A distributed and centralized cloud energy storage scheduling method

Abstract

The present invention discloses a cloud energy storage scheduling method that combines distributed and centralized energy storage. According to the typical operating scenario in which the existing distributed energy storage is equipped with clean energy output and the shared energy storage is backed by the power grid, the energy storage power constraint, charge continuity constraint, etc. are comprehensively considered to establish the operation model of the two energy storages. Finally, the target cascade analysis method is used to realize the decoupling and information transmission of the two energy storages. After multiple iterations, the convergence conditions are met to realize the joint scheduling of distributed energy storage and centralized shared energy storage, so that the energy storage equipment can be flexibly scheduled in the form of cloud energy storage, and the economy of each participating entity is improved. It can also realize peak shaving and valley filling to smooth out power grid fluctuations, reduce the behaviors of distributed users such as purchasing electricity and sending electricity back to the power grid, and increase the security of the power grid. It can greatly improve the absorption rate of clean energy such as wind and solar, deepen the penetration rate of clean energy, and promote the progress of the "dual carbon" goals.

Description

Technical Field

[0001] The present invention relates to the field of cloud storage in power systems, and in particular to a distributed and centralized cloud energy storage scheduling method. Background Art

[0002] The penetration rate of clean energy in the power system is gradually increasing, and the installed capacity of new energy will also increase further. However, the random waveform brought by clean energy such as wind and solar power poses a severe challenge to the power grid. Therefore, energy storage, which has the ability to smooth the output fluctuations of renewable energy and "peak shaving and valley filling", has reached an unprecedented height. Therefore, it is necessary to carry out relevant research on energy storage.

[0003] At present, distributed energy storage is mainly concentrated on user-side energy storage. In order to reduce the operating costs of market players, scholars have conducted research on the dispatching control and configuration planning of energy storage. Lin Zhenfeng proposed a user-side distributed energy storage optimization dispatching method based on a robust game model, which transforms complex problems based on the cooperation theory of Nash bargaining. Gan Wei and other scholars decomposed the distributed energy storage cooperative sharing problem into capacity trading sub-problems and cost sharing sub-problems, and improved the utilization and economy of energy storage resources through time-sharing reuse. Gao Song used a deep reinforcement learning algorithm based on a competitive deep Q network to perform self-learning optimization on energy storage action decisions, thereby determining the optimal configuration of distributed energy storage points and their parameters.

[0004] "Shared energy storage" has greatly reduced its own equipment investment costs with economies of scale, and has become an effective means to solve the user-side energy storage cost problem and improve the consumption rate. Wang Shijun introduced the concept of sharing and established a community comprehensive energy system model with shared energy storage, verifying that shared energy storage helps users to reasonably optimize energy use arrangements, thereby reducing energy costs. Li Xianshan rented shared energy storage from multiple microgrid grid-connected systems to form a microgrid alliance to participate in active distribution network scheduling, which is conducive to the efficient application of energy storage and promotes the consumption of new energy.

[0005] Distributed energy consumption rate has a positive effect, but it cannot avoid over-reliance on and impact on the power grid, and due to the high cost of energy storage investment, there is a lack of market players to guide the consumption rate to further increase. However, integrated shared energy storage can only be in a single state of charging or discharging at any time, and cannot take into account the electricity needs of different types of users, lacking flexibility. Summary of the invention

[0006] The purpose of the present invention is to provide a cloud energy storage scheduling method that combines distributed and centralized energy storage. According to the typical operation scenario of existing distributed energy storage equipped with clean energy output and shared energy storage backed by the power grid, the energy storage power constraint, charge continuity constraint, etc. are comprehensively considered to establish the operation model of the two energy storages. Finally, the target cascade analysis method is used to realize the decoupling and information transmission of the two energy storages. After multiple iterations, the convergence conditions are met to realize the joint scheduling of distributed energy storage and centralized shared energy storage, so as to flexibly schedule energy storage equipment in the form of cloud energy storage and improve the economic efficiency of each participating entity.

[0007] The present invention is achieved through the following technical solutions:

[0008] A distributed and centralized cloud energy storage scheduling method, comprising:

[0009] An upper-level centralized shared energy storage model and a lower-level distributed energy storage model are constructed. The upper-level centralized shared energy storage model has the largest economic benefit and the lower-level distributed energy storage model has the smallest daily operating cost. The two models are jointly scheduled through the target cascade analysis method to achieve decoupling and information transmission. A penalty function is set to iterate the joint scheduling method, and the accuracy standard of the joint scheduling is improved by updating and rolling the penalty function.

[0010] The joint dispatching process of the target cascade analysis method is as follows: after reading in the user's electric load and energy output, the equipment operating parameters are set, optimized in the upper centralized shared energy storage model, and the optimized parameters are passed to the lower distributed energy storage model and the lower objective function is updated; the lower distributed energy storage model is optimized through the new lower objective function, and it is determined whether the optimization result meets the convergence condition; if it does, the optimization result is output; if not, the optimization result is fed back to the upper centralized shared energy storage model, and then the upper objective function is updated, and then the optimization is iteratively optimized in the upper centralized shared energy storage model, and the steps are repeated until the convergence result is output.

[0011] Furthermore, the upper centralized shared energy storage model includes a shared energy storage objective function, which includes:

[0012] The objective function is determined to maximize daily economic benefits, where the economic benefits include shared energy storage service fees and electricity sales benefits; the objective function is:

[0013] F 1 =C ser +C sel

[0014] Among them, F 1 It is the daily economic benefit taking into account the whole life cycle cost; Cser is the service fee for the user group to use the shared energy storage power station; Csel is the cost of the user group to purchase electricity from the shared energy storage power station;

[0015]

[0016]

[0017] Where N is the number of user groups; T is the dispatching duration; ξ(t) is the unit service fee for shared energy storage; P sb,k (t) is the power purchased by user k from the energy storage power station in time period t; P ss,k (t) is the power sold by user k to the energy storage power station in time period t; δ(t) is the unit time; Ω(t) is the electricity purchase price of the energy storage power station in time period t.

[0018] Furthermore, the upper centralized shared energy storage model also has constraints, including energy storage power station charging and discharging power constraints and energy storage charge continuity constraints;

[0019] The conditions for charging and discharging power constraints of energy storage power stations are:

[0020]

[0021] Among them, E ess (t+1), E ess (t) are the power consumption of the energy storage power station at time periods t+1 and t respectively; The maximum charging and discharging power of the energy storage power station; They are respectively the charging and discharging status bits of the energy storage power station, which are Boolean variables;

[0022] The energy storage charge continuity constraint condition is:

[0023]

[0024]

[0025] Where, χ is the energy storage self-discharge power; η abs , η relea They are the charging and discharging efficiency of energy storage respectively; are the charging and discharging power of the energy storage power station at time t respectively; It is the maximum charge state of the energy storage power station.

[0026] Furthermore, the lower distributed energy storage model includes a distributed energy storage objective function, which includes:

[0027] The objective function is determined to be the lowest daily operating cost, where the daily operating cost includes the cost of the user group purchasing electricity from the power grid, the service fee for the user group to use the shared energy storage power station, the cost of the user group purchasing and selling electricity from the shared energy storage power station, and the cost of using energy storage within the user group; then the objective function is:

[0028] f 2 =C grid+C ser +C sta +C cost

[0029] Among them, F 2 is the daily operating economic cost of cloud energy storage for producers and sellers; Cgrid is the cost of electricity purchased by the user group from the power grid; Csta is the cost of electricity purchased and sold by the user group from the shared energy storage power station; Ccost is the cost of energy storage use within the user group;

[0030]

[0031] Among them, α(t) is the electricity price purchased from the power grid during this period; P g,k (t) is the power purchased by user k from the grid in time period t; is the electricity price of the energy storage power station at time period t; μ is the power loss cost coefficient of energy storage; P os,k (t), P ob,k (t) are the charging power of user k using its own energy storage in time period t.

[0032] Furthermore, the lower distributed energy storage model also has constraints, including power balance constraints, energy storage charge continuity constraints, self-energy storage charge and discharge power constraints, and energy storage charge and discharge balance constraints;

[0033] The power balance constraints are:

[0034] P pv,k (t)+P wind,k (t)+P ob,k (t)+P sb,k (t)

[0035] +P g,k (t) = P os,k (t)+P ss,k (t)+P load,k (t)

[0036] Among them, P pv,k (t), P wind,k (t) are the photovoltaic and wind power outputs of user k in time period t; P lod,k (t) is the power load of user k in time period t;

[0037] The constraints of self-storage charge continuity are:

[0038]

[0039] Among them, E(t+1) and E(t) are the power of the energy storage at time periods t+1 and t respectively; P abs (t), P relea(t) are the charging and discharging power of the energy storage at time t; Emax is the maximum charge state of the energy storage power station;

[0040] The constraints of the self-storage charging and discharging power are:

[0041]

[0042] Among them, P max The maximum value of its own energy storage charging and discharging power; U b , U s They are the energy storage charging and discharging status bits, which are Boolean variables;

[0043] The self-storage charge and discharge balance constraint conditions are:

[0044] The sum of electricity purchased and sold by the user group to the energy storage power station is equal to the charging and discharging power of the energy storage power station, and the sum of the charging and discharging of the user group to its own energy storage is equal to the charging and discharging power of its own energy storage, so:

[0045]

[0046] Furthermore, the target cascade analysis method is used to divide the cloud energy storage dispatching system into multiple sub-models, continuously divert the design indicators from top to bottom, and at the same time, the response results are fed back from bottom to top. Each layer of the model is solved independently, and the solution results are fed back to each other until the convergence conditions are met; the variable optimization model of each layer is:

[0047]

[0048] Among them, f(x) is the initial objective function of the layer system; g(x) < 0 and h(x) = 0 are the constraints of the layer system; t ij Design variables passed from the upper system to the lower system; ij is the response result fed back to the upper layer by the lower layer system; π(t ij -r ij ) is the penalty function.

[0049] Furthermore, the upper centralized shared energy storage model will optimize the energy storage result P ess (t) is sent to the lower distributed energy storage model as a design variable, and the lower distributed energy storage model transmits the optimized power consumption P(t) as a feedback variable to the upper centralized shared energy storage model; considering the simplicity and solvability of the penalty function and the function model in the comprehensive target cascade analysis method, in the kth iteration, the objective functions of the upper and lower layers need to be updated, and then:

[0050]

[0051]

[0052] in, and P k (t) are the optimization results of the total power supply and power demand after the kth iteration; τ and ψ are coefficients greater than 0, and their values ​​are respectively based on F 1 and F 2 Depends on the size.

[0053] Furthermore, it also includes the convergence function:

[0054]

[0055]

[0056] Among them, ξ 1 and 2 are the convergence accuracy of the two convergence conditions respectively; C A and C B are the economic benefits of the upper and lower layers respectively; the first formula indicates that the difference between the communication variables transmitted between the upper and lower layers is less than the convergence accuracy, and the physical meaning of the second formula is the ratio of the difference between the benefits of the whole system and the benefits of the previous iteration to the benefits of the whole system;

[0057] After adding the penalty function term, the deviation between the design variables of the upper centralized shared energy storage model and the feedback variables of the lower layer will make the current solution a non-optimal solution; the convergence function is used to adjust the approach speed of the current optimization layer variables to their upper or lower layer variables; as the number of iterations increases, the error between the design variables and the feedback variables gradually decreases, and the influence of the penalty function term on the objective function decreases accordingly. Through repeated iterations, until the error between the design variable and the feedback variable is less than the set value ξ, the iteration stops and the optimal solution is obtained.

[0058] Furthermore, the scheduling process of the target cascade analysis method includes the following steps:

[0059] S1: Input energy storage parameters and load data, set the initial values ​​of coupling variables and penalty function multipliers, and set the number of iterations k = 1; coupling variables include interaction power;

[0060] S2: The upper centralized shared energy storage model and the lower distributed energy storage model are solved according to their respective optimization problems. On the one hand, the distributed energy storage pursues the economic operation goal, and at the same time, the distributed energy storage data is used as a shared variable to approximate the virtual storage data transmitted to the shared energy storage, and the solved virtual data is passed to the upper centralized shared energy storage model; the distributed energy storage is solved in parallel, and the initial value of the coupling variable is used by default in the first iteration;

[0061] S3: After the upper centralized shared energy storage model sends the value to the lower distributed energy storage model, it optimizes itself while approaching the value transmitted by the lower distributed energy storage model. On the one hand, the shared energy storage pursues the economic operation goal, and at the same time, the output storage power value is used as a shared variable to approach the virtual required storage value transmitted by the lower distributed energy storage model, and the solved virtual value is transmitted to the lower distributed energy storage model.

[0062] S4: Check the convergence conditions. If they are met at the same time, terminate the iteration process and output the optimal scheduling result and objective function value. Otherwise, update the penalty function multiplier, set k=k+1, and return to the above step S2 to continue solving. The upper centralized shared energy storage model and the lower distributed energy storage model are repeatedly iterated until the convergence conditions are met and the results are output.

[0063] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0064] In addition to flexibly dispatching energy storage equipment in the form of cloud energy storage, the present invention improves the economic efficiency of all participating entities, and also realizes peak shaving and valley filling to smooth out grid fluctuations, reduces the purchase of electricity and reverse transmission of electricity to the grid by distributed users, and increases the safety of the grid. It greatly improves the consumption rate of clean energy such as wind and solar, deepens the penetration rate of clean energy, and promotes the progress of the "dual carbon" goal. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0066] Figure 1 A schematic diagram of a distributed and centralized cloud energy storage scheduling structure provided in Example 1 of the present invention;

[0067] Figure 2 A schematic diagram of the target cascade analysis method provided in Example 1 of the present invention;

[0068] Figure 3 A schematic diagram of the logic flow of the target cascade analysis method provided in Example 1 of the present invention;

[0069] Figure 4 A schematic diagram of user electrical load and energy output provided by Embodiment 2 of the present invention;

[0070] Figure 5 A distributed energy storage charging and discharging curve diagram provided in Example 2 of the present invention;

[0071] Figure 6 A shared energy storage charge and discharge curve diagram provided in Example 2 of the present invention;

[0072] Figure 7A convergence process diagram under convergence condition 1 provided in Example 2 of the present invention;

[0073] Figure 8 This is a convergence process diagram under convergence condition 2 provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0074] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0075] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] Example 1

[0077] See also Figure 1-Figure 3 This embodiment provides a distributed and centralized cloud energy storage scheduling method, which takes into account the coexistence of various participating entities in view of the multi-energy coupling characteristics of the cloud energy storage system. First, the operation mode of the cloud energy storage system is sorted out, and by setting up a distributed energy storage aggregation and centralized energy storage sharing mechanism, the cloud energy storage joint complementarity is realized, and the actual operation scenario of the scheduling method is obtained;

[0078] Secondly, the joint dispatch of the two energy storages is realized by the target cascade analysis method to achieve decoupling and information transmission. The penalty function is set to promote the repeated iteration of the cloud energy storage system, and the penalty function is updated during the iteration, and multiple rounds of rolling meet the joint dispatch accuracy requirements. The orderly transmission of energy flow, economic flow and information flow between various entities is realized;

[0079] Finally, the upper layer takes the maximum economic benefit and the lower layer takes the minimum daily operating cost as the goal. Considering the constraints of energy storage power station charging and discharging power, power balance constraints, energy storage charge continuity, etc., a cloud energy storage scheduling method combining distributed and centralized is proposed, and the commercial software CPLEX is used to solve the mixed integral linear problem.

[0080] In this embodiment, it includes constructing an upper centralized shared energy storage model and a lower distributed energy storage model, taking the maximum economic benefit of the upper centralized shared energy storage model and the minimum daily operating cost of the lower distributed energy storage model as the objective function, and jointly scheduling the two models through the target cascade analysis method to achieve decoupling and information transmission; and setting a penalty function to iterate the joint scheduling method, and improving the accuracy standard of the joint scheduling by updating and rolling the penalty function; wherein

[0081] The joint dispatching process of the target cascade analysis method is as follows: after reading in the user's electric load and energy output, the equipment operating parameters are set, optimized in the upper centralized shared energy storage model, and the optimized parameters are passed to the lower distributed energy storage model and the lower objective function is updated; the lower distributed energy storage model is optimized through the new lower objective function, and it is determined whether the optimization result meets the convergence condition; if it does, the optimization result is output; if not, the optimization result is fed back to the upper centralized shared energy storage model, and then the upper objective function is updated, and then the optimization is iteratively optimized in the upper centralized shared energy storage model, and the steps are repeated until the convergence result is output.

[0082] Centralized shared energy storage Large-scale energy storage equipment can fully utilize the scale effect and reduce the initial investment cost. With "sharing" as the main value orientation, it improves resource utilization efficiency by sharing energy storage resources among users, thereby reducing the overall cost, and on this basis, it can further meet the energy storage needs of more users.

[0083] Therefore, the upper centralized shared energy storage model includes a shared energy storage objective function, which includes:

[0084] The objective function is determined to maximize daily economic benefits, where the economic benefits include shared energy storage service fees and electricity sales benefits; the objective function is:

[0085] F 1 =C ser +C sel

[0086] Among them, F 1 It is the daily economic benefit taking into account the whole life cycle cost; Cser is the service fee for the user group to use the shared energy storage power station; Csel is the cost of the user group to purchase electricity from the shared energy storage power station;

[0087]

[0088]

[0089] Where N is the number of user groups; T is the dispatching duration; ξ(t) is the unit service fee for shared energy storage; P sb,k (t) is the power purchased by user k from the energy storage power station in time period t; P ss,k (t) is the power sold by user k to the energy storage power station in time period t; δ(t) is the unit time; Ω(t) is the electricity purchase price of the energy storage power station in time period t.

[0090] The upper-level centralized shared energy storage model also has constraints, including the energy storage power station charging and discharging power constraints and the energy storage charge continuity constraints;

[0091] The conditions for charging and discharging power constraints of energy storage power stations are:

[0092]

[0093] Among them, E ess (t+1), E ess (t) are the power consumption of the energy storage power station at time periods t+1 and t respectively; The maximum charging and discharging power of the energy storage power station; They are respectively the charging and discharging status bits of the energy storage power station, which are Boolean variables;

[0094] The energy storage charge continuity constraint condition is:

[0095]

[0096]

[0097] Where, χ is the energy storage self-discharge power; η abs , η relea They are the charging and discharging efficiency of energy storage respectively; are the charging and discharging power of the energy storage power station at time t respectively; It is the maximum charge state of the energy storage power station.

[0098] Distributed energy storage has corresponding application modes in all aspects of the power system, which can effectively eliminate the peak-valley difference between day and night, enhance the utilization efficiency of equipment, promote the consumption of new energy, regulate voltage and frequency, smooth the power fluctuation of new energy, participate in demand-side response, etc. Distributed energy storage resources in cloud energy storage can improve the utilization rate of existing idle energy storage. So:

[0099] The lower distributed energy storage model includes the distributed energy storage objective function:

[0100] The objective function is determined to be the lowest daily operating cost, where the daily operating cost includes the cost of the user group purchasing electricity from the power grid, the service fee for the user group to use the shared energy storage power station, the cost of the user group purchasing and selling electricity from the shared energy storage power station, and the cost of using energy storage within the user group; then the objective function is:

[0101] f 2 =C grid +C ser +C sta +C cost

[0102] Among them, F 2 is the daily operating economic cost of cloud energy storage for producers and sellers; Cgrid is the cost of electricity purchased by the user group from the power grid; Csta is the cost of electricity purchased and sold by the user group from the shared energy storage power station; Ccost is the cost of energy storage use within the user group;

[0103]

[0104] Among them, α(t) is the electricity price purchased from the power grid during this period; P g,k (t) is the power purchased by user k from the grid in time period t; is the electricity price of the energy storage power station at time period t; μ is the power loss cost coefficient of energy storage; P os,k (t), P ob,k (t) are the charging power of user k using its own energy storage in time period t.

[0105] The lower distributed energy storage model also has constraints, including power balance constraints, energy storage charge continuity constraints, self-energy storage charge and discharge power constraints, and energy storage charge and discharge balance constraints.

[0106] The power balance constraints are:

[0107] P pv,k (t)+P wind,k (t)+P ob,k (t)+P sb,k (t) +P g,k (t) = P os,k (t)+P ss,k (t)+P load,k (t)

[0108] Among them, P pv,k (t), P wind,k (t) are the photovoltaic and wind power outputs of user k in time period t; P lod,k (t) is the power load of user k in time period t;

[0109] The constraints of self-storage charge continuity are:

[0110]

[0111] Among them, E(t+1) and E(t) are the power of the energy storage at time periods t+1 and t respectively; P abs (t), P relea (t) are the charging and discharging power of the energy storage at time t; Emax is the maximum charge state of the energy storage power station;

[0112] The constraints of the self-storage charging and discharging power are:

[0113]

[0114] Among them, P max The maximum value of its own energy storage charging and discharging power; U b , U s They are the energy storage charging and discharging status bits, which are Boolean variables;

[0115] The self-storage charge and discharge balance constraint conditions are:

[0116] The sum of electricity purchased and sold by the user group to the energy storage power station is equal to the charging and discharging power of the energy storage power station, and the sum of the charging and discharging of the user group to its own energy storage is equal to the charging and discharging power of its own energy storage, so:

[0117]

[0118] The target cascade analysis method is a design method that uses parallel thinking to solve complex system problems. In this embodiment, the cloud energy storage dispatching system is divided into multiple sub-models, and the design indicators are continuously diverted from top to bottom. At the same time, the response results are fed back from bottom to top. Each layer of the model is solved independently, and the solution results are fed back to each other until the convergence conditions are met. The variable optimization model of each layer is:

[0119]

[0120] Among them, f(x) is the initial objective function of the layer system; g(x) < 0 and h(x) = 0 are the constraints of the layer system; t ij Design variables passed from the upper system to the lower system; ij is the response result fed back to the upper layer by the lower layer system; π(t ij -r ij ) is the penalty function. Specifically, the upper centralized shared energy storage model takes the optimized energy storage result P ess (t) is sent to the lower distributed energy storage model as a design variable, and the lower distributed energy storage model transmits the optimized power consumption P(t) as a feedback variable to the upper centralized shared energy storage model; considering the simplicity and solvability of the penalty function and the function model in the comprehensive target cascade analysis method, in the kth iteration, the objective functions of the upper and lower layers need to be updated, and then:

[0121]

[0122]

[0123] in, and P k (t) are the optimization results of the total power supply and power demand after the kth iteration; τ and ψ are coefficients greater than 0, and their values ​​are respectively based on F 1 and F 2 Depends on the size.

[0124] And, also includes the convergence function:

[0125]

[0126]

[0127] Among them, ξ 1 and 2 are the convergence accuracy of the two convergence conditions respectively; C A and C B are the economic benefits of the upper and lower layers respectively; the first formula indicates that the difference between the communication variables transmitted between the upper and lower layers is less than the convergence accuracy, and the physical meaning of the second formula is the ratio of the difference between the benefits of the whole system and the benefits of the previous iteration to the benefits of the whole system;

[0128] It can be seen that after adding the penalty function term, the deviation between the design variables of the upper centralized shared energy storage model and the feedback variables of the lower layer will make the current solution a non-optimal solution; the convergence function is used to adjust the approach speed of the current optimization layer variables to their upper or lower layer variables; as the number of iterations increases, the error between the design variables and the feedback variables gradually decreases, and the influence of the penalty function term on the objective function decreases accordingly. Through repeated iterations, until the error between the design quantity and the feedback quantity is less than the set value ξ, the iteration stops and the optimal solution is obtained.

[0129] In summary, in order to achieve the expected effect, the scheduling process of the target cascade analysis method of this embodiment includes the following steps:

[0130] S1: Input energy storage parameters and load data, set the initial values ​​of coupling variables and penalty function multipliers, and set the number of iterations k = 1; coupling variables include interaction power;

[0131] S2: The upper centralized shared energy storage model and the lower distributed energy storage model are solved according to their respective optimization problems. On the one hand, the distributed energy storage pursues the economic operation goal, and at the same time, the distributed energy storage data is used as a shared variable to approximate the virtual storage data transmitted to the shared energy storage, and the solved virtual data is passed to the upper centralized shared energy storage model; the distributed energy storage is solved in parallel, and the initial value of the coupling variable is used by default in the first iteration;

[0132] S3: After the upper centralized shared energy storage model sends the value to the lower distributed energy storage model, it optimizes itself while approaching the value transmitted by the lower distributed energy storage model. On the one hand, the shared energy storage pursues the economic operation goal, and at the same time, the output storage power value is used as a shared variable to approach the virtual required storage value transmitted by the lower distributed energy storage model, and the solved virtual value is transmitted to the lower distributed energy storage model.

[0133] S4: Check the convergence conditions. If they are met at the same time, terminate the iteration process and output the optimal scheduling result and objective function value. Otherwise, update the penalty function multiplier, set k=k+1, and return to the above step S2 to continue solving. The upper centralized shared energy storage model and the lower distributed energy storage model are repeatedly iterated until the convergence conditions are met and the results are output.

[0134] Example 2

[0135] See also Figure 4-Figure 8 In order to verify the distributed and centralized cloud energy storage scheduling method proposed in Example 1, this embodiment proposes the following example:

[0136] Please refer again Figure 4 The cloud energy storage system used in this embodiment includes three users. Users A, B and C are selected from clothing and daily necessities wholesale factories, electrified railways and engineering manufacturing industries respectively. They are all equipped with a storage capacity of 500KWh and a charging and discharging power limit of 100KW. The typical daily operating load and clean energy output of the user group are as follows: Figure 4 As shown. The power purchase price of the power grid adopts the time-of-use electricity price in Sichuan Province, and the purchase and sale prices of electricity between the user group and the energy storage power station are shown in Table 1. The service fee of the shared energy storage power station is 0.05 yuan / KWh, and the power cost loss coefficient of the user group's own energy storage is 0.01 yuan / KWh. The charging and discharging efficiency of the energy storage power station is 0.98, the state of charge range is 0.1-0.9, and the initial capacity is 0.2. The capacity cost of the energy storage power station adopts the winning bid price of 1,200 yuan / KWh for the Hubei shared energy storage project in 2022, the power cost is 1,000 yuan / KWh, the operation and maintenance cost is 72 yuan / (a·KW), and the life cycle is 8 years.

[0137] The electricity price parameters are:

[0138]

[0139] To verify the rationality of Example 1, this example compares and analyzes the following three scenarios based on the above calculation examples. The optimization model takes 24 hours as an operation cycle, each period is 15 minutes long, and contains 96 periods.

[0140] Scenario 1: Only using own energy storage;

[0141] Scenario 2: Using only distributed energy storage;

[0142] Scenario 3: Using cloud energy storage model.

[0143] According to different usage scenarios, the following table can be obtained:

[0144]

[0145] First, consider the user group cost and clean energy consumption rate:

[0146] As shown in Table 2, the total operating cost of scenario 1 is much higher than that of scenarios 2 and 3, but the cost of energy storage is very low. This is because each user can only use their own energy storage, which cannot play a good role in peak shaving and valley filling, resulting in a large amount of power purchase and abandonment of power by the power grid, so the consumption rate is only 72.9%. In scenario 2, users can call each other's energy storage, which reflects the complementarity of users. The total operating cost is reduced by 64.6% compared with scenario 1, and more clean energy is consumed, and the consumption rate is increased to 90.3%. However, the increase in the cost of energy storage is caused by the call of energy storage between users at various time periods. The configuration result of the shared energy storage power station in scenario 3 is a capacity of 8172.3KW·h, and the maximum charging and discharging power is 397KW. On the basis of scenario 2, the total operating cost of scenario 3 is reduced by 26.7%, and the cost of purchasing electricity from the power grid is reduced by 91%, which greatly reduces the pressure on the power grid. The increase in the cost of energy storage is mainly due to the increase in the service fee of shared energy storage. Clean energy also increased to 98.2%, which shows that the cloud energy storage model can realize the integrated use of distributed energy storage, achieve user complementarity, and significantly improve the clean energy consumption rate.

[0147] Then, refer to Figure 5 The charging and discharging curve of the user group's own energy storage in scenario 3 is shown in the figure. It can be seen from the figure that the three users repeatedly use their own energy storage for charging and discharging according to their own electricity needs, so the curve fluctuates greatly. And compared with the peak power of 484KW when the user calls their own energy storage, Figure 5 The peak value is 294KW. Analysis shows that there are multiple users charging and discharging at the same time in a unit of time, so the actual energy storage usage will be reduced, reflecting the complementarity of users.

[0148] Afterwards, please refer to Figure 6 The charge and discharge curve of the shared energy storage power station in scenario 3 is shown in the figure. The shared energy storage power station is charged from 0:00 to 9:30, reaching the peak power of 7350KW at 9:30, and the energy storage power station is called to discharge from 9:30 to 23:00. Finally, it is charged again from 23:00 to 24:00, so that the power station finally returns to the initial state of 20%. That is, charging is carried out in the low electricity price stage and discharging is carried out in the high electricity price stage. Fully promote peak shaving and valley filling, transfer of electricity load, and realize efficient consumption of clean energy.

[0149] Finally, the target cascade analysis method is used for scenario 3 to obtain the convergence results. Please refer to Figure 7 and Figure 8 , the convergence results of the target cascade method for the cloud energy storage system in scenario 3 are as follows Figure 7 and Figure 8As shown in the figure, the two figures represent the iterative convergence process of convergence conditions 1 and 2 respectively. Convergence condition 1 represents the difference between the power supply and demand of the upper and lower layers. Therefore, there is a large gap in the first iteration, and it begins to drop rapidly in the second iteration, showing the good convergence characteristics of the target cascade method. Finally, at the 26th iteration, it reached 5×10 -3 The convergence accuracy requirement. Convergence condition 2 represents the proportional difference between the previous iteration and the current iteration of each energy storage system in the cloud energy storage system, which reflects the stability of the system. Therefore, the iteration accuracy starts to decrease from 1, and after multiple oscillations, it reaches the convergence accuracy requirement of 0.00187613.

[0150] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A distributed and centralized cloud energy storage scheduling method. It is characterized in that include: Construct an upper-level centralized shared energy storage model and a lower-level distributed energy storage model. Take the maximum economic benefit of the upper-level centralized shared energy storage model and the minimum daily operating cost of the lower-level distributed energy storage model as the objective function, and use the target cascade analysis method to jointly schedule the two models to achieve decoupling and information transmission; set a penalty function to iterate the joint scheduling method, and improve the accuracy standard of the joint scheduling by updating and rolling the penalty function; in The joint dispatching process of the target cascade analysis method is as follows: after reading in the user's electric load and energy output, the equipment operating parameters are set, optimized in the upper centralized shared energy storage model, and the optimized parameters are passed to the lower distributed energy storage model and the lower objective function is updated; the lower distributed energy storage model is optimized through the new lower objective function, and it is determined whether the optimization result meets the convergence condition; if it does, the optimization result is output; if it does not, the optimization result is fed back to the upper centralized shared energy storage model, and then the upper objective function is updated, and then iterative optimization is performed in the upper centralized shared energy storage model, and the steps are repeated until the convergence result is output; The upper centralized shared energy storage model includes a shared energy storage objective function, which includes: The objective function is determined to maximize daily economic benefits, where the economic benefits include shared energy storage service fees and electricity sales benefits; the objective function is: F 1 =C ser +C sel Among them, F 1 It is the daily economic benefit taking into account the whole life cycle cost; Cser is the service fee for the user group to use the shared energy storage power station; Csel is the cost of the user group to purchase electricity from the shared energy storage power station; Where N is the number of user groups; T is the dispatching duration; ξ(t) is the unit service fee for shared energy storage; P sb,k (t) is the power purchased by user k from the energy storage power station in time period t; P ss,k (t) is the power sold by user k to the energy storage power station in time period t; δ(t) is the unit time; Ω(t) is the purchase price of electricity from the energy storage power station in time period t; The upper centralized shared energy storage model also has constraints, which include energy storage power station charging and discharging power constraints and energy storage charge continuity constraints; The conditions for charging and discharging power constraints of energy storage power stations are: Among them, E ess (t+1), E ess (t) are the power consumption of the energy storage power station at time periods t+1 and t respectively; The maximum charging and discharging power of the energy storage power station; They are respectively the charging and discharging status bits of the energy storage power station, which are Boolean variables; The energy storage charge continuity constraint condition is: Where, χ is the energy storage self-discharge power; η abs , η relea They are the charging and discharging efficiency of energy storage respectively; are respectively the charging and discharging power of the energy storage power station at time t; It is the maximum charge state of the energy storage power station.

2. According to the distributed and centralized cloud energy storage scheduling method described in claim 1, It is characterized in that The lower distributed energy storage model includes a distributed energy storage objective function, which includes: The objective function is determined to be the lowest daily operating cost, where the daily operating cost includes the cost of the user group purchasing electricity from the power grid, the service fee for the user group to use the shared energy storage power station, the cost of the user group purchasing and selling electricity from the shared energy storage power station, and the cost of using energy storage within the user group; then the objective function is: minF 2 =C grid +C ser +C sta +C cost Among them, F 2 is the daily operating economic cost of cloud energy storage for producers and sellers; Cgrid is the cost of electricity purchased by the user group from the power grid; Csta is the cost of electricity purchased and sold by the user group from the shared energy storage power station; Ccost is the cost of energy storage use within the user group; Among them, α(t) is the electricity price purchased from the power grid during this period; P g,k (t) is the power purchased by user k from the grid in time period t; is the electricity price of the energy storage power station at time period t; μ is the power loss cost coefficient of energy storage; P os,k (t), P ob,k (t) are the charging power of user k using its own energy storage in time period t.

3. According to the distributed and centralized cloud energy storage scheduling method described in claim 2, It is characterized in that The lower distributed energy storage model is also provided with constraints, including power balance constraints, energy storage charge continuity constraints, self-energy storage charge and discharge power constraints, and energy storage charge and discharge balance constraints; The power balance constraints are: P pv,k (t)+P wind,k (t)+P ob,k (t)+P sb,k (t) +P g,k (t)=P os,k (t)+P ss,k (t)+P load,k (t) Among them, P pv,k (t), P wind,k (t) are the photovoltaic and wind power outputs of user k in time period t; P lod,k (t) is the power load of user k in time period t; The constraints of self-storage charge continuity are: Among them, E(t+1) and E(t) are the power of the energy storage at time periods t+1 and t respectively; P abs (t), P relea (t) are the charging and discharging power of the energy storage at time t; Emax is the maximum charge state of the energy storage power station; The constraints of the self-storage charging and discharging power are: Among them, P max The maximum value of its own energy storage charging and discharging power; U b , U s They are the energy storage charging and discharging status bits, which are Boolean variables; The self-storage charge and discharge balance constraint conditions are: The sum of electricity purchased and sold by the user group to the energy storage power station is equal to the charging and discharging power of the energy storage power station, and the sum of the charging and discharging of the user group to its own energy storage is equal to the charging and discharging power of its own energy storage, so:

4. According to the distributed and centralized cloud energy storage scheduling method of claim 1, It is characterized in that The target cascade analysis method is used to divide the cloud energy storage dispatching system into multiple sub-models, continuously divert the design indicators from top to bottom, and at the same time, the response results are fed back from bottom to top. Each layer of the model is solved independently, and the solution results are fed back to each other until the convergence conditions are met; The variable optimization model of each layer is: Among them, f(x) is the initial objective function of the layer system; g(x) < 0 and h(x) = 0 are the constraints of the layer system; t ij Design variables passed from the upper system to the lower system; ij is the response result fed back to the upper layer by the lower layer system; π(t ij -r ij ) is the penalty function.

5. According to the distributed and centralized cloud energy storage scheduling method of claim 4, It is characterized in that The upper centralized shared energy storage model will optimize the energy storage result P ess (t) is sent to the lower distributed energy storage model as a design variable, and the lower distributed energy storage model transmits the optimized power consumption P(t) as a feedback variable to the upper centralized shared energy storage model; considering the simplicity and solvability of the penalty function and the function model in the comprehensive target cascade analysis method, in the kth iteration, the objective functions of the upper and lower layers need to be updated, and then: Among them, P k ess (t) and P k (t) are the optimization results of the total power supply and power demand after the kth iteration; τ and ψ are coefficients greater than 0, and their values ​​are respectively based on F 1 and F 2 Depends on the size.

6. A distributed and centralized cloud energy storage scheduling method according to claim 5, It is characterized in that Also includes convergence functions: Among them, ξ 1 and 2 are the convergence accuracy of the two convergence conditions respectively; C A and C B are the economic benefits of the upper and lower layers respectively; the first formula indicates that the difference between the communication variables transmitted between the upper and lower layers is less than the convergence accuracy, and the physical meaning of the second formula is the ratio of the difference between the benefits of the whole system and the benefits of the previous iteration to the benefits of the whole system; After adding the penalty function term, the deviation between the design variables of the upper centralized shared energy storage model and the feedback variables of the lower layer will make the current solution a non-optimal solution. The convergence function is used to adjust the approach speed of the current optimization layer variables to their upper or lower layer variables; as the number of iterations increases, the error between the design variables and the feedback variables gradually decreases, and the influence of the penalty function term on the objective function decreases accordingly. Through repeated iterations, until the error between the design quantity and the feedback quantity is less than the set value ξ, the iteration stops and the optimal solution is obtained.

7. According to claim 1, a distributed and centralized cloud energy storage scheduling method, It is characterized in that The scheduling process of the target cascade analysis method includes the following steps: S1: Input energy storage parameters and load data, set the initial values ​​of coupling variables and penalty function multipliers, and set the number of iterations k=1; the coupling variables include interaction power; S2: The upper centralized shared energy storage model and the lower distributed energy storage model are solved according to their respective optimization problems. On the one hand, the distributed energy storage pursues the economic operation goal, and at the same time, the distributed energy storage data is used as a shared variable to approximate the virtual storage data transmitted to the shared energy storage, and the solved virtual data is passed to the upper centralized shared energy storage model; the distributed energy storage is solved in parallel, and the initial value of the coupling variable is used by default in the first iteration; S3: After the upper centralized shared energy storage model sends the value to the lower distributed energy storage model, it optimizes itself while approaching the value transmitted by the lower distributed energy storage model. On the one hand, the shared energy storage pursues the economic operation goal, and at the same time, the output storage power value is used as a shared variable to approach the virtual required storage value transmitted by the lower distributed energy storage model, and the solved virtual value is transmitted to the lower distributed energy storage model. S4: Check the convergence conditions. If they are met at the same time, terminate the iteration process and output the optimal scheduling result and objective function value. Otherwise, update the penalty function multiplier, set k=k+1, and return to the above step S2 to continue solving. The upper centralized shared energy storage model and the lower distributed energy storage model are repeatedly iterated until the convergence conditions are met and the results are output.

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