Wind-solar power generation optimization method, device and equipment based on cooperation of wind-solar energy storage and carbon emission, and storage medium

Abstract

The invention discloses a wind-solar power generation optimization method, device and equipment based on cooperation of wind-solar energy storage and carbon emission, and a storage medium. The method comprises the following steps: firstly, obtaining power grid data of a power grid at the current moment and a power data limit value; constructing a multi-objective optimization model and constraint conditions of the multi-objective optimization model by taking the sum of energy storage cost and carbon emission cost as an objective according to power grid data and the power data limit value; and finally, solving under each constraint to obtain the wind power generation capacity and the photovoltaic power generation capacity when the sum of the energy storage cost and the carbon emission cost is minimum. By implementing the method, the wind power generation capacity and the photovoltaic power generation capacity under the condition that the energy storage cost and the carbon emission cost are minimized can be optimized, and the power grid can be conveniently scheduled based on the wind power generation capacity and the photovoltaic power generation capacity.

Description

Technical Field

[0001] The present invention relates to the field of wind-solar power generation optimization technology, and in particular to a wind-solar power generation optimization method, device, equipment and storage medium based on the synergy of wind-solar energy storage and carbon emissions. Background Art

[0002] In the current energy system, wind power and photovoltaic power generation are important components of renewable energy, and their power generation has an important impact on the stability and economy of the power grid.

[0003] In the existing technology, since wind power generation and photovoltaic power generation are restricted by natural resources such as wind speed and sunshine intensity, which are uncontrollable factors, it is usually difficult to optimize wind power generation and photovoltaic power generation while minimizing energy storage costs and carbon emission costs, resulting in difficulty in relevant scheduling of the power grid based on the optimized wind power generation and photovoltaic power generation. Summary of the invention

[0004] The present invention provides a wind-solar power generation optimization method, device, equipment and storage medium based on the coordination of wind-solar energy storage and carbon emissions, which can optimize wind power generation and photovoltaic power generation while minimizing energy storage cost and carbon emission cost.

[0005] An embodiment of the present invention provides a wind-solar power generation optimization method based on wind-solar energy storage and carbon emission coordination, comprising:

[0006] Obtain the grid data and power data limit values ​​of the current moment of the grid; wherein the above-mentioned grid data include: energy storage system power, energy storage system capacity, annual power generation of the energy storage system, wind turbine power generation, photovoltaic power generation, external power purchase, external power carbon emissions, wind power carbon emissions and photovoltaic carbon emissions; the above-mentioned power data limit values ​​include: maximum energy storage system power, maximum energy storage system capacity, maximum wind turbine power generation, maximum photovoltaic power generation, maximum external power purchase, lower limit of grid load demand, maximum external power carbon emissions corresponding to maximum external power purchase, maximum wind power carbon emissions corresponding to maximum wind turbine power generation, maximum photovoltaic carbon emissions corresponding to maximum photovoltaic power generation and upper limit of carbon emission cost;

[0007] Based on the above power grid data, the energy storage cost and carbon emission cost at the current moment are calculated;

[0008] According to the above-mentioned power grid data and the above-mentioned power data limit values, a multi-objective optimization model and the constraints of the above-mentioned multi-objective optimization model are constructed with the goal of minimizing the sum of the above-mentioned energy storage cost and the above-mentioned carbon emission cost; wherein the above-mentioned constraints include: energy storage system power capacity constraints, power generation capacity constraints, load demand constraints and carbon emission constraints;

[0009] Under various constraints, the multi-objective optimization model is solved to obtain the wind power generation and photovoltaic power generation when the sum of the energy storage cost and the carbon emission cost is minimized.

[0010] Furthermore, obtaining the above power data limit value includes:

[0011] Obtaining preset power data limit values;

[0012] According to the above preset power data limit value, a preset unit investment value, a preset operation and maintenance cost, a preset kilowatt-hour cost, a preset first carbon emission cost, and a preset second carbon emission cost are calculated;

[0013] The preset energy storage cost is calculated based on the sum of the preset unit investment value, the preset operation and maintenance cost, and the preset kilowatt-hour cost;

[0014] Calculate the preset carbon emission cost according to the average of the preset first carbon emission cost and the preset second carbon emission cost;

[0015] The preset maximum grid load demand is calculated based on the sum of the preset maximum energy storage system power, the preset maximum wind turbine generator set power generation, the preset maximum photovoltaic generator set power generation and the preset maximum external power purchase amount;

[0016] Compare the preset energy storage cost with the preset energy storage cost upper limit, the preset carbon emission cost with the preset carbon emission cost upper limit, and the preset maximum grid load demand with the preset grid load demand lower limit;

[0017] If the above-mentioned preset energy storage cost is not greater than the above-mentioned preset energy storage cost upper limit, the above-mentioned preset carbon emission cost is not greater than the above-mentioned preset carbon emission cost upper limit and the above-mentioned preset maximum grid load demand is not less than the above-mentioned grid load demand lower limit, then the above-mentioned preset power data limit value shall be used as the above-mentioned power data limit value; otherwise, the size of each of the above-mentioned preset power data limit values ​​shall be adjusted, and the above-mentioned preset energy storage cost, preset carbon emission cost and preset maximum grid load demand shall be recalculated until the above-mentioned preset energy storage cost is not greater than the above-mentioned preset energy storage cost upper limit, the above-mentioned preset carbon emission cost is not greater than the above-mentioned preset carbon emission cost upper limit and the above-mentioned preset maximum grid load demand is not less than the above-mentioned grid load demand lower limit.

[0018] Furthermore, the preset unit investment value, the preset operation and maintenance cost, the preset kilowatt-hour cost, the preset first carbon emission cost and the preset second carbon emission cost are calculated based on the preset power data limit value, including:

[0019] Obtain correction factors, base discount rates, energy storage operation period, unit power investment cost coefficient, unit capacity investment cost coefficient, unit power annual operation and maintenance cost coefficient, unit capacity annual operation and maintenance cost coefficient, conversion efficiency, carbon emission factor, and carbon social cost;

[0020] The preset unit investment value is calculated based on the preset maximum energy storage system power, the preset maximum energy storage system capacity, the correction factor, the base discount rate, the energy storage operation period, the unit power investment cost coefficient and the unit capacity investment cost coefficient;

[0021] The preset operation and maintenance cost is calculated based on the preset maximum energy storage system power, the preset maximum energy storage system capacity, the annual operation and maintenance cost coefficient per unit power, and the annual operation and maintenance cost coefficient per unit capacity;

[0022] The preset cost per kilowatt-hour is calculated based on the preset unit investment value, preset operation and maintenance cost, conversion efficiency and annual power generation of the energy storage system;

[0023] The preset first carbon emission cost is calculated based on the preset maximum wind turbine power generation, the preset maximum photovoltaic power generation, the preset maximum external electricity purchase amount and the carbon emission factor;

[0024] The above-mentioned preset second carbon emission cost is calculated based on the preset maximum external electricity carbon emissions, the preset maximum wind power carbon emissions, the preset maximum photovoltaic carbon emissions and the above-mentioned carbon social cost.

[0025] Furthermore, the energy storage cost and carbon emission cost at the current moment are calculated based on the above power grid data, including:

[0026] The unit investment value is calculated based on the energy storage system power, energy storage system capacity, correction factor, base discount rate, energy storage operation period, unit power investment cost coefficient and unit capacity investment cost coefficient;

[0027] The operation and maintenance cost is calculated based on the energy storage system power, energy storage system capacity, annual operation and maintenance cost coefficient per unit power, and annual operation and maintenance cost coefficient per unit capacity;

[0028] The cost per kilowatt-hour is calculated based on the above unit investment value, operation and maintenance cost, conversion efficiency and annual power generation of the energy storage system;

[0029] The first carbon emission cost is calculated based on the power generation of the wind turbine unit, the power generation of the photovoltaic unit, the external power purchase amount and the carbon emission factor;

[0030] The second carbon emission cost is calculated based on the above external electricity carbon emissions, wind power carbon emissions, photovoltaic carbon emissions and the above carbon social cost;

[0031] The energy storage cost is calculated based on the sum of the unit investment value, operation and maintenance cost, and cost per kilowatt-hour.

[0032] The carbon emission cost is calculated according to an average value of the first carbon emission cost and the second carbon emission cost.

[0033] Furthermore, the objective function of the above multi-objective optimization model is:

[0034] min(C storage +C emission );

[0035] In the formula, C storage represents the above energy storage cost, C emission represents the above carbon emission cost.

[0036] Furthermore, the power capacity constraint of the above energy storage system is:

[0037] 0≤P ESS ≤P ESS,max ;

[0038] 0≤E ESS ≤E ESS,max ;

[0039] Where P ESS Represents the power of the energy storage system, P ESS,max Indicates the maximum energy storage system power limit, E ESS Represents the capacity of the energy storage system, E ESS,max Indicates the maximum energy storage system capacity;

[0040] The above power generation capacity constraint is:

[0041] 0≤P wind ≤P wind,max ;

[0042] 0≤P solar ≤P solar,max ;

[0043] Where P wind represents the power generation of wind turbines, P wind,max Indicates the maximum wind turbine power generation, P solar Represents the power generation of the photovoltaic unit, P solar,max Indicates the maximum power generation of the photovoltaic unit.

[0044] Furthermore, the above load demand constraint is:

[0045] P ESS +P wind +P solar +P gird ≥P 负荷需求 ;

[0046] Where P gird represents the amount of external electricity purchased, P 负荷需求 Indicates the lower limit of grid load demand;

[0047] The above carbon emission constraints are:

[0048] C emission ≤C emission,max ;

[0049] In the formula, C emission,max Represents the upper limit of carbon emission cost.

[0050] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0051] The present invention provides a wind-solar power generation optimization device based on wind-solar energy storage and carbon emission synergy, comprising:

[0052] Data acquisition module, cost calculation module, optimization model building module and model solving module;

[0053] The above-mentioned data acquisition module is used to obtain the grid data and power data limit values ​​of the current moment of the grid; wherein the above-mentioned grid data include: energy storage system power, energy storage system capacity, annual power generation of the energy storage system, wind turbine power generation, photovoltaic power generation, external power purchase, external power carbon emissions, wind power carbon emissions and photovoltaic carbon emissions; the above-mentioned power data limit values ​​include: maximum energy storage system power, maximum energy storage system capacity, maximum wind turbine power generation, maximum photovoltaic power generation, maximum external power purchase, lower limit of grid load demand, maximum external power carbon emissions corresponding to maximum external power purchase, maximum wind power carbon emissions corresponding to maximum wind turbine power generation, maximum photovoltaic carbon emissions corresponding to maximum photovoltaic power generation and upper limit of carbon emission cost;

[0054] The cost calculation module is used to calculate the energy storage cost and carbon emission cost at the current moment according to the power grid data;

[0055] The optimization model building module is used to build a multi-objective optimization model and constraints of the multi-objective optimization model based on the power grid data and the power data limit value, with the goal of minimizing the sum of the energy storage cost and the carbon emission cost; wherein the constraints include: energy storage system power capacity constraints, power generation capacity constraints, load demand constraints and carbon emission constraints;

[0056] The model solving module is used to solve the multi-objective optimization model under various constraints to obtain the wind power generation and photovoltaic power generation when the sum of the energy storage cost and the carbon emission cost is minimized.

[0057] Based on the above method embodiment, the present invention provides a terminal device embodiment;

[0058] The present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a wind-solar power generation optimization method based on the synergy of wind-solar energy storage and carbon emissions as described in any embodiment of the present invention.

[0059] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0060] The present invention provides a storage medium, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a wind-solar power generation optimization method based on the synergy of wind-solar energy storage and carbon emissions as described in any embodiment of the present invention.

[0061] The embodiments of the present invention have the following beneficial effects:

[0062] The present invention provides a wind-solar power generation optimization method, device, terminal equipment and storage medium based on the coordination of wind-solar energy storage and carbon emissions; the above method first obtains the power grid data and power data limit values ​​of the power grid at the current moment; wherein the above power grid data include: energy storage system power, energy storage system capacity, annual power generation of the energy storage system, wind turbine power generation, photovoltaic power generation, external power purchase, external power carbon emissions, wind power carbon emissions and photovoltaic carbon emissions; the above power data limit values ​​include: maximum energy storage system power, maximum energy storage system capacity, maximum wind turbine power generation, maximum photovoltaic power generation, maximum external power purchase, lower limit of power grid load demand, maximum external power carbon emissions corresponding to the maximum external power purchase, and maximum wind turbine power generation to The maximum wind power carbon emissions, the maximum photovoltaic carbon emissions corresponding to the maximum photovoltaic unit power generation, and the upper limit of the carbon emission cost; then, according to the above-mentioned power grid data, the energy storage cost and carbon emission cost at the current moment are calculated; then, according to the above-mentioned power grid data and the above-mentioned power data limit value, with the goal of minimizing the sum of the above-mentioned energy storage cost and the above-mentioned carbon emission cost, a multi-objective optimization model and the constraints of the above-mentioned multi-objective optimization model are constructed; wherein the above-mentioned constraints include: energy storage system power capacity constraints, power generation capacity constraints, load demand constraints, and carbon emission constraints; finally, under each constraint, the above-mentioned multi-objective optimization model is solved to obtain the wind power generation and photovoltaic power generation when the sum of the above-mentioned energy storage cost and the above-mentioned carbon emission cost is the smallest. Therefore, the present invention utilizes the characteristic that power generation will indirectly affect the cost and carbon emissions by meeting the load demand of the power grid. In the multi-objective optimization model, the calculation of wind power generation and photovoltaic power generation is associated with the cost and carbon emissions in the power grid, and then the optimized wind power generation and photovoltaic power generation are obtained, which is convenient for scheduling the power grid based on wind power generation and photovoltaic power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of a wind-solar power generation optimization method based on the coordination of wind-solar energy storage and carbon emissions provided by an embodiment of the present invention.

[0064] Figure 2 It is a structural schematic diagram of a wind-solar power generation optimization device based on the coordination of wind-solar energy storage and carbon emissions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] like Figure 1 As shown, an embodiment of the present invention provides a wind-solar power generation optimization method based on wind-solar energy storage and carbon emission synergy, including:

[0067] Step S101: Obtain the grid data and power data limit values ​​of the current moment of the grid; wherein the above-mentioned grid data include: energy storage system power, energy storage system capacity, annual power generation of the energy storage system, wind turbine power generation, photovoltaic power generation, external power purchase, external power carbon emissions, wind power carbon emissions and photovoltaic carbon emissions; the above-mentioned power data limit values ​​include: maximum energy storage system power, maximum energy storage system capacity, maximum wind turbine power generation, maximum photovoltaic power generation, maximum external power purchase, lower limit of grid load demand, maximum external power carbon emissions corresponding to maximum external power purchase, maximum wind power carbon emissions corresponding to maximum wind turbine power generation, maximum photovoltaic carbon emissions corresponding to maximum photovoltaic power generation and upper limit of carbon emission cost;

[0068] In a preferred embodiment, the acquisition of the power data limit value includes:

[0069] Obtaining preset power data limit values;

[0070] According to the above preset power data limit value, a preset unit investment value, a preset operation and maintenance cost, a preset kilowatt-hour cost, a preset first carbon emission cost, and a preset second carbon emission cost are calculated;

[0071] The preset energy storage cost is calculated based on the sum of the preset unit investment value, the preset operation and maintenance cost, and the preset kilowatt-hour cost;

[0072] Specifically, the preset energy storage cost is calculated according to the following formula:

[0073] C′ storage =C′ in +C′ OM +C′ ESS

[0074] In the formula, C′ storage represents the preset energy storage cost, C′ in Indicates the preset unit investment value, C′ OM represents the preset operation and maintenance cost, C′ ESS Indicates the preset cost per kilowatt-hour.

[0075] Calculate the preset carbon emission cost according to the average of the preset first carbon emission cost and the preset second carbon emission cost;

[0076] The preset maximum grid load demand is calculated based on the sum of the preset maximum energy storage system power, the preset maximum wind turbine generator set power generation, the preset maximum photovoltaic generator set power generation and the preset maximum external power purchase amount;

[0077] Specifically, the preset maximum grid load demand is calculated according to the following formula:

[0078] P′ 负荷需求 =P′ ESS +P′ wind +P′s olar +P′ gird

[0079] In the formula, P′ 负荷需求 Represents the preset maximum grid load demand, P′ ESS Represents the preset maximum energy storage system power, P′ wind Represents the preset maximum wind turbine power generation, P′ solar Indicates the preset maximum photovoltaic unit power generation, P′ gird Indicates the preset maximum external electricity purchase amount.

[0080] Compare the preset energy storage cost with the preset energy storage cost upper limit, the preset carbon emission cost with the preset carbon emission cost upper limit, and the preset maximum grid load demand with the preset grid load demand lower limit;

[0081] If the above-mentioned preset energy storage cost is not greater than the above-mentioned preset energy storage cost upper limit, the above-mentioned preset carbon emission cost is not greater than the above-mentioned preset carbon emission cost upper limit and the above-mentioned preset maximum grid load demand is not less than the above-mentioned grid load demand lower limit, then the above-mentioned preset power data limit value shall be used as the above-mentioned power data limit value; otherwise, the size of each of the above-mentioned preset power data limit values ​​shall be adjusted, and the above-mentioned preset energy storage cost, preset carbon emission cost and preset maximum grid load demand shall be recalculated until the above-mentioned preset energy storage cost is not greater than the above-mentioned preset energy storage cost upper limit, the above-mentioned preset carbon emission cost is not greater than the above-mentioned preset carbon emission cost upper limit and the above-mentioned preset maximum grid load demand is not less than the above-mentioned grid load demand lower limit.

[0082] Preferably, Boundary Value Analysis (BVA) is a commonly used black box testing method, which is a supplementary means based on the equivalence class partitioning method. The core idea of ​​this method is that errors in software are more likely to occur at the boundaries of the input or output range rather than in the middle area. Therefore, boundary value analysis focuses on testing input values ​​that are close to or at the boundaries. The advantage of boundary value analysis is its simplicity and efficiency, which can help testers find errors in potential high-risk areas.

[0083] In this preferred embodiment, the power data limit value is determined by using a boundary value analysis method, so that the setting of the power data limit value is more reasonable.

[0084] In another preferred embodiment, the preset unit investment value, the preset operation and maintenance cost, the preset kilowatt-hour cost, the preset first carbon emission cost and the preset second carbon emission cost are calculated according to the preset power data limit value, including:

[0085] Obtain correction factors, base discount rates, energy storage operation period, unit power investment cost coefficient, unit capacity investment cost coefficient, unit power annual operation and maintenance cost coefficient, unit capacity annual operation and maintenance cost coefficient, conversion efficiency, carbon emission factor, and carbon social cost;

[0086] The preset unit investment value is calculated based on the preset maximum energy storage system power, the preset maximum energy storage system capacity, the correction factor, the base discount rate, the energy storage operation period, the unit power investment cost coefficient and the unit capacity investment cost coefficient;

[0087] Specifically, the preset unit investment value is calculated according to the following formula:

[0088] C i ′ n =C(r,n)×(C P ×P E ′ SS +C E ×E ′ ESS )

[0089] In the formula, C(r,n) represents the correction factor, which is used to consider the time value of money, r represents the base discount rate, n represents the energy storage operation period, and C P Represents the unit power investment cost coefficient, C E Represents the unit capacity investment cost coefficient, E′ ESS Indicates the preset maximum energy storage system power.

[0090] The preset operation and maintenance cost is calculated based on the preset maximum energy storage system power, the preset maximum energy storage system capacity, the annual operation and maintenance cost coefficient per unit power, and the annual operation and maintenance cost coefficient per unit capacity;

[0091] Specifically, the preset operation and maintenance cost is calculated according to the following formula:

[0092] C′ OM =K O ×P′ ESS +K M ×E′ ESS

[0093] In the formula, C′ OM represents the preset operation and maintenance cost, K O Represents the annual operation and maintenance cost coefficient per unit power, K MRepresents the annual operation and maintenance cost coefficient per unit capacity.

[0094] The preset cost per kilowatt-hour is calculated based on the preset unit investment value, preset operation and maintenance cost, conversion efficiency and annual power generation of the energy storage system;

[0095] Specifically, the preset electricity cost is calculated according to the following formula:

[0096]

[0097] In the formula, C′ ESS represents the preset cost per kWh, η represents the conversion efficiency, Q ESS Represents the annual power generation of the energy storage system.

[0098] The preset first carbon emission cost is calculated based on the preset maximum wind turbine power generation, the preset maximum photovoltaic power generation, the preset maximum external electricity purchase amount and the carbon emission factor;

[0099] Specifically, the preset first carbon emission cost is calculated according to the following formula:

[0100] C′ emission_1 =(P′ gird +P′ wind +P′ solar )×δ

[0101] In the formula, C′ emission_1 represents the preset first carbon emission cost, P′ gird represents the preset maximum external electricity purchase amount, δ represents the carbon emission factor, which represents the carbon emissions generated per unit of electricity, which can be determined based on the average emission factor of the power generation type and region.

[0102] The above-mentioned preset second carbon emission cost is calculated based on the preset maximum external electricity carbon emissions, the preset maximum wind power carbon emissions, the preset maximum photovoltaic carbon emissions and the above-mentioned carbon social cost.

[0103] Specifically, the preset second carbon emission cost is calculated according to the following formula:

[0104] C′ emission_2 =SCC×∑ t (G′ gird (t)+G′ wind (t)+G′ solar (t))

[0105] In the formula, C′ emission_2 represents the preset second carbon emission cost, SCC represents the social cost of carbon, G′ gird (t) represents the preset maximum external electricity carbon emissions generated by purchasing grid electricity at time t, G′ wind(t) represents the preset maximum wind power carbon emission at time t, G′ solar (t) represents the preset maximum photovoltaic carbon emissions at time t.

[0106] In this preferred embodiment, the preset unit investment value, the preset operation and maintenance cost, the preset cost per kilowatt-hour, the preset first carbon emission cost and the preset second carbon emission cost are calculated by presetting the power data limit value.

[0107] Step S102: Calculate the energy storage cost and carbon emission cost at the current moment according to the above power grid data;

[0108] In a preferred embodiment, the energy storage cost and carbon emission cost at the current moment are calculated based on the above-mentioned power grid data, including:

[0109] The unit investment value is calculated based on the energy storage system power, energy storage system capacity, correction factor, base discount rate, energy storage operation period, unit power investment cost coefficient and unit capacity investment cost coefficient;

[0110] Specifically, the unit investment value is calculated according to the following formula:

[0111] C in =C(r,n)×(C P ×P ESS +C E ×E ESS )

[0112] In the formula, C in Represents the unit investment value, P ESS Represents the power of the energy storage system, E ESS Represents the capacity of the energy storage system.

[0113] The operation and maintenance cost is calculated based on the energy storage system power, energy storage system capacity, annual operation and maintenance cost coefficient per unit power, and annual operation and maintenance cost coefficient per unit capacity;

[0114] Specifically, the operation and maintenance cost is calculated according to the following formula:

[0115] C OM =K O ×P ESS +K M ×E ESS

[0116] In the formula, C OM Indicates operation and maintenance cost.

[0117] The cost per kilowatt-hour is calculated based on the above unit investment value, operation and maintenance cost, conversion efficiency and annual power generation of the energy storage system;

[0118] Specifically, the cost per kilowatt-hour is calculated according to the following formula:

[0119]

[0120] In the formula, C ESS Represents the cost per kilowatt-hour, which is the core indicator for measuring the economic feasibility of energy storage projects.

[0121] The first carbon emission cost is calculated based on the power generation of the wind turbine unit, the power generation of the photovoltaic unit, the external power purchase amount and the carbon emission factor;

[0122] Specifically, the first carbon emission is calculated according to the following formula:

[0123] C emission_1 =(P gird +P wind +P solar )×δ

[0124] In the formula, C emission_1 Indicates the first carbon emission, P gird represents the amount of external electricity purchased, P wind Represents the power generation of wind turbines, P solar Indicates the power generation of the photovoltaic unit.

[0125] The second carbon emission cost is calculated based on the above external electricity carbon emissions, wind power carbon emissions, photovoltaic carbon emissions and the above carbon social cost;

[0126] Specifically, the second carbon emission is calculated according to the following formula:

[0127] C emission_2 =SCC×∑ t (G gird (t)+G wind (t)+G solar (t))

[0128] In the formula, C emission_2 Represents the second carbon emission, G gird (t) represents the external electricity carbon emissions generated by purchasing grid electricity at time t, which can be obtained by calculating the product of the external electricity purchase amount and the carbon emission factor, G wind (t) represents the power generation of the wind turbine at time t, G solar (t) represents the power generation of the photovoltaic unit at time t.

[0129] The energy storage cost is calculated based on the sum of the unit investment value, operation and maintenance cost, and cost per kilowatt-hour.

[0130] Specifically, the energy storage cost is calculated according to the following formula:

[0131] Cstorage =C in +C OM +C ESS

[0132] In the formula, C storage Represents the energy storage cost.

[0133] The carbon emission cost is calculated according to an average value of the first carbon emission cost and the second carbon emission cost.

[0134] In this preferred embodiment, the energy storage cost and carbon emission cost at the current moment are calculated through the data of each power grid.

[0135] Step S103: Based on the above-mentioned power grid data and the above-mentioned power data limit value, a multi-objective optimization model and the constraints of the above-mentioned multi-objective optimization model are constructed with the goal of minimizing the sum of the above-mentioned energy storage cost and the above-mentioned carbon emission cost; wherein the above-mentioned constraints include: energy storage system power capacity constraint, power generation capacity constraint, load demand constraint and carbon emission constraint;

[0136] Specifically, the parameters and constraints in the above multi-objective optimization model can be adjusted according to specific circumstances.

[0137] In a preferred embodiment, the objective function of the multi-objective optimization model is:

[0138] min(C storage +C emission );

[0139] In the formula, C storage represents the above energy storage cost, C emission Represents the above-mentioned carbon emission cost, which means the economic loss that can be avoided by reducing unit carbon emissions.

[0140] In this preferred embodiment, the minimum value of the sum of energy storage cost and carbon emission cost is used as the objective function of the entire multi-objective optimization model.

[0141] In another preferred embodiment, the power capacity constraint of the energy storage system is:

[0142] 0≤P ESS ≤P ESS,max ;

[0143] 0≤E ESS ≤E ESS,max ;

[0144] Where P ESS Represents the power of the energy storage system, P ESS,max Indicates the maximum energy storage system power limit, E ESS Represents the capacity of the energy storage system, EESS,max Indicates the maximum energy storage system capacity;

[0145] The above power generation capacity constraint is:

[0146] 0≤P wind ≤P wind,max ;

[0147] 0≤P solar ≤P solar,max ;

[0148] Where P wind represents the power generation of wind turbines, P wind,max Indicates the maximum wind turbine power generation, P solar Represents the power generation of the photovoltaic unit, P solar,max Indicates the maximum power generation of the photovoltaic unit.

[0149] In this preferred embodiment, energy storage system power capacity constraints and power generation capacity constraints are determined.

[0150] In another preferred embodiment, the load demand constraint is:

[0151] P ESS +P wind +P solar +P gird ≥P 负荷需求 ;

[0152] Where P gird represents the amount of external electricity purchased, P 负荷需求 Indicates the lower limit of grid load demand;

[0153] The above carbon emission constraints are:

[0154] C emission ≤C emission,max ;

[0155] In the formula, C emission,max Represents the upper limit of carbon emission cost.

[0156] In this preferred embodiment, load demand constraints and carbon emission constraints are determined.

[0157] Step S104: Under various constraints, the multi-objective optimization model is solved to obtain the wind power generation and photovoltaic power generation when the sum of the energy storage cost and the carbon emission cost is minimized.

[0158] Specifically, a mathematical programming method (such as linear programming, nonlinear programming, integer programming, etc.) is used to solve the multi-objective optimization model. Matlab, CPLEX or other optimization software may also be used to solve the model.

[0159] Preferably, sensitivity analysis can be combined to determine the degree of impact of changes in key parameters in the model on the model output.

[0160] Specifically, the sensitivity analysis is as follows: the carbon emission factor, energy storage system capacity and energy storage system efficiency in the multi-objective optimization model are taken as key parameters, and a reasonable range of variation is set for these key parameters. The determination of this range of variation is based on historical data, forecasts, expert opinions or hypothetical scenarios; the value of the key parameter is changed one by one or in combination in the multi-objective optimization model, and the output result of the model is recorded after each solution; by comparing the output results of the model under different key parameter values, it can be analyzed which parameters have the greatest impact on the energy storage cost and carbon emission cost, as well as the specific direction and degree of impact.

[0161] Preferably, based on the above analysis results, the influence of key parameter changes on model output can be summarized, for example, whether an increase in a key parameter value always leads to an increase in energy storage costs or a decrease in carbon emission costs.

[0162] Preferably, through sensitivity analysis, decision makers can better understand the potential changes in model output under different operating strategies or external conditions, so as to make more informed decisions. For example, if it is found that energy storage costs are very sensitive to electricity prices, then during periods of high electricity prices, it may be necessary to consider increasing the use of energy storage systems or adjusting the generation of renewable energy.

[0163] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0164] like Figure 2 As shown, an embodiment of the present invention provides a wind-solar power generation optimization device based on wind-solar energy storage and carbon emission coordination, including: a data acquisition module, a cost calculation module, an optimization model construction module and a model solving module;

[0165] The above-mentioned data acquisition module is used to obtain the grid data and power data limit values ​​of the current moment of the grid; wherein the above-mentioned grid data include: energy storage system power, energy storage system capacity, annual power generation of the energy storage system, wind turbine power generation, photovoltaic power generation, external power purchase, external power carbon emissions, wind power carbon emissions and photovoltaic carbon emissions; the above-mentioned power data limit values ​​include: maximum energy storage system power, maximum energy storage system capacity, maximum wind turbine power generation, maximum photovoltaic power generation, maximum external power purchase, lower limit of grid load demand, maximum external power carbon emissions corresponding to maximum external power purchase, maximum wind power carbon emissions corresponding to maximum wind turbine power generation, maximum photovoltaic carbon emissions corresponding to maximum photovoltaic power generation and upper limit of carbon emission cost;

[0166] The cost calculation module is used to calculate the energy storage cost and carbon emission cost at the current moment according to the power grid data;

[0167] The optimization model building module is used to build a multi-objective optimization model and constraints of the multi-objective optimization model based on the power grid data and the power data limit value, with the goal of minimizing the sum of the energy storage cost and the carbon emission cost; wherein the constraints include: energy storage system power capacity constraints, power generation capacity constraints, load demand constraints and carbon emission constraints;

[0168] The model solving module is used to solve the multi-objective optimization model under various constraints to obtain the wind power generation and photovoltaic power generation when the sum of the energy storage cost and the carbon emission cost is minimized.

[0169] It should be noted that the device embodiments described above are merely schematic, wherein the modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. Ordinary technicians in this field can understand and implement it without creative work. The above schematic diagram is only an example of a wind-solar power generation optimization device based on the coordination of wind-solar energy storage and carbon emissions, and does not constitute a limitation on a wind-solar power generation optimization device based on the coordination of wind-solar energy storage and carbon emissions, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components.

[0170] Based on the above method item embodiments, the present invention provides corresponding terminal device item embodiments.

[0171] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements a wind-solar power generation optimization method based on the synergy of wind-solar energy storage and carbon emissions as described in any embodiment of the present invention.

[0172] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the device;

[0173] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The device may include, but is not limited to, a processor, a memory;

[0174] The processor referred to herein may be a central processing unit (CPU), 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. The general-purpose processor may be a microprocessor or any conventional processor, etc. The above-mentioned processor is the control center of the above-mentioned device, and uses various interfaces and lines to connect various parts of the entire device;

[0175] The above-mentioned memory can be used to store the above-mentioned computer programs and / or modules. The above-mentioned processor realizes various functions of the above-mentioned device by running or executing the computer programs and / or modules stored in the above-mentioned memory, and calling the data stored in the memory. The above-mentioned memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; in addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0176] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0177] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a wind-solar power generation optimization method based on the synergy of wind-solar energy storage and carbon emissions as described in any embodiment of the present invention.

[0178] In this embodiment, the storage medium is a computer-readable storage medium, the computer program includes computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0179] Compared with the prior art, by implementing the above-mentioned embodiments of the present invention, the wind power generation and photovoltaic power generation can be optimized under the condition of minimizing the energy storage cost and carbon emission cost.

[0180] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A wind-solar power generation optimization method based on wind-solar energy storage and carbon emission synergy, characterized in that: include: Obtain the grid data and power data limit values ​​of the current moment of the grid; wherein the grid data include: energy storage system power, energy storage system capacity, annual power generation of the energy storage system, wind turbine power generation, photovoltaic power generation, external power purchase, external power carbon emissions, wind power carbon emissions and photovoltaic carbon emissions; the power data limit values ​​include: maximum energy storage system power, maximum energy storage system capacity, maximum wind turbine power generation, maximum photovoltaic power generation, maximum external power purchase, lower limit of grid load demand, maximum external power carbon emissions corresponding to maximum external power purchase, maximum wind power carbon emissions corresponding to maximum wind turbine power generation, maximum photovoltaic carbon emissions corresponding to maximum photovoltaic power generation and upper limit of carbon emission cost; Calculate the energy storage cost and carbon emission cost at the current moment according to the power grid data; According to the grid data and the power data limit value, a multi-objective optimization model and constraints of the multi-objective optimization model are constructed with the goal of minimizing the sum of the energy storage cost and the carbon emission cost; wherein the constraints include: energy storage system power capacity constraints, power generation capacity constraints, load demand constraints and carbon emission constraints; Under various constraints, the multi-objective optimization model is solved to obtain the wind power generation and photovoltaic power generation when the sum of the energy storage cost and the carbon emission cost is minimized.

2. According to claim 1, a wind-solar power generation optimization method based on wind-solar energy storage and carbon emission synergy is characterized in that: The acquisition of the power data limit value includes: Obtaining preset power data limit values; According to the preset power data limit value, a preset unit investment value, a preset operation and maintenance cost, a preset kilowatt-hour cost, a preset first carbon emission cost, and a preset second carbon emission cost are calculated; Calculate the preset energy storage cost according to the sum of the preset unit investment value, the preset operation and maintenance cost, and the preset kilowatt-hour cost; Calculating a preset carbon emission cost according to an average value of the preset first carbon emission cost and the preset second carbon emission cost; The preset maximum grid load demand is calculated based on the sum of the preset maximum energy storage system power, the preset maximum wind turbine generator set power generation, the preset maximum photovoltaic generator set power generation and the preset maximum external power purchase amount; Compare the preset energy storage cost with the preset energy storage cost upper limit, the preset carbon emission cost with the preset carbon emission cost upper limit, and the preset maximum grid load demand with the grid load demand lower limit; If the preset energy storage cost is not greater than the preset energy storage cost upper limit, the preset carbon emission cost is not greater than the preset carbon emission cost upper limit, and the preset maximum grid load demand is not less than the grid load demand lower limit, then the preset power data limit value is used as the power data limit value; otherwise, adjust the size of each preset power data limit value, recalculate the preset energy storage cost, preset carbon emission cost and preset maximum grid load demand until the preset energy storage cost is not greater than the preset energy storage cost upper limit, the preset carbon emission cost is not greater than the preset carbon emission cost upper limit, and the preset maximum grid load demand is not less than the grid load demand lower limit.

3. The method for optimizing wind-solar power generation based on wind-solar energy storage and carbon emission synergy according to claim 2 is characterized in that: The step of calculating the preset unit investment value, the preset operation and maintenance cost, the preset kilowatt-hour cost, the preset first carbon emission cost, and the preset second carbon emission cost according to the preset power data limit value includes: Obtain correction factors, base discount rates, energy storage operation period, unit power investment cost coefficient, unit capacity investment cost coefficient, unit power annual operation and maintenance cost coefficient, unit capacity annual operation and maintenance cost coefficient, conversion efficiency, carbon emission factor, and carbon social cost; The preset unit investment value is calculated according to the preset maximum energy storage system power, the preset maximum energy storage system capacity, the correction factor, the base discount rate, the energy storage operation period, the unit power investment cost coefficient and the unit capacity investment cost coefficient; The preset operation and maintenance cost is calculated according to the preset maximum energy storage system power, the preset maximum energy storage system capacity, the annual operation and maintenance cost coefficient per unit power, and the annual operation and maintenance cost coefficient per unit capacity; The preset kilowatt-hour cost is calculated based on the preset unit investment value, the preset operation and maintenance cost, the conversion efficiency and the annual power generation of the energy storage system; The preset first carbon emission cost is calculated based on the preset maximum wind turbine generator set power generation, the preset maximum photovoltaic generator set power generation, the preset maximum external electricity purchase amount and the carbon emission factor; The preset second carbon emission cost is calculated based on the preset maximum external electricity carbon emissions, the preset maximum wind power carbon emissions, the preset maximum photovoltaic carbon emissions and the carbon social cost.

4. The method for optimizing wind-solar power generation based on wind-solar energy storage and carbon emission synergy according to claim 3 is characterized in that: The energy storage cost and carbon emission cost at the current moment are calculated based on the power grid data, including: Calculate the unit investment value according to the energy storage system power, energy storage system capacity, correction factor, base discount rate, energy storage operation period, unit power investment cost coefficient and unit capacity investment cost coefficient; Calculate the operation and maintenance cost according to the energy storage system power, energy storage system capacity, annual operation and maintenance cost coefficient per unit power, and annual operation and maintenance cost coefficient per unit capacity; The cost per kilowatt-hour is calculated based on the unit investment value, operation and maintenance cost, conversion efficiency and annual power generation of the energy storage system; Calculate a first carbon emission cost according to the power generation of the wind turbine set, the power generation of the photovoltaic set, the external power purchase amount and the carbon emission factor; Calculating a second carbon emission cost according to the external electricity carbon emission, wind power carbon emission, photovoltaic carbon emission and the carbon social cost; The energy storage cost is calculated according to the sum of the unit investment value, the operation and maintenance cost, and the cost per kilowatt-hour; The carbon emission cost is calculated according to an average value of the first carbon emission cost and the second carbon emission cost.

5. The method for optimizing wind-solar power generation based on wind-solar energy storage and carbon emission synergy according to claim 4 is characterized in that: The objective function of the multi-objective optimization model is: min(C storage +C emission ); In the formula, C storage represents the energy storage cost, C emission represents the carbon emission cost.

6. The method for optimizing wind-solar power generation based on wind-solar energy storage and carbon emission synergy according to claim 5 is characterized in that: The energy storage system power capacity constraint is: 0≤P ESS ≤P ESS,max ; 0≤E ESS ≤E ESS,max ; Where P ESS Represents the power of the energy storage system, P ESS,max Indicates the maximum energy storage system power limit, E ESS Represents the capacity of the energy storage system, E ESS,max Indicates the maximum energy storage system capacity; The power generation capacity constraint is: 0≤P wind ≤P wind,max ; 0≤P solar ≤P solar,max ; Where P wind Indicates the power generation of wind turbines, P wind,max Indicates the maximum wind turbine power generation, P solar Indicates the power generation of the photovoltaic unit, P solar,max Indicates the maximum power generation of the photovoltaic unit.

7. The method for optimizing wind-solar power generation based on wind-solar energy storage and carbon emission synergy according to claim 6 is characterized in that: The load demand constraint is: P ESS +P wind +P solar +P gird ≥P 负荷需求 ; Where P gird represents the amount of external electricity purchased, P 负荷需求 Indicates the lower limit of grid load demand; The carbon emission constraints are: C emission ≤C emission,max ; In the formula, C emission,max Represents the upper limit of carbon emission cost.

8. A wind-solar power generation optimization device based on wind-solar energy storage and carbon emission synergy, characterized in that: include: Data acquisition module, cost calculation module, optimization model building module and model solving module; The data acquisition module is used to obtain the grid data and power data limit values ​​of the grid at the current moment; wherein the grid data includes: energy storage system power, energy storage system capacity, annual power generation of the energy storage system, wind turbine power generation, photovoltaic power generation, external power purchase, external power carbon emissions, wind power carbon emissions and photovoltaic carbon emissions; the power data limit values ​​include: maximum energy storage system power, maximum energy storage system capacity, maximum wind turbine power generation, maximum photovoltaic power generation, maximum external power purchase, lower limit of grid load demand, maximum external power carbon emissions corresponding to maximum external power purchase, maximum wind power carbon emissions corresponding to maximum wind turbine power generation, maximum photovoltaic carbon emissions corresponding to maximum photovoltaic power generation and upper limit of carbon emission cost; The cost calculation module is used to calculate the energy storage cost and carbon emission cost at the current moment according to the power grid data; The optimization model construction module is used to construct a multi-objective optimization model and constraints of the multi-objective optimization model based on the power grid data and the power data limit value, with the goal of minimizing the sum of the energy storage cost and the carbon emission cost; wherein the constraints include: energy storage system power capacity constraints, power generation capacity constraints, load demand constraints and carbon emission constraints; The model solving module is used to solve the multi-objective optimization model under various constraints to obtain the wind power generation and photovoltaic power generation when the sum of the energy storage cost and the carbon emission cost is minimized.

9. A terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a wind-solar power generation optimization method based on the synergy of wind-solar energy storage and carbon emissions as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a wind-solar power generation optimization method based on the synergy of wind-solar energy storage and carbon emissions as described in any one of claims 1 to 7.

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