Electric power system operation balance adjusting method and device based on independent energy storage

Abstract

The invention discloses a power system operation balance adjusting method and device based on independent energy storage. The method comprises the following steps: acquiring historical electricity market data of all independent energy storage equipment in each historical time period, and predicting a first parameter corresponding to the independent energy storage equipment in a future time period according to the historical electricity market data; taking the first parameter as a boundary condition, initializing a preset auxiliary decision model, solving the auxiliary decision model, and obtaining adjustment data of each independent energy storage device for adjusting the balance of the power system in a future time period; the adjustment data comprises charging and discharging data and frequency modulation data of the independent energy storage equipment in each time period in the future; and according to the charging and discharging data and the frequency modulation data, respectively adjusting the charging and discharging power and the frequency modulation capacity of the independent energy storage device in the corresponding future time period. According to the invention, time division multiplexing of the energy storage equipment can be realized, so that the flexibility and the effective utilization hours of the energy storage equipment during power system adjustment are improved.

Description

Technical Field

[0001] The present application relates to the field of energy storage scheduling, and in particular to a method and device for adjusting the operation balance of an electric power system based on independent energy storage. Background Art

[0002] In the current electricity market trading environment, energy storage can be used for frequency regulation auxiliary services and electric energy market services to obtain profits. Through the market-based call of energy storage, the power system operation balance can be adjusted. However, this method is limited by the regulation constraints of the energy storage's own operating characteristics on the one hand; on the other hand, the benefits of energy storage participating in regulation must also be considered.

[0003] Existing technologies usually only maximize the benefits of energy storage charging and discharging, on the basis of ensuring that the charging and discharging power of energy storage in each time period meets the maximum and minimum discharge power and charge state constraints, without considering the regulation ability of energy storage to provide auxiliary services such as frequency regulation, wasting the regulation potential of energy storage in scenarios other than "peak shifting and valley filling" in power system operation. In addition, existing technologies construct optimization regulation models and algorithms for energy storage to participate in multiple power trading products under the model of joint clearing of energy storage in the market for electric energy and auxiliary services such as frequency regulation and standby. The above method is inconsistent with the actual situation of energy storage participating in different categories of market transactions in "time-sharing reuse" in current practice. Therefore, it is impossible to maximize the effective utilization level of energy storage in different time periods and different operating scenarios, and it is also impossible to guide energy storage to reasonably formulate the optimal charging and discharging plan in the future operation stage.

[0004] Therefore, how to ensure the benefits of energy storage while optimizing the frequency regulation auxiliary services and electric energy market services that the same energy storage participates in at different time periods to improve the utilization rate of energy storage in the "time-sharing reuse" scenario is a technical problem that needs to be solved at present. Summary of the invention

[0005] The present application provides a method and device for balancing and regulating the operation of an electric power system based on independent energy storage, so as to solve the technical problem of low utilization rate when independent energy storage equipment participates in the market and regulates the balance of the electric power system in the "time-sharing multiplexing" scenario.

[0006] In order to solve the above technical problems, in a first aspect, an embodiment of the present application provides a method for adjusting the operation balance of a power system based on independent energy storage, comprising:

[0007] Obtaining historical electricity market data of all independent energy storage devices in each historical period, and predicting a first parameter corresponding to the independent energy storage device in a future period based on the historical electricity market data; the first parameter includes: a mileage capacity ratio of the independent energy storage device;

[0008] Taking the first parameter as the boundary condition, initializing a preset auxiliary decision model, and solving the auxiliary decision model to obtain the regulation data of each independent energy storage device for regulating the balance of the power system in the future period; the auxiliary decision model is obtained by optimizing the regulation data of each independent energy storage device on the power system in different future periods by considering the mutual exclusivity of the independent energy storage devices participating in different markets in the same period; the regulation data includes: the charging and discharging data and the frequency modulation data of the independent energy storage devices in each future period;

[0009] According to the charging and discharging data and the frequency modulation data, the charging and discharging power and the frequency modulation capacity of the independent energy storage device corresponding to the future time period are adjusted respectively.

[0010] Compared with the prior art, the embodiments of the present application have the following beneficial effects: due to the time-sharing reuse scenario, independent energy storage devices can participate in different markets at different time periods to provide subsequent services for regulating the balance of the power system. Therefore, by using the historical electricity market data of the historical time period, predicting the future electricity market data, obtaining the first parameter, and using the first parameter as the boundary condition of the subsequent auxiliary decision-making model, obtaining the independent processing of the charging and discharging power and frequency regulation capacity in each future time period, so that the subsequent independent energy storage device can dynamically configure the capacity for providing peak shifting and valley filling and frequency regulation auxiliary services in each time period according to a more reasonable charging operation plan and according to the operation needs of the power system, so as to achieve the purpose of time-sharing reuse of energy storage devices and increase the number of effective utilization hours, thereby improving the flexibility and utilization rate of energy storage resources in regulating the power system.

[0011] In some embodiments of the first aspect of the present application, predicting, based on the historical electricity market data, a first parameter corresponding to the independent energy storage device in a future period includes:

[0012] The first parameter also includes: the predicted electric energy price and predicted frequency regulation clearing price of the independent energy storage device in the future period; the historical electricity market data includes: the historical electricity price, historical frequency regulation clearing price, winning capacity and actual frequency regulation mileage of the corresponding node of the independent energy storage device in each of the historical periods;

[0013] According to the historical electricity prices of the nodes corresponding to the independent energy storage devices in each of the historical time periods, the predicted electricity price of the independent energy storage devices in the future time period is obtained through a preset autoregressive sliding average model;

[0014] According to the historical frequency modulation clearing price of the independent energy storage device in each of the historical time periods, obtaining the predicted frequency modulation clearing price of the independent energy storage device in a future time period;

[0015] The mileage-capacity ratio of the independent energy storage device is obtained according to the successful bid capacity and the actual frequency regulation mileage of the independent energy storage device in each of the historical time periods.

[0016] Compared with the prior art, the above embodiment has the following beneficial effects: in order to better characterize the node price of the spot electricity market, the predicted electricity price in the future period is obtained through historical price data, and further based on the predicted electricity price of the location of the energy storage device in the future period, the predicted frequency regulation clearing price and the mileage capacity ratio, the subsequent energy storage device theme is guided to carry out decision-making, so as to maximize the application potential of energy storage resources in peak shifting and frequency regulation scenarios.

[0017] In some embodiments of the first aspect of the present application, the step of obtaining the predicted electric energy price of the independent energy storage device in a future period by a preset autoregressive moving average model according to the historical electricity price of the node corresponding to the independent energy storage device in each historical period includes:

[0018] Calculate the historical average electricity price according to the historical electricity price of the node corresponding to the independent energy storage device in each historical period;

[0019] Calculate the electricity price deviation corresponding to each of the historical time periods of the independent energy storage device according to the historical average electricity price;

[0020] Inputting the electricity price deviation corresponding to the independent energy storage device in each of the historical time periods into the autoregressive moving average model to obtain the predicted electricity price deviation in each future time period;

[0021] The predicted electricity price corresponding to the future time period is obtained according to the predicted electricity price deviation and the historical average electricity price.

[0022] Compared with the prior art, the above embodiment has the following beneficial effects: after extracting the price deviation component sequence of the same period of historical operating days, the sliding regression self-averaging model is used to fit the predicted value of the price deviation component of the same period of future operating days. After obtaining the deviation component, the predicted energy price is obtained in combination with the average value of the same period in history. Under the condition of ensuring economic feasibility, the subsequent energy storage equipment theme is guided to carry out decision-making, so as to maximize the application potential of energy storage resources in peak shifting and frequency regulation scenarios.

[0023] In some embodiments of the first aspect of the present application, before inputting the electricity price deviation corresponding to each of the historical time periods of the independent energy storage device into the autoregressive moving average model, the method further includes:

[0024] According to the number of samples of the electricity price deviation, the first model parameters corresponding to the combination of each regression order and each sliding average order are obtained by using the maximum likelihood estimation method;

[0025] Determining the optimal autoregressive order according to the first model parameters corresponding to the combination of each autoregressive order with different sliding average orders by using the minimum information criterion;

[0026] The optimal sliding average order is determined according to the first model parameters corresponding to the combinations of each sliding average order with different autoregressive orders using the Bayesian information criterion.

[0027] Compared with the prior art, the above embodiment has the following beneficial effects: by optimizing the parameters of the autoregressive moving average model, the accuracy and stability of the autoregressive moving average model are significantly improved. The maximum likelihood estimation method and the information quantity criterion are used to ensure the selection of the optimal autoregressive and moving average orders, thereby effectively reducing the risk of overfitting.

[0028] In some embodiments of the first aspect of the present application, the auxiliary decision model is obtained by optimizing the regulation data of the power system of each independent energy storage device in different future time periods by considering the mutual exclusivity of the independent energy storage devices participating in different markets in the same time period, including:

[0029] The auxiliary decision model includes: a first objective function and a first constraint condition;

[0030] According to the first parameter, the first objective function is constructed with the goal of maximizing the comprehensive benefit of the independent energy storage device in the future period;

[0031] The first constraint condition is constructed by constraining the independent energy storage device not to participate in different markets during the same period of time and combining the operation constraints of the independent energy storage device.

[0032] Compared with the prior art, the above embodiment has the following beneficial effects: based on the predicted electricity price, predicted frequency regulation clearing price and mileage capacity ratio of the location of the independent energy storage device in the future time period, the mutual exclusion constraint of the independent energy storage device participating in two markets in the same time period and the operation regulation constraint of the energy storage are considered, and an auxiliary decision-making model with the greatest comprehensive benefit is constructed, which can obtain the charging / discharging capacity and frequency regulation capacity in different future time periods, realize the purpose of time-sharing reuse of independent energy storage devices and increase the effective utilization hours, thereby improving the flexibility and utilization rate of energy storage resources in regulating the power system.

[0033] In some embodiments of the first aspect of the present application, the constraint that the independent energy storage device cannot participate in different markets during the same period includes:

[0034] The following formula is used to constrain the independent energy storage device from participating in different markets during the same period:

[0035] 0≤x i,t+y i,t +z i,t ≤1

[0036] Among them, x i,t ,y i,t 、z i,t They respectively represent indicator variables of whether the independent energy storage device i is in charging, discharging or frequency modulation in the future period t. When the sum of the three indicator variables is 0, it means that the independent energy storage device does not participate in any market; when the sum of the three indicator variables is 1, it means that the independent energy storage device participates in the electric energy market and executes discharge or charging decisions, or the independent energy storage device participates in the frequency modulation service market and executes frequency modulation data.

[0037] Compared with the prior art, the above embodiment has the following beneficial effects: by limiting the sum of the decision variables to be less than or equal to 1, it is ensured that the energy storage device cannot be used for calling services in different markets at the same time, thereby ensuring the stability of subsequent power system operation and regulation.

[0038] In a second aspect, an embodiment of the present application further provides a power system operation balance adjustment device based on independent energy storage, comprising: a first parameter acquisition module, an adjustment data acquisition module and an adjustment module;

[0039] The first parameter acquisition module is used to acquire the historical electricity market data of all independent energy storage devices in each historical period, and predict the first parameter corresponding to the independent energy storage device in the future period according to the historical electricity market data; the first parameter includes: the mileage capacity ratio of the independent energy storage device;

[0040] The regulating data acquisition module is used to initialize a preset auxiliary decision model with the first parameter as a boundary condition, and solve the auxiliary decision model to obtain the regulating data of each independent energy storage device for regulating the balance of the power system in the future period; the auxiliary decision model is obtained by optimizing the regulating data of each independent energy storage device on the power system in different future periods by considering the mutual exclusivity of the independent energy storage devices participating in different markets in the same period; the regulating data includes: charging and discharging data and frequency modulation data of the independent energy storage devices in each future period;

[0041] The regulating module is used to respectively regulate the charging and discharging power and the frequency modulation capacity of the independent energy storage device corresponding to the future time period according to the charging and discharging data and the frequency modulation data.

[0042] In some embodiments of the second aspect of the present application, the first parameter acquisition module is used to predict the first parameter corresponding to the independent energy storage device in a future period according to the historical electricity market data, including:

[0043] The first parameter also includes: the predicted electric energy price and predicted frequency regulation clearing price of the independent energy storage device in the future period; the historical electricity market data includes: the historical electricity price, historical frequency regulation clearing price, winning capacity and actual frequency regulation mileage of the corresponding node of the independent energy storage device in each of the historical periods;

[0044] According to the historical electricity prices of the nodes corresponding to the independent energy storage devices in each of the historical time periods, the predicted electricity price of the independent energy storage devices in the future time period is obtained through a preset autoregressive sliding average model;

[0045] According to the historical frequency modulation clearing price of the independent energy storage device in each of the historical time periods, obtaining the predicted frequency modulation clearing price of the independent energy storage device in a future time period;

[0046] The mileage-capacity ratio of the independent energy storage device is obtained according to the successful bid capacity and the actual frequency regulation mileage of the independent energy storage device in each of the historical time periods.

[0047] In some embodiments of the second aspect of the present application, the auxiliary decision model is obtained by optimizing the regulation data of the power system of each independent energy storage device in different future time periods by considering the mutual exclusivity of the independent energy storage devices participating in different markets in the same time period, including:

[0048] The auxiliary decision model includes: a first objective function and a first constraint condition;

[0049] According to the first parameter, the first objective function is constructed with the goal of maximizing the comprehensive benefit of the independent energy storage device in the future period;

[0050] The first constraint condition is constructed by constraining the independent energy storage device not to participate in different markets during the same period of time and combining the operation constraints of the independent energy storage device.

[0051] In some embodiments of the second aspect of the present application, the constraint that the independent energy storage device cannot participate in different markets during the same period includes:

[0052] The following formula is used to constrain the independent energy storage device from participating in different markets during the same period:

[0053] 0≤x i,t +y i,t +z i,t ≤1

[0054] Among them, x i,t ,y i,t 、z i,tThey respectively represent indicator variables of whether the independent energy storage device i is in charging, discharging or frequency modulation in the future period t. When the sum of the three indicator variables is 0, it means that the independent energy storage device does not participate in any market; when the sum of the three indicator variables is 1, it means that the independent energy storage device participates in the electric energy market and executes discharge or charging decisions, or the independent energy storage device participates in the frequency modulation service market and executes frequency modulation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic flow chart of a method for adjusting the operation balance of a power system based on independent energy storage provided in some embodiments of the present application;

[0056] Figure 2 This is a schematic diagram of the structure of a power system operation balance adjustment device based on independent energy storage provided in some embodiments of the present application. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] Embodiment 1

[0059] Please refer to Figure 1 , a method for adjusting the operation balance of a power system based on independent energy storage provided in an embodiment of the present application, including S10 to S30, specifically:

[0060] S10: Obtain historical electricity market data of all independent energy storage devices in each historical period, and predict a first parameter corresponding to the independent energy storage device in a future period based on the historical electricity market data; the first parameter includes: the mileage capacity ratio of the independent energy storage device.

[0061] Further, in some embodiments of the present application, predicting the first parameter corresponding to the independent energy storage device in the future period according to the historical electricity market data includes:

[0062] The first parameter also includes: the predicted electric energy price and predicted frequency regulation clearing price of the independent energy storage device in the future period; the historical electricity market data includes: the historical electricity price, historical frequency regulation clearing price, winning capacity and actual frequency regulation mileage of the corresponding node of the independent energy storage device in each of the historical periods;

[0063] According to the historical electricity prices of the nodes corresponding to the independent energy storage devices in each of the historical time periods, the predicted electricity price of the independent energy storage devices in the future time period is obtained through a preset autoregressive sliding average model;

[0064] According to the historical frequency modulation clearing price of the independent energy storage device in each of the historical time periods, obtaining the predicted frequency modulation clearing price of the independent energy storage device in a future time period;

[0065] The mileage-capacity ratio of the independent energy storage device is obtained according to the successful bid capacity and the actual frequency regulation mileage of the independent energy storage device in each of the historical time periods.

[0066] In order to better characterize the node prices in the spot electricity market, the predicted electricity prices in future time periods are obtained through historical price data. Further, based on the predicted electricity prices in future time periods at the location of the energy storage equipment, the predicted frequency regulation clearing prices and the mileage capacity ratio, the subsequent energy storage equipment theme is guided to carry out decision-making, so as to maximize the application potential of energy storage resources in peak shifting and frequency regulation scenarios.

[0067] Further, in some embodiments of the present application, the predicted electricity price of the independent energy storage device in the future period is obtained by a preset autoregressive moving average model according to the historical electricity price of the node corresponding to the independent energy storage device in each historical period, including:

[0068] Calculate the historical average electricity price according to the historical electricity price of the node corresponding to the independent energy storage device in each historical period;

[0069] Calculate the electricity price deviation corresponding to each of the historical time periods of the independent energy storage device according to the historical average electricity price;

[0070] Inputting the electricity price deviation corresponding to the independent energy storage device in each of the historical time periods into the autoregressive moving average model to obtain the predicted electricity price deviation in each future time period;

[0071] The predicted electricity price corresponding to the future time period is obtained according to the predicted electricity price deviation and the historical average electricity price.

[0072] Compared with the prior art, the above embodiment has the following beneficial effects: after extracting the price deviation component sequence of the same period of historical operating days, the sliding regression self-averaging model is used to fit the predicted value of the price deviation component of the same period of future operating days. After obtaining the deviation component, the predicted energy price is obtained in combination with the average value of the same period in history. Under the condition of ensuring economic feasibility, the subsequent energy storage equipment theme is guided to carry out decision-making, so as to maximize the application potential of energy storage resources in peak shifting and frequency regulation scenarios.

[0073] Furthermore, in some embodiments of the present application, before inputting the electricity price deviation corresponding to each of the historical time periods of the independent energy storage device into the autoregressive moving average model, the method further includes:

[0074] According to the number of samples of the electricity price deviation, the first model parameters corresponding to the combination of each regression order and each sliding average order are obtained by using the maximum likelihood estimation method;

[0075] Determining the optimal autoregressive order according to the first model parameters corresponding to the combination of each autoregressive order with different sliding average orders by using the minimum information criterion;

[0076] The optimal sliding average order is determined according to the first model parameters corresponding to the combinations of each sliding average order with different autoregressive orders using the Bayesian information criterion.

[0077] By optimizing the parameters of the autoregressive moving average model, the accuracy and stability of the autoregressive moving average model are significantly improved. The maximum likelihood estimation method and information criterion are used to ensure the selection of the best autoregressive and moving average orders, thereby effectively reducing the risk of overfitting.

[0078] Preferably, in some embodiments of the present application, the first parameter can be obtained by the following preferred implementation:

[0079] First, the predicted electricity price of the independent energy storage device in the future period is obtained through the following steps S11 to S14:

[0080] S11: Determine the electricity price deviation of each independent energy storage device in the historical period of each historical operation day through the following steps S111 to S113:

[0081] S111: The average electricity price of all historical periods on the historical operation day is obtained by the following formula:

[0082]

[0083] in, is the average price of independent energy storage device i on the jth historical operation day; M j is the total number of electric energy market operation periods on the jth historical operation day; p i,j,t is the node historical electricity price of independent energy storage device i in historical period t on the jth historical operation day.

[0084] S112: Obtain the node historical electricity price relative to each historical period through the following formula The amount of deviation;

[0085]

[0086] Among them, τ i,j,t It is the deviation of the node historical electricity price of independent energy storage device i in historical period t on the jth historical operation day relative to the average price of the corresponding historical operation day.

[0087] S113: Set the target operation day (i.e., the future operation day) as the jth operation day, and obtain the deviations of the previous m historical operation days according to S111 to S112, thereby obtaining the following sample set:

[0088]

[0089] in, It is a sample set containing the deviation amount from the target operation day m days before, which is used to input into the autoregressive moving average model to obtain the predicted electricity price deviation amount in the future period.

[0090] S12: In Before inputting into the autoregressive moving average model, the relevant parameters of the autoregressive moving average model are obtained through the following steps S121 to S124 (the relevant parameters include the order k of the autoregressive component and the order q of the moving average in the autoregressive moving average model, the coefficients of the moving average model, and the coefficients of the autoregressive model):

[0091] S121: forming different order combinations of k and q according to a preset range;

[0092] S122: According to the sample set obtained in S113, the maximum likelihood estimation method is used to fit the coefficients of the moving average model and the coefficients of the autoregressive moving average model in the autoregressive moving average model under different order combinations;

[0093] S123: Starting from the low-order combination, the minimum information criterion (AI nformation Cr iterion, AIC) and the Bayesian information criterion (Bayesian information cr iter ion, BIC) are used in turn to compare and select the most appropriate autoregressive order k and sliding average order q, specifically:

[0094] According to the following minimum information criterion, the most appropriate autoregressive order is determined:

[0095]

[0096] in, is the variance of the maximum likelihood fitting residual corresponding to the k-order autoregressive model; AIC(k) is the AIC criterion function; m is the number of historical samples; k pro is the most appropriate autoregressive order; It means that after traversing the AIC(k) values ​​corresponding to different k values, the best k value is selected as k pro .

[0097] According to the following Bayesian information criterion, the most appropriate sliding average order is determined:

[0098]

[0099] in, is the variance of the maximum likelihood fitting residual corresponding to the q-order moving average model; BIC(q) is the BIC criterion function; m is the number of historical samples; q pro is the most appropriate sliding average order; It means that after traversing the BIC(q) values ​​corresponding to different q values, the best q value is selected as q pro .

[0100] S124: According to the autoregressive fitting and sliding average fitting results of S122 and S123, and the corresponding orders of the AIC and BI Criteria, the parameters of the autoregressive sliding average model in the following S13 can be obtained by comparison.

[0101] S13: obtained according to S11 As well as the parameters of the autoregressive moving average model obtained by S12, the autoregressive moving average model shown below is used to obtain the predicted electricity price deviation in the future period:

[0102]

[0103] Among them, k and q represent the order of the autoregressive component and the order of the moving average in the model respectively; to Represents the price deviation τ used to predict the t-th period of the operation day i,j,t The coefficients of the autoregressive model; θ 1,t to θ q,t represents the coefficient of the moving average model; ε t to ε j-q,t Represents the white noise of the price deviation time series of the t-th period of the historical running day.

[0104] It should be mentioned that an autoregressive moving average model needs to be constructed for each future period t. Based on the assumption that the target operation day is operation day j, the sample of the historical price deviation of the tth period from the historical operation day j-1 to jm is input into the corresponding autoregressive moving average model, and the predicted price deviation value of the tth period of the jth operation day can be output. Thus, the sequence A is obtained. i,j =(τ i,j,1 ,τ i,j,2 ,…,τ i,j,E ), where Ai,j It contains the node electricity price deviation forecast value of independent energy storage device i in each operating period (i.e. future period) on the j-th operating day (equivalent to the future operating day), and E is the total number of future periods.

[0105] S14: Based on the principle in S111, obtain B i,j,m It contains the average electricity price of all historical periods of the m historical operating days before the jth operating day, and i,j,m Select the samples that are the same working day or rest day as the jth operating day, denoted as Further from seeking B i ′ ,j,m The mean of all samples As the average of the predicted node electricity prices on the jth operation day, the predicted electricity prices for each operation period (i.e., future period) on the jth operation day are finally obtained according to the following formula:

[0106]

[0107]

[0108] Among them, p i,j,1 ,p i,j,2 ,…,p i,j,t ,…,p i,j,E That is, the predicted electricity price for each operating period (i.e. future period) on the jth operating day.

[0109] Afterwards, the predicted frequency regulation clearing price of the independent energy storage device in the future period is obtained according to S15:

[0110] S15: Considering that the clearing price of the frequency regulation market is independent of the location of the node where the independent energy storage is located (i.e., the frequency regulation market is priced according to the marginal clearing principle), the market price fluctuations in different operating days and the same period are small. Therefore, the time-segment frequency regulation market prices of the m historical operating days before the j-th operating day (i.e., the historical frequency regulation clearing prices) are used as samples, and the average value is directly calculated to obtain the frequency regulation market prices of each period of the j-th operating day (i.e., the predicted frequency regulation clearing prices). Therefore, the predicted frequency regulation clearing prices of independent energy storage equipment in future periods are obtained through the following formula:

[0111] L j,t =avg(L j-1,t ,L j-2,t ,…,L j-m,t )

[0112] Among them, L j,t is the predicted FM clearing price for the future period t on the j-th operating day.

[0113] Finally, the mileage capacity ratio of the independent energy storage device is obtained according to S16:

[0114] S16: Count the ratio of the actual frequency regulation mileage in each period to the bid-winning capacity in each period during the historical operation day, and obtain the mileage capacity ratio of the independent energy storage device through the following formula:

[0115]

[0116] Among them, g r,i represents the mileage capacity ratio of the independent energy storage device i on the jth operating day to the m previous historical operating days; T m represents the number of frequency modulation periods in the m historical operating days before the jth operating day; C i,t is the frequency regulation capacity of independent energy storage device i in the corresponding period; M i,t It is the actual frequency regulation mileage of independent energy storage device i in the corresponding period.

[0117] S20: Initialize a preset auxiliary decision model with the first parameter as the boundary condition, and solve the auxiliary decision model to obtain the regulation data of each independent energy storage device for regulating the balance of the power system in the future time period; the auxiliary decision model is obtained by optimizing the regulation data of the power system by each independent energy storage device in different future time periods by considering the mutual exclusivity of the independent energy storage devices participating in different markets in the same time period; the regulation data includes: the charging and discharging data and the frequency modulation data of the independent energy storage devices in each future time period.

[0118] Furthermore, in some embodiments of the present application, the auxiliary decision model is obtained by optimizing the regulation data of the power system by each independent energy storage device in different future time periods by considering the mutual exclusivity of the independent energy storage devices participating in different markets in the same time period, including:

[0119] The auxiliary decision model includes: a first objective function and a first constraint condition;

[0120] According to the first parameter, the first objective function is constructed with the goal of maximizing the comprehensive benefit of the independent energy storage device in the future period;

[0121] The first constraint condition is constructed by constraining the independent energy storage device not to participate in different markets during the same period of time and combining the operation constraints of the independent energy storage device.

[0122] Based on the predicted electricity price, predicted frequency regulation clearing price and mileage capacity ratio of the location of the independent energy storage device in the future time period, and considering the mutual exclusion constraints of the independent energy storage device participating in the two markets in the same time period and the operation regulation constraints of the energy storage, an auxiliary decision-making model with the greatest comprehensive benefit is constructed. The charging / discharging capacity and frequency regulation capacity in different future time periods can be obtained, so as to achieve the purpose of time-sharing reuse of independent energy storage equipment and increase the effective utilization hours, thereby improving the flexibility and utilization rate of energy storage resources in regulating the power system.

[0123] Preferably, in some embodiments of the present application, the auxiliary decision model can be constructed and obtained by the following steps:

[0124] S21: The first objective function of the decision-making support model is constructed using the following formula:

[0125]

[0126] Among them, T j is the total number of time periods on the jth operating day; and They are the charging and discharging power decisions of independent energy storage device i participating in market calls in period t to regulate the operation of the power system; and are charge and discharge efficiency respectively; The frequency regulation capacity of the independent energy storage device i participating in the market call in period t to adjust the operation of the power system; and L j,t They are the frequency regulation capacity compensation standard and frequency regulation market clearing price in period t, respectively, and are usually constants.

[0127] S22: The first constraint condition of the auxiliary decision model is constructed by the following formulas from S221 to S224:

[0128] S221: In the time-sharing reuse mode, independent energy storage devices cannot participate in the electric energy market and frequency regulation auxiliary services at the same time. Therefore, it is necessary to add the following constraints: the energy storage is selectively in the indication state of charging, discharging or providing frequency regulation capacity:

[0129] 0≤x i,t +y i,t +z i,t ≤1

[0130] Among them, x i,t ,y i,t 、z i,tThey respectively represent indicator variables of whether the independent energy storage device i is in charging, discharging or frequency modulation in the future period t. When the sum of the three indicator variables is 0, it means that the independent energy storage device does not participate in any market; when the sum of the three indicator variables is 1, it means that the independent energy storage device participates in the electric energy market and executes discharge or charging decisions, or the independent energy storage device participates in the frequency modulation service market and executes frequency modulation data.

[0131] S222: Maximum and minimum power constraints of independent energy storage devices:

[0132]

[0133] Among them, P i,min and P i,max are the maximum and minimum output power limits of independent energy storage device i respectively.

[0134] S223: Energy storage charge state constraints, considering that when energy storage participates in market calls in different time periods, it should not exceed the charge state limits of energy storage in each operating period:

[0135]

[0136] Among them, SOC i,t SOC is the state of charge of independent energy storage device i in period t; i,max and SOC i,min are the maximum and minimum state of charge limits of independent energy storage device i; E i es is the rated power of independent energy storage device i.

[0137] S224: Maximum charge and discharge times of energy storage during operation day:

[0138]

[0139] in, is the maximum number of cycles of independent energy storage device i.

[0140] S30: According to the charging and discharging data and the frequency modulation data, respectively adjust the charging and discharging power and the frequency modulation capacity of the independent energy storage device corresponding to the future time period.

[0141] From the above, it can be seen that the method for adjusting the balance of power system operation based on independent energy storage provided by the embodiment of the present application has the following beneficial effects: due to the time-sharing reuse scenario, independent energy storage equipment can participate in different markets at different time periods to provide subsequent services for adjusting the balance of the power system. Therefore, by using the historical electricity market data of the historical time period, predicting the future electricity market data, obtaining the first parameter, and using the first parameter as the boundary condition of the subsequent auxiliary decision-making model, the independent processing of the charging and discharging power and frequency regulation capacity in each future time period is obtained, so that the subsequent independent energy storage equipment can dynamically configure the capacity of each time period for providing peak shifting and valley filling and frequency regulation auxiliary services according to a more reasonable charging operation plan and the operation needs of the power system, so as to achieve the purpose of time-sharing reuse of energy storage equipment and increase the number of effective utilization hours, thereby improving the flexibility and utilization rate of energy storage resources in regulating the power system.

[0142] Embodiment 2

[0143] refer to Figure 2 , a power system operation balance adjustment device based on independent energy storage provided in an embodiment of the present application, includes: a first parameter acquisition module 11, an adjustment data acquisition module 12 and an adjustment module 13.

[0144] Furthermore, in some embodiments of the present application, the first parameter acquisition module 11 is used to obtain historical electricity market data of all independent energy storage devices in each historical period, and predict the first parameter corresponding to the independent energy storage device in the future period based on the historical electricity market data; the first parameter includes: the mileage capacity ratio of the independent energy storage device; the adjustment data acquisition module 12 is used to initialize a preset auxiliary decision model with the first parameter as the boundary condition, and solve the auxiliary decision model to obtain the adjustment data of each independent energy storage device for adjusting the balance of the power system in the future period; the auxiliary decision model is obtained by optimizing the adjustment data of each independent energy storage device on the power system in different future periods by considering the mutual exclusivity of the independent energy storage devices participating in different markets in the same period; the adjustment data includes: the charging and discharging data and the frequency modulation data of the independent energy storage devices in each future period; the adjustment module 13 is used to adjust the charging and discharging power and the frequency modulation capacity of the independent energy storage devices in the corresponding future period according to the charging and discharging data and the frequency modulation data.

[0145] Further, in some embodiments of the present application, the first parameter acquisition module 11 is used to predict the first parameter corresponding to the independent energy storage device in the future time period according to the historical electricity market data, including: the first parameter also includes: the predicted electric energy price and predicted frequency modulation clearing price of the independent energy storage device in the future time period; the historical electricity market data includes: the historical electricity price of the corresponding node of the independent energy storage device in each historical time period, the historical frequency modulation clearing price, the winning capacity and the actual frequency modulation mileage; according to the historical electricity price of the corresponding node of the independent energy storage device in each historical time period, the predicted electric energy price of the independent energy storage device in the future time period is obtained through a preset autoregressive moving average model; according to the historical frequency modulation clearing price of the independent energy storage device in each historical time period, the predicted frequency modulation clearing price of the independent energy storage device in the future time period is obtained; according to the winning capacity and the actual frequency modulation mileage of the independent energy storage device in each historical time period, the mileage-to-capacity ratio of the independent energy storage device is obtained.

[0146] Furthermore, in some embodiments of the present application, the predicted electricity price of the independent energy storage device in the future time period is obtained through a preset autoregressive sliding average model based on the historical electricity prices of the nodes corresponding to the independent energy storage device in each of the historical time periods, including: calculating the historical average electricity price based on the historical electricity prices of the nodes corresponding to the independent energy storage device in each of the historical time periods; calculating the electricity price deviation corresponding to the independent energy storage device in each of the historical time periods based on the historical average electricity price; inputting the electricity price deviation corresponding to the independent energy storage device in each of the historical time periods into the autoregressive sliding average model to obtain the predicted electricity price deviation for each future time period; and obtaining the predicted electricity price corresponding to the future time period based on the predicted electricity price deviation and the historical average electricity price.

[0147] Furthermore, in some embodiments of the present application, before the electricity price deviation corresponding to the independent energy storage device in each of the historical time periods is input into the autoregressive moving average model, it also includes: according to the sample size of the electricity price deviation, obtaining the first model parameters corresponding to the combination of each regression order and each sliding average order through the maximum likelihood estimation method; according to the first model parameters corresponding to the combination of each autoregressive order with different sliding average orders, determining the optimal autoregressive order through the minimum information criterion; according to the first model parameters corresponding to the combination of each sliding average order with different autoregressive orders, determining the optimal sliding average order through the Bayesian information criterion.

[0148] Furthermore, in some embodiments of the present application, the auxiliary decision model is obtained by optimizing the regulation data of each independent energy storage device on the power system in different future time periods by considering the mutual exclusivity of the independent energy storage devices participating in different markets in the same time period, including: the auxiliary decision model includes: a first objective function and a first constraint condition; according to the first parameter, the first objective function is constructed with the goal of maximizing the comprehensive benefits of the independent energy storage devices in the future time period; the first constraint condition is constructed by constraining the independent energy storage devices from participating in different markets in the same time period, combined with the operation constraints of the independent energy storage devices.

[0149] Furthermore, in some embodiments of the present application, the constraint that the independent energy storage device cannot participate in different markets during the same period includes:

[0150] The following formula is used to constrain the independent energy storage device from participating in different markets during the same period:

[0151] 0≤x i,t +y i,t +z i,t ≤1

[0152] Among them, x i,t ,y i,t 、z i,t They respectively represent indicator variables of whether the independent energy storage device i is in charging, discharging or frequency modulation in the future period t. When the sum of the three indicator variables is 0, it means that the independent energy storage device does not participate in any market; when the sum of the three indicator variables is 1, it means that the independent energy storage device participates in the electric energy market and executes discharge or charging decisions, or the independent energy storage device participates in the frequency modulation service market and executes frequency modulation data.

[0153] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present invention. The embodiment of the present invention provides an electric power system operation balance adjustment device based on independent energy storage, which can implement any method item embodiment of the present invention, that is, the electric power system operation balance adjustment method based on independent energy storage provided in embodiment one.

[0154] From the above, it can be seen that the power system operation balance adjustment device based on independent energy storage provided by the embodiment of the present application has the following beneficial effects: due to the time-sharing reuse scenario, the independent energy storage device can participate in different markets at different time periods to provide subsequent services for adjusting the balance of the power system. Therefore, through the historical electricity market data of the historical time period, the future electricity market data is predicted, the first parameter is obtained, and the first parameter is used as the boundary condition of the subsequent auxiliary decision-making model to obtain the independent processing of the charging and discharging power and frequency regulation capacity in each future time period, so that the subsequent independent energy storage device can dynamically configure the capacity of each time period for providing peak shifting and valley filling and frequency regulation auxiliary services according to a more reasonable charging operation plan and the operation needs of the power system, so as to achieve the purpose of time-sharing reuse of energy storage equipment and increase the number of effective utilization hours, thereby improving the flexibility and utilization rate of energy storage resources in regulating the power system.

[0155] Embodiment 3

[0156] Based on the above-mentioned embodiment of the power system operation balancing regulation method based on independent energy storage, another embodiment of the present application provides a power system operation balancing regulation terminal device based on independent energy storage, and the power system operation balancing regulation terminal device based on independent energy storage includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the power system operation balancing regulation method based on independent energy storage of any embodiment of the present application is implemented.

[0157] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, and the one or more modules are stored in the memory and executed by the processor to complete the present application. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the independent energy storage-based power system operation balancing regulation device.

[0158] The power system operation balance regulation device based on independent energy storage can be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The power system operation balance regulation terminal device based on independent energy storage can include, but is not limited to, a processor and a memory.

[0159] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the power system operation balance regulation device based on independent energy storage, and uses various interfaces and lines to connect the various parts of the power system operation balance regulation device based on independent energy storage. The memory may be used to store the computer program and / or module, and the processor realizes various functions of the power system operation balance regulation device based on independent energy storage by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the mobile phone, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMed i aCard, SMC), a secure digital (SecureDi gi tal, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0160] Embodiment 4

[0161] Based on the above-mentioned embodiment of the power system operation balance regulation method based on independent energy storage, another embodiment of the present application provides a storage medium, wherein the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the power system operation balance regulation method based on independent energy storage according to any embodiment of the present application.

[0162] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0163] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for adjusting the operation balance of a power system based on independent energy storage, characterized in that: include: Obtaining historical electricity market data of all independent energy storage devices in each historical period, and predicting a first parameter corresponding to the independent energy storage device in a future period based on the historical electricity market data; the first parameter includes: a mileage capacity ratio of the independent energy storage device; Taking the first parameter as the boundary condition, initializing a preset auxiliary decision model, and solving the auxiliary decision model to obtain the regulation data of each independent energy storage device for regulating the balance of the power system in the future period; the auxiliary decision model is obtained by optimizing the regulation data of each independent energy storage device on the power system in different future periods by considering the mutual exclusivity of the independent energy storage devices participating in different markets in the same period; the regulation data includes: the charging and discharging data and the frequency modulation data of the independent energy storage devices in each future period; According to the charging and discharging data and the frequency modulation data, the charging and discharging power and the frequency modulation capacity of the independent energy storage device corresponding to the future time period are adjusted respectively.

2. A method for adjusting the operation balance of a power system based on independent energy storage according to claim 1, characterized in that: The predicting, based on the historical electricity market data, a first parameter corresponding to the independent energy storage device in a future period includes: The first parameter also includes: the predicted electric energy price and predicted frequency regulation clearing price of the independent energy storage device in the future period; the historical electricity market data includes: the historical electricity price, historical frequency regulation clearing price, winning capacity and actual frequency regulation mileage of the corresponding node of the independent energy storage device in each of the historical periods; According to the historical electricity prices of the nodes corresponding to the independent energy storage devices in each of the historical time periods, the predicted electricity price of the independent energy storage devices in the future time period is obtained through a preset autoregressive sliding average model; According to the historical frequency modulation clearing price of the independent energy storage device in each of the historical time periods, obtaining the predicted frequency modulation clearing price of the independent energy storage device in a future time period; The mileage-capacity ratio of the independent energy storage device is obtained according to the successful bid capacity and the actual frequency regulation mileage of the independent energy storage device in each of the historical time periods.

3. A method for adjusting the operation balance of a power system based on independent energy storage as claimed in claim 2, characterized in that: The method of obtaining the predicted electric energy price of the independent energy storage device in a future time period by using a preset autoregressive sliding average model according to the corresponding node historical electricity price of the independent energy storage device in each historical time period includes: Calculate the historical average electricity price according to the historical electricity price of the node corresponding to the independent energy storage device in each historical period; Calculate the electricity price deviation corresponding to each of the historical time periods of the independent energy storage device according to the historical average electricity price; Inputting the electricity price deviation corresponding to the independent energy storage device in each of the historical time periods into the autoregressive moving average model to obtain the predicted electricity price deviation in each future time period; The predicted electricity price corresponding to the future time period is obtained according to the predicted electricity price deviation and the historical average electricity price.

4. A method for adjusting the operation balance of a power system based on independent energy storage as claimed in claim 3, characterized in that: Before inputting the electricity price deviation corresponding to the independent energy storage device in each of the historical time periods into the autoregressive moving average model, the method further includes: According to the number of samples of the electricity price deviation, the first model parameters corresponding to the combination of each regression order and each sliding average order are obtained by using the maximum likelihood estimation method; Determining the optimal autoregressive order according to the first model parameters corresponding to the combination of each autoregressive order with different sliding average orders by using the minimum information criterion; The optimal sliding average order is determined according to the first model parameters corresponding to the combinations of each sliding average order with different autoregressive orders using the Bayesian information criterion.

5. A method for adjusting the operation balance of a power system based on independent energy storage as claimed in claim 1, characterized in that: The auxiliary decision model is obtained by optimizing the regulation data of the power system by each independent energy storage device in different future time periods by considering the mutual exclusivity of the independent energy storage devices participating in different markets in the same time period, and includes: The auxiliary decision model includes: a first objective function and a first constraint condition; According to the first parameter, the first objective function is constructed with the goal of maximizing the comprehensive benefit of the independent energy storage device in the future period; The first constraint condition is constructed by constraining the independent energy storage device not to participate in different markets during the same period of time and combining the operation constraints of the independent energy storage device.

6. A method for adjusting the operation balance of a power system based on independent energy storage as claimed in claim 5, characterized in that: The constraints that the independent energy storage device cannot participate in different markets during the same period include: The following formula is used to constrain the independent energy storage device from participating in different markets during the same period: 0≤x i,t +y i,t +z i,t ≤1 Among them, x i,t ,y i,t 、z i,t They respectively represent indicator variables of whether the independent energy storage device i is in charging, discharging or frequency modulation in the future period t. When the sum of the three indicator variables is 0, it means that the independent energy storage device does not participate in any market; when the sum of the three indicator variables is 1, it means that the independent energy storage device participates in the electric energy market and executes discharge or charging decisions, or the independent energy storage device participates in the frequency modulation service market and executes frequency modulation data.

7. A power system operation balance adjustment device based on independent energy storage, characterized in that: include: A first parameter acquisition module, an adjustment data acquisition module and an adjustment module; The first parameter acquisition module is used to acquire the historical electricity market data of all independent energy storage devices in each historical period, and predict the first parameter corresponding to the independent energy storage device in the future period according to the historical electricity market data; the first parameter includes: the mileage capacity ratio of the independent energy storage device; The regulating data acquisition module is used to initialize a preset auxiliary decision model with the first parameter as a boundary condition, and solve the auxiliary decision model to obtain the regulating data of each independent energy storage device for regulating the balance of the power system in the future period; the auxiliary decision model is obtained by optimizing the regulating data of each independent energy storage device on the power system in different future periods by considering the mutual exclusivity of the independent energy storage devices participating in different markets in the same period; the regulating data includes: charging and discharging data and frequency modulation data of the independent energy storage devices in each future period; The regulating module is used to respectively regulate the charging and discharging power and the frequency modulation capacity of the independent energy storage device corresponding to the future time period according to the charging and discharging data and the frequency modulation data.

8. The power system operation balance regulating device based on independent energy storage as claimed in claim 7, characterized in that: The first parameter acquisition module is used to predict the first parameter corresponding to the independent energy storage device in a future period according to the historical electricity market data, including: The first parameter also includes: the predicted electric energy price and predicted frequency regulation clearing price of the independent energy storage device in the future period; the historical electricity market data includes: the historical electricity price, historical frequency regulation clearing price, winning capacity and actual frequency regulation mileage of the corresponding node of the independent energy storage device in each of the historical periods; According to the historical electricity prices of the nodes corresponding to the independent energy storage devices in each of the historical time periods, the predicted electricity price of the independent energy storage devices in the future time period is obtained through a preset autoregressive sliding average model; According to the historical frequency modulation clearing price of the independent energy storage device in each of the historical time periods, obtaining the predicted frequency modulation clearing price of the independent energy storage device in a future time period; The mileage-capacity ratio of the independent energy storage device is obtained according to the successful bid capacity and the actual frequency regulation mileage of the independent energy storage device in each of the historical time periods.

9. The power system operation balance regulating device based on independent energy storage according to claim 7, characterized in that: The auxiliary decision model is obtained by optimizing the regulation data of the power system by each independent energy storage device in different future time periods by considering the mutual exclusivity of the independent energy storage devices participating in different markets in the same time period, and includes: The auxiliary decision model includes: a first objective function and a first constraint condition; According to the first parameter, the first objective function is constructed with the goal of maximizing the comprehensive benefit of the independent energy storage device in the future period; The first constraint condition is constructed by constraining the independent energy storage device not to participate in different markets during the same period of time and combining the operation constraints of the independent energy storage device.

10. The power system operation balance regulating device based on independent energy storage according to claim 9, characterized in that: The constraints that the independent energy storage device cannot participate in different markets during the same period include: The following formula is used to constrain the independent energy storage device from participating in different markets during the same period: 0≤x i,t +y i,t +z i,t ≤1 Among them, x i,t ,y i,t 、z i,t They respectively represent indicator variables of whether the independent energy storage device i is in charging, discharging or frequency modulation in the future period t. When the sum of the three indicator variables is 0, it means that the independent energy storage device does not participate in any market; when the sum of the three indicator variables is 1, it means that the independent energy storage device participates in the electric energy market and executes discharge or charging decisions, or the independent energy storage device participates in the frequency modulation service market and executes frequency modulation data.

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