A multi-objective optimization method for energy storage capacity considering low carbon and balanced load fluctuation

Abstract

The application discloses a kind of low carbon and balanced load fluctuation's energy storage capacity multi-objective optimization configuration method, relating to energy storage capacity optimization field, a kind of low carbon and balanced load fluctuation's energy storage capacity multi-objective optimization configuration method and system are presented.The scheme not only considers the operating parameter of power system and the operating characteristics of energy storage power station, but also fuses energy structure, load fluctuation and low carbon emission reduction and other various factors, realizes the global optimization of energy storage capacity.In addition, the advanced intelligent optimization algorithm is also adopted in the present patent technology, and the solving efficiency and accuracy are improved.By comprehensively considering various factors and using advanced optimization algorithm, the present patent technology can provide more scientific and reasonable guidance for energy storage configuration optimization, and provide strong support for stable operation and sustainable development of power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage capacity optimization, and in particular to a multi-objective optimization configuration method for energy storage capacity considering low carbon and balanced load fluctuation. BACKGROUND

[0002] With the transformation of global energy structure and the rapid development of renewable energy, the power system is facing unprecedented challenges. The intermittency and uncertainty of renewable energy bring great pressure to the supply and demand balance of the power system. Energy storage technology, as a key means to regulate the supply and demand balance of the power system, has multiple functions such as smoothing renewable energy output fluctuation, improving power grid stability, reducing curtailment of wind and solar power, supporting power market transactions, and optimizing power resource configuration. Therefore, the optimization configuration of energy storage capacity is crucial for improving the overall efficiency and reliability of the power system, and is a key element of building a green, low-carbon and intelligent power system.

[0003] Although energy storage technology has been widely used in the power system, the existing energy storage capacity configuration method still has some significant shortcomings. Traditional methods often only configure energy storage capacity based on historical load data and renewable energy output prediction, ignoring the influence of low-carbon emission reduction, energy structure change and the operation characteristics of energy storage power stations. This single-dimensional configuration method often leads to inaccurate configuration of energy storage capacity, which cannot fully utilize the advantages of energy storage technology. In addition, some methods lack a global optimization perspective, only focusing on the optimization of a single energy storage power station or a local power grid, and fail to achieve the overall optimization of the entire power system.

[0004] The optimization configuration of energy storage capacity will face more challenges and opportunities. On the one hand, the continuous emergence of new energy storage technologies will provide more choices for the optimization configuration of energy storage capacity, such as lithium-ion batteries, sodium-sulfur batteries, compressed air energy storage, etc. These new energy storage technologies have different performance characteristics and application scenarios, and need to consider factors such as economy, safety and reliability for configuration. On the other hand, future energy storage capacity optimization configuration technology needs to be more green and low-carbon. SUMMARY

[0005] In order to overcome the above technical deficiencies, the present application provides a multi-objective optimization configuration method for energy storage capacity considering low carbon and balanced load fluctuation, which can provide engineering reference for energy storage capacity optimization scheme when balancing load fluctuation and controlling carbon emissions.

[0006] The technical solution adopted by the present application to overcome the technical problems is:

[0007] A multi-objective optimization configuration method for energy storage capacity considering low carbon and balanced load fluctuation, comprising:

[0008] S1. Constructing a target function f1;

[0009] S2. Construct carbon emission constraint expression Y1 and adjustment rate constraint expression Y2 for objective function f1;

[0010] S3. Construct the objective function f2;

[0011] S4. Construct system power balance constraint expression Y3, wind power generation constraint expression Y4, photovoltaic power generation constraint expression Y5, system power source side upward margin constraint expression Y6, and system power source side downward margin constraint expression Y7 for objective function f2.

[0012] S5. Construct the objective function f3;

[0013] S6. Construct the energy storage constraint expression Y8 for the objective function f3;

[0014] S7. Iterate through objective functions f1, f2, and f3, and output the value of the optimal solution based on each constraint condition;

[0015] S8. Output the value of the optimal solution to the user.

[0016] Furthermore, in step S1, the formula is used... The objective function f1 is calculated, where σ is the carbon emission tax factor and ρ is the indirect carbon emission tax factor. This refers to the carbon emissions from coal-fired power units within the system. This refers to the indirect coal emissions during the lifecycle of the unit equipment within the system.

[0017] Furthermore, through the formula The carbon emissions from coal-fired power units within the system were calculated. In the formula, τ is the carbon emission factor, and A P B P C P All are coal fuel quantity coefficients for thermal power units, P p,t Let t be the power output of the thermal power unit.

[0018] Through formula The indirect coal emissions during the lifecycle of the unit equipment within the system were calculated. In the formula, 'a' represents the indirect coal emission conversion factor during the life cycle of thermal power unit equipment, 'b' represents the indirect coal emission conversion factor during the life cycle of wind power unit equipment, 'c' represents the indirect coal emission conversion factor during the life cycle of photovoltaic unit equipment, and 'd' represents the indirect coal emission conversion factor during the life cycle of energy storage equipment. P wpp,t Let P be the power output of the new energy wind turbine at time t. pv,t Let P be the power output of the new energy photovoltaic unit at time t. esd,t Let t be the power of the energy storage system at time t.

[0019] Further, the carbon emission constraint expression Y1 is:

[0020] wherein is a direct carbon emission threshold, is an indirect carbon emission threshold;

[0021] For the thermal power unit adjustment space, the adjustment rate constraint expression Y2 is:

[0022] wherein P Lower is a unit adjustment space, which is a lower adjustment space value of the unit, is a unit adjustment space, which is an upper adjustment space value of the unit, p,t-1 is a thermal power unit power at t-1, is a unit adjustment rate, which is an upper limit value of an upper adjustment rate of the unit, V up is a unit adjustment rate, which is a lower limit value of an upper adjustment rate of the unit, is a unit adjustment rate, which is an upper limit value of a lower adjustment rate of the unit, V dw is a unit adjustment rate, which is a lower limit value of a lower adjustment rate of the unit.

[0023] Further, the target function f2 is calculated by the formula in step S3, wherein T D is a system scheduling period, n,t is a defined balance power, nav,t is an average value of the balance power in the scheduling period.

[0024] Further, the defined balance power P n,t is calculated by the formula f,t wpp,t pv,t r,t n,t , wherein P r,t is a power grid disturbance power at t, f,t is a system demand load at t, and the average value P of the balance power in the scheduling period is calculated by the formula nav,t .

[0025] Further, the system power balance constraint expression Y3 is:

[0026] P p,t + P wpp,t + P pv,t + P​​​​esd,t +P r,t =P f,t ;

[0027] The constraint Y4 for wind power generation is:

[0028] in P represents the power limitation value of the wind turbine unit at time t. wpp,t-1 The power of the new energy wind turbine at time t-1. V wpp,up Given the lower limit of the upward adjustment power change rate of the wind turbine, Given the upper limit of the upward adjustment power change rate of the wind turbine, V wpp,dw Given the lower limit of the downward adjustment power change rate of the wind turbine, The upper limit of the downward adjustment power change rate for a given wind turbine unit;

[0029] The photovoltaic power generation constraint Y5 is:

[0030] Where P pv,t-1 The power of the new energy photovoltaic unit at time t-1 The power limit of the photovoltaic generator unit at time t. V pv,up Given the lower limit of the upward adjustment power change rate of the photovoltaic generator set, Given the upper limit of the upward adjustment power change rate of the photovoltaic generator set, V pv,dw Given the lower limit of the downward regulation power change rate of a photovoltaic generator set, This represents the upper limit of the downward adjustment power change rate for a given photovoltaic generator set;

[0031] The expression for the upward margin constraint Y6 on the system power supply side is:

[0032] in Let t be the upper limit of the power output of the thermal power unit. Let S be the upper limit of the energy storage system power at time t. up a is the upward margin on the system power supply side. fb a is the electrical load margin measurement factor. wppb a is the wind power margin coefficient. pvb This is the margin coefficient for photovoltaic power generation;

[0033] The expression for the downward margin constraint Y7 on the system power supply side is:

[0034] inP p,t is the lower limit of the power of the thermal power generating unit at time t, S dw is the downward margin of the power supply side of the system, is the lower limit margin coefficient of the thermal power generating unit.

[0035] Further, the target function f3 is calculated in step S5 by the formula f3 = min (μP esd,n + θP esd,t +W), wherein P esd,n is the rated device capacity of the energy storage system, μ is the equivalent daily capacity cost of the energy storage system, θ is the equivalent power cost of the energy storage system, and W is the equivalent daily average maintenance cost of the energy storage system.

[0036] Further, the energy storage constraint condition expression Y8 is:

[0037] wherein P esd,t-1 is the power of the energy storage system at time t-1, is the device capacity limit value of the energy storage system, is the operating power limit value of the energy storage system at time t, and K r is the operating power limit factor.

[0038] Further, the target function f1, the target function f2, and the target function f3 are iteratively solved in step S7 by the multi-objective grey wolf algorithm, and the value of the optimal solution is output.

[0039] The beneficial effects of the present application are: a multi-objective optimization configuration method and system for energy storage capacity considering low carbon and balanced load fluctuation are proposed. The scheme not only considers the operating parameters of the power system and the operating characteristics of the energy storage power station, but also integrates various factors such as energy structure, load fluctuation, and low-carbon emission reduction, thereby realizing global optimization of the energy storage capacity. In addition, the present patent technology also adopts an advanced intelligent optimization algorithm, thereby improving the solving efficiency and accuracy. By comprehensively considering various factors and adopting an advanced optimization algorithm, the present patent technology can provide more scientific and reasonable guidance for energy storage configuration optimization, and provide strong support for stable operation and sustainable development of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 is the method roadmap of the present application;

[0041] Figure 2 is the method flowchart of the present application. DETAILED DESCRIPTION

[0042] The present application will be further described below with reference to the accompanying Figure 1 , the accompanying Figure 2 of the present application.

[0043] A multi-objective optimization configuration method of energy storage capacity considering low carbon and balanced load fluctuation, comprising:

[0044] S1. Construct a target function f1.

[0045] S2. Construct a carbon emission constraint condition expression Y1 and an adjustment rate constraint condition expression Y2 for the target function f1.

[0046] S3. Construct a target function f2.

[0047] S4. Construct a system power balance constraint condition expression Y3, a wind power generation constraint condition expression Y4, a photovoltaic power generation constraint condition expression Y5, an upward margin constraint condition expression Y6 of the system power supply side, and a downward margin constraint condition expression Y7 of the system power supply side for the target function f2.

[0048] S5. Construct a target function f3.

[0049] S6. Construct an energy storage constraint condition expression Y8 for the target function f3.

[0050] S7. Iteratively solve the target function f1, the target function f2, and the target function f3, and output the value of the optimal solution.

[0051] S8. Output the value of the optimal solution to the user.

[0052] The carbon emission cost on the source side is mainly derived from the direct carbon emission of the combustion of fossil fuels and the indirect carbon emission in the life cycle of the operation and maintenance of various source-side units, so in an embodiment of the present application, the target function f1 is calculated by the formula in the step S1, wherein σ is a carbon emission tax factor, ρ is an indirect carbon emission tax factor, is the coal-fired carbon emission of the thermal power unit in the system, is the indirect coal carbon emission in the life cycle of the unit equipment in the system.

[0053] In an embodiment of the present application, the coal-fired carbon emission of the thermal power unit in the system is calculated by the formula wherein τ is a carbon emission factor, A P , B P , C P are coal carbon fuel quantity coefficients of the thermal power unit, P p,t is the power of the thermal power unit at time t;

[0054] The indirect coal carbon emission in the life cycle of the unit equipment in the system is calculated by the formula ​​Wherein a is the indirect coal carbon emission conversion factor of the thermal power unit equipment in the life cycle, b is the indirect coal carbon emission conversion factor of the wind power unit equipment in the life cycle, c is the indirect coal carbon emission conversion factor of the photovoltaic unit equipment in the life cycle, d is the indirect coal carbon emission conversion factor of the energy storage equipment in the life cycle, P wpp,t P pv,t P esd,t P

[0055] In an embodiment of the present application, the carbon emission constraint condition expression Y1 is:

[0056] Wherein is a direct carbon emission threshold, is an indirect carbon emission threshold.

[0057] For the thermal power unit adjustment space, the adjustment rate constraint condition expression Y2 is:

[0058] Wherein P Lower is the unit adjustment space, which is the lower adjustment space value of the unit, is the unit adjustment space, which is the upper adjustment space value of the unit, P p,t-1 is the thermal power unit power at t-1, is the adjustment rate of the thermal power unit, which is the upper limit value of the upper adjustment rate of the unit, V up is the adjustment rate of the thermal power unit, which is the lower limit value of the upper adjustment rate of the unit, is the adjustment rate of the thermal power unit, which is the upper limit value of the lower adjustment rate of the unit, V dw is the adjustment rate of the thermal power unit, which is the lower limit value of the lower adjustment rate of the unit.

[0059] The system takes the normal user demand load as a benchmark, and in the case of ensuring the normal demand load, the new energy power integrated into the power grid and the power grid disturbance power caused by the fault are taken as the objects that need to be balanced and smoothed by the system, and a balance power is defined, which is composed of the wind power unit actual power, the photovoltaic power unit power and the power grid side disturbance power, so in an embodiment of the present application, the target function f2 is calculated by the formula in step S3, wherein T D is the scheduling period of the system, P n,t is the defined balance power, P nav,t is the average value of the balance power in the scheduling period.

[0060] The formula P n,t =Pf,t -P wpp,t -P pv,t -P r,t The defined balance power P is calculated n,t , wherein P r,t is the grid disturbance power at time t, P f,t is the system demand power at time t, and the average value P of the balance power in the dispatching period is calculated through the formula nav,t The system power balance constraint condition expression Y3 is:

[0061] P p,t + P wpp,t + P pv,t + P esd,t + P r,t = P f,t .

[0062] The wind power generation constraint condition Y4 is:

[0063] wherein P is the power limit value of the wind turbine at time t, P wpp,t-1 is the new energy wind turbine power at time t-1, V wpp,up is the lower limit value of the upward adjustment power change rate of the given wind turbine, is the upper limit value of the upward adjustment power change rate of the given wind turbine, V wpp,dw is the lower limit value of the downward adjustment power change rate of the given wind turbine, is the upper limit value of the downward adjustment power change rate of the given wind turbine.

[0064] The photovoltaic power generation constraint condition Y5 is:

[0065] wherein P pv,t-1 is the new energy photovoltaic turbine power at time t-1, is the power limit of the photovoltaic turbine at time t, V pv,up is the lower limit value of the upward adjustment power change rate of the given photovoltaic turbine, is the upper limit value of the upward adjustment power change rate of the given photovoltaic turbine, V pv,dw is the lower limit value of the downward adjustment power change rate of the given photovoltaic turbine, is the upper limit value of the downward adjustment power change rate of the given photovoltaic turbine.

[0066] The upward margin constraint condition expression Y6 of the system power supply side is:

[0067] wherein is the upper limit value of the power of the thermal power generating unit at time t, is the upper limit value of the power of the energy storage system at time t, up is the upward margin of the power supply side of the system, fb is the load margin coefficient of the power supply side of the system, wppb is the wind power margin coefficient, pvb is the photovoltaic power margin coefficient.

[0068] The downward margin constraint condition expression Y7 of the power supply side of the system is:

[0069] wherein P p,t is the lower limit of the power of the thermal power generating unit at time t, dw is the downward margin of the power supply side of the system, is the lower limit margin coefficient of the thermal power generating unit.

[0070] The energy storage capacity optimization of the present patent is not only the single cost optimization of the energy storage, but also the simultaneous optimization of the above two objective functions, so in an embodiment of the present patent, the objective function f3 is calculated by formula f3 = min (μP esd,n + θP esd,t + W) in step S5, wherein P esd,n is the rated device capacity of the energy storage system, μ is the equivalent daily capacity cost of the energy storage system, θ is the equivalent power cost of the energy storage system, and W is the equivalent daily average maintenance cost of the energy storage system.

[0071] In an embodiment of the present patent, the energy storage constraint condition expression Y8 is:

[0072] wherein P esd,t-1 is the power of the energy storage system at time t-1, is the device capacity limit value of the energy storage system, is the operating power limit value of the energy storage system at time t, r is a unit charging rate guarantee defined for the safe operation of the energy storage system, so that the charge and discharge amount within adjacent time periods will not exceed the operating power limit factor of the energy storage system.

[0073] In one embodiment of the present application, the target function f1, the target function f2, and the target function f3 are iteratively solved by the multi-objective grey wolf algorithm in step S7, and according to the set carbon emission constraint condition Y1, the adjustment rate constraint condition Y2, the system power balance constraint condition Y3, the wind power generation constraint condition Y4, the photovoltaic power generation constraint condition Y5, the upward margin constraint condition Y6 of the system power side, the downward margin constraint condition Y7 of the system power side, and the energy storage constraint condition Y8, the optimal solution satisfying all the constraint conditions is obtained, and the value of the optimal solution is output to the user.

[0074] Finally, it should be noted that: the above only for the preferred embodiments of the present application, and not for limiting the present application, although the foregoing embodiments of the present application are described in detail, for those skilled in the art, it still can be modified, or part of the technical features of the equivalent replacement of the technical solutions recorded in the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-objective optimization method for energy storage capacity considering low carbon emissions and load balancing fluctuations, characterized in that, include: S1. Construct the objective function f1; S2. Construct carbon emission constraint expression Y1 and adjustment rate constraint expression Y2 for objective function f1; S3. Construct the objective function f2; S4. Construct system power balance constraint expression Y3, wind power generation constraint expression Y4, photovoltaic power generation constraint expression Y5, system power source side upward margin constraint expression Y6, and system power source side downward margin constraint expression Y7 for objective function f2. S5. Construct the objective function f3; S6. Construct the energy storage constraint expression Y8 for the objective function f3; S7. Iterate through objective functions f1, f2, and f3, and output the value of the optimal solution based on each constraint condition; S8. Output the value of the optimal solution to the user.

2. The multi-objective optimization configuration method for energy storage capacity considering low carbon emissions and load balancing fluctuations as described in claim 1, characterized in that: In step S1, the formula is used The objective function f1 is calculated, where σ is the carbon emission tax factor and ρ is the indirect carbon emission tax factor. This refers to the carbon emissions from coal-fired power units within the system. This refers to the indirect coal emissions during the lifecycle of the unit equipment within the system.

3. The multi-objective optimization configuration method for energy storage capacity considering low carbon emissions and load balancing fluctuations as described in claim 2, characterized in that: Through formula The carbon emissions from coal-fired power units within the system were calculated. In the formula, τ is the carbon emission factor, and A P B P C P All are coal fuel quantity coefficients for thermal power units, P p,t Let t be the power output of the thermal power unit. Through formula The indirect coal emissions during the lifecycle of the unit equipment within the system were calculated. In the formula, 'a' represents the indirect coal emission conversion factor during the life cycle of thermal power unit equipment, 'b' represents the indirect coal emission conversion factor during the life cycle of wind power unit equipment, 'c' represents the indirect coal emission conversion factor during the life cycle of photovoltaic unit equipment, and 'd' represents the indirect coal emission conversion factor during the life cycle of energy storage equipment. P wpp,t Let P be the power output of the new energy wind turbine at time t. pv,t Let P be the power output of the new energy photovoltaic unit at time t. esd,t Let t be the power of the energy storage system at time t.

4. The multi-objective optimization configuration method for energy storage capacity considering low carbon emissions and load balancing fluctuations according to claim 3, characterized in that: The carbon emission constraint expression Y1 is: in The threshold for direct carbon emissions. This is a threshold for indirect carbon emissions; For the regulation space of thermal power units, the expression for the regulation rate constraint Y2 is: in P Lower The unit's regulation space is the unit's lower regulation space value. The unit's regulation space is the regulation space value on the unit, P. p,t-1 Let be the power of the thermal power unit at time t-1. The regulating rate of a thermal power unit is the upper limit of the unit's regulating rate. V up The regulating rate of a thermal power unit is the lower limit of the unit's upper regulating rate. The regulating rate of a thermal power unit is the upper limit of the unit's lower regulating rate. V dw The regulating rate of a thermal power unit is the lower limit of the unit's lower regulating rate.

5. The multi-objective optimization configuration method for energy storage capacity considering low carbon emissions and load balancing fluctuations as described in claim 3, characterized in that: In step S3, the formula is used. The objective function f2 is calculated, where T D P is the system's scheduling period. n,t For the defined balanced power, P nav,t This is the average value of the balancing power during the scheduling cycle.

6. The multi-objective optimization configuration method for energy storage capacity considering low carbon emissions and load balancing fluctuations as described in claim 5, characterized in that: Through formula P n,t =P f,t -P wpp,t -P pv,t -P r,t The calculated equilibrium power P is obtained. n,t In the formula P r,t Let P be the power of the power grid disturbance at time t. f,t Let be the system's required electrical load at time t, expressed by the formula... The average value P of the balanced power during the scheduling period was calculated. nav,t .

7. The multi-objective optimization configuration method for energy storage capacity considering low carbon emissions and load balancing fluctuations as described in claim 6, characterized in that: The system power balance constraint expression Y3 is: P p,t +P wpp,t +P pv,t +P esd,t +P r,t =P f,t ; The constraint Y4 for wind power generation is: in P represents the power limitation value of the wind turbine unit at time t. wpp,t-1 The power of the new energy wind turbine at time t-1. V wpp,up Given the lower limit of the upward adjustment power change rate of the wind turbine, Given the upper limit of the upward adjustment power change rate of the wind turbine, V wpp,dw Given the lower limit of the downward adjustment power change rate of the wind turbine, The upper limit of the downward adjustment power change rate for a given wind turbine unit; The photovoltaic power generation constraint Y5 is: Where P pv,t-1 The power of the new energy photovoltaic unit at time t-1 The power limit of the photovoltaic generator unit at time t. V pv,up Given the lower limit of the upward adjustment power change rate of the photovoltaic generator set, Given the upper limit of the upward adjustment power change rate of the photovoltaic generator set, V pv,dw Given the lower limit of the downward regulation power change rate of a photovoltaic generator set, This represents the upper limit of the downward adjustment power change rate for a given photovoltaic generator set; The expression for the upward margin constraint Y6 on the system power supply side is: in Let t be the upper limit of the power output of the thermal power unit. Let S be the upper limit of the energy storage system power at time t. up For the upward margin on the system power supply side, a fb a is the power load margin measurement factor. wppb a is the wind power margin coefficient. pvb This is the margin coefficient for photovoltaic power generation; The expression for the downward margin constraint Y7 on the system power supply side is: in P p,t S represents the lower limit of the power output of the thermal power unit at time t. dw This is the downside margin on the system power supply side. This is the lower limit margin coefficient for thermal power units.

8. The multi-objective optimization configuration method for energy storage capacity considering low carbon emissions and load balancing fluctuations according to claim 3, characterized in that: In step S5, the formula f3 = min(μP) is used. esd,n +θP esd,t The objective function f3 is obtained by calculating +W, where P... esd,n Let μ be the rated capacity of the energy storage system, μ be the equivalent daily capacity cost of the energy storage system, θ be the equivalent power cost of the energy storage system, and W be the equivalent daily unit average maintenance cost of the energy storage system.

9. The multi-objective optimization configuration method for energy storage capacity considering low carbon emissions and load balancing fluctuations as described in claim 8, characterized in that: The energy storage constraint expression Y8 is: Where P esd,t-1 Let be the power of the energy storage system at time t-1. This refers to the device capacity limit value for energy storage systems. Let K be the operating power limit of the energy storage system at time t. r This is the operating power limiting factor.

10. The multi-objective optimization configuration method for energy storage capacity considering low carbon emissions and load balancing fluctuations according to claim 1, characterized in that: In step S7, the objective functions f1, f2, and f3 are iteratively solved using the multi-objective gray wolf algorithm, and the optimal solution is output.

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