Gravity battery hybrid energy storage optimal configuration method and device considering multiple scenes

Abstract

The invention discloses an optimal configuration method and device for gravity battery hybrid energy storage considering multiple scenes, and the method comprises the steps: building a mathematical model of the gravity battery hybrid energy storage based on the operation characteristics of a wind and light storage system containing the gravity battery hybrid energy storage; based on the operation economy of the gravity battery hybrid energy storage and in combination with the application scenarios of peak regulation and fluctuation stabilization, an objective function of a mathematical model is established; based on the operation characteristics of the wind and light storage system containing the gravity battery hybrid energy storage, giving an optimization constraint condition of the gravity battery hybrid energy storage; the optimal operation economical efficiency of the wind and light storage system containing the gravity battery hybrid energy storage is taken as a target, a particle swarm algorithm is used for solving, and an optimal configuration scheme of the gravity battery hybrid energy storage is given. The optimal operation economy of the wind and light storage system is taken as a target, two application scenes of peak regulation and fluctuation stabilization are considered, the optimal power / capacity ratio of gravity energy storage and battery energy storage is searched, and optimal configuration of the wind and light storage system is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric energy storage configuration, and specifically relates to an optimization configuration method and device for gravity battery hybrid energy storage taking into account multiple scenarios. Background Art

[0002] In recent years, facing the dual pressures of environmental pollution and energy shortage, people have begun to actively explore new energy power generation technologies, among which wind power and photovoltaic power generation have attracted much attention. However, due to the uncertainty of wind and solar primary resources, the output of new energy shows the characteristics of randomness and volatility. Therefore, the large-scale access of new energy puts forward higher requirements for power system peak regulation and wind and solar fluctuation smoothing. As an "energy-type" energy storage, gravity energy storage has outstanding advantages such as long cycle charging and discharging service life, high safety, large capacity, no pollution, and large-scale construction. It is suitable for peak regulation in power systems, black start of microgrids and other scenarios, but its response speed is slow and its energy density is not high.

[0003] As a new type of energy storage technology, the hybrid energy storage system with gravity energy storage can combine other types of energy storage with gravity energy storage, comprehensively utilize the advantages of multiple energy storage technologies, and make up for the shortcomings of single gravity energy storage. This hybrid energy storage system can not only improve the flexibility and efficiency of energy storage, but also show higher adaptability in different application scenarios. For example, battery energy storage can provide fast-response short-term power supply, while gravity energy storage is suitable for long-term, large-scale energy storage. Through the combination of the two, the hybrid energy storage system can provide more stable and reliable solutions in terms of grid peak regulation, wind and solar fluctuation smoothing, and promoting the consumption of new energy, further promoting energy transformation and sustainable development.

[0004] However, the application of gravity energy storage is still in its infancy. Although relevant technical research on the grid-connected control of gravity energy storage has been carried out, the focus of the research is mainly on the control strategy within the gravity energy storage system and the grid-connected control strategy of a single unit. There are still obvious deficiencies in the capacity configuration of gravity energy storage, especially the research on the capacity optimization configuration of gravity energy storage combined with other energy storage technologies is relatively lacking, which is not conducive to improving the performance and economy of hybrid energy storage systems containing gravity energy storage. Summary of the invention

[0005] In view of the defects in the above-mentioned prior art, the present invention provides a method and device for optimizing the configuration of gravity-battery hybrid energy storage in consideration of multiple scenarios. With the goal of optimizing the economic operation of the wind-solar-storage system, taking into account the two application scenarios of peak load regulation and fluctuation smoothing, the optimal power / capacity ratio of gravity energy storage and battery energy storage is sought to achieve the optimal configuration of the wind-solar-storage system.

[0006] In a first aspect, the present invention provides a method for optimizing configuration of gravity battery hybrid energy storage taking into account multiple scenarios, comprising:

[0007] Based on the operating characteristics of the wind-solar-storage system with gravity-battery hybrid energy storage, a mathematical model of gravity-battery hybrid energy storage is established;

[0008] Based on the operation economy of gravity-battery hybrid energy storage, and combined with the application scenarios of peak load regulation and fluctuation smoothing, the objective function of the mathematical model is established;

[0009] Based on the operating characteristics of the wind-solar-storage system with gravity-battery hybrid energy storage, the optimization constraints of gravity-battery hybrid energy storage are given;

[0010] With the goal of optimizing the operating economy of the wind-solar-storage system with gravity-battery hybrid energy storage, the particle swarm algorithm is used to solve the optimal configuration scheme of gravity-battery hybrid energy storage.

[0011] Furthermore, based on the operating characteristics of the wind-solar-storage system with gravity-battery hybrid energy storage, a mathematical model of gravity-battery hybrid energy storage is established, including:

[0012] Based on the power and efficiency of gravity energy storage charging and discharging in the wind-solar-storage system, the storage capacity model of gravity energy storage is determined; based on the rated capacity of gravity energy storage in the wind-solar-storage system, as well as the power and efficiency of charging and discharging, the charge state model of gravity energy storage is given;

[0013] Based on the power and efficiency of battery energy storage charging and discharging in the wind-solar-storage system, the storage capacity model of the battery energy storage is determined; based on the rated capacity of the battery energy storage in the wind-solar-storage system, as well as the power and efficiency of charging and discharging, the charge state model of the battery energy storage is given.

[0014] Furthermore, the storage capacity model of gravity energy storage satisfies the following relationship:

[0015]

[0016] In the formula, E GB (t+1) is the amount of electricity stored in the gravity energy storage at the time period t+1; E GB (t) is the amount of electricity stored in the gravity energy storage during the period t; is the charging power of gravity energy storage in period t; is the charging efficiency of gravity energy storage in period t; is the discharge power of gravity energy storage in period t; is the discharge efficiency of gravity energy storage in time period t, Δt is the state duration at time t;

[0017] The state of charge model of gravity energy storage satisfies the following relationship:

[0018]

[0019] In the formula, SOCGB (t+1) is the state of charge of the gravity energy storage at the time period t+1; SOC GB (t) is the charge state of the gravity energy storage in period t; E GBN is the rated capacity of gravity energy storage;

[0020] The storage capacity model of battery energy storage satisfies the following relationship:

[0021]

[0022] In the formula, E ES (t+1) is the amount of electricity stored in the battery during the period t+1; E ES (t) is the amount of electricity stored in the battery during the period t; is the charging power of the battery energy storage during period t; is the charging efficiency of the battery energy storage during the period t; is the discharge power of the battery energy storage during period t; is the discharge efficiency of battery energy storage in period t;

[0023] The state of charge model of battery energy storage satisfies the following relationship:

[0024]

[0025] In the formula, SOC ES (t+1) is the state of charge of the battery energy storage at the time period t+1; SOC ES (t) is the state of charge of the battery energy storage during the period t; E ESN The rated capacity of the battery energy storage.

[0026] Furthermore, based on the operation economy of gravity-battery hybrid energy storage and combined with the application scenarios of peak load regulation and fluctuation smoothing, the objective function of the mathematical model is established, including:

[0027] Determine the revenue from electricity sales based on the power of the grid tie line, the charge and discharge power of the gravity battery hybrid energy storage, and the grid-connected electricity price;

[0028] Determine the peak load benefits based on the charging and discharging power of gravity-battery hybrid energy storage and time-of-use electricity prices;

[0029] Based on the charging and discharging power of the gravity-battery hybrid energy storage and combined with the unit operation and maintenance cost of the gravity-battery hybrid energy storage, determine the operation and maintenance cost of the gravity-battery hybrid energy storage;

[0030] Based on the power and fluctuation penalty coefficient of the grid tie line, the tie line fluctuation penalty cost is given;

[0031] Based on the electricity sales revenue, peak load regulation revenue, operation and maintenance cost of gravity battery hybrid energy storage and interconnection line fluctuation penalty cost, the objective function of the mathematical model is given.

[0032] Furthermore, the objective function of the mathematical model satisfies the following relationship:

[0033] minC=-(F1+F2)+(C1+C2)

[0034]

[0035] C1=(P d,t +P c,t )c ope Δt

[0036] C2=(P g,t -P g,t-1 ) 2 c l Δt

[0037] In the formula, C is the total cost, F1 is the electricity sales income of the wind and solar system; F2 is the peak-shaving income of the gravity battery hybrid energy storage; C1 is the operation and maintenance cost of the gravity battery hybrid energy storage; C2 is the tie line fluctuation penalty, P g,t is the power of the tie line at time t; P c,t is the charging power of gravity battery hybrid energy storage at time t; P d,t is the discharge power of gravity battery hybrid energy storage at time t; c1 is the wind and solar grid-connected electricity price; Δt is the state duration at time t, c2 is the time-of-use electricity price, c ope is the unit operation and maintenance cost of gravity battery hybrid energy storage, P g,t-1 is the power of the tie line at time t-1; c l is the tie line fluctuation penalty coefficient.

[0038] Furthermore, based on the operating characteristics of the wind-solar-storage system with gravity-battery hybrid energy storage, the optimization constraints of gravity-battery hybrid energy storage are given, including:

[0039] Based on the actual output of gravity energy storage and wind-solar storage systems, and combined with the power of the grid tie line, the power balance constraints of gravity-battery hybrid energy storage are determined;

[0040] Based on the maximum charge and discharge power of the battery energy storage, determine the charge and discharge power constraints of the battery energy storage; based on the minimum and maximum charge and discharge power of the gravity energy storage, determine the charge and discharge power constraints of the gravity energy storage; based on the charge and discharge power of the gravity energy storage, determine the climbing rate constraints of the gravity energy storage;

[0041] Based on the minimum and maximum state of charge of gravity-battery hybrid energy storage and combined with the charging and discharging power and efficiency of gravity-battery hybrid energy storage, the state of charge constraints of gravity-battery hybrid energy storage are given.

[0042] Furthermore, the wind-solar-storage system also includes the power balance constraints of wind power, photovoltaic power, and gravity battery hybrid energy storage, satisfying the following relationship:

[0043] P wind,t +P pv,t +P ES,t +P BG,t =P g,t

[0044] Where P wind,t is the actual output of wind power at time t; P pv,t is the actual output of photovoltaic power at time t; P BG,t is the actual output of gravity energy storage at time t; P ES,t is the actual output of the battery energy storage at time t.

[0045] Battery energy storage charging and discharging power constraints satisfy the following relationship

[0046]

[0047] Where P ES (t) The charging and discharging power allocated to the battery energy storage for the gravity-battery hybrid energy storage; Maximum charging power for battery energy storage; The maximum discharge power of the battery energy storage.

[0048] The gravity energy storage charging and discharging power constraints satisfy the following relationship

[0049]

[0050] Where P GB (t) the charging and discharging power allocated to the gravity energy storage for the gravity battery hybrid energy storage; is the minimum charging power for gravity energy storage; The maximum charging power for gravity energy storage; is the minimum discharge power of gravity energy storage; is the maximum discharge power of gravity energy storage.

[0051] The climbing rate constraint of gravity energy storage satisfies the following relationship

[0052]

[0053] Where K is the climbing rate, ΔP GB (t) is the rate of change of the charging and discharging power allocated to the gravity energy storage by the gravity battery hybrid energy storage.

[0054] The state of charge constraints of gravity battery hybrid energy storage satisfy the following relationship

[0055]

[0056] SOC min ≤SOC t ≤SOC max

[0057] In the formula, SOC t+1 SOC is the state of charge of the gravity battery hybrid energy storage at time t+1; t SOC is the state of charge of the gravity battery hybrid energy storage at time t; min SOC is the minimum state of charge of gravity battery hybrid energy storage. max is the maximum state of charge of gravity battery hybrid energy storage; η c is the charging efficiency of gravity battery hybrid energy storage; η d is the discharge efficiency of gravity battery hybrid energy storage; E N It is the rated capacity of gravity-battery hybrid energy storage.

[0058] Furthermore, with the goal of optimizing the operating economy of the wind-solar-storage system with gravity-battery hybrid energy storage, the particle swarm algorithm is used to solve and give the optimal configuration scheme of gravity-battery hybrid energy storage, including:

[0059] Initialize the particle swarm algorithm, iteratively update the historical optimal position and global optimal position of the particle, as well as the speed and position of each particle, and output the optimal energy storage configuration plan, where the energy storage configuration plan includes the rated power and rated capacity of gravity energy storage and battery energy storage, as well as the optimized total cost.

[0060] Furthermore, the historical optimal position and global optimal position of the particle as well as the speed and position of each particle are iteratively updated, including:

[0061] In each iteration, the rated capacity and rated power of the corresponding gravity-battery hybrid energy storage are calculated according to the energy storage capacity and power parameters of the current particle, including the rated power and capacity of gravity energy storage and battery energy storage;

[0062] For each time period, the optimization constraints are determined, and the objective function of the mathematical model is defined as minimizing the operating cost of the gravity battery hybrid energy storage; based on the optimization constraints, the objective function of the mathematical model is optimized and solved, and the corresponding fitness is calculated. If the solution is successful, the total cost of the current particle is recorded. If the solution fails, the fitness is set to infinity;

[0063] The fitness of each solution is compared with the fitness of the historical optimal position and the global optimal position, and the historical optimal position and the global optimal position of the particle as well as the speed and position of each particle are updated.

[0064] Furthermore, the YALMIP toolbox and Gurobi solver are used to optimize the objective function of the mathematical model based on the optimization constraints.

[0065] In a second aspect, the present invention further provides an optimization configuration device for gravity battery hybrid energy storage taking into account multiple scenarios, using the above-mentioned optimization configuration method for gravity battery hybrid energy storage taking into account multiple scenarios, the optimization configuration device comprises:

[0066] A model building module, which is used to establish a mathematical model of gravity battery hybrid energy storage based on the operating characteristics of the wind-solar storage system containing gravity battery hybrid energy storage;

[0067] The objective function building module is used to establish the objective function of the mathematical model based on the operation economy of gravity-battery hybrid energy storage and combined with the application scenarios of peak load regulation and fluctuation smoothing;

[0068] A constraint condition determination module, which is used to provide the optimization constraint conditions of the gravity battery hybrid energy storage based on the operating characteristics of the wind-solar storage system containing the gravity battery hybrid energy storage;

[0069] The optimization solution module is used to optimize the operating economy of the wind-solar-storage system containing gravity battery hybrid energy storage, and use the particle swarm algorithm to solve and provide an optimal configuration plan for the gravity battery hybrid energy storage.

[0070] Furthermore, the model building module includes:

[0071] Based on the power and efficiency of gravity energy storage charging and discharging in the wind-solar-storage system, the storage capacity model of gravity energy storage is determined; based on the rated capacity of gravity energy storage in the wind-solar-storage system, as well as the power and efficiency of charging and discharging, the charge state model of gravity energy storage is given;

[0072] Based on the power and efficiency of battery energy storage charging and discharging in the wind-solar-storage system, the storage capacity model of the battery energy storage is determined; based on the rated capacity of the battery energy storage in the wind-solar-storage system, as well as the power and efficiency of charging and discharging, the charge state model of the battery energy storage is given.

[0073] Furthermore, the objective function building module includes:

[0074] Determine the revenue from electricity sales based on the power of the grid tie line, the charge and discharge power of the gravity battery hybrid energy storage, and the grid-connected electricity price;

[0075] Determine the peak load benefits based on the charging and discharging power of gravity-battery hybrid energy storage and time-of-use electricity prices;

[0076] Based on the charging and discharging power of the gravity-battery hybrid energy storage and combined with the unit operation and maintenance cost of the gravity-battery hybrid energy storage, determine the operation and maintenance cost of the gravity-battery hybrid energy storage;

[0077] Based on the power and fluctuation penalty coefficient of the grid tie line, the tie line fluctuation penalty cost is given;

[0078] Based on the electricity sales revenue, peak load regulation revenue, operation and maintenance cost of gravity battery hybrid energy storage and interconnection line fluctuation penalty cost, the objective function of the mathematical model is given.

[0079] Furthermore, the constraint determination module includes:

[0080] Based on the actual output of gravity energy storage and wind-solar storage systems, and combined with the power of the grid tie line, the power balance constraints of the gravity-battery hybrid energy storage are determined;

[0081] Based on the maximum charge and discharge power of the battery energy storage, determine the charge and discharge power constraints of the battery energy storage; based on the minimum and maximum charge and discharge power of the gravity energy storage, determine the charge and discharge power constraints of the gravity energy storage; based on the charge and discharge power of the gravity energy storage, determine the climbing rate constraints of the gravity energy storage;

[0082] Based on the minimum and maximum state of charge of gravity-battery hybrid energy storage and combined with the charging and discharging power and efficiency of gravity-battery hybrid energy storage, the state of charge constraints of gravity-battery hybrid energy storage are given.

[0083] Furthermore, the optimization solution module includes:

[0084] Initialize the particle swarm algorithm, iteratively update the historical optimal position and global optimal position of the particle, as well as the speed and position of each particle, and output the optimal energy storage configuration plan, where the energy storage configuration plan includes the rated power and rated capacity of gravity energy storage and battery energy storage, as well as the optimized total cost.

[0085] Furthermore, the optimization solution module also includes:

[0086] In each iteration, the rated capacity and rated power of the corresponding gravity-battery hybrid energy storage are calculated according to the energy storage capacity and power parameters of the current particle, including the rated power and capacity of gravity energy storage and battery energy storage;

[0087] For each time period, the optimization constraints are determined, and the objective function of the mathematical model is defined as minimizing the operating cost of the gravity battery hybrid energy storage; based on the optimization constraints, the objective function of the mathematical model is optimized and solved, and the corresponding fitness is calculated. If the solution is successful, the total cost of the current particle is recorded. If the solution fails, the fitness is set to infinity;

[0088] The fitness of each solution is compared with the fitness of the historical optimal position and the global optimal position, and the historical optimal position and the global optimal position of the particle as well as the speed and position of each particle are updated.

[0089] The present invention provides a method and device for optimizing the configuration of gravity battery hybrid energy storage taking into account multiple scenarios, which at least includes the following beneficial effects:

[0090] Considering the demand for new energy storage (gravity energy storage and battery energy storage) in different scenarios, two application scenarios of peak shaving and fluctuation smoothing are selected for research; the operating characteristics of the wind-solar-storage system containing "gravity-battery" hybrid energy storage are analyzed, and a mathematical model of gravity-battery hybrid energy storage is established; considering the two application scenarios of peak shaving and fluctuation smoothing and the economic efficiency of system operation, the objective function of the model is established; considering the system operation characteristics, optimization constraints such as system power balance constraints and energy storage constraints are formulated; with the goal of optimal system operation economy, the particle swarm algorithm is used to solve the optimal configuration model of gravity-battery hybrid energy storage considering multiple application scenarios. The present invention comprehensively considers the two application scenarios of peak shaving and fluctuation smoothing, takes the optimal system operation economy as the goal, finds the optimal power / capacity ratio of gravity energy storage and battery energy storage, realizes the optimal configuration of the hybrid energy storage system, provides a reference for the optimal configuration of the wind-solar-storage system containing gravity energy storage, and further improves the economy and reliability of the wind-solar-storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 A flowchart of a method for optimizing configuration of gravity battery hybrid energy storage taking into account multiple scenarios provided by the present invention;

[0092] Figure 2 A flowchart of iterative update provided for an embodiment of the present invention;

[0093] Figure 3 A schematic diagram of the combined operation results of the gravity-battery hybrid energy storage provided by an embodiment of the present invention;

[0094] Figure 4 Schematic diagram of the gravity battery hybrid energy storage fluctuation smoothing effect provided by an embodiment of the present invention

[0095] Figure 5 A schematic diagram of an optimized configuration device for gravity battery hybrid energy storage taking into account multiple scenarios provided by the present invention. DETAILED DESCRIPTION

[0096] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. 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 are within the scope of protection of the present invention.

[0097] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0098] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0099] like Figure 1 As shown, the present invention provides an optimization configuration method for gravity battery hybrid energy storage taking into account multiple scenarios, which may include:

[0100] Based on the operating characteristics of the wind-solar-storage system with gravity battery hybrid energy storage, a mathematical model of gravity battery hybrid energy storage is established; wherein the wind-solar-storage system refers to a combined power generation system composed of wind power, photovoltaics and gravity battery hybrid energy storage, that is, the wind-solar-storage system = wind power + photovoltaics + gravity battery hybrid energy storage;

[0101] Based on the operation economy of gravity-battery hybrid energy storage, and combined with the application scenarios of peak load regulation and fluctuation smoothing, the objective function of the mathematical model is established;

[0102] Based on the operating characteristics of the wind-solar-storage system with gravity-battery hybrid energy storage, the optimization constraints of gravity-battery hybrid energy storage are given;

[0103] With the goal of optimizing the operating economy of the wind-solar-storage system with gravity-battery hybrid energy storage, the particle swarm algorithm is used to solve the optimal configuration scheme of gravity-battery hybrid energy storage.

[0104] The present invention can effectively meet the requirements for optimal configuration of gravity battery hybrid energy storage, realize clean and efficient utilization of energy, and has significant economic and social benefits.

[0105] In actual application scenarios, the present invention considers the demand for new energy storage in different scenarios, and selects two application scenarios, peak load regulation and fluctuation smoothing, for research;

[0106] The two application scenarios of peak load regulation and fluctuation smoothing are consistent in time scale, both have high requirements for power demand, and show great profit potential. In addition, they also show certain complementarity and correlation.

[0107] For peak load-shaving application scenarios, gravity battery hybrid energy storage achieves peak load-shaving on the interconnection line through a low-buy-high-sell charging and discharging strategy, effectively alleviating the contradiction between power supply and demand and improving the economic efficiency of power grid operation;

[0108] For fluctuation smoothing application scenarios, gravity battery hybrid energy storage has the function of fluctuation smoothing, which is mainly used to smooth the power fluctuations of interconnection lines.

[0109] When establishing the mathematical model of gravity battery hybrid energy storage, the operating characteristics of the wind-solar storage system containing gravity battery hybrid energy storage are analyzed and the mathematical model is finally constructed; among them, for the mathematical model of gravity energy storage, its working state is divided into two kinds, namely charging state and discharging state, and its storage power during charging and discharging (charging and discharging) satisfies:

[0110]

[0111] In the formula, E GB (t+1) is the amount of electricity stored in the gravity energy storage at the time period t+1; E GB (t) is the amount of electricity stored in the gravity energy storage during the period t; is the charging power of gravity energy storage in period t; is the charging efficiency of gravity energy storage in period t; is the discharge power of gravity energy storage in period t; is the discharge efficiency of gravity energy storage in period t.

[0112] During charging and discharging, its SOC (state of charge) satisfies:

[0113]

[0114] In the formula, SOC GB (t+1) is the state of charge of the gravity energy storage at the time period t+1; SOC GB (t) is the amount of electricity stored in the gravity energy storage during the period t; E GBN is the rated capacity of gravity energy storage.

[0115] For the mathematical model of battery energy storage, its working state is divided into two types, namely charging state and discharging state. During charging and discharging, the stored power meets the following requirements:

[0116]

[0117] In the formula, E ES (t+1) is the amount of electricity stored in the battery during the period t+1; E ES (t) is the amount of electricity stored in the battery during the period t; is the charging power of the battery energy storage during period t; is the charging efficiency of the battery energy storage during the period t; is the discharge power of the battery energy storage during period t; is the discharge efficiency of the battery energy storage during period t.

[0118] During charging and discharging, the SOC meets the following requirements:

[0119]

[0120] In the formula, SOC ES (t+1) is the state of charge of the battery energy storage at the time period t+1; SOC ES (t) is the amount of electricity stored in the battery during the period t; E ESN The rated capacity of the battery energy storage.

[0121] After analyzing and constructing the mathematical model of gravity-battery hybrid energy storage, the objective function of the mathematical model is established by considering the two application scenarios of peak load regulation and fluctuation smoothing as well as the operating economy of gravity-battery hybrid energy storage;

[0122] The objective function of this mathematical model includes four parts: electricity sales revenue, peak load regulation revenue, energy storage operation and maintenance cost, and tie line fluctuation penalty. Its expression is:

[0123] minC=-(F1+F2)+(C1+C2)

[0124] In the formula, C is the total cost, F1 is the electricity sales revenue of the wind and solar system; F2 is the peak-shaving revenue of the gravity-battery hybrid energy storage; C1 is the operation and maintenance cost of the gravity-battery hybrid energy storage; C2 is the interconnection line fluctuation penalty.

[0125] The electricity sales revenue refers to the grid-connected revenue of the wind-solar-storage system containing gravity-battery hybrid energy storage, and its expression is:

[0126]

[0127] Where P g,t is the power of the tie line at time t; P c,t is the charging power of gravity battery hybrid energy storage at time t; P d,tis the discharge power of the gravity battery hybrid energy storage at time t; c1 is the wind-solar grid-connected electricity price; Δt is the state duration at time t, which is 1h. This state duration refers to the Δt time in the charging state or the discharging state.

[0128] The peak-shaving benefit refers to the fact that the gravity battery hybrid energy storage is charged when the load is low and discharged when the load is peak, and the time-of-use electricity price is used to participate in peak-valley arbitrage. When calculating the peak-shaving benefit, the present invention uses the gravity battery hybrid energy storage discharge power minus the gravity battery hybrid energy storage charging power as the total peak-shaving power, and its expression is:

[0129]

[0130] Where c2 is the time-of-use electricity price.

[0131] The energy storage operation and maintenance cost refers to the dynamic cost investment caused by the necessary inspection and maintenance of related facilities to ensure the stable operation of the wind-solar storage system containing gravity battery hybrid energy storage, and its expression is:

[0132] C1=(P d,t +P c,t )c ope Δt

[0133] In the formula, c ope Unit operation and maintenance costs for gravity-battery hybrid energy storage.

[0134] The tie line fluctuation penalty refers to the economic cost due to the power fluctuation of the tie line, and its expression is:

[0135] C2=(P g,t -P g,t-1 ) 2 c l Δt

[0136] Where P g,t-1 is the power of the tie line at time t-1; c l is the tie line fluctuation penalty coefficient.

[0137] After constructing the objective function of the mathematical model, the optimization constraints such as system power balance constraint and energy storage constraint are formulated by considering the operating characteristics of the wind-solar-storage system with gravity-battery hybrid energy storage;

[0138] Specifically, the constraints of the mathematical model include:

[0139] (1) Power balance constraints of wind, solar and storage systems:

[0140] P wind,t +P pv,t +P ES,t +PBG,t =P g,t

[0141] Where P wind,t is the actual output of wind power at time t; P pv,t is the actual output of photovoltaic power at time t; P BG,t is the actual output of gravity energy storage at time t; P ES,t is the actual output of the battery energy storage at time t.

[0142] (2) Charging and discharging power constraints of gravity battery hybrid energy storage:

[0143] ①Battery energy storage charging and discharging power constraints:

[0144]

[0145] Where P ES (t) is the charging and discharging power (charging power, discharging power) allocated to the battery energy storage by the gravity-battery hybrid energy storage; Maximum charging power for battery energy storage; The maximum discharge power of the battery energy storage.

[0146] ② Gravity energy storage charging and discharging power constraints:

[0147]

[0148] Where P GB (t) The charging and discharging power allocated to the gravity energy storage by the hybrid energy storage system; is the minimum charging power for gravity energy storage; The maximum charging power for gravity energy storage; is the minimum discharge power of gravity energy storage; is the maximum discharge power of gravity energy storage.

[0149] ③ Gravity energy storage climbing rate constraints:

[0150]

[0151] Where K is the climbing rate, ΔP GB (t) is the rate of change of the charging and discharging power allocated to the gravity energy storage by the gravity battery hybrid energy storage.

[0152] (3) State of charge constraints for gravity-battery hybrid energy storage:

[0153]

[0154] SOC min ≤SOC t ≤SOC max

[0155] In the formula, SOC t+1 is the state of charge of the energy storage at time t+1; SOC t is the state of charge of the energy storage at time t; SOC min SOC is the minimum state of charge for energy storage. max is the maximum state of charge of energy storage; η c is the charging efficiency of energy storage; η d is the discharge efficiency of energy storage; E N is the rated capacity of the energy storage. Among them, the state of charge is the ratio of the stored power of the energy storage at time t to the rated power, that is, SOC = E / E N In practical applications, the initial state of charge (i.e., the state of charge at time 0) is a known parameter, and the state of charge at other times is calculated in sequence by the formula. The charge and discharge efficiency is determined by the type of energy storage. For example, the charge and discharge efficiency of battery energy storage is 0.9.

[0156] With the goal of achieving economic optimization of the wind-solar-storage system with gravity-battery hybrid energy storage, the particle swarm algorithm is used to solve the optimal configuration model of gravity-battery hybrid energy storage considering multiple application scenarios, and the final optimal configuration scheme is given, including:

[0157] Initialize the particle swarm algorithm, iteratively update the historical optimal position and global optimal position of the particle, as well as the speed and position of each particle, and output the optimal energy storage configuration plan, where the energy storage configuration plan includes the rated power and rated capacity of gravity energy storage and battery energy storage, as well as the optimized total cost.

[0158] Among them, Figure 2 As shown, the historical optimal position and global optimal position of the particle as well as the speed and position of each particle are iteratively updated, including:

[0159] In each iteration, the rated capacity and rated power of the corresponding gravity-battery hybrid energy storage are calculated according to the energy storage capacity and power parameters of the current particle, including the rated power and capacity of gravity energy storage and battery energy storage; energy storage capacity refers to the amount of electricity that can be stored in energy storage, and the unit is generally kWh; energy storage power refers to the amount of power that can be emitted or absorbed by energy storage per unit time, and the unit is generally kW; for application scenarios that require long-term power supply, a larger energy storage capacity is required; and for application scenarios that require rapid response and charging and discharging, a larger energy storage power is required;

[0160] For each time period, the optimization constraints are determined, and the objective function of the mathematical model is defined as minimizing the operating cost of the gravity battery hybrid energy storage; based on the optimization constraints, the objective function of the mathematical model is optimized and solved, and the corresponding fitness is calculated. If the solution is successful, the total cost of the current particle is recorded. If the solution fails, the fitness is set to infinity;

[0161] The fitness of each solution is compared with the fitness of the historical optimal position and the global optimal position, and the historical optimal position and the global optimal position of the particle as well as the speed and position of each particle are updated.

[0162] When the fitness of each solution is compared with the fitness of the historical optimal position and the global optimal position, when the fitness of the new particle position solved is better than the historical optimal position and / or the global optimal position, the historical optimal position and / or the global optimal position, as well as the speed and position of each particle are updated accordingly. Among them, the optimal energy storage configuration scheme finally output by the present invention is not necessarily the global optimal position. The historical optimal position of a particle refers to the optimal solution found by the particle itself, and the global optimal position refers to the optimal solution found by all particles in the entire particle swarm. The energy storage configuration scheme that is finally outputted as the best is the best one between the historical optimal position and the global optimal position of the particle, that is, based on the comparison between the historical optimal position and the global optimal position, when the historical optimal position is better than the global optimal position, the output energy storage configuration scheme is the historical optimal position, otherwise it is the global optimal position.

[0163] In practical applications, the specific steps for solving the mathematical model may include:

[0164] (1) Initialization of the particle swarm algorithm, including: first determining the number of particles and the number of iterations, secondly setting the inertia weight to control the influence of particle velocity, setting the learning factor and social learning factor to represent the degree to which the particle moves toward its own historical optimal position and the global optimal position, and finally initializing the random position and velocity of each particle in the search space, recording the historical optimal position and the global optimal position of each particle, and the initial value is a randomly generated position.

[0165] (2) The iterative solution steps of the particle swarm algorithm include: Step 1, in each iteration, according to the energy storage capacity and power parameters of the current particle, calculate the capacity and power of the corresponding energy storage, including the rated power and capacity of gravity energy storage and battery energy storage; Step 2, for each time period, set constraints such as power balance constraints, energy storage charging and discharging state constraints, energy storage charging and discharging power constraints, and energy storage SOC constraints; Step 3, define the objective function of the mathematical model as minimizing the operating cost of the wind-solar-storage system containing gravity battery hybrid energy storage, including four parts: electricity sales revenue, peak load revenue, energy storage operation and maintenance costs, and interconnection line fluctuation penalty; Step 4, use the YALMIP toolbox and Gurobi solver to optimize and solve. In each iteration, according to the energy storage capacity and power parameters of the current particle, solve the optimization problem and calculate its fitness. If the solution is successful, record the total cost of the current particle. If the solution fails, set the fitness to infinity; Step 5, update the historical optimal position and global optimal position of the particle as well as the speed and position of each particle.

[0166] (3) The optimization results of the particle swarm algorithm include: after completing all iterations, outputting the optimal energy storage configuration plan, including the rated power and capacity of gravity energy storage and battery energy storage, and the corresponding optimized total cost.

[0167] The present invention takes a wind-solar combined power station in a certain place connected to a "gravity-battery" hybrid energy storage device (i.e., the gravity-battery hybrid energy storage in the present invention) as an example to simulate and verify the optimal configuration and operation of the "gravity-battery" hybrid energy storage device. The wind-solar combined power station specifically described in the specific embodiment of the present invention includes a wind power generation system with a maximum power of 10MW and a photovoltaic power generation system with a peak capacity of 6MW.

[0168] The on-grid electricity price of the model described in the present invention is shown in Table 1, the time-of-use electricity price is shown in Table 2, and the operating parameters of the gravity battery hybrid energy storage are shown in Table 3.

[0169] Table 1 On-grid electricity price

[0170]

[0171] Table 2 Time-of-use electricity prices

[0172]

[0173]

[0174] Table 3 Operation parameters of gravity battery hybrid energy storage

[0175]

[0176] The established gravity battery hybrid energy storage optimization configuration model considering multiple application scenarios and the economic efficiency of wind-solar-storage system operation is solved, and the optimization configuration results of gravity battery hybrid energy storage are shown in Table 4, and the various costs and benefits of the wind-solar-storage system are shown in Table 5. In the result representation of this specific embodiment, the cost of the wind-solar-storage system is a positive value, such as the energy storage operation and maintenance cost and the tie line fluctuation penalty, and the benefit of the wind-solar-storage system is a negative value, such as the electricity sales income and peak load income.

[0177] Table 4 Optimal configuration results of gravity battery hybrid energy storage

[0178]

[0179] Table 5 Costs and benefits of wind, solar and storage systems

[0180]

[0181] At this time, the combined operation results of gravity battery hybrid energy storage are as follows: Figure 3As shown in the figure, by analyzing the energy storage output, it can be seen that the overall trend of energy storage output is to perform two charging operations and two discharging operations within 24 hours a day. From the perspective of peak load regulation, during the flat and valley period of time-of-use electricity prices, energy storage uses the surplus electricity of wind and solar power generation for charging operations, effectively storing low-priced electricity to prepare for subsequent power supply, while reducing the phenomenon of wind and solar power generation abandonment; during 9:00-15:00 and 19:00-22:00 every day, that is, the peak period of time-of-use electricity prices, the power demand of the power grid rises sharply, and energy storage performs discharge operations to sell electricity to the power grid to meet the power demand of the power system. Therefore, the energy storage system not only meets the peak load regulation demand of the power system through charging and discharging operations, but also realizes peak-valley arbitrage.

[0182] The effect of gravity battery hybrid energy storage fluctuation smoothing is as follows Figure 4 As shown in the figure, by comparing the interconnection line power before and after optimization, it can be seen that the power fluctuation of the interconnection line of the wind-solar-storage system is significantly smaller after optimization. When the wind-solar output is unstable, the gravity-battery hybrid energy storage can effectively adjust the power of the wind-solar-storage system interconnection line by absorbing or releasing electric energy, thereby reducing its power fluctuation. Therefore, the gravity-battery hybrid energy storage plays a significant role in smoothing the power fluctuation of the interconnection line and improving the stability of the power system.

[0183] In order to verify the economy and effectiveness of the gravity battery hybrid energy storage optimization configuration model (objective function of the mathematical model) proposed in the present invention that considers multiple application scenarios and the economic operation of the wind-solar-storage system, three schemes are set for comparison. The specific schemes are as follows:

[0184] (1) Option 1: Wind-solar-storage system equipped with gravity-battery hybrid energy storage;

[0185] (2) Option 2: The wind-solar-storage system is equipped with gravity energy storage only;

[0186] (3) Option 3: The wind-solar-storage system is equipped with battery energy storage only.

[0187] The system costs and benefits of the three solutions are compared, as shown in Table 6.

[0188] Table 6 Comparison of system costs and benefits of three solutions

[0189]

[0190] In summary, the scheme 1 proposed in the present invention can not only charge when the load is low and discharge when the load is peak, thereby realizing system peak regulation and peak-valley arbitrage, but also smooth the power fluctuation of the interconnection line, perform fluctuation suppression, and reduce the interconnection line fluctuation penalty, and its economic performance is the best among the three schemes.

[0191] like Figure 5As shown, the present invention also provides an optimization configuration device for gravity battery hybrid energy storage considering multiple scenarios, and adopts the above-mentioned optimization configuration method for gravity battery hybrid energy storage considering multiple scenarios, and the optimization configuration device includes:

[0192] A model building module, which is used to establish a mathematical model of gravity battery hybrid energy storage based on the operating characteristics of the wind-solar storage system containing gravity battery hybrid energy storage;

[0193] The objective function building module is used to establish the objective function of the mathematical model based on the operation economy of gravity-battery hybrid energy storage and combined with the application scenarios of peak load regulation and fluctuation smoothing;

[0194] A constraint condition determination module, which is used to provide the optimization constraint conditions of the gravity battery hybrid energy storage based on the operating characteristics of the wind-solar storage system containing the gravity battery hybrid energy storage;

[0195] The optimization solution module is used to optimize the operating economy of the wind-solar-storage system containing gravity battery hybrid energy storage, and use the particle swarm algorithm to solve and provide an optimal configuration plan for the gravity battery hybrid energy storage.

[0196] Furthermore, the model building module includes:

[0197] Based on the power and efficiency of gravity energy storage charging and discharging in the wind-solar-storage system, the storage capacity model of gravity energy storage is determined; based on the rated capacity of gravity energy storage in the wind-solar-storage system, as well as the power and efficiency of charging and discharging, the charge state model of gravity energy storage is given;

[0198] Based on the power and efficiency of battery energy storage charging and discharging in the wind-solar-storage system, the storage capacity model of the battery energy storage is determined; based on the rated capacity of the battery energy storage in the wind-solar-storage system, as well as the power and efficiency of charging and discharging, the charge state model of the battery energy storage is given.

[0199] Furthermore, the objective function building module includes:

[0200] Determine the revenue from electricity sales based on the power of the grid tie line, the charge and discharge power of the gravity battery hybrid energy storage, and the grid-connected electricity price;

[0201] Determine the peak load benefits based on the charging and discharging power of gravity-battery hybrid energy storage and time-of-use electricity prices;

[0202] Based on the charging and discharging power of the gravity-battery hybrid energy storage and combined with the unit operation and maintenance cost of the gravity-battery hybrid energy storage, determine the operation and maintenance cost of the gravity-battery hybrid energy storage;

[0203] Based on the power and fluctuation penalty coefficient of the grid tie line, the tie line fluctuation penalty cost is given;

[0204] Based on the electricity sales revenue, peak load regulation revenue, operation and maintenance cost of gravity battery hybrid energy storage and interconnection line fluctuation penalty cost, the objective function of the mathematical model is given.

[0205] Furthermore, the constraint determination module includes:

[0206] Based on the actual output of gravity energy storage and wind-solar storage systems, and combined with the power of the grid tie line, the power balance constraints of the gravity-battery hybrid energy storage are determined;

[0207] Based on the maximum charge and discharge power of the battery energy storage, determine the charge and discharge power constraints of the battery energy storage; based on the minimum and maximum charge and discharge power of the gravity energy storage, determine the charge and discharge power constraints of the gravity energy storage; based on the charge and discharge power of the gravity energy storage, determine the climbing rate constraints of the gravity energy storage;

[0208] Based on the minimum and maximum state of charge of gravity-battery hybrid energy storage and combined with the charging and discharging power and efficiency of gravity-battery hybrid energy storage, the state of charge constraints of gravity-battery hybrid energy storage are given.

[0209] Furthermore, the optimization solution module includes:

[0210] Initialize the particle swarm algorithm, iteratively update the historical optimal position and global optimal position of the particle, as well as the speed and position of each particle, and output the optimal energy storage configuration plan, where the energy storage configuration plan includes the rated power and rated capacity of gravity energy storage and battery energy storage, as well as the optimized total cost.

[0211] Furthermore, the optimization solution module also includes:

[0212] In each iteration, the rated capacity and rated power of the corresponding gravity-battery hybrid energy storage are calculated according to the energy storage capacity and power parameters of the current particle, including the rated power and capacity of gravity energy storage and battery energy storage;

[0213] For each time period, the optimization constraints are determined, and the objective function of the mathematical model is defined as minimizing the operating cost of the gravity battery hybrid energy storage; based on the optimization constraints, the objective function of the mathematical model is optimized and solved, and the corresponding fitness is calculated. If the solution is successful, the total cost of the current particle is recorded. If the solution fails, the fitness is set to infinity;

[0214] The fitness of each solution is compared with the fitness of the historical optimal position and the global optimal position, and the historical optimal position and the global optimal position of the particle as well as the speed and position of each particle are updated.

[0215] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing the configuration of gravity battery hybrid energy storage considering multiple scenarios, characterized in that: include: Based on the operating characteristics of the wind-solar-storage system with gravity-battery hybrid energy storage, a mathematical model of gravity-battery hybrid energy storage is established; Based on the operation economy of gravity-battery hybrid energy storage, and combined with the application scenarios of peak load regulation and fluctuation smoothing, the objective function of the mathematical model is established; Based on the operating characteristics of the wind-solar-storage system with gravity-battery hybrid energy storage, the optimization constraints of gravity-battery hybrid energy storage are given; With the goal of optimizing the operating economy of the wind-solar-storage system with gravity-battery hybrid energy storage, the particle swarm algorithm is used to solve the optimal configuration scheme of gravity-battery hybrid energy storage.

2. The method for optimizing the configuration of gravity battery hybrid energy storage according to claim 1, characterized in that: Based on the operating characteristics of the wind-solar-storage system with gravity-battery hybrid energy storage, a mathematical model of gravity-battery hybrid energy storage is established, including: Based on the power and efficiency of gravity energy storage charging and discharging in the wind-solar-storage system, the storage capacity model of gravity energy storage is determined; based on the rated capacity of gravity energy storage in the wind-solar-storage system, as well as the power and efficiency of charging and discharging, the charge state model of gravity energy storage is given; Based on the power and efficiency of battery energy storage charging and discharging in the wind-solar-storage system, the storage capacity model of the battery energy storage is determined; based on the rated capacity of the battery energy storage in the wind-solar-storage system, as well as the power and efficiency of charging and discharging, the charge state model of the battery energy storage is given.

3. The method for optimizing the configuration of gravity battery hybrid energy storage according to claim 2, characterized in that: The storage capacity model of gravity energy storage satisfies the following relationship: In the formula, E GB (t+1) is the amount of electricity stored in the gravity energy storage at the time period t+1; E GB (t) is the amount of electricity stored in the gravity energy storage during the period t; is the charging power of gravity energy storage in period t; is the charging efficiency of gravity energy storage in period t; is the discharge power of gravity energy storage in period t; is the discharge efficiency of gravity energy storage in time period t, Δt is the state duration at time t; The state of charge model of gravity energy storage satisfies the following relationship: In the formula, SOC GB (t+1) is the state of charge of the gravity energy storage at the time period t+1; SOC GB (t) is the charge state of the gravity energy storage in period t; E GBN is the rated capacity of gravity energy storage; The storage capacity model of battery energy storage satisfies the following relationship: In the formula, E ES (t+1) is the amount of electricity stored in the battery during the period t+1; E ES (t) is the amount of electricity stored in the battery during the period t; is the charging power of the battery energy storage during period t; is the charging efficiency of the battery energy storage during the period t; is the discharge power of the battery energy storage during period t; is the discharge efficiency of battery energy storage in period t; The state of charge model of battery energy storage satisfies the following relationship: In the formula, SOC ES (t+1) is the state of charge of the battery energy storage at the time period t+1; SOC ES (t) is the state of charge of the battery energy storage during the period t; E ESN The rated capacity of the battery energy storage.

4. The method for optimizing configuration of gravity battery hybrid energy storage according to claim 1, characterized in that: Based on the operation economy of gravity-battery hybrid energy storage and combined with the application scenarios of peak load regulation and fluctuation smoothing, the objective function of the mathematical model is established, including: Determine the revenue from electricity sales based on the power of the grid tie line, the charge and discharge power of the gravity battery hybrid energy storage, and the grid-connected electricity price; Determine the peak load benefits based on the charging and discharging power of gravity-battery hybrid energy storage and time-of-use electricity prices; Based on the charging and discharging power of the gravity-battery hybrid energy storage and combined with the unit operation and maintenance cost of the gravity-battery hybrid energy storage, determine the operation and maintenance cost of the gravity-battery hybrid energy storage; Based on the power and fluctuation penalty coefficient of the grid tie line, the tie line fluctuation penalty cost is given; Based on the electricity sales revenue, peak load regulation revenue, operation and maintenance cost of gravity battery hybrid energy storage and interconnection line fluctuation penalty cost, the objective function of the mathematical model is given.

5. The method for optimizing configuration of gravity battery hybrid energy storage according to claim 4, characterized in that: The objective function of the mathematical model satisfies the following relationship: minC=-(F1+F2)+(C1+C2) C1=(P d,t +P c,t )c ope Δt C2=(P g,t -P g,t-1 ) 2 c l Δt In the formula, C is the total cost, F1 is the electricity sales income of the wind and solar system; F2 is the peak-shaving income of the gravity battery hybrid energy storage; C1 is the operation and maintenance cost of the gravity battery hybrid energy storage; C2 is the tie line fluctuation penalty, P g,t is the power of the tie line at time t; P c,t is the charging power of gravity battery hybrid energy storage at time t; P d,t is the discharge power of the gravity battery hybrid energy storage at time t; c1 is the wind and solar grid-connected electricity price; Δt is the state duration at time t, c2 is the time-of-use electricity price, c ope is the unit operation and maintenance cost of gravity battery hybrid energy storage, P g,t-1 is the power of the tie line at time t-1; c l is the tie line fluctuation penalty coefficient.

6. The method for optimizing configuration of gravity battery hybrid energy storage according to claim 4 or 5, characterized in that: Based on the operating characteristics of the wind-solar-storage system with gravity-battery hybrid energy storage, the optimization constraints of gravity-battery hybrid energy storage are given, including: Based on the actual output of gravity energy storage and wind-solar storage systems, and combined with the power of the grid tie line, the power balance constraints of gravity-battery hybrid energy storage are determined; Based on the maximum charge and discharge power of the battery energy storage, determine the charge and discharge power constraints of the battery energy storage; based on the minimum and maximum charge and discharge power of the gravity energy storage, determine the charge and discharge power constraints of the gravity energy storage; based on the charge and discharge power of the gravity energy storage, determine the climbing rate constraints of the gravity energy storage; Based on the minimum and maximum state of charge of gravity-battery hybrid energy storage and combined with the charging and discharging power and efficiency of gravity-battery hybrid energy storage, the state of charge constraints of gravity-battery hybrid energy storage are given.

7. The method for optimizing configuration of gravity battery hybrid energy storage according to claim 6, characterized in that: The power balance constraint of gravity-battery hybrid energy storage satisfies the following relationship: P wind,t +P pv,t +P ES,t +P BG,t =P g,t Where P wind,t is the actual output of wind power at time t; P pv,t is the actual output of photovoltaic power at time t; P BG,t is the actual output of gravity energy storage at time t; P ES,t is the actual output of the battery energy storage at time t; The battery energy storage charging and discharging power constraints satisfy the following relationship: Where P ES (t) The charging and discharging power allocated to the battery energy storage for the gravity-battery hybrid energy storage; Maximum charging power for battery energy storage; The maximum discharge power of the battery energy storage; The gravity energy storage charging and discharging power constraints satisfy the following relationship: Where P GB (t) the charging and discharging power allocated to the gravity energy storage for the gravity battery hybrid energy storage; is the minimum charging power for gravity energy storage; The maximum charging power for gravity energy storage; is the minimum discharge power of gravity energy storage; is the maximum discharge power of gravity energy storage; The climbing rate constraint of gravity energy storage satisfies the following relationship: Where K is the climbing rate, ΔP GB (t) is the rate of change of the charging and discharging power allocated to the gravity energy storage by the gravity battery hybrid energy storage; The state of charge constraints of gravity battery hybrid energy storage satisfy the following relationship: SOC min ≤SOC t ≤SOC max In the formula, SOC t+1 SOC is the state of charge of the gravity battery hybrid energy storage at time t+1; t SOC is the state of charge of the gravity battery hybrid energy storage at time t; min It is the minimum state of charge of gravity battery hybrid energy storage; SOC max It is the maximum state of charge of gravity battery hybrid energy storage; η c Charging efficiency for gravity-battery hybrid energy storage; η d is the discharge efficiency of gravity battery hybrid energy storage; E N It is the rated capacity of gravity-battery hybrid energy storage.

8. The method for optimizing configuration of gravity battery hybrid energy storage according to claim 1, characterized in that: With the goal of optimizing the operating economy of the wind-solar-storage system with gravity-battery hybrid energy storage, the particle swarm algorithm is used to solve the optimal configuration scheme of gravity-battery hybrid energy storage, including: Initialize the particle swarm algorithm, iteratively update the historical optimal position and global optimal position of the particle, as well as the speed and position of each particle, and output the optimal energy storage configuration plan, where the energy storage configuration plan includes the rated power and rated capacity of gravity energy storage and battery energy storage, as well as the optimized total cost.

9. The method for optimizing configuration of gravity battery hybrid energy storage according to claim 8, characterized in that: Iteratively update the historical optimal position and global optimal position of the particle as well as the speed and position of each particle, including: In each iteration, the rated capacity and rated power of the corresponding gravity battery hybrid energy storage are calculated according to the energy storage capacity and power parameters of the current particle; For each time period, the optimization constraints are determined, and the objective function of the mathematical model is defined as minimizing the operating cost of the gravity battery hybrid energy storage; based on the optimization constraints, the objective function of the mathematical model is optimized and solved, and the corresponding fitness is calculated. If the solution is successful, the total cost of the current particle is recorded. If the solution fails, the fitness is set to infinity; The fitness of each solution is compared with the fitness of the historical optimal position and the global optimal position, and the historical optimal position and the global optimal position of the particle as well as the speed and position of each particle are updated.

10. An optimized configuration device for gravity battery hybrid energy storage considering multiple scenarios, characterized in that: The optimization configuration method of gravity battery hybrid energy storage taking into account multiple scenarios as described in any one of claims 1 to 9 is adopted, and the optimization configuration device includes: A model building module, which is used to establish a mathematical model of gravity battery hybrid energy storage based on the operating characteristics of the wind-solar storage system containing gravity battery hybrid energy storage; The objective function building module is used to establish the objective function of the mathematical model based on the operation economy of gravity-battery hybrid energy storage and combined with the application scenarios of peak load regulation and fluctuation smoothing; A constraint condition determination module, which is used to provide the optimization constraint conditions of the gravity battery hybrid energy storage based on the operating characteristics of the wind-solar storage system containing the gravity battery hybrid energy storage; The optimization solution module is used to optimize the operating economy of the wind-solar-storage system containing gravity-battery hybrid energy storage, and use the particle swarm algorithm to solve and provide an optimal configuration plan for the gravity-battery hybrid energy storage.

Images