A method and device for joint optimization of multi-element energy storage operation control and site selection and capacity determination

Abstract

The present invention discloses a method for joint optimization of multi-element energy storage operation regulation and site selection and sizing and a device for implementing the method, and relates to the technical field of power planning and dispatching. It includes: constructing a joint optimization framework for joint operation regulation and site selection and sizing of multi-element energy storage; constructing a model for the working principle of multi-element energy storage; and constructing a joint optimization model for operation regulation and site selection and sizing of multi-element energy storage. The present invention can deeply explore the correlation mechanism between energy storage dispatching and energy storage planning, and formulate reasonable multi-element energy storage site selection and sizing configuration schemes and operation dispatching strategies from the two levels of planning and operation, thereby realizing the joint optimization of energy storage operation regulation and site selection and sizing, guiding the development of the regional energy storage industry, and providing guidance for regional energy storage construction in terms of site selection and sizing and operation dispatching, providing support for the development of future energy storage technology and the energy storage industry, as well as the construction of an integrated energy base of wind, solar, thermal, and nuclear storage.

Description

Technical Field

[0001] The invention relates to the technical field of electric power planning and dispatching. Background Art

[0002] With the continuous advancement of the "carbon peak and carbon neutrality" goals and the continuous deepening of the construction of new power systems, technologies such as new energy, electric vehicles, flexible loads, and smart buildings have made significant progress in recent years. While various emerging technologies are promoting energy transformation and power system reform, they are also increasing the uncertainty of the power grid on both the source and load sides. In view of the continuous transformation trend of the power system towards low carbonization and cleanness, the proportion of clean energy installed, mainly wind turbines and photovoltaics, will be further increased and gradually become the main form of power supply in the power grid; and load terminal power replacement technologies represented by coal-to-electricity will also be further promoted. The simultaneous reform of both the source and load sides has also derived three major energy problems: First, the relationship between the continuous growth of energy demand and the requirements of clean and low-carbon energy, while also effectively dealing with the volatility and intermittency of new energy; second, the relationship between the diversity of application scenarios and the diversification of technical routes; and third, the relationship between energy security and reliability and economic feasibility. Therefore, how to further ensure the safety and reliability of power grid operation while ensuring the sustainable transformation of energy is an important issue that needs to be solved urgently.

[0003] Energy storage is an important emerging technology. It can adjust the energy structure and generate new forms of energy utilization. It can further transform the traditional source-grid-load power supply mode into a source-grid-load-storage coordinated interactive mode. At the same time, it also has the advantages of smoothing the volatility of new energy output, diverse technical routes, and adapting to a variety of application scenarios. It has received more and more attention in recent years. At present, the research on energy storage technology is mainly focused on the working principle of energy storage, energy storage operation and control, and energy storage planning and configuration. However, the current research on energy storage operation and planning is still in the stage of independent and separate research, and the correlation between scheduling and planning technology in time and space is not considered. Control technology can provide safety constraint verification, operation cost analysis and other information for energy storage planning and configuration by formulating short-time scale operation plans; while planning technology can provide equipment component information for energy storage operation and control by formulating long-time scale configuration plans, so as to provide power grid operators with operation and control analysis. Operation and planning are optimized through alternating optimization, so as to jointly achieve the optimization of energy storage configuration under the premise of ensuring the safety of power grid operation. Summary of the invention

[0004] The purpose of the present invention is to make up for the deficiencies and defects of the existing technology and to provide a method for joint optimization of multi-energy storage operation control and site selection and capacity determination and a device for implementing the method.

[0005] The technical solution adopted by the present invention to achieve the above-mentioned purpose is: a method for joint optimization of multi-element energy storage operation control and site selection and capacity determination, comprising the following steps:

[0006] S100, build a joint optimization framework for multi-energy storage joint operation and control and site selection and capacity determination;

[0007] S200, construct a multi-element energy storage working principle model;

[0008] S300, construct a joint optimization model for multi-energy storage operation control and site selection and capacity determination.

[0009] In step S100, the multi-energy storage includes pumped storage, compressed air energy storage, hydrogen energy storage, liquid flow battery energy storage, and load-side distributed energy storage. The power source is a large wind farm and a photovoltaic power station. The power source side uses pumped storage and compressed air energy storage as power source supporting support, the grid side uses hydrogen energy storage and liquid flow battery energy storage as operation support, and the load side uses load-side distributed energy storage.

[0010] The multi-element energy storage working principle model in S200 includes:

[0011] S201. The working principle model of pumped storage is represented by formula (1), which includes two main principle steps: power generation and pumping. From the perspective of operating power, the specific expression of the working principle model of pumped storage is as follows:

[0012] ; (1)

[0013] In formula (1): is the operating power of pumped storage a at time t; , are respectively the generating power and pumping power of pumped storage a at time t;

[0014] At the same time, the relationship between the pumped storage operating power and the reservoir capacity is expressed as follows:

[0015] ; (2)

[0016] In formula (2): is the storage capacity of the upper reservoir of pumped storage a at time t; , are the conversion coefficients between the average water volume and electricity volume of pumped storage a in the pumping and power generation states, respectively; , are the generating power and pumping power of pumped storage a at time t-1 respectively; is the scheduling time interval;

[0017] S202. The working principle model of compressed air energy storage is represented by formula (3), which includes two main principle steps: compression and expansion. From the perspective of operating power, the specific expression of the working principle model of compressed air energy storage in the compression stage is as follows:

[0018] ; (3)

[0019] In formula (3): is the compression power consumed by the compressed air energy storage b at time t; is the constant pressure specific heat capacity of air; is the mass of air compressed by the compressed air energy storage b at time t; is the intake temperature of the compressed air energy storage b; is the rated compression ratio of compressed air energy storage b; β is the specific heat ratio of ideal air;

[0020] From the perspective of operating power, the working principle model of compressed air energy storage in the expansion stage is shown in formula (4):

[0021] ; (4)

[0022] In formula (4): is the compression power consumed by the compressed air energy storage b at time t; is the constant pressure specific heat capacity of air; is the mass of air compressed by the compressed air energy storage b at time t; is the intake temperature of the compressed air energy storage b; is the rated compression ratio of compressed air energy storage b; β is the specific heat ratio of ideal air; is the expansion efficiency of compressed air energy storage b;

[0023] S203. The working principle model of hydrogen energy storage is represented by formula (5). When the power output of the power supply is greater than the load demand of the power grid, the excess power is consumed by the electrolyzer, hydrogen is produced by electrolyzing water, and the hydrogen is stored in the hydrogen storage tank. When the power output of the power supply is less than the load demand of the power grid, the hydrogen fuel cell will use hydrogen and oxygen as raw materials to produce electricity by chemical reaction to meet the load demand. Therefore, the working process of hydrogen energy storage mainly includes three main links: electrolyzer, fuel cell and hydrogen storage tank. From the perspective of operating power, the specific expression of the working principle model of hydrogen energy storage in the electrolyzer link is as follows:

[0024] ; (5)

[0025] In formula (5), is the output power of the hydrogen energy storage c electrolyzer at time t; is the input power of the hydrogen energy storage c electrolyzer at time t; is the efficiency of the hydrogen storage c electrolyzer;

[0026] From the perspective of operating power, the working principle model of hydrogen energy storage in the fuel cell link is shown in formula (6):

[0027] ; (6)

[0028] In formula (6), is the output power of the hydrogen energy storage c fuel cell at time t; is the input power of the hydrogen energy storage c fuel cell at time t; for hydrogen energy storage c fuel cell efficiency;

[0029] From the perspective of operating power, the working principle model of hydrogen energy storage in the hydrogen storage tank link is shown in equations (7) and (8). When the hydrogen storage tank of hydrogen energy storage stores hydrogen:

[0030] ; (7)

[0031] When the hydrogen storage tank of hydrogen energy storage discharges hydrogen:

[0032] ; (8)

[0033] In formula (7) and formula (8), E c,t is the capacity of the hydrogen storage tank at time t; is the output power of the hydrogen energy storage c fuel cell at time t; is the efficiency of the hydrogen storage c electrolyzer; For hydrogen energy storage c the storage efficiency of the hydrogen storage tank; is the scheduling time interval;

[0034] S204. The working principle model of liquid flow battery energy storage is shown in formula (9). All chemical reactions are carried out in solution. After the reaction is completed, the solution can be pumped out of the battery by a pump. At the same time, a new solution to be reacted is injected into the battery from the storage tank. From the perspective of operating power, the amount of liquid flow battery energy storage is related to factors such as charge and discharge power, charge and discharge efficiency, and self-discharge rate. The specific expression of the working principle model is as follows:

[0035] E d , t = ( 1 − η d ) E d , t − 1 + [ η out, d lc P out, d , t lc + η in, d lc P in, d , t lc ] Δ t ; (9)

[0036] In formula (9), E d,t is the capacity of the flow battery energy storage d at time t; is the output power of the flow battery energy storage d at time t; is the input power of the flow battery energy storage d at time t; and They are the discharge and charge efficiencies of flow battery energy storage d respectively; is the self-discharge loss efficiency of the flow battery energy storage d;

[0037] S205. The load-side distributed energy storage working principle model is represented by formula (9). The energy status of electric vehicle batteries is usually measured by ampere-hour method, as shown in formula (10):

[0038] ; (10)

[0039] In formula (10), E f,t is the power of electric car f at time t; E f,0 is the initial charge of the electric vehicle f; E S is the rated capacity of the electric vehicle battery; η f is the average charge and discharge efficiency of the electric vehicle; I is the current flowing through the battery of the electric vehicle. From the perspective of operating power, the relationship between the power of the electric vehicle f and the charge and discharge power is shown in formula (11):

[0040] ; (11)

[0041] In formula (11), E f,t is the power of electric vehicle f at time t; and are the charging power obtained by electric vehicle f from the grid and the discharging power of electric vehicle f to the grid respectively; and are the efficiency coefficients of charging and discharging of electric vehicles f, respectively; is the amount of electricity consumed by electric vehicle f during driving at time t.

[0042] The multi-element energy storage operation control and site selection and capacity determination joint optimization model in S300 includes two levels of optimization models: the first is the optimization at the planning level; the second is the optimization at the scheduling level.

[0043] include:

[0044] S301. Objective function. The planning and operation levels involved in the joint optimization model for multi-energy storage operation regulation and site selection and capacity determination are both aimed at optimizing the renewable energy consumption rate. The objective function of the joint optimization model for multi-energy storage operation regulation and site selection and capacity determination is shown in formula (12):

[0045] ; (12)

[0046] In formula (12), is the renewable energy consumption rate of the planned area, and its specific calculation method is shown in formula (13):

[0047] ; (13)

[0048] In formula (13), is the power generation of the new energy power station k at time t; is the amount of electricity consumed by the new energy power station k at time t; t0 is the start time of scheduling; K is the total number of new energy power stations in the area to be planned; T is the end time of scheduling; is the pumping power of pumped storage a at time t; is the compression power consumed by the compressed air energy storage b at time t; is the input power of the hydrogen energy storage c electrolyzer at time t; is the input power of the flow battery energy storage d at time t; is the charging power obtained by the electric vehicle f from the power grid;

[0049] S302, constraints. Based on the objective function of the multi-energy storage operation control and site selection and capacity joint optimization model constructed in S301, the constructed multi-energy storage operation control and site selection and capacity joint optimization model should also meet the main constraints such as power balance. Among them, the power balance constraint in the planned area is shown in formula (14):

[0050] ; (14)

[0051] In formula (14), , are respectively the generating power and pumping power of pumped storage a at time t; is the compression power consumed by the compressed air energy storage b at time t; is the compression power consumed by the compressed air energy storage b at time t; is the output power of the hydrogen energy storage c fuel cell at time t; is the input power of the hydrogen energy storage c fuel cell at time t; is the output power of the flow battery energy storage d at time t; is the input power of the flow battery energy storage d at time t; and are the charging power obtained by electric vehicle f from the grid and the discharging power of electric vehicle f to the grid respectively; is the power load demand of the power grid at time t. A, B, C, D, and F are the number of pumped storage, compressed air energy storage, hydrogen energy storage, flow battery energy storage, and load-side distributed energy storage in the area to be planned; L is the number of load points in the area to be planned.

[0052] In step S300, a quantum genetic algorithm is used to solve and calculate different levels of operation and planning involved in the model.

[0053] A device for realizing a joint optimization method for multi-element energy storage operation control and site selection and capacity determination, characterized in that it comprises:

[0054] A multi-energy storage joint operation control and site selection and capacity determination joint optimization framework construction module 210 is used to clarify the area to be planned, input the power load demand of the area to be planned, and input the installed capacity of new energy generators in the area to be planned;

[0055] A multi-energy storage working principle model building module 220 is used to clarify the capacity, output constraints and other technical parameters of different types of energy storage in the planned area;

[0056] The multi-energy storage operation control and site selection and capacity determination joint optimization model construction module 230 is used to solve the problems of energy storage type selection, site selection and capacity determination, and scheduling operation strategy in the planning area.

[0057] The system also includes a terminal device, which includes a processor 300 and a first memory 301, a second memory 302 and a third memory 303 connected to the processor.

[0058] The first memory 301 is used to store the program code of the joint operation control and site selection and capacity joint optimization framework modeling of the multi-energy storage, and transmit the program code to the processor 300;

[0059] The second memory 302 is used to store the program code constructed by the multivariate energy storage working principle model and transmit the program code to the processor 300;

[0060] The third memory 303 is used to store program codes for constructing a joint optimization model for multi-element energy storage operation control and site selection and capacity determination, and transmit the program codes to the processor 300;

[0061] The processor 300 is used to execute the steps in the above-mentioned multi-element energy storage operation control and site selection and capacity joint optimization method according to the instructions in the above-mentioned program code.

[0062] The device of the present invention realizes the joint optimization method of multi-energy storage operation regulation and site selection and sizing, deeply explores the correlation mechanism between energy storage scheduling and energy storage planning through typical energy storage technologies such as pumped storage, compressed air energy storage, hydrogen energy storage, liquid flow battery energy storage, and load-side distributed energy storage, and formulates reasonable multi-energy storage site selection and sizing configuration plans and operation scheduling strategies from both planning and operation levels, thereby realizing the joint optimization of energy storage operation regulation and site selection and sizing, guiding the development of the regional energy storage industry, and providing support for the development of future energy storage technology and the construction of an integrated energy base of wind, solar, thermal and nuclear storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of a joint optimization method for multi-energy storage operation control and site selection and capacity determination according to the present invention.

[0064] Figure 2 To invent the schematic diagram of the device for joint optimization of multi-energy storage operation control and site selection and capacity determination.

[0065] Figure 3 It is a schematic diagram of the computer terminal device of the present invention.

[0066] Figure 4 This is a schematic diagram of the layout of an urban new energy project according to an embodiment of the present invention.

[0067] Figure 5 This is a schematic diagram of the urban energy storage development plan of the present invention. DETAILED DESCRIPTION

[0068] like Figure 1 As shown, a multi-element energy storage operation control and site selection and capacity determination joint optimization method of the present invention is as follows Figure 1 As shown, the following steps are included:

[0069] S100, build a joint optimization framework for multi-energy storage joint operation and control and site selection and capacity determination;

[0070] S200, construct a multi-element energy storage working principle model;

[0071] S300, construct a joint optimization model for multi-energy storage operation control and site selection and capacity determination.

[0072] The following are specific instructions:

[0073] S100. The joint optimization framework for joint operation control and site selection and capacity determination of multiple energy storage constructed by the present invention refers to comprehensive consideration of factors such as technology maturity, applicable scenarios, and response time. The constructed planning area includes multiple energy storage equipment such as pumped storage, compressed air storage, hydrogen storage, flow battery storage, and load-side distributed storage. Among them, the power source considers clean energy power sources such as large wind farms and photovoltaic power stations; the power supply side considers pumped storage and compressed air storage as power supply supporting; the grid side considers hydrogen storage and flow battery storage as operation support; the load side considers load-side distributed storage. Through synergy and complementarity, multiple energy storage can further improve the safety and efficiency of grid operation while reducing grid planning and operation costs.

[0074] S200. The multi-energy storage working principle model constructed by the present invention refers to the construction of a multi-energy storage joint operation control and site selection and capacity joint optimization framework. In the steps of constructing the multi-energy storage working principle model, five types of energy storage equipment, namely pumped storage, compressed air energy storage, hydrogen energy storage, flow battery energy storage, and load-side distributed energy storage, are mainly considered. The operation mechanism of multi-energy storage, the relationship between the energy storage capacity and the energy storage operation power, and the factors affecting the energy storage charging and discharging are specifically modeled and analyzed, including:

[0075] Pumped storage working principle model. As shown in formula (1), the pumped storage working process considered in the present invention mainly includes two main principle steps: power generation and pumping. From the perspective of operating power, the specific expression of the pumped storage working principle model is as follows:

[0076] ; (1)

[0077] In formula (1): is the operating power of pumped storage a at time t; , are the generating power and pumping power of pumped storage a at time t respectively.

[0078] At the same time, the working principle model of pumped storage should also consider the relationship between the operating power of pumped storage and the storage capacity of the reservoir. The specific expression is shown in formula (2):

[0079] ; (2)

[0080] In formula (2): is the storage capacity of the upper reservoir of pumped storage a at time t; , are the conversion coefficients between the average water volume and electricity volume of pumped storage a in the pumping and power generation states, respectively; , are the generating power and pumping power of pumped storage a at time t-1 respectively; The scheduling time interval.

[0081] Compressed air energy storage working principle model. As shown in formula (3), the compressed air energy storage working process considered by the present invention mainly includes two main principle steps of compression and expansion. From the perspective of operating power, the specific expression of the working principle model of compressed air energy storage in the compression stage is as follows:

[0082] ; (3)

[0083] In formula (3): is the compression power consumed by the compressed air energy storage b at time t; is the constant pressure specific heat capacity of air; is the mass of air compressed by the compressed air energy storage b at time t; is the intake temperature of the compressed air energy storage b; is the rated compression ratio of compressed air energy storage b; β is the specific heat ratio of ideal air.

[0084] From the perspective of operating power, the working principle model of compressed air energy storage in the expansion stage is shown in formula (4):

[0085] ; (4)

[0086] In formula (4): is the compression power consumed by the compressed air energy storage b at time t; is the constant pressure specific heat capacity of air; is the mass of air compressed by the compressed air energy storage b at time t; is the intake temperature of the compressed air energy storage b; is the rated compression ratio of compressed air energy storage b; β is the specific heat ratio of ideal air; is the expansion efficiency of compressed air energy storage b.

[0087] The working principle model of hydrogen energy storage is represented by formula (5). The working process of hydrogen energy storage considered in the present invention is that when the power output of the power supply is greater than the load demand of the power grid, the excess power is consumed by the electrolyzer, hydrogen is produced by electrolyzing water, and the hydrogen is stored in the hydrogen storage tank; when the power output of the power supply is less than the load demand of the power grid, the hydrogen fuel cell will use hydrogen and oxygen as raw materials to produce electricity by chemical reaction to meet the load demand. Therefore, the working process of hydrogen energy storage mainly includes three main links: electrolyzer, fuel cell and hydrogen storage tank. From the perspective of operating power, the specific expression of the working principle model of hydrogen energy storage in the electrolyzer link is as follows:

[0088] ; (5)

[0089] In formula (5), is the output power of the hydrogen energy storage c electrolyzer at time t; is the input power of the hydrogen energy storage c electrolyzer at time t; is the efficiency of the electrolyzer for hydrogen storage c.

[0090] From the perspective of operating power, the working principle model of hydrogen energy storage in the fuel cell link is shown in formula (6):

[0091] ; (6)

[0092] In formula (6), is the output power of the hydrogen energy storage c fuel cell at time t; is the input power of the hydrogen energy storage c fuel cell at time t; The efficiency of the fuel cell for hydrogen energy storage c.

[0093] Taking gaseous hydrogen storage as an example, from the perspective of operating power, the working principle model of hydrogen energy storage in the hydrogen storage tank link is shown in equations (7) and (8). When the hydrogen storage tank of hydrogen energy storage stores hydrogen:

[0094] ; (7)

[0095] When the hydrogen storage tank of hydrogen energy storage discharges hydrogen:

[0096] ; (8)

[0097] In formula (7) and formula (8), E c,t is the capacity of the hydrogen storage tank at time t; is the output power of the hydrogen energy storage c fuel cell at time t; is the efficiency of the hydrogen storage c electrolyzer; For hydrogen energy storage c the storage efficiency of the hydrogen storage tank; The scheduling time interval.

[0098] The working principle model of liquid flow battery energy storage is expressed as formula (9). The liquid flow battery energy storage considered in the present invention belongs to a secondary energy storage battery processing technology that stores active chemical substances in liquid electrolyte. All chemical reactions are carried out in solution. After the reaction is completed, the solution can be pumped out of the battery by a pump, and the new solution to be reacted is injected into the battery from the storage tank. From the perspective of operating power, the storage capacity of liquid flow battery energy storage is related to factors such as charging and discharging power, charging and discharging efficiency, and self-discharge rate. The specific expression of the working principle model is as follows:

[0099] E d , t = ( 1 − η d ) E d , t − 1 + [ η out, d lc P out, d , t lc + η in, d lc P in, d , t lc ] Δ t ; (9)

[0100] In formula (9), E d,t is the capacity of the flow battery energy storage d at time t; is the output power of the flow battery energy storage d at time t; is the input power of the flow battery energy storage d at time t; and They are the discharge and charge efficiencies of flow battery energy storage d respectively; is the self-discharge loss efficiency of the flow battery energy storage d.

[0101] The load-side distributed energy storage working principle model is represented by formula (9). The load-side distributed energy storage considered in the present invention takes electric vehicles as an example. The energy status of electric vehicle batteries is usually measured by ampere-hour method, as shown in formula (10):

[0102] ; (10)

[0103] In formula (10), E f,t is the power of electric car f at time t; E f,0 is the initial charge of the electric vehicle f; E S is the rated capacity of the electric vehicle battery; η f is the average charge and discharge efficiency of the electric vehicle; I is the current flowing through the battery of the electric vehicle. From the perspective of operating power, the relationship between the power of the electric vehicle f and the charge and discharge power is shown in formula (11):

[0104] ; (11)

[0105] In formula (11), E f,t is the power of electric vehicle f at time t; and are the charging power obtained by electric vehicle f from the grid and the discharging power of electric vehicle f to the grid respectively; and are the efficiency coefficients of charging and discharging of electric vehicles f, respectively; is the amount of electricity consumed by electric vehicle f during driving at time t.

[0106] S300. The multi-energy storage operation regulation and site selection and capacity joint optimization model constructed by the present invention is based on the constructed multi-energy storage joint operation regulation and site selection and capacity joint optimization framework, and the multi-energy storage working principle model, to construct a joint optimization model including operation regulation at the operation level and site selection and capacity at the planning level. The model specifically includes two levels of optimization models. The first is the optimization at the planning level, that is, to optimize the type, capacity and quantity of energy storage in the planning area. In the present invention, the types of energy storage consider five types: pumped storage, compressed air energy storage, hydrogen energy storage, flow battery energy storage, and load-side distributed energy storage. The second is the optimization at the scheduling level, that is, to optimize the charging and discharging state and charging and discharging power of the energy storage in the planning area. Including:

[0107] Objective function. The planning and operation levels involved in the joint optimization model for multi-energy storage operation regulation and site selection and capacity determination are both aimed at optimizing the renewable energy consumption rate. The objective function of the joint optimization model for multi-energy storage operation regulation and site selection and capacity determination is shown in formula (12):

[0108] ; (12)

[0109] In formula (12), is the renewable energy consumption rate of the planned area, and its specific calculation method is shown in formula (13):

[0110] ; (13)

[0111] In formula (13), is the power generation of the new energy power station k at time t; is the amount of electricity consumed by the new energy power station k at time t; t0 is the start time of scheduling; K is the total number of new energy power stations in the area to be planned; T is the end time of scheduling; is the pumping power of pumped storage a at time t; is the compression power consumed by the compressed air energy storage b at time t; is the input power of the hydrogen energy storage c electrolyzer at time t; is the input power of the flow battery energy storage d at time t; is the charging power obtained by electric vehicle f from the power grid.

[0112] Constraints. On the basis of constructing the objective function of the multi-energy storage operation regulation and site selection and capacity joint optimization model in S301, the multi-energy storage operation regulation and site selection and capacity joint optimization model constructed by the present invention should also meet the main constraints such as power balance, among which the power balance constraint in the planned area is shown in formula (14):

[0113] ; (14)

[0114] In formula (14), , are respectively the generating power and pumping power of pumped storage a at time t; is the compression power consumed by the compressed air energy storage b at time t; is the compression power consumed by the compressed air energy storage b at time t; is the output power of the hydrogen energy storage c fuel cell at time t; is the input power of the hydrogen energy storage c fuel cell at time t; is the output power of the flow battery energy storage d at time t; is the input power of the flow battery energy storage d at time t; and are the charging power obtained by electric vehicle f from the grid and the discharging power of electric vehicle f to the grid respectively; is the power load demand of the power grid at time t. A, B, C, D, and F are the number of pumped storage, compressed air energy storage, hydrogen energy storage, flow battery energy storage, and load-side distributed energy storage in the area to be planned; L is the number of load points in the area to be planned.

[0115] The constraints also take into account the power flow constraints of the power system and the output constraints and capacity constraints of the energy storage equipment.

[0116] In terms of model solving, the present invention uses quantum genetic algorithm to solve and calculate the different levels of operation and planning involved in the model. Specifically, the first is the optimization of planning, that is, the configuration level of energy storage equipment. Based on the overall load level of the planned area, considering the constraints on regional power load demand, among the five types of energy storage equipment, namely pumped storage, compressed air storage, hydrogen storage, flow battery storage, and load-side distributed storage, the type of energy storage and the number of energy storage configurations are used as optimization variables for the objective function to give the location and capacity of energy storage equipment in the planned area; from the perspective of the algorithm, the optimization problem at the planning level belongs to a nonlinear integer programming problem, and it is difficult to obtain an analytical solution. Therefore, the present invention uses a quantum genetic algorithm to solve the optimization problem of the planning layer model, genetically encodes the optimization variables of the planning layer, and generates an initial chromosome solution set for the planning layer, that is, randomly generates an initial chromosome composed of types and capacity variables of different types of energy storage, sets the initial type and initial energy of the energy storage equipment, and sets the parameters such as the encoding of the genes in the algorithm, the probability of crossover mutation, and the number of iterations, so that the number of iterations is 1.

[0117] Secondly, the operation is the optimization at the operation scheduling level. For the hourly load demand curve in the entire planning period, the power balance of the planned area and the output constraints and capacity constraints of the energy storage equipment are considered. Based on the equipment site selection and capacity optimization results of the planning layer, the start and stop and output of different types of energy storage equipment are used as optimization variables to provide hourly optimization scheduling plans for different types of energy storage. From the perspective of the algorithm, the optimization problem at the scheduling level is a mixed nonlinear optimization problem. The present invention uses a quantum genetic algorithm to solve it. Gene encoding is performed on the optimization variables of the planning layer to generate the initial chromosome solution set of the planning layer. The storage capacity and output range of different types of energy storage equipment are analyzed, which are used as the optimization range for gene selection crossover mutation. The optimization results of the operation layer are returned to the planning layer, and the planning layer model is optimized and calculated.

[0118] Figure 2 The invention is a device for realizing joint optimization of multi-element energy storage operation control and site selection and capacity setting, comprising:

[0119] A multi-energy storage joint operation control and site selection and capacity determination joint optimization framework construction module 210 is used to clarify the area to be planned, input the power load demand of the area to be planned, and input the installed capacity of new energy generators in the area to be planned;

[0120] A multi-energy storage working principle model building module 220 is used to clarify the capacity, output constraints and other technical parameters of different types of energy storage in the planned area;

[0121] The multi-energy storage operation control and site selection and capacity determination joint optimization model construction module 230 is used to solve the problems of energy storage type selection, site selection and capacity determination, and scheduling operation strategy in the planning area.

[0122] Figure 3 The computer terminal device is used to complete all or part of the steps of the above-mentioned multi-element energy storage operation control and site selection and capacity joint optimization method. The device includes a processor 300 and a first memory 301, a second memory 302 and a third memory 303.

[0123] The first memory 301 is used to store program codes for modeling the joint operation control and site selection and capacity determination joint optimization framework of multiple energy storage, and transmit the program codes to the processor 300;

[0124] The second memory 302 is used to store the program code for constructing the multivariate energy storage working principle model, and transmit the program code to the processor 300;

[0125] The third memory 303 is used to store program codes for constructing a joint optimization model for multi-element energy storage operation control and site selection and capacity determination, and transmit the program codes to the processor 300;

[0126] The processor 300 is used to execute the steps in the above-mentioned multi-element energy storage operation control and site selection and capacity joint optimization method according to the instructions in the above-mentioned program code.

[0127] The computer programs in the first memory 301, the second memory 302, and the third memory 303 may be divided into one or more modules / units, which are stored in the corresponding memory and executed by the processor 300 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the terminal device 30.

[0128] The terminal device 30 may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include but is not limited to: a processor 300, a first memory 301, a second memory 302, and a third memory 303. The terminal device may also include input and output devices, network access devices, buses, etc.

[0129] The processor 300 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0130] The first memory 301, the second memory 302 and the third memory 303 may be internal storage units of the terminal device 30, such as a hard disk or memory of the terminal device 30. The first memory 301, the second memory 302 and the third memory 303 may also be external storage devices of the terminal device 30, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 30. The first memory 301, the second memory 302 and the third memory 303 may also include both internal storage units and external storage devices of the terminal device 30. The first memory 301, the second memory 302 and the third memory 303 are used to store the computer program and other programs and data required by the terminal device. The first memory 301, the second memory 302 and the third memory 303 may also be used to temporarily store data that has been output or is to be output.

[0131] Taking a northern city as an example, based on the city's new energy layout planning and the method proposed in this invention, the energy storage type selection and energy storage site selection and capacity planning are carried out for the city's energy storage development. Figure 4 shown.

[0132] Based on the proposed multi-energy storage operation control and site selection and capacity joint optimization method, Figure 4 The energy storage development plan is carried out in the planning area shown in the figure, and the schematic diagram of the urban energy storage development plan is obtained as follows Figure 5 shown.

[0133] Through Figure 5 From the analysis of the schematic diagram of urban energy storage development planning shown in the figure, it can be seen that the multi-energy storage operation control and site selection and capacity determination joint optimization method proposed in the present invention can effectively analyze the planning and configuration of energy storage and provide support for the safe and stable operation of regional power.

[0134] In summary, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for joint optimization of multi-element energy storage operation control and site selection and capacity determination, characterized in that: The following steps are involved: S100, build a joint optimization framework for multi-energy storage joint operation and control and site selection and capacity determination; S200, build a multi-element energy storage working principle model, including: S201. The working principle model of pumped storage is represented by formula (1), which includes two main principle steps: power generation and pumping. From the perspective of operating power, the specific expression of the working principle model of pumped storage is as follows: P g-s,a,t =P g,a,t -P s,a,t (1) In formula (1): P g-s,a,t is the operating power of pumped storage a at time t; P g,a,t , P s,a,t are respectively the generating power and pumping power of pumped storage a at time t; At the same time, the relationship between the pumped storage operating power and the reservoir capacity is expressed as follows: W a,t =W a,t-1 +n s,a P s,a,t-1 △t-η g,a P g,a,t-1 △t (2) In formula (2): W a,t is the storage capacity of the upper reservoir of pumped storage a at time t; η s,a , η g,a are the conversion coefficients between the average water volume and electricity volume of pumped storage a in the pumping and power generation states; P g,a,t-1 , P s,a,t-1 are the generating power and pumping power of pumped storage a at time t-1 respectively; △t is the scheduling time interval; S202. The working principle model of compressed air energy storage is represented by formula (3), which includes two main principle steps: compression and expansion. From the perspective of operating power, the specific expression of the working principle model of compressed air energy storage in the compression stage is as follows: In formula (3): P c,b,t is the compression power consumed by the compressed air energy storage b at time t; C air is the constant pressure specific heat capacity of air; m b,t is the mass of air compressed by the compressed air energy storage b at time t; T b,in is the intake temperature of the compressed air energy storage b; η b is the rated compression ratio of compressed air energy storage b; β is the specific heat ratio of ideal air; From the perspective of operating power, the working principle model of compressed air energy storage in the expansion stage is shown in formula (4): In formula (4): P f,b,t is the compression power consumed by the compressed air energy storage b at time t; C air is the constant pressure specific heat capacity of air; m b,t is the mass of air compressed by the compressed air energy storage b at time t; T b,in is the intake temperature of the compressed air energy storage b; η b is the rated compression ratio of compressed air energy storage b; β is the specific heat ratio of ideal air; η f is the expansion efficiency of compressed air energy storage b; S203. The working principle model of hydrogen energy storage is represented by formula (5). When the power output of the power supply is greater than the load demand of the power grid, the excess power is consumed by the electrolyzer, hydrogen is produced by electrolyzing water, and the hydrogen is stored in the hydrogen storage tank. When the power output of the power supply is less than the load demand of the power grid, the hydrogen fuel cell will use hydrogen and oxygen as raw materials to produce electricity by chemical reaction to meet the load demand. Therefore, the working process of hydrogen energy storage mainly includes three main links: electrolyzer, fuel cell and hydrogen storage tank. From the perspective of operating power, the specific expression of the working principle model of hydrogen energy storage in the electrolyzer link is as follows: In formula (5), is the output power of the hydrogen energy storage c electrolyzer at time t; is the input power of the hydrogen energy storage c electrolyzer at time t; is the efficiency of the hydrogen storage c electrolyzer; From the perspective of operating power, the working principle model of hydrogen energy storage in the fuel cell link is shown in formula (6): In formula (6), is the output power of the hydrogen energy storage c fuel cell at time t; is the input power of the hydrogen energy storage c fuel cell at time t; for hydrogen energy storage c fuel cell efficiency; From the perspective of operating power, the working principle model of hydrogen energy storage in the hydrogen storage tank link is shown in equations (7) and (8). When the hydrogen storage tank of hydrogen energy storage stores hydrogen: When the hydrogen storage tank of hydrogen energy storage discharges hydrogen: In formula (7) and formula (8), E c,t is the capacity of the hydrogen storage tank at time t; is the output power of the hydrogen energy storage c fuel cell at time t; is the efficiency of the hydrogen storage electrolyzer; η c is the storage efficiency of hydrogen energy storage c hydrogen storage tank; △t is the scheduling time interval; S204. The working principle model of liquid flow battery energy storage is shown in formula (9). All chemical reactions are carried out in solution. After the reaction is completed, the solution can be pumped out of the battery by a pump. At the same time, a new solution to be reacted is injected into the battery from the storage tank. From the perspective of operating power, the storage capacity of liquid flow battery energy storage is related to the charging and discharging power, charging and discharging efficiency and self-discharge rate. The specific expression of the working principle model is as follows: In formula (9), E d,t is the capacity of the flow battery energy storage d at time t; is the output power of the flow battery energy storage d at time t; is the input power of the flow battery energy storage d at time t; and are the discharge and charge efficiencies of flow battery energy storage d; η d is the self-discharge loss efficiency of the flow battery energy storage d; S205. The load-side distributed energy storage working principle model is represented by formula (9). The energy status of the electric vehicle battery is measured by the ampere-hour method, as shown in formula (10): In formula (10), E f,t is the power of electric car f at time t; E f,0 is the initial charge of the electric vehicle f; E S is the rated capacity of the electric vehicle battery; η f is the average charge and discharge efficiency of the electric vehicle; I is the current flowing through the battery of the electric vehicle. From the perspective of operating power, the relationship between the power of the electric vehicle f and the charge and discharge power is shown in formula (11): In formula (11), E f,t is the power of electric vehicle f at time t; and are the charging power obtained by electric vehicle f from the grid and the discharging power of electric vehicle f to the grid respectively; and are the efficiency coefficients of charging and discharging of electric vehicles respectively; P ev,f,t is the amount of electricity consumed by the electric vehicle f during driving at time t; S300, constructing a joint optimization model for multi-energy storage operation regulation and site selection and capacity determination. The joint optimization model for multi-energy storage operation regulation and site selection and capacity determination in S300 includes two levels of optimization models: the first is the optimization at the planning level; the second is the optimization at the scheduling level. include: S301. Objective function. The planning and operation levels involved in the joint optimization model for multi-energy storage operation regulation and site selection and capacity determination are both aimed at optimizing the renewable energy consumption rate. The objective function of the joint optimization model for multi-energy storage operation regulation and site selection and capacity determination is shown in formula (12): max{Ψ} (12) In formula (12), Ψ is the renewable energy consumption rate of the planned area, and its specific calculation method is shown in formula (13): In formula (13), is the power generation of the new energy power station k at time t; is the amount of electricity consumed by the new energy power station k at time t; t0 is the start time of scheduling; K is the total number of new energy power stations in the area to be planned; T is the end time of scheduling; P s,a,t is the pumping power of pumped storage a at time t; P c,b,t is the compression power consumed by the compressed air energy storage b at time t; is the input power of the hydrogen energy storage c electrolyzer at time t; is the input power of the flow battery energy storage d at time t; is the charging power obtained by the electric vehicle f from the power grid; S302, constraints. Based on the objective function of the multi-energy storage operation control and site selection and capacity joint optimization model constructed in S301, the constructed multi-energy storage operation control and site selection and capacity joint optimization model should also meet the main constraints of power balance. Among them, the power balance constraints in the planned area are shown in formula (14): In formula (14), P g,a,t , P s,a,t are respectively the generating power and pumping power of pumped storage a at time t; P f,b,t P is the compression power consumed by the compressed air energy storage b at time t; c,b,t is the compression power consumed by the compressed air energy storage b at time t; is the output power of the hydrogen energy storage c fuel cell at time t; is the input power of the hydrogen energy storage c fuel cell at time t; is the output power of the flow battery energy storage d at time t; is the input power of the flow battery energy storage d at time t; and are the charging power obtained by electric vehicle f from the grid and the discharging power of electric vehicle f to the grid respectively; P l,t is the power load demand of the power grid at time t; A, B, C, D, and F are the numbers of pumped storage, compressed air energy storage, hydrogen energy storage, flow battery energy storage, and load-side distributed energy storage in the area to be planned, respectively; L is the number of load points in the area to be planned.

2. A method for joint optimization of multi-element energy storage operation control and site selection and capacity determination according to claim 1, characterized in that: In step S100, the multi-energy storage includes pumped storage, compressed air energy storage, hydrogen energy storage, liquid flow battery energy storage, and load-side distributed energy storage. The power source is a large wind farm and a photovoltaic power station. The power source side uses pumped storage and compressed air energy storage as power source supporting support, the grid side uses hydrogen energy storage and liquid flow battery energy storage as operation support, and the load side uses load-side distributed energy storage.

3. A method for joint optimization of multi-element energy storage operation control and site selection and capacity determination according to claim 1, characterized in that: In step S300, a quantum genetic algorithm is used to solve and calculate different levels of operation and planning involved in the model.

4. A device for realizing a joint optimization method for multi-element energy storage operation control and site selection and capacity determination, characterized in that: include: A multi-energy storage joint operation control and site selection and capacity determination joint optimization framework construction module (210) is used to clarify the area to be planned, input the power load demand of the area to be planned, and input the installed capacity of new energy generators in the area to be planned; The multi-energy storage working principle model building module (220) is used to clarify the capacity and output constraint technical parameters of different types of energy storage in the planned area. The working principle model of pumped storage is expressed as formula (1), which includes two main principle steps: power generation and pumping. From the perspective of operating power, the specific expression of the working principle model of pumped storage is as follows: P g-s,a,t =P g,a,t -P s,a,t (1) In formula (1): P g-s,a,t is the operating power of pumped storage a at time t; P g,a,t , P s,a,t are respectively the generating power and pumping power of pumped storage a at time t; At the same time, the relationship between the pumped storage operating power and the reservoir capacity is expressed as follows: W a,t =W a,t-1 +n s,a P s,a,t-1 △t-η g,a P g,a,t-1 △t (2) In formula (2): W a,t is the storage capacity of the upper reservoir of pumped storage a at time t; η s,a , η g,a are the conversion coefficients between the average water volume and electricity volume of pumped storage a in the pumping and power generation states; P g,a,t-1 , P s,a,t-1 are the generating power and pumping power of pumped storage a at time t-1 respectively; △t is the scheduling time interval; The working principle model of compressed air energy storage, as expressed in formula (3), includes two main principle steps: compression and expansion. From the perspective of operating power, the specific expression of the working principle model of compressed air energy storage in the compression stage is as follows: In formula (3): P c,b,t is the compression power consumed by the compressed air energy storage b at time t; C air is the constant pressure specific heat capacity of air; m b,t is the mass of air compressed by the compressed air energy storage b at time t; T b,in is the intake temperature of the compressed air energy storage b; η b is the rated compression ratio of compressed air energy storage b; β is the specific heat ratio of ideal air; From the perspective of operating power, the working principle model of compressed air energy storage in the expansion stage is shown in formula (4): In formula (4): P f,b,t is the compression power consumed by the compressed air energy storage b at time t; C air is the constant pressure specific heat capacity of air; m b,t is the mass of air compressed by the compressed air energy storage b at time t; T b,in is the intake temperature of the compressed air energy storage b; η b is the rated compression ratio of compressed air energy storage b; β is the specific heat ratio of ideal air; η f is the expansion efficiency of compressed air energy storage b; The working principle model of hydrogen energy storage is expressed as follows: when the power output of the power supply is greater than the load demand of the power grid, the excess power is consumed by the electrolyzer, hydrogen is produced by electrolyzing water, and the hydrogen is stored in the hydrogen storage tank; when the power output of the power supply is less than the load demand of the power grid, the hydrogen fuel cell will use hydrogen and oxygen as raw materials to undergo chemical reactions to produce electricity to meet the load demand. Therefore, the working process of hydrogen energy storage mainly includes three main links: electrolyzer, fuel cell and hydrogen storage tank. From the perspective of operating power, the specific expression of the working principle model of hydrogen energy storage in the electrolyzer link is as follows: In formula (5), is the output power of the hydrogen energy storage c electrolyzer at time t; is the input power of the hydrogen energy storage c electrolyzer at time t; is the efficiency of the hydrogen storage c electrolyzer; From the perspective of operating power, the working principle model of hydrogen energy storage in the fuel cell link is shown in formula (6): In formula (6), is the output power of the hydrogen energy storage c fuel cell at time t; is the input power of the hydrogen energy storage c fuel cell at time t; for hydrogen energy storage c fuel cell efficiency; From the perspective of operating power, the working principle model of hydrogen energy storage in the hydrogen storage tank link is shown in equations (7) and (8). When the hydrogen storage tank of hydrogen energy storage stores hydrogen: When the hydrogen storage tank of hydrogen energy storage discharges hydrogen: In formula (7) and formula (8), E c,t is the capacity of the hydrogen storage tank at time t; is the output power of the hydrogen energy storage c fuel cell at time t; is the efficiency of the hydrogen storage electrolyzer; η c is the storage efficiency of hydrogen energy storage c hydrogen storage tank; △t is the scheduling time interval; The working principle model of liquid flow battery energy storage is shown in formula (9). All chemical reactions are carried out in solution. After the reaction is completed, the solution can be pumped out of the battery by a pump. At the same time, the new solution to be reacted is injected into the battery from the storage tank. From the perspective of operating power, the storage capacity of liquid flow battery energy storage is related to the charging and discharging power, charging and discharging efficiency and self-discharge rate. The specific expression of the working principle model is as follows: In formula (9), E d,t is the capacity of the flow battery energy storage d at time t; is the output power of the flow battery energy storage d at time t; is the input power of the flow battery energy storage d at time t; and They are the discharge and charge efficiencies of flow battery energy storage d respectively; η d is the self-discharge loss efficiency of the flow battery energy storage d; The working principle model of distributed energy storage on the load side is expressed as formula (9). The energy status of electric vehicle batteries is measured using the ampere-hour method, as shown in formula (10): In formula (10), E f,t is the power of electric car f at time t; E f,0 is the initial charge of the electric vehicle f; E S is the rated capacity of the electric vehicle battery; η f is the average charge and discharge efficiency of the electric vehicle; I is the current flowing through the battery of the electric vehicle. From the perspective of operating power, the relationship between the power of the electric vehicle f and the charge and discharge power is shown in formula (11): In formula (11), E f,t is the power of electric vehicle f at time t; and are the charging power obtained by electric vehicle f from the grid and the discharging power of electric vehicle f to the grid respectively; and are the efficiency coefficients of charging and discharging of electric vehicles respectively; P ev,f,t is the amount of electricity consumed by the electric vehicle f during driving at time t; The multi-energy storage operation control and site selection and capacity determination joint optimization model construction module (230) is used to solve the problems of type selection, site selection and capacity determination, and scheduling operation strategy of energy storage in the planning area. Objective function, the planning and operation levels involved in the joint optimization model of multi-energy storage operation regulation and site selection and capacity determination are both aimed at optimizing the renewable energy consumption rate. The objective function of the joint optimization model of multi-energy storage operation regulation and site selection and capacity determination is shown in formula (12): max{Ψ} (12) In formula (12), Ψ is the renewable energy consumption rate of the planned area, and its specific calculation method is shown in formula (13): In formula (13), is the power generation of the new energy power station k at time t; is the amount of electricity consumed by the new energy power station k at time t; t0 is the start time of scheduling; K is the total number of new energy power stations in the area to be planned; T is the end time of scheduling; P s,a,t is the pumping power of pumped storage a at time t; P c,b,t is the compression power consumed by the compressed air energy storage b at time t; is the input power of the hydrogen energy storage c electrolyzer at time t; is the input power of the flow battery energy storage d at time t; is the charging power obtained by the electric vehicle f from the power grid; Constraints: Based on the objective function of the multi-energy storage operation control and site selection and capacity joint optimization model constructed in S301, the constructed multi-energy storage operation control and site selection and capacity joint optimization model should also meet the main constraints of power balance. Among them, the power balance constraints in the planned area are shown in formula (14): In formula (14), P g,a,t , P s,a,t are respectively the generating power and pumping power of pumped storage a at time t; P f,b,t P is the compression power consumed by the compressed air energy storage b at time t; c,b,t is the compression power consumed by the compressed air energy storage b at time t; is the output power of the hydrogen energy storage c fuel cell at time t; is the input power of the hydrogen energy storage c fuel cell at time t; is the output power of the flow battery energy storage d at time t; is the input power of the flow battery energy storage d at time t; and are the charging power obtained by electric vehicle f from the grid and the discharging power of electric vehicle f to the grid respectively; P l,t is the power load demand of the power grid at time t; A, B, C, D, and F are the numbers of pumped storage, compressed air energy storage, hydrogen energy storage, flow battery energy storage, and load-side distributed energy storage in the area to be planned, respectively; L is the number of load points in the area to be planned.

5. The device for realizing the joint optimization method of multi-element energy storage operation control and site selection and capacity determination according to claim 4 is characterized in that: Also included is a terminal device, comprising a processor (300) and a first memory (301), a second memory (302) and a third memory (303) connected to the processor, The first memory (301) is used to store program codes for modeling a joint optimization framework for multi-energy storage joint operation control and site selection and capacity determination, and transmit the program codes to the processor (300); The second memory (302) is used to store program codes for constructing a multivariate energy storage working principle model, and transmit the program codes to the processor (300); The third memory (303) is used to store program codes for constructing a joint optimization model for multi-element energy storage operation control and site selection and capacity determination, and transmit the program codes to the processor (300); The processor (300) is used to execute the steps in the above-mentioned multi-element energy storage operation control and site selection and capacity joint optimization method according to the instructions in the above-mentioned program code.

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