Optimization method and device for installed capacity of wind and light energy storage system based on carbon emission reduction

Abstract

The invention provides a method and a device for optimizing installed capacity of a wind and light energy storage system based on carbon emission reduction, and belongs to the field of energy storage capacity configuration. The method comprises the following steps: acquiring a typical characteristic curve of a to-be-optimized region in the whole time of a typical day in each season in a historical period; based on a pre-constructed wind-solar matching model, a wind power generation characteristic curve and a photovoltaic power generation characteristic curve, determining the optimal installed capacity matching of wind power installation and photovoltaic power generation installation of the to-be-optimized region under wind-solar complementation; the wind-solar matching model is constructed based on the minimum wind-solar abandoned power quantity, the minimum power shortage rate and the minimum output volatility; and determining the optimal wind power installed capacity, the optimal photovoltaic power generation installed capacity and the optimal energy storage capacity based on the optimal installed capacity ratio of the wind power installation and the photovoltaic power generation installation, the power utilization ratio curve and a pre-constructed energy storage capacity optimal configuration model. According to the invention, the installed capacity of the wind and light energy storage system can be reasonably configured.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage capacity configuration, and in particular to an optimization method and device for the installed capacity of a wind-solar energy storage system based on carbon emission reduction. Background Art

[0002] With the continuous strengthening of carbon emission control, the global demand for renewable energy is also increasing continuously. Renewable energy has excellent environmental protection performance, wide resource distribution, and is suitable for local development and utilization. In recent years, wind-solar energy storage distributed generation systems with energy storage devices have been more and more widely used.

[0003] However, as intermittent power sources, the outputs of wind power and photovoltaic power generation both show great uncertainty, volatility and randomness, and the output characteristics of wind power and photovoltaic power generation have the characteristics of seasonal complementarity and day-night complementarity. Reasonably adjusting the capacity ratio of wind power and photovoltaic power and configuring energy storage devices helps to suppress the fluctuation of output power and mitigate the impact of new energy on the power system.

[0004] At present, for the configuration of the installed capacity of wind-solar energy storage systems, most are targeted at maximizing the respective main body benefits or maximizing the total benefits, and the load characteristics of the power system are not fully considered, so that the current installed capacity cannot reach the optimal configuration. Summary of the Invention

[0005] The embodiments of the present invention provide an optimization method and device for the installed capacity of a wind-solar energy storage system based on carbon emission reduction to solve the problem of unreasonable configuration of the installed capacity of the current wind-solar energy storage system.

[0006] In a first aspect, the embodiments of the present invention provide an optimization method for the installed capacity of a wind-solar energy storage system based on carbon emission reduction, including:

[0007] Obtain the typical characteristic curves of each season's typical day in the historical period in the area to be optimized. The typical characteristic curves include the electricity consumption ratio curve, the wind power generation characteristic curve, and the photovoltaic power generation characteristic curve;

[0008] Based on the pre-constructed wind-solar ratio model, the wind power generation characteristic curve, and the photovoltaic power generation characteristic curve, determine the optimal installed capacity ratio of wind power installation and photovoltaic power installation under wind-solar complementarity in the area to be optimized; the wind-solar ratio model is constructed based on minimizing wind-solar curtailment, minimizing the power shortage rate, and minimizing output volatility;

[0009] Based on the optimal installed capacity ratio of wind power installation and photovoltaic power installation, the electricity consumption ratio curve, and the pre-constructed energy storage capacity optimization configuration model, determine the optimal wind power installed capacity, the optimal photovoltaic power installed capacity, and the optimal energy storage capacity.

[0010] In a possible implementation, the energy storage capacity optimization configuration model is constructed based on minimizing the operating cost of the wind-solar energy storage system, minimizing the system load power outage rate, and optimizing the system peak shaving performance.

[0011] In a possible implementation, the wind-solar ratio model R is:

[0012] R = min(F 1 + F 2 + F 3 );

[0013] where F 1 is the wind-solar curtailment amount, F 2 is the power deficiency rate, and F 3 is the output volatility;

[0014] The energy storage capacity optimization configuration model T is:

[0015] T = min(F 4 + F 5 + F 6 );

[0016] where F 4 is the operating cost of the wind-solar energy storage system, F 5 is the system load power outage rate, and F 6 is the system peak shaving performance.

[0017] In a possible implementation, the wind-solar curtailment amount F 1 is:

[0018]

[0019] where C WG = M × (αP W + βP G ); M is the total installed capacity of wind and solar, α + β = 1, α is the proportion of wind power installed capacity, β is the proportion of photovoltaic installed capacity, P W is the per-unit value of wind power output at the target time t, P G is the per-unit value of photovoltaic output at the target time t, Δt is the sampling frequency within the statistical time, C X is the accommodation at the target time t, C WG is the total output of wind and solar at the target time t, and T is a period of time;

[0020] The power deficiency rate F 2 is:

[0021]

[0022] where ΔP is the credible capacity of the newly added capacity when the ratio changes, and C ac is the difference in the newly added wind-solar capacity;

[0023] Output fluctuation F 3 is:

[0024] F 3 = std(N) + X(N);

[0025] where N is the wind-solar power output sequence converted from the wind-solar power generation characteristic curves of typical days in all seasons during the historical period, N = αL W + βL G , L W is the output sequence converted from the wind power generation characteristic curves of typical days in all seasons during the historical period, L G is the output sequence converted from the photovoltaic power generation characteristic curves of typical days in all seasons during the historical period, std(N) is the standard deviation of sequence N, X(N) is the range of sequence N, α + β = 1, α is the wind power installation ratio, and β is the photovoltaic power installation ratio.

[0026] In a possible implementation, the operating cost F of the wind-solar energy storage system 4 is:

[0027] F 4 = T 总 + T 管 ;

[0028] where T 总 is the fixed investment cost of wind power, photovoltaic power, and energy storage, and T 管 is the operating and management costs of different types;

[0029] System load power shortage rate F 5 is:

[0030] F 5 = (T 需 - T 风 - T 光 )Δt;

[0031] where T 需 is the load demand at the target time t, T 风 is the average power of wind power generation at the target time t, and T 光 is the average power of photovoltaic power generation at the target time t;

[0032] System peak shaving performance F 6 is:

[0033]

[0034] where C WG = M×(αP W + βP G ), CWG is the total wind-solar power output at the target time t, P 2 is the output power of the energy storage at the target time t, C X is the accommodation at the target time t, M is the total installed capacity of wind and solar, α + β = 1, α is the proportion of wind power installed capacity, β is the proportion of photovoltaic installed capacity, P W is the per-unit value of wind power output at the target time t, P G is the per-unit value of photovoltaic output at the target time t.

[0035] In a possible implementation, determining the optimal installed capacity ratio of wind power installation and photovoltaic power installation under wind-solar complementarity in the area to be optimized includes:

[0036] Based on the wind power generation characteristic curve, the photovoltaic power generation characteristic curve, and the preset wind-solar constraint conditions, use the improved non-dominated sorting genetic algorithm to solve the wind-solar ratio model, and determine the optimal installed capacity ratio of wind power installation and photovoltaic power installation under wind-solar complementarity in the area to be optimized; among them, the preset wind-solar constraint conditions include area constraint and penetration rate constraint.

[0037] In a possible implementation, determining the optimal wind power installed capacity, the optimal photovoltaic power installed capacity, and the optimal energy storage capacity includes:

[0038] Based on the optimal installed capacity ratio of wind power installation and photovoltaic power installation, the electricity consumption ratio curve, and the energy storage installation constraint conditions, use the improved non-dominated sorting genetic algorithm to solve the energy storage capacity optimization configuration model, and determine the optimal wind power installed capacity, the optimal photovoltaic power installed capacity, and the optimal energy storage capacity; among them, the energy storage installation constraint conditions include the total installed capacity, and the combined output rate of wind power and photovoltaic power during the annual load trough period is not higher than the daily minimum load rate.

[0039] In a possible implementation, obtaining the typical characteristic curves of each season's typical days in full time in the historical period in the area to be optimized includes:

[0040] Obtain the electricity consumption data of the four seasons in the historical period in the area to be optimized, and perform normalization processing on the obtained electricity consumption data to obtain the electricity consumption ratio curves of each season's typical days in full time in the historical period in the area to be optimized;

[0041] Obtain the photovoltaic power generation data of the four seasons in the historical period in the area to be optimized, and perform normalization processing on the obtained photovoltaic power generation data to obtain the photovoltaic power generation characteristic curves of each season's typical days in full time in the historical period in the area to be optimized;

[0042] Obtain the wind power generation data of the four seasons in the historical period in the area to be optimized, and perform normalization processing on the obtained wind power generation data to obtain the wind power generation characteristic curves of each season's typical days in full time in the historical period in the area to be optimized.

[0043] In a possible implementation, the area to be optimized includes heavy industrial areas, light industrial areas, logistics parks or agricultural industrial parks.

[0044] In a second aspect, an optimization device for the installed capacity of a wind-solar energy storage system based on carbon emission reduction provided by an embodiment of the present invention includes:

[0045] An acquisition module, configured to acquire the typical characteristic curves of each season's typical day in the historical period of the area to be optimized, and the typical characteristic curves include electricity consumption ratio curves, wind power generation characteristic curves and photovoltaic power generation characteristic curves;

[0046] A determination ratio module, configured to determine the optimal installed capacity ratio of wind power installation and photovoltaic power installation under wind-solar complementary in the area to be optimized based on a pre-constructed wind-solar ratio model, wind power generation characteristic curves and photovoltaic power generation characteristic curves; the wind-solar ratio model is constructed based on minimizing wind-solar curtailment, minimizing the power shortage rate and minimizing the output volatility;

[0047] A determination capacity module, configured to determine the optimal wind power installed capacity, the optimal photovoltaic power installed capacity and the optimal energy storage capacity based on the optimal installed capacity ratio of wind power installation and photovoltaic power installation, the electricity consumption ratio curve and a pre-constructed energy storage capacity optimization configuration model.

[0048] The optimization method for the installed capacity of a wind-solar energy storage system based on carbon emission reduction provided by an embodiment of the present invention first acquires the typical characteristic curves of each season's typical day in the historical period of the area to be optimized, and the typical characteristic curves include electricity consumption ratio curves, wind power generation characteristic curves and photovoltaic power generation characteristic curves, so as to provide a data basis for wind power installation, photovoltaic installation and energy storage capacity based on the acquired typical characteristic curves of each season's typical day. Then, based on the pre-constructed wind-solar ratio model, wind power generation characteristic curves and photovoltaic power generation characteristic curves, the optimal installed capacity ratio of wind power installation and photovoltaic power installation under wind-solar complementary in the area to be optimized can be determined. After determining the optimal installed capacity ratio, the optimal wind power installed capacity, the optimal photovoltaic power installed capacity and the optimal energy storage capacity can be determined based on the optimal installed capacity ratio of wind power installation and photovoltaic power installation, the electricity consumption ratio curve and the pre-constructed energy storage capacity optimization configuration model. When determining the optimal installed capacity of the wind-solar energy storage system, the present invention takes into account wind-solar curtailment, the power shortage rate and output volatility, so as to obtain the optimal installed capacity ratio, and thus can more accurately determine each installed capacity. Description of the Drawings

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0050] Figure 1 is the implementation flowchart of the optimization method for the installed capacity of the wind-solar energy storage system based on carbon emission reduction provided by the embodiments of the present invention;

[0051] Figure 2 is the electricity consumption ratio curve of a typical full day in each season in a certain heavy industry area during the historical period provided by the embodiments of the present invention;

[0052] Figure 3 is the electricity consumption ratio curve of a typical full day in each season in a certain light industry area during the historical period provided by the embodiments of the present invention;

[0053] Figure 4 is the electricity consumption ratio curve of a typical full day in each season in a certain logistics park during the historical period provided by the embodiments of the present invention;

[0054] Figure 5 is the photovoltaic power generation characteristic curve of a typical full day in each season in a certain place during the historical period provided by the embodiments of the present invention;

[0055] Figure 6 is the wind power generation characteristic curve of a typical full day in each season in a certain place during the historical period provided by the embodiments of the present invention;

[0056] Figure 7 is the structural schematic diagram of the optimization device for the installed capacity of the wind-solar energy storage system based on carbon emission reduction provided by the embodiments of the present invention. Detailed implementation manners

[0057] In the following description, specific details such as specific system structures and technologies are proposed for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, the detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0058] To make the purpose, technical solutions, and advantages of the present invention clearer, the following will be described through specific embodiments in conjunction with the accompanying drawings.

[0059] Figure 1The implementation flowchart of the optimization method for the installed capacity of the wind-solar energy storage system based on carbon emission reduction provided by the embodiments of the present invention is described in detail as follows:

[0060] S110. Obtain the typical characteristic curves of each season's typical days in the historical period in the area to be optimized throughout the day.

[0061] Among them, the typical characteristic curves include the electricity consumption ratio curve, the wind power generation characteristic curve, and the photovoltaic power generation characteristic curve.

[0062] In some embodiments, the area to be optimized may be: a heavy industrial area, a light industrial area, a logistics park, or an agricultural industrial park.

[0063] The heavy industrial park scenario includes industries such as metal smelting, forging, processing, and mineral mining. Enterprises usually have large-scale production capabilities, so their power load density is relatively high. At the same time, the production process is often highly continuous and the load is relatively stable. Unlike light industries or commercial areas, the load does not fluctuate with the working day and time. However, the starting moment of large equipment may cause a load peak at a specific time, and there may be characteristics of short-term impact load during the process, with a large power demand for the power grid in a short time. At the same time, the load in the heavy industrial park may be affected by seasons. Additional power may be required for heating in winter, while more power may be needed for cooling in summer.

[0064] Enterprises in the light industrial park are usually of relatively small scale, so their power load density is relatively low. The light industrial production process may not be as continuous and stable as that of heavy industries, so its load fluctuates greatly. The load may fluctuate with the working day, specific production tasks, or seasons. The daily load curve in the light industrial park may show obvious morning and evening peaks, which are related to the working hours of employees and the time of production activities. Relatively few power electronic devices are used in the light industrial park, so the harmonic content generated is relatively low, and the impact on the power quality of the power grid is small. Compared with heavy industries, there are fewer impact loads in the light industrial production process, and the instantaneous pressure on the power grid is small. The light industrial production is less affected by seasons, so the seasonal change of its load is not as obvious as that of heavy industries.

[0065] The load characteristics of a logistics park are mainly manifested in the following aspects: The load of a logistics park often has obvious periodicity. For example, during the periods when goods arrive or are distributed intensively, the power load will peak. During other periods, the load is relatively low. The peak load periods usually coincide with activities such as cargo handling, sorting, and packing, which may require a large number of mechanical equipment driven by electricity. However, some logistics equipment such as rapid doors and elevators may generate short-term impact loads when starting or stopping. Generally, the overall impact is small. To meet the demand for 24-hour operation, the load in the logistics park may also maintain a certain level at night, especially for facilities such as cold chain logistics that require continuous power supply. At the same time, the load of the logistics park may be affected by seasons. For example, during e-commerce promotion seasons, the quantity of goods to be processed will increase significantly, resulting in an increase in the load. At the same time, part of the load in the logistics park is controllable. For example, by optimizing the operation plan and equipment scheduling, the peak shaving and valley filling of the load can be achieved to a certain extent.

[0066] Agricultural production is greatly affected by seasons, and the load of agricultural industrial parks also has obvious seasonality. During the seasons of crop planting and harvesting, the electricity consumption will increase significantly. At the same time, during the agricultural production process, the use of some equipment is also intermittent, such as irrigation equipment and agricultural product processing equipment. This will cause the load in the park to fluctuate greatly within a certain period of time. Agricultural industrial parks are usually relatively scattered, and may involve multiple different regions and production links, which makes the load distribution relatively scattered. At the same time, some agricultural characteristic parks may use a large number of power equipment, such as lighting, ventilation, and heating equipment in greenhouse sheds, resulting in high energy consumption. Some links in agricultural production have high requirements for the continuity and reliability of power supply, such as agricultural product storage and processing. Once a power outage occurs, it may cause significant economic losses.

[0067] In this embodiment, the typical characteristic curves of all times on typical days in each season in the historical period of the area to be optimized are obtained, including:

[0068] The electricity consumption data in the four seasons in the historical period of the area to be optimized are obtained, and the obtained electricity consumption data are normalized to obtain the electricity consumption ratio curves of all times on typical days in each season in the historical period of the area to be optimized.

[0069] The photovoltaic power generation data in the four seasons in the historical period of the area to be optimized are obtained, and the obtained photovoltaic power generation data are normalized to obtain the photovoltaic power generation characteristic curves of all times on typical days in each season in the historical period of the area to be optimized.

[0070] The wind power generation data in the four seasons in the historical period of the area to be optimized are obtained, and the obtained wind power generation data are normalized to obtain the wind power generation characteristic curves of all times on typical days in each season in the historical period of the area to be optimized.

[0071] Taking the above-mentioned partially optimized area as an example, the electricity consumption data of a heavy industry area, a light industry area, a logistics park, and an agricultural industrial park in a certain place are obtained respectively, and the electricity consumption ratio curves of the area to be optimized at all times on typical days in each season during the historical period are drawn as shown in Figures 2 - 4 . And the photovoltaic power generation data in the four seasons during the historical period of this area and the wind power generation data in the four seasons during the historical period are obtained, and the photovoltaic power generation characteristic curves at all times on typical days in each season during the historical period and the wind power generation characteristic curves at all times on typical days in each season during the historical period are drawn as shown in Figures 5 - 6 .

[0072] S120. Based on the pre-constructed wind-solar ratio model, wind power generation characteristic curve, and photovoltaic power generation characteristic curve, determine the optimal installed capacity ratio of wind power installation and photovoltaic power installation under wind-solar complementary in the area to be optimized.

[0073] Among them, the wind-solar ratio model is constructed based on the minimum wind-solar curtailment, the lowest power shortage rate, and the minimum output volatility.

[0074] In some embodiments, the wind-solar ratio model R is:

[0075] R = min(F 1 + F 2 + F 3 );

[0076] Among them, F 1 is the wind-solar curtailment, F 2 is the power shortage rate, and F 3 is the output volatility.

[0077] In this embodiment, the wind-solar curtailment F 1 is:

[0078]

[0079] Among them, C WG = M×(αP W + βP G ); M is the total installed capacity of wind and solar, α + β = 1, α is the wind power installation ratio, β is the photovoltaic power installation ratio, P W is the per-unit value of wind power output at the target time t, P G is the per-unit value of photovoltaic power output at the target time t, Δt is the sampling frequency within the statistical time, C X is the consumption at the target time t, C WG is the total output of wind and solar at the target time t, and T is a period of time.

[0080] The power shortage rate F 2 is:

[0081]

[0082] Among them, ΔP is the credible capacity of the newly added capacity when the ratio changes, and C ac is the difference in the newly added wind and solar capacity.

[0083] The output volatility F 3 is:

[0084] F 3 = std(N) + X(N);

[0085] Among them, N is the wind-solar power output sequence converted from the wind-solar power generation characteristic curves of typical days in each season during the historical period, N = αL W + βL G , L W is the output sequence converted from the wind power generation characteristic curves of typical days in each season during the historical period, L G is the output sequence converted from the photovoltaic power generation characteristic curves of typical days in each season during the historical period, std(N) is the standard deviation of the sequence N, X(N) is the range of the sequence N, α + β = 1, α is the wind power installation ratio, and β is the photovoltaic installation ratio.

[0086] In some embodiments, based on the wind power generation characteristic curve, the photovoltaic power generation characteristic curve, and the wind-solar preset constraint conditions, an improved non-dominated sorting genetic algorithm can be used to solve the wind-solar ratio model to determine the optimal installed capacity ratio of the wind power installation and the photovoltaic power installation under wind-solar complementarity in the area to be optimized.

[0087] In this embodiment, the wind-solar preset constraint conditions include area constraint and penetration rate constraint.

[0088] The area constraint is:

[0089]

[0090] P s = nP su ,P w = mP wu ;

[0091] Among them, P wu , P su are the single-machine capacities of wind power and solar cells respectively, S wu , S su are the single-machine floor areas of wind power and photovoltaic respectively, λ s , λ w are the maximum constructible area coefficients of photovoltaic and wind power respectively, S is the area of the research area, P w , P sare the installed capacities of wind power and solar cells respectively, and both m and n are non-negative integers.

[0092] The penetration rate constraint is:

[0093]

[0094] Among them, P ws (t) is the wind power output rate, P ss (t) is the photovoltaic output rate, r is the renewable energy penetration rate requirement, L(t) is the load demand of the area to be optimized at time t, H is the total duration, and Δt is the calculation time interval.

[0095] S130. Determine the optimal wind power installed capacity, the optimal photovoltaic installed capacity, and the optimal energy storage capacity based on the optimal installed capacity ratio of wind power and photovoltaic power generation, the power consumption ratio curve, and the pre-constructed energy storage capacity optimization configuration model.

[0096] In some embodiments, the energy storage capacity optimization configuration model is constructed based on the minimum operating cost of the wind-solar-energy storage system, the lowest system load power outage rate, and the optimal system peak shaving performance.

[0097] In this embodiment, the energy storage capacity optimization configuration model T is:

[0098] T = min(F 4 + F 5 + F 6 );

[0099] Among them, F 4 is the operating cost of the wind-solar-energy storage system, F 5 is the system load power outage rate, and F 6 is the system peak shaving performance.

[0100] The operating cost F 4 of the wind-solar-energy storage system is:

[0101] F 4 = T 总 + T 管 ;

[0102] Among them, T 总 is the fixed investment cost of wind power, photovoltaic power, and energy storage, and T 管 is the operating and management costs of different types;

[0103] The system load power outage rate F 5 is:

[0104] F 5 = (T 需 - T 风 - T 光 )Δt;

[0105] Among them, T 需 is the load demand at the target time t, and T 风 is the average power of wind power generation at the target time t, and T 光 is the average power generated by photovoltaic at the target time t;

[0106] The system peak shaving performance F 6 is:

[0107]

[0108] Among them, C WG = M×(αP W + βP G ), C WG is the total output of wind and light at the target time t, P 2 is the output power of energy storage at the target time t, C X is the accommodation at the target time t, M is the total installed capacity of wind and light, α + β = 1, α is the installed capacity ratio of wind power, β is the installed capacity ratio of photovoltaic, P W is the per-unit value of wind power output at the target time t, P G is the per-unit value of photovoltaic output at the target time t.

[0109] In some embodiments, based on the optimal installed capacity ratio of wind power installation and photovoltaic power installation, the electricity consumption ratio curve, and the energy storage installation constraint conditions, an improved non-dominated sorting genetic algorithm is used to solve the energy storage capacity optimization configuration model to determine the optimal wind power installed capacity, the optimal photovoltaic power installed capacity, and the optimal energy storage capacity.

[0110] In this embodiment, the energy storage installation constraint conditions include the total installed capacity, and the combined output rate of wind power and photovoltaic during the annual load valley period is not higher than the daily minimum load rate.

[0111] The combined output rate P(t) of wind power and photovoltaic during the annual load valley period is:

[0112] P(t)= Pws(t)×CL ws + P ss (t)×CL ss ;

[0113] Normalize the combined output of wind power and photovoltaic at each moment of the whole year to obtain the combined output rate P(t)1 of wind power and photovoltaic at each moment of the whole year:

[0114] P(t)1 = P(t) / P max ;

[0115] The difference between the combined output rate P(t)1 of wind power and photovoltaic during the daily load valley period and the daily minimum load rate F min is the energy storage power demand P 2, P 2 = P(t)1 - F min .

[0116] The total capacity constraint is as follows:

[0117] 0 ≤ CL ws ≤ Z, 0 ≤ CL ss ≤ Z, 0 ≤ CL cs ≤ Z;

[0118] Among them, P ws (t) is the wind power output rate, P ss (t) is the photovoltaic power output rate, CL ws is the wind power installed capacity, CL ss is the photovoltaic installed capacity, CL cs is the photovoltaic installed capacity.

[0119] The optimization method for the installed capacity of the wind-solar energy storage system based on carbon emission reduction provided by the embodiments of the present invention first obtains the typical characteristic curves of each season's typical days in the historical period of the area to be optimized. The typical characteristic curves include the electricity consumption ratio curve, the wind power generation characteristic curve, and the photovoltaic power generation characteristic curve, so as to provide a data basis for wind power installation, photovoltaic installation, and energy storage capacity based on the obtained typical characteristic curves of each season's typical days in the whole day. Then, based on the pre-constructed wind-solar ratio model, the wind power generation characteristic curve, and the photovoltaic power generation characteristic curve, the optimal installed capacity ratio of wind power installation and photovoltaic power installation under wind-solar complementary in the area to be optimized can be determined. After determining the optimal installed capacity ratio, based on the optimal installed capacity ratio of wind power installation and photovoltaic power installation, the electricity consumption ratio curve, and the pre-constructed energy storage capacity optimization configuration model, the optimal wind power installed capacity, the optimal photovoltaic power installed capacity, and the optimal energy storage capacity can be determined. When determining the optimal installed capacity of the wind-solar energy storage system, the present invention takes into account the wind-solar abandonment power, the power shortage rate, and the output volatility, so as to obtain the optimal installed capacity ratio, and thus can more accurately determine each installed capacity.

[0120] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0121] The following is the device embodiment of the present invention. For the details not described in detail, reference can be made to the corresponding method embodiments above.

[0122] Figure 7 The structural schematic diagram of the optimization device for the installed capacity of the wind-solar energy storage system based on carbon emission reduction provided by the embodiments of the present invention is shown. For the sake of convenience of description, only the parts related to the embodiments of the present invention are shown and are described in detail as follows:

[0123] As Figure 7 shown, the optimization device 700 for the installed capacity of the wind-solar energy storage system based on carbon emission reduction includes:

[0124] An acquisition module 710, configured to acquire the typical characteristic curves of each typical day in all seasons within the historical period in the area to be optimized, where the typical characteristic curves include the electricity consumption ratio curve, the wind power generation characteristic curve, and the photovoltaic power generation characteristic curve;

[0125] A determination ratio module 720, configured to determine the optimal installed capacity ratio of the wind power installation and the photovoltaic power installation under wind-solar complementary based on the pre-constructed wind-solar ratio model, the wind power generation characteristic curve, and the photovoltaic power generation characteristic curve; the wind-solar ratio model is constructed based on minimizing the wind-solar abandonment power, minimizing the power shortage rate, and minimizing the output volatility;

[0126] A determination capacity module 730, configured to determine the optimal wind power installed capacity, the optimal photovoltaic power installed capacity, and the optimal energy storage capacity based on the optimal installed capacity ratio of the wind power installation and the photovoltaic power installation, the electricity consumption ratio curve, and the pre-constructed energy storage capacity optimization configuration model.

[0127] In a possible implementation manner, the energy storage capacity optimization configuration model is constructed based on minimizing the operating cost of the wind-solar energy storage system, minimizing the system load power shortage rate, and optimizing the system peak shaving performance.

[0128] In a possible implementation manner, the wind-solar ratio model R is:

[0129] R = min(F 1 + F 2 + F 3 );

[0130] Wherein, F 1 is the wind-solar abandonment power, F 2 is the power shortage rate, and F 3 is the output volatility;

[0131] The energy storage capacity optimization configuration model T is:

[0132] T = min(F 4 + F 5 + F 6 );

[0133] Wherein, F 4 is the operating cost of the wind-solar energy storage system, F 5 is the system load power shortage rate, and F 6 is the system peak shaving performance.

[0134] In a possible implementation manner, the wind-solar abandonment power F 1is:

[0135]

[0136] wherein, C WG = M×(αP W + βP G ); M is the total installed capacity of wind and light, α + β = 1, α is the proportion of wind power installed capacity, β is the proportion of photovoltaic installed capacity, P W is the per-unit value of wind power output at the target time t, P G is the per-unit value of photovoltaic output at the target time t, Δt is the sampling frequency within the statistical time, C X is the accommodation at the target time t, C WG is the total output of wind and light at the target time t, T is a period of time;

[0137] Power shortage rate F 2 is:

[0138]

[0139] wherein, AP is the credible capacity of the newly added capacity when the ratio is changed, C ac is the difference in the newly added wind and light capacity;

[0140] Output volatility F 3 is:

[0141] F 3 = std(N) + X(N);

[0142] wherein, N is the wind and light output sequence converted from the wind and light power generation characteristic curves of typical days in each season during the historical period, N = αL W + βL G , L W is the output sequence converted from the wind power generation characteristic curve of typical days in each season during the historical period, L G is the output sequence converted from the photovoltaic power generation characteristic curve of typical days in each season during the historical period, std(N) is the standard deviation of the sequence N, X(N) is the range of the sequence N, α + β = 1, α is the proportion of wind power installed capacity, β is the proportion of photovoltaic installed capacity.

[0143] In a possible implementation manner, the operating cost F 4 of the wind and light energy storage system is:

[0144] F 4 = T 总 + T 管 ;

[0145] wherein, T 总 is the fixed investment cost of wind power, photovoltaic and energy storage, T 管For different types of operation and management expenses;

[0146] System load power shortage rate F 5 is:

[0147] F 5 =(T 需 -T 风 -T 光 )Δt;

[0148] Among them, T 需 is the load demand at the target time t, T 风 is the average power of wind power generation at the target time t, T 光 The average power generated by photovoltaic at the target time t;

[0149] System peak shaving performance F 6 is:

[0150]

[0151] Among them, C WG =M×(αP W +βP G ), C WG is the total output of wind and light at the target time t, P 2 is the output power of energy storage at the target time t, C X is the accommodation at the target time t, M is the total installed capacity of wind and light, α+β=1, α is the installed capacity ratio of wind power, β is the installed capacity ratio of photovoltaic power, P W is the per-unit value of wind power output at the target time t, P G is the per-unit value of photovoltaic power output at the target time t.

[0152] In a possible implementation, a determination ratio module 720 is provided, which is used to solve the wind-light ratio model by using an improved non-dominated sorting genetic algorithm based on the wind power generation characteristic curve, the photovoltaic power generation characteristic curve, and the preset wind-light constraint conditions, and determine the optimal installed capacity ratio of wind power installation and photovoltaic power installation under wind-light complementarity in the area to be optimized; among them, the preset wind-light constraint conditions include area constraint and penetration rate constraint.

[0153] In a possible implementation, a determination capacity module 730 is provided, which is used to solve the energy storage capacity optimization configuration model by using an improved non-dominated sorting genetic algorithm based on the optimal installed capacity ratio of wind power installation and photovoltaic power installation, the electricity consumption ratio curve, and the energy storage installation constraint conditions, and determine the optimal wind power installed capacity, the optimal photovoltaic power installed capacity, and the optimal energy storage capacity; among them, the energy storage installation constraint conditions include the total installed capacity, and the combined output rate of wind power and photovoltaic power during the annual load trough period is not higher than the daily minimum load rate.

[0154] In a possible implementation, an acquisition module 710 is configured to acquire the power consumption data of the area to be optimized in the four seasons during the historical period, and perform normalization processing on the obtained power consumption data to obtain the power consumption ratio curves of the typical days of each season in the area to be optimized throughout the day.

[0155] Acquire the photovoltaic power generation data of the area to be optimized in the four seasons during the historical period, and perform normalization processing on the obtained photovoltaic power generation data to obtain the photovoltaic power generation characteristic curves of the typical days of each season in the area to be optimized throughout the day.

[0156] Acquire the wind power generation data of the area to be optimized in the four seasons during the historical period, and perform normalization processing on the obtained wind power generation data to obtain the wind power generation characteristic curves of the typical days of each season in the area to be optimized throughout the day.

[0157] In a possible implementation, the area to be optimized includes a heavy industry area, a light industry area, a logistics park or an agricultural industrial park.

[0158] The optimization device for the installed capacity of the wind-solar energy storage system based on carbon emission reduction provided by the embodiments of the present invention first acquires the typical characteristic curves of the typical days of each season in the area to be optimized throughout the day. The typical characteristic curves include the power consumption ratio curve, the wind power generation characteristic curve and the photovoltaic power generation characteristic curve, so that a data basis can be provided for the wind power installation, the photovoltaic power installation and the energy storage capacity based on the acquired typical characteristic curves of the typical days of each season. Then, based on the pre-constructed wind-solar ratio model, the wind power generation characteristic curve and the photovoltaic power generation characteristic curve, the optimal installed capacity ratio of the wind power installation and the photovoltaic power installation under the wind-solar complementary condition in the area to be optimized can be determined. After determining the optimal installed capacity ratio, the optimal wind power installed capacity, the optimal photovoltaic power installed capacity and the optimal energy storage capacity can be determined based on the optimal installed capacity ratio of the wind power installation and the photovoltaic power installation, the power consumption ratio curve and the pre-constructed energy storage capacity optimization configuration model. When determining the optimal installed capacity of the wind-solar energy storage system, the present invention takes into account the wind-solar abandonment power, the power shortage rate and the output volatility, so that the optimal installed capacity ratio can be obtained, and thus the installed capacity of each component can be determined more accurately.

[0159] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0160] Those of ordinary skill in the art can realize that the templates, units, and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented in electronic hardware or in a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0161] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-described various embodiments of the optimization method for the installed capacity of the wind-solar energy storage system based on carbon emission reduction. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0162] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for optimizing the installed capacity of a wind-solar-energy storage system based on carbon emission reduction, characterized in that: include: Obtain typical characteristic curves of the area to be optimized for typical days and hours in each season in the historical period, wherein the typical characteristic curves include a power consumption ratio curve, a wind power generation characteristic curve, and a photovoltaic power generation characteristic curve; Based on the pre-built wind-solar ratio model, the wind power generation characteristic curve and the photovoltaic power generation characteristic curve, determine the optimal installed capacity ratio of wind power installed capacity and photovoltaic power installed capacity in the area to be optimized under wind-solar complementarity; The wind-solar ratio model is constructed based on the minimum wind-solar power abandonment, the lowest power shortage rate and the minimum output volatility; Based on the optimal installed capacity ratio of the wind power installed capacity and the photovoltaic power generation installed capacity, the electricity consumption ratio curve and the pre-constructed energy storage capacity optimization configuration model, the optimal wind power installed capacity, the optimal photovoltaic power generation installed capacity and the optimal energy storage capacity are determined.

2. The method for optimizing the installed capacity of a wind-solar-energy storage system based on carbon emission reduction according to claim 1, characterized in that: The energy storage capacity optimization configuration model is constructed based on the minimum operating cost of the wind and solar energy storage system, the lowest system load power shortage rate and the optimal system peak load performance.

3. The method for optimizing the installed capacity of a wind-solar-energy storage system based on carbon emission reduction according to claim 2, characterized in that: The wind-solar ratio model R is: R = min (F1 + F2 + F3); Among them, F1 is the amount of wind and solar power curtailment, F2 is the power shortage rate, and F3 is the output volatility; The energy storage capacity optimization configuration model T is: T = min (F4 + F5 + F6); Among them, F4 is the operating cost of the wind, solar and energy storage system, F5 is the system load power shortage rate, and F6 is the system peak regulation performance.

4. The method for optimizing the installed capacity of a wind-solar-energy storage system based on carbon emission reduction according to claim 1, characterized in that: The wind and solar power curtailment F1 is: Among them, C WG =M×(αP W +βP G ), M is the total installed capacity of wind and solar power, α+β=1, α is the proportion of wind power installed capacity, β is the proportion of photovoltaic installed capacity, P W is the wind power output per unit value at the target time t, P G is the photovoltaic output per unit value at the target time t, Δt is the sampling frequency within the statistical time, C X is the consumption at the target time t, C WG is the total wind and solar power output at the target time t, and T is a period of time; The power shortage rate F2 is: Among them, ΔP is the credible capacity of the newly added capacity when the ratio changes, C ac The newly added wind and solar capacity is poor; The output fluctuation F3 is: F3 = std(N) + X(N); Among them, N is the wind and solar power output sequence converted from the full-time wind and solar power generation characteristic curve of typical days in each season in the historical period, N = αL W +βL G , L W is the output sequence converted from the full-time wind power generation characteristic curve of typical days in each season in the historical period, L G It is the output sequence converted from the full-time photovoltaic power generation characteristic curve of typical days in each season in the historical period. std(N) is the standard deviation of sequence N, X(N) is the range of sequence N, α+β=1, α is the proportion of wind power installed capacity, and β is the proportion of photovoltaic installed capacity.

5. The method for optimizing the installed capacity of a wind-solar-energy storage system based on carbon emission reduction according to claim 2, characterized in that: The operating cost F4 of the wind-solar energy storage system is: F4=T 总 +T 管 ; Among them, T 总 is the fixed investment cost of wind power, photovoltaic power and energy storage, T 管 For different types of operating and management costs; The system load power failure rate F5 is: F5=(T 需 -T 风 -T 光 )Δt; Among them, T 需 is the load demand at target time t, T 风 is the average power of wind power generation at the target time t, T 光 The average power of photovoltaic power generation at the target time t; The system peak load performance F6 is: Among them, C WG =M×(αP W +βP G ), C WG is the total wind and solar power output at the target time t, P2 is the output power of the energy storage at the target time t, C X is the consumption at the target time t, M is the total installed capacity of wind and solar power, α+β=1, α is the proportion of wind power installed capacity, β is the proportion of photovoltaic installed capacity, P W is the wind power output per unit value at the target time t, P G is the per-unit value of photovoltaic output at the target time t.

6. The method for optimizing the installed capacity of a wind-solar-energy storage system based on carbon emission reduction according to any one of claims 1 to 5, characterized in that: The determining of the optimal installed capacity ratio of wind power installed capacity and photovoltaic power installed capacity in the area to be optimized under wind-solar complementarity includes: Based on the wind power generation characteristic curve, photovoltaic power generation characteristic curve, and wind-solar preset constraints, an improved non-inferior classification genetic algorithm is used to solve the wind-solar ratio model to determine the optimal installed capacity ratio of wind power installed capacity and photovoltaic power installed capacity in the area to be optimized under wind-solar complementarity; wherein the wind-solar preset constraints include area constraints and permeability constraints.

7. The method for optimizing the installed capacity of a wind-solar-energy storage system based on carbon emission reduction according to any one of claims 1 to 5, characterized in that: The determination of the optimal wind power installed capacity, the optimal photovoltaic power generation installed capacity and the optimal energy storage capacity includes: Based on the optimal installed capacity ratio of the wind power installed capacity and the photovoltaic power generation installed capacity, the electricity consumption ratio curve and the energy storage installed capacity constraints, an improved non-inferior classification genetic algorithm is used to solve the energy storage capacity optimization configuration model to determine the optimal wind power installed capacity, the optimal photovoltaic power generation installed capacity and the optimal energy storage capacity; wherein the energy storage installed capacity constraints include the total installed capacity and the combined output rate of wind power and photovoltaic power during the annual load valley period not being higher than the daily minimum load rate.

8. The method for optimizing the installed capacity of a wind-solar-energy storage system based on carbon emission reduction according to any one of claims 1 to 5, characterized in that: The method of obtaining the typical characteristic curve of the area to be optimized for a typical day and a full time in each season in the historical period includes: Obtaining the electricity consumption data of the area to be optimized in four seasons during the historical period, and normalizing the obtained electricity consumption data to obtain a full-time electricity consumption ratio curve of a typical day in each season during the historical period for the area to be optimized; Acquire the photovoltaic power generation data of the region to be optimized in four seasons in the historical period, and perform normalization processing on the obtained photovoltaic power generation data to obtain the photovoltaic power generation characteristic curve of the region to be optimized on a typical day in each season in the historical period; The wind power generation data of the region to be optimized in four seasons during the historical period are obtained, and the obtained wind power generation data are normalized to obtain the full-time wind power generation characteristic curve of a typical day in each season during the historical period for the region to be optimized.

9. The method for optimizing the installed capacity of a wind-solar-energy storage system based on carbon emission reduction according to any one of claims 1 to 5, characterized in that: The areas to be optimized include heavy industrial areas, light industrial areas, logistics parks or agricultural industrial parks.

10. A device for optimizing the installed capacity of a wind-solar-energy storage system based on carbon emission reduction, characterized in that: include: An acquisition module is used to acquire typical characteristic curves of typical days and hours in each season of the area to be optimized in the historical period, wherein the typical characteristic curves include a power consumption ratio curve, a wind power generation characteristic curve, and a photovoltaic power generation characteristic curve; A ratio determination module is used to determine the optimal installed capacity ratio of wind power installed capacity and photovoltaic power installed capacity in the area to be optimized under wind-solar complementarity based on a pre-built wind-solar ratio model, the wind power generation characteristic curve and the photovoltaic power generation characteristic curve; The wind-solar ratio model is constructed based on the minimum wind-solar power abandonment, the lowest power shortage rate and the minimum output volatility; The capacity determination module is used to determine the optimal wind power installed capacity, the optimal photovoltaic power generation installed capacity and the optimal energy storage capacity based on the optimal installed capacity ratio of the wind power installed capacity and the photovoltaic power generation installed capacity, the electricity consumption ratio curve and the pre-built energy storage capacity optimization configuration model.

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