power of energy storage unit b at time t; is a 0-1 variable, indicating the discharge state of the energy storage unit b at time t. Indicates the discharge status; The energy storage unit SOC constraint includes: Assume that the charging capacity of the energy storage unit at each moment is: Where: is the charging capacity of the energy storage unit at time b; α is the charging efficiency coefficient of the energy storage unit; Δ t is the segment length; The discharge amount of the energy storage unit at each moment is: Where: is the discharge amount of the energy storage unit at time b; β is the discharge efficiency coefficient of the energy storage unit; The expression of the energy storage unit storage capacity at each moment is: The SOC constraint expression of the energy storage unit at each moment is: SOC b,min ≤SOC b,t ≤SOC b,max Where: SOC b,max and SOC b,min They represent the maximum SOC limit and minimum SOC limit of energy storage unit b respectively; SOC b,t represents the SOC value of energy storage unit b at time t; Indicates the rated capacity of energy storage unit b The expected SOC constraint of the energy storage unit is: Where: is the planned SOC value of energy storage unit b at time t.

8. A system for calculating energy storage rolling optimization considering prediction deviation, characterized in that: include: The day-ahead module is used to build a day-ahead time scale unit power generation plan compilation model that takes into account the operation constraints of conventional thermal power and energy storage units, and to carry out day-ahead power generation plan compilation; The intraday module is used to construct a generation plan compilation model for the unit on the intraday time scale, based on the intraday ultra-short-term new energy forecast value and the day-ahead short-term new energy forecast value deviation, taking into account the system reserve reserve, the system new energy absorption capacity, and the transmission line delivery capacity factors. The day-ahead energy storage unit SOC plan value of the fixed time period calculated by the day-ahead power generation plan compilation is used as the expected value, and a piecewise cost function considering the absorption of new energy is constructed as the SOC expected value adjustment bandwidth of the intraday energy storage unit, so as to realize the absorption of the intraday ultra-short-term new energy forecast deviation according to the rated capacity ratio of the energy storage unit, carry out the pre-compilation of the intraday power generation plan, and form the output plan curve of the intraday energy storage unit and the SOC plan curve of the intraday energy storage unit; The real-time module is used to form an adjustment bandwidth based on the SOC planning value of the day-ahead energy storage unit in a fixed time period of the day-ahead power generation plan preparation result in real-time time scale, and to build a real-time time scale unit power generation plan preparation model to carry out real-time unit power generation plan preparation.

9. A computing device, characterized in that include: One or more processors, one or more memories, and one or more programs, wherein the programs are stored in the memories and are configured to be executed by the processors, and when the programs are loaded into the processors, the steps of the energy storage rolling optimization calculation method considering prediction deviations according to any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the processor executes the steps of the energy storage rolling optimization calculation method considering prediction deviation according to any one of claims 1 to 7.

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