Electric power system optimal configuration method and system containing vertical matrix type gravity energy storage

Abstract

The invention discloses a power system optimal configuration method and system containing vertical matrix type gravity energy storage, and the method comprises the steps: constructing a power system optimal configuration model considering the collaborative optimization of a gravity energy storage system and an electrochemical energy storage system, and combining the gravity energy storage system with the electrochemical energy storage system to enhance the complementarity; the optimal configuration model of the power system aims to meet the maximum annual equivalent system net income of the vertical matrix type gravity energy storage system under the condition of wind and light absorption. And comprehensively considering constraint conditions of a vertical matrix type gravity energy storage system, an electrochemical energy storage system, a wind power station and a photovoltaic power station in the power system, and solving the model to obtain a power system optimal configuration scheme comprising configuration capacities of the vertical matrix type gravity energy storage system and the electrochemical energy storage system. According to the method, the gravity energy storage system and the electrochemical energy storage system are cooperatively considered, and the advantages of gravity energy storage and electrochemical energy storage can be exerted at the same time.

Description

Technical Field

[0001] The present invention belongs to the technical field of gravity energy storage, and in particular relates to a method and system for optimizing configuration of an electric power system containing vertical matrix gravity energy storage. Background Art

[0002] In recent years, the penetration rate of new energy in the power system has increased significantly, and the resulting inertia of the power system has decreased, which has brought great challenges to the stability of the power system. Reasonable planning of the capacity of wind farms, photovoltaic power stations and energy storage is a prerequisite for ensuring the economic efficiency and reliability of new power systems. The gravity energy storage matrix gravity energy storage system is a new type of gravity energy storage system control method proposed on the basis of tower crane gravity energy storage technology, which performs charging and discharging through the orderly lifting of mass blocks. Gravity energy storage has the advantages of simple principle, stable structure, wide installation site selection range, low investment cost, and environmental protection. At the same time, gravity energy storage also has the problem of high initial investment cost; electrochemical energy storage system has the functions of rapid response to power demand in a short time and smooth fluctuations, but it has the disadvantages of limited battery cycle times and high maintenance cost. Summary of the invention

[0003] In order to solve the problems existing in the prior art, the present invention proposes a method and system for optimizing the configuration of an electric power system containing vertical matrix-type gravity energy storage.

[0004] The technical solution of the present invention is as follows:

[0005] A method for optimizing the configuration of an electric power system containing vertical matrix gravity energy storage, wherein the electric power system is provided with a vertical matrix gravity energy storage system, an electrochemical energy storage system, a wind power station and a photovoltaic power station, the method comprising:

[0006] Construct an optimal configuration model for the power system considering the coordinated optimization of the gravity energy storage system and the electrochemical energy storage system. The optimal configuration model for the power system aims to maximize the annual equivalent system net benefit of the vertical matrix gravity energy storage system under the conditions of wind and solar power consumption, and takes system safety constraints, cleanliness constraints, vertical matrix gravity energy storage system operation constraints, electrochemical energy storage system operation constraints, wind power station operation constraints, and photovoltaic power station operation constraints as constraints, and takes the configuration capacity of the vertical matrix gravity energy storage system and the electrochemical energy storage system as decision variables;

[0007] Under the given annual output time series of wind power stations and photovoltaic power stations, the power system optimization configuration model is solved to obtain the power system optimization configuration scheme including the configuration capacity of the vertical matrix gravity energy storage system and the electrochemical energy storage system.

[0008] Furthermore, the maximum target C of the annual equivalent system net benefit of the vertical matrix energy storage power system is as follows:

[0009] maxC=I EB -C tz -C yw -C zj (1)

[0010] Where C represents the annual net income of the vertical matrix energy storage power system; I EB Represents the system's annual comprehensive income; C tz Represents the annual operation and maintenance cost of the system; C yw Represents the system operation and maintenance cost; C zj represents the system depreciation cost;

[0011] Among them, the system annual comprehensive income I EB for:

[0012]

[0013] Where λ EB represents the system electricity selling price; p L (t) is the system's external power transmission in period t; t is the period number, which is the number of optimized periods throughout the year; Δt is the length of the unit period.

[0014] System annual operation and maintenance cost C tz The calculation is as follows:

[0015]

[0016] In the formula, z represents the discount rate, m represents the system operation life; C tz,n , n = 1, 2, 3, 4 represent the investment costs of wind power station, photovoltaic power station, gravity energy storage system, and electrochemical energy storage system respectively, and the calculation method is as follows:

[0017]

[0018] In the formula, C W and λ W Respectively represent the installed capacity of wind power station and the unit installed investment cost; C V and λ V They represent the installed capacity of photovoltaic power stations and the unit installed investment cost respectively; C B and λ B1 They represent the installed capacity and unit installed investment cost of electrochemical energy storage system respectively; E B and λ B2 Respectively represent the battery capacity and unit capacity investment cost of vertical matrix gravity energy storage system; C G and λ GThey respectively represent the installed capacity and unit installed investment cost of the vertical matrix gravity energy storage system.

[0019] System operation and maintenance cost C yw and depreciation cost C zj The calculation is as follows:

[0020]

[0021] Where: yw represents the operation and maintenance cost coefficient; δ zj Represents the residual value coefficient.

[0022] Furthermore, the operation constraints of the vertical matrix energy storage system include power constraints, charge and discharge state constraints and charge state constraints; wherein the power constraints are:

[0023]

[0024] In the formula, p G,f (t), p G,c (t) represent the discharge power and charging power of the vertical matrix gravity energy storage system in time period t, u G,f (t),u G,c (t) respectively represent the discharge output state and charging output state of the vertical matrix gravity energy storage system in time period t, which is a 0-1 variable, C G is the gravity energy storage capacity;

[0025] The charge and discharge state constraints and the state of charge constraints are:

[0026] u G,f (t)+u G,c (t)≤1(9)

[0027] e G (t+1)=e G (t)+ζ G,c p G,c (t)-p G,f (t) / ζ G,f (10)

[0028] Where: e G (t) represents the storage capacity of the vertical matrix gravity energy storage system in time period t; G,c and G,f They represent the charging efficiency and discharging efficiency of the vertical matrix gravity energy storage system respectively.

[0029] Furthermore, the electrochemical energy storage system operation constraints include charge and discharge power range constraints, charge and discharge state constraints, and charge state constraints; wherein the charge and discharge power range constraints are:

[0030]

[0031] In the formula, p B,f (t), p B,c (t) represent the discharge power and charging power of the electrochemical energy storage system in time period t, u B,f (t),u B,c (t) respectively represent the discharge output state and charge output state of the electrochemical energy storage system in time period t, which are 0-1 variables, and C B The inverter capacity of the electrochemical energy storage power station;

[0032] The charge and discharge state constraints and charge state constraints of electrochemical energy storage are:

[0033] u B,f (t)+u B,c (t)≤1(12)

[0034] e B (t+1)=e B (t)+ζ B,c p B,c (t)-p B,f (t) / ζ B,f (13)

[0035] Where: e B (t) represents the amount of electricity stored in the electrochemical energy storage system during the period t; B,c and B,f They represent the charging efficiency and discharging efficiency of the energy storage power station respectively.

[0036] Furthermore, the wind power station operation constraints and photovoltaic power station operation constraints include wind power and photovoltaic power generation constraints, wind power and photovoltaic utilization constraints, and wind power and photovoltaic power abandonment state constraints; wherein the wind power and photovoltaic power generation constraints are:

[0037]

[0038] The utilization constraints of wind power and photovoltaic power are:

[0039]

[0040] In the formula, σ W (t) and σ V (t) represents the theoretical normalized output of the wind farm and photovoltaic power station in the time period t; Indicates the maximum power abandonment rate of wind power and photovoltaic power;

[0041] Wind power / photovoltaic power abandonment status constraints:

[0042] (1-x(t))·(p W (t)+pV (t))≤p W (t)+p V (t)≤(2-x(t))·(p W (t)+p V (t))-m(11)

[0043] In the formula, m represents a very small positive number.

[0044] Furthermore, the power system optimization configuration model is solved by using a particle swarm algorithm, and the solution process of the particle swarm algorithm is as follows:

[0045] Initialize the particle swarm, give the swarm size N, and randomly generate the position X of each particle i , speed V i ; i is

[0046] Calculate the fitness values ​​fitness1[i] and fitness2[i] of each particle for the objective functions C1 and C2 respectively;

[0047] Under the two objective functions C1 and C2, the individual extreme values ​​pbest[1,i] and pbest[2,i] are obtained for each particle respectively;

[0048] For the two objective functions C1 and C2, find two global extreme values ​​gbest[1] and gbest[2] respectively;

[0049] Calculate the mean agbest and distance dgbest of the two global extreme values ​​gbest[1] and gbest[2];

[0050] Calculate the distance dgbest[i] between each particle pbest[1,i] and pbest[2,i];

[0051] Calculate the update velocity V for each particle i and position X i The individual extreme value pbest[i] to be used when

[0052] Update the velocity V of each particle based on agbest and pbest[i] i and the position X of each particle i ;

[0053] The velocity V of each particle is updated by i and the position X of each particle i Get the new mean agbest of the two global vectors and the iteration of the individual extreme value pbest[i], and terminate the iteration with the iteration target.

[0054] Furthermore, the calculation method of the global extreme value is as follows:

[0055]

[0056] In the formula, i represents the i-th particle in the particle swarm, and n is the total size of the particle swarm;

[0057] The method to update the particle velocity is:

[0058] v in (t+1)=c1a1(p in -x in (t))+c2a2(p gn -x in (t)) (15)

[0059] In the formula, v in (t+1) represents the velocity of the particle in the nth dimension in the t+1th iteration; p in represents the position of the n-dimensional individual extreme point of particle i in the tth iteration; p gn It represents the position of all particles at the global extreme point of the nth dimension; a1 and a2 are the first and second random numbers between [0,1]; c1 and c2 are the first and second acceleration factors, respectively.

[0060] Method to update particle position:

[0061] x in (t+1)=x in (t)+v in (t+1) (16)

[0062] In the formula, x in (t) represents the position of particle i in the nth dimension in the tth iteration.

[0063] A power system optimization configuration system containing vertical matrix gravity energy storage, wherein the power system is provided with a vertical matrix gravity energy storage system, an electrochemical energy storage system, a wind power station and a photovoltaic power station, and comprises a model building module and a model solving module;

[0064] The model building module is used to construct an electric power system optimization configuration model that considers the coordinated optimization of the gravity energy storage system and the electrochemical energy storage system. The electric power system optimization configuration model aims to maximize the annual equivalent system net benefit of the vertical matrix gravity energy storage system under the situation of wind and solar power consumption, and takes system safety constraints, cleanliness constraints, vertical matrix gravity energy storage system operation constraints, electrochemical energy storage system operation constraints, wind power station operation constraints and photovoltaic power station operation constraints as constraints, and takes the configuration capacity of the vertical matrix gravity energy storage system and the electrochemical energy storage system as decision variables;

[0065] The model solving module is used to solve the power system optimization configuration model under the given wind power station and photovoltaic power station annual output time series, and obtain the power system optimization configuration plan including the configuration capacity of the vertical matrix gravity energy storage system and the electrochemical energy storage system.

[0066] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0067] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] The present invention proposes a method and system for optimizing the configuration of a power system containing vertical matrix gravity energy storage. The method constructs an optimal configuration model for a power system that considers the coordinated optimization of a gravity energy storage system and an electrochemical energy storage system. Combining gravity energy storage with an electrochemical energy storage system can enhance complementarity and significantly improve energy storage efficiency. The power system optimization configuration model aims to maximize the annual equivalent system net benefit of the vertical matrix gravity energy storage system under the conditions of wind and solar power consumption. It comprehensively considers the constraints related to the vertical matrix gravity energy storage system, electrochemical energy storage system, wind power station and photovoltaic power station in the power system. By solving the model, an optimal configuration scheme for the power system including the configuration capacity of the vertical matrix gravity energy storage system and the electrochemical energy storage system is obtained. The method considers the gravity energy storage system and the electrochemical energy storage system in a coordinated manner, and can give full play to the advantages of gravity energy storage and electrochemical energy storage at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 Schematic diagram of energy conversion for gravity energy storage;

[0071] Figure 2 This is the grid-connected topology diagram of the gravity energy storage system;

[0072] Figure 3 A flow chart of a method for optimizing configuration of a power system;

[0073] Figure 4 This is the REPS result diagram under the optimized configuration of shared energy storage in a northwestern province. DETAILED DESCRIPTION

[0074] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0075] According to the group standard "Technical Requirements for Synchronous Grid Connection of Gravity Energy Storage Systems", a gravity energy storage system is defined as a system that uses a motor to store energy by ascending discrete energy storage media, and drives the generator motor to generate electricity by descending the energy storage medium. It generally includes gravity energy storage media, transmission systems, generator motors and their auxiliary equipment. Vertical gravity energy storage is defined as a gravity system in which the trajectory of the energy storage medium is perpendicular to the horizontal plane. The energy conversion diagram of gravity energy storage is shown in the figure below. Figure 1 As shown, the grid-connected topology of the gravity energy storage system is as follows Figure 2 shown.

[0076] For the vertical gravity energy storage system during the energy storage process, the expression for the force balance of the heavy object is:

[0077] F g =mg+F μ

[0078] In the formula, F g Improve traction for the electric motor; F μ is the friction of the pulley system; m is the mass of the weight; g is the acceleration due to gravity.

[0079] The expression of the motor increasing traction power is:

[0080] P g =F g ·v g =(mg+F)·v g

[0081] Where: P g To increase the traction power of the vertical energy storage motor; g is the uniform rising speed of the weight.

[0082] When the gravity energy storage mode is in operation, the new energy power station or the upper power grid provides electricity to drive the traction motor of the high-altitude energy storage platform, lift the standard weight block on the low-altitude platform vertically at a uniform speed, and adjust the time taken by controlling the running speed through the fixed motor and transmission system to convert the electrical energy into high-quality potential mechanical energy. When there is a power shortage and the power generation mode is in operation, the high-altitude energy storage platform lowers the weight block, and the mechanical energy is generated by the gravity work during the uniform descent, which drives the generator on the high-altitude platform to convert the mechanical energy back into electrical energy to meet the electricity demand.

[0083] Embodiment 1:

[0084] The present invention provides an optimization configuration method for an electric power system containing vertical matrix gravity energy storage, wherein the electric power system is provided with a vertical matrix gravity energy storage system, an electrochemical energy storage system, a wind power station and a photovoltaic power station, such as Figure 3 As shown, the method comprises the following specific steps:

[0085] S1. Construct an optimal configuration model for the power system that considers the coordinated optimization of the gravity energy storage system and the electrochemical energy storage system. The optimal configuration model for the power system aims to maximize the annual equivalent system net benefit of the vertical matrix gravity energy storage system under the conditions of wind and solar power consumption, and takes system safety constraints, cleanliness constraints, vertical matrix gravity energy storage system operation constraints, electrochemical energy storage system operation constraints, wind power station operation constraints, and photovoltaic power station operation constraints as constraints, and takes the configuration capacity of the vertical matrix gravity energy storage system and the electrochemical energy storage system as decision variables;

[0086] S2. Given the annual output time series of wind power stations and photovoltaic power stations, the power system optimization configuration model is solved to obtain the power system optimization configuration plan including the configuration capacity of the vertical matrix gravity energy storage system and the electrochemical energy storage system.

[0087] Embodiment 2:

[0088] This embodiment is further designed on the basis of the first embodiment in that the maximum target C of the annual equivalent system net benefit of the vertical matrix energy storage power system in this embodiment is as follows:

[0089] maxC=I EB -C tz -C yw -C zj (1)

[0090] Where C represents the annual net income of the vertical matrix energy storage power system; I EB represents the system's annual comprehensive income; C tz Represents the annual operation and maintenance cost of the system; C yw Represents the system operation and maintenance cost; C zj represents the system depreciation cost;

[0091] Among them, the system annual comprehensive income I EB (i.e., electricity sales revenue) is:

[0092]

[0093] Where λ EB represents the system electricity selling price; p L (t) is the system's external power transmission in period t; t is the period number, which is the number of optimized periods throughout the year; Δt is the length of the unit period.

[0094] System annual operation and maintenance cost C tz The calculation is as follows:

[0095]

[0096] In the formula, z represents the discount rate, m represents the system operation life; C tz,n , n = 1, 2, 3, 4 represent the investment costs of wind power station, photovoltaic power station, gravity energy storage system, and electrochemical energy storage system respectively, and the calculation method is as follows:

[0097]

[0098] In the formula, C W and λ W Respectively represent the installed capacity of wind power station and the unit installed investment cost; C V and λ V They represent the installed capacity of photovoltaic power stations and the unit installed investment cost respectively; C B and λ B1 They represent the installed capacity and unit installed investment cost of electrochemical energy storage system respectively; E B and λ B2 Respectively represent the battery capacity and unit capacity investment cost of vertical matrix gravity energy storage system; C G and λ G They respectively represent the installed capacity and unit installed investment cost of the vertical matrix gravity energy storage system.

[0099] System operation and maintenance cost C yw and depreciation cost C zj The calculation is as follows:

[0100]

[0101] Where: yw represents the operation and maintenance cost coefficient, which is 2%; δ zj Represents the residual value coefficient, which is 10%.

[0102] Embodiment three:

[0103] This embodiment is further designed on the basis of the second embodiment in that the operation constraints of the vertical matrix energy storage system in this embodiment include power constraints, charge and discharge state constraints and charge state constraints; wherein the power constraints are:

[0104]

[0105] In the formula, p G,f (t), p G,c (t) represent the discharge power and charging power of the vertical matrix gravity energy storage system in time period t, u G,f (t),uG,c (t) respectively represent the discharge output state and charging output state of the vertical matrix gravity energy storage system in time period t, which is a 0-1 variable, C G is the gravity energy storage capacity;

[0106] The charge and discharge state constraints and the state of charge constraints are:

[0107] u G,f (t)+u G,c (t)≤1(9)

[0108] e G (t+1)=e G (t)+ζ G,c p G,c (t)-p G,f (t) / ζ G,f (10)

[0109] Where: e G (t) represents the storage capacity of the vertical matrix gravity energy storage system in period t, which is the optimization variable; ζ G,c and G,f They represent the charging efficiency and discharging efficiency of the vertical matrix gravity energy storage system respectively.

[0110] Embodiment 4:

[0111] This embodiment is further designed on the basis of the first embodiment in that the operation constraints of the electrochemical energy storage system in this embodiment include a charge and discharge power range constraint, a charge and discharge state constraint, and a charge state constraint; wherein the charge and discharge power range constraint is:

[0112]

[0113] In the formula, p B,f (t), p B,c (t) represent the discharge power and charging power of the electrochemical energy storage system in time period t, u B,f (t),u B,c (t) respectively represent the discharge output state and charge output state of the electrochemical energy storage system in time period t, which are 0-1 variables, and C B The inverter capacity of the electrochemical energy storage power station;

[0114] The charge and discharge state constraints and charge state constraints of electrochemical energy storage are:

[0115] u B,f (t)+u B,c (t)≤1 (12)

[0116] e B (t+1)=e B (t)+ζB,c p B,c (t)-p B,f (t) / ζ B,f (13)

[0117] Where: e B (t) represents the storage capacity of the electrochemical energy storage system in time period t, which is the optimization variable; B,c and B,f They represent the charging efficiency and discharging efficiency of the energy storage power station respectively, and are taken as 0.9.

[0118] Embodiment five:

[0119] This embodiment is further designed on the basis of the first embodiment in that the wind power station operation constraints and the photovoltaic power station operation constraints in this embodiment include wind power and photovoltaic power generation constraints, wind power and photovoltaic utilization constraints, and wind power and photovoltaic power abandonment state constraints; wherein the wind power and photovoltaic power generation constraints are:

[0120]

[0121] The utilization constraints of wind power and photovoltaic power are:

[0122]

[0123] In the formula, σ W (t) and σ V (t) represents the theoretical normalized output of the wind farm and photovoltaic power station in the time period t; Indicates the maximum power abandonment rate of wind power and photovoltaic power;

[0124] Wind power / photovoltaic power abandonment status constraints:

[0125] (1-x(t))·(p W (t)+p V (t))≤p W (t)+p V (t)≤(2-x(t))·(p W (t)+p V (t))-m (11)

[0126] In the formula, m represents a very small positive number.

[0127] After the addition of new energy stations, the active power output of new energy units has a greater impact on the distribution of line power flow. The active power flow of the line may be reshaped due to the fluctuation of new energy output. Constraining the active power flow of the line is a necessary measure to ensure the safe transmission of power. The safety constraints of the active power flow of the line are:

[0128] P t,b =∑P t,g +∑Pt,f,w -∑P t,l (12)

[0130] Where P t，b is the active power injected into node b at time t, P t，g is the active power of the thermal power unit at time t, P t，f，w is the active power of the new energy station node f at time t, P t,l is the load power of node j in the system at time t.

[0131] The system cleanliness constraint is:

[0132]

[0133] In the formula, E CR is the clean energy ratio, E C is the clean energy power generation, E T is the total power generation, E CRtar is the target value for the proportion of clean energy.

[0134] Embodiment six:

[0135] This embodiment is further designed on the basis of the first embodiment in that the particle swarm algorithm is used to solve the power system optimization configuration model in this embodiment. The solution process of the particle swarm algorithm is as follows:

[0136] Initialize the particle swarm, give the swarm size N, and randomly generate the position X of each particle i , speed V i ; i is

[0137] Calculate the fitness values ​​fitness1[i] and fitness2[i] of each particle for the objective functions C1 and C2 respectively;

[0138] Under the two objective functions C1 and C2, the individual extreme values ​​pbest[1,i] and pbest[2,i] are obtained for each particle respectively;

[0139] For the two objective functions C1 and C2, find two global extreme values ​​gbest[1] and gbest[2] respectively;

[0140] Calculate the mean agbest (the mean can be the average value or the weighted average value) and the distance dgbest of the two global extreme values ​​gbest[1] and gbest[2];

[0141] Calculate the distance dgbest[i] between each particle pbest[1,i] and pbest[2,i];

[0142] Calculate the update velocity V for each particle i and position X i The individual extreme value pbest[i] to be used when

[0143] Update the velocity V of each particle based on agbest and pbest[i] i and the position X of each particle i ;

[0144] The velocity V of each particle is updated by i and the position X of each particle i Obtain the new mean agbest of the two global vectors and the iteration of the individual extreme value pbest[i], and terminate the iteration with the iteration target. The iteration target is generally i>50.

[0145] Embodiment seven:

[0146] This embodiment is further designed on the basis of the sixth embodiment in that the calculation method of the global extreme value in this embodiment is as follows:

[0147]

[0148] In the formula, i represents the i-th particle in the particle swarm, and n is the total size of the particle swarm;

[0149] The method to update the particle velocity is:

[0150] v in (t+1)=c1a1(p in -x in (t))+c2a2(p gn -x in (t)) (15)

[0151] In the formula, v in (t+1) represents the velocity of the particle in the nth dimension in the t+1th iteration; p in represents the position of the n-dimensional individual extreme point of particle i in the tth iteration; p gn It represents the position of all particles at the global extreme point of the nth dimension; a1 and a2 are the first and second random numbers between [0,1]; c1 and c2 are the first and second acceleration factors, respectively.

[0152] Method to update particle position:

[0153] x in (t+1)=x in (t)+v in (t+1) (16)

[0154] In the formula, x in(t) represents the position of particle i in the nth dimension in the tth iteration.

[0155] Embodiment eight:

[0156] The present invention provides an electric power system optimization configuration system containing vertical matrix gravity energy storage, wherein the electric power system is provided with a vertical matrix gravity energy storage system, an electrochemical energy storage system, a wind power station and a photovoltaic power station, and comprises a model building module and a model solving module;

[0157] The model building module is used to construct an optimal configuration model of the power system considering the coordinated optimization of the gravity energy storage system and the electrochemical energy storage system. The optimal configuration model of the power system aims to maximize the annual equivalent system net benefit of the vertical matrix gravity energy storage system under the situation of wind and solar power consumption, and takes system safety constraints, cleanliness constraints, vertical matrix gravity energy storage system operation constraints, electrochemical energy storage system operation constraints, wind power station operation constraints and photovoltaic power station operation constraints as constraints, and takes the configuration capacity of the vertical matrix gravity energy storage system and the electrochemical energy storage system as decision variables;

[0158] The model solving module is used to solve the power system optimization configuration model under the given wind power station and photovoltaic power station annual output time series, and obtain the power system optimization configuration plan including the configuration capacity of the vertical matrix gravity energy storage system and the electrochemical energy storage system.

[0159] Embodiment nine:

[0160] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method described in any of the above embodiments.

[0161] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0162] Application examples:

[0163] This example adopts the method of the present invention to conduct simulation tests on the power grid system of a province in northwest my country. The system has a wind power installed capacity of 123,750MW, a photovoltaic installed capacity of 31,500MW, and annual wind / light utilization hours of 1,970h and 1,800h respectively. The system consumption rate is set to be no less than 95%, the cost of electrochemical energy storage is 0.51 yuan / kWh, the cost of gravity energy storage is 0.47 yuan / kWh, the installed capacity of electrochemical energy storage is 3,500MW, and the installed capacity of compressed air energy storage is 6,000MW.

[0164] Based on the above boundary conditions, the energy storage configuration with the lowest cost of the shared energy storage system is calculated through the 8760h time-series production simulation throughout the year: electrochemical energy storage 6000MW / 6000WMh; gravity energy storage 3274.2MW / 19645.2MWh. At this time, the cost of hybrid energy storage is 34.3194 billion yuan.

[0165] In order to verify the above results, the new energy production simulation software (REPS) was used to model and simulate the shared energy storage system. Under the above boundary conditions, referring to the forecast of various power installed capacity in a northwestern province in 2025, pumped storage power stations, thermal power units, hydropower units, nuclear power plants and other modules were added, and different energy storage power station capacity configurations were imported for simulation calculation, and the system absorption rate and cost were observed. The calculation results are shown in Table 1. Figure 4 This is the result report output interface of the REPS software under the optimal configuration.

[0166] Table 1. Hybrid energy storage configuration capacity, cost and consumption rate simulation results

[0167]

[0168] Through Table 1 and Figure 4 It can be seen that in the results calculated by the new energy production simulation software, gravity energy storage 3274.2MW / 19645.2MWh; electrochemical energy storage 6000MW / 6000WMh are the lowest cost optimization configurations, and the new energy consumption rate is above 95%, which verifies the correctness of the calculation method.

[0169] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing the configuration of an electric power system containing vertical matrix gravity energy storage, wherein the electric power system is provided with a vertical matrix gravity energy storage system, an electrochemical energy storage system, a wind power station and a photovoltaic power station, characterized in that: The method comprises: Construct an optimal configuration model for the power system considering the coordinated optimization of the gravity energy storage system and the electrochemical energy storage system. The optimal configuration model for the power system aims to maximize the annual equivalent system net benefit of the vertical matrix gravity energy storage system under the conditions of wind and solar power consumption, and takes system safety constraints, cleanliness constraints, vertical matrix gravity energy storage system operation constraints, electrochemical energy storage system operation constraints, wind power station operation constraints, and photovoltaic power station operation constraints as constraints, and takes the configuration capacity of the vertical matrix gravity energy storage system and the electrochemical energy storage system as decision variables; Under the given annual output time series of wind power stations and photovoltaic power stations, the power system optimization configuration model is solved to obtain the power system optimization configuration scheme including the configuration capacity of the vertical matrix gravity energy storage system and the electrochemical energy storage system.

2. The method for optimizing the configuration of a power system containing vertical matrix gravity energy storage according to claim 1, characterized in that: The maximum annual equivalent system net benefit target C of the vertical matrix energy storage power system is as follows: maxC=I EB -C tz -C yw -C zj (1) Where C represents the annual net income of the vertical matrix energy storage power system; I EB represents the system's annual comprehensive income; C tz Represents the annual operation and maintenance cost of the system; C yw Represents the system operation and maintenance cost; C zj represents the system depreciation cost; Among them, the system annual comprehensive income I EB for: Where λ EB represents the system electricity selling price; p L (t) is the system's external power delivery in time period t; t is the time period number, which is the number of optimized time periods throughout the year; Δt is the unit time period length; System annual operation and maintenance cost C tz The calculation is as follows: In the formula, z represents the discount rate, m represents the system operation life; C tz,n , n = 1, 2, 3, 4 represent the investment costs of wind power station, photovoltaic power station, gravity energy storage system, and electrochemical energy storage system respectively, and the calculation method is as follows: In the formula, C W and λ W Respectively represent the installed capacity of wind power station and the unit installed investment cost; C V and λ V Respectively represent the installed capacity of photovoltaic power stations and the unit installed investment cost; C B and λ B1 They represent the installed capacity and unit installed investment cost of electrochemical energy storage system respectively; E B and λ B2 Respectively represent the battery capacity and unit capacity investment cost of vertical matrix gravity energy storage system; C G and λ G They represent the installed capacity and unit installed investment cost of the vertical matrix gravity energy storage system respectively; System operation and maintenance cost C yw and depreciation cost C zj The calculation is as follows: Where: yw represents the operation and maintenance cost coefficient; δ zj Represents the residual value coefficient.

3. The method for optimizing the configuration of a power system containing vertical matrix gravity energy storage according to claim 1, characterized in that: The operation constraints of the vertical matrix energy storage system include power constraints, charge and discharge state constraints and charge state constraints; wherein the power constraints are: In the formula, p G,f (t), p G,c (t) represent the discharge power and charging power of the vertical matrix gravity energy storage system in time period t, u G,f (t),u G,c (t) respectively represent the discharge output state and charging output state of the vertical matrix gravity energy storage system in time period t, which is a 0-1 variable, C G is the gravity energy storage capacity; The charge and discharge state constraints and charge state constraints are: u G,f (t)+u G,c (t)≤1(9) e G (t+1)=e G (t)+ζ G,c p G,c (t)-p G,f (t) / ζ G,f (10) Where: e G (t) represents the storage capacity of the vertical matrix gravity energy storage system in time period t; G,c and G,f They represent the charging efficiency and discharging efficiency of the vertical matrix gravity energy storage system respectively.

4. The method for optimizing the configuration of a power system containing vertical matrix gravity energy storage according to claim 1, characterized in that: The electrochemical energy storage system operation constraints include charge and discharge power range constraints, charge and discharge state constraints, and charge state constraints; wherein the charge and discharge power range constraints are: In the formula, p B,f (t), p B,c (t) represent the discharge power and charging power of the electrochemical energy storage system in time period t, u B,f (t),u B,c (t) respectively represent the discharge output state and charge output state of the electrochemical energy storage system in time period t, which are 0-1 variables, and C B The inverter capacity of the electrochemical energy storage power station; The charge and discharge state constraints and charge state constraints of electrochemical energy storage are: u B,f (t)+u B,c (t)≤1(12) e B (t+1)=e B (t)+ζ B,c p B,c (t)-p B,f (t) / ζ B,f (13) Where: e B (t) represents the storage capacity of the electrochemical energy storage system in time period t; B,c and B,f They represent the charging efficiency and discharging efficiency of the energy storage power station respectively.

5. The method for optimizing the configuration of a power system containing vertical matrix gravity energy storage according to claim 1, characterized in that: The wind power station operation constraints and photovoltaic power station operation constraints include wind power and photovoltaic power generation constraints, wind power and photovoltaic utilization constraints, and wind power and photovoltaic power abandonment state constraints; wherein the wind power and photovoltaic power generation constraints are: The utilization constraints of wind power and photovoltaic power are: In the formula, σ W (t) and σ V (t) represents the theoretical normalized output of the wind farm and photovoltaic power station in the time period t; Indicates the maximum power abandonment rate of wind power and photovoltaic power; Wind power / photovoltaic power abandonment status constraints: (1-x(t))·(p W (t)+p V (t))≤p W (t)+p V (t)≤(2-x(t))·(p W (t)+p V (t))-m(11)In the formula, m represents a very small positive number.

6. The method for optimizing the configuration of a power system containing vertical matrix gravity energy storage according to claim 1, characterized in that: The particle swarm algorithm is used to solve the power system optimization configuration model. The solution process of the particle swarm algorithm is as follows: Initialize the particle swarm, give the swarm size N, and randomly generate the position X of each particle i , speed V i ; i is Calculate the fitness values ​​fitness1[i] and fitness2[i] of each particle for the objective functions C1 and C2 respectively; Under the two objective functions C1 and C2, the individual extreme values ​​pbest[1,i] and pbest[2,i] are obtained for each particle respectively; For the two objective functions C1 and C2, find two global extreme values ​​gbest[1] and gbest[2] respectively; Calculate the mean agbest and distance dgbest of the two global extreme values ​​gbest[1] and gbest[2]; Calculate the distance dgbest[i] between each particle pbest[1,i] and pbest[2,i]; Calculate the update velocity V for each particle i and position X i The individual extreme value pbest[i] to be used when Update the velocity V of each particle based on agbest and pbest[i] i and the position X of each particle i ; The velocity V of each particle is updated by i and the position X of each particle i Get the new mean agbest of the two global vectors and the iteration of the individual extreme value pbest[i], and terminate the iteration with the iteration target.

7. The method for optimizing the configuration of a power system containing vertical matrix gravity energy storage according to claim 6, characterized in that: The calculation method of the global extreme value is as follows: In the formula, i represents the i-th particle in the particle swarm, and n is the total size of the particle swarm; The method to update the particle velocity is: v in (t+1)=c1a1(p in -x in (t))+c2a2(p gn -x in (t)) (15) In the formula, v in (t+1) represents the velocity of the particle in the nth dimension in the t+1th iteration; p in represents the position of the n-dimensional individual extreme point of particle i in the tth iteration; p gn It represents the position of all particles at the global extreme point of the nth dimension; a1 and a2 are the first and second random numbers between [0,1]; c1 and c2 are the first and second acceleration factors respectively; Method to update particle position: x in (t+1)=x in (t)+v in (t+1) (16) In the formula, x in (t) represents the position of particle i in the nth dimension in the tth iteration.

8. An electric power system optimization configuration system containing vertical matrix gravity energy storage, wherein the electric power system is provided with a vertical matrix gravity energy storage system, an electrochemical energy storage system, a wind power station and a photovoltaic power station, characterized in that: It includes a model building module and a model solving module; The model building module is used to construct an electric power system optimization configuration model that considers the coordinated optimization of the gravity energy storage system and the electrochemical energy storage system. The electric power system optimization configuration model aims to maximize the annual equivalent system net benefit of the vertical matrix gravity energy storage system under the situation of wind and solar power consumption, and takes system safety constraints, cleanliness constraints, vertical matrix gravity energy storage system operation constraints, electrochemical energy storage system operation constraints, wind power station operation constraints and photovoltaic power station operation constraints as constraints, and takes the configuration capacity of the vertical matrix gravity energy storage system and the electrochemical energy storage system as decision variables; The model solving module is used to solve the power system optimization configuration model under the given wind power station and photovoltaic power station annual output time series, and obtain the power system optimization configuration plan including the configuration capacity of the vertical matrix gravity energy storage system and the electrochemical energy storage system.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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