Energy storage capacity multi-objective optimization configuration method considering low-carbon property and balanced load fluctuation

Abstract

The invention discloses an energy storage capacity multi-objective optimization configuration method considering low carbon and balanced load fluctuation, relates to the technical field of energy storage capacity optimization, and provides an energy storage capacity multi-objective optimization configuration method and system considering low carbon and balanced load fluctuation. According to the scheme, operation parameters of a power system and operation characteristics of an energy storage power station are considered, various factors such as an energy structure, load fluctuation and low-carbon emission reduction are fused, and global optimization of the energy storage capacity is achieved. In addition, an advanced intelligent optimization algorithm is adopted in the technology of the patent, and the solving efficiency and precision are improved. By comprehensively considering various factors and adopting an advanced optimization algorithm, the technology of the patent can provide more scientific and reasonable guidance for energy storage configuration optimization, and provides powerful support for stable operation and sustainable development of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage capacity optimization, and in particular to a multi-objective optimization configuration method for energy storage capacity taking low carbon characteristics and balanced load fluctuation into consideration. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, the power system is facing unprecedented challenges. The intermittent and uncertain nature of renewable energy has brought tremendous pressure to the balance of supply and demand in the power system. As a key means of regulating the balance of supply and demand in the power system, energy storage technology has multiple functions such as smoothing the fluctuation of renewable energy output, improving the stability of the power grid, reducing the phenomenon of wind and solar power abandonment, supporting power market transactions and optimizing the allocation of power resources. Therefore, the optimal configuration of energy storage capacity is crucial to improving the overall efficiency and reliability of the power system, and is a key element in building a green, low-carbon, and intelligent power system.

[0003] Although energy storage technology has been widely used in power systems, there are still some significant deficiencies in existing energy storage capacity configuration methods. Traditional methods often configure energy storage capacity based only on historical load data and renewable energy output forecasts, ignoring the impact of low-carbon emission reduction, changes in energy structure, and the operating characteristics of energy storage power stations themselves. This single-dimensional configuration method often leads to inaccurate configuration of energy storage capacity and fails to give full play to the advantages of energy storage technology. In addition, some methods lack a global optimization perspective and only focus on the optimization of a single energy storage power station or a local power grid, failing to achieve the overall optimization of the entire power system.

[0004] The optimal 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 options for the optimal 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 be configured by comprehensively considering factors such as their economy, safety and reliability. On the other hand, the future energy storage capacity optimization configuration technology needs to be more green and low-carbon. Summary of the invention

[0005] In order to overcome the deficiencies of the above technologies, the present invention provides a multi-objective optimization configuration method for energy storage capacity that takes into account low carbon and balanced load fluctuations and can provide an engineering reference for energy storage capacity optimization solutions when balancing load fluctuations and controlling carbon emissions.

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

[0007] A multi-objective optimization configuration method for energy storage capacity taking into account low carbon performance and balanced load fluctuations, comprising:

[0008] S1. Construct objective function f1;

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

[0010] S3. Construct objective function f2;

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

[0012] S5. construct objective function f3;

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

[0014] S7. Iteratively solve the objective function f1, objective function f2, and objective function f3, and output the value of the optimal solution according to each constraint condition;

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

[0016] Furthermore, in step S1, the formula The objective function f1 is calculated, where σ is the carbon emission tax factor and ρ is the indirect carbon emission tax factor. is the carbon emission of coal burning in the thermal power units in the system, It refers to the indirect coal carbon emissions during the life cycle of the unit equipment in the system.

[0017] Furthermore, through the formula Calculate the carbon emissions of coal-fired power units in the system Where τ is the carbon emission factor, A P , B P , C P All are coal carbon fuel coefficients of thermal power units, P p,t is the power of the thermal power unit at time t;

[0018] By formula Calculate the indirect coal carbon emissions during the life cycle of the unit equipment in the system Where a is the indirect coal carbon emission conversion factor of thermal power unit equipment during its life cycle, b is the indirect coal carbon emission conversion factor of wind turbine equipment during its life cycle, c is the indirect coal carbon emission conversion factor of photovoltaic unit equipment during its life cycle, d is the indirect coal carbon emission conversion factor of energy storage equipment during its life cycle, P wpp,t is the power of the new energy wind turbine at time t, P pv,t is the power of the new energy photovoltaic unit at time t, P esd,t is the power of the energy storage system at time t.

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

[0020] in is the direct carbon emission threshold, is the indirect carbon emissions threshold;

[0021] For the regulation space of thermal power units, the regulation rate constraint expression Y2 is:

[0022] in P Lower The adjustment space for the unit is the adjustment space value for the unit. The adjustment space of the unit is the adjustment space value of the unit, P p,t-1 is the power of the thermal power unit at time t-1, The regulation rate of the thermal power unit is the upper limit of the upper regulation rate of the unit. V up The regulation rate of the thermal power unit is the lower limit of the upper regulation rate of the unit. The regulation rate of the thermal power unit is the upper limit of the lower regulation rate of the unit. V dw The regulation rate of the thermal power unit is the lower limit of the unit's lower regulation rate.

[0023] Furthermore, in step S3, the formula Calculate the objective function f2, where T D is the system scheduling period, P n,t is the defined equilibrium power, P nav,t It is the average value of the balanced power during the scheduling period.

[0024] Furthermore, through the formula P n,t =P f,t -P wpp,t -P pv,t -P r,t The defined balanced power P is calculated n,t , where P r,t is the grid disturbance power at time t, P f,t is the system power load required at time t, through the formula Calculate the average value P of the balanced power during the scheduling period nav,t .

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

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

[0027] Wind power generation constraint Y4 is:

[0028] in is the power limit value of the wind turbine at time t, P wpp,t-1 is the power of the new energy wind turbine at time t-1, V wpp,up To adjust the lower limit of the power change rate upward for a given wind turbine, To adjust the upper limit of the power change rate of a given wind turbine upward, V wpp,dw The lower limit of the power change rate for a given wind turbine is adjusted downward. Adjusting downward the upper limit of the power change rate for a given wind turbine group;

[0029] Photovoltaic power generation constraint Y5 is:

[0030] Where P pv,t-1 is the power of the new energy photovoltaic unit at time t-1, is the power limit of the photovoltaic generator set at time t, V pv,up For a given photovoltaic generator set, adjust the power change rate lower limit upward, For a given photovoltaic generator set, the upper limit of the power change rate is adjusted upward. V pv,dw is the lower limit of the downward power change rate of a given photovoltaic generator set, Adjusting downward the upper limit of the power change rate for a given photovoltaic power generation group;

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

[0032] in is the upper limit of thermal power unit power at time t, is the upper limit of the energy storage system power at time t, S up is the upward margin of the system power supply side, a fb is the power load margin coefficient, a wppb is the wind power margin coefficient, a pvb is the photovoltaic power generation margin coefficient;

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

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

[0035] Further, in step S5, the formula f3=min(μP esd,n +θP esd,t +W) to calculate the objective function f3, where 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 unit average maintenance cost of the energy storage system.

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

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

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

[0039] The beneficial effect of the present invention is that a multi-objective optimization configuration method and system for energy storage capacity taking into account low carbon and balanced load fluctuations is proposed. This scheme not only takes into account the operating parameters of the power system and the operating characteristics of the energy storage power station, but also integrates multiple factors such as energy structure, load fluctuations, and low-carbon emission reduction, thereby achieving global optimization of energy storage capacity. In addition, this patented technology also adopts advanced intelligent optimization algorithms to improve the efficiency and accuracy of the solution. By comprehensively considering multiple factors and adopting advanced optimization algorithms, this patented technology can provide more scientific and reasonable guidance for the optimization of energy storage configuration, and provide strong support for the stable operation and sustainable development of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A method roadmap for the present invention;

[0041] Figure 2 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0042] The following is combined with Figure 1 , Attachment Figure 2 The present invention is further described.

[0043] A multi-objective optimization configuration method for energy storage capacity taking into account low carbon performance and balanced load fluctuations, comprising:

[0044] S1. Construct the objective function f1.

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

[0046] S3. Construct the objective function f2.

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

[0048] S5. Construct objective function f3.

[0049] S6. Construct energy storage constraint expression Y8 for objective function f3.

[0050] S7. Iteratively solve objective function f1, objective function f2, and objective 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 mainly comes from direct carbon emissions from the combustion of fossil fuels and indirect carbon emissions during the life cycle such as operation and maintenance of various source-side units. Therefore, in one embodiment of the present invention, in step S1, the formula The objective function f1 is calculated, where σ is the carbon emission tax factor and ρ is the indirect carbon emission tax factor. is the carbon emission of coal burning in the thermal power units in the system, It refers to the indirect coal carbon emissions during the life cycle of the unit equipment in the system.

[0053] In one embodiment of the present invention, by formula Calculate the carbon emissions of coal-fired power units in the system Where τ is the carbon emission factor, A P , B P , C P All are coal carbon fuel coefficients of thermal power units, P p,t is the power of the thermal power unit at time t;

[0054] By formula Calculate the indirect coal carbon emissions during the life cycle of the unit equipment in the system Where a is the indirect coal carbon emission conversion factor of thermal power unit equipment during its life cycle, b is the indirect coal carbon emission conversion factor of wind turbine equipment during its life cycle, c is the indirect coal carbon emission conversion factor of photovoltaic unit equipment during its life cycle, d is the indirect coal carbon emission conversion factor of energy storage equipment during its life cycle, P wpp,t is the power of the new energy wind turbine at time t, P pv,t is the power of the new energy photovoltaic unit at time t, P esd,t is the power of the energy storage system at time t.

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

[0056] in is the direct carbon emission threshold, is the indirect carbon emissions threshold.

[0057] For the regulation space of thermal power units, the regulation rate constraint expression Y2 is:

[0058] in P Lower The adjustment space for the unit is the adjustment space value for the unit. The adjustment space of the unit is the adjustment space value of the unit, P p,t-1 is the power of the thermal power unit at time t-1, The regulation rate of the thermal power unit is the upper limit of the upper regulation rate of the unit. V up The regulation rate of the thermal power unit is the lower limit of the upper regulation rate of the unit. The regulation rate of the thermal power unit is the upper limit of the lower regulation rate of the unit. V dw The regulation rate of the thermal power unit is the lower limit of the unit's lower regulation rate.

[0059] The system takes the normal user demand load as the benchmark. Under the condition of ensuring the normal demand load, the new energy power connected to the grid and the grid disturbance power caused by the fault are taken as the objects that the system needs to balance and balance, and defines a balanced power. The balanced power is composed of the actual power of the wind turbine, the power of the photovoltaic generator and the grid-side disturbance power. Therefore, in one embodiment of the present invention, in step S3, the formula Calculate the objective function f2, where T D is the system scheduling period, P n,t is the defined equilibrium power, P nav,t It is the average value of the balanced power during the scheduling period.

[0060] By formula P n,t =Pf,t -P wpp,t -P pv,t -P r,t The defined balanced power P is calculated n,t , where P r,t is the grid disturbance power at time t, P f,t is the system power load required at time t, through the formula Calculate the average value P of the balanced power during the scheduling period nav,t The system power balance constraint expression Y3 is:

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

[0062] Wind power generation constraint Y4 is:

[0063] in is the power limit value of the wind turbine at time t, P wpp,t-1 is the power of the new energy wind turbine at time t-1, V wpp,up To adjust the lower limit of the power change rate upward for a given wind turbine, To adjust the upper limit of the power change rate of a given wind turbine upward, V wpp,dw The lower limit of the power change rate for a given wind turbine is adjusted downward. Adjust the upper limit of the power change rate downward for a given wind turbine.

[0064] Photovoltaic power generation constraint Y5 is:

[0065] Where P pv,t-1 is the power of the new energy photovoltaic unit at time t-1, is the power limit of the photovoltaic generator set at time t, V pv,up For a given photovoltaic generator set, adjust the power change rate lower limit upward, For a given photovoltaic generator set, the upper limit of the power change rate is adjusted upward. V pv,dw is the lower limit of the downward power change rate of a given photovoltaic generator set, Adjust the upper limit of the power change rate downward for a given photovoltaic generator set.

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

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

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

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

[0070] The energy storage capacity optimization of this patent is not only a single cost optimization of energy storage, but also a simultaneous optimization of the above two objective functions. Therefore, in one embodiment of the present invention, in step S5, the formula f3=min(μP esd,n +θP esd,t +W) to calculate the objective function f3, where 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 unit average maintenance cost of the energy storage system.

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

[0072] Where P esd,t-1 is the power of the energy storage system at time t-1, is the device capacity limit of the energy storage system, is the operating power limit value of the energy storage system at time t, K r In order to ensure the safe operation of the energy storage system, a storage unit charging rate is defined to ensure that the charge and discharge amounts in adjacent moments will not exceed the operating power limit factor of the energy storage system.

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

[0074] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-objective optimization configuration method for energy storage capacity taking into account low carbon and balanced load fluctuations, characterized in that: include: S1. Construct objective function f1; S2. Constructing the carbon emission constraint condition expression Y1 and the adjustment rate constraint condition expression Y2 for the objective function f1; S3. Construct objective function f2; S4. Construct the system power balance constraint expression Y3, the wind power generation constraint expression Y4, the photovoltaic power generation constraint expression Y5, the upward margin constraint expression Y6 on the system power supply side and the downward margin constraint expression Y7 on the system power supply side for the objective function f2; S5. construct objective function f3; S6. Constructing energy storage constraint expression Y8 for objective function f3; S7. Iteratively solve the objective function f1, objective function f2, and objective function f3, and output the value of the optimal solution according to 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 taking into account low carbon performance and balanced load fluctuation according to claim 1 is characterized in that: In step S1, the formula The objective function f1 is calculated, where σ is the carbon emission tax factor and ρ is the indirect carbon emission tax factor. is the carbon emission of coal combustion in the thermal power units in the system, It refers to the indirect coal carbon emissions during the life cycle of the unit equipment in the system.

3. The multi-objective optimization configuration method for energy storage capacity taking into account low carbon performance and balanced load fluctuations according to claim 2 is characterized in that: By formula Calculate the carbon emissions of coal-fired power units in the system Where τ is the carbon emission factor, A P , B P , C P All are coal carbon fuel coefficients of thermal power units, P p,t is the power of the thermal power unit at time t; By formula Calculate the indirect coal carbon emissions during the life cycle of the unit equipment in the system Where a is the indirect coal carbon emission conversion factor of thermal power unit equipment during its life cycle, b is the indirect coal carbon emission conversion factor of wind turbine equipment during its life cycle, c is the indirect coal carbon emission conversion factor of photovoltaic unit equipment during its life cycle, d is the indirect coal carbon emission conversion factor of energy storage equipment during its life cycle, P wpp,t is the power of the new energy wind turbine at time t, P pv,t is the power of the new energy photovoltaic unit at time t, P esd,t is the power of the energy storage system at time t.

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

5. The multi-objective optimization configuration method for energy storage capacity taking into account low carbon performance and balanced load fluctuations according to claim 3 is characterized in that: In step S3, the formula Calculate the objective function f2, where T D is the system scheduling period, P n,t is the defined equilibrium power, P nav,t It is the average value of the balanced power during the scheduling period.

6. The multi-objective optimization configuration method for energy storage capacity taking into account low carbon performance and balanced load fluctuations according to claim 5 is characterized in that: By formula P n,t =P f,t -P wpp,t -P pv,t -P r,t The defined balanced power P is calculated n,t , where P r,t is the grid disturbance power at time t, P f,t is the system power load required at time t, through the formula Calculate the average value P of the balanced power during the scheduling period nav,t .

7. The multi-objective optimization configuration method for energy storage capacity taking into account low carbon performance and balanced load fluctuations according to claim 6 is 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 ; Wind power generation constraint Y4 is: in is the power limit value of the wind turbine at time t, P wpp,t-1 is the power of the new energy wind turbine at time t-1, V wpp,up To adjust the lower limit of the power change rate upward for a given wind turbine, To adjust the upper limit of the power change rate of a given wind turbine upward, V wpp,dw The lower limit of the power change rate for a given wind turbine is adjusted downward. Adjusting downward the upper limit of the power change rate for a given wind turbine group; Photovoltaic power generation constraint Y5 is: Where P pv,t-1 is the power of the new energy photovoltaic unit at time t-1, is the power limit of the photovoltaic generator set at time t, V pv,up For a given photovoltaic generator set, adjust the power change rate lower limit upward, For a given photovoltaic generator set, the upper limit of the power change rate is adjusted upward. V pv,dw is the lower limit of the downward power change rate of a given photovoltaic generator set, Adjusting downward the upper limit of the power change rate for a given photovoltaic power generation group; The upward margin constraint condition expression Y6 on the system power supply side is: in is the upper limit of thermal power unit power at time t, is the upper limit of the energy storage system power at time t, S up is the upward margin of the system power supply side, a fb is the power load margin coefficient, a wppb is the wind power margin coefficient, a pvb is the photovoltaic power generation margin coefficient; The downward margin constraint condition expression Y7 on the system power supply side is: in P p,t is the lower limit of thermal power unit power at time t, S dw is the downward margin of the system power supply side, It is the lower limit margin coefficient of thermal power unit.

8. The multi-objective optimization configuration method for energy storage capacity taking into account low carbon performance and balanced load fluctuations according to claim 3 is characterized in that: In step S5, the formula f3=min(μP esd,n +θP esd,t +W) to calculate the objective function f3, where 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 unit average maintenance cost of the energy storage system.

9. The multi-objective optimization configuration method for energy storage capacity taking into account low carbon performance and balanced load fluctuations according to claim 8 is characterized in that: The energy storage constraint expression Y8 is: Where P esd,t-1 is the power of the energy storage system at time t-1, is the device capacity limit of the energy storage system, is the operating power limit value of the energy storage system at time t, K r is the operating power limiting factor.

10. The multi-objective optimization configuration method for energy storage capacity taking into account low carbon performance and balanced load fluctuations according to claim 1 is characterized in that: In step S7, the objective function f1, objective function f2, and objective function f3 are iteratively solved by a multi-objective grey wolf algorithm, and the value of the optimal solution is output.

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