Energy storage system operation optimization method, system, server and storage medium

Abstract

An operation optimization method, system, server and storage medium for an energy storage system. The method includes obtaining the probability distribution of the total load prediction deviation as the input of the uncertain factors on the load side; obtaining the maximum state of charge and the minimum state of charge allowed by the multi-point distributed energy storage system on the distribution network side and the charge and discharge power constraints as the constraint conditions for the power output; setting the energy storage system output as consisting of a decision-making output without considering the load uncertainty factors and a decision-making output for bearing the load prediction deviation. For the multi-point distributed energy storage system, setting the load prediction deviation bearing coefficients of each energy storage system, and the sum of all the bearing coefficients is 1; setting the probabilistic constraint of the energy storage system output; setting the objective function of the energy storage system operation optimization as minimizing the peak value of the main grid injection power; obtaining the operation optimization control model of the energy storage system under the probabilistic constraint, performing equivalent transformation and solving to obtain the energy storage system output plan. The present invention can reduce the impact on the main grid caused by the uncertain factors on the load side.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation planning, and in particular to an operation optimization method, system, server and storage medium for an energy storage system. Background Art

[0002] From a traditional perspective, electric energy cannot be stored on a large scale, and the operation of the power system needs to maintain a balance between supply and demand at all times, that is, the deviation on the load side at each moment needs to be adjusted by the power generation side to ensure power balance and avoid load shedding. However, in recent years, the booming development of energy storage technology is gradually subverting this feature, because its bidirectional charging and discharging ability can carry out the spatio-temporal transfer of energy.

[0003] Previous research on energy storage mainly focused on suppressing the fluctuations of new energy on the source side and absorbing abandoned wind and light, but less focused on balancing the uncertain factors on the load side. In recent years, with the large-scale access of high-power loads such as electric vehicles to the distribution network, the volatility and uncertainty on the load side have gradually increased, and the impact on the main grid has become more obvious. How to use the energy storage system as a flexible regulation resource means on the user side or the distribution network side with a lower voltage level and achieve in-situ balance on the basis of considering the uncertain factors of the load, so as to avoid introducing more uncertain factors into the main grid is a technical problem to be solved in this technical field.

[0004] The traditional control optimization theory based on the deterministic idea cannot take into account the influence of uncertain factors in the decision-making process. However, the operation of the power system is a multi-time scale process. For example, the dispatching department will prepare the day-ahead power generation plan of thermal power units according to the load forecast and new energy power generation situation of the next day. However, the actual situations of the load and new energy usually deviate from the forecast to a certain extent. At this time, the control optimization theory based on the deterministic idea cannot take into account the influence of uncertain factors, which may lead to insufficient operation margin of the power grid or non-optimal economy. To solve the above problems, other technical solutions have applied the idea of stochastic programming for operation control, that is, first generate scenarios to obtain the load situations under different scenarios and the expectations corresponding to these scenarios, and then study the optimization control strategies considering all scenarios for weighting. However, the operation scenarios cannot be accurately generated, and when there are too many scenarios, the solution time will be too long, which cannot meet the requirements of many projects for the calculation time. Summary of the Invention

[0005] The purpose of the present invention is to provide an operation optimization method, system, server and storage medium for an energy storage system aiming at the above problems in the prior art, which can take into account the influence of the prediction deviation of the load on the power supply and demand balance, use the energy storage as a flexible regulation resource to balance the load in-situ, increase the autonomy of the regional power grid operation, reduce the impact of the load-side uncertain factors on the main grid, and increase the system operation margin.

[0006] To achieve the above object, the present invention has the following technical solutions:

[0007] In a first aspect, a method for optimizing the operation of an energy storage system is provided, including the following steps:

[0008] Obtain the probability distribution of the total load prediction deviation based on historical load prediction and actual data, and use it as the input of the uncertain factors on the load side of the energy storage system;

[0009] Obtain the maximum state of charge allowed for the multi-point distributed energy storage system on the distribution network side and the minimum state of charge as well as the charge and discharge power constraints as the constraint conditions for the power output of the energy storage system;

[0010] Set the output of the energy storage system as the decision-making output without considering load uncertainty factors and the decision-making output for bearing the load prediction deviation. For the multi-point distributed energy storage system, set the load prediction deviation bearing coefficient for each energy storage system, and the sum of all load prediction deviation bearing coefficients is 1;

[0011] Set the probability constraint of the energy storage system output according to the input of the uncertain factors on the load side of the energy storage system, the constraint conditions of the energy storage system power output, the energy storage system output, and the load prediction deviation bearing coefficients of all multi-point distributed energy storage systems;

[0012] Set the objective function of the energy storage system operation optimization as minimizing the peak value of the power injected into the main grid;

[0013] Integrate the probability constraint of the energy storage system output and the objective function of the energy storage system operation optimization to obtain the operation optimization control model of the energy storage system under probability constraints;

[0014] Perform equivalent transformation on the operation optimization control model of the energy storage system under probability constraints and solve it to obtain the energy storage system output plan.

[0015] Preferably, in the step of obtaining the probability distribution of the total load prediction deviation based on historical load prediction and actual data and using it as the input of the uncertain factors on the load side of the energy storage system, obtain the expectation μ of the load prediction deviation probability distribution at each node i in each time period t i,t and the variance

[0016] Obtain the probability expectation of the load prediction deviation based on a data-driven method and the probability variance Var[ω i,t of the load prediction deviation, and

[0017] the total load prediction deviation

[0018] The expectation of the total load prediction deviation is as follows:

[0019]

[0020] The variance of the total load prediction deviation is as follows:

[0021]

[0022] Obtain the probability distribution Ω of the total load prediction deviation as the input of the uncertain factors on the load side of the energy storage system.

[0023] Furthermore, in the step of obtaining the maximum state of charge and the minimum state of charge allowed for the multi-point distributed energy storage system on the distribution network side and the charge and discharge power constraints as the constraint conditions for the power output of the energy storage system, if node i is not connected to any energy storage system, then set the maximum state of charge to 0, and the charge and discharge power constraint to 0.

[0024] Preferably, in the step of setting the output of the energy storage system as the decision-making output without considering the load uncertainty factor and the decision-making output for bearing the load prediction deviation, for the multi-point distributed energy storage system, set the load prediction deviation bearing coefficient of each energy storage system, and the sum of all the load prediction deviation bearing coefficients is 1. Set the actual output of each active power source as:

[0025]

[0026] In the formula, p 0,t is the injection power of the superior power grid at each time period t, is the injection power of the superior power grid without considering the load prediction deviation at each time period, α 0,t Ω t is the power for balancing the load prediction deviation borne by the superior power grid at each time period; is the charging power of the energy storage system at each node at different time periods considering the load prediction deviation, is the charging power of the energy storage system without considering the load prediction deviation, is the power of the load prediction deviation borne by the charging power of the energy storage system at each time period; is the discharging power of the energy storage system at each node at different time periods considering the load prediction deviation, is the discharging power of the energy storage system without considering the load prediction deviation, is the power of the load prediction deviation borne by the discharging power of the energy storage system at each time period;

[0027] The expression that the sum of all the load prediction deviation bearing coefficients is 1 is: ​

[0028]

[0029] Furthermore, the probabilistic constraint of the energy storage system output is as follows:

[0030]

[0031]

[0032]

[0033]

[0034] In the formula, 1 - ε ch and 1 - ε dis are respectively the confidence coefficients of the charge-discharge power step limit of the energy storage system;

[0035] SOC i,t is the state of charge of each node's energy storage system at each time period, SOC i,t-1 is the state of charge of each node's energy storage system in the previous time period, η i is the charge-discharge conversion efficiency of the energy storage system, pess max is the maximum charge-discharge power of the energy storage system, is the probability distribution satisfying the constraint, is the expected value of the probability distribution satisfying the constraint.

[0036] Furthermore, the objective function of the operation optimization of the energy storage system is as follows:

[0037]

[0038] In the formula, p 0,t is the injection power of the superior power grid at each time period t, t ∈ T is the set of time periods, and T is the total number of time periods within a day.

[0039] Furthermore, the objective function of the operation optimization of the energy storage system is equivalently transformed as follows:

[0040]

[0041] Then the expression of the equivalently transformed objective function of the operation optimization of the energy storage system is:

[0042]

[0043] Furthermore, in the probabilistic constraint of the energy storage system output, for the expressions and use the inverse function of the probability distribution of the load prediction deviation for equivalent transformation, and the equivalently transformed expression is as follows:

[0044]

[0045]

[0046]

[0047]

[0048] Wherein, Φ -1 (1 - ε ch ), Φ -1 (1 - ε dis ) are respectively the inverse functions of the probability distributions of the charge and discharge powers of the energy storage system in the corresponding confidence intervals, is the standard deviation of the load prediction deviation;

[0049] After equivalent transformation of the expression the following is obtained:

[0050]

[0051] Second, a system for optimizing the operation of an energy storage system is provided, including:

[0052] An uncertain factor input module, configured to obtain the probability distribution of the total load prediction deviation based on historical load predictions and actual data as the uncertain factor input on the load side of the energy storage system;

[0053] A power output constraint condition acquisition module, configured to obtain the maximum state of charge and the minimum state of charge allowed for the multi-point distributed energy storage system on the distribution network side and the charge and discharge power constraints

[0054] as the constraint conditions for the power output of the energy storage system;

[0055] A system output setting module, configured to set the output of the energy storage system as the decision-making output without considering load uncertainties and the decision-making output for bearing the load prediction deviation. For the multi-point distributed energy storage system, set the load prediction deviation bearing coefficients of each energy storage system, and the sum of all load prediction deviation bearing coefficients is 1;

[0056] A system output probability constraint setting module, configured to set the probability constraints of the energy storage system output according to the uncertain factor input on the load side of the energy storage system, the constraint conditions of the energy storage system power output, the energy storage system output, and the load prediction deviation bearing coefficients of all the multi-point distributed energy storage systems;

[0057] An operation optimization control model establishment module, which is used to integrate the probabilistic constraints of the energy storage system output and the objective function of the energy storage system operation optimization to obtain an operation optimization control model of the energy storage system under probabilistic constraints;

[0058] A model solving module, which is used to equivalently transform and solve the operation optimization control model of the energy storage system under the probabilistic constraints to obtain an energy storage system output plan.

[0059] Preferably, the uncertainty factor input module obtains the expectation μ of the probability distribution of the load prediction deviation at each node i in each time period t i,t and variance

[0060] The probability expectation of the load prediction deviation is obtained based on a data-driven method and the probability variance Var[ω i,t of the load prediction deviation, and

[0061] The total load prediction deviation

[0062] The expectation of the total load prediction deviation is:

[0063]

[0064] The variance of the total load prediction deviation is:

[0065]

[0066] The probability distribution Ω of the total load prediction deviation is obtained as the uncertainty factor input on the load side of the energy storage system.

[0067] Furthermore, if node i is not connected to any energy storage system, the power output constraint condition acquisition module sets the maximum state of charge to 0, and the charge-discharge power constraint is 0.

[0068] Preferably, the system output setting module sets the actual output of each active power source as:

[0069]

[0070] In the formula, p 0,t is the injection power of the superior power grid in each time period t, is the injection power of the superior power grid when not considering the load prediction deviation in each time period, α 0,t Ω t is the power for balancing the load prediction deviation borne by the superior power grid in each time period; is the charging power of the energy storage system at each node in different time periods considering the load prediction deviation, is the charging power of the energy storage system without considering the load forecasting deviation, is the load forecasting deviation power borne by the charging power of the energy storage system in each period; is the discharging power of the energy storage system at different periods of each node considering the load forecasting deviation, is the discharging power of the energy storage system without considering the load forecasting deviation, is the load forecasting deviation power borne by the discharging power of the energy storage system in each period;

[0071] The expression that the sum of all the load forecasting deviation bearing coefficients is 1 is:

[0072]

[0073] Furthermore, the probability constraint of the energy storage system output set by the system output probability constraint setting module is:

[0074]

[0075]

[0076]

[0077]

[0078] In the formula, 1 - ε ch and 1 - ε dis are respectively the confidence coefficients of the charge-discharge power step limit of the energy storage system;

[0079] SOC i,t is the state of charge of the energy storage system at each node and each period, SOC i,t-1 is the state of charge of the energy storage system at the previous period of each node, η i is the charge-discharge conversion efficiency of the energy storage system, pess max is the maximum charge-discharge power of the energy storage system, is the probability distribution that meets the constraint, is the expected value of the probability distribution that meets the constraint.

[0080] Furthermore, the objective function for the operation optimization of the energy storage system set by the objective function setting module is:

[0081]

[0082] In the formula, p 0,t is the power injected by the superior power grid in each period, t ∈ T is the set of periods, and T is all the periods in a day.

[0083] Furthermore, the model solving module equivalently transforms the objective function for the operation optimization of the energy storage system in the following manner:

[0084]

[0085] Then, the expression of the equivalently transformed objective function for the operation optimization of the energy storage system is:

[0086]

[0087] Furthermore, the model solving module equivalently transforms the expressions and in the probabilistic constraint of the energy storage system output using the inverse function of the probability distribution of the load prediction deviation, and the following equivalently transformed expressions are obtained:

[0088]

[0089]

[0090]

[0091]

[0092] In the formula, Φ -1 (1 - ε ch ), Φ -1 (1 - ε dis ) are respectively the inverse functions of the probability distributions of the charging and discharging power of the energy storage system in the corresponding confidence intervals, is the standard deviation of the load prediction deviation;

[0093] After equivalently transforming the expression the following is obtained:

[0094]

[0095] In the third aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements an energy storage system operation optimization method as described in the first aspect.

[0096] In the fourth aspect, a server is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements an energy storage system operation optimization method as described in the first aspect.

[0097] Compared with the prior art, the first aspect of the present invention has at least the following beneficial effects:

[0098] Based on historical load forecasting and actual data, the present invention obtains the probability distribution of load forecasting deviation through data-driven and curve fitting methods. Considering the maximum and minimum state of charge allowed by the multi-point distributed energy storage system on the distribution network side as the constraint conditions for the power output of the energy storage system, and setting the output of the energy storage system as the decision-making output without considering load uncertainty factors and the decision-making output for bearing load forecasting deviation. For the multi-point distributed energy storage system, the load forecasting deviation bearing coefficient of each energy storage system is set, and the sum of all load forecasting deviation bearing coefficients is 1. Based on this, the probability constraint of the energy storage system output is set, and the operation optimization control model of the energy storage system under probability constraints can take into account the impact of load forecasting deviation on the power supply-demand balance. The operation optimization control model of the energy storage system under probability constraints is equivalently transformed based on probability theory and solved to obtain the energy storage system output plan. The present invention realizes an operation optimization strategy for the energy storage system considering load uncertainty factors, which can use the energy storage as a flexible regulation resource to locally balance the load, increase the autonomy of the regional power grid operation, reduce the impact of load-side uncertainty factors on the main grid, and thus increase the system operation margin.

[0099] It can be understood that the beneficial effects of the second to fourth aspects of the present invention can be referred to the relevant descriptions in the first aspect above, and will not be repeated here. Description of the Drawings

[0100] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0101] Figure 1 Flowchart of an operation optimization method for an energy storage system according to an embodiment of the present invention;

[0102] Figure 2 Block diagram of the structure of an operation optimization system for an energy storage system according to an embodiment of the present invention. Detailed Embodiments

[0103] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0104] In addition, in the description of the specification and the appended claims of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0105] Embodiment 1

[0106] Please refer to Figure 1 , Figure 1 which shows the flow of an operation optimization method for an energy storage system according to an embodiment of the present invention, mainly including:

[0107] Step 1: Obtain the probability distribution of the total load prediction deviation based on historical load prediction and actual data as the input of the uncertain factors on the load side of the energy storage system;

[0108] Step 2: Obtain the maximum state of charge allowed for the multi-point distributed energy storage system on the distribution network side and the minimum state of charge as well as the charge and discharge power constraints as the constraint conditions for the power output of the energy storage system;

[0109] Step 3: Set the output of the energy storage system as two parts: the decision-making output without considering the load uncertainty factors and the decision-making output for bearing the load prediction deviation. For the multi-point distributed energy storage system, set the load prediction deviation bearing coefficients of each energy storage system, and the sum of all the load prediction deviation bearing coefficients is 1;

[0110] Step 4: Set the probability constraint of the energy storage system output according to the input of the uncertain factors on the load side of the energy storage system, the constraint conditions for the power output of the energy storage system, the energy storage system output, and the load prediction deviation bearing coefficients of all the multi-point distributed energy storage systems;

[0111] Step 5: Set the objective function of the operation optimization of the energy storage system to minimize the peak value of the power injected into the main grid;

[0112] Step 6: Integrate the probability constraint of the energy storage system output and the objective function of the operation optimization of the energy storage system to obtain the operation optimization control model of the energy storage system under the probability constraint;

[0113] Step 7: Perform equivalent transformation on the operation optimization control model of the energy storage system under the probability constraint and solve it to obtain the energy storage system output plan.

[0114] In a possible implementation manner, in the step 1, the probability distribution of the load prediction deviation is obtained by means of data-driven and curve fitting, and the expectation μ of the load prediction deviation probability distribution of each node i at each time period t is obtained i,t and the variance

[0115] The probability expectation of the load prediction deviation is obtained based on the data-driven method The variance Var[ω i,t of the deviation from the load forecast, and

[0116] the total load forecast deviation Since the load forecast deviations are independent of each other, they are additive.

[0117] The expectation of the total load forecast deviation is:

[0118]

[0119] The variance of the total load forecast deviation is:

[0120]

[0121] The probability distribution Ω of the total load forecast deviation is obtained as the input of the uncertain factors on the load side of the energy storage system.

[0122] In a possible implementation manner, in step 2, if node i is not connected to any energy storage system, the maximum state of charge is set to 0, and the charge and discharge power constraint is set to 0. This step is mainly to avoid overcharging and over-discharging of the energy storage system.

[0123] In a possible implementation manner, in step 3, the actual output of each active power source is set to:

[0124]

[0125] In the formula, p 0,t is the injection power of the superior power grid at each time period t, is the injection power of the superior power grid when the load forecast deviation is not considered, α 0,t Ω t is the power for balancing the load forecast deviation borne by the superior power grid at each time period; is the charging power of the energy storage system at each node considering the load forecast deviation, is the charging power of the energy storage system without considering the load forecast deviation, is the power of the load forecast deviation borne by the charging power of the energy storage system at each time period; is the discharging power of the energy storage system at each node considering the load forecast deviation, is the discharging power of the energy storage system without considering the load forecast deviation, is the power of the load forecast deviation borne by the discharging power of the energy storage system at each time period;

[0126] To ensure that the additional power reserved for the uncertain factors on the load side can exactly balance the load prediction deviation, the sum of the sharing coefficients of all active power sources needs to be 1. According to Equation (3), the expression for the sum of all load prediction deviation sharing coefficients being 1 is:

[0127]

[0128] In a possible implementation manner, the probability constraint for setting the output of the energy storage system in Step 4 is as follows:

[0129]

[0130]

[0131]

[0132]

[0133] In the formula, Equations (5) and (6) constrain that the charge and discharge power of the energy storage system does not exceed the limit within a certain confidence interval, 1 - ε ch and 1 - ε dis are respectively the confidence coefficients for the charge and discharge power of the energy storage system to step over the limit, and their values can be selected according to the degree of autonomy of the regional power grid;

[0134] SOC i,t is the state of charge of the energy storage system at each node and each time period, SOC i,t-1 is the state of charge of the energy storage system at the previous time period at each node, η i is the charge and discharge conversion efficiency of the energy storage system, pess max is the maximum charge and discharge power of the energy storage system, is the probability distribution that satisfies the constraint, is the expected value of the probability distribution that satisfies the constraint.

[0135] In a possible implementation manner, the objective function for the operation optimization of the energy storage system set in Step 5 is:

[0136]

[0137] In the formula, p 0,t is the injection power of the superior power grid at each time period t, t ∈ T is the set of time periods, and T is the total number of time periods within a day.

[0138] Furthermore, Step 6 integrates the probability constraint of the output of the energy storage system and the objective function of the operation optimization of the energy storage system to obtain the operation optimization control model of the energy storage system under probability constraints, which is Equations (4) to (9).

[0139] In a possible implementation manner, step 7 performs equivalent transformation on formulas (4) to (9) as follows:

[0140] The objective function for the operation optimization of the energy storage system is equivalently transformed as follows:

[0141]

[0142] Then, the expression of the equivalently transformed objective function for the operation optimization of the energy storage system in formula (9) is:

[0143]

[0144] Formulas (5) and (6) in the probabilistic constraint on the output of the energy storage system are equivalently transformed by using the inverse function of the probability distribution of the load prediction deviation, and the equivalently transformed expressions are as follows:

[0145]

[0146]

[0147]

[0148]

[0149] In the formula, Φ -1 (1 - ε ch ), Φ -1 (1 - ε dis ) are respectively the inverse functions of the probability distributions of the charging and discharging power of the energy storage system in the corresponding confidence intervals, is the standard deviation of the load prediction deviation;

[0150] Similarly, after equivalently transforming formula (7), we get:

[0151]

[0152] Furthermore, step 7 equivalently transforms and solves the operation optimization control model of the energy storage system under probabilistic constraints based on probability theory to obtain the output plan of the energy storage system. An operation optimization control model with the objective function being formula (11) and the constraint conditions being (4), (8), (12) to (16) is obtained. This model is a linear model and can be directly solved by a solver.

[0153] The present invention proposes an operation optimization strategy for an energy storage system considering load uncertainty factors, which can take into account the influence of load prediction deviation on the power supply - demand balance, use the energy storage as a flexible regulation resource to locally balance the load, increase the autonomy of the regional power grid operation, reduce the impact on the main grid caused by load - side uncertainty factors, and increase the system operation margin.

[0154] Embodiment 2

[0155] Please refer to Figure 2 , this embodiment provides an energy storage system operation optimization system, including an uncertain factor input module 1, a power output constraint condition acquisition module 2, a system output setting module 3, a system output probability constraint setting module 4, a target function setting module 5, an operation optimization control model establishment module 6, and a model solving module 7. The main functions and uses of each module are reflected in the following aspects:

[0156] The uncertain factor input module 1 is used to obtain the probability distribution of the total load prediction deviation according to historical load prediction and actual data, as the uncertain factor input on the load side of the energy storage system;

[0157] The power output constraint condition acquisition module 2 is used to obtain the maximum state of charge and the minimum state of charge allowed for the multi-point distributed energy storage system on the distribution network side and the charge and discharge power constraint as the constraint conditions for the power output of the energy storage system; The system output setting module 3 is used to set the output of the energy storage system as the decision-making output without considering load uncertainty factors and the decision-making output for bearing the load prediction deviation. For the multi-point distributed energy storage system, set the load prediction deviation bearing coefficients of each energy storage system, and the sum of all load prediction deviation bearing coefficients is 1;

[0158] The system output probability constraint setting module 4 is used to set the probability constraint of the energy storage system output according to the uncertain factor input on the load side of the energy storage system, the constraint conditions of the energy storage system power output, the energy storage system output, and the load prediction deviation bearing coefficients of all multi-point distributed energy storage systems;

[0159] The target function setting module 5 is used to set the target function of the energy storage system operation optimization as minimizing the peak value of the power injected into the main grid;

[0160] The operation optimization control model establishment module 6 is used to integrate the probability constraint of the energy storage system output and the target function of the energy storage system operation optimization to obtain the operation optimization control model of the energy storage system under probability constraints;

[0161] The model solving module 7 is used to perform equivalent transformation on the operation optimization control model of the energy storage system under probability constraints and solve it to obtain the energy storage system output plan.

[0162] Furthermore, the uncertain factor input module 1 obtains the expectation μ of the load prediction deviation probability distribution of each node i at each time period t

[0163] and the variance i,t and

[0164] Obtain the probability expectation of the load prediction deviation based on a data-driven approach And the probability variance Var[ω of the load prediction deviation i,t , and

[0165] Total load prediction deviation

[0166] The expectation of the total load prediction deviation is:

[0167]

[0168] The variance of the total load prediction deviation is:

[0169]

[0170] Obtain the probability distribution Ω of the total load prediction deviation as the input of the uncertain factors on the load side of the energy storage system

[0171] Furthermore, if node i is not connected to any energy storage system, the power output constraint condition acquisition module 2 sets the maximum state of charge to 0, and the charge and discharge power constraint is 0

[0172] Furthermore, the system output setting module 3 sets the actual output of each active power source to:

[0173]

[0174] In the formula, p 0,t is the injection power of the superior power grid at each time period t, is the injection power of the superior power grid when not considering the load prediction deviation at each time period, α 0,t Ω t is the power for balancing the load prediction deviation borne by the superior power grid at each time period; is the charging power of the energy storage system at each node and different time periods considering the load prediction deviation, is the charging power of the energy storage system without considering the load prediction deviation, is the power of the load prediction deviation borne by the charging power of the energy storage system at each time period; is the discharging power of the energy storage system at each node and different time periods considering the load prediction deviation, is the discharging power of the energy storage system without considering the load prediction deviation, is the power of the load prediction deviation borne by the discharging power of the energy storage system at each time period;

[0175] The expression that the sum of all load prediction deviation sharing coefficients is 1 is:

[0176]

[0177] Furthermore, the probability constraint of the energy storage system output set by the system output probability constraint setting module 4 is as follows:

[0178]

[0179]

[0180]

[0181]

[0182] In the formula, 1 - ε ch and 1 - ε dis are respectively the confidence coefficients of the charge and discharge power step - over limits of the energy storage system;

[0183] SOC i,t is the state of charge of the energy storage system at each node and each time period, SOC i,t-1 is the state of charge of the energy storage system at the previous time period of each node, η i is the charge - discharge conversion efficiency of the energy storage system, pess max is the maximum charge - discharge power of the energy storage system, is the probability distribution satisfying the constraint, is the expected value of the probability distribution satisfying the constraint.

[0184] Furthermore, the objective function setting module 5 sets the objective function for the operation optimization of the energy storage system as follows:

[0185]

[0186] In the formula, p 0,t is the power injected by the superior power grid at each time period, t ∈ T is the set of time periods, and T is all time periods within a day.

[0187] Furthermore, the model solving module 7 equivalently transforms the objective function for the operation optimization of the energy storage system in the following manner:

[0188]

[0189] Then the expression of the equivalently transformed objective function for the operation optimization of the energy storage system is:

[0190]

[0191] On the other hand, the model solving module 7 equivalently transforms the expressions and in the probability constraint of the energy storage system output by using the inverse function of the probability distribution of the load prediction deviation, and the following equivalently transformed expressions are obtained:

[0192]

[0193]

[0194]

[0195]

[0196] In the formula, Φ -1 (1 - ε ch ), Φ -1 (1 - ε dis ) are respectively the inverse functions of the probability distributions of the charging and discharging powers of the energy storage system in the corresponding confidence intervals, is the standard deviation of the load forecasting deviation;

[0197] Similarly, after equivalent transformation of the expression , the following is obtained:

[0198]

[0199] The present invention proposes a method for constructing an optimized operation model of an energy storage system considering load forecasting deviation and an equivalent model transformation method, which avoids introducing uncertain factors into the main grid and can be applied to the preparation of the output plan of the energy storage system considering load forecasting deviation.

[0200] Embodiment 3

[0201] Another embodiment of the present invention also proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the energy storage system operation optimization method described in Embodiment 1 of the present invention. The computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate forms, etc. The computer-readable storage medium can include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included 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. For the sake of convenience of description, only the part related to the embodiment of the present invention is shown above. For the specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. This computer-readable storage medium is non-transitory and can be stored in a storage device formed by various electronic devices, and can implement the execution process described in the method of the embodiment of the present invention.

[0202] Example 4

[0203] Another embodiment of the present invention further provides a server, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the energy storage system operation optimization method described in Embodiment 1 of the present invention. Similarly, for ease of description, the above content only shows the part related to the embodiments of the present invention. For specific technical details not disclosed, please refer to the method part of the embodiments of the present invention.

[0204] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0205] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0206] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0207] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1Steps of the functions specified in one or more boxes.

[0208] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that modifications or equivalent replacements can still be made to the specific implementation manners of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for optimizing the operation of an energy storage system, characterized in that, It includes the following steps: Obtain the probability distribution of the total load forecasting deviation based on historical load forecasting and actual data, and use it as the input of the uncertain factors on the load side of the energy storage system; Obtain the maximum state of charge allowed for the multi-point distributed energy storage system on the distribution network side and the minimum state of charge as well as the charge and discharge power constraints as the constraint conditions for the power output of the energy storage system; Set the output of the energy storage system as two parts: the decision-making output without considering the load uncertainty factors and the decision-making output for bearing the load forecasting deviation. For the multi-point distributed energy storage system, set the load forecasting deviation bearing coefficients of each energy storage system, and the sum of all the load forecasting deviation bearing coefficients is 1; Set the probabilistic constraint of the energy storage system output according to the input of the uncertain factors on the load side of the energy storage system, the constraint conditions of the energy storage system power output, the energy storage system output, and the load forecasting deviation bearing coefficients of all the multi-point distributed energy storage systems; Set the objective function of the energy storage system operation optimization as minimizing the peak value of the power injected into the main grid; Integrate the probabilistic constraint of the energy storage system output and the objective function of the energy storage system operation optimization to obtain the operation optimization control model of the energy storage system under probabilistic constraints; Perform equivalent transformation on the operation optimization control model of the energy storage system under probabilistic constraints and solve it to obtain the energy storage system output plan; In the step of obtaining the probability distribution of the total load prediction deviation based on historical load prediction and actual data as the input of the uncertain factors on the load side of the energy storage system, the expectation μ of the load prediction deviation probability distribution of each node i at each time period t is obtained i,t and variance Obtaining the probability expectation of the load forecasting deviation based on a data-driven approach and the variance Var[ω of the load forecasting deviation i,t , and Total load prediction deviation The expectation of the total load forecasting deviation is: The variance of the total load forecasting deviation is: Obtain the probability distribution Ω of the total load forecasting deviation as the input of the uncertain factors on the load side of the energy storage system; In the step of setting the output of the energy storage system as two parts: the decision-making output without considering the load uncertainty factors and the decision-making output for bearing the load forecasting deviation. For the multi-point distributed energy storage system, set the load forecasting deviation bearing coefficients of each energy storage system, and the sum of all the load forecasting deviation bearing coefficients is 1, set the actual output of each active power source as: where p 0,t is the injection power of the superior power grid at each time period t, is the injection power of the superior power grid without considering the load forecasting deviation at each time period, and α 0,t Ω t is the power for balancing the load forecasting deviation borne by the superior power grid at each time period; is the charging power of the energy storage system at each node and different time periods considering the load forecasting deviation, is the charging power of the energy storage system without considering the load forecasting deviation, is the power of the load forecasting deviation borne by the charging power of the energy storage system at each time period; is the discharging power of the energy storage system at each node and different time periods considering the load forecasting deviation, is the discharging power of the energy storage system without considering the load forecasting deviation, is the power of the load forecasting deviation borne by the discharging power of the energy storage system at each time period; The expression that the sum of all the load forecasting deviation bearing coefficients is 1 is:

2. The method for optimizing the operation of an energy storage system according to claim 1, characterized in that, In the step of obtaining the maximum state of charge and the minimum state of charge allowed by the multi-point distributed energy storage system on the power distribution side and the charge and discharge power constraints as the constraint conditions for the power output of the energy storage system, if node i is not connected to any energy storage system, the maximum state of charge is set to 0, and the charge and discharge power constraints are set to 0. ​ 3. The method for optimizing the operation of an energy storage system according to claim 2, characterized in that, The probabilistic constraint of the energy storage system output is: where 1-ε ch and 1-ε dis are the confidence coefficients for the charging and discharging power step - over limits of the energy storage system respectively; SOC i,t is the state of charge of each node's energy storage system at each time period, SOC i,t-1 is the state of charge of each node's energy storage system in the previous time period, η i is the charge-discharge conversion efficiency of the energy storage system, pess max is the maximum charge-discharge power of the energy storage system, is the probability distribution that satisfies the constraint, is the expected value of the probability distribution that satisfies the constraint.

4. The method for optimizing the operation of an energy storage system according to claim 3, characterized in that, The objective function of the energy storage system operation optimization is: where p 0,t is the injection power of the superior power grid at each time period t, t ∈ T is the set of time periods, and T is the total number of time periods within a day.

5. The method for optimizing the operation of an energy storage system according to claim 4, characterized in that, The objective function of the energy storage system operation optimization is equivalently transformed in the following way: Then the expression of the equivalently transformed objective function of the energy storage system operation optimization is:

6. A method for optimizing the operation of an energy storage system according to claim 3, characterized in that In the probabilistic constraints of the output of the energy storage system, for the expression and Use the inverse function of the probability distribution of the load forecasting deviation for equivalent transformation, and the equivalent transformed expression is as follows: Where, Φ -1 (1 - ε ch ), Φ -1 (1 - ε dis ) are the inverse functions of the probability distributions of the charge and discharge powers of the energy storage system in the corresponding confidence intervals, is the standard deviation of the load prediction deviation; After equivalent transformation of the expression we get:

7. An energy storage system operation optimization system, characterized in that It includes: An uncertain factor input module, which is used to obtain the probability distribution of the total load forecasting deviation based on historical load forecasting and actual data, and use it as the input of the uncertain factors on the load side of the energy storage system; The power output constraint acquisition module is used to obtain the maximum state of charge allowed for the multi-point distributed energy storage system on the distribution network side and the minimum state of charge as well as the charge and discharge power constraints as the constraint conditions for the power output of the energy storage system; A system output setting module, which is used to set the output of the energy storage system as two parts: the decision-making output without considering the load uncertainty factors and the decision-making output for bearing the load forecasting deviation. For the multi-point distributed energy storage system, set the load forecasting deviation bearing coefficients of each energy storage system, and the sum of all the load forecasting deviation bearing coefficients is 1; A system output probabilistic constraint setting module, which is used to set the probabilistic constraint of the energy storage system output according to the input of the uncertain factors on the load side of the energy storage system, the constraint conditions of the energy storage system power output, the energy storage system output, and the load forecasting deviation bearing coefficients of all the multi-point distributed energy storage systems; An objective function setting module, which is used to set the objective function of the energy storage system operation optimization as minimizing the peak value of the power injected into the main grid; An operation optimization control model establishment module, which is used to integrate the probabilistic constraint of the energy storage system output and the objective function of the energy storage system operation optimization to obtain the operation optimization control model of the energy storage system under probabilistic constraints; The model solving module is used to perform equivalent transformation on the operation optimization control model of the energy storage system under the probability constraint and solve it to obtain the output plan of the energy storage system; The uncertainty factor input module obtains the expectation μ of the probability distribution of the load forecasting deviation of each node i at each time period t i,t and the variance Obtain the probability expectation of the load forecasting deviation based on a data-driven approach and the probability variance Var[ω of the load forecasting deviation i,t , and Total load prediction deviation The expectation of the total load prediction deviation is: The variance of the total load prediction deviation is: Obtain the probability distribution Ω of the total load prediction deviation as the input of the uncertain factors on the load side of the energy storage system; The system output setting module sets the actual output of each active power source as: where p 0,t is the injection power of the superior power grid at each time period t, is the injection power of the superior power grid without considering the load forecasting deviation at each time period, and α 0,t Ω t is the power for balancing the load forecasting deviation borne by the superior power grid at each time period; is the charging power of the energy storage system at each node and different time periods considering the load forecasting deviation, is the charging power of the energy storage system without considering the load forecasting deviation, is the power of the load forecasting deviation borne by the charging power of the energy storage system at each time period; is the discharging power of the energy storage system at each node and different time periods considering the load forecasting deviation, is the discharging power of the energy storage system without considering the load forecasting deviation, is the power of the load forecasting deviation borne by the discharging power of the energy storage system at each time period; The expression that the sum of all load prediction deviation bearing coefficients is 1 is:

8. An energy storage system operation optimization system according to claim 7, characterized in that: If node i is not connected to any energy storage system, the power output constraint acquisition module sets the maximum state of charge to 0, and the charge and discharge power constraint is 0.

9. An energy storage system operation optimization system according to claim 8, characterized in that The probability constraint of the energy storage system output set by the system output probability constraint setting module is: where 1-ε ch and 1-ε dis are the confidence coefficients of the charge and discharge power step limits of the energy storage system respectively; SOC i,t is the state of charge of each node's energy storage system at each time period, SOC i,t-1 is the state of charge of each node's energy storage system in the previous time period, η i is the charge-discharge conversion efficiency of the energy storage system, pess max is the maximum charge-discharge power of the energy storage system, is the probability distribution that satisfies the constraints, is the expected value of the probability distribution that satisfies the constraints.

10. An energy storage system operation optimization system according to claim 9, characterized in that The objective function setting module sets the objective function for the operation optimization of the energy storage system as: where p 0,t is the power injected by the superior power grid in each period, t ∈ T is the set of periods, and T is all periods within a day.

11. The operation optimization system of an energy storage system according to claim 10, wherein, The model solving module performs equivalent transformation on the objective function for the operation optimization of the energy storage system in the following manner: Then the expression of the objective function for the operation optimization of the energy storage system after equivalent transformation is:

12. The operation optimization system of an energy storage system according to claim 9, wherein, The expression in the probabilistic constraint of the output of the energy storage system solved by the model solving module and are equivalently transformed using the inverse function of the probability distribution of the load forecasting deviation, and the equivalently transformed expression is as follows: where Φ -1 (1 - ε ch ), Φ -1 (1 - ε dis ) are the inverse functions of the probability distributions of the charging and discharging powers of the energy storage system in the corresponding confidence intervals, is the standard deviation of the load prediction deviation; After equivalent transformation of the expression we get:

13. A computer-readable storage medium storing a computer program, wherein, When the computer program is executed by the processor, it implements an energy storage system operation optimization method according to any one of claims 1 to 6.

14. A server, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, When the processor executes the computer program, it implements an energy storage system operation optimization method according to any one of claims 1 to 6.

Images