Configuration Method of Hydrogen-Electric Hybrid Energy Storage System for Power Transmission Network with High Proportion of Wind and Photovoltaic Access

Abstract

The present invention relates to the technical field of power transmission networks, and discloses a configuration method for a hydrogen-electric hybrid energy storage system in a power transmission network with a high proportion of wind and light access, including: S1, modeling according to the characteristics of the hydrogen-electric hybrid energy storage system, and establishing a hydrogen-electric HESS model and a wind-light output model; S2, designing a cooperation strategy for the hydrogen-electric HESS model according to the charging and discharging power of the hydrogen-electric hybrid energy storage system; S3, establishing a two-layer programming model of the hydrogen-electric HESS based on the wind-light output model and the hydrogen-electric HESS model, wherein the objective of the upper-layer model is to minimize the annual comprehensive cost of the power transmission network, and the objective of the lower-layer model is to minimize the wind and light curtailment rate; S4, finding the optimal solution of the lower-layer model corresponding to the premise that the objective of the upper-layer model has an optimal solution. The present invention improves the ability to temporally transfer the output of wind power and photovoltaic power, reduces the consumption of high-carbon fossil energy, and reduces the wind and light curtailment rate.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission networks, and particularly to a configuration method for a hydrogen - electricity hybrid energy storage system in a power transmission network with a high proportion of wind and photovoltaic power access. Background Technique

[0002] Currently, there is a problem of generally high wind and light abandonment rates in power transmission networks with a high proportion of wind and photovoltaic power access. Configuring an energy storage system, as a means with potential utility, has received much favor in current research. Therefore, in view of the problem of energy storage configuration in power transmission networks, in order to achieve the purpose of reducing the comprehensive cost and the wind and light abandonment rates, the present application proposes a configuration method for a hydrogen - electricity hybrid energy storage system in a power transmission network with a high proportion of wind and photovoltaic power access. Summary of the Invention

[0003] The present invention provides a configuration method for a hydrogen - electricity hybrid energy storage system in a power transmission network with a high proportion of wind and photovoltaic power access, which can configure a larger capacity and power at lower construction and maintenance costs, thereby improving the ability to temporally shift the output of wind power and photovoltaic power, reducing the consumption of high - carbon fossil energy, and lowering the wind and light abandonment rates.

[0004] The present invention is realized through the following technical solutions:

[0005] A configuration method for a hydrogen - electricity hybrid energy storage system in a power transmission network with a high proportion of wind and photovoltaic power access includes:

[0006] S1. Model according to the characteristics of the hydrogen - electricity hybrid energy storage system, and establish a hydrogen - electricity HESS model and a wind and light output model;

[0007] S2. Design the cooperation strategy of the hydrogen - electricity HESS model according to the charge and discharge power of the hydrogen - electricity hybrid energy storage system;

[0008] S3. Establish a two - layer programming model of the hydrogen - electricity HESS based on the wind and light output model and the hydrogen - electricity HESS model, wherein the objective of the upper - layer model is to minimize the annual comprehensive cost of the power transmission network, and the objective of the lower - layer model is to minimize the wind and light abandonment rate;

[0009] S4. Obtain the optimal solution of the lower - layer model corresponding to the premise that the objective of the upper - layer model has an optimal solution.

[0010] As an optimization, the hydrogen - electricity HESS model includes a battery energy storage model and a hydrogen energy storage model, wherein,

[0011] The battery energy storage model includes a battery energy storage charge - discharge model and battery energy storage constraint conditions;

[0012] The battery energy storage charge - discharge model is:

[0013]

[0014] Where: SOC i,t is the charge quantity of battery energy storage i at time t; SOC i，t+1 is the charge quantity of battery energy storage i at time t + 1; P cha,i,t , P dis,i,t are the charging and discharging powers of battery energy storage i respectively; η cha , η dis are the charging and discharging efficiencies of battery energy storage i respectively; I cha,i,t represents the charging state of battery energy storage i at time t, and its variables are 0 and 1 respectively. Among them, when I cha,i,t is 1, it means that battery energy storage i is charging, and when I cha,i,t is 0, it means that battery energy storage i is not charging; I dis,i,t represents the discharging state of battery energy storage i at time t, and its variables are 0 and 1 respectively. When I dis,i,t is 1, it means that battery energy storage i is discharging, and when I dis,i,t is 0, it means that battery energy storage i is not discharging; and the battery energy storage cannot charge and discharge simultaneously. Therefore, I cha,i,t I dis,i,t = 0; E BS,i,rate is the rated capacity of battery energy storage i; δ BS is the self-discharge rate of battery energy storage; Δt represents the time scale;

[0015] The constraint conditions of the battery energy storage are as follows:

[0016] SOC min ≤SOC i,t+1 ≤SOC max ;

[0017] SOC i,1 = SOC i,T ;

[0018] 0 ≤ P cha,i,t ≤ I cha,i,t P BS,i,rate ;

[0019] 0 ≤ P dis,i,t ≤ I dis,i,t P BS,i,rate ;

[0020] Where: SOC max , SOC min are the maximum and minimum charge quantities of battery energy storage i respectively; T is the configuration period; P BS,i,rate is the rated power of battery energy storage i, and SOC i，l represents the charge quantity at the first time, that is, the initial charge of the battery energy storage;

[0021] The hydrogen energy storage model includes a hydrogen energy storage charge-discharge model and hydrogen energy storage constraint conditions;

[0022] The hydrogen energy storage charge-discharge model is as follows:

[0023]

[0024] Where: P PEME,j,t 、P PEMFC,j,t are the charge-discharge powers of hydrogen energy storage j respectively; η PEME and η PEMFC are the charge and discharge efficiencies of hydrogen energy storage j respectively; is the equivalent charge quantity of hydrogen energy storage j at time t; is the equivalent charge quantity of hydrogen energy storage j at time t + 1, u PEME,j,t and u PEMFC,j,t are the start-stop state variables of PEME and PEMFC of hydrogen energy storage j at time t, 1 represents the start state, and 0 represents the stop state; is the rated capacity of hydrogen energy storage j, PEME is proton exchange membrane electrolyzer, and PEMFC is proton exchange membrane fuel cell;

[0025] The hydrogen energy storage constraint conditions are:

[0026]

[0027]

[0028]

[0029] (T PEMFC,on,j,t-1 -T PEMFC,on,min )(u PEMFC,j,t-1 -u PEMFC,j,t )≥0;

[0030] (T PEMFC,off,j,t-1 -T PEMFC,off,min )(u PEMFC,j,t -u PEMFC,j,t-1 )≥0;

[0031] (T PEME,on,j,t-1 -T PEME,on,min )(u PEME,j,t-1 -u PEME,j,t )≥0;

[0032] (T PEME,off,j,t-1 -T PEME,off,min )(u PEME,j,t -u PEME,j,t-1 )≥0;

[0033] u PEMFC,j,t +u PEME,j,t <2;

[0034] Wherein: is the rated power of the hydrogen energy storage j; is the minimum power when the hydrogen energy storage j is working; T PEME,on,j,t and T PEME,off,j,t are the continuous operation and outage times of the PEME of the hydrogen energy storage j at time t, respectively; T PEMFC,on,j,t and T PEMFC,off,j,t are the continuous operation and outage times of the PEMFC of the hydrogen energy storage j at time t, respectively; T PEME,on,min and T PEME,off,min are the minimum continuous operation time and minimum continuous outage time of the PEME, respectively; T PEMFC,on,min and T PEMFC,off,min are the minimum continuous operation time and minimum continuous outage time of the PEMFC, respectively, u PEME,j,t and u PEMFC,j,t are the start-stop state variables of the PEME and PEMFC of the hydrogen energy storage j at time t, respectively, u PEME，j，t-1 and u PEMFC，j，t-1 are the start-stop state variables of the PEME and PEMFC of the hydrogen energy storage j at time t-1, respectively. t-1 in the formula represents the continuous operation and outage time of the previous moment, and t represents the current moment.

[0035] As an optimization, the cooperation strategy of the hydrogen-electric HESS model is specifically as follows:

[0036] Mode 1: When the charge and discharge power of the hydrogen-electric HESS model is less than the mode switching power threshold P HESS,th , the hydrogen energy storage model is turned off and the battery energy storage model is turned on for charge and discharge;

[0037] Mode 2: When the charge and discharge power of the hydrogen-electric HESS model is greater than the mode switching power threshold P HESS,th , the hydrogen energy storage model starts the proton exchange membrane fuel cell or proton exchange membrane electrolyzer according to the positive and negative of the operating power.

[0038] As an optimization, the switching process between Mode 1 and Mode 2 is specifically as follows:

[0039] A1. Read the charge and discharge power P HESS，t of the hydrogen-electric HESS model and the current mode switching variable b. The current mode switching variable b is obtained by comparing the charge and discharge power P HESS，t of the hydrogen-electric HESS model with the mode switching power threshold P HESS,th ; P HESS，t is the charge and discharge power of the hydrogen-electric HESS model at time t;

[0040] A2. Switch the mode through the energy storage mode selection hysteresis loop;

[0041] A3. Switch the model according to the selected mode to achieve the switching of the hydrogen-electric HESS model.

[0042] As an optimization, in A2, the energy storage mode selection hysteresis is specifically:

[0043] When the charging and discharging power P of the hydrogen-electric HESS model HESS，t is greater than P HESS，th *ξ, the current mode switches from mode 1 to mode 2, where ξ is the return coefficient and its value is greater than 1; when the charging and discharging power P of the hydrogen-electric HESS model HESS，t is less than P HESS，th / ξ, the current mode switches from mode 2 to mode 1.

[0044] As an optimization, the wind-solar output model includes a wind power output model and a photovoltaic power output model, where

[0045] The wind power output model is:

[0046]

[0047] Where: is the ideal output value of wind farm m at time t; v m,t is the wind speed of wind farm m at time t; P r,m is the rated power of wind farm m; v c,m is the cut-in wind speed of wind farm m; v r,m is the rated wind speed of wind farm m; v f,m is the cut-out wind speed of wind farm m;

[0048] The photovoltaic power output model is:

[0049]

[0050] θ n =θ TEST [1 - φ(K n,t - K TEST )];

[0051] Where: is the ideal output value of photovoltaic station n at time t; L n,t is the solar irradiance of photovoltaic station n at time t; M n is the light-receiving area of photovoltaic station n; θ n is the power generation efficiency of photovoltaic station n; θ TEST is the conversion power under standard test conditions; φ is the power temperature coefficient; K n,t is the actual temperature of photovoltaic station n at time t; K TEST is the temperature under standard test conditions.

[0052] As an optimization, the objective function of the upper-layer model is:

[0053] minF1 = C I + C OM + C P + C L ;

[0054] where: F1 is the objective function of the upper-layer model, i.e., the annual comprehensive cost; C I is the equivalent annual value installation cost of the hydrogen-electric HESS model; C OM is the annual operation and maintenance cost of the hydrogen-electric HESS model; C P is the annual power generation and maintenance cost of each unit; C L is the annual network loss cost;

[0055] The constraint conditions of the upper-layer model include the capacity constraint and power constraint of the hydrogen-electric HESS model;

[0056] The capacity constraint of the hydrogen-electric HESS model is:

[0057] 0 ≤ E BS,i,rate ≤ E BS,max ;

[0058]

[0059] where: E BS,max , are the maximum capacities that can be built for battery energy storage i and hydrogen energy storage j at each node, respectively, E BS,i,rate is the rated capacity of battery energy storage i, is the rated capacity of hydrogen energy storage j;

[0060] The power constraint of the hydrogen-electric HESS model is:

[0061] 0 ≤ P BS,i,rate ≤ P BS,max ;

[0062]

[0063] where: P BS,max , are the maximum powers that can be built for battery energy storage i and hydrogen energy storage j at each node, respectively, P BS,i,rate is the rated power of battery energy storage i, is the rated power of hydrogen energy storage j;

[0064] The objective function of the lower-layer model is:

[0065]

[0066] Among them: F2 is the lower-layer objective function, i.e., the curtailment rate of wind and light, N W 、N PV are the numbers of wind farms and photovoltaic power stations respectively, is the ideal output value of wind farm m at time t, P W，m，t is the actual power output of the wind farm, P W，m，t is a variable, curtailment of wind = P 0 W，m，t -P W，m，t ; is the ideal output value of photovoltaic station n at time t, P PV，n，t is the actual power output of the photovoltaic power station, P PV，n，t is a variable, curtailment of light = P 0 PV，n，t -P PV，n，t ;

[0067] The constraint conditions of the lower-layer model include power balance constraint, node power balance constraint, node voltage limit constraint, branch capacity constraint, and reserve constraint;

[0068] The power balance constraint is:

[0069]

[0070] Among them: N is the number of nodes, P load,k,t is the load power of node k at time t, P G,l,t 、P W,m,t 、P PV,n,t are the actual power outputs of thermal power, wind power, and photovoltaic power respectively, N G 、N W 、N PV are the numbers of thermal power units, wind farms, and photovoltaic power stations respectively, P loss,t is the active power loss, P BS,t is the total power of battery energy storage at time t; is the total power of hydrogen energy storage at time t;

[0071] The node power balance constraint, node voltage limit constraint, and branch capacity constraint are:

[0072]

[0073] U i,min ≤U i,t ≤U i,max ;

[0074] S ij,t ≤S ij,max ;

[0075] Among them: Pi,t , Q i,t are the active and reactive power injections of node i at time t; U i,t , U j,t are the voltage magnitudes of node i and node j at time t; G ij , B ij are the conductance and susceptance of branch ij at time t; θ ij is the phase angle difference between the voltages of node i and node j; U i,max , U i,min are the upper and lower limits of the voltage magnitude of node i; S ij,t is the power value of branch ij at time t; S ij,max is the maximum transmission power of branch ij;

[0076] The reserve constraint is:

[0077]

[0078] Where: ΔP G,l,t is the reserve capacity that the thermal power plant l can provide at time t, ΔP W,m,t , ΔP PV,n,t , ΔP load,k,t are the prediction errors of wind power, photovoltaic power output and load at time t respectively, and the prediction errors are obtained by normal distribution, ΔP BS,t , are the reserve capacities that the battery energy storage and hydrogen energy storage can provide at time t respectively, P loss,t is the active power loss, and β is the confidence level.

[0079] As an optimization, the equivalent annual value installation cost C I of the hydrogen-electric HESS model is:

[0080]

[0081] Where,

[0082]

[0083]

[0084]

[0085]

[0086] Where: C I_BS is the equivalent annual value installation cost of the battery energy storage; is the equivalent annual value installation cost of the hydrogen energy storage; r is the discount rate; τ is the equipment life cycle; c I_BS,p , c I_BS,e , They are the investment cost per unit power of battery energy storage, the investment cost per unit capacity of battery energy storage, the investment cost per unit power of hydrogen energy storage model, and the investment cost per unit capacity of hydrogen energy storage; P BS,rate 、E BS,rate 、 They are the total rated power of battery energy storage, the total rated capacity of battery energy storage, the total rated power of hydrogen energy storage, and the total rated capacity of hydrogen energy storage; N BS 、 They are the quantities of battery energy storage and hydrogen energy storage respectively;

[0087] The annual operation and maintenance cost C OM of the hydrogen - electric HESS model is:

[0088]

[0089]

[0090]

[0091] Where: C OM_BS is the annual operation and maintenance cost of battery energy storage; The annual operation and maintenance cost of hydrogen energy storage; c OM_BS 、 They are the unit operation power costs of battery energy storage and hydrogen energy storage respectively; P BS,t is the total power of battery energy storage at time t; is the total power of hydrogen energy storage at time t;

[0092] The annual power generation and maintenance cost C P of each unit is:

[0093]

[0094] Where: C G 、C W 、C PV They are the annual power generation and maintenance costs of thermal power, wind power, and photovoltaic power respectively; N G 、N W 、N PV They are the quantities of thermal power units, wind farms, and photovoltaic power stations respectively; c G 、c W 、c PV They are the sums of the unit power generation and maintenance costs of thermal power, wind power, and photovoltaic power respectively; P G,l,t 、P W,m,t 、P PV,n,t They are the actual output powers of thermal power, wind power, and photovoltaic power respectively, and T is the total simulation time, i.e., the configuration time;

[0095] The annual network loss cost C L is:

[0096]

[0097] where: c loss is the unit network loss cost; P loss,t is the active power loss, T is the total simulation time, i.e., the configuration time.

[0098] As an optimization, the upper-layer model is solved using the particle swarm optimization algorithm. Among them, the upper-layer particles of the upper-layer model are: the variable L of whether to configure the hydrogen-electric HESS model at node i i , when L i =0, it means not to configure the hydrogen-electric HESS model at node i; when L i =1, it means to configure the hydrogen-electric HESS model at node i.

[0099] As an optimization, the lower-layer model is solved using the particle swarm optimization algorithm combined with power flow calculation. Among them, each lower-layer particle of the lower-layer model is the hydrogen-electric HESS model connected to each node. The lower-layer particles are the rated capacity and rated power of the hydrogen-electric HESS model, including: the rated capacity E BS,i,rate of the battery energy storage connected to each node, the rated power P BS,i,rate of the battery energy storage, the rated capacity of the hydrogen energy storage, and the rated power of the hydrogen energy storage. When the upper-layer particle L i =0, the rated capacity and rated power of the hydrogen-electric HESS model at node i are both 0.

[0100] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0101] 1. Since the ability of the energy storage system to transfer the output of wind power and photovoltaic power in time series is mainly limited by capacity and power, the hydrogen-electric HESS model proposed by the present invention can configure a larger capacity and power at a lower construction and maintenance cost than the traditional single energy storage system, thereby improving the ability to transfer the output of wind power and photovoltaic power in time series, reducing the consumption of high-carbon fossil energy, and reducing the curtailment rate of wind and light;

[0102] 2. According to the decrease in the cost of thermal power (i.e., the decrease in output) in the simulation table in the embodiment, and the decrease in the overall output curve of thermal power in FIGS. 8 and 9, it shows that the remaining capacity of thermal power increases at each moment and there is enough reserve to cope with load fluctuations. That is, the hydrogen-electric HESS model of the present invention can not only reduce the output of thermal power as a whole and improve the absorption of wind and light, but also reduce the overall output curve of thermal power through the analysis of typical days, making the standby of thermal power more sufficient and improving the stability of the system;

[0103] 3. The coordination strategy of the hydrogen-electric HESS model of the present invention can effectively avoid incorrect switching during fluctuations near the switching power threshold P HESS,th and enable the hydrogen-electric HESS model to operate in the optimal state. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] To clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0105] Figure 1 is the flowchart of the switching of the distribution strategy mode of the hydrogen-electric HESS model of the configuration method of the hydrogen-electric hybrid energy storage system with a high proportion of wind and light access to the transmission network according to the present invention;

[0106] Figure 2 is the comparison diagram of the energy storage duration and energy storage capacity of different energy storage forms in the configuration method of the hydrogen-electric hybrid energy storage system with a high proportion of wind and light access to the transmission network according to the present invention;

[0107] Figure 3 is the network topology structure diagram of the example in the embodiment;

[0108] Figure 4 is the measured data diagram of the ideal output of wind and light in 2019;

[0109] Figure 5 is Figure 4 the local load power per hour in

[0110] Figure 6 is Figure 4 the power of the exported load per hour in

[0111] Figure 7 is the monthly wind and light curtailment of each scenario;

[0112] Figure 8(a) is the operation result diagram of the spatio-temporal distribution of the operation conditions of nodes 1-30 in a typical day during the heating period without energy storage simulated by simulation software;

[0113] Figure 8(b) is the operation result diagram of the spatio-temporal distribution of the operation conditions of nodes 1-30 in a typical day during the heating period with a configured hydrogen-electric HESS model simulated by simulation software;

[0114] Figure 9(a) is the operation result diagram of the spatio-temporal distribution of the operation conditions of nodes 31-60 in a typical day during the non-heating period without energy storage simulated by simulation software;

[0115] Figure 9(b) is the operation result diagram of the spatio-temporal distribution of the operation conditions of nodes 31-60 in a typical day during the non-heating period of the hydrogen-electric HESS model simulated by simulation software;

[0116] Figure 10 It is the charge and discharge power curve of the hydrogen-electric HESS model on a certain day. Specific implementation manner

[0117] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments and drawings. The illustrative embodiments and descriptions thereof of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0118] A method for configuring a hydrogen-electric hybrid energy storage system for a power transmission network with a high proportion of wind and light access includes:

[0119] S1. Model according to the characteristics of the hydrogen-electric hybrid energy storage system, and establish a hydrogen-electric HESS model and a wind and light output model.

[0120] The battery energy storage model and the hydrogen energy storage model are two parts of the hydrogen-electric HESS model. Among them, the hydrogen energy storage model (hydrogen energy storage system) consists of a proton exchange membrane electrolyzer (PEME), a proton exchange membrane fuel cell (PEMFC), and a hydrogen storage tank (HS), and their functions are "charging", "discharging", and "storing hydrogen" respectively.

[0121] Next, a specific introduction to the hydrogen-electric HESS model will be given.

[0122] ① Battery energy storage model:

[0123] The battery energy storage model includes a battery energy storage charge and discharge model and battery energy storage constraint conditions.

[0124] Specifically, the battery energy storage charge and discharge model is:

[0125]

[0126] Among them: SOC i,t is the charge amount of the battery energy storage i at time t; SOC i，t+1 is the charge amount of the battery energy storage i at time t+1; P cha,i,t and P dis,i,t are the charge and discharge powers of the battery energy storage i respectively; η cha and η dis are the charge and discharge efficiencies of the battery energy storage i respectively; Icha,i,t Indicates the charging state of the battery energy storage i at time t, and its variables are 0 and 1 respectively. Among them, I cha,i,t When it is 1, it indicates that the battery energy storage i is charging, I cha,i,t When it is 0, it indicates that the battery energy storage i is not charging; I dis,i,t Indicates the discharging state of the battery energy storage i at time t, and its variables are 0 and 1 respectively, I dis,i,t When it is 1, it indicates that the battery energy storage i is discharging, I dis,i,t When it is 0, it indicates that the battery energy storage i is not discharging; and the battery energy storage cannot charge and discharge simultaneously. Therefore, I cha,i,t I dis,i,t = 0; E BS,i,rate Is the rated capacity of the battery energy storage i; δ BS Is the self-discharge rate of the battery energy storage; Δt represents the time scale;

[0127] The battery energy storage constraint conditions are:

[0128] SOC min ≤ SOC i,t+1 ≤ SOC max (2);

[0129] SOC i,1 = SOC i,T (3);

[0130] 0 ≤ P cha,i,t ≤ I cha,i,t P BS,i,rate (4);

[0131] 0 ≤ P dis,i,t ≤ I dis,i,t P BS,i,rate (5);

[0132] Among them: SOC max 、SOC min Are respectively the maximum and minimum charge amounts of the battery energy storage i; T is the configuration period; P BS,i,rate Is the rated power of the battery energy storage i, SOC i，l Represents the charge amount at the first time, that is, the initial charge of the battery energy storage.

[0133] The hydrogen energy storage model includes the hydrogen energy storage charge and discharge model and the hydrogen energy storage constraint conditions.

[0134] Specifically, the hydrogen energy storage charge and discharge model is:

[0135]

[0136] Among them: P PEME,j,t 、P PEMFC,j,tare the charging and discharging powers of hydrogen energy storage j; η PEME and η PEMFC are the charging and discharging efficiencies of hydrogen energy storage j respectively; is the equivalent charge quantity of hydrogen energy storage j at time t; is the equivalent charge quantity of hydrogen energy storage j at time t+1, u PEME,j,t and u PEMFC,j,t are the start-stop state variables of PEME and PEMFC of hydrogen energy storage j at time t respectively. When it is 1, it represents the start state, and when it is 0, it represents the stop state; is the rated capacity of hydrogen energy storage j, PEME is proton exchange membrane electrolyzer, and PEMFC is proton exchange membrane fuel cell;

[0137] The hydrogen energy storage constraint conditions are:

[0138]

[0139]

[0140]

[0141] (T PEMFC,on,j,t-1 -T PEMFC,on,min )(u PEMFC,j,t-1 -u PEMFC,j,t )≥0 (10);

[0142] (T PEMFC,off,j,t-1 -T PEMFC,off,min )(u PEMFC,j,t -u PEMFC,j,t-1 )≥0 (11);

[0143] (T PEME,on,j,t-1 -T PEME,on,min )(u PEME,j,t-1 -u PEME,j,t )≥0 (12);

[0144] (T PEME,off,j,t-1 -T PEME,off,min )(u PEME,j,t -u PEME,j,t-1 )≥0 (13);

[0145] u PEMFC,j,t +u PEME,j,t <2 (14);

[0146] Where: is the rated power of hydrogen energy storage j; is the minimum power when hydrogen energy storage j is working; T PEME,on,j,t and T PEME,off,j,t are the continuously operating and shutdown times of PEME of hydrogen energy storage j at time t respectively; T PEMFC,on,j,tand T PEMFC,off,j,t are the continuous operation time and outage time of the PEMFC of the hydrogen energy storage j at time t; T PEME,on,min and T PEME,off,min are the minimum continuous operation time and minimum continuous outage time of the PEME, respectively; T PEMFC,on,min and T PEMFC,off,min are the minimum continuous operation time and minimum continuous outage time of the PEMFC, respectively, u PEME,j,t and u PEMFC,j,t are the start-stop state variables of the PEME and PEMFC of the hydrogen energy storage j at time t, u PEME，j，t-1 and u PEMFC，j，t-1 are the start-stop state variables of the PEME and PEMFC of the hydrogen energy storage j at time t-1, respectively.

[0147] In this embodiment, the wind-solar power output model includes a wind power output model and a photovoltaic power output model. Among them,

[0148] The wind power output is positively correlated with the wind speed. The historical data of the wind speed v m,t is used to calculate the ideal wind power output. The specific wind power output model is:

[0149]

[0150] Where: is the ideal power output value of the wind farm m at time t; v m,t is the wind speed of the wind farm m at time t; P r,m is the rated power of the wind farm m; v c,m is the cut-in wind speed of the wind farm m; v r,m is the rated wind speed of the wind farm m; v f,m is the cut-out wind speed of the wind farm m;

[0151] The historical data of the solar irradiance L n,t is used to calculate the ideal photovoltaic power output. The specific photovoltaic power output model is:

[0152]

[0153] θ n = θ TEST [1 - φ(K n,t - K TEST )] (17);

[0154] Where: is the ideal power output value of the photovoltaic station n at time t; L n,t is the solar irradiance of the photovoltaic station n at time t; M n is the light-receiving area of the photovoltaic station n; θ n is the power generation efficiency of the photovoltaic station n; θ TESTis the conversion power under standard test conditions; φ is the power temperature coefficient; K n,t is the actual temperature of the photovoltaic station n at time t; K TEST is the temperature under standard test conditions.

[0155] S2. Design the cooperation strategy of the hydrogen-electric HESS model according to the charging and discharging power of the hydrogen-electric hybrid energy storage system. The hydrogen energy storage model has higher efficiency and lower comprehensive cost under high-power operating conditions. Therefore, the hydrogen energy storage model should be operated at a relatively high power.

[0156] As Figure 2 shown, the battery energy storage model and the hydrogen energy storage model are complementary in terms of energy storage duration and energy storage power; and because the power of the hydrogen energy storage model is positively correlated with the efficiency, after the power reaches a certain threshold, the comprehensive operating cost of the hydrogen energy storage model is lower than that of the battery energy storage model. Therefore, by combining the two energy storage models, a cooperation strategy of the hydrogen-electric HESS model is designed to achieve the optimal solution of the charging and discharging of the hydrogen-electric HESS model. Assume that the power when the comprehensive costs of the battery energy storage model and the hydrogen energy storage model are equal is the mode switching power threshold P HESS,th .

[0157] In this embodiment, the cooperation strategy of the hydrogen-electric HESS model is specifically as follows:

[0158] Mode 1: When the charging and discharging power of the hydrogen-electric HESS model is less than the mode switching power threshold P HESS,th , the hydrogen energy storage model is turned off and the battery energy storage model is turned on for charging and discharging;

[0159] Mode 2: When the charging and discharging power of the hydrogen-electric HESS model is greater than the mode switching power threshold P HESS,th , the hydrogen energy storage model starts the proton exchange membrane fuel cell or the proton exchange membrane electrolyzer according to the positive and negative of the operating power.

[0160] To prevent the charging and discharging power of the hydrogen-electric HESS model from fluctuating near the mode switching power threshold P HESS,th and causing the hydrogen-electric HESS model to repeatedly switch between two energy storage modes, a hysteresis loop for energy storage mode selection as Figure 1 shown is set.

[0161] In this embodiment, the switching process between Mode 1 and Mode 2 is specifically as follows:

[0162] A1. Read the charging and discharging power P HESS，t of the hydrogen-electric HESS model and the current mode switching variable b. The current mode switching variable b is based on the charging and discharging power P HESS，t of the hydrogen-electric HESS model and the mode switching power threshold PHESS,th Obtained by comparison; P HESS，t is the charging and discharging power of the hydrogen-electric HESS model at time t, b = 1 represents Mode 1, and b = 2 represents Mode 2;

[0163] A2. Switch the mode through the energy storage mode selection hysteresis loop;

[0164] A3. Switch the model according to the selected mode to achieve the switching of the hydrogen-electric HESS model.

[0165] In this embodiment, in A2, the energy storage mode selection hysteresis loop is specifically:

[0166] When the charging and discharging power P of the hydrogen-electric HESS model HESS，t is greater than P HESS，th *ξ, the current mode switches from Mode 1 to Mode 2, where ξ is the return coefficient and its value is greater than 1; when the charging and discharging power P of the hydrogen-electric HESS model HESS，t is less than P HESS，th / ξ, the current mode switches from Mode 2 to Mode 1.

[0167] The configuration optimization objectives of the present invention are the annual comprehensive cost and the wind and light abandonment rates. According to these two optimization objectives, a two-layer programming model of the hydrogen-electric HESS is established, that is,

[0168] S3. Based on the wind and light output model and the hydrogen-electric HESS model, establish a two-layer programming model of the hydrogen-electric HESS, where the objective of the upper-layer model is to minimize the annual comprehensive cost of the transmission grid, and the objective of the lower-layer model is to minimize the wind and light abandonment rates.

[0169] In the upper-layer model, the objective is to minimize the annual comprehensive cost of the transmission grid; the decision variable is the location where the hydrogen-electric HESS is connected; the constraint conditions include the capacity constraint of the hydrogen-electric HESS model and the power constraint of the hydrogen-electric HESS model.

[0170] In this embodiment, the objective function of the upper-layer model is:

[0171] minF1 = C I + C OM + C P + C L (18);

[0172] Where: F1 is the objective function of the upper-layer model, that is, the annual comprehensive cost; C I is the equivalent annual value installation cost of the hydrogen-electric HESS model; C OM is the annual operation and maintenance cost of the hydrogen-electric HESS model; C P is the annual power generation and maintenance cost of each unit; C L is the annual network loss cost;

[0173] In this embodiment, the equivalent annual installation cost C of the hydrogen-electric HESS model I is as follows:

[0174]

[0175] Among them,

[0176]

[0177]

[0178]

[0179]

[0180] Among them: C I_BS is the equivalent annual installation cost of battery energy storage; is the equivalent annual installation cost of hydrogen energy storage; r is the discount rate; τ is the equipment life cycle; c I_BS,p and c I_BS,e and are the unit power investment cost of battery energy storage, the unit capacity investment cost of battery energy storage, the unit power investment cost of hydrogen energy storage, and the unit capacity investment cost of hydrogen energy storage respectively; P BS,rate and E BS,rate and are the total rated power of battery energy storage, the total rated capacity of battery energy storage, the total rated power of hydrogen energy storage, and the total rated capacity of hydrogen energy storage respectively; N BS and are the quantities of battery energy storage and hydrogen energy storage respectively;

[0181] The annual operation and maintenance cost C of the hydrogen-electric HESS model OM is as follows:

[0182]

[0183]

[0184]

[0185] Among them: C OM_BS is the annual operation and maintenance cost of battery energy storage; is the annual operation and maintenance cost of hydrogen energy storage; c OM_BS and are the unit operation power costs of battery energy storage and hydrogen energy storage respectively; P BS,t is the total power of battery energy storage at time t; is the total power of hydrogen energy storage at time t;

[0186] The annual power generation and maintenance cost C of each unit P is as follows:

[0187]

[0188] Where: C G , C W , C PV are the annual power generation and maintenance costs of thermal power, wind power, and photovoltaic power respectively; N G , N W , N PV are the numbers of thermal power units, wind farms, and photovoltaic power stations respectively; c G , c W , c PV are the sums of the unit power generation and maintenance costs of thermal power, wind power, and photovoltaic power respectively; P G,l,t , P W,m,t , P PV,n,t are the actual output powers of thermal power, wind power, and photovoltaic power respectively. T is the total simulation time, i.e., the configuration time;

[0189] The annual network loss cost C L is as follows:

[0190]

[0191] Where: c loss is the unit network loss cost; P loss,t is the active power loss, and T is the total simulation time.

[0192] The capacity constraint of the hydrogen - electric HESS model is:

[0193] 0 ≤ E BS,i,rate ≤ E BS,max (29);

[0194]

[0195] Where: E BS,max , are the maximum capacities that can be built for battery energy storage and hydrogen energy storage at each node respectively, E BS,i,rate is the rated capacity of battery energy storage i, is the rated capacity of hydrogen energy storage j. Some nodes are connected to power stations, and some nodes are connected to loads;

[0196] The power constraint of the hydrogen - electric HESS model is:

[0197] 0 ≤ P BS,i,rate ≤ P BS,max (31);

[0198]

[0199] Where: P BS,max and are the maximum power that can be built by battery energy storage and hydrogen energy storage at each node respectively, P BS,i,rate is the rated power of battery energy storage i, is the rated power of hydrogen energy storage j.

[0200] In the lower-level model, the goal is to minimize the curtailment rate of wind and light; its decision variables are the rated capacity and rated power of grid-connected battery energy storage and hydrogen energy storage; its constraint conditions include power balance constraint, node power balance constraint, node voltage limit constraint, branch capacity constraint and reserve constraint.

[0201] The objective function of the lower-level model is as follows:

[0202]

[0203] Where: F2 is the lower-level objective function, that is, the curtailment rate of wind and light, N W and N PV are the numbers of wind farms and photovoltaic power stations respectively, is the ideal output value of wind farm m at time t, P W，m，t is the actual power output of the wind farm, P W，m，t is a variable, curtailment of wind = P0W,m,t - PW,m,t; is the ideal output value of photovoltaic station n at time t, PPV,n,t is the actual power output of the photovoltaic power station, P PV，n，t is a variable, curtailment of light = P 0 PV，n，t - P PV，n，t .

[0204] The power balance constraint is as follows:

[0205]

[0206] Where: N is the number of nodes, P load,k,t is the load power of node k at time t, P G,l,t and P W,m,t and P PV,n,t are the actual power outputs of thermal power, wind power and photovoltaic power respectively, N G and N W and N PV are the numbers of thermal power units, wind farms and photovoltaic power stations respectively, P loss,t is the active power loss, P BS,t is the total power of battery energy storage at time t; is the total power of hydrogen energy storage at time t;

[0207] The node power balance constraint, node voltage limit constraint, and branch capacity constraint are as follows:

[0208]

[0209] U i,min ≤U i,t ≤U i,max (36);

[0210] S ij,t ≤S ij,max (37);

[0211] Where: P i,t and Q i,t are the active and reactive power injections of node i at time t, respectively; U i,t and U j,t are the voltage amplitudes of node i and node j at time t, respectively; G ij and B ij are the conductance and susceptance of branch ij at time t, respectively; θ ij is the phase angle difference between the voltages of node i and node j; U i,max and U i,min are the upper and lower limits of the voltage amplitude of node i, respectively; S ij,t is the power value of branch ij at time t; S ij,max is the maximum transmission power of branch ij.

[0212] The reserve constraint is as follows:

[0213]

[0214] Where: ΔP G,l,t is the reserve capacity that thermal power plant l can provide at time t, ΔP W,m,t , ΔP PV,n,t , and ΔP load,k,t are the prediction errors of wind power, PV output, and load at time t, respectively. The prediction errors are obtained using a normal distribution. ΔP BS,t , are the reserve capacities that battery energy storage and hydrogen energy storage can provide at time t, respectively. P loss,t is the active power loss, and β is the confidence level. The role of the reserve constraint is that the power grid reserves enough capacity to cope with situations such as load fluctuations.

[0215] S4. Find the optimal solution of the corresponding lower-level model on the premise that the objective of the upper-level model has an optimal solution.

[0216] In the actual energy storage configuration problem, it is necessary to first consider the optimal economy, and then on this basis, consider the lowest curtailment rate of wind and light, that is, to obtain the lower-layer optimum under the premise of the upper-layer objective optimum. At the same time, considering that the optimal configuration of the transmission network is a non-linear multi-objective problem and it is difficult to obtain the global optimal solution, a two-layer iterative particle swarm optimization algorithm combined with power flow calculation is used for solution. The particle swarm optimization algorithm has the advantages of high calculation efficiency and simple implementation. Combining with the research object and operation control strategy of this patent, the particle swarm optimization algorithm is used to iteratively calculate the upper and lower layers of the optimal configuration of the transmission network to obtain the optimal solution.

[0217] Specifically, in this embodiment, the upper-layer model is solved by using the particle swarm optimization algorithm. Among them, the upper-layer particle of the upper-layer model is: the variable L of whether to configure the hydrogen-electric HESS model at node i i , when L i = 0, it means that the hydrogen-electric HESS model is not configured at node i; when L i = 1, it means that the hydrogen-electric HESS model is configured at node i; the lower-layer model is solved by using the particle swarm optimization algorithm combined with power flow calculation. Among them, each lower-layer particle of the lower-layer model is the hydrogen-electric HESS model connected to each node, and the lower-layer particle is the rated capacity and rated power of the hydrogen-electric HESS model, including: the rated capacity E of the battery energy storage connected to each node BS,i,rate , the rated power P of the battery energy storage BS,i,rate , the rated capacity of the hydrogen energy storage and the rated power of the hydrogen energy storage When the upper-layer particle L i = 0, the rated capacity and rated power of the hydrogen-electric HESS model at node i are both 0.

[0218] In the iterative process, the upper-layer particle needs to be input into the lower layer as a parameter to determine the initialization and update of the lower-layer particle at each node; the optimized results of the capacity and power of the lower layer need to be input into the upper layer to calculate the upper-layer objective, update the optimal value and fitness.

[0219] The specific implementation steps are as follows:

[0220] (1) Step 1, initialize the upper-layer particle swarm. According to the value range of the upper-layer planning decision variable, initialize the speed, position, individual optimal value and group optimal value of the particle swarm, and set the current iteration number iter1 = 0.

[0221] (2) Step 2, input the upper-layer particle into the lower-layer planning model as a parameter, and update the iteration number iter1 = iter1 + 1.

[0222] (3) Step 3, lower-layer optimization. The steps are as follows:

[0223] ① Initialize the lower-layer particle swarm. Based on the value ranges of the upper-layer particles and the lower-layer planning decision variables, initialize the velocities and positions of the lower-layer particles, initialize the individual optimal values and the population optimal values, and set the current iteration number iter2 = 0.

[0224] ② Calculate the fitness of the lower-layer particles. According to the lower-layer particle data, update the rated capacity and rated power of the hydrogen-electric HESS connected to each node in the power flow program of the power grid. Conduct power flow calculations to obtain the fitness of the lower-layer particle swarm.

[0225] ③ Update the individual optimal value, individual optimal fitness, population optimal value, and population optimal fitness of the lower-layer particle swarm. Compare the fitness of the particle swarm with the current corresponding individual optimal fitness in sequence to update the individual optimal value and individual optimal fitness. Then compare the individual optimal fitness with the current group optimal fitness in sequence to update the group optimal value and group optimal fitness.

[0226] ④ Update the lower-layer particle swarm. Update the velocities and positions of the lower-layer particles, and determine whether the updated values meet the conditions: if the velocities before and after the update are the same, multiply the current velocity by a random number between (0, 1); if the updated particles go out of bounds, make the out-of-bounds particles equal to the nearest boundary value. Update the iteration number iter2 = iter2 + 1.

[0227] ⑤ Determine the iteration number. Determine whether the condition iter2 < max iter2 is met. If it is met, return to ②; otherwise, use the current population optimal value and population optimal fitness as the optimization result and turn to step 4.

[0228] (4) Step 4, calculate the fitness of the upper-layer particles. According to the current population particle data, obtain the particle fitness.

[0229] (5) Step 5, update the upper-layer particle swarm. The same as step 3.

[0230] (6) Step 6, update the individual optimal value, individual optimal fitness, group optimal value, and group optimal fitness of the upper-layer particle swarm. The same as step 3.

[0231] (7) Step 7, determine the iteration number. Determine whether the condition iter1 < max iter1 is met. If it is met, return to step 2; otherwise, output the optimization result of the double-layer hydrogen-electric HESS configuration.

[0232] Next, verify through a numerical example whether the configuration method of the present invention can achieve the purpose of reducing the comprehensive cost and the curtailment rate of wind and solar power.

[0233] Embodiment

[0234] Description of the numerical example:

[0235] The network simulated in the example is a transmission network with 80 nodes in a certain area and a high proportion of wind power and photovoltaic access. The network topology is as follows: Figure 3 shown.

[0236] The grid structure for this example consists of 80 nodes, of which node 1 is a transmission node, 38 load nodes, 6 thermal power nodes, and 36 wind and photovoltaic nodes. The access nodes for each type of power station are shown in Table A1. The network has two voltage levels: 220kV and 500kV. Wind and photovoltaic output and load power data are based on actual measurements in the region in 2019. The total installed capacity of thermal power generation is 1600MW; the total installed capacity of wind power generation is 5075MW; and the total installed capacity of photovoltaic power generation is 194MW. Before the deployment of energy storage, the transmission load of the entire grid accounted for 48.23% of the total load, making it a typical power transmission grid.

[0237] Table A1 Access nodes of various types of power stations

[0238]

[0239]

[0240] like Figure 4 、 Figure 5 and Figure 6 As shown in the figure, the ideal wind and solar power output curve shows that spring and autumn are the peak seasons for wind and solar power generation, followed by winter, and the lowest in summer. The annual average wind and solar power output is 1769.74 MW. Local load is highest in summer and relatively evenly distributed across spring, autumn, and winter. The annual average local load is 763.34 MW. The trend of external load is similar to that of the ideal wind and solar power output, showing a positive correlation. The annual average external load is 711.14 MW.

[0241] The simulation parameters of the particle swarm algorithm are set as follows: the upper population size is 30 populations; the lower population size is 50 populations; the upper iteration number is 50; the lower iteration number is 100; the maximum inertia weight coefficient of the upper and lower layers is 0.6, the minimum inertia weight coefficient is 0.4, the confidence level is 0.9, the configuration period is one year, that is, 8760 hours, and the return coefficient ξ is 1.05.

[0242] In order to verify the effectiveness of the model and coordination strategy, four different scenarios were selected for comparison, and the energy storage configuration issues in different situations were analyzed.

[0243] 1. Configuration Scenario

[0244] Scenario 1: No energy storage.

[0245] Scenario 2: Only the battery energy storage model is configured. Configuring a single energy storage model does not require an energy storage coordination strategy.

[0246] Scenario 3: Only configure the hydrogen energy storage model. Similar to Scenario 2, no energy storage coordination strategy is required.

[0247] Scenario 4: Configure the hydrogen-electric HESS model and adopt the hydrogen-electric HESS coordination strategy proposed by the present invention.

[0248] II. Configuration Results of Each Scenario

[0249] The configuration results of the battery energy storage model in Scenario 2 are shown in Table A2, with 31 configuration nodes. The total rated capacity of the energy storage is 3423.48 MWh, and the total rated power is 699.91 MW / h.

[0250] Table A2 Configuration Results Table of Battery Energy Storage in Scenario 2

[0251]

[0252]

[0253]

[0254] The configuration results of Scenario 3 for the hydrogen energy storage model are shown in Table A3, with 12 configuration nodes. The total rated capacity of the energy storage is 2273.69 MWh, and the total rated power is 565.96 MW / h.

[0255] Table A3 Configuration Results Table of Hydrogen Energy Storage in Scenario 3

[0256]

[0257]

[0258] The configuration results of the hydrogen-electric HESS model in Scenario 4 are shown in Table A4, with 30 configuration nodes, among which 9 nodes are configured with hydrogen-electric HESS, and the other 21 nodes are only configured with battery energy storage. The total rated capacity of the energy storage is 3715.38 MWh, and the total rated power is 729.22 MW / h.

[0259] Table A4 Configuration Results Table of Hydrogen-Electric HESS in Scenario 4

[0260]

[0261]

[0262]

[0263]

[0264] The site selection results of Scenario 2 and Scenario 4 are generally the same; the site selection result of Scenario 3 is quite different from those of Scenario 2 and Scenario 4. This is mainly because the constraints on the minimum capacity and power of the hydrogen energy storage model prevent some nodes that require low-power energy storage from being configured, resulting in a relatively small total rated capacity and total rated power.

[0265] The costs of each scenario are shown in Table 1. Scenario 1 is compared with Scenario 2, Scenario 3, and Scenario 4 to analyze the impact of energy storage configuration on the transmission grid. The thermal power generation costs of Scenario 2, Scenario 3, and Scenario 4 are reduced by 37.81%, 18.54%, and 46.68% respectively compared with Scenario 1, and the annual comprehensive costs are reduced by 4.14%, 1.84%, and 4.78% respectively. This shows that after the installation of energy storage in the transmission grid, the output of wind power and photovoltaic power can be effectively shifted in time series, thus effectively reducing the consumption of high-carbon fossil energy. The wind and light curtailment rates corresponding to each scenario are shown in Table 2 and Figure 7 as follows. The wind and light curtailment rates of Scenario 2, Scenario 3, and Scenario 4 are reduced by 3.95%, 2.37%, and 5.48% respectively compared with Scenario 1. It can be concluded that the result of Scenario 4 is the best. After the installation of energy storage, the reduction of the wind and light curtailment rate is not much. However, the purpose of installing energy storage is not only to reduce the wind and light curtailment rate, but also to consider the utilization rate of energy storage, and configure energy storage under the condition of the lowest comprehensive cost.

[0266] The unit rated power cost of the hydrogen energy storage model is about twice that of the battery energy storage, and the unit capacity cost of the hydrogen storage tank in the hydrogen energy storage model is about 1 / 15 of that of the battery energy storage. Scenario 4 is compared with Scenario 2 and Scenario 3 to analyze the differences between the configurations of the single battery energy storage model and the single hydrogen energy storage model and the configuration of the hydrogen-electric HESS model. In Table 1, the thermal power generation costs of Scenario 4 are reduced by 14.26% and 34.54% respectively compared with Scenario 2 and Scenario 3, and the installation cost is also lower. At the same time, the total rated capacity and total rated power of the hydrogen-electric HESS in Scenario 4 are higher. In Table 2, the wind and light curtailment rates of Scenario 4 are reduced by 1.53% and 3.11% respectively compared with Scenario 2 and Scenario 3. In addition, due to the constraints on the minimum capacity and power of the hydrogen energy storage model installed in Scenario 3, the installation locations of hydrogen energy storage are fewer and the total installed capacity is smaller, so the ability to shift the output of wind power and photovoltaic power in time series is also weaker. To sum up, Scenario 4 configures higher capacity and power at a lower cost, can more effectively shift the output of wind power and photovoltaic power in time series, reduce the consumption of more high-carbon fossil energy, and further reduce the wind and light curtailment rate.

[0267] Table 1 Costs and investment recovery periods of each scenario

[0268]

[0269] Table 2 Wind and light curtailment rates and annual network losses of each scenario

[0270]

[0271] In addition, comparing the payback periods of the three scenarios in Table 1, the payback period of Scenario 2 is the longest and that of Scenario 4 is the shortest. Since this patent only considers the configuration cost of energy storage and does not consider the land and facility costs during the construction of energy storage, the payback periods of all scenarios are relatively short.

[0272] The annual network losses of each scenario in Table 2 vary little because the power grid in this area highly relies on power export, and the exported power accounts for nearly half of the generated power. Shifting the output of wind power and photovoltaic power in time sequence by energy storage cannot effectively reduce the power flowing through the line.

[0273] In summary, the hydrogen-electric HESS proposed in this patent can configure a larger energy storage at a lower comprehensive cost, significantly reduce the consumption of high-carbon fossil energy, and at the same time reduce the curtailment rates of wind and light.

[0274] III. Analysis of the operating conditions of the typical daily system

[0275] The heating period and the non-heating period in this area are both half a year. Therefore, typical days are selected for analysis during the heating period and the non-heating period respectively. The typical days in the heating period and the non-heating period are in the peak season and the off-season of wind power respectively. Figures 8 and 9 are the spatio-temporal distribution diagrams of the operating conditions of Nodes 1-30 on the typical day in the heating period and Nodes 31-60 on the typical day in the non-heating period respectively, where the load and energy storage charging are positive, and the thermal power, wind-solar power generation, and energy storage discharging are negative.

[0276] Comparing Figure 8(a) with (b), the hydrogen-electric HESS configured at Nodes 7, 14, and 29 is charged during the peak power generation period and discharged during the low power generation period; the thermal power generation at Nodes 3 and 16 has been significantly reduced, with a total reduction of 31.67%; the actual output of wind and solar power at Nodes 24, 25, and 26 has increased significantly, with a total increase of 9.35%. This shows that during the peak season of wind power, the hydrogen-electric HESS can effectively shift the output of wind power and photovoltaic power in time sequence, increase the consumption of wind and solar power, and reduce the consumption of fossil energy. It can be seen from the time aspect that the energy storage discharges from 1h to 9h, and at the same time the thermal power output decreases; the energy storage is charged from 10h to 16h, and at the same time the wind and solar power output increases. From the spatial aspect, for thermal power or wind and solar power, the energy storage with the nearest adjustment ability is used for adjustment.

[0277] Comparing Figure 9(a) with (b), the thermal power generation of nodes 34, 47, and 50 has been significantly reduced, with a total reduction of 29.34%; the hydrogen-electric HESS configured at nodes 46, 48, 49, and 51 is in the discharge state most of the time to reduce the thermal power output; at this time, it is the off-season of wind power, and all the wind and light output has been consumed. This shows that the hydrogen-electric HESS can effectively reduce the thermal power output, increase the grid reserve, and improve the system stability in the off-season of wind power. It can be seen from the time that the energy storage discharges from 1h to 5h, and at the same time, the thermal power output decreases significantly; the energy storage discharge slows down from 6h to 10h, and at the same time, the thermal power output decreases slightly. Spatially, for thermal power or wind and light, the nearest energy storage with adjustment ability is used for adjustment.

[0278] In summary, this section analyzes the typical daily operating conditions during the heating period and the non-heating period, indicating that configuring the hydrogen-electric HESS has a good effect on increasing the consumption of wind and light, reducing the consumption of high-carbon fossil energy, and improving the grid stability.

[0279] IV. Comparison of the cooperation strategies with and without the hydrogen-electric HESS model

[0280] According to the configuration results obtained from Scenario 4, the cooperation strategies with and without the hydrogen-electric HESS model are compared. The hydrogen-electric HESS model with a strategy uses the hydrogen-electric HESS cooperation strategy with a hysteresis loop proposed by the present invention to control the switching of the energy storage mode. The hydrogen-electric HESS without a strategy immediately switches the energy storage mode when the switching power reaches the threshold P HESS,th .

[0281] Figure 10 It is the charge and discharge power curve of the hydrogen-electric HESS model for a certain day. At 5h, the switching power of the hydrogen-electric HESS is lower than the threshold P HESS,th , but greater than P HESS,th / ξ. At this time, the hydrogen-electric HESS without a strategy performs a mode switch, while the hydrogen-electric HESS with a strategy does not. From 6h to 8h, the switching power is greater than P HESS,th ·ξ. However, the hydrogen-electric HESS without a strategy cannot start because the hydrogen energy storage does not meet the minimum stop time, resulting in the inability to switch the mode; at 9h, the hydrogen energy storage performs a mode switch after reaching the minimum stop time. At 10h, the switching power is less than P HESS,th / ξ. The hydrogen-electric HESS without a strategy cannot switch the mode because the hydrogen energy storage does not meet the minimum start time, while the hydrogen-electric HESS with a strategy performs a mode switch. The situation from 18h to 21h is similar to the previous one. Therefore, it can be seen that the charge and discharge results of the hydrogen-electric HESS with a strategy are better than those without a strategy.

[0282] In summary, it can be concluded that the hydrogen-electric HESS cooperation strategy can effectively avoid the switching power at the threshold P HESS,thError switching during nearby fluctuations enables the hydrogen-electric HESS to operate in the best state.

[0283] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A configuration method for a hydrogen-electric hybrid energy storage system with a high proportion of wind and light access to the transmission network, characterized in that, Including: S1. Model according to the characteristics of the hydrogen-electric hybrid energy storage system, and establish a hydrogen-electric HESS model and a wind-solar power output model; S2. Design the cooperation strategy of the hydrogen-electric HESS model according to the charge and discharge power of the hydrogen-electric hybrid energy storage system; S3. Establish a two-layer programming model of the hydrogen-electric HESS based on the wind-solar power output model and the hydrogen-electric HESS model. Among them, the goal of the upper-layer model is to minimize the annual comprehensive cost of the transmission grid, and the goal of the lower-layer model is to minimize the wind and light abandonment rate; The objective function of the upper-layer model is: minF1 = C I + C OM + C P + C L ; Among them: F1 is the objective function of the upper-layer model, i.e., the annual comprehensive cost; C I is the equivalent annual value installation cost of the hydrogen-electric HESS model; C OM is the annual operation and maintenance cost of the hydrogen-electric HESS model; C P is the annual power generation and maintenance cost of each unit; C L is the annual network loss cost; The objective function of the lower-layer model is: Where: F2 is the lower-layer objective function, i.e., the curtailment rate of wind and solar power, N W , N PV are the numbers of wind farms and photovoltaic power stations respectively, is the ideal output value of wind farm m at time t, P W,m,t is the actual power output of the wind farm, is the ideal output value of photovoltaic power station n at time t, P PV,n,t is the actual power output of the photovoltaic power station; S4. Obtain the optimal solution of the lower-layer model corresponding to the premise that the objective of the upper-layer model has an optimal solution.

2. The configuration method of the hydrogen-electric hybrid energy storage system for a power transmission network with a high proportion of wind and solar power access according to claim 1, wherein The hydrogen-electric HESS model includes a battery energy storage model and a hydrogen energy storage model. Among them, The battery energy storage model includes a battery energy storage charge and discharge model and battery energy storage constraint conditions; The battery energy storage charge and discharge model is: Where: SOC i,t is the charge quantity of the battery energy storage i at time t; SOC i,t+1 is the charge quantity of the battery energy storage i at time t + 1; P cha,i,t and P dis,i,t are the charge and discharge powers of the battery energy storage i respectively; η cha and η dis are the charge and discharge efficiencies of the battery energy storage i respectively; I cha,i,t represents the charging state of the battery energy storage i at time t, and its variables are 0 and 1 respectively. Among them, when I cha,i,t is 1, it means the battery energy storage i is charging, and when I cha,i,t is 0, it means the battery energy storage i is not charging; I dis,i,t represents the discharging state of the battery energy storage i at time t, and its variables are 0 and 1 respectively. When I dis,i,t is 1, it means the battery energy storage i is discharging, and when I dis,i,t is 0, it means the battery energy storage i is not discharging; and the battery energy storage cannot charge and discharge simultaneously. Therefore, I cha,i,t I dis,i,t = 0; E BS,i,rate is the rated capacity of the battery energy storage i; δ BS is the self-discharge rate of the battery energy storage; Δt represents the time scale; The battery energy storage constraint conditions are: SOC min ≤ SOC i,t+1 ≤ SOC max ; SOC i,1 = SOC i,T ; 0 ≤ P cha,i,t ≤ I cha,i,t P BS,i,rate ; 0 ≤ P dis,i,t ≤ I dis,i,t P BS,i,rate ; Where: SOC max and SOC min are the maximum and minimum charge amounts of the battery energy storage i respectively; T is the configuration period; P BS,i,rate is the rated power of the battery energy storage i, and SOC i,l represents the charge amount at the first time, that is, the initial charge of the battery energy storage; The hydrogen energy storage model includes a hydrogen energy storage charge and discharge model and hydrogen energy storage constraint conditions; The hydrogen energy storage charge and discharge model is: Where: P PEME,j,t , P PEMFC,j,t are the charge and discharge powers of hydrogen energy storage j respectively; η PEME and η PEMFC are the charge and discharge efficiencies of hydrogen energy storage j respectively; is the equivalent charge quantity of hydrogen energy storage j at time t; u PEME,j,t and u PEMFC,j,t are the start-stop state variables of PEME and PEMFC of hydrogen energy storage j at time t respectively. When it is 1, it represents the start state, and when it is 0, it represents the stop state; is the rated capacity of hydrogen energy storage j. PEME is proton exchange membrane electrolyzer, and PEMFC is proton exchange membrane fuel cell; The hydrogen energy storage constraint conditions are: (T PEMFC,on,j,t-1 -T PEMFC,on,min )(u PEMFC,j,t-1 -u PEMFC,j,t )≥0; (T PEMFC,off,j,t-1 -T PEMFC,off,min )(u PEMFC,j,t -u PEMFC,j,t-1 )≥0; (T PEME,on,j,t-1 -T PEME,on,min )(u PEME,j,t-1 -u PEME,j,t )≥0; (T PEME,off,j,t-1 -T PEME,off,min )(u PEME,j,t -u PEME,j,t-1 )≥0; u PEMFC,j,t +u PEME,j,t <2; Wherein: is the rated power of the hydrogen energy storage j; is the minimum power during the operation of the hydrogen energy storage j; T PEME,on,j,t and T PEME,off,j,t are the continuous operation and outage times of the PEME of the hydrogen energy storage j at time t, respectively; T PEMFC,on,j,t and T PEMFC,off,j,t are the continuous operation and outage times of the PEMFC of the hydrogen energy storage j at time t, respectively; T PEME,on,min and T PEME,off,min are the minimum continuous operation time and minimum continuous outage time of the PEME, respectively; T PEMFC,on,min and T PEMFC,off,min are the minimum continuous operation time and minimum continuous outage time of the PEMFC, respectively, u PEME,j,t and u PEMFC,j,t are the start-stop state variables of the PEME and PEMFC of the hydrogen energy storage j at time t, respectively, u PEME,j,t-1 and u PEMFC,j,t-1 are the start-stop state variables of the PEME and PEMFC of the hydrogen energy storage j at time t-1, respectively.

3. The method for configuring a hydrogen-electric hybrid energy storage system for a power transmission network with a high proportion of wind and solar power access according to claim 2, wherein, The cooperation strategy of the hydrogen-electric HESS model is specifically: Mode 1: When the charging and discharging power of the hydrogen-electric HESS model is less than the mode switching power threshold P HESS,th , the hydrogen energy storage model is turned off and the battery energy storage model is turned on for charging and discharging; Mode 2: When the charging and discharging power of the hydrogen-electric HESS model is greater than the mode switching power threshold P HESS,th , the hydrogen energy storage model starts the proton exchange membrane fuel cell or the proton exchange membrane electrolyzer according to the positive and negative of the operating power.

4. The configuration method of the hydrogen-electric hybrid energy storage system for a power transmission network with a high proportion of wind and solar power access according to claim 3, characterized in that, The switching process between Mode 1 and Mode 2 is specifically: A1. Read the charging and discharging power P of the hydrogen-electric HESS model HESS,t and the current mode switching variable b, where the current mode switching variable b is based on the charging and discharging power P of the hydrogen-electric HESS model HESS,t and the mode switching power threshold P HESS,th obtained by comparison; P HESS,t is the charging and discharging power of the hydrogen-electric HESS model at time t; A2. Switch the mode through the energy storage mode selection hysteresis loop; A3. Switch the model according to the selected mode to achieve the switching of the hydrogen-electric HESS model.

5. The configuration method of the hydrogen-electric hybrid energy storage system for a power transmission network with a high proportion of wind and solar power access according to claim 4, wherein In A2, the energy storage mode selection hysteresis loop is specifically: When the charging and discharging power P of the hydrogen-electric HESS model HESS,t is greater than P HESS,th *ξ, the current mode switches from mode 1 to mode 2, where ξ is the return coefficient with a value greater than 1; when the charging and discharging power P of the hydrogen-electric HESS model HESS,t is less than P HESS,th / ξ, the current mode switches from mode 2 to mode 1.

6. The configuration method of the hydrogen-electric hybrid energy storage system for a power transmission network with a high proportion of wind and light access according to claim 5, wherein, The wind-solar power output model includes a wind power output model and a photovoltaic power output model. Among them, The wind power output model is: Wherein: is the ideal output value of wind farm m at time t; v m,t is the wind speed of wind farm m at time t; P r,m is the rated power of wind farm m; v c,m is the cut-in wind speed of wind farm m; v r,m is the rated wind speed of wind farm m; v f,m is the cut-out wind speed of wind farm m; The photovoltaic power output model is: θ n = θ TEST [1 - φ(K n,t - K TEST )]; Wherein: is the ideal output value of the photovoltaic power station n at time t; L n,t is the solar irradiance of the photovoltaic power station n at time t; M n is the light-receiving area of the photovoltaic power station n; θ n is the power generation efficiency of the photovoltaic power station n; θ TEST is the conversion power under standard test conditions; φ is the power temperature coefficient; K n,t is the actual temperature of the photovoltaic power station n at time t; K TEST is the temperature under standard test conditions.

7. The method for configuring a hydrogen-electric hybrid energy storage system for a power transmission network with a high proportion of wind and solar power access according to claim 6, characterized in that The constraint conditions of the upper-layer model include the capacity constraint of the hydrogen-electric HESS model and the power constraint of the hydrogen-electric HESS model; The capacity constraint of the hydrogen-electric HESS model is: 0 ≤ E BS,i,rate ≤ E BS,max ; Where: E BS,max and are the maximum capacities that can be built for battery energy storage i and hydrogen energy storage j at each node, respectively. E BS,i,rate is the rated capacity of battery energy storage i, is the rated capacity of hydrogen energy storage j; The power constraint of the hydrogen-electric HESS model is: 0 ≤ P BS,i,rate ≤ P BS,max ; Where: P BS,max and are the maximum power that can be built by the battery energy storage i and the hydrogen energy storage j at each node, respectively. P BS,i,rate is the rated power of the battery energy storage i, is the rated power of the hydrogen energy storage j; The constraint conditions of the lower-layer model include power balance constraint, node power balance constraint, node voltage limit constraint, branch capacity constraint, and reserve constraint; The power balance constraint is: Where: N is the number of nodes, P load,k,t is the load power of node k at time t, P G,l,t , P W,m,t , P PV,n,t are the actual output powers of thermal power, wind power, and photovoltaic power respectively, N G , N W , N PV are the numbers of thermal power units, wind farms, and photovoltaic power stations respectively, P loss,t is the active power loss, P BS,t So the total power of the battery energy storage at time t; So the total power of the hydrogen energy storage at time t; The node power balance constraint, node voltage limit constraint, and branch capacity constraint are: U i,min ≤U i,t ≤U i,max ; S ij,t ≤S ij,max ; where: P i,t , Q i,t are the active and reactive power injections of node i at time t, respectively; U i,t , U j,t are the voltage magnitudes of node i and node j at time t, respectively; G ij , B ij are the conductance and susceptance of branch ij at time t, respectively; θ ij is the phase angle difference between the voltages of node i and node j; U i,max , U i,min are the upper and lower limits of the voltage magnitude of node i, respectively; S ij,t is the power value of branch ij at time t; S ij,max is the maximum transmission power of branch ij; The reserve constraint is: where: △P G,l,t is the reserve capacity that thermal power plant l can provide at time t, and △P W,m,t , △P PV,n,t , and △P load,k,t are the prediction errors of wind power, PV output, and load at time t respectively. The prediction errors are obtained using a normal distribution. △P BS,t , are the reserve capacities that battery energy storage and hydrogen energy storage can provide at time t respectively. P loss,t is the active power loss, and β is the confidence level.

8. The configuration method of the hydrogen-electric hybrid energy storage system for a power transmission network with a high proportion of wind and solar power access according to claim 7, characterized in that, The equivalent annual installation cost C of the hydrogen-electric HESS model I is as follows: Among them, Where: C I_BS is the equivalent annual installation cost of battery energy storage; is the equivalent annual installation cost of hydrogen energy storage; r is the discount rate; τ is the equipment life cycle; c I_BS,p and c I_BS,e and are the unit power investment cost of battery energy storage, the unit capacity investment cost of battery energy storage, the unit power investment cost of hydrogen energy storage, and the unit capacity investment cost of hydrogen energy storage respectively; P BS,rate and E BS,rate and are the total rated power of battery energy storage, the total rated capacity of battery energy storage, the total rated power of hydrogen energy storage, and the total rated capacity of hydrogen energy storage respectively; N BS and are the quantities of battery energy storage and hydrogen energy storage respectively; The annual operation and maintenance cost C of the hydrogen-electric HESS model OM is as follows: Among them: C OM_BS is the annual operation and maintenance cost of battery energy storage; is the annual operation and maintenance cost of hydrogen energy storage; c OM_BS and are the unit operation power costs of battery energy storage and hydrogen energy storage respectively; P BS,t is the total power of battery energy storage at time t; is the total power of hydrogen energy storage at time t; The annual power generation and maintenance cost C of each unit P is as follows: Among them: C G 、C W 、C PV are the annual power generation and maintenance costs of thermal power, wind power, and photovoltaic respectively; N G 、N W 、N PV are the numbers of thermal power units, wind farms, and photovoltaic power stations respectively; c G 、c W 、c PV are the sums of the unit power generation and maintenance costs of thermal power, wind power, and photovoltaic respectively; P G,l,t 、P W,m,t 、P PV,n,t are the actual output powers of thermal power, wind power, and photovoltaic respectively, and T is the total simulation time, i.e., the configuration time; The annual network loss cost C L is as follows: where: c loss is the unit network loss cost; P loss,t is the active power loss, and T is the total simulation time, i.e., the configuration time.

9. The configuration method of the hydrogen-electric hybrid energy storage system for a power transmission network with a high proportion of wind and solar power access according to claim 1, wherein The upper-layer model is solved using the particle swarm optimization algorithm. Among them, the upper-layer particles of the upper-layer model are: the variable L of whether to configure the hydrogen-electric HESS model at node i i , when L i = 0, it means that the hydrogen-electric HESS model is not configured at node i; when L i = 1, it means that the hydrogen-electric HESS model is configured at node i.

10. The method for configuring a hydrogen-electric hybrid energy storage system for a power transmission network with a high proportion of wind and solar power access according to claim 9, wherein, The lower-layer model is solved by using a particle swarm algorithm combined with power flow calculation. Among them, each lower-layer particle of the lower-layer model is a hydrogen-electric HESS model connected to each node, and the lower-layer particle is the rated capacity and rated power of the hydrogen-electric HESS model, including: the rated capacity E of the battery energy storage connected to each node BS,i,rate , the rated power P of the battery energy storage BS,i,rate , the rated capacity E of the hydrogen energy storage H2,i,rate and the rated power P of the hydrogen energy storage H2,i,rate . When the upper-layer particle L i = 0, the rated capacity and rated power of the hydrogen-electric HESS model at node i are both 0.

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