Low-carbon economic optimal configuration method and system for electrochemical energy storage

Abstract

The invention discloses a low-carbon economic optimization configuration method and system for electrochemical energy storage, and relates to the technical field of low-carbon economic optimization, and the method comprises the steps: constructing a multi-target energy storage configuration optimization model, and optimizing the operation cost of a system and the peak-valley difference of an operation curve of a power system according to the energy storage configuration power and capacity; constructing a power system operation optimization model, and optimizing the typical daily operation total cost according to the charging and discharging power of the energy storage device and the equipment operation state; a double-layer optimization model is constructed based on parameter transmission, energy storage configuration is optimized through a multi-objective genetic algorithm, and system operation is optimized through linear programming. According to the method, the contribution of the energy storage equipment to new energy consumption is quantified into economic benefits by constructing the carbon emission reduction income model, and the value of the energy storage system in low-carbon economy is improved; a double-layer optimization model is adopted, the system operation cost and the carbon emission reduction income are integrated, and collaborative optimization of energy storage configuration and operation is achieved; and by dividing peak and valley periods and gentle periods, the calculation complexity is reduced, and the optimization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon economic optimization, and in particular to a low-carbon economic optimization configuration method and system for electrochemical energy storage. Background Art

[0002] As the global energy structure transforms towards a low-carbon and clean direction, electrochemical energy storage technology has become an important part of the modern energy system. In recent years, energy storage technology has developed rapidly, especially in the power system. Energy storage can achieve peak shaving and valley filling, smooth out fluctuations in renewable energy, and enhance system scheduling flexibility. At present, electrochemical energy storage is widely used in wind and solar power stations, microgrids, and user-side energy management, becoming an important technical means to improve renewable energy consumption and energy utilization efficiency. In addition, the economic benefits and carbon emission reduction benefits of energy storage technology have received increasing attention. Although existing technologies have made significant progress in energy storage configuration optimization and operation scheduling, traditional optimization methods often focus on economy and fail to fully consider the potential benefits of energy storage in reducing carbon emissions and new energy consumption.

[0003] Current energy storage optimization configuration methods focus on operation optimization and configuration cost optimization. However, these methods have significant shortcomings in solving practical problems. On the one hand, most methods only consider the role of energy storage in balancing system load fluctuations, but ignore the carbon emission reduction benefits brought by energy storage by promoting the consumption of renewable energy, making the optimization results unable to fully meet the needs of a low-carbon economy. On the other hand, when dealing with peak and valley fluctuations in the power grid operation curve, traditional optimization models do not distinguish between peak and valley and flat periods, resulting in high computational complexity and slow convergence speed. In addition, existing technologies usually adopt a single-layer optimization model, which cannot achieve dynamic linkage between energy storage configuration and operation scheduling, and it is difficult to simultaneously optimize the configuration cost of energy storage and the system operation cost. Therefore, existing methods cannot meet the comprehensive needs of future power grids for efficient energy storage configuration. An innovative method that can take into account both low-carbon economic benefits and operation cost optimization is urgently needed. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing energy storage optimization configuration method does not fully consider the carbon emission reduction benefits, has high calculation complexity and low efficiency, and is difficult to achieve dynamic linkage optimization of configuration and operation, as well as how to optimize energy storage configuration and operation while taking into account both economy and low-carbon benefits.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a low-carbon economic optimization configuration method for electrochemical energy storage, including constructing a multi-objective energy storage configuration optimization model, optimizing the system operating cost and the peak-to-valley difference of the power system operation curve according to the energy storage configuration power and capacity; constructing a power system operation optimization model, optimizing the typical daily operating total cost according to the energy storage device charging and discharging power and equipment operating status; constructing a two-layer optimization model based on parameter transfer, optimizing the energy storage configuration through a multi-objective genetic algorithm, and optimizing the system operation through linear programming

[0007] As a preferred solution of the low-carbon economic optimization configuration method of electrochemical energy storage described in the present invention, wherein: the construction of a multi-objective energy storage configuration optimization model includes optimizing the energy storage site selection and equipment investment cost through 0-1 variables according to the energy storage power capacity variable in the energy storage candidate location, establishing a carbon emission reduction benefit model, promoting the change of purchased electricity volume for new energy consumption through energy storage, calculating carbon tax benefits, and jointly optimizing with the operating economic benefits; establishing a total cost function, expressed as:

[0008] F 1 =min(C ALL -B OP -B CD )

[0009] Among them, C ALL represents the life cycle cost of energy storage, B OP represents the economic benefit of operation optimization, B CD represents the carbon emission reduction benefit; the life cycle cost of the energy storage C ALL Including investment and construction costs C INV , total maintenance cost C RE and equipment residual value C FIN ; The cost and benefit are converted to the annual formula, expressed as:

[0010]

[0011] Among them, γ represents the discount rate, which is represented by the bank's annual interest rate, and L represents the planned life cycle of the energy storage configuration; the one-time investment construction cost, maintenance cost and equipment residual value calculation method are expressed as:

[0012]

[0013] Where m represents the number of candidate locations for electrochemical energy storage, a E.i A 0-1 state variable indicating whether energy storage is configured at position i or j, c E.P represents the investment and construction cost of unit power energy storage, a CE.i The state quantity indicating whether the location i is a candidate location for centralized energy storage in the energy hub, c E.E represents the investment cost of electric energy storage per unit capacity, cE.A It represents the additional cost of centralized electric energy storage per unit power due to investment in boosting equipment, etc., P E.i 、E E.i They represent the power and capacity of the electric energy storage configured at position i, β E represents the maintenance cost coefficient of electric energy storage, c E.FIN Represents the residual value of electric energy storage per unit capacity; the residual value of electric energy storage takes into account the metal recovery and scrapping costs of waste batteries; the economic benefit of the operation optimization B OP The direct economic benefits of energy storage configuration each year; the operating economic benefits B OP The formula is expressed as:

[0014] B OP =C OP -C OP.0

[0015] Among them, C OP represents the annual operating cost of the system before energy storage configuration, C OP.0 Represents the annual operating cost of the system after energy storage configuration; the carbon emission reduction benefit B CD The carbon emission reduction benefits converted from the carbon emission reduction brought by energy storage to promote the consumption of new energy are obtained by taking the result data of operation optimization; carbon emission reduction benefits B CD The calculation formula is expressed as:

[0016]

[0017] Among them, ω represents carbon tax, Respectively represent the purchase amount of electricity and natural gas, W e , W g They represent the carbon emission intensity of power plants and natural gas respectively, and the unit is kgCO2 / kWh.

[0018] As a preferred solution of the low-carbon economic optimization configuration method of electrochemical energy storage described in the present invention, the construction of a multi-objective energy storage configuration optimization model also includes optimizing the balanced distribution of power grid load in the power system operation curve with the goal of minimizing the difference between peak power and valley power; establishing a power system imbalance function, expressed as:

[0019] F 2 =min(P MAX -P MIN )

[0020] Among them, P MAX Represents the peak power of the power system, P MIN represents the valley power of the power system; sets energy storage configuration constraints, wherein the energy storage configuration constraints include power capacity ratio restrictions, energy storage device quantity restrictions, and location selection constraints; the constraints are expressed as:

[0021]

[0022] Among them, P E.i.max 、E E.i.max They represent the maximum power and capacity allowed for electrochemical energy storage at position i, respectively, and k E.max , k E.min Respectively represent the upper and lower limits of the electrochemical energy storage capacity power ratio, n E.max Indicates the maximum number of electrochemical energy storage configurations allowed.

[0023] As a preferred solution of the low-carbon economic optimization configuration method of electrochemical energy storage described in the present invention, wherein: the construction of the power system operation optimization model includes establishing a charging and discharging model of energy storage equipment, optimizing the charging and discharging strategy of energy storage during peak and valley periods by storing energy quantity, charging and discharging power and efficiency, optimizing the demand for energy station electricity and gas purchase, combining wind and solar power generation, gas power generation and energy storage operation status, dynamically balancing the power demand and economic cost of the system; the charging and discharging model of energy storage equipment is expressed as:

[0024]

[0025] P ES.c.i (t)·P ES.d.i (t) = 0

[0026] W i min (t)≤W i (t)≤W i max (t)

[0027] Among them, W i (t) represents the energy storage capacity of energy storage i at time t, δ i represents the self-release energy coefficient, η ES.i Indicates the charging and discharging efficiency, P ES.c.i (t), P ES.d.i (t) respectively represent the charging and discharging power of energy storage at time t, Indicates the maximum charge and discharge power, W i max (t), W i min (t) represents the upper and lower limits of energy storage respectively; for the gas power generation model, it is expressed as:

[0028]

[0029] Among them, P GV (t) represents the power generated by the gas power plant at a specific time t, η GH represents the energy conversion efficiency, η HVrepresents the thermal efficiency of natural gas, C gas It represents the calorific value of natural gas, and V(t) represents the volume of natural gas consumed within time t.

[0030] As a preferred solution of the low-carbon economic optimization configuration method of electrochemical energy storage described in the present invention, wherein: the construction of the power system operation optimization model also includes dividing the operation time period into peak and valley periods and smooth periods within a typical day, dynamically optimizing the peak and valley periods every 15 minutes, optimizing the smooth periods every hour, and maintaining the system stable operation after optimization through bus power balance constraints, electrochemical energy storage continuous operation constraints and equipment power upper and lower limit constraints; the operation optimization model is expressed as:

[0031] F 3 =min C OP

[0032] Among them, C OP Indicates the annual operating cost of the system; the annual operating cost of the system C op The annual system operating cost C is obtained through the operation optimization of a typical day. op It is expressed as:

[0033]

[0034] Among them, C OP.i represents the operating cost of the system on the ith typical day, d i represents the number of days corresponding to the i-th typical day, c W 、c S 、c G They represent the unit power operation cost of wind power generation, photovoltaic power generation, and gas power generation at time t, respectively. W (t), P S (t), P G (t) represents the power generation of wind power, photovoltaic power generation and gas power generation at time t, m E 、m G represents the cost of purchasing electricity and gas from the upper energy grid at time t, P E (t) represents the power purchased from the upper energy grid at time t, V G (t) represents the gas volume purchased from the upper energy network at time t; the bus power balance constraint is expressed as:

[0035] P load (t)+P ES.c.i (t) = P W (t)+P P (t)+P G (t)+P ES.d.i (t)

[0036] Among them, P load(t) represents the electric load power at time t, P W (t), P P (t), P G (t) respectively represent the wind turbine power supply, photovoltaic power supply, and gas power generation at time t, P ES.c.i (t), P ES.d.i (t) represents the charging power and discharging power of the electrochemical energy storage at time t respectively; the upper and lower limits of the equipment power are expressed as:

[0037]

[0038] Among them, P G.i (t) represents the power generation of the gas power plant at time t, represents the maximum power generation of the gas power plant; the continuous operation constraint of the electrochemical energy storage is expressed as:

[0039] W i.1 =W i.36 =W i.0

[0040] Among them, W i.1 , W i.36 Respectively represent the storage capacity of energy storage i at the beginning and end of each day, W i.0 Indicates the initial value of the energy storage charge state.

[0041] As a preferred scheme of the low-carbon economic optimization configuration method of electrochemical energy storage described in the present invention, wherein: the construction of a two-layer optimization model based on parameter transfer includes the upper optimization model transferring energy storage power, capacity and location parameters to the lower model, optimizing the impact of energy storage configuration on system operation; the lower optimization model feeds back to the upper energy storage configuration model through the optimization result of operating cost, and further adjusts the energy storage equipment configuration plan.

[0042] As a preferred scheme of the low-carbon economic optimization configuration method of electrochemical energy storage described in the present invention, the solution of the two-layer optimization model includes the upper model using the NSGA-Ⅱ genetic algorithm to perform non-dominated degree sorting, selection, crossover and mutation operations on the randomly generated initial population, and output the Pareto optimal solution set; the lower model uses a linear programming solver to optimize the operating cost of each typical day, and collaboratively optimizes the two-layer model through iterative cycles.

[0043] Another object of the present invention is to provide a low-carbon economic optimization configuration system for electrochemical energy storage, which can solve the technical problem that the current energy storage optimization configuration technology is difficult to take into account both low-carbon economic benefits and stable system operation through a multi-objective optimization model and a two-layer optimization method.

[0044] As a preferred solution of the low-carbon economic optimization configuration system for electrochemical energy storage described in the present invention, it includes: a multi-objective energy storage configuration optimization module, an operation optimization module, and a system operation optimization module; the multi-objective energy storage configuration optimization module is used to construct a multi-objective energy storage configuration optimization model, and optimizes the system operation cost and the peak-to-valley difference of the power system operation curve according to the energy storage configuration power and capacity; the operation optimization module is used to construct a power system operation optimization model, and optimizes the typical daily total operation cost according to the charging and discharging power of the energy storage device and the equipment operating status; the system operation optimization module is used to construct a two-layer optimization model based on parameter transfer, optimize the energy storage configuration through a multi-objective genetic algorithm, and optimize the system operation through linear programming.

[0045] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a low-carbon economic optimization configuration method for electrochemical energy storage.

[0046] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for low-carbon economic optimization configuration of electrochemical energy storage.

[0047] Beneficial effects of the present invention: The low-carbon economy optimization configuration method of electrochemical energy storage provided by the present invention quantifies the contribution of energy storage equipment to the consumption of new energy as economic benefits by constructing a carbon emission reduction benefit model, thereby optimizing the energy storage configuration plan and improving the actual value of the energy storage system in the low-carbon economy; through the optimal configuration of energy storage, the peak and valley fluctuations in the power system operation curve are smoothed, which not only enables renewable energy to be more stably connected to the power grid, but also improves the new energy consumption rate; through the two-layer optimization model, the system operation cost and carbon emission reduction benefits are comprehensively considered, which not only solves the problem that the traditional single-objective optimization model is difficult to balance low carbon and economy, but also realizes the coordinated optimization of energy storage configuration; by dividing the power operation period into peak and valley and flat periods, adjusting the number of state variables, not only the calculation complexity is reduced, but also the convergence speed and calculation efficiency of the optimization algorithm are improved; through the precise adjustment of energy storage in the peak and valley periods, not only the grid load fluctuation is reduced, but also the stability and safety of the grid operation are enhanced; through the two-layer optimization model based on parameter transfer, the upper layer performs multi-objective energy storage configuration optimization, and the lower layer performs grid operation optimization, which not only solves the problem that configuration and operation cannot be linked in the traditional method, but also improves the integrity of the optimization results and the actual application effect. The present invention achieves better effects in adaptability and flexibility in power systems of different sizes. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0049] Figure 1 An overall flow chart of a low-carbon economic optimization configuration method for electrochemical energy storage provided in the first embodiment of the present invention.

[0050] Figure 2 A specific flow chart of a low-carbon economic optimization configuration method for electrochemical energy storage provided in the first embodiment of the present invention.

[0051] Figure 3 A schematic diagram of the relationship between upper and lower optimization models of a low-carbon economic optimization configuration method for electrochemical energy storage provided in the first embodiment of the present invention.

[0052] Figure 4 A multi-objective dual optimization algorithm flow chart of a low-carbon economic optimization configuration method for electrochemical energy storage provided in the first embodiment of the present invention.

[0053] Figure 5 An overall flow chart of a low-carbon economic optimization configuration system for electrochemical energy storage provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0055] Example 1, reference Figure 1-Figure 4 , which is an embodiment of the present invention, provides a low-carbon economic optimization configuration method for electrochemical energy storage, comprising:

[0056] S1: Construct a multi-objective energy storage configuration optimization model to optimize the system operating cost and the peak-to-valley difference of the power system operation curve according to the energy storage configuration power and capacity.

[0057] Furthermore, the construction of a multi-objective energy storage configuration optimization model includes optimizing the energy storage site selection and equipment investment cost through 0-1 variables according to the energy storage power capacity variable in the energy storage candidate location, establishing a carbon emission reduction benefit model, promoting the change of purchased electricity volume for new energy consumption through energy storage, calculating carbon tax benefits, and optimizing them together with the operating economic benefits; establishing a total cost function, expressed as:

[0058] F 1 =min(C ALL -B OP -B CD )

[0059] Among them, C ALL represents the life cycle cost of energy storage, B OP represents the economic benefit of operation optimization, B CD represents the carbon emission reduction benefits; the life cycle cost of energy storage C ALL Including investment and construction costs C INV , total maintenance cost C RE and equipment residual value C FIN ; The cost and benefit are converted to the annual formula, expressed as:

[0060]

[0061] Among them, γ represents the discount rate, which is represented by the bank's annual interest rate, and L represents the planned life cycle of the energy storage configuration; the one-time investment construction cost, maintenance cost and equipment residual value calculation method are expressed as:

[0062]

[0063] Where m represents the number of candidate locations for electrochemical energy storage, a E.i A 0-1 state variable indicating whether energy storage is configured at position i or j, c E.P represents the investment and construction cost of unit power energy storage, a CE.i The state quantity indicating whether the location i is a candidate location for centralized energy storage in the energy hub, c E.E represents the investment cost of electric energy storage per unit capacity, c E.A It represents the additional cost of centralized electric energy storage per unit power due to investment in boosting equipment, etc., P E.i 、E E.i They represent the power and capacity of the electric energy storage configured at position i, β E represents the maintenance cost coefficient of electric energy storage, c E.FIN Indicates the residual value of electric energy storage per unit capacity; the residual value of electric energy storage takes into account the metal recovery and scrapping costs of waste batteries; the economic benefit of operation optimization B OP is the direct economic benefit of energy storage configuration each year, that is, the difference in annual system operating costs before and after energy storage configuration; operating economic benefit BOP The formula is expressed as:

[0064] B OP =C OP -C OP.0

[0065] Among them, C OP represents the annual operating cost of the system before energy storage configuration, C OP.0 represents the annual operating cost of the system after energy storage configuration; carbon emission reduction benefit B CD The carbon emission reduction benefits converted from energy storage to promote the consumption of new energy are taken from the result data of operation optimization, that is, the increase in the amount of electricity and gas purchased by the new energy network after energy storage configuration compared with the previous one; carbon emission reduction benefits B CD The calculation formula is expressed as:

[0066]

[0067] Among them, ω represents carbon tax, Respectively represent the purchase amount of electricity and natural gas, W e , W g They represent the carbon emission intensity of power plants and natural gas respectively, and the unit is kgCO2 / kWh.

[0068] It should be noted that the construction of a multi-objective energy storage configuration optimization model also includes optimizing the balanced distribution of power grid load by minimizing the difference between peak power and valley power in the power system operation curve; and establishing a power system imbalance function by minimizing the peak-valley difference of the power system operation curve, which is expressed as:

[0069] F 2 =min(P MAX -P MIN )

[0070] Among them, P MAX Represents the peak power of the power system, P MIN Indicates the valley power of the power system; sets energy storage configuration constraints, which include power capacity ratio restrictions, energy storage equipment quantity restrictions, and location selection constraints; constraints are expressed as:

[0071]

[0072] Among them, P E.i.max 、E E.i.max They represent the maximum power and capacity allowed for electrochemical energy storage at position i, respectively, and k E.max , k E.min Respectively represent the upper and lower limits of the electrochemical energy storage capacity power ratio, n E.max Indicates the maximum number of electrochemical energy storage configurations allowed.

[0073] It should also be noted that the total cost function is established with the goal of minimizing the system operating cost and minimizing the peak-to-valley difference of the power system operating curve.

[0074] S2: Construct a power system operation optimization model to optimize the total cost of typical day operation based on the charging and discharging power of the energy storage device and the equipment operating status.

[0075] Furthermore, constructing the power system operation optimization model includes establishing a charging and discharging model for energy storage equipment, optimizing the charging and discharging strategy of energy storage during peak and valley periods by energy storage capacity, charging and discharging power, and efficiency, optimizing the demand for energy station electricity and gas purchases, and dynamically balancing the power demand and economic cost of the system by combining wind and solar power generation, gas power generation, and energy storage operation status; the charging and discharging model for energy storage equipment is expressed as:

[0076]

[0077] P ES.c.i (t)·P ES.d.i (t) = 0

[0078] W i min (t)≤W i (t)≤W i max (t)

[0079] Among them, W i (t) represents the energy storage capacity of energy storage i at time t, which specifically corresponds to the state of charge of the battery, δ i represents the self-release energy coefficient, η ES.i Indicates the charging and discharging efficiency, P ES.c.i (t), P ES.d.i (t) respectively represent the charging and discharging power of energy storage at time t, Indicates the maximum charge and discharge power, W i max (t), W i min (t) represents the upper and lower limits of energy storage respectively; for the gas power generation model, it is expressed as:

[0080]

[0081] Among them, P GV (t) represents the power generated by the gas power plant at a specific time t, η GH represents the energy conversion efficiency, η HV represents the thermal efficiency of natural gas, C gas It represents the calorific value of natural gas, and V(t) represents the volume of natural gas consumed within time t.

[0082] It should be noted that the construction of the power system operation optimization model also includes dividing the operation time period into peak and valley periods and smooth periods within a typical day, dynamically optimizing the peak and valley periods every 15 minutes and optimizing the smooth periods every hour, and maintaining the stable operation of the system after optimization through bus power balance constraints, electrochemical energy storage continuous operation constraints, and equipment power upper and lower limit constraints; the operation optimization model is expressed as:

[0083] F 3 =min C OP

[0084] Among them, C OP Indicates the annual operating cost of the system; the annual operating cost of the system C op The annual system operating cost C is obtained through the operation optimization of a typical day. op It is expressed as:

[0085]

[0086] Among them, C OP.i represents the operating cost of the system on the ith typical day, d i represents the number of days corresponding to the i-th typical day, c W 、c S 、c G They represent the unit power operation cost of wind power generation, photovoltaic power generation, and gas power generation at time t, respectively. W (t), P S (t), P G (t) represents the power generation of wind power, photovoltaic power generation and gas power generation at time t, m E 、m G represents the cost of purchasing electricity and gas from the upper energy grid at time t, P E (t) represents the power purchased from the upper energy grid at time t, V G (t) represents the gas volume purchased from the upper energy network at time t; the bus power balance constraint is expressed as:

[0087] P load (t)+P ES.c.i (t) = P W (t)+P P (t)+P G (t)+P ES.d.i (t)

[0088] Among them, P load (t) represents the electric load power at time t, P W (t), P P (t), P G (t) respectively represent the wind turbine power supply, photovoltaic power supply, and gas power generation at time t, P ES.c.i(t), P ES.d.i (t) represents the charging power and discharging power of the electrochemical energy storage at time t respectively; the upper and lower limits of the equipment power are expressed as:

[0089]

[0090] Among them, P G.i (t) represents the power generation of the gas power plant at time t, represents the maximum power generation of the gas power plant; the continuous operation constraint of the electrochemical energy storage is expressed as:

[0091] W i.1 =W i.36 =W i.0

[0092] Among them, W i.1 , W i.36 Respectively represent the storage capacity of energy storage i at the beginning and end of each day, W i.0 Indicates the initial value of the energy storage charge state.

[0093] It should also be noted that the optimization goal of the operation optimization model is to minimize the total operating cost within a typical day, and the optimization variables are the energy supply of the energy hub to the superior energy grid in each time period, the output of each unit, and the charging and discharging of the energy storage device; the optimization variables include the power, capacity and location of the energy storage configuration.

[0094] S3: A two-layer optimization model is constructed based on parameter transfer, energy storage configuration is optimized through a multi-objective genetic algorithm, and system operation is optimized through linear programming.

[0095] Furthermore, a two-layer optimization model is constructed based on parameter transfer, including an upper-layer optimization model transferring energy storage power, capacity and location parameters to a lower-layer model to optimize the impact of energy storage configuration on system operation; the lower-layer optimization model feeds back the optimization results of operating costs to the upper-layer energy storage configuration model to further adjust the energy storage equipment configuration plan.

[0096] It should be noted that the solution of the two-layer optimization model includes the upper model using the NSGA-Ⅱ genetic algorithm to perform non-dominated degree sorting, selection, crossover and mutation operations on the randomly generated initial population, and output the Pareto optimal solution set; the lower model uses a linear programming solver to optimize the operating cost of each typical day, and collaboratively optimizes the two-layer model through iterative cycles.

[0097] Example 2 is an embodiment of the present invention, which provides a low-carbon economic optimization configuration method for electrochemical energy storage. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0098] First, this embodiment aims to verify the effectiveness of the electrochemical energy storage configuration method based on the double-layer optimization model in the low-carbon economic optimization. A typical regional power grid is selected, and the system includes multiple energy forms, such as wind power, photovoltaic, gas power generation and power load. The performance of the unoptimized, single-layer optimization model and the double-layer optimization model of the present invention are compared experimentally. By adjusting the energy storage location, capacity, power and other parameters, the system operating cost, carbon emissions and new energy utilization rate are comprehensively analyzed.

[0099] Secondly, in the experimental steps, a baseline model was constructed with the optimized configuration without energy storage as the baseline, and parameters such as system operating cost, carbon emissions, and renewable energy utilization rate were recorded. The single-layer optimization experiment used traditional optimization methods to perform single-objective optimization on the energy storage configuration, and analyzed the impact on system operating cost and carbon emission reduction. The double-layer optimization experiment used a multi-objective genetic algorithm to optimize the energy storage configuration through the upper-layer model, with the goal of minimizing system operating cost and minimum peak-to-valley difference. The lower-layer model used linear programming to optimize energy storage scheduling, with the goal of minimizing typical daily operating cost. The upper and lower layers were iteratively optimized through parameter transfer until convergence.

[0100] The experiment records key indicators such as operating cost, carbon emissions, optimization time, system stability, etc. under each scheme, and obtains the data in Table 1 below.

[0101] Table 1 Test scenario indicators data

[0102]

[0103]

[0104] It can be seen from the experimental data that the electrochemical energy storage configuration method based on the double-layer optimization model significantly reduces the system operating cost, and the annual operating cost drops from the unoptimized US$1.5 million to US$1.25 million, a reduction of about 17%, and is better than the US$1.4 million of the single-layer optimization model; the carbon emissions of the double-layer optimization model of the method of the present invention are reduced from 50,000 tons in the baseline state to 42,000 tons, and the emission reduction rate reaches 16%, which reflects the obvious effect of energy storage on promoting the consumption of renewable energy; the method of the present invention increases the utilization rate of renewable resources from 70% to 85%, which is higher than the 75% of the single-layer optimization model, and improves the utilization rate of new energy; the method of the present invention significantly reduces the computational complexity by distinguishing between valley peaks and flat periods, the optimization time is moderate, and the system stability index reaches 0.95, showing a strong stable regulation ability.

[0105] In summary, the experimental results prove the innovation and superiority of the method of the present invention, which not only makes up for the shortcomings of the existing technology, but also provides an efficient and practical solution for the low-carbon economic optimization of the power system.

[0106] Example 3, reference Figure 5, which is an embodiment of the present invention, provides a low-carbon economic optimization configuration system for electrochemical energy storage, including a multi-objective energy storage configuration optimization module, an operation optimization module, and a system operation optimization module.

[0107] The multi-objective energy storage configuration optimization module is used to construct a multi-objective energy storage configuration optimization model, and optimize the system operating cost and the peak-to-valley difference of the power system operation curve according to the energy storage configuration power and capacity; the operation optimization module is used to construct a power system operation optimization model, and optimize the typical daily operating total cost according to the energy storage device charging and discharging power and equipment operating status; the system operation optimization module is used to construct a two-layer optimization model based on parameter transfer, optimize the energy storage configuration through a multi-objective genetic algorithm, and optimize the system operation through linear programming.

[0108] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0109] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0110] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0111] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0112] It should be noted that 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A low-carbon economic optimization configuration method for electrochemical energy storage, characterized in that: include: Construct a multi-objective energy storage configuration optimization model to optimize the system operating cost and the peak-to-valley difference of the power system operation curve according to the energy storage configuration power and capacity; Construct a power system operation optimization model to optimize the total cost of typical day operation based on the charging and discharging power of the energy storage device and the equipment operating status; A two-layer optimization model is constructed based on parameter transfer, the energy storage configuration is optimized through a multi-objective genetic algorithm, and the system operation is optimized through linear programming.

2. The low-carbon economic optimization configuration method for electrochemical energy storage according to claim 1, characterized in that: The multi-objective energy storage configuration optimization model is constructed, including optimizing the energy storage site selection and equipment investment cost through 0-1 variables according to the energy storage power capacity variable in the energy storage candidate location, establishing a carbon emission reduction benefit model, promoting the change of purchased electricity volume for new energy consumption through energy storage, calculating carbon tax benefits, and optimizing together with the operation economic benefits; The total cost function is established and expressed as: F1=min(C ALL -B OP -B CD ) Among them, C ALL represents the life cycle cost of energy storage, B OP represents the economic benefit of operation optimization, B CD represents the carbon reduction benefit; The total life cycle cost of the energy storage is C ALL Including investment and construction costs C INV , total maintenance cost C RE and equipment residual value C FIN ; The cost and benefit are converted to the annual formula, expressed as: Among them, γ represents the discount rate, which is represented by the bank's annual interest rate, and L represents the planned life cycle of the configured energy storage; The calculation method of one-time investment construction cost, maintenance cost and equipment residual value is expressed as: Where m represents the number of candidate locations for electrochemical energy storage, a E.i A 0-1 state variable indicating whether energy storage is configured at position i or j, c E.P represents the investment and construction cost of unit power energy storage, a CE.i The state quantity indicating whether the location i is a candidate location for centralized energy storage in the energy hub, c E.E represents the investment cost of electric energy storage per unit capacity, c E.A It represents the additional cost of centralized electric energy storage per unit power due to investment in boosting equipment, etc., P E.i 、E E.i They represent the power and capacity of the electric energy storage configured at position i, β E represents the maintenance cost coefficient of electric energy storage, c E.FIN Indicates the residual value of unit capacity electric energy storage; The residual value of electric energy storage takes into account the metal recovery and scrapping costs of waste batteries; The operation optimization economic benefit B OP To configure the direct economic benefits of energy storage each year; Operation economic benefit OP The formula is expressed as: B OP =C OP -C OP.0 Among them, C OP represents the annual operating cost of the system before energy storage configuration, C OP.0 Represents the annual operating cost of the system after energy storage configuration; The carbon emission reduction benefit B CD The carbon emission reduction brought about by energy storage and the consumption of new energy is converted into emission reduction benefits, and the result data of operation optimization is obtained; Carbon emission reduction benefits B CD The calculation formula is expressed as: Among them, ω represents carbon tax, Respectively represent the purchase amount of electricity and natural gas, W e , W g They represent the carbon emission intensity of power plants and natural gas respectively, and the unit is kgCO2 / kWh.

3. The low-carbon economic optimization configuration method for electrochemical energy storage according to claim 2, characterized in that: The construction of the multi-objective energy storage configuration optimization model also includes optimizing the balanced distribution of the power grid load with the goal of minimizing the difference between the peak power and the valley power in the power system operation curve; The power system unbalance function is established and expressed as: F2=min(P MAX -P MIN ) Among them, P MAX Represents the peak power of the power system, P MIN Indicates the valley power of the power system; Setting energy storage configuration constraints, wherein the energy storage configuration constraints include power capacity ratio restrictions, energy storage device quantity restrictions, and location selection constraints; The constraints are expressed as: Among them, P E.i.max 、E E.i.max They represent the maximum power and capacity allowed for electrochemical energy storage at position i, respectively, and k E.max , k E.min Respectively represent the upper and lower limits of the electrochemical energy storage capacity power ratio, n E.max Indicates the maximum number of electrochemical energy storage configurations allowed.

4. The low-carbon economic optimization configuration method for electrochemical energy storage according to claim 3, characterized in that: The construction of the power system operation optimization model includes establishing a charging and discharging model for energy storage equipment, optimizing the charging and discharging strategy of energy storage during peak and valley periods by energy storage capacity, charging and discharging power and efficiency, optimizing the demand for power and gas purchases at energy stations, and combining wind and solar power generation, gas power generation and energy storage operation status to dynamically balance the power demand and economic cost of the system; The charging and discharging model of energy storage equipment is expressed as: P ES.c.i (t)·P ES.d.i (t)=0 W i min (t)≤W i (t)≤W i max (t) Among them, W i (t) represents the energy storage capacity of energy storage i at time t, δ i represents the self-release energy coefficient, η ES.i Indicates the charging and discharging efficiency, P ES.c.i (t), P ES.d.i (t) respectively represent the charging and discharging power of energy storage at time t, Indicates the maximum charge and discharge power, W i max (t), W i min (t) represent the upper and lower limits of energy storage respectively; The gas-fired power generation model is expressed as: Among them, P GV (t) represents the power generated by the gas power plant at a specific time t, η GH represents the energy conversion efficiency, η HV represents the thermal efficiency of natural gas, C gas It represents the calorific value of natural gas, and V(t) represents the volume of natural gas consumed within time t.

5. The low-carbon economic optimization configuration method for electrochemical energy storage according to claim 4, characterized in that: The construction of the power system operation optimization model also includes dividing the operation time period into peak and valley periods and flat periods within a typical day, dynamically optimizing the peak and valley periods every 15 minutes and optimizing the flat periods every hour, and maintaining the stable operation of the system after optimization through bus power balance constraints, electrochemical energy storage continuous operation constraints, and equipment power upper and lower limit constraints; Run the optimization model, expressed as: <h2 style=";text-align:left;direction:ltr">F3 = minC<h2 style=";text-align:left;direction:ltr"> OP Among them, C OP Indicates the annual operating cost of the system; System annual operating cost C op The annual system operating cost C is obtained through the operation optimization of a typical day. op It is expressed as: Among them, C OP.i represents the operating cost of the system on the ith typical day, d i represents the number of days corresponding to the i-th typical day, c W 、c S 、c G They represent the unit power operation cost of wind power generation, photovoltaic power generation, and gas power generation at time t, respectively. W (t), P S (t), P G (t) represents the power generation of wind power, photovoltaic power generation and gas power generation at time t, m E 、m G represents the cost of purchasing electricity and gas from the upper energy grid at time t, P E (t) represents the power purchased from the upper energy grid at time t, V G (t) represents the gas volume purchased from the superior energy network at time t; The bus power balance constraint is expressed as: P load (t)+P ES.c.i (t)=P W (t)+P P (t)+P G (t)+P ES.d.i (t) Among them, P load (t) represents the electric load power at time t, P W (t), P P (t), P G (t) respectively represent the wind turbine power supply, photovoltaic power supply, and gas power generation at time t, P ES.c.i (t), P ES.d.i (t) respectively represent the charging power and discharging power of the electrochemical energy storage at time t; The upper and lower limits of device power are expressed as: Among them, P G.i (t) represents the power generation of the gas power plant at time t, Indicates the maximum power generation capacity of a gas-fired power plant; The continuous operation constraint of electrochemical energy storage is expressed as: IN i.1 =In i.36 =In i.0 Among them, W i.1 , W i.36 Respectively represent the storage capacity of energy storage i at the beginning and end of each day, W i.0 Indicates the initial value of the energy storage charge state.

6. The low-carbon economic optimization configuration method for electrochemical energy storage according to claim 5, characterized in that: The construction of a two-layer optimization model based on parameter transfer includes the upper layer optimization model transferring energy storage power, capacity and location parameters to the lower layer model, optimizing the impact of energy storage configuration on system operation; The lower-level optimization model feeds back the optimization results of operating costs to the upper-level energy storage configuration model to further adjust the energy storage equipment configuration plan.

7. The low-carbon economic optimization configuration method for electrochemical energy storage according to claim 6, characterized in that: The solution of the two-layer optimization model includes the upper layer model using NSGA-Ⅱ genetic algorithm to perform non-dominated degree sorting, selection, crossover and mutation operations on the randomly generated initial population, and output the Pareto optimal solution set; The lower model uses a linear programming solver to optimize the operating cost of each typical day, and the two-layer model is collaboratively optimized through iterative cycles.

8. A system using the low-carbon economy optimization configuration method of electrochemical energy storage as claimed in any one of claims 1 to 7, characterized in that: Includes multi-objective energy storage configuration optimization module, operation optimization module, and system operation optimization module; The multi-objective energy storage configuration optimization module is used to construct a multi-objective energy storage configuration optimization model to optimize the system operation cost and the peak-to-valley difference of the power system operation curve according to the energy storage configuration power and capacity; The operation optimization module is used to construct an operation optimization model for the power system, and optimize the total cost of typical daily operation according to the charging and discharging power of the energy storage device and the operating status of the equipment; The system operation optimization module is used to construct a two-layer optimization model based on parameter transfer, optimize the energy storage configuration through a multi-objective genetic algorithm, and optimize the system operation through linear programming.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the low-carbon economic optimization configuration method for electrochemical energy storage according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the low-carbon economic optimization configuration method for electrochemical energy storage according to any one of claims 1 to 7 are implemented.

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