Power distribution network distributed energy storage optimization method and system based on double-layer model, and medium

Abstract

The invention discloses a power distribution network distributed energy storage optimization method and system based on a double-layer model and a medium, and belongs to the technical field of power distribution network optimization operation, and the method comprises the steps: simulating and obtaining a load output error of a node based on a random scene method, carrying out the modeling of illumination intensity through employing Beta distribution, and constructing a double-layer operation model; setting a node voltage minimum risk coefficient as an inner-layer optimization target based on a maximum entropy criterion of a random variable; determining the energy storage power output as an outer-layer optimization target based on the energy efficiency function; obtaining a constraint data set of the double-layer model based on a system constraint condition; and the node voltage safety margin and the comprehensive operation benefit are considered, the double-layer model is solved based on a superior and inferior solution distance method, the optimal value of the double-layer model is obtained, and an optimal energy storage operation plan conforming to the node voltage safety margin of the power distribution network is formulated. According to the invention, the safety and operation efficiency of the power distribution network can be improved, the smooth adjustment effect of the distributed energy storage device is exerted, and the operation flexibility of the power distribution network is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network optimization operation, and specifically relates to a distribution network distributed energy storage optimization method, system and medium based on a double-layer model. Background Art

[0002] New energy sources have gradually become the supporting power source of the energy structure. Distributed photovoltaic power generation follows the principles of adapting measures to local conditions, clean and efficient, decentralized layout and nearby utilization, making full use of solar energy resources and reducing the consumption of fossil energy. However, due to the randomness and intermittency of photovoltaic output, when distributed photovoltaic is connected to the distribution network, it is easy to cause voltage over-limit, increased abandonment rate and increased network loss, which in turn affects the operation safety and efficiency of the distribution network.

[0003] In view of the above problems caused by distributed photovoltaic access, it is possible to consider configuring distributed energy storage devices in the distribution network. Distributed energy storage devices have the characteristics of fast charging and discharging response and smooth output, and can realize functions such as voltage support, promote distributed photovoltaic consumption, and enhance the flexibility of distribution network operation. The frequent charging and discharging of distributed energy storage devices is not conducive to the extension of the full cycle life of energy storage and the later maintenance of equipment. It is necessary to explore the optimized operation method of distributed energy storage suitable for efficient regulation of distribution networks.

[0004] For example, Chinese patent application CN115800347A discloses a method and system for optimizing the configuration of energy storage in a distribution network based on a planning-operation double-layer model. This method is suitable for access to new urban distribution networks containing distributed renewable energy sources. Under the premise of considering the safety constraints of the distribution network flow, this method takes the lowest annual average operating cost as the goal, comprehensively considers the cost indicators such as the construction cost of the entire life cycle of energy storage and the operating cost of the distribution network, and proposes a planning-operation double-layer model for the energy storage system; and finds the key coupling factors of the double-layer model, converts the double-layer model into a single-layer model; and uses a solver to solve, thereby obtaining an optimal configuration plan for energy storage capacity. The organic coordinated flow of energy is achieved between the distribution network, energy storage, distributed renewable energy, and loads, providing support for the distribution network load to respond to the peak and valley electricity prices of the power grid, alleviate the pressure of power grid energy supply, and improve the operating efficiency of the distribution network.

[0005] However, the above-mentioned existing technologies do not take into account the losses caused by frequent operation of energy storage. At the same time, the uncertain output of loads and photovoltaics will increase the complexity of the distribution network flow, thereby affecting the node voltage safety. Therefore, studying the optimized operation of distributed energy storage in the distribution network under uncertain loads and photovoltaic access can ensure the safe operation of the distribution network and improve the operating efficiency and economic benefits. Summary of the invention

[0006] To solve the above problems, the present invention provides a distributed energy storage optimization method, system and medium for a distribution network based on a double-layer model to solve the problems in the prior art.

[0007] In order to achieve the above-mentioned invention object, the present invention proposes a distributed energy storage optimization method for distribution network based on a double-layer model, comprising: Step S1: multiple nodes are set based on the transmission mode of the distribution network, the load output error of the node is simulated and obtained based on the random scenario method, and a double-layer model is established for the light intensity of the node based on the load output error and Beta distribution, wherein the double-layer model includes an inner model and an outer model; Step S2: Calculate the probability flow of the distribution network based on the semi-invariant method, generate a node voltage probability density function based on the probability flow according to the first criterion, calculate the risk coefficient of the node voltage based on the node voltage probability density function, and set the minimum risk coefficient as the inner optimization target of the inner model; Step S3: The distributed energy storage of the distribution network constructs an energy efficiency function based on the acquired first variable value and second variable value, and calculates an outer optimization target of the outer model based on the energy efficiency function; Step S4: setting a plurality of constraint conditions, and obtaining a constraint data set of the double-layer model based on the constraint conditions, wherein the constraint conditions include system flow constraint, photovoltaic output constraint, energy storage charge state and capacity constraint; Step S5: The two-layer model obtains the node voltage safety margin and the operating benefit based on the node voltage probability density function and the energy efficiency function, and solves the two-layer model based on the superior-inferior solution distance method to obtain the optimal value of the two-layer model, and generates an energy storage operation plan based on the node voltage safety margin and the optimal value of the two-layer model.

[0008] Furthermore, establishing the two-layer model comprises the following steps: The random scenario method combines a hyperbolic distribution function and a probability density function and describes the load output error based on a first formula, which is: ,in, is the probability density function, π , ξ , μ and δ are the parameters of the hyperbolic distribution function, K λ ( ξ )for λ The modified Bessel function of the third kind is is the load output error of the node in the random scenario method; Based on the second formula, the load output error and the Beta distribution are combined to generate the double-layer model to represent the photovoltaic output. , the second formula is: ,in, α and βare the parameters of the Beta distribution, P max is the maximum value of photovoltaic active power, is the photovoltaic active power; Based on the third formula, the actual load demand of the i-th node in the distribution network at time t is calculated , the second formula is: ,in, , and are the user load, photovoltaic output and energy storage charging and discharging power of the i-th node at time t, is the load output error of the i-th node at time t in the random scenario method; If the i-th node is not connected to a photovoltaic device or an energy storage device at time t, then or The value of is zero.

[0009] Furthermore, the semi-invariant method includes: The power reference point of the distribution network is obtained, a Taylor first-order form of the AC power flow equation is generated based on the power reference point, and the fluctuation change of the node power is obtained based on the Taylor first-order form. The mathematical form of the AC power flow equation is: ,in, is a first-order semi-invariant, X 0 represents the expected value at the power reference point, is the power reference point, J 0 is the Jacobian matrix of the power reference point, W 0 is J The inverse matrix of 0, S Inject power fluctuation amount into the node; Calculate the r-order semi-invariant based on the fourth formula , the fourth formula is: , where r is a positive integer, For the matrix W The e-th power of each element in 0, is the r-order semi-invariant of the photovoltaic active power, is the r-order semi-invariant of load power; Replacement of r-order semi-invariants based on conversion formula , the conversion formula is: , , where C is the probability combination operator symbol.

[0010] Further, generating the node voltage probability density function includes: The first criterion refers to the maximum entropy criterion of the random variable, and the first criterion is set based on a mathematical model, and the mathematical model is: ,in, is the entropy value, To satisfy the constraint symbol, is the node voltage probability density function, is the constraint function; The optimal solution of the node voltage probability density function is output based on the mathematical model, and a Lagrangian function is constructed based on the maximum entropy value. The Lagrangian function is: ,in, λ 0 and λ r is the Lagrange multiplier, m is any positive integer; The extreme value condition of the Lagrangian function is set based on the third formula, and the third formula is: ,exist When , the node voltage probability density function is: , where exp is the exponential function symbol.

[0011] Furthermore, obtaining the inner layer optimization target includes: Obtain node voltage deviation, and establish the i-th node voltage comprehensive function based on the node voltage deviation , the i-th node voltage comprehensive function is: ,in, is the voltage deviation of the ith node, and These are voltage deviation parameters. is the voltage amplitude of the ith node; The node voltage probability density function calculates the risk coefficient of the node voltage based on the fourth formula , the fourth formula is: , where the minimum risk factor is set as the inner optimization target , is the maximum amplitude of the voltage at the i-th node, is the minimum amplitude of the voltage at the i-th node.

[0012] Further, calculating the outer optimization target includes: The distributed energy storage determines the change value of the photovoltaic output, charges during the peak period of the photovoltaic output, and discharges during the valley period when the photovoltaic output is less than the load demand, and calculates the first variable value based on the fifth formula , the fifth formula is: ,in, and are the electricity price during discharge and the electricity price during charge, and are the energy storage discharge power and energy storage charging power respectively, and are the valley time period and the peak time period respectively, and are respectively the discharge efficiency and the charging efficiency of the distributed energy storage; The second variable value is calculated based on the sixth formula , the sixth formula is: ,in, C s and are the line loss cost and energy storage charging and discharging loss cost of the distributed energy storage, The system operation period, P s is the line loss power during the period; The energy efficiency function outputs the maximum operating benefit based on the seventh formula , and determine the energy storage output power, the seventh formula is: , the energy storage output power under the maximum operating benefit is set as the outer optimization target .

[0013] Furthermore, obtaining the constraint data set includes: The system power flow constraint and the node voltage safety margin are set as the first constraint of the inner optimization target in the double-layer model, the photovoltaic output constraint, the energy storage charge state and the capacity constraint are set as the second constraint of the outer optimization target in the double-layer model, and the first constraint and the second constraint are combined to generate the constraint data set of the double-layer model; The first constraint is: , ,in, and are the power source active power, photovoltaic active power, energy storage active power and load active power of the i-th node respectively. and are the power source reactive power, photovoltaic reactive power, energy storage reactive power and load reactive power of the i-th node respectively, and are the voltage amplitudes of the i-th node and the j-th node respectively, is the voltage phase between the i-th node and the j-th node, and are respectively the system conductance and the system susceptance of the distribution network; The second constraint is: , ,in, and The minimum value and the maximum value corresponding to the photovoltaic active power in the photovoltaic output constraint are respectively, and are respectively the minimum and maximum values ​​corresponding to the photovoltaic reactive power in the photovoltaic output constraint, is the value corresponding to the energy storage charge state, is the value corresponding to the capacity constraint.

[0014] Further, obtaining the optimal value of the two-layer model includes: The inner model in the double-layer model transmits the photovoltaic output and the node voltage safety margin to the outer model based on the inner optimization target, and the outer model formulates the energy storage operation plan based on the outer optimization target and the risk coefficient of the distributed energy storage, and provides the probabilistic power flow information to the inner model; The maximum index of the inner optimization target and the outer optimization target of the two-layer model are calculated based on the good and bad solution distance method, and the good and bad solution distance method is: ,in, When is the inner model, When is the outer model, is the maximum size index of model n in the f-th solution, is the optimization target of model n in the f-th solution, is the minimum optimization target value of model n in all solutions, is the maximum value of the optimization target of model n among all the schemes, f is a positive integer from 1 to F, and F is the number of schemes; The extremely large index is standardized based on the eighth formula to generate a standard index. The eighth formula is: ,in, is the standard index of model n in the f-th scheme; Based on the ninth formula, the first distance between the standard index of the model n in the f-th scheme and the corresponding maximum value is calculated respectively. , the second distance to the corresponding minimum , the ninth formula is: , based on the tenth formula, calculate the fit of all standard indicators , the tenth formula is: , extract the standard index corresponding to the maximum fit and set it as the optimal value of the two-layer model.

[0015] The present invention also provides a distribution network distributed energy storage optimization system based on a double-layer model, which is used to implement the above-mentioned distribution network distributed energy storage optimization method based on a double-layer model. The system mainly includes: A node analysis module, which sets a plurality of nodes based on the transmission mode of the distribution network, simulates and obtains the load output error of the node based on the random scenario method, and establishes a double-layer model for the light intensity of the node based on the load output error and Beta distribution, wherein the double-layer model includes an inner model and an outer model; An optimization setting module, which calculates the probability flow of the distribution network based on a semi-invariant method, generates a node voltage probability density function based on a first criterion, calculates the risk coefficient of the node voltage based on the node voltage probability density function, sets the minimum risk coefficient as the inner optimization target of the inner model, constructs an energy efficiency function of the distributed energy storage of the distribution network based on the acquired first variable value and second variable value, and calculates the outer optimization target of the outer model based on the energy efficiency function; A model constraint module, which sets a plurality of constraint conditions and obtains a constraint data set of the double-layer model based on the constraint conditions, wherein the constraint conditions include system flow constraint, photovoltaic output constraint, energy storage charge state and capacity constraint; A model output module, wherein the double-layer model obtains the node voltage safety margin and the operation benefit based on the node voltage probability density function and the energy efficiency function, solves the double-layer model based on the superior-inferior solution distance method, obtains the optimal value of the double-layer model, and generates an energy storage operation plan based on the node voltage safety margin and the optimal value of the double-layer model.

[0016] The present invention also provides a computer medium storing program instructions, wherein when the program instructions are executed, the device where the computer medium is located is controlled to execute the above-mentioned distribution network distributed energy storage optimization method based on the double-layer model.

[0017] Compared with the prior art, the beneficial effects of the present invention are at least as follows: The present invention first sets a random scenario method to obtain the load output error. It can be considered that the uncertainty scenarios of load and photovoltaic will increase the complexity of the distribution network flow and affect the node voltage safety in the distribution network. Then, based on the uncertainty of photovoltaic output and the random volatility of load, the semi-invariant method is used to calculate the probability flow of the distribution network. Finally, the node voltage safety probability density function is generated through the maximum entropy criterion of random variables, thereby obtaining the safety margin index of the node voltage.

[0018] The present invention also combines the relationship between energy efficiency functions to determine the optimized energy efficiency function, and forms a two-layer model with the node voltage safety margin index. The optimal energy storage operation plan is generated based on the optimal value of the two-layer model to achieve safe operation and optimization of economic benefits of distributed energy storage in the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1The present invention is a flow chart of a distributed energy storage optimization method for a distribution network based on a double-layer model; Figure 2 This is a structural diagram of a distributed energy storage optimization system for a distribution network based on a double-layer model of the present invention; Figure 3 A simulation model diagram of a distribution network including a distributed energy storage system in the present invention; Figure 4 is the output curve of the system load and a single photovoltaic in the simulation model of the present invention; Figure 5 It is a voltage diagram of key nodes before optimization in the simulation model of the present invention; Figure 6 is a curve of active power variation of distributed energy storage in the simulation model of the present invention; Figure 7 It is the voltage diagram of key nodes after optimization in the simulation model of the present invention; Figure 8 This is a diagram of system loss changes before and after optimization in the simulation model of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0022] like Figure 1 As shown, the distributed energy storage optimization method for distribution network based on the double-layer model includes: Step S1: multiple nodes are set based on the transmission mode of the distribution network, the load output error of the node is simulated and obtained based on the random scenario method, and a double-layer model is established for the light intensity of the node based on the load output error and Beta distribution. The double-layer model includes an inner model and an outer model.

[0023] Specifically, in this embodiment, the nodes of the distribution network refer to the points where different power components in the distribution network are interconnected, including but not limited to physical connection points and virtual nodes, such as the points where loads, power generation equipment and energy storage equipment are connected to the power grid. The random scenario method is used to process and evaluate the uncertainty and randomness in the system. The load output error refers to the difference between the actual load power and the expected value of the load power in the uncertainty scenario. The load output error can be described by the Beta distribution of the corresponding random variable, combined with the light intensity of the node and a two-layer model is formed, in which the light intensity is the most important factor affecting the photovoltaic output of the node. The two-layer model refers to a two-layer optimization model that correlates the optimization objectives and constraints of the inner and outer layers, which can achieve safe operation and efficiency optimization of distributed energy storage in the distribution network.

[0024] Step S2: Calculate the probability flow of the distribution network based on the semi-invariant method, generate the node voltage probability density function based on the first criterion, calculate the risk coefficient of the node voltage based on the node voltage probability density function, and set the minimum risk coefficient as the inner optimization target of the inner model.

[0025] Specifically, in this embodiment, the semi-invariant method can be used to process random variables and probability distributions in the distribution network and generate the probability flow of the distribution network, wherein the probability flow can quantify the randomness and uncertainty of the distribution network. The node voltage probability density function refers to the safety probability density function of the node voltage, and the risk coefficient of the node voltage can be calculated. The inner optimization target refers to the optimization target of the inner model in the double-layer model.

[0026] Step S3: The distributed energy storage of the distribution network constructs an energy efficiency function based on the acquired first variable value and second variable value, and calculates an outer optimization target of the outer model based on the energy efficiency function.

[0027] Specifically, in this embodiment, distributed energy storage refers to energy storage devices or systems dispersed in different locations in the distribution network, the first variable value refers to the operating benefit value of the distributed energy storage, and the second variable value refers to the system loss value of the distributed energy storage. The energy efficiency function is a comprehensive function used to measure and analyze distributed energy storage, and the outer optimization target refers to the optimization target of the outer model in the double-layer model.

[0028] Step S4: setting a variety of constraint conditions, and obtaining a constraint data set of the double-layer model based on the constraint conditions, wherein the constraint conditions include system flow constraint, photovoltaic output constraint, energy storage charge state and capacity constraint.

[0029] Specifically, in this embodiment, the constraint condition is used to limit the feasible solution space of the two-layer model. In the distribution network, the constraint condition ensures that the corresponding constraint data set will not be violated when optimizing the operation plan of the energy storage device, so as to achieve safe and efficient operation of the distribution network. The system flow constraint refers to the constraint condition corresponding to the probability flow of the distribution network, the photovoltaic output constraint refers to the constraint condition of the photovoltaic output fluctuation, the energy storage charge state refers to the constraint condition of the energy storage device charging and discharging, and the capacity constraint refers to the constraint condition of the capacity size of the energy storage device.

[0030] Step S5: The double-layer model obtains the node voltage safety margin and operation benefit based on the node voltage probability density function and the energy efficiency function, and solves the double-layer model based on the superior-inferior solution distance method to obtain the optimal value of the double-layer model, and generates an energy storage operation plan based on the optimal value of the node voltage safety margin and the energy efficiency function.

[0031] Specifically, in this embodiment, the node voltage safety margin refers to the difference between the voltage level of each node in the distribution network and the voltage range required for its safe operation. The size of the node voltage safety margin can be used to quantify the risk factor of the node voltage, and can also reflect the stability and reliability of the distribution network. Therefore, an energy storage operation plan for the distribution network is formulated based on the node voltage safety margin and the economic benefits of operation. The energy storage operation plan determines the operation strategy of the distributed energy storage device or system in the distribution network through the optimal value in the two-layer model to achieve a series of predetermined target values, which can improve the stability and reliability of the distribution network and improve energy utilization efficiency.

[0032] As a preferred technical solution of the present invention, establishing a double-layer model includes the following steps: The random scenario method combines the hyperbolic distribution function and the probability density function and describes the load output error based on the first formula, which is: ,in, is the probability density function, π , ξ , μ and δ are the parameters of the hyperbolic distribution function, K λ ( ξ )for λ The modified Bessel function of the third kind is is the load output error of the node in the random scenario method.

[0033] Based on the second formula, the load output error and Beta distribution are combined to generate a two-layer model to represent the photovoltaic output , the second formula is: ,in, α and β are the parameters of the Beta distribution, Pmax is the maximum value of photovoltaic active power, P PV is the photovoltaic active power.

[0034] Based on the third formula, the actual load demand of the i-th node in the distribution network at time t is calculated , the second formula is: ,in, , and are the user load, photovoltaic output and energy storage charging and discharging power of the i-th node at time t, is the load output error of the i-th node at time t in the random scenario method.

[0035] If the i-th node is not connected to a photovoltaic device or an energy storage device at time t, then or The value of is zero.

[0036] Specifically, in this embodiment, the random scenario method can clearly describe the node load output error by combining multiple functional formulas. The third type of modified Bessel function is a Hankel function used to deal with fluctuation problems. Photovoltaic output refers to the photovoltaic active power generated by the photovoltaic power generation system, that is, the power of the photovoltaic system converting solar energy into electrical energy. Substituting the photovoltaic active power into the second formula can represent the corresponding photovoltaic output.

[0037] As a preferred technical solution of the present invention, the semi-invariant method includes: Obtain the power reference point of the distribution network, generate the Taylor first-order form of the AC power flow equation based on the power reference point, and obtain the fluctuation of node power based on the Taylor first-order form. The mathematical form of the AC power flow equation is: ,in, is a first-order semi-invariant, X 0 represents the expected value at the power reference point, is the power reference point, J 0 is the Jacobian matrix of the power reference point, W 0 is J The inverse matrix of 0, S Inject power fluctuation amount into the node.

[0038] Calculate the r-order semi-invariant based on the fourth formula , the fourth formula is: , where r is a positive integer, For the matrix W The e-th power of each element in 0, is the r-order semi-invariant of the photovoltaic active power, is the r-order semi-invariant of load power.

[0039] Replacement of r-order semi-invariants based on conversion formula , the conversion formula is: , ,in, is the probability combination operator symbol.

[0040] Specifically, in this embodiment, the power reference point refers to a reference node in the distribution network for power flow analysis and calculation, which is used to represent the operating state of the distribution network under standard conditions. The semi-invariant method has the mathematical property of linear additivity, so in the fourth formula, the photovoltaic active power and the load power can be added to generate semi-invariants of various orders.

[0041] As a preferred technical solution of the present invention, generating a node voltage probability density function includes: The first criterion refers to the maximum entropy criterion of random variables. The first criterion is set based on a mathematical model, which is: ,in, is the entropy value, To satisfy the constraint symbol, is the node voltage probability density function, is the constraint function.

[0042] Based on the mathematical model, the node voltage probability density function is output and the Lagrangian function is constructed. The Lagrangian function is: ,in, λ 0 and λ r is the Lagrange multiplier, and m is any positive integer.

[0043] The extreme value condition of the Lagrangian function is set based on the third formula, which is: ,exist When , the node voltage probability density function is: , where exp is the exponential function symbol.

[0044] Specifically, the node voltage probability density function is generated by combining the Lagrangian function with the probability density function and the semi-invariant method.

[0045] As a preferred technical solution of the present invention, obtaining the inner layer optimization target includes: Obtain node voltage deviation and establish the i-th node voltage comprehensive function based on the node voltage deviation , the comprehensive function of the voltage at the i-th node is: ,in, is the voltage deviation of the ith node, and These are voltage deviation parameters. is the voltage amplitude of the ith node.

[0046] The node voltage probability density function calculates the risk factor of the node voltage based on the fourth formula , the fourth formula is: , where the minimum risk factor is set as the inner optimization target , is the maximum amplitude of the voltage at the ith node, is the minimum amplitude of the voltage at the ith node.

[0047] Specifically, the node voltage comprehensive function is a mathematical function used to quantify the impact of node voltage deviation in the distribution network on the efficiency of the distribution network system. It combines factors such as the size of the voltage deviation, voltage stability and power efficiency. By combining the node voltage probability density function and the node voltage comprehensive function to generate the fourth formula, the risk coefficient of the inner model can be calculated.

[0048] As a preferred technical solution of the present invention, calculating the outer layer optimization target includes: According to the change in photovoltaic output, the distributed energy storage is charged during the peak period of photovoltaic output and discharged during the valley period when the photovoltaic output is less than the load demand. The first variable value is calculated based on the fifth formula , the fifth formula is: ,in, and are the electricity price during discharge and the electricity price during charge, and are the energy storage discharge power and energy storage charging power respectively, and They are valley period and peak period respectively. and They are the discharge efficiency and charging efficiency of distributed energy storage respectively; Calculate the second variable value based on the sixth formula , the sixth formula is: ,in, C s and They are the line loss cost of distributed energy storage and the energy storage charging and discharging loss cost, The system operation period, P s is the line loss power in the time period; The energy efficiency function outputs the maximum operating benefit based on the seventh formula , and determine the energy storage output power, the seventh formula is: , set the energy storage output power under the maximum operating benefit as the outer optimization target .

[0049] Specifically, in this embodiment, the energy storage power refers to the power of distributed energy storage in the distribution network. Setting the energy storage output power under maximum operating benefit as the outer optimization target can maximize the efficiency of the device corresponding to the distributed energy storage.

[0050] As a preferred technical solution of the present invention, obtaining the constraint data set includes: The system flow constraint and node voltage safety margin are set as the first constraint of the inner optimization target in the two-layer model, and the photovoltaic output constraint, energy storage charge state and capacity constraint are set as the second constraint of the outer optimization target in the two-layer model. The first constraint and the second constraint are combined to generate the constraint data set of the two-layer model.

[0051] The first constraint is: , ,in, and are the power source active power, photovoltaic active power, energy storage active power and load active power of the i-th node respectively. and are the reactive power of the power supply, photovoltaic reactive power, energy storage reactive power and load reactive power of the i-th node respectively. and are the voltage amplitudes of the i-th node and the j-th node respectively, is the voltage phase between the i-th node and the j-th node, and are the system conductance and system susceptance of the distribution network respectively.

[0052] The second constraint is: , ,in, and The minimum and maximum values ​​of the photovoltaic active power in the photovoltaic output constraint are respectively, and are the minimum and maximum values ​​of photovoltaic reactive power in photovoltaic output constraints, respectively. is the value corresponding to the energy storage charge state, is the value corresponding to the capacity constraint.

[0053] Specifically, in this embodiment, by setting the optimization target of the double-layer model respectively through the first constraint and the second constraint, the accuracy of distributed energy storage can be improved, and the safe operation of the distribution network can be guaranteed.

[0054] As a preferred technical solution of the present invention, obtaining the optimal value of the double-layer model includes: In the double-layer model, the inner model transfers the photovoltaic output and node voltage safety margin to the outer model based on the inner optimization target. The outer model formulates the energy storage operation plan based on the outer optimization target and risk factor of the distributed energy storage, and provides probabilistic power flow information to the inner model.

[0055] Based on the good and bad solution distance method, the maximum index of the inner optimization target and the outer optimization target of the two-layer model are calculated respectively. The good and bad solution distance method is: ,in, When is the inner model, When is the outer model, is the maximum size index of model n in the f-th solution, is the optimization target of model n in the f-th solution, is the minimum optimization target value of model n in all solutions, is the maximum value of the optimization target of model n among all options, f is a positive integer from 1 to F, and F is the number of options.

[0056] Based on the eighth formula, the extremely large index is standardized and the standard index is generated. The eighth formula is: ,in, is the standard index of model n in the f-th solution.

[0057] Based on the ninth formula, the first distance between the standard index of model n in the f-th scheme and the corresponding maximum value is calculated respectively. , the second distance to the corresponding minimum , the ninth formula is: , based on the tenth formula, calculate the fit of all standard indicators , the tenth formula is: , extract the standard index corresponding to the maximum fit and set it as the optimal value of the two-layer model.

[0058] Specifically, in this embodiment, solving the optimal value of the two-layer model can be used to formulate an energy storage operation plan, wherein the number of schemes refers to the total number of all schemes that can solve the two-layer model, and the energy storage operation plan refers to the operation plan of the device or system corresponding to the distributed energy storage, that is, the charging and discharging plan, which is formulated by the optimal value of the two-layer model, can ensure the safe operation of the distribution network and improve the operating efficiency.

[0059] like Figure 2 As shown, the present invention also provides a distribution network distributed energy storage optimization system based on a double-layer model, which is used to implement the above-mentioned distribution network distributed energy storage optimization method based on a double-layer model. The system mainly includes: The node analysis module sets multiple nodes based on the transmission mode of the distribution network, simulates and obtains the load output error of the node based on the random scenario method, and establishes a double-layer model for the light intensity of the node based on the load output error and Beta distribution. The double-layer model includes an inner model and an outer model.

[0060] The optimization setting module calculates the probability flow of the distribution network based on the semi-invariant method. The probability flow generates the node voltage probability density function based on the first criterion, calculates the risk coefficient of the node voltage based on the node voltage probability density function, and sets the minimum risk coefficient as the inner optimization target of the inner model. The distributed energy storage of the distribution network constructs an energy efficiency function based on the obtained first variable value and second variable value, and calculates the outer optimization target of the outer model based on the energy efficiency function.

[0061] The model constraint module sets multiple constraint conditions and obtains the constraint data set of the double-layer model based on the constraint conditions. The constraint conditions include system flow constraint, photovoltaic output constraint, energy storage charge state and capacity constraint.

[0062] Model output module, the two-layer model obtains the node voltage safety margin based on the node voltage probability density function and the energy efficiency function, and solves the two-layer model based on the superior-inferior solution distance method to obtain the optimal value of the two-layer model, and generates an energy storage operation plan based on the node voltage safety margin and the optimal value of the two-layer model.

[0063] Specifically, in this embodiment, the IEEE33 node standard model is improved through a simulation model, wherein the IEEE33 node model is a benchmark system model widely used in power system dynamic research and teaching, which includes 33 nodes and 31 transmission lines, with different generator, load and switchgear configurations, such as Figure 3 As shown in the figure, a distribution network simulation model with a distributed energy storage system is built. In the simulation model, the system base voltage is set to 12.66kV, and photovoltaic power stations (PV) with a rated capacity of 400kW are installed at nodes 12, 20, 25, and 28 respectively. Distributed energy storage devices (ESS) are set at nodes 3 and 9, with a maximum charge and discharge power of 300kW and a charge and discharge efficiency of 0.9; the rated capacity of the transformer is 2.5kVA.

[0064] like Figure 4 The following are the system load and the output curve of a single photovoltaic: Figure 5As shown in the figure, the photovoltaic access nodes (12, 20, 25, 28) and the feeder end nodes (18, 33) are selected as key nodes. When the distributed energy storage output is not optimized, the voltage of photovoltaic access nodes 12, 20, and 28 rises rapidly during the photovoltaic output peak period (10-16 o'clock), so that the voltage exceeds the upper limit, and exceeds 1.06pu at some times. At night (21-24 o'clock, 0-4 o'clock), the voltage of feeder end nodes 18 and 33 drops due to insufficient power, and is lower than 0.93pu at some times, which is not conducive to the safe operation of the system. Figure 6 The figure shows the active power output plan of distributed energy storage after double-layer optimization operation. A positive output indicates energy storage charging, and a negative output indicates energy storage discharging. Figure 7 The figure shows the voltage of key nodes after the distributed energy storage charging and discharging output is optimized. Figure 7 It can be seen that after optimization, the voltage of key nodes at each moment fluctuates within the safe range of 0.95~1.05pu. Figure 8 The diagram of system loss changes before and after optimization shows that the average system loss decreases by 23.6% after energy storage optimization operation compared to the case without energy storage device. The above simulation results show that the distributed energy storage optimization method, system and medium for distribution network based on the double-layer model proposed in the present invention can realize the safe operation of distribution network and improve the operation efficiency.

[0065] The present invention also provides a computer medium storing program instructions, wherein when the program instructions are executed, the device where the computer medium is located is controlled to execute the above-mentioned distribution network distributed energy storage optimization method based on the double-layer model.

[0066] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0067] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0068] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0069] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0070] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed energy storage optimization method for distribution network based on a two-layer model, characterized in that: The method comprises the following steps: Step S1: multiple nodes are set based on the transmission mode of the distribution network, the load output error of the node is simulated and obtained based on the random scenario method, and a double-layer model is established for the light intensity of the node based on the load output error and Beta distribution, wherein the double-layer model includes an inner model and an outer model; Step S2: Calculate the probability flow of the distribution network based on the semi-invariant method, generate a node voltage probability density function based on the probability flow according to the first criterion, calculate the risk coefficient of the node voltage based on the node voltage probability density function, and set the minimum risk coefficient as the inner optimization target of the inner model; Step S3: The distributed energy storage of the distribution network constructs an energy efficiency function based on the acquired first variable value and second variable value, and calculates an outer optimization target of the outer model based on the energy efficiency function; Step S4: setting a plurality of constraint conditions, and obtaining a constraint data set of the double-layer model based on the constraint conditions, wherein the constraint conditions include system flow constraint, photovoltaic output constraint, energy storage charge state and capacity constraint; Step S5: The two-layer model obtains the node voltage safety margin and the operating benefit based on the node voltage probability density function and the energy efficiency function, and solves the two-layer model based on the superior-inferior solution distance method to obtain the optimal value of the two-layer model, and generates an energy storage operation plan based on the node voltage safety margin and the optimal value of the two-layer model.

2. The method according to claim 1, characterized in that Establishing the two-layer model includes the following steps: The random scenario method combines a hyperbolic distribution function and a probability density function and describes the load output error based on a first formula, which is: ,in, is the probability density function, π , ξ , μ and δ are the parameters of the hyperbolic distribution function, K λ ( ξ )for λ The modified Bessel function of the third kind is is the load output error of the node in the random scenario method; Based on the second formula, the load output error and the Beta distribution are combined to generate the double-layer model to represent the photovoltaic output. , the second formula is: ,in, α and β are the parameters of the Beta distribution, P max is the maximum value of photovoltaic active power, is the photovoltaic active power; Based on the third formula, the actual load demand of the i-th node in the distribution network at time t is calculated , the second formula is: ,in, , and are the user load, photovoltaic output and energy storage charging and discharging power of the i-th node at time t, is the load output error of the i-th node at time t in the random scenario method; If the i-th node is not connected to a photovoltaic device or an energy storage device at time t, then or The value of is zero.

3. The method according to claim 1, characterized in that The semi-invariant method includes: The power reference point of the distribution network is obtained, a Taylor first-order form of the AC power flow equation is generated based on the power reference point, and the fluctuation change of the node power is obtained based on the Taylor first-order form. The mathematical form of the AC power flow equation is: ,in, is a first-order semi-invariant, X 0 represents the expected value at the power reference point, is the power reference point, J 0 is the Jacobian matrix of the power reference point, W 0 is J The inverse matrix of 0, S Inject power fluctuation amount into the node; Calculate the r-order semi-invariant based on the fourth formula , the fourth formula is: , where r is a positive integer, For the matrix W The e-th power of each element in 0, is the r-order semi-invariant of the photovoltaic active power, is the r-order semi-invariant of load power; Replacement of r-order semi-invariants based on conversion formula , the conversion formula is: , , where C is the probability combination operator symbol.

4. The method according to claim 3, characterized in that Generating the node voltage probability density function includes: The first criterion refers to the maximum entropy criterion of the random variable, and the first criterion is set based on a mathematical model, and the mathematical model is: ,in, is the entropy value, To satisfy the constraint symbol, is the node voltage probability density function, is the constraint function; The optimal solution of the node voltage probability density function is output based on the mathematical model, and a Lagrangian function is constructed based on the maximum entropy value. The Lagrangian function is: ,in, λ 0 and λ r is the Lagrange multiplier, m is any positive integer; The extreme value condition of the Lagrangian function is set based on the third formula, and the third formula is: ,exist When , the node voltage probability density function is: , where exp is the exponential function symbol.

5. The method according to claim 4, characterized in that Obtaining the inner layer optimization target includes: Obtain node voltage deviation, and establish the i-th node voltage comprehensive function based on the node voltage deviation , the i-th node voltage comprehensive function is: ,in, is the voltage deviation of the ith node, and These are voltage deviation parameters. is the voltage amplitude of the ith node; The node voltage probability density function calculates the risk coefficient of the node voltage based on the fourth formula , the fourth formula is: , where the minimum risk factor is set as the inner optimization target , is the maximum amplitude of the voltage at the i-th node, is the minimum amplitude of the voltage at the i-th node.

6. The method according to claim 1, characterized in that Calculating the outer optimization target includes: Distributed energy storage determines the change value of the photovoltaic output, charges during the peak period of the photovoltaic output, and discharges during the valley period when the photovoltaic output is less than the load demand, and calculates the first variable value based on the fifth formula , the fifth formula is: ,in, and are the electricity prices during discharge and charging respectively, and are the energy storage discharge power and energy storage charging power respectively, and are the valley time period and the peak time period respectively, and are respectively the discharge efficiency and the charging efficiency of the distributed energy storage; The second variable value is calculated based on the sixth formula , the sixth formula is: ,in, C s and are the line loss cost and energy storage charging and discharging loss cost of the distributed energy storage, The system operation period, P s is the line loss power in the period; the energy efficiency function outputs the maximum operating benefit based on the seventh formula , and determine the energy storage output power, the seventh formula is: , the energy storage output power under the maximum operating benefit is set as the outer optimization target .

7. The method according to claim 1, characterized in that Obtaining constraint data sets includes: The system power flow constraint and the node voltage safety margin are set as the first constraint of the inner optimization target in the double-layer model, the photovoltaic output constraint, the energy storage charge state and the capacity constraint are set as the second constraint of the outer optimization target in the double-layer model, and the first constraint and the second constraint are combined to generate the constraint data set of the double-layer model; The first constraint is: , ,in, and are the power source active power, photovoltaic active power, energy storage active power and load active power of the i-th node respectively. and are the power source reactive power, photovoltaic reactive power, energy storage reactive power and load reactive power of the i-th node respectively, and are the voltage amplitudes of the i-th node and the j-th node respectively, is the voltage phase between the i-th node and the j-th node, and are respectively the system conductance and the system susceptance of the distribution network; The second constraint is: , ,in, and The minimum value and the maximum value corresponding to the photovoltaic active power in the photovoltaic output constraint are respectively, and are respectively the minimum and maximum values ​​corresponding to the photovoltaic reactive power in the photovoltaic output constraint, is the value corresponding to the energy storage charge state, is the value corresponding to the capacity constraint.

8. The method according to claim 1, characterized in that Obtaining the optimal value of the two-layer model includes: The inner model in the double-layer model transmits the photovoltaic output and the node voltage safety margin to the outer model based on the inner optimization target, and the outer model formulates the energy storage operation plan based on the outer optimization target and the risk coefficient of the distributed energy storage, and provides the probabilistic power flow information to the inner model; The maximum index of the inner optimization target and the outer optimization target of the two-layer model are calculated based on the good and bad solution distance method, and the good and bad solution distance method is: ,in, When is the inner model, When is the outer model, is the maximum size index of model n in the f-th solution, is the optimization target of model n in the f-th solution, is the minimum optimization target value of model n in all solutions, is the maximum value of the optimization target of model n among all the schemes, f is a positive integer from 1 to F, and F is the number of schemes; The extremely large index is standardized based on the eighth formula to generate a standard index. The eighth formula is: ,in, is the standard index of model n in the f-th scheme; Based on the ninth formula, the first distance between the standard index of the model n in the f-th scheme and the corresponding maximum value is calculated respectively. , the second distance to the corresponding minimum , the ninth formula is: , based on the tenth formula, calculate the fit of all standard indicators , the tenth formula is: , extract the standard index corresponding to the maximum fit and set it as the optimal value of the two-layer model.

9. A distributed energy storage optimization system for a distribution network based on a two-layer model, used to implement the method according to any one of claims 1 to 8, characterized in that: The system includes the following modules: A node analysis module, which sets a plurality of nodes based on the transmission mode of the distribution network, simulates and obtains the load output error of the node based on the random scenario method, and establishes a double-layer model for the light intensity of the node based on the load output error and Beta distribution, wherein the double-layer model includes an inner model and an outer model; An optimization setting module, which calculates the probability flow of the distribution network based on a semi-invariant method, generates a node voltage probability density function based on a first criterion, calculates the risk coefficient of the node voltage based on the node voltage probability density function, sets the minimum risk coefficient as the inner optimization target of the inner model, constructs an energy efficiency function of the distributed energy storage of the distribution network based on the acquired first variable value and second variable value, and calculates the outer optimization target of the outer model based on the energy efficiency function; A model constraint module, which sets a plurality of constraint conditions and obtains a constraint data set of the double-layer model based on the constraint conditions, wherein the constraint conditions include system flow constraint, photovoltaic output constraint, energy storage charge state and capacity constraint; A model output module, wherein the double-layer model obtains the node voltage safety margin based on the node voltage probability density function and the energy efficiency function, solves the double-layer model based on the superior-inferior solution distance method, obtains the optimal value of the double-layer model, and generates an energy storage operation plan based on the node voltage safety margin and the optimal value of the double-layer model.

10. A computer medium, characterized in that The computer medium stores program instructions, wherein when the program instructions are executed, the device where the computer medium is located is controlled to execute the method according to any one of claims 1 to 8.

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