Independent energy storage-based method and apparatus for regulating operational balance of power system

Abstract

An independent energy storage-based method and apparatus for regulating operational balance of a power system. The method comprises: acquiring historical electricity market data of all independent energy storage devices across historical time periods, and on the basis of the historical electricity market data, predicting a first parameter of a corresponding independent energy storage device in a future time period; using the first parameter as a boundary condition to initialize a preset auxiliary decision-making model, and solving the auxiliary decision-making model; acquiring regulation data of each independent energy storage device for regulating the balance of a power system in the future time period, the regulation data comprising charging / discharging data and frequency regulation data of the independent energy storage device in each future time period; and on the basis of the charging / discharging data and the frequency regulation data, respectively regulating charging / discharging power and frequency regulation capacity of the corresponding independent energy storage device in the corresponding future time period. In this way, time-division multiplexing of the independent energy storage device is achieved, thereby improving the flexibility and effective utilization hours of the independent energy storage device when regulating the power system.

Description

A power system operation balance regulation method and device based on independent energy storage TECHNICAL FIELD

[0001] The present application relates to the field of energy storage scheduling, in particular to a power system operation balance regulation method and device based on independent energy storage. BACKGROUND

[0002] In the current power market trading environment, energy storage can be used for frequency modulation auxiliary services and energy market services to obtain profits. Through the marketization of energy storage, the operation balance of the power system is regulated. However, this method is limited by the regulation constraints of the operating characteristics of the energy storage itself on the one hand, and the benefits of the energy storage participating in the regulation on the other hand.

[0003] The prior art usually maximizes the benefits of energy storage charging and discharging on the basis of ensuring that the charging and discharging power of energy storage in each period meets the maximum and minimum discharging power and state of charge constraints, and does not consider the regulation capacity of energy storage providing frequency modulation and other auxiliary services, which wastes the regulation potential of energy storage in scenarios other than peak load shifting in the power system. In addition, the prior art considers the mode of energy storage participating in the joint clearing of energy and frequency modulation, standby and other auxiliary service markets, and constructs an optimization control model and algorithm for energy storage participating in multiple power trading varieties. The above method does not conform to the actual situation of energy storage participating in different market transactions in the current practice, and therefore cannot maximize the effective utilization level of energy storage in different time periods and different operating scenarios, nor can it guide energy storage to reasonably develop the optimal charging and discharging plan in the future operating stage.

[0004] Therefore, how to ensure the benefits of energy storage while optimizing the frequency modulation auxiliary services and energy market services of the same energy storage in different time periods to improve the utilization of energy storage in the "time-sharing multiplexing" scenario is a technical problem to be solved at present. SUMMARY

[0005] The present application provides a power system operation balance regulation method and device based on independent energy storage to solve the technical problem of low utilization of independent energy storage devices participating in market and balancing the power system in the "time-sharing multiplexing" scenario.

[0006] To solve the above technical problem, in a first aspect, the present application provides a power system operation balance regulation method 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 of the independent energy storage device in a future period according to the historical electricity market data; the first parameter includes a mileage capacity ratio of the independent energy storage device;

[0008] The first parameter is used as a boundary condition to initialize a preset auxiliary decision model, and the auxiliary decision model is solved to obtain adjustment data of each independent energy storage device for regulating power system balance in future time periods; the auxiliary decision model is obtained by considering the mutual exclusivity of the independent energy storage device participating in different markets in the same time period, and optimizing adjustment data of each independent energy storage device for the power system in different future time periods; the adjustment data includes charge and discharge data and frequency modulation data of the independent energy storage device in future time periods;

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

[0010] Compared with the prior art, the embodiments of the present application have the following beneficial effects: in the time-sharing multiplexing scenario, the independent energy storage device can participate in different markets in different time periods to provide subsequent adjustment services for power system balance. Therefore, by predicting future electricity market data from historical electricity market data of historical time periods, obtaining the first parameter, and using the first parameter as a boundary condition of a subsequent auxiliary decision model, the charge and discharge power and the frequency modulation capacity of the independent energy storage device in future time periods are obtained, so that the subsequent independent energy storage device can dynamically configure the capacity of each time period for providing peak shaving and frequency modulation auxiliary services according to a more reasonable charging operation plan and the operation needs of the power system, realize time-sharing multiplexing of the energy storage device, and improve the effective utilization hours, thereby improving the flexibility and utilization rate of the energy storage resource when regulating the power system.

[0011] In some embodiments of the first aspect of the present application, the first parameter corresponding to the independent energy storage device in the future time period is predicted according to the historical electricity market data, including:

[0012] The first parameter further includes a predicted electricity energy price and a predicted frequency modulation clearing price of the independent energy storage device in the future time period; and the historical electricity market data includes corresponding node historical electricity prices, historical frequency modulation clearing prices, bid capacities and actual frequency modulation distances of the independent energy storage device in each historical time period;

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

[0014] According to the historical frequency modulation clearing prices 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;

[0015] According to the bid capacity and the actual frequency modulation mileage of the independent energy storage device in each historical period, the mileage capacity ratio of the independent energy storage device is obtained.

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

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

[0018] According to the corresponding node historical electricity price of the independent energy storage device in each historical period, a historical average electricity price is calculated;

[0019] According to the historical average electricity price, the corresponding electricity price deviation amount of the independent energy storage device in each historical period is calculated;

[0020] The corresponding electricity price deviation amount of the independent energy storage device in each historical period is input into the autoregressive moving average model to obtain the predicted electricity price deviation amount of each future period;

[0021] According to the predicted electricity price deviation amount and the historical average electricity price, the predicted electricity price of the corresponding future period is obtained.

[0022] Compared with the prior art, the above-mentioned embodiment has the following beneficial effects: after extracting the price deviation component sequence of the same period of the historical operation day, the predicted value of the price deviation component of the same period of the future operation day is obtained by fitting the sliding regression self-average model, when the deviation component is obtained, the predicted energy price is obtained by combining the average value of the same period of the historical operation day, which ensures that the economic feasibility is guided to make decisions for the subsequent energy storage device theme, and the application potential of the energy storage resource in the peak load shifting and frequency modulation scenarios is maximized.

[0023] In some embodiments of the first aspect of the present application, before the corresponding electricity price deviation amount of the independent energy storage device in each historical period is input into the autoregressive moving average model, further comprising:

[0024] According to the sample number of the electricity price deviation amount, the first model parameter corresponding to the combination of each autoregressive order and each moving average order is obtained by maximum likelihood estimation method.

[0025] According to the first model parameters corresponding to each combination of the autoregressive order and the different moving average order, the optimal autoregressive order is determined by the minimum information criterion.

[0026] According to the first model parameters corresponding to each combination of the moving average order and the different autoregressive order, the optimal moving average order is determined by 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 criterion are used to ensure that the best autoregressive and moving average order is selected, thereby effectively reducing the risk of overfitting.

[0028] In some embodiments of the first aspect of the application, the auxiliary decision model, by considering the exclusivity of the independent energy storage device participating in different markets in the same period, optimizes the adjustment data of each independent energy storage device to the power system in different future periods, and comprises:

[0029] The auxiliary decision model comprises a first objective function and a first constraint condition.

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

[0031] By constraining the independent energy storage device from participating in different markets in the same period, the first constraint condition is constructed in combination with 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 energy price, the predicted frequency modulation clearing price and the mileage capacity ratio of the location where the independent energy storage device is located in the future period, considering the exclusivity constraint of the independent energy storage device participating in two markets in the same period and the operation adjustment constraint of the energy storage, the auxiliary decision model with the maximum comprehensive benefit is constructed, the charging / discharging capacity and the frequency modulation capacity in different future periods can be obtained, the independent energy storage device is time-sharing multiplexed, the effective utilization hours are improved, and the flexibility and utilization rate of the energy storage resource in adjusting the power system are improved.

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

[0034] The constraint that the independent energy storage device cannot participate in different markets in the same period is constrained by the following formula: 0≤x i,t +y i,t +zi,t ≤1

[0035] wherein x i,t , y i,t , z i,t represent the indicator variables of the independent energy storage device i in the future time period t in charging, discharging or frequency modulation, respectively, 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 electricity market and executes the discharging or charging decision, or the independent energy storage device participates in the frequency modulation service market and executes the frequency modulation data.

[0036] Compared with the prior art, the above embodiment has the following beneficial effects: by limiting the sum of each decision variable to be less than or equal to 1, it is ensured that the energy storage device cannot be used for calling services of different markets at the same time, and the stability of subsequent power system operation regulation is ensured.

[0037] In a second aspect, the embodiments of the present application also provide a power system operation balance regulation device based on independent energy storage, comprising: a first parameter acquisition module, a regulation data acquisition module and a regulation module.

[0038] The first parameter acquisition module is configured to acquire historical electricity market data of all independent energy storage devices in each historical time period, and predict first parameters of the independent energy storage devices in future time periods according to the historical electricity market data; the first parameters include: mileage capacity ratios of the independent energy storage devices.

[0039] The regulation data acquisition module is configured to initialize a preset auxiliary decision model with the first parameters as boundary conditions, and solve the auxiliary decision model to acquire regulation data of each independent energy storage device for regulating power system balance in future time periods; the auxiliary decision model is obtained by optimizing regulation data of each independent energy storage device for regulating 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; the regulation data includes: charging and discharging data and frequency modulation data of the independent energy storage devices in future time periods.

[0040] The regulation module is configured to regulate charging and discharging power and frequency modulation capacity of the independent energy storage devices in corresponding future time periods according to the charging and discharging data and the frequency modulation data, respectively.

[0041] In some embodiments of the second aspect of the present application, the first parameter acquisition module is configured to predict first parameters of the independent energy storage devices in future time periods according to the historical electricity market data, comprising:

[0042] The first parameter further comprises: a predicted electricity energy price and a predicted frequency modulation clearing price of the independent energy storage device in a future period; and the historical electricity market data comprises: corresponding node historical electricity prices, historical frequency modulation clearing prices, winning capacities and actual frequency modulation mileages of the independent energy storage device in each historical period.

[0043] According to the corresponding node historical electricity prices of the independent energy storage device in each historical period, the predicted electricity energy price of the independent energy storage device in a future period is obtained through a preset autoregressive moving average model.

[0044] According to the historical frequency modulation clearing prices of the independent energy storage device in each historical period, the predicted frequency modulation clearing price of the independent energy storage device in a future period is obtained.

[0045] According to the winning capacities and the actual frequency modulation mileages of the independent energy storage device in each historical period, the mileage capacity ratio of the independent energy storage device is obtained.

[0046] In some embodiments of the second aspect of the present application, the auxiliary decision model is obtained by optimizing adjustment data of each independent energy storage device to the power system in different future periods by considering mutual exclusivity of the independent energy storage device participating in different markets in the same period, and comprises:

[0047] The auxiliary decision model comprises: a first objective function and a first constraint condition.

[0048] According to the first parameter, a first objective function is constructed with the maximum comprehensive benefit of the independent energy storage device in a future period as a target.

[0049] The first constraint condition is constructed by combining operation constraints of the independent energy storage device by restricting that the independent energy storage device cannot participate in different markets in the same period.

[0050] In some embodiments of the second aspect of the present application, the restriction that the independent energy storage device cannot participate in different markets in the same period comprises:

[0051] The independent energy storage device cannot participate in different markets in the same period is restricted by the following formula: 0≤x i,t +y i,t +z i,t ≤1

[0052] Wherein, x i,t , y i,t , z i,trespectively represent the indication variables of the independent energy storage device i in the future time period t in charging, discharging or frequency modulation, when the sum of the three indication variables is 0, it means that the independent energy storage device does not participate in any market; when the sum of the three indication variables is 1, it represents that the independent energy storage device participates in the electricity market and executes the discharging or charging decision, or the independent energy storage device participates in the frequency modulation service market and executes the frequency modulation data. BRIEF DESCRIPTION OF DRAWINGS

[0053] Fig. 1 is a flowchart of a method for balancing and regulating power system operation based on independent energy storage according to some embodiments of the present application;

[0054] Fig. 2 is a structural diagram of a device for balancing and regulating power system operation based on independent energy storage according to some embodiments of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0056] Embodiment one

[0057] Please refer to Fig. 1, which is a method for balancing and regulating power system operation based on independent energy storage according to some embodiments of the present application, including S10 to S30, specifically:

[0058] S10: Obtain historical electricity market data of all independent energy storage devices in each historical time period, and predict a first parameter corresponding to the independent energy storage device in the future time period according to the historical electricity market data; the first parameter includes: the mileage capacity ratio of the independent energy storage device.

[0059] Further, in some embodiments of the present application, the first parameter corresponding to the independent energy storage device in the future time period is predicted according to the historical electricity market data, including:

[0060] The first parameter further includes: the predicted electricity price and the predicted frequency modulation clearing price of the independent energy storage device in the future time period; the historical electricity market data includes: the corresponding node historical price, the historical frequency modulation clearing price, the winning capacity and the actual frequency modulation mileage of the independent energy storage device in each historical time period;

[0061] According to the corresponding node historical price of the independent energy storage device in each historical time period, the predicted electricity price of the independent energy storage device in the future time period is obtained through a preset autoregressive moving average model;

[0062] obtaining the predicted frequency modulation out-clearing price of the independent energy storage device in a future period according to the historical frequency modulation out-clearing price of the independent energy storage device in each historical period;

[0063] obtaining the mileage-capacity ratio of the independent energy storage device according to the bid capacity and the actual frequency modulation mileage of the independent energy storage device in each historical period.

[0064] In order to better characterize the node price of the spot electricity energy market, the predicted electricity energy price in a future period is obtained through historical price data, and further based on the predicted electricity energy price, the predicted frequency modulation out-clearing price and the mileage-capacity ratio of the location where the energy storage device is located in the future period, subsequent decision-making of the energy storage device theme is guided, and the application potential of the energy storage resource in the peak load shifting and frequency modulation scenarios is maximized.

[0065] Further, in some embodiments of the present application, the obtaining the predicted electricity energy price of the independent energy storage device in a future period according to the corresponding node historical electricity price of the independent energy storage device in each historical period through a preset autoregressive moving average model comprises:

[0066] calculating a historical average electricity price according to the corresponding node historical electricity price of the independent energy storage device in each historical period;

[0067] calculating the electricity price deviation amount corresponding to each historical period of the independent energy storage device according to the historical average electricity price;

[0068] inputting the electricity price deviation amount corresponding to each historical period of the independent energy storage device into the autoregressive moving average model to obtain the predicted electricity price deviation amount of each future period;

[0069] obtaining the predicted electricity energy price corresponding to a future period according to the predicted electricity price deviation amount and the historical average electricity price.

[0070] Compared with the prior art, the above-mentioned embodiments have the following beneficial effects: after extracting the price deviation component sequence of the same period of the historical operation day, the predicted price deviation component value of the same period of the future operation day is fitted according to the sliding regression self-average model, when the deviation component is obtained, the predicted energy price is obtained by combining the average value of the same period of the historical day, the subsequent decision-making of the energy storage device theme is guided under the condition of ensuring economic feasibility, and the application potential of the energy storage resource in the peak load shifting and frequency modulation scenarios is maximized.

[0071] Further, in some embodiments of the present application, before inputting the electricity price deviation amount corresponding to each historical period of the independent energy storage device into the autoregressive moving average model, the method further comprises:

[0072] According to the sample number of the electricity price deviation amount, the first model parameters corresponding to the combination of the respective autoregressive order and the respective moving average order are obtained by maximum likelihood estimation method;

[0073] According to the first model parameters corresponding to the combination of the respective autoregressive order and the different moving average order, the optimal autoregressive order is determined by minimum information criterion;

[0074] According to the first model parameters corresponding to the combination of the respective moving average order and the different autoregressive order, the optimal moving average order is determined by Bayesian information criterion.

[0075] 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 criterion are used to ensure the selection of the best autoregressive and moving average order, thereby effectively reducing the risk of overfitting.

[0076] Preferably, in some embodiments of the present application, the first parameter can be obtained by the following preferred implementation:

[0077] First, the predicted electricity price of the independent energy storage device in the future period is obtained by the following steps S11 to S14:

[0078] S11: The electricity price deviation amount of each independent energy storage device in each historical operating day in each historical period is determined by the following steps S111 to S113:

[0079] S111: The average price of all historical periods of the historical operating day is obtained by the following formula:

[0080] Wherein, is the average price of the jth historical operating day of the independent energy storage device i; M j is the total number of electricity market operating periods of the jth historical operating day; p i,j,t is the node historical electricity price of the independent energy storage device i in the historical period t of the jth historical operating day.

[0081] S112: The deviation amount of the node historical electricity price of each historical period with respect to is obtained by the following formula:

[0082] Wherein, τ i,j,t is the deviation amount of the node historical electricity price of the independent energy storage device i in the historical period t of the jth historical operating day with respect to the corresponding historical operating day average price.

[0083] S113: set the target operation day (i.e. future operation day) as the jth operation day, and sequentially obtain the deviation amount of the previous m historical operation days according to S111 to S112, thereby obtaining the following sample set:

[0084] wherein, is a sample set containing the deviation amount of the distance of the target operation day for m days, which is used as input to the autoregressive moving average model to obtain the predicted electricity price deviation amount of the future period.

[0085] S12: in the case of Before inputting to the autoregressive moving average model, the relevant parameters of the autoregressive moving average model (the relevant parameters include the order k of the autoregressive component in the autoregressive moving average model and the order q of the moving average, the coefficients of the moving average model and the coefficients of the autoregressive model) are obtained by the following steps S121 to S124:

[0086] S121: according to the preset range, different order combinations of k and q are composed;

[0087] S122: according to the sample set obtained in S113, the maximum likelihood estimation method is used to respectively fit the coefficients of the moving average model and the coefficients of the autoregressive model in the autoregressive moving average model under different order combinations;

[0088] S123: starting from the low-order order combination, the A-Information Criterion (AIC) and the Bayesian information criterion (BIC) are sequentially compared to select the most appropriate autoregressive order k and moving average order q, specifically:

[0089] According to the following minimum information criterion, the most appropriate autoregressive order is determined:

[0090] wherein, 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; represents that after traversing the AIC(k) values corresponding to different k values, the best k value is selected as k pro .

[0091] According to the following Bayesian information criterion, the most appropriate moving average order is determined:

[0092] wherein, is the variance of the maximum likelihood fitting residual corresponding to the qth order moving average model; BIC(q) is the BIC criterion function; m is the number of historical samples; q is the order of the moving average model pro is the most appropriate moving average order; represents the BIC(q) value corresponding to different q values, and the best q value is selected as q pro .

[0093] S124: According to the autoregressive fitting and moving average fitting results of S122 and S123, and the order of the corresponding AIC and BIC criteria, the parameters of the autoregressive moving average model in S13 can be obtained by comparison.

[0094] S13: According to the parameters of the autoregressive moving average model obtained in S11 and the autoregressive moving average model obtained in S12, the autoregressive moving average model shown below is used to obtain the predicted price deviation amount of the future period:

[0095] wherein k and q represent the order of the autoregressive component and the order of the moving average in the model, respectively; to represent the coefficients of the autoregressive model used to predict the price deviation amount τ i,j,t of the tth period of the target operation day; θ 1,t to θ q,t represent the coefficients of the moving average model; ε t to ε j-q,t represent the white noise of the tth period price deviation time series of the historical operation day.

[0096] It should be noted that a 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 historical price deviation amount samples of the tth period of the historical operation days j-1 to j-m are input into the corresponding moving average model, and the predicted price deviation value of the tth period of the jth operation day can be output. Thus, the sequence Α i,j = (τ i,j,1 , τ i,j,2 , …, τ i,j,E ) is obtained, wherein Α i,j contains the node price deviation prediction value of each operation period (i.e. future period) of the independent energy storage device i in the jth operation day (equivalent to the future operation day), and E is the total number of future periods.

[0097] S14: Based on the principle in S111, the following is obtained: B i,j,m contains the average price of all historical periods of the first m historical operation days of the jth operation day, and B i,j,mThe samples selected on the same weekday or rest day as the jth operation day are denoted as Further, B' is obtained by solving i,j,m The mean value of all samples As the predicted node electricity price mean value of the jth operation day, the predicted electricity energy price of each operation period (i.e. future period) of the jth operation day is finally obtained according to the following formula:

[0098] Wherein, p i,j,1 ,p i,j,2 ,…,p i,j,t ,…,p i,j,E is the predicted electricity energy price of each operation period (i.e. future period) of the jth operation day.

[0099] Then, the predicted frequency modulation out-clearing price of the independent energy storage device in the future period is obtained according to S15:

[0100] S15: Considering that the out-clearing price of the frequency modulation market is irrelevant to the location of the node where the independent energy storage is located (i.e. the frequency modulation market is priced according to the marginal out-clearing principle), the market price fluctuation of different operation days in the same period is small, so the sub-period frequency modulation market price (i.e. historical frequency modulation out-clearing price) of the first m historical operation days of the jth operation day is used as a sample, and the mean value is directly obtained to obtain the frequency modulation market price of each period of the jth operation day (i.e. predicted frequency modulation out-clearing price), so that the predicted frequency modulation out-clearing price of the independent energy storage device in the future period is obtained through the following formula: j,t = avg(L j-1,t ,L j-2,t ,…,L j-m,t )

[0101] Wherein, L j,t is the predicted frequency modulation out-clearing price of the future period t of the jth operation day.

[0102] Finally, the mileage-capacity ratio of the independent energy storage device is obtained according to S16:

[0103] S16: The ratio of the actual frequency modulation mileage of each period to the winning capacity of the period in the historical operation days is counted, and the mileage-capacity ratio of the independent energy storage device is obtained through the following formula:

[0104] Wherein, g r,i represents the mileage-capacity ratio of the independent energy storage device i in the first m historical operation days of the jth operation day; T m represents the number of frequency modulation periods of the first m historical operation days of the jth operation day; C i,t is the frequency modulation capacity of the independent energy storage device i in the corresponding period; M i,tThe actual frequency modulation mileage of the independent energy storage device i in the corresponding time period is executed.

[0105] S20: A preset auxiliary decision model is initialized with the first parameter as a boundary condition, and the auxiliary decision model is solved to obtain adjustment data of each independent energy storage device for adjusting power system balance in a future time period; the auxiliary decision model is obtained by considering mutual exclusivity of the independent energy storage device participating in different markets in the same time period, and optimizing adjustment data of each independent energy storage device for the power system in different future time periods; the adjustment data includes charge and discharge data and frequency modulation data of the independent energy storage device in each future time period.

[0106] Further, in some embodiments of the present application, the auxiliary decision model is obtained by considering mutual exclusivity of the independent energy storage device participating in different markets in the same time period, and optimizing adjustment data of each independent energy storage device for the power system in different future time periods, and includes:

[0107] The auxiliary decision model includes a first objective function and a first constraint condition;

[0108] According to the first parameter, a first objective function is constructed with the maximum comprehensive benefit of the independent energy storage device in the future time period as the target;

[0109] The first constraint condition is constructed by combining the operation constraints of the independent energy storage device and by constraining the independent energy storage device from participating in different markets in the same time period.

[0110] Based on the predicted electricity price, the predicted frequency modulation clearing price and the mileage capacity ratio of the location where the independent energy storage device is located in the future time period, considering the mutual exclusivity constraint of the independent energy storage device participating in two markets in the same time period and the operation adjustment constraint of the energy storage, an auxiliary decision model with the maximum comprehensive benefit is constructed, the charge / discharge capacity and the frequency modulation capacity in different future time periods can be obtained, the purpose of time-sharing multiplexing of the independent energy storage device and improving the effective utilization hours is achieved, and thus the flexibility and utilization rate of the energy storage resource in adjusting the power system are improved.

[0111] Preferably, in some embodiments of the present application, the auxiliary decision model can be constructed and obtained by the following steps:

[0112] S21: A first objective function of the auxiliary decision model is built by the following formula:

[0113] Wherein, T j is the total number of time periods of the jth operation day; and are charge and discharge power decisions of the independent energy storage device i participating in market calling to adjust the power system operation in the t time period; And respectively are the charge and discharge efficiency; is the frequency regulation capacity of the independent energy storage device i participating in the market calling to adjust the operation of the power system at the t period; and L j,t respectively are the frequency regulation capacity compensation standard and the frequency regulation market clearing price of the t period, which are usually constants.

[0114] S22: Build the first constraint condition of the auxiliary decision model through the formulas in S221 to S224 as follows:

[0115] S221: Since in the time-sharing multiplexing mode, the independent energy storage device cannot participate in the electricity market and the frequency regulation auxiliary service at the same period, it is necessary to add: the indication state constraint of the energy storage selectively being in charging, discharging or providing frequency regulation capacity: 0≤x i,t +y i,t +z i,t ≤1

[0116] wherein x i,t , y i,t , z i,t respectively represent the indication variables of the independent energy storage device i being in charging, discharging or frequency regulation at the future period t, when the sum of the three indication variables is 0, it represents that the independent energy storage device does not participate in any market; when the sum of the three indication variables is 1, it represents that the independent energy storage device participates in the electricity market and executes the discharging or charging decision, or the independent energy storage device participates in the frequency regulation service market and executes the frequency regulation data.

[0117] S222: Maximum and minimum power constraints of the independent energy storage device:

[0118] wherein P i,min and P i,max are the maximum and minimum output power limits of the independent energy storage device i.

[0119] S223: State of charge constraint of the energy storage, considering that the energy storage should not exceed the state of charge limit of the energy storage at each operating period when participating in the market calling:

[0120] wherein SOC i,t is the state of charge of the independent energy storage device i at the t period; SOC i,max and SOC i,min are the maximum and minimum state of charge limits of the independent energy storage device i; is the rated power of the independent energy storage device i.

[0121] S224: Maximum charging and discharging times limit of the energy storage within the operating day:

[0122] wherein, Nmax,i is the maximum number of cycles of the independent energy storage device i.

[0123] S30: adjusting the charging and discharging power and the frequency modulation capacity of the independent energy storage device corresponding to the future period according to the charging and discharging data and the frequency modulation data.

[0124] As can be seen from the above, the method for balancing and adjusting the operation of the power system based on the independent energy storage provided by the embodiments has the following beneficial effects: in the time-sharing multiplexing scenario, the independent energy storage device can participate in different markets in different periods to provide services for subsequent adjustment of the power system balance. Therefore, by using the historical electricity market data of the historical period, the future electricity market data is predicted, the first parameter is obtained, the first parameter is used as the boundary condition of the subsequent auxiliary decision model, and the charging and discharging power and the frequency modulation capacity in each future period are obtained, so that the subsequent independent energy storage device dynamically configures the capacity for providing peak shaving and frequency modulation auxiliary services in each period according to a more reasonable charging operation plan and according to the operation needs of the power system, thereby achieving time-sharing multiplexing of the energy storage device, improving the effective utilization hours, and improving the flexibility and utilization rate of the energy storage resource when adjusting the power system.

[0125] Embodiment Two

[0126] Referring to FIG. 2, the device for balancing and adjusting the operation of the power system based on the independent energy storage provided by the embodiments comprises a first parameter acquisition module 11, an adjustment data acquisition module 12, and an adjustment module 13.

[0127] Further, in some embodiments of the present application, the first parameter acquisition module 11 is configured to obtain the historical electricity market data of all independent energy storage devices in each historical period, and predict the first parameter of the independent energy storage device in the future period according to the historical electricity market data; the first parameter comprises the mileage capacity ratio of the independent energy storage device; the adjustment data acquisition module 12 is configured 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 balancing the power system in the future period; the auxiliary decision model is obtained by optimizing the adjustment data of each independent energy storage device for the power system in different future periods by considering the mutual exclusivity of the independent energy storage device participating in different markets in the same period; the adjustment data comprises the charging and discharging data and the frequency modulation data of the independent energy storage device in each future period; and the adjustment module 13 is configured to adjust the charging and discharging power and the frequency modulation capacity of the independent energy storage device corresponding to the future period according to the charging and discharging data and the frequency modulation data.

[0128] Further, in some embodiments of the present application, the first parameter acquisition module 11 is configured to predict, according to the historical electricity market data, a first parameter of the independent energy storage device in a future period, and the first parameter further comprises a predicted electricity energy price and a predicted frequency modulation clearing price of the independent energy storage device in the future period, the historical electricity market data comprises corresponding node historical electricity prices, historical frequency modulation clearing prices, winning capacities and actual frequency modulation mileages of the independent energy storage device in each historical period, the predicted electricity energy price of the independent energy storage device in the future period is obtained by a preset autoregressive moving average model according to the corresponding node historical electricity prices of the independent energy storage device in each historical period, the predicted frequency modulation clearing price of the independent energy storage device in the future period is obtained according to the historical frequency modulation clearing prices of the independent energy storage device in each historical period, and the mileage capacity ratio of the independent energy storage device is obtained according to the winning capacities and the actual frequency modulation mileages of the independent energy storage device in each historical period.

[0129] Further, in some embodiments of the present application, the predicted electricity energy price of the independent energy storage device in the future period is obtained by the preset autoregressive moving average model according to the corresponding node historical electricity prices of the independent energy storage device in each historical period, and the method comprises the following steps: calculating a historical average electricity price according to the corresponding node historical electricity prices of the independent energy storage device in each historical period; calculating an electricity price deviation amount corresponding to each historical period of the independent energy storage device according to the historical average electricity price; inputting the electricity price deviation amount corresponding to each historical period of the independent energy storage device into the autoregressive moving average model to obtain a predicted electricity price deviation amount of each future period; and obtaining the predicted electricity energy price corresponding to the future period according to the predicted electricity price deviation amount and the historical average electricity price.

[0130] Further, in some embodiments of the present application, before the electricity price deviation amount corresponding to each historical period of the independent energy storage device is inputted into the autoregressive moving average model, the method further comprises the following steps: obtaining a first model parameter corresponding to a combination of each autoregressive order and each moving average order by maximum likelihood estimation according to a sample number of the electricity price deviation amount; determining an optimal autoregressive order by minimum information criterion according to the first model parameter corresponding to the combination of each autoregressive order and different moving average orders; and determining an optimal moving average order by Bayesian information criterion according to the first model parameter corresponding to the combination of each moving average order and different autoregressive orders.

[0131] Further, in some embodiments of the present application, the auxiliary decision model is obtained by optimizing the adjustment data of each independent energy storage device to the power system in different future time periods by considering the exclusivity of the independent energy storage device participating in different markets in the same time period, and the auxiliary decision model comprises a first objective function and a first constraint condition; the first objective function is constructed by maximizing the comprehensive benefits of the independent energy storage device in the future time period according to the first parameter; and the first constraint condition is constructed by combining the operation constraints of the independent energy storage device by restricting that the independent energy storage device cannot participate in different markets in the same time period.

[0132] Further, in some embodiments of the present application, the restriction that the independent energy storage device cannot participate in different markets in the same time period comprises:

[0133] The restriction that the independent energy storage device cannot participate in different markets in the same time period is constructed by the following formula: 0≤x i,t +y i,t +z i,t ≤1

[0134] Wherein, x i,t , y i,t , z i,t respectively represent the indicator variables of the independent energy storage device i in the future time period t in the charging, discharging or frequency modulation state, and when the sum of the three indicator variables is 0, it indicates that the independent energy storage device does not participate in any market; and when the sum of the three indicator variables is 1, it represents that the independent energy storage device participates in the electric energy market and executes the discharging or charging decision, or the independent energy storage device participates in the frequency modulation service market and executes the frequency modulation data.

[0135] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present application, and the independent energy storage-based power system operation balance adjustment device provided by the embodiments of the present application can realize any one of the method item embodiments of the present application, that is, the independent energy storage-based power system operation balance adjustment method provided by the first embodiment.

[0136] In summary, the embodiment of the application provides a kind of power system operation balancing regulation device based on independent energy storage, with the following beneficial effects: in the time-sharing multiplexing scenario, independent energy storage equipment can participate in different markets in different time periods to provide subsequent adjustment services for power system balancing.Therefore, by predicting future electricity market data from historical electricity market data of historical period, obtaining the first parameter, and taking the first parameter as the boundary condition of subsequent auxiliary decision model, the charging and discharging power and frequency modulation capacity in each future period are obtained, so that the subsequent independent energy storage equipment dynamically configures the capacity for providing peak load shifting and frequency modulation auxiliary services in each period according to more reasonable charging operation plan and according to the operation needs of power system, realizes the purpose of time-sharing multiplexing of energy storage equipment and improves the effective utilization hours, so as to improve the flexibility and utilization rate of energy storage resource when regulating power system.

[0137] Embodiment three

[0138] On the basis of the above-mentioned embodiment of the power system operation balancing regulation method based on independent energy storage, another embodiment of the application provides a terminal device for power system operation balancing regulation based on independent energy storage. The terminal device for power system operation balancing regulation 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 application is implemented.

[0139] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the application. The one or more modules can be a series of computer program instruction segments that can complete a specific function, which are used to describe the execution process of the computer program in the power system operation balancing regulation device based on independent energy storage.

[0140] The power system operation balancing regulation device based on independent energy storage can be a desktop computer, a notebook computer, a palm computer, a cloud server, and other computing devices. The terminal device for power system operation balancing regulation based on independent energy storage can include, but is not limited to, a processor and a memory.

[0141] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the independent energy storage based power system operation balance adjustment device, and connects various parts of the independent energy storage based power system operation balance adjustment device through various interfaces and lines. The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the independent energy storage based power system operation balance adjustment device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application required by a function, etc. The data storage area can store data created according to use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0142] Embodiment Four

[0143] On the basis of the above-mentioned embodiments of the independent energy storage based power system operation balance adjustment method, another embodiment of the present application provides a storage medium including a stored computer program, wherein when the computer program runs, the device where the storage medium is located is controlled to execute the independent energy storage based power system operation balance adjustment method of any one of the embodiments of the present application.

[0144] In this embodiment, the storage medium described above is a computer readable storage medium, the computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer readable medium can 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, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0145] The specific embodiments described above further illustrate the purposes, technical solutions and beneficial effects of the present application. It should be understood that the above description is only for specific embodiments of the present application and is not intended to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for balancing the operation of an independent energy storage-based power system, characterized in that, The method comprises the following steps: obtaining historical electricity market data of all independent energy storage devices in each historical period, and predicting a first parameter of the independent energy storage device corresponding to a future period according to the historical electricity market data; the first parameter comprises a mileage capacity ratio of the independent energy storage device; initializing a preset auxiliary decision model with the first parameter as a boundary condition, and solving the auxiliary decision model to obtain adjustment data of each independent energy storage device for adjusting power system balance in a future period; the auxiliary decision model is obtained by considering the mutual exclusivity of the independent energy storage device participating in different markets in the same period, and optimizing the adjustment data of each independent energy storage device for the power system in different future periods; the adjustment data comprises charge and discharge data and frequency modulation data of the independent energy storage device in each future period; adjusting the charge and discharge power and the frequency modulation capacity of the independent energy storage device corresponding to each future period according to the charge and discharge data and the frequency modulation data.

2. The method of claim 1, wherein the method further comprises: The method further comprises the following steps: the first parameter further comprises a predicted electricity price and a predicted frequency modulation clearing price of the independent energy storage device in a future period; the historical electricity market data comprises a corresponding node historical electricity price, a historical frequency modulation clearing price, a winning capacity and an actual frequency modulation mileage of the independent energy storage device in each historical period; obtaining the predicted electricity price of the independent energy storage device in a future period by a preset autoregressive moving average model according to the corresponding node historical electricity price of the independent energy storage device in each historical period; obtaining the predicted frequency modulation clearing price of the independent energy storage device in a future period according to the historical frequency modulation clearing price of the independent energy storage device in each historical period; obtaining the mileage capacity ratio of the independent energy storage device according to the winning capacity and the actual frequency modulation mileage of the independent energy storage device in each historical period.

3. The method of claim 2, wherein the method further comprises: The method further comprises the following steps: calculating a historical average electricity price according to the corresponding node historical electricity price of the independent energy storage device in each historical period; calculating an electricity price deviation amount corresponding to each historical period of the independent energy storage device according to the historical average electricity price; inputting the electricity price deviation amount corresponding to each historical period of the independent energy storage device into the autoregressive moving average model to obtain a predicted electricity price deviation amount of each future period; obtaining the predicted electricity price corresponding to each future period according to the predicted electricity price deviation amount and the historical average electricity price.

4. The method of claim 3, wherein the method further comprises: The method further comprises the following steps before inputting the electricity price deviation amount corresponding to each historical period of the independent energy storage device into the autoregressive moving average model: obtaining a first model parameter corresponding to each autoregressive order and each moving average order combination by maximum likelihood estimation according to the sample number of the electricity price deviation amount; According to the first model parameters corresponding to combinations of each autoregressive order and different moving average orders, an optimal autoregressive order is determined by a minimum information criterion; According to the first model parameters corresponding to combinations of each moving average order and different autoregressive orders, an optimal moving average order is determined by a Bayesian information criterion.

5. The method of claim 1, wherein the method further comprises: determining a power balance between the first power source and the second power source; and adjusting the power balance between the first power source and the second power source based on the determined power balance. The auxiliary decision model is obtained by optimizing adjustment data of each independent energy storage device for the power system in different future time periods by considering the exclusivity of the independent energy storage device participating in different markets in the same time period, and comprises: The auxiliary decision model comprises a first objective function and a first constraint condition; According to the first parameter, a first objective function is constructed with the goal of maximizing the comprehensive benefit of the independent energy storage device in the future time period; The first constraint condition is constructed by combining the operation constraints of the independent energy storage device by restricting the independent energy storage device from participating in different markets in the same time period.

6. The method of claim 5, wherein the method further comprises: The restriction that the independent energy storage device cannot participate in different markets in the same time period comprises: The restriction that the independent energy storage device cannot participate in different markets in the same time period is determined by the following formula: 0 ≤ x i,t + y i,t + z i,t ≤ 1 where x i,t , y i,t , z i,t represent the indicator variables of the independent energy storage device i being in charge, discharge or frequency regulation at future time period t, respectively. When the sum of the three indicator variables is 0, it means the independent energy storage device does not participate in any market; when the sum of the three indicator variables is 1, it means the independent energy storage device participates in the electricity market and executes the discharge or charge decision, or the independent energy storage device participates in the frequency regulation service market and executes the frequency regulation data.

7. An independent energy storage-based power system operation balancing adjustment device, characterized in that, It comprises: a first parameter acquisition module, an adjustment data acquisition module, and an adjustment module; The first parameter acquisition module is configured to acquire historical electricity market data of all independent energy storage devices in each historical time period, and predict a first parameter of the independent energy storage device in a future time period according to the historical electricity market data; the first parameter comprises a mileage capacity ratio of the independent energy storage device; The adjustment data acquisition module is configured to initialize a preset auxiliary decision model with the first parameter as a boundary condition, and solve the auxiliary decision model to acquire adjustment data of each independent energy storage device for adjusting power system balance in the future time period; the auxiliary decision model is obtained by optimizing adjustment data of each independent energy storage device for the power system in different future time periods by considering the exclusivity of the independent energy storage device participating in different markets in the same time period; the adjustment data comprises charge and discharge data and frequency modulation data of the independent energy storage device in each future time period; The adjustment module is configured to adjust charge and discharge power and frequency modulation capacity of the independent energy storage device in each future time period according to the charge and discharge data and the frequency modulation data, respectively.

8. The independent energy storage based power system operation balancing adjustment device of claim 7, wherein, The first parameter acquisition module is configured to predict a first parameter of the independent energy storage device in a future time period according to historical electricity market data, comprising: The first parameter further comprises a predicted electricity energy price and a predicted frequency modulation clearing price of the independent energy storage device in the future time period; the historical electricity market data comprises corresponding node historical electricity prices, historical frequency modulation clearing prices, winning capacities, and actual frequency modulation mileages of the independent energy storage device in each historical time period; According to the corresponding node historical electricity prices of the independent energy storage device in each historical time period, the predicted electricity energy price of the independent energy storage device in the future time period is acquired by a preset autoregressive moving average model; According to the historical frequency modulation out-clearing price of the independent energy storage device in each historical time period, the predicted frequency modulation out-clearing price of the independent energy storage device in a future time period is obtained; According to the bid capacity and the actual frequency modulation mileage of the independent energy storage device in each historical time period, the mileage-capacity ratio of the independent energy storage device is obtained.

9. The independent energy storage based power system operational balancing regulation apparatus of claim 7, wherein, The auxiliary decision model is obtained by considering the mutual exclusivity of the independent energy storage device participating in different markets in the same time period, and optimizing the adjustment data of each independent energy storage device to the power system in different future time periods, and comprises: The auxiliary decision model comprises a first objective function and a first constraint condition. According to the first parameter, a first objective function is constructed with the maximum comprehensive benefit of the independent energy storage device in a future time period as the target. The first constraint condition is constructed by constraining the independent energy storage device from participating in different markets in the same time period and combining the operation constraints of the independent energy storage device.

10. The independent energy storage based power system operating balance adjustment apparatus of claim 9, wherein, The constraint that the independent energy storage device cannot participate in different markets in the same time period comprises: The constraint that the independent energy storage device cannot participate in different markets in the same time period is realized by the following formula: 0 ≤ x i,t + y i,t + z i,t ≤ 1 where x i,t , y i,t , z i,t represent the indicator variables of the independent energy storage device i being in charge, discharge or frequency regulation at future time period t, respectively. When the sum of the three indicator variables is 0, it means the independent energy storage device does not participate in any market; when the sum of the three indicator variables is 1, it means the independent energy storage device participates in the electricity market and executes the discharge or charge decision, or the independent energy storage device participates in the frequency regulation service market and executes the frequency regulation data.

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