Energy storage optimal configuration method and device, equipment and storage medium

Abstract

The invention discloses an energy storage optimization configuration method, device and equipment and a storage medium, and the method comprises the steps: taking user side distributed renewable energy historical output data and predicted output data as input, building a distributed renewable energy strong uncertainty model based on Wharisstein divergence, and building a distributed renewable energy strong uncertainty model based on a Wharisstein distribution robust optimization theory; the method comprises the steps of constructing a user side operation cost and energy storage income model, constructing a distribution network side comprehensive operation cost model based on a conditional value-at-risk theory, solving the user side operation cost and energy storage income model based on the constraint of a distributed renewable energy source strong uncertainty model, and obtaining an energy storage configuration scheme enabling the user side energy consumption cost to be the lowest. And solving the distribution network side comprehensive operation cost model to obtain an energy storage configuration scheme enabling the comprehensive operation cost of the power distribution network to be minimum, so that the problems of consumption and power fluctuation of the distributed power supply can be effectively solved, and a user side and distribution network side shared energy storage configuration strategy is more reasonable.

Description

Technical Field

[0001] The present invention relates to a distribution network optimization technology, and more particularly to an energy storage optimization configuration method, device, equipment and storage medium. Background Art

[0002] It has become an inevitable trend to build a new power system characterized by a high proportion of new energy. The user distributed resources close to the power demand side have the potential to support the clean, low-carbon, safe and efficient development of the new power system with their huge scale and flexible adjustment capabilities. Therefore, the penetration rate of distributed new energy represented by household photovoltaics in the distribution network is constantly increasing.

[0003] Due to the inherent intermittent and uncertain nature of renewable energy output, the large-scale access of distributed renewable energy will inevitably affect the operation of the distribution network, resulting in increased system losses, deterioration of power quality, wind and solar power abandonment, and other issues. In recent years, battery energy storage technology has developed rapidly. Energy storage equipment can be used to solve the problem of renewable energy consumption and eliminate system congestion due to its energy time-shifting characteristics. The configuration and planning of energy storage in power systems has also become a hot topic of current research.

[0004] However, with the access of large-scale distributed resources, the prediction accuracy of distributed photovoltaic output has not yet reached the requirements for ensuring the safe operation of the power grid. The distribution network faces a series of severe challenges such as new energy consumption, network congestion, power fluctuations, etc. At the same time, the need to transform and expand the lines and transformers has caused an increase in transmission and distribution costs. Therefore, how to deal with the uncertainty of distributed new energy output is the key and difficulty of optimizing configuration. Summary of the invention

[0005] The present invention provides an energy storage optimization configuration method, device, equipment and storage medium to make the energy storage configuration strategy shared by the user side and the distribution network side more reasonable.

[0006] In a first aspect, the present invention provides a method for optimizing energy storage configuration, comprising:

[0007] Taking the historical output data and predicted output data of distributed renewable energy on the user side as input, a distributed renewable energy strong uncertainty model is constructed based on Wasserstein divergence.

[0008] Based on Wasserstein's distributed bar optimization theory, a user-side operating cost and energy storage benefit model is constructed;

[0009] Based on the conditional risk value theory, a comprehensive operation cost model for the distribution network is constructed;

[0010] Based on the constraint solving of the distributed renewable energy strong uncertainty model, the user-side operation cost and energy storage benefit model is obtained to obtain the energy storage configuration plan that minimizes the user-side energy cost.

[0011] Solve the comprehensive operating cost model of the distribution network side and obtain the energy storage configuration plan that minimizes the comprehensive operating cost of the distribution network.

[0012] Optionally, the historical output data and predicted output data of distributed renewable energy on the user side are used as input, and a distributed renewable energy strong uncertainty model is constructed based on Wasserstein divergence, including:

[0013] Calculate the difference between historical output data and predicted output data to obtain historical prediction error;

[0014] Taking the historical forecast error of the output power of distributed renewable energy units as a random variable, an empirical distribution of the forecast error is constructed;

[0015] A fuzzy set is constructed with the empirical distribution of historical prediction errors as the center as a strong uncertainty model for distributed renewable energy. The Wasserstein divergence is used in the fuzzy set to characterize the distance between the empirical distribution of historical prediction errors and the true probability distribution.

[0016] Optionally, the empirical distribution of historical forecast errors is:

[0017]

[0018] in, is the empirical distribution of the historical prediction error of the i-th output power of the distributed renewable energy unit, N sam is the number of samples of historical forecast errors, d k is the Dirac measure of the kth historical forecast error.

[0019] Optionally, the fuzzy set is:

[0020]

[0021] in, is a fuzzy set, which can be regarded as a radius by The Wasserstein sphere with the center as the center, is the true probability distribution of historical forecast errors, is the distance between the empirical distribution of historical forecast errors and the true probability distribution, The specific expressions are as follows:

[0022]

[0023] In the formula, is δ i The confidence level of; η is an auxiliary variable; is the average of historical forecast errors, δ i,k is the kth historical forecast error, It can be found by the equal division search method.

[0024] Optionally, based on Wasserstein distributed blue stick optimization theory, a user-side operating cost and energy storage benefit model is constructed, including:

[0025] Taking the minimization of user-side energy consumption cost as the first objective function, and taking power balance constraints, energy storage device operation constraints, electricity trading constraints, renewable energy operation constraints, and Wasserstein fuzzy set constraints as constraints, a user-side operating cost and energy storage benefit model is constructed.

[0026] Optionally, the first objective function is:

[0027]

[0028] In the formula, r is the rth user function area; R is the function area set; is the price of electricity purchased by the distribution network from the user side, The price of electricity sold by the distribution network to users; The amount of electricity purchased by the distribution network from the user side. The amount of electricity sold by the distribution network to users; is the energy storage discharge of functional area r at time t, is the energy storage charge of functional area r at time t; Y is the decision variable of the max model, including B is the decision variable of the min model, in is the photovoltaic output of functional area r, which is an uncertain variable;

[0029] Power balance constraints:

[0030]

[0031] Energy storage device operation constraints:

[0032]

[0033] In the formula, are the maximum charging and discharging powers allowed for energy storage in functional area r respectively; is the capacity of energy storage at time t; is the maximum and minimum capacity allowed for the energy storage device; η loss is the loss coefficient of the energy storage device; η ch,ESr , η dis,ESr is the charge and discharge coefficient of the energy storage device; and within a scheduling cycle, the initial and final capacity states of the energy storage device should remain consistent;

[0034] Electricity trading constraints:

[0035]

[0036] In the formula, and The maximum amount of electricity that a grid operator can sell and purchase from users;

[0037] Renewable energy operation constraints:

[0038]

[0039] In the formula, Represents the maximum and minimum output of distributed photovoltaic power on the user side;

[0040] Wasserstein fuzzy set constraints:

[0041]

[0042] In the formula, is the probability distribution of distributed photovoltaic output samples; is the bad probability distribution of the predicted value of distributed photovoltaic output; θ p , p Obey Random variable with distribution; Φ p is θ p , p The joint probability distribution of; ε is the radius of the Wasserstein sphere for distributed photovoltaic output; ρ PV is the confidence level of distributed photovoltaic output.

[0043] Optionally, based on the conditional value at risk theory, a comprehensive operation cost model for the distribution network side is constructed, including:

[0044] Taking the minimization of the comprehensive operation cost of the distribution network as the second objective function, and taking power balance constraints, energy storage configuration constraints, energy storage operation constraints, power trading constraints and conditional risk constraints as constraints, a comprehensive operation cost model of the distribution network side is constructed;

[0045] The second objective function is:

[0046]

[0047] In the formula, C inv The cost of investing in energy storage for grid operators; s is the operating cost; L is the risk factor; is the conditional risk value of the distribution network operation cost; φ is the risk value of the distribution network operation cost; α is the confidence level; p s is the probability of scenario s occurring; z s R is the value of the distribution network operation cost exceeding the risk value; delay To delay the investment in grid upgrades by configuring shared energy storage on the distribution network side;

[0048] Power balance constraints:

[0049]

[0050] in, The amount of electricity purchased by the distribution network from the upper power grid. It is the power sent back from the distribution network to the upper grid;

[0051] Energy storage configuration constraints:

[0052]

[0053] η min M ES,DN ≤P ES,DN ≤η max M ES,DN

[0054] In the formula, are the maximum energy storage capacity and maximum energy storage power that can be configured respectively; η min and η max are the minimum and maximum power coefficients of energy storage, respectively;

[0055] Energy storage operation constraints:

[0056]

[0057] In the formula, They are the maximum charging and discharging powers allowed by the energy storage on the distribution network side; is the capacity of the energy storage on the distribution network side at time t; The maximum and minimum capacity allowed for the energy storage device on the distribution network side; η loss is the loss coefficient of the energy storage device on the distribution network side; η ch,ES,DN , η dis,ES,DN The distribution network side is the charge and discharge coefficient of the energy storage device; and within a dispatch cycle, the initial and final capacity states of the energy storage device on the distribution network side should remain consistent;

[0058] Electricity trading constraints:

[0059]

[0060]

[0061] In the formula, It is the upper limit of the power that the distribution network can purchase from the upper power grid. It is the upper limit constraint on the power transmission from the distribution network to the upper grid;

[0062] Conditional risk constraints:

[0063] z s≥0;

[0064] z s ≥f s -φ

[0065] Where, is the value by which the distribution network operation cost exceeds the risk value, f s is the operating cost, and φ is the risk value of the distribution network operating cost.

[0066] In a second aspect, the present invention further provides an energy storage optimization configuration device, comprising:

[0067] The first model building module is used to take the historical output data and predicted output data of distributed renewable energy on the user side as input, and to build a distributed renewable energy strong uncertainty model based on Wasserstein divergence;

[0068] The second model building module is used to build a user-side operating cost and energy storage benefit model based on Wasserstein distributed blue stick optimization theory;

[0069] The third model building module is used to build a comprehensive operation cost model of the distribution network side based on the conditional risk value theory;

[0070] The first solving module is used to solve the user-side operation cost and energy storage benefit model based on the constraints of the distributed renewable energy strong uncertainty model, and obtain the energy storage configuration plan that minimizes the user-side energy cost;

[0071] The second solution module is used to solve the comprehensive operation cost model of the distribution network side and obtain the energy storage configuration plan that minimizes the comprehensive operation cost of the distribution network.

[0072] In a third aspect, the present invention further provides an electronic device, comprising:

[0073] one or more processors;

[0074] A storage device for storing one or more programs;

[0075] When the one or more programs are executed by the one or more processors, the one or more processors implement the energy storage optimization configuration method provided in the first aspect of the present invention.

[0076] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the energy storage optimization configuration method provided in the first aspect of the present invention.

[0077] The energy storage optimization configuration method provided by the present invention takes the historical output data and predicted output data of distributed renewable energy on the user side as input, constructs a distributed renewable energy strong uncertainty model based on the Wasserstein divergence, constructs a user side operating cost and energy storage benefit model based on the Wasserstein divergence robust optimization theory, constructs a distribution network side comprehensive operating cost model based on the conditional risk value theory, solves the user side operating cost and energy storage benefit model based on the constraints of the distributed renewable energy strong uncertainty model, obtains an energy storage configuration plan that minimizes the energy consumption cost on the user side, solves the distribution network side comprehensive operating cost model, and obtains an energy storage configuration plan that minimizes the comprehensive operating cost of the distribution network. The method can effectively handle the problems of distributed power supply absorption and power fluctuation, and makes the shared energy storage configuration strategy between the user side and the distribution network side more reasonable.

[0078] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0080] Figure 1 A flow chart of an energy storage optimization configuration method provided by the present invention;

[0081] Figure 2 It is the result diagram of the optimal dispatch of various resources in the distribution network;

[0082] Figure 3 This is the charging and discharging strategy diagram for shared energy storage on the distribution network side;

[0083] Figure 4 A schematic diagram of the structure of an energy storage optimization configuration device provided by the present invention;

[0084] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0085] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0086] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0087] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0088] Figure 1 This is a flow chart of a method for optimizing energy storage configuration provided by the present invention. This embodiment can be applied to optimizing energy storage on the user side and the distribution network side. The method can be executed by the energy storage optimization configuration device provided by the present invention. The device can be implemented by software and / or hardware, and is usually configured in an electronic device, such as Figure 1 As shown, the energy storage optimization configuration method includes the following steps:

[0089] S101. Taking the historical output data and predicted output data of distributed renewable energy on the user side as input, a distributed renewable energy strong uncertainty model is constructed based on Wasserstein divergence.

[0090] In an embodiment of the present invention, historical output data and predicted output data of distributed renewable energy on the user side are selected as input, and a strong uncertainty model of distributed renewable energy is constructed based on the Wasserstein divergence.

[0091] In some embodiments of the present invention, the difference between historical output data and predicted output data is calculated to obtain a historical prediction error; the historical prediction error of the output power of the distributed renewable energy unit is used as a random variable to construct an empirical distribution of the prediction error; a fuzzy set is constructed with the empirical distribution of the historical prediction error as the center as a strong uncertainty model of distributed renewable energy, wherein the Wasserstein divergence is used in the fuzzy set to characterize the distance between the empirical distribution of the historical prediction error and the true probability distribution.

[0092] Exemplarily, the specific process is as follows:

[0093] The prediction error of the i-th output power of the distributed renewable energy unit in the system is a random variable δ i In practical applications, the prediction error of limited historical samples is (N sam is the number of historical samples) can provide information about the true probability distribution Reliable probability information to extract an empirical distribution

[0094]

[0095] in, is the empirical distribution of the historical prediction error of the i-th output power of the distributed renewable energy unit, N sam is the number of samples of historical forecast errors, d k is the Dirac measure of the kth historical forecast error.

[0096] Then with Construct fuzzy sets for the centers To ensure Has good properties, using Wasserstein divergence to accurately describe and For a given support space and two probability distributions Its Wasserstein distance It can be expressed as:

[0097]

[0098] In the formula, For the empirical distribution A random variable; J is and The joint distribution of is the distance between two random variables, and the 1-norm is used due to its superior numerical traceability in distributionally robust optimization problems.

[0099] According to the above definition, the fuzzy set It can be expressed as:

[0100]

[0101] in, is a fuzzy set, which can be regarded as a radius by The Wasserstein sphere with the center as the center, is the true probability distribution of historical forecast errors, is the distance between the empirical distribution of historical forecast errors and the true probability distribution, The specific expressions are as follows:

[0102]

[0103] In the formula, is δ i The confidence level of; η is an auxiliary variable; is the average of historical forecast errors, δ i,k is the kth historical forecast error, It can be found by the equal division search method.

[0104] S102. Based on Wasserstein distributed robust optimization theory, a user-side operating cost and energy storage benefit model is constructed.

[0105] In an embodiment of the present invention, the minimization of user-side energy consumption cost is taken as the first objective function, and power balance constraints, energy storage device operation constraints, electricity trading constraints, renewable energy operation constraints, and Wasserstein fuzzy set constraints are taken as constraints to construct a user-side operating cost and energy storage benefit model.

[0106] The first objective function is:

[0107]

[0108] In the formula, r is the rth user function area; R is the function area set; is the price of electricity purchased by the distribution network from the user side, The price of electricity sold by the distribution network to users; The amount of electricity purchased by the distribution network from the user side. The amount of electricity sold by the distribution network to users; is the energy storage discharge of functional area r at time t, is the energy storage charge of functional area r at time t; Y is the decision variable of the max model, including B is the decision variable of the min model, in is the photovoltaic output of functional area r, which is an uncertain variable.

[0109] Power balance constraints:

[0110]

[0111] Energy storage device operation constraints:

[0112]

[0113] In the formula, are the maximum charging and discharging powers allowed for energy storage in functional area r respectively; is the capacity of energy storage at time t; is the maximum and minimum capacity allowed for the energy storage device; η loss is the loss coefficient of the energy storage device; η ch,ESr , η dis,ESr is the charge and discharge coefficient of the energy storage device; and within a scheduling cycle, the initial and final capacity states of the energy storage device should remain consistent;

[0114] Electricity trading constraints:

[0115]

[0116] In the formula, and The maximum amount of electricity that a grid operator can sell and purchase from users;

[0117] Renewable energy operation constraints:

[0118]

[0119] In the formula, Represents the maximum and minimum output of distributed photovoltaic power on the user side;

[0120] Wasserstein fuzzy set constraints:

[0121]

[0122] In the formula, is the probability distribution of distributed photovoltaic output samples; is the bad probability distribution of the predicted value of distributed photovoltaic output; θ p , p Obey Random variable with distribution; Φ p is θ p , p The joint probability distribution of; ε is the radius of the Wasserstein sphere for distributed photovoltaic output; ρ PV is the confidence level of distributed photovoltaic output.

[0123] S103. Based on the conditional risk value theory, a comprehensive operation cost model for the distribution network is constructed.

[0124] In an embodiment of the present invention, the minimization of the comprehensive operating cost of the distribution network is taken as the second objective function, and the power balance constraint, energy storage configuration constraint, energy storage operation constraint, power trading constraint and conditional risk constraint are taken as constraints to construct a comprehensive operating cost model of the distribution network side.

[0125] The second objective function is:

[0126]

[0127]

[0128] In the formula, C inv The cost of investing in energy storage for grid operators; s is the operating cost; L is the risk factor; is the conditional risk value of the distribution network operation cost; φ is the risk value of the distribution network operation cost; α is the confidence level; p s is the probability of scenario s occurring; z s R is the value of the distribution network operation cost exceeding the risk value; delay To configure shared energy storage on the distribution network side to delay the benefits of grid upgrade investment.

[0129] The calculation formulas are as follows:

[0130] f s =C ele +C LT ;

[0131]

[0132] Where: C ele is the electricity transaction cost; C LT To reverse the cost of electricity transmission; The price at which the distribution network purchases electricity from the upper grid; The amount of electricity purchased by the distribution network from the upper power grid; is the energy storage investment cost on the distribution network side, The operation and maintenance cost of energy storage on the distribution network side; and are the unit capacity and unit power investment and construction costs of distribution network energy storage devices respectively; M ES,DN and They are the capacity and maximum power of energy storage configuration on the distribution network side; is the unit power maintenance cost of the energy storage device in the distribution network; is the charging and discharging power of the energy storage in the distribution network; p LT It is the transmission and distribution cost caused by reverse power transmission per unit power; is the power sent back from the distribution network to the upper grid; γ is the proportionality coefficient; C up is the cost of upgrading the unit capacity power grid; ΔL is the change in the maximum load demand of the distribution network before and after the storage.

[0133] Power balance constraints:

[0134]

[0135] in, The amount of electricity purchased by the distribution network from the upper power grid. It is the power sent back from the distribution network to the upper grid.

[0136] Energy storage configuration constraints:

[0137]

[0138] η min M ES,DN ≤P ES,DN ≤η max M ES,DN

[0139] In the formula, are the maximum energy storage capacity and maximum energy storage power that can be configured respectively; η min and η max are the minimum and maximum power coefficients of energy storage respectively.

[0140] Energy storage operation constraints:

[0141]

[0142] In the formula, They are the maximum charging and discharging powers allowed by the energy storage on the distribution network side; is the capacity of the energy storage on the distribution network side at time t; The maximum and minimum capacity allowed for the energy storage device on the distribution network side; η loss is the loss coefficient of the energy storage device on the distribution network side; η ch,ES,DN , η dis,ES,DN The distribution network side is the charge and discharge coefficient of the energy storage device; and within a scheduling cycle, the initial and final capacity states of the energy storage device on the distribution network side should remain consistent.

[0143] Electricity trading constraints:

[0144]

[0145] In the formula, It is the upper limit of the power that the distribution network can purchase from the upper power grid. It is the upper limit constraint on the power transmission from the distribution network to the upper grid.

[0146] Conditional risk constraints:

[0147] z s ≥0;

[0148] z s ≥f s -φ

[0149] Where, is the value by which the distribution network operation cost exceeds the risk value, f s is the operating cost, and φ is the risk value of the distribution network operating cost.

[0150] S104. Solve the user-side operating cost and energy storage benefit model based on the constraints of the distributed renewable energy strong uncertainty model to obtain an energy storage configuration plan that minimizes the user-side energy cost.

[0151] In an embodiment of the present invention, the user-side operating cost and energy storage benefit model are solved based on the constraints of the distributed renewable energy strong uncertainty model to obtain an energy storage configuration plan that minimizes the user-side energy cost, and through an optimization algorithm, the minimum value of the first objective function under the constraints of the first objective function is solved. The specific optimization algorithm is not limited in this embodiment of the present invention.

[0152] S105. Solve the comprehensive operation cost model of the distribution network side to obtain an energy storage configuration plan that minimizes the comprehensive operation cost of the distribution network.

[0153] In the embodiment of the present invention, the minimum value of the second objective function under the constraint condition of the second objective function is solved by an optimization algorithm, and the specific optimization algorithm is not limited in the embodiment of the present invention.

[0154] The energy storage optimization configuration method provided by the present invention takes the historical output data and predicted output data of distributed renewable energy on the user side as input, constructs a distributed renewable energy strong uncertainty model based on the Wasserstein divergence, constructs a user side operating cost and energy storage benefit model based on the Wasserstein divergence robust optimization theory, constructs a distribution network side comprehensive operating cost model based on the conditional risk value theory, solves the user side operating cost and energy storage benefit model based on the constraints of the distributed renewable energy strong uncertainty model, obtains an energy storage configuration plan that minimizes the energy consumption cost on the user side, solves the distribution network side comprehensive operating cost model, and obtains an energy storage configuration plan that minimizes the comprehensive operating cost of the distribution network. The method can effectively handle the problems of distributed power supply absorption and power fluctuation, and makes the shared energy storage configuration strategy between the user side and the distribution network side more reasonable.

[0155] In order to verify the effectiveness and accuracy of the proposed distribution network shared energy storage optimization configuration method and system based on conditional risk value, a distribution system with four functional areas is taken as the research object as an example. First, the economic benefits of the construction of shared energy storage on the distribution network side are analyzed, and two optimization scenarios are set: 1) no energy storage is configured on the distribution network side; 2) shared energy storage is configured on the distribution network side. The energy storage configuration results of scenario 2 are shown in Table 1; the economic benefit comparison of the distribution network is shown in Table 2. When the distribution network side is not configured with shared energy storage, the distribution network needs to bear the cost of distributed photovoltaic abandonment, and because there is a mismatch between the output of distributed photovoltaics and the load power consumption time, the distribution network needs to purchase more electricity from the upper power grid to meet the energy demand of each functional area, so the electricity cost is the highest. After configuring the shared energy storage on the distribution network side, it can first effectively promote the consumption of renewable energy and avoid the phenomenon of reverse power transmission to the upper power grid through energy storage; on the other hand, due to the call of the shared energy storage on the distribution network side by each functional area, the role of peak shaving and valley filling of the shared energy storage on the distribution network side can be effectively played, reducing the cost of power trading for distribution network operators. Therefore, although the construction of shared energy storage on the distribution network side requires certain investment and construction costs and operation and maintenance costs, the commissioning of energy storage effectively reduces the total operating costs on the distribution network side, while achieving new energy consumption and improving the economic benefits of the system.

[0156] Table 1. Configuration results of shared energy storage on the distribution network side

[0157]

[0158] Table 2 Comparison of distribution network operating costs before and after energy storage configuration

[0159]

[0160]

[0161] Then, the results of the optimized dispatch of shared energy storage on the distribution network side are analyzed. Figure 2 This is the result diagram of the optimal dispatch of various resources in the distribution network. Figure 3 This is the charging and discharging strategy diagram of the shared energy storage on the distribution network side. Figure 2 and Figure 3 As shown in the figure, between 10:00 and 17:00, the functional areas on the user side are in the period of high photovoltaic power generation, and it is impossible to realize the local consumption of new energy. Therefore, it is sent back to the distribution network layer, and the shared energy storage on the distribution network side is charged during this period; during the peak period of nighttime electricity consumption from 19:00 to 22:00, the energy storage on the distribution network side is discharged to meet the energy demand on the user side. It is worth noting that as a public utility project, the charging and discharging behavior of distribution network energy storage is different from that of traditional energy storage for profit, which usually makes profits in the form of low interest and high output. Instead, it is charged at the time when photovoltaic output is the largest, realizing the consumption of distributed photovoltaic at the distribution network layer.

[0162] Figure 4A schematic diagram of the structure of an energy storage optimization configuration device provided by the present invention is shown in FIG. Figure 4 As shown, the energy storage optimization configuration device includes:

[0163] The first model building module 201 is used to build a distributed renewable energy strong uncertainty model based on Wasserstein divergence by taking the historical output data and predicted output data of the distributed renewable energy on the user side as input;

[0164] The second model building module 202 is used to build a user side operating cost and energy storage benefit model based on Wasserstein distribution blue stick optimization theory;

[0165] The third model building module 203 is used to build a comprehensive operation cost model of the distribution network side based on the conditional risk value theory;

[0166] The first solving module 204 is used to solve the user-side operation cost and energy storage benefit model based on the constraints of the distributed renewable energy strong uncertainty model to obtain an energy storage configuration solution that minimizes the user-side energy cost;

[0167] The second solution module 205 is used to solve the comprehensive operation cost model of the distribution network side to obtain the energy storage configuration plan that minimizes the comprehensive operation cost of the distribution network.

[0168] In some embodiments of the present invention, the first model building module 201 includes:

[0169] The error calculation submodule is used to calculate the difference between the historical output data and the predicted output data to obtain the historical prediction error;

[0170] An empirical distribution construction submodule is used to construct an empirical distribution of prediction errors using the historical prediction errors of the output power of distributed renewable energy units as random variables;

[0171] The first model building submodule is used to build a fuzzy set centered on the empirical distribution of historical prediction errors as a strong uncertainty model of distributed renewable energy, wherein the Wasserstein divergence is used in the fuzzy set to characterize the distance between the empirical distribution of historical prediction errors and the true probability distribution.

[0172] In some embodiments of the present invention, the empirical distribution of historical prediction errors is:

[0173]

[0174] in, is the empirical distribution of the historical prediction error of the i-th output power of the distributed renewable energy unit, N sam is the number of samples of historical forecast errors, d k is the Dirac measure of the kth historical forecast error.

[0175] In some embodiments of the present invention, the fuzzy set is:

[0176]

[0177] in, is a fuzzy set, which can be regarded as a radius by The Wasserstein sphere with the center as the center, is the true probability distribution of historical forecast errors, is the distance between the empirical distribution of historical forecast errors and the true probability distribution, The specific expressions are as follows:

[0178]

[0179] In the formula, is δ i The confidence level of; η is an auxiliary variable; is the average of historical forecast errors, δ i,k is the kth historical forecast error, It can be found by the equal division search method.

[0180] In some embodiments of the present invention, the second model building module 202 includes:

[0181] The second model construction submodule is used to take the minimization of user-side energy consumption cost as the first objective function, and use power balance constraints, energy storage device operation constraints, electricity trading constraints, renewable energy operation constraints, and Wasserstein fuzzy set constraints as constraints to construct a user-side operating cost and energy storage benefit model.

[0182] In some embodiments of the present invention, the first objective function is:

[0183]

[0184] In the formula, r is the rth user function area; R is the function area set; is the price of electricity purchased by the distribution network from the user side, The price of electricity sold by the distribution network to users; The amount of electricity purchased by the distribution network from the user side. The amount of electricity sold by the distribution network to users; is the energy storage discharge of functional area r at time t, is the energy storage charge of functional area r at time t; Y is the decision variable of the max model, including B is the decision variable of the min model, in is the photovoltaic output of functional area r, which is an uncertain variable;

[0185] Power balance constraints:

[0186]

[0187] Energy storage device operation constraints:

[0188]

[0189] In the formula, are the maximum charging and discharging powers allowed for energy storage in functional area r respectively; is the capacity of energy storage at time t; is the maximum and minimum capacity allowed for the energy storage device; η loss is the loss coefficient of the energy storage device; η ch,ESr , η dis,ESr is the charge and discharge coefficient of the energy storage device; and within a scheduling cycle, the initial and final capacity states of the energy storage device should remain consistent;

[0190] Electricity trading constraints:

[0191]

[0192] In the formula, and The maximum amount of electricity that a grid operator can sell and purchase from users;

[0193] Renewable energy operation constraints:

[0194]

[0195] In the formula, Represents the maximum and minimum output of distributed photovoltaic power on the user side;

[0196] Wasserstein fuzzy set constraints:

[0197]

[0198] In the formula, is the probability distribution of distributed photovoltaic output samples; is the bad probability distribution of the predicted value of distributed photovoltaic output; θ o , p Obey Random variable with distribution; Φ p is θ p , p The joint probability distribution of; ε is the radius of the Wasserstein sphere for distributed photovoltaic output; ρ PV is the confidence level of distributed photovoltaic output.

[0199] In some embodiments of the present invention, the third model building module 203 includes:

[0200] The third model building submodule is used to take the minimum comprehensive operation cost of the distribution network as the second objective function, and to build a comprehensive operation cost model of the distribution network side with power balance constraints, energy storage configuration constraints, energy storage operation constraints, electric energy trading constraints and conditional risk constraints as constraints;

[0201] The second objective function is:

[0202]

[0203] In the formula, C inv The cost of investing in energy storage for grid operators; s is the operating cost; L is the risk factor; is the conditional risk value of the distribution network operation cost; φ is the risk value of the distribution network operation cost; α is the confidence level; p s is the probability of scenario s occurring; z s R is the value of the distribution network operation cost exceeding the risk value; delay To delay the investment in grid upgrades by configuring shared energy storage on the distribution network side;

[0204] Power balance constraints:

[0205]

[0206] in, The amount of electricity purchased by the distribution network from the upper power grid. It is the power sent back from the distribution network to the upper grid;

[0207] Energy storage configuration constraints:

[0208]

[0209] η min M ES,DN ≤P ES,DN ≤η max M ES,DN

[0210] In the formula, are the maximum energy storage capacity and maximum energy storage power that can be configured respectively; η min and η max are the minimum and maximum power coefficients of energy storage, respectively;

[0211] Energy storage operation constraints:

[0212]

[0213] In the formula, They are the maximum charging and discharging powers allowed by the energy storage on the distribution network side; is the capacity of the energy storage on the distribution network side at time t; The maximum and minimum capacity allowed for the energy storage device on the distribution network side; η loss is the loss coefficient of the energy storage device on the distribution network side; η ch,ES,DN , η dis,ES,DN The distribution network side is the charge and discharge coefficient of the energy storage device; and within a dispatch cycle, the initial and final capacity states of the energy storage device on the distribution network side should remain consistent;

[0214] Electricity trading constraints:

[0215]

[0216] In the formula, It is the upper limit of the power that the distribution network can purchase from the upper power grid. It is the upper limit constraint on the power transmission from the distribution network to the upper grid;

[0217] Conditional risk constraints:

[0218] z s ≥0;

[0219] z s ≥f s -φ

[0220] Where, is the value by which the distribution network operation cost exceeds the risk value, f s is the operating cost, and φ is the risk value of the distribution network operating cost.

[0221] The above-mentioned energy storage optimization configuration device can execute the energy storage optimization configuration method provided by the above-mentioned embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the energy storage optimization configuration method.

[0222] Figure 5 A schematic diagram of the structure of an electronic device provided for an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0223] like Figure 5As shown, the electronic device includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0224] A number of components in the electronic device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0225] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the energy storage optimization configuration method.

[0226] In some embodiments, the energy storage optimization configuration method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the energy storage optimization configuration method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the energy storage optimization configuration method in any other appropriate manner (e.g., by means of firmware).

[0227] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0228] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0229] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0230] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0231] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0232] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0233] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the energy storage optimization configuration method provided in any embodiment of the present application.

[0234] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0235] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0236] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing energy storage configuration, characterized in that: include: Taking the historical output data and predicted output data of distributed renewable energy on the user side as input, a distributed renewable energy strong uncertainty model is constructed based on Wasserstein divergence. Based on Wasserstein's distributed bar optimization theory, a user-side operating cost and energy storage benefit model is constructed; Based on the conditional risk value theory, a comprehensive operation cost model for the distribution network is constructed; Based on the constraint solving of the distributed renewable energy strong uncertainty model, the user-side operation cost and energy storage benefit model is obtained to obtain the energy storage configuration plan that minimizes the user-side energy cost. Solve the comprehensive operating cost model of the distribution network side and obtain the energy storage configuration plan that minimizes the comprehensive operating cost of the distribution network.

2. The energy storage optimization configuration method according to claim 1, characterized in that: Taking the historical output data and predicted output data of distributed renewable energy on the user side as input, a distributed renewable energy strong uncertainty model is constructed based on Wasserstein divergence, including: Calculate the difference between historical output data and predicted output data to obtain historical prediction error; Taking the historical forecast error of the output power of distributed renewable energy units as a random variable, an empirical distribution of the forecast error is constructed; A fuzzy set is constructed with the empirical distribution of historical prediction errors as the center as a strong uncertainty model for distributed renewable energy. The Wasserstein divergence is used in the fuzzy set to characterize the distance between the empirical distribution of historical prediction errors and the true probability distribution.

3. The energy storage optimization configuration method according to claim 2, characterized in that: The empirical distribution of historical forecast errors is: in, is the empirical distribution of the historical prediction error of the i-th output power of the distributed renewable energy unit, N sam is the number of samples of historical forecast errors, d k is the Dirac measure of the kth historical forecast error.

4. The energy storage optimization configuration method according to claim 3, characterized in that: The fuzzy sets are: in, is a fuzzy set, which can be regarded as a radius by The Wasserstein sphere with the center as the center, is the true probability distribution of historical forecast errors, is the distance between the empirical distribution of historical forecast errors and the true probability distribution, The specific expressions are as follows: In the formula, is δ i The confidence level of; η is an auxiliary variable; is the average of historical forecast errors, δ i,k is the kth historical forecast error, It can be found by the equal division search method.

5. The energy storage optimization configuration method according to any one of claims 1 to 4, characterized in that: Based on Wasserstein distributed blue stick optimization theory, a user-side operating cost and energy storage benefit model is constructed, including: Taking the minimization of user-side energy consumption cost as the first objective function, and taking power balance constraints, energy storage device operation constraints, electricity trading constraints, renewable energy operation constraints, and Wasserstein fuzzy set constraints as constraints, a user-side operating cost and energy storage benefit model is constructed.

6. The energy storage optimization configuration method according to claim 5, characterized in that: The first objective function is: In the formula, r is the rth user function area; R is the function area set; is the price of electricity purchased by the distribution network from the user side, The price of electricity sold by the distribution network to users; The amount of electricity purchased by the distribution network from the user side. The amount of electricity sold by the distribution network to users; is the energy storage discharge of functional area r at time t, is the energy storage charge of functional area r at time t; Y is the decision variable of the max model, including B is the decision variable of the min model, in is the photovoltaic output of functional area r, which is an uncertain variable; Power balance constraints: Energy storage device operation constraints: In the formula, are the maximum charging and discharging powers allowed for energy storage in functional area r respectively; is the capacity of energy storage at time t; The maximum and minimum capacity allowed for an energy storage device; η loss is the loss coefficient of the energy storage device; η ch,ESr , η dis,ESr is the charge and discharge coefficient of the energy storage device; and within a scheduling cycle, the initial and final capacity states of the energy storage device should remain consistent; Electricity trading constraints: In the formula, and The maximum amount of electricity that a grid operator can sell and purchase from users; Renewable energy operation constraints: In the formula, Represents the maximum and minimum output of distributed photovoltaic power on the user side; Wasserstein fuzzy set constraints: In the formula, is the probability distribution of distributed photovoltaic output samples; is the bad probability distribution of the predicted value of distributed photovoltaic output; θ p , p Obey Random variable with distribution; Φ p is θ p , p The joint probability distribution of; ε is the radius of the Wasserstein sphere for distributed photovoltaic output; ρ PV is the confidence level of distributed photovoltaic output.

7. The energy storage optimization configuration method according to claim 6, characterized in that: Based on the conditional risk value theory, a comprehensive operation cost model for the distribution network is constructed, including: Taking the minimization of the comprehensive operation cost of the distribution network as the second objective function, and taking power balance constraints, energy storage configuration constraints, energy storage operation constraints, power trading constraints and conditional risk constraints as constraints, a comprehensive operation cost model of the distribution network side is constructed; The second objective function is: In the formula, C inv The cost of investing in energy storage for grid operators; s is the operating cost; L is the risk factor; is the conditional risk value of the distribution network operation cost; φ is the risk value of the distribution network operation cost; α is the confidence level; p s is the probability of scenario s occurring; z s R is the value of the distribution network operation cost exceeding the risk value; delay To delay the investment in grid upgrades by configuring shared energy storage on the distribution network side; Power balance constraints: in, The amount of electricity purchased by the distribution network from the upper power grid. It is the power sent back from the distribution network to the upper grid; Energy storage configuration constraints: or min M ES,DN ≤P ES,DN ≤η max M ES,DN In the formula, are the maximum energy storage capacity and maximum energy storage power that can be configured respectively; η min and η max are the minimum and maximum power coefficients of energy storage, respectively; Energy storage operation constraints: In the formula, They are the maximum charging and discharging powers allowed by the energy storage on the distribution network side; is the capacity of the energy storage on the distribution network side at time t; The maximum and minimum capacity allowed for the energy storage device on the distribution network side; η loss is the loss coefficient of the energy storage device on the distribution network side; η ch,ES,DN , η dis,ES,DN The distribution network side is the charge and discharge coefficient of the energy storage device; and within a dispatch cycle, the initial and final capacity states of the energy storage device on the distribution network side should remain consistent; Electricity trading constraints: In the formula, It is the upper limit of the power that the distribution network can purchase from the upper power grid. It is the upper limit constraint on the power transmission from the distribution network to the upper grid; Conditional risk constraints: z s ≥0； z s ≥f s -φ Where, is the value by which the distribution network operation cost exceeds the risk value, f s is the operating cost, and φ is the risk value of the distribution network operating cost.

8. An energy storage optimization configuration device, characterized in that: include: The first model building module is used to take the historical output data and predicted output data of distributed renewable energy on the user side as input, and to build a distributed renewable energy strong uncertainty model based on Wasserstein divergence; The second model building module is used to build a user-side operating cost and energy storage benefit model based on Wasserstein distributed blue stick optimization theory; The third model building module is used to build a comprehensive operation cost model of the distribution network side based on the conditional risk value theory; The first solving module is used to solve the user-side operation cost and energy storage benefit model based on the constraints of the distributed renewable energy strong uncertainty model, and obtain the energy storage configuration plan that minimizes the user-side energy cost; The second solution module is used to solve the comprehensive operation cost model of the distribution network side and obtain the energy storage configuration plan that minimizes the comprehensive operation cost of the distribution network.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the energy storage optimization configuration method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the energy storage optimization configuration method as described in any one of claims 1 to 7 is implemented.

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