A method and system for optimizing DC transmission curves of a multi-energy complementary integrated transmission base

Abstract

The present invention discloses a method and system for optimizing the DC transmission curve of a multi-energy complementary integrated transmission base. According to the annual DC transmission curve based on resource matching and load matching under the same DC utilization hours, combined with the clean energy curtailment rate and the receiving-end load matching, the optimal annual transmission curve is determined; according to the optimal annual transmission curve and the typical daily load curve of the receiving-end power grid, the optimal daily transmission curve that matches the receiving-end power demand is determined; based on the typical daily load curve of the receiving-end power grid, the peak-shaving slope time sequence is obtained; according to the peak-shaving slope time sequence, the slope sequence is combined with the mode sequence comparison table to obtain the peak-shaving mode sequence; based on the peak-shaving mode sequence, the peak-shaving time sequence transmission upper limit is calculated; based on the peak-shaving time sequence transmission upper limit, the optimal daily transmission curve is corrected. The present invention can effectively take into account the needs of the transmitting and receiving ends and optimize the DC transmission curve in power generation systems with a high proportion of clean electricity, such as large-scale hydro-wind-solar, wind-solar-thermal storage integration.

Description

Technical Field

[0001] The present invention relates to a method and system for optimizing a DC transmission curve of a multi-energy complementary integrated transmission base based on trend pattern matching, taking into account sending-end resource matching, receiving-end load characteristics and peak-shaving needs, and belongs to the field of new power system design. Background Art

[0002] As my country's economic development enters a new normal, the energy industry is undergoing a profound shift. To achieve the "30.60" dual carbon goals, the proportion of non-fossil energy consumption in terminal energy consumption must be increased urgently. At the same time, to accelerate the construction of a new power system dominated by renewable energy, the penetration rate of renewable energy will continue to rise. How to rationally allocate renewable energy resources, enhance the grid's absorption capacity, and reduce the rate of renewable energy curtailment while ensuring grid security and stability have become critical issues that need to be addressed.

[0003] Therefore, in-depth research has been conducted both domestically and internationally on the multi-energy complementarity of large-scale wind, solar, hydro, thermal, and energy storage power bases, focusing primarily on the optimal scheduling of multi-generation units, power capacity allocation methods, and sequential production simulation. First, the optimal scheduling of multi-generation units is achieved through the establishment of a multi-scale clean energy spatiotemporal model, an optimized coordinated scheduling algorithm, and a comprehensive operation and control mechanism. For example, for wind, solar, and hydro multi-energy power systems, short-term optimal scheduling methods based on stochastic programming or the POS algorithm have been proposed to solve the optimization objective function of minimizing water curtailment and coal consumption. Second, for power generation capacity allocation, particle swarm optimization and mixed integer linear programming algorithms have been proposed to solve the optimal capacity allocation of hybrid power generation systems. However, most of these studies have focused on typical wind and photovoltaic output scenarios and are unable to fully simulate the complexities of system operation. Therefore, sequential production simulation incorporating the characteristics of renewable energy sources has become a research focus. For example, by considering the output characteristics of hydro, wind, and solar power, load characteristics, unit peak-shaving capacity, and grid network transmission constraints, clean energy sequential production simulation models have been established. Sequential operation simulation techniques have also been used to verify the interconnection benefits of a specific region in my country.

[0004] In summary, research on large-scale multi-energy complementary development focuses primarily on unit modeling and optimized scheduling models, as well as time-series production simulation based on rigid constraints. However, large-scale multi-energy complementary bases typically transmit power to load centers via ultra-high voltage AC / DC channels. Balancing the characteristics of sending-end resources with the load and peak-shaving characteristics of receiving ends, and optimizing power source ratios and transmission curves, are key factors affecting the clean energy absorption capacity and quality of the power transmitted from these bases. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for optimizing the DC transmission curve of a multi-energy complementary integrated transmission base, so as to solve the problem that the relationship between the sending-end resource matching, the receiving-end load characteristics and the peak-shaving demand is not considered in a multi-energy complementary power generation system with a high proportion of clean electricity.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] On the one hand, a method for optimizing a DC transmission curve of a multi-energy complementary integrated transmission base includes:

[0008] Based on the annual DC transmission curve based on resource matching and load matching under the same DC utilization hours, the optimal annual transmission curve is determined by combining the clean energy curtailment rate and the receiving-end load matching degree;

[0009] Determining an optimal daily power transmission curve that matches the power demand of the receiving end based on the optimal annual power transmission curve and the typical daily load curve of the receiving end power grid;

[0010] Based on the typical daily load curve of the receiving power grid, a peak-shaving balance sequence of the receiving power grid is calculated, and a peak-shaving slope time sequence is obtained by calculating the slope of the peak-shaving balance sequence; according to the peak-shaving slope time sequence, a peak-shaving mode sequence is obtained by combining the slope sequence with a mode sequence comparison table;

[0011] Based on the peak-shaving mode sequence, the peak-shaving time sequence power transmission upper limit is calculated; based on the peak-shaving time sequence power transmission upper limit, the optimal daily power transmission curve is corrected to obtain the optimal daily power transmission curve that takes into account the receiving end power demand and the peak-shaving demand.

[0012] Furthermore, the method of determining the optimal annual power transmission curve based on the annual DC power transmission curve based on resource matching and load matching under the same DC utilization hours, combined with the clean energy curtailment rate and the receiving end load matching, includes:

[0013] Establish an objective function that includes the clean energy curtailment rate and the matching degree of the receiving load:

[0014] min R g,k +λ k R l,k (1)

[0015] Where λ k is the weight factor, R g,k is the clean energy curtailment rate in the kth month, R l,k is the receiving end load matching degree in the kth month;

[0016] The constraints of the objective function are:

[0017]

[0018]

[0019] 0≤R g,k ≤R g,kmax (4)

[0020]

[0021] Where, E g,k 、E l,k are the power transmission amount based on the resource distribution of the sending end and the power transmission amount based on the load demand of the receiving end in the kth month under the same DC utilization hours, E o,k is the optimized DC transmission capacity in the kth month, ω(k) is the resource curtailment indicator function in the kth month, and E c,k is the estimated electricity space of the sending grid in the kth month, R g,kmax is the limit value of the clean energy curtailment rate in the kth month, T dc For the same DC utilization hours, P omax is the rated capacity of the DC transmission channel;

[0022] Based on the constraints, the objective function is solved to obtain the optimal annual power transmission curve E o =[E o,1 ,E o,2 ,…,E o,k ,…,E o,12 ].

[0023] Furthermore, the electricity space E of the sending-end power grid in the kth month c,k Estimated according to the following method:

[0024] The power balance profit or loss of the sending power grid at time t in month k is calculated according to the following formula

[0025]

[0026] Where, They represent the power output and load of the sending-end power grid at time t in month k;

[0027] Select The minimum value is taken as the power profit or loss P at the kth month installed control moment c,k , then the electricity space E for the kth month in this area c,k , estimated by the following formula:

[0028]

[0029] Where N k represents the number of days in the kth month, ΔT is the duration of the period, T d Calculate the total duration for a day.

[0030] Furthermore, determining the optimal daily power transmission curve that matches the power demand of the receiving end based on the optimal annual power transmission curve and the typical daily load curve of the receiving end power grid includes:

[0031] Determine the DC daily power transmission curve for the kth month based on the optimal annual power transmission curve Where T d Calculate the total duration for a day;

[0032] According to the typical daily load curve of the receiving power grid in the kth month Determine the power transmission weight for each period of the day

[0033]

[0034] Where, represents the load level of the receiving power grid at time t in the kth month, L o,kmax 、L o,kmin L o,k The maximum and minimum values ​​in ;

[0035] Based on the typical daily load curve of the receiving power grid in the kth month and the power transmission weights of each period of the day, the objective function is established:

[0036]

[0037] Where, is the DC power transmission quantity at time t in the kth month, P omax is the rated capacity of the DC transmission channel, N k is the number of days in the kth month;

[0038] The constraints of the objective function are:

[0039]

[0040] Where ΔT is the time period, E o,k is the DC power transmission quantity in the kth month;

[0041] Based on the constraints, the objective function is iteratively solved to obtain the optimal daily power transmission curve that matches the power demand of the receiving end.

[0042] Furthermore, the receiving-end power grid peak balancing sequence Calculated according to the following formula:

[0043]

[0044] Where, is the minimum output of the conventional power supply unit of the receiving grid at time t, are the wind power and photovoltaic output of the receiving power grid at time t respectively.

[0045] Furthermore, the peak shaving slope timing sequence is calculated according to the following formula:

[0046]

[0047] Where C' o,k is the peak slope timing sequence, for The slope of change, C o,kmax For sequence C o,k The absolute maximum value, is the compression factor.

[0048] Furthermore, the peak shaving mode sequence According to the following table:

[0049] Slope sequence and pattern sequence comparison table

[0050]

[0051] in, is the lower threshold for the slope pattern sequence change, is the middle threshold of the slope pattern sequence change, is the high threshold of the slope pattern sequence change, ε c is the unit change of the peak-shaving mode sequence.

[0052] Furthermore, the peak load timing power transmission upper limit is calculated according to the following formula:

[0053]

[0054] Where, is the upper limit of peak-shaving power transmission at time t.

[0055] Furthermore, the modifying of the optimal daily power transmission curve based on the peak-shaving time sequence power transmission upper limit includes:

[0056] Daily power transmission curve Peak load transmission constraints should be met:

[0057]

[0058] Where, is the upper limit of peak load transmission at time t;

[0059] Combining equations (9) and (14) to calculate the daily power transmission curve Perform iterative optimization to obtain the optimal daily power transmission curve that takes into account both the power demand of the receiving end and the peak load demand

[0060] On the other hand, a multi-energy complementary integrated transmission base DC transmission curve optimization system includes:

[0061] The module for determining the optimal annual power transmission curve is configured to determine the optimal annual power transmission curve based on the annual DC power transmission curve based on resource matching and load matching under the same DC utilization hours, combined with the clean energy curtailment rate and the receiving end load matching degree;

[0062] an optimal daily power transmission curve determination module, configured to determine an optimal daily power transmission curve that matches the power demand of the receiving end based on the optimal annual power transmission curve and a typical daily load curve of the receiving end power grid;

[0063] A peak shaving mode sequence determination module is configured to calculate a peak shaving balance sequence of the receiving-end power grid based on a typical daily load curve of the receiving-end power grid, and obtain a peak shaving slope time sequence sequence by calculating the slope of the peak shaving balance sequence; and obtain a peak shaving mode sequence based on the peak shaving slope time sequence sequence in combination with a slope sequence and a mode sequence comparison table;

[0064] The optimal daily power transmission curve correction module is configured to calculate the peak-shaving time sequence power transmission upper limit based on the peak-shaving time sequence power transmission upper limit, and correct the optimal daily power transmission curve based on the peak-shaving time sequence power transmission upper limit to obtain the optimal daily power transmission curve that takes into account the receiving end power demand and the peak-shaving demand.

[0065] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0066] In large-scale power generation systems with a high proportion of clean electricity, such as hydropower, wind power, solar power, and wind power, solar power, and thermal storage, this invention can effectively optimize the DC transmission curve by taking into account the needs of the sending and receiving ends, reasonably optimize the power supply ratio of clean energy bases, and improve the quality of power transmission with a high proportion of new energy. With the current "carbon peak" and "carbon neutrality" goals, the power system is facing a structural transformation. The power system based on new energy will bring revolutionary changes to the power supply structure, power supply ratio, power flow, and grid topology of the traditional power grid. When evaluating the power supply scheme of the sending end of the DC transmission system, this invention can reasonably design the DC transmission curve based on the sending end resources, receiving end load, and receiving end peak regulation demand, thereby providing a supporting reference for DC operation mode and power supply organization. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is the overall flow chart of the method of the present invention;

[0068] Figure 2 This is the hydro-level annual output curve for the Jinsha River Basin;

[0069] Figure 3 Typical output characteristics of wind power and photovoltaic power in the Jinsha River Basin: (a) average output of wind power throughout the year, (b) typical output characteristics of wind power in four seasons, (c) average output of photovoltaic power throughout the year, and (d) typical output curve of photovoltaic power in four seasons.

[0070] Figure 4 The profit and loss trend of power in Sichuan Province;

[0071] Figure 5 The optimal annual power transmission curve for Baihetan to Jiangsu, (a) annual power transmission curve optimization (low scheme), (b) annual power transmission curve under different utilization hours;

[0072] Figure 6 The load and renewable energy output curves for typical days in Jiangsu Province during the four seasons, (a) high load scenario, (b) anti-peak load scenario;

[0073] Figure 7 The typical peak-shaving time series curves for each season;

[0074] Figure 8 Optimal power transmission curves for typical days in four seasons: (a) spring, (b) summer, (c) autumn, and (d) winter.

[0075] Figure 9 Schematic diagram of the morphological pattern of the time series. DETAILED DESCRIPTION

[0076] The present invention will be further described below in conjunction with specific examples. The following examples are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0077] like Figure 1 As shown, a method for optimizing a DC transmission curve of a multi-energy complementary integrated transmission base includes the following steps:

[0078] Step S1: Determine the optimal annual power transmission curve based on the annual DC power transmission curve based on resource matching and load matching under the same DC utilization hours, combined with the clean energy curtailment rate and the receiving end load matching degree;

[0079] In combination with the existing power transmission situation of DC transmission channels, taking into account the full absorption of sending-end resources, and referring to the utilization hours level of existing DC transmission, the DC utilization hours are selected to determine the matching DC utilization hours.

[0080] For the kth month of the sending grid, the power balance profit and loss at time t is calculated according to the following formula:

[0081]

[0082] Where, They represent the power output and load of the sending-end power grid at time t in the kth month respectively.

[0083] Select The minimum value is taken as the power profit or loss P at the kth month installed control moment c,k , then the electricity space E for the kth month in this area c,k , can be estimated by the following formula:

[0084]

[0085] Where N k represents the number of days in the kth month, ΔT is the duration of the period, T d Calculate the total duration of a day. c,k >0 indicates that the sending-end power grid has sufficient space to absorb the surplus power of the multi-energy complementary power generation system; E c,k <0 indicates that the sending-end grid has surplus power that can be transmitted via the DC channel.

[0086] The optimization of the annual power transmission curve is mainly based on the consideration of the changing trend of the load curve, and further matching the water, wind and solar resources at the sending end.

[0087] To this end, an objective function including the clean energy curtailment rate and the matching degree of the receiving load is established:

[0088] minR g,k +λ k R l,k (3)

[0089] Where λ k is the weight factor, R g,k is the clean energy curtailment rate in the kth month, R l,k is the receiving end load matching degree in the kth month.

[0090] Clean energy curtailment rate R g,k Matching degree with receiving end load R l,k The estimation method is as follows:

[0091]

[0092]

[0093] Where, E g,k 、E l,k are the power transmission amount based on the resource distribution of the sending end and the power transmission amount based on the load demand of the receiving end in the kth month under the same DC utilization hours, E o,k is the optimized DC transmission capacity in the kth month, ω(k) is the resource curtailment indicator function in the kth month, and E c,k is the estimated electricity space of the sending grid in the kth month.

[0094] Clean energy curtailment rate R g,k The power abandonment rate limit should not be exceeded, i.e.

[0095] 0≤R g,k ≤R g,kmax (6)

[0096] Where R g,kmax is the limit value of the clean energy curtailment rate in the kth month.

[0097] E o,k It should also meet the constraints of annual power transmission under the same DC utilization hours, namely:

[0098]

[0099] Where, T dc For the same DC utilization hours, P omax is the rated capacity of the DC transmission channel.

[0100] Therefore, the constraints of the objective function are:

[0101]

[0102]

[0103] 0≤R g,k ≤R g,kmax

[0104]

[0105] Based on the objective function of formula (3), the optimal annual power transmission curve E is obtained by optimizing and iterating under the above constraints. o =[E o,1 ,E o,2 ,…,E o,k ,…,E o,12 ].

[0106] Step S2, determining an optimal daily power transmission curve that matches the power demand of the receiving end based on the optimal annual power transmission curve and the typical daily load curve of the receiving end power grid;

[0107] The optimal annual power transmission curve is determined through step S1, thereby determining the power transmission amount E for each month. o,k On this basis, the DC daily power transmission curve It should match the daily load characteristics of the receiving end, and also take into account the anti-peak characteristics of the supporting new energy in the receiving area to reduce the absorption pressure at the receiving end.

[0108] For the typical daily load curve of the receiving power grid in the kth month Due to the limitation of power resources at the sending end, it is difficult to transmit power completely according to the load change trend, but the power demand during the peak load period should be guaranteed. Therefore, the power transmission weights at different time periods are set. As shown in the following formula:

[0109]

[0110] Where, represents the load level of the receiving power grid at time t in the kth month, L o,kmax 、L o,kmin L o,k The maximum and minimum values ​​in .

[0111] In order to match the power demand of the receiving end, the daily power transmission curve is further optimized and the objective function is established as follows:

[0112]

[0113] Where, is the DC power transmission quantity at time t in month k, N k is the number of days in the kth month.

[0114] The power constraints of the annual power transmission curve should be met during the optimization process, namely:

[0115]

[0116] Where ΔT is the time period, E o,k is the DC power transmission amount in the kth month.

[0117] Based on the constraint conditions of formula (10), the objective function of formula (9) is iteratively solved to obtain the optimal daily power transmission curve that matches the power demand of the receiving end.

[0118] Step S3: Based on the typical daily load curve of the receiving power grid, the receiving power grid peak shaving balance sequence is calculated, and the slope of the peak shaving balance sequence is calculated to obtain a peak shaving slope time sequence sequence; according to the peak shaving slope time sequence sequence, the slope sequence is combined with a mode sequence comparison table to obtain a peak shaving mode sequence;

[0119] Obtain the optimal daily power transmission curve that matches the power demand of the receiving end Finally, the curve should further meet the peak load constraints of the receiving power grid.

[0120] The peak-shaving balance sequence of the receiving end in month k is calculated according to the following formula:

[0121]

[0122] Where, is the minimum output of the conventional power supply unit of the receiving grid at time t, are the wind power and photovoltaic output of the receiving power grid at time t respectively. This indicates that it is difficult for wind and solar power to peak-shave in the receiving power grid, and DC power transmission should reduce its output to participate in peak-shaving.

[0123] Sequence C o,k It reflects the changing trend of the output and load of new energy in the receiving power grid. In order to reflect the trend of peak load change in detail, the trend is divided into 7 situations: rapid rise, rapid rise, slow rise, flat, slow fall, rapid fall, rapid fall, respectively, with {3ε c ,2ε c ,ε c ,0,-ε c ,-2ε c ,-3ε c} is described by the pattern sequence, ε c is the unit change of the pattern sequence. The morphological change trend and pattern description of the sequence are as follows: Figure 9 shown.

[0124] The slope is a variable that reflects the speed of curve change. In order to accurately describe the morphological pattern of the peak-shaving sequence, the peak-shaving slope time series is calculated according to the following slope calculation method of the peak-shaving balance sequence:

[0125]

[0126] Where C' o,k is the peak slope timing sequence, for The slope of change, C o,kmax For sequence C o,k The absolute maximum value, is the compression factor.

[0127] Get the peak slope timing sequence C' o,k Finally, refer to Table 1 to get the relationship between the slope sequence and the pattern sequence. The value of can be obtained by comparing with Table 1. is the lower threshold for the slope pattern sequence change, is the middle threshold of the slope pattern sequence change, is the high threshold of the slope pattern sequence change, ε c is the unit change of the peak-shaving mode sequence.

[0128] Table 1 Comparison table of slope sequence and pattern sequence

[0129]

[0130] If the peak-shaving timing sequence and This indicates that the peak sequence is in a rapid rising state, and the morphological sequence If the peak-shaving timing sequence and This indicates that the peak sequence is in a rapid decline, and the morphological sequence Other forms can be derived by analogy.

[0131] Step S4, based on the peak-shaving mode sequence, calculate the peak-shaving time sequence power transmission upper limit; based on the peak-shaving time sequence power transmission upper limit, correct the optimal daily power transmission curve to obtain the optimal daily power transmission curve that takes into account the receiving end power demand and peak-shaving demand.

[0132] Based on the peak load mode sequence obtained in step S3, the peak load transmission limit at time t is calculated according to the following formula:

[0133]

[0134] Daily power transmission curve The peak load transmission constraints should be met, namely:

[0135]

[0136] Combining equations (9) and (14) to calculate the daily power transmission curve Perform iterative optimization to obtain the optimal daily power transmission curve that takes into account both the power demand of the receiving end and the peak load demand

[0137] The following are specific implementation cases:

[0138] Taking the Jinsha River Basin multi-energy complementary transmission system as an example, the rationality of the method of the present invention is verified. This implementation case takes a single DC transmission channel as the core, and considers the joint regulation of the Jinshang 7-level power station, Jinxia Wudongde, Baihetan, and Xiluodu power stations in the Jinsha River Basin. The Xiangjiaba Hydropower Station in the Jinsha River Basin is not included in the research scope. Among them, the average annual output curve of the Jinshang 7-level power station, Jinxia Wudongde, Baihetan, and Xiluodu power stations in the Jinsha River Basin is as follows Figure 2 As shown in Figure 2, the typical output characteristics of wind power and photovoltaic power in the Jinsha River Basin are as follows: Figure 3 shown.

[0139] Without considering the Xiangjiaba Hydropower Station, the total capacity of the supporting DC transmission of the hydropower stations in the Jinsha River Basin is 5,500 kilowatts. Combined with the power transmission situation of the existing DC transmission channels, and considering the full absorption of hydropower, the power transmission of wind power and photovoltaic power will be appropriately increased. Consideration will be given to formulating high, medium and low annual power transmission plans for each DC under different utilization hours. See Table 2 for details.

[0140] Table 2

[0141]

[0142] Taking the Baihetan Left Bank power transmission to Jiangsu as an example, the DC sending end is Sichuan Province and the receiving end is Jiangsu Province. Based on the output characteristics of the sending end resources of the Baihetan Left Bank in the Jinsha River Basin and the load characteristics of the receiving end power grid in Jiangsu Province, the DC annual power transmission curve based on resource matching and load matching is obtained under the same DC utilization hours. g,k 、E l,k They are respectively the power transmission amount based on the distribution of sending-end resources on the left bank of Baihetan and the power transmission amount based on the load demand of receiving end in Jiangsu Province under the same DC utilization hours.

[0143] The power supply and demand in Sichuan Province is characterized by being dominated by water and having prominent structural contradictions between flood and drought. From the demand side, the flood and drought ratio is about 50%:50%; from the supply side, the installed capacity structure is dominated by hydropower. Since the overall output characteristics of hydropower are characterized by more floods and fewer droughts, the flood and drought ratio is about 60%:40%. The mismatch between hydropower output characteristics and load characteristics has caused Sichuan to be prone to the structural problem of "surplus floods and shortages". With the subsequent hydropower development in the three major river basins, the overall scale of hydropower transmission in Sichuan has been further expanded. Combined with the development of its own load, Sichuan Province has gradually developed into a situation of "shortages in both floods and droughts" and the coexistence of water abandonment problems in the flood season. The development trend of electricity profits and losses in Sichuan Province is as follows. Figure 4 shown.

[0144] Depend on Figure 4 It can be seen that Sichuan Province will have a seasonal gap in 2025, with the maximum gap reaching 3.53 million kilowatts. Based on the above power profit and loss results, the power space that can be absorbed by the Sichuan power grid is estimated according to formula (2). g,k Matching degree with receiving end load R l,k The annual power transmission curve of Baihetan Left Bank power transmission to Jiangsu is iteratively optimized, where the control parameter R g,kmax =10%, λ k = 0.2 to obtain the optimal annual power transmission curve from the left bank of Baihetan to Jiangsu, such as Figure 5 shown.

[0145] from Figure 5 As can be seen from the figure, to ensure the curtailment rate of the hydropower, wind, and solar hybrid power generation system at the sending end, the optimization curve primarily considers the resource endowment of the sending end. Considering that Sichuan hydropower has surplus hydropower that can be transmitted during the boom season, the transmission curve is appropriately adjusted upward to meet the power support needs of the receiving end. During the dry season, when the sending end experiences a local power deficit and the receiving end experiences insufficient power demand in the spring and autumn, the transmission curve is appropriately adjusted downward to meet local demand at the sending end.

[0146] Furthermore, the daily transmission curve needs to take into account different scenarios in the receiving area, mainly including high-load scenarios and counter-peak shaving scenarios. In high-load scenarios, the DC daily transmission curve should ensure power support, while in counter-peak shaving scenarios, it should cooperate with peak shaving control.

[0147] Under the national strategic background of “carbon peak and carbon neutrality”, the renewable energy in the receiving provinces will also develop rapidly. The increase in renewable energy penetration will bring significant pressure to the grid. Select the load and renewable energy output curve of typical days in four seasons in the receiving area, such as Figure 6 shown.

[0148] Taking Jiangsu Province as an example, peak load on the receiving grid occurs primarily in summer and winter, with lower loads in spring and autumn. During peak load scenarios, wind power output is low in summer and high in winter, while photovoltaic power exhibits the opposite trend. In counter-peak load scenarios, the probability of strong wind and solar power generation in spring and autumn is higher, while wind power output variability is also higher, placing greater demands on the peak load regulation depth of the sending-end DC power grid. A statistical analysis of annual output data for typical wind and photovoltaic power generation in receiving regions is presented in Table 3.

[0149] Table 3

[0150]

[0151] Based on the statistical results of renewable energy output and typical wind and solar output curves in the receiving area, peak load balance time series calculation is carried out in the receiving area to generate peak load balance sequence. Typical peak load time series curves in each season are as follows: Figure 7 shown.

[0152] From Table 3 and Figure 7 It can be seen that during the summer load peak, the output rates of wind power and photovoltaic power with a cumulative probability of 95% are only 27% and 40% respectively. There is a large margin for peak-shaving in the receiving areas, and the daily power transmission curve should be mainly based on ensuring power demand; the peak wind power output in winter is higher, and the daily power transmission load should actively participate in peak-shaving during the period of high wind power generation; the peak-shaving pressure is the greatest in spring, and the output rates of wind power and photovoltaic power with a cumulative probability of 95% reach 66% and 50% respectively. The power transmission curve should be lowered as much as possible to reduce the pressure on the receiving end.

[0153] Pick ε c =0.1, generate the pattern matching sequence of the peak-shaving time sequence. Taking into account the peak-shaving time sequence pattern matching and the receiving end load curve fitting, the daily power transmission curve at each typical time in the receiving end area is optimized. Taking the low-cost scheme from Baihetan to Jiangsu as an example, the optimization process of the daily power transmission curve for typical days in four seasons is as follows: Figure 8 shown.

[0154] from Figure 8It can be seen from the figure that during the peak hours in summer, the receiving system has a large peak-shaving margin, and DC can transmit power at full capacity. It is subject to the amount of water, wind, and solar resources at the sending end, and is weighted by the power transmission weight coefficient to ensure power during the peak load period. During the low hours in spring and autumn, DC should fully consider the impact of wind and photovoltaic output, participate in peak-shaving at night when wind power is prone to peak generation and at noon when photovoltaic power is most generated, and limit DC output.

[0155] The method of the present invention estimates the capacity of the sending-end power grid to absorb clean power sources based on the load characteristics and power balance results of the sending-end power grid. Furthermore, based on the exploitable amount and output characteristics of the sending-end clean power sources and the load characteristics and power demand of the receiving-end power grid, the method establishes an optimization objective function by calculating the clean energy curtailment rate and the matching degree of the receiving-end load, and obtains the optimal annual DC power transmission curve based on sending-end resource matching and receiving-end load matching at the same level of DC channel utilization hours.

[0156] After obtaining the optimal annual DC transmission curve, the daily transmission curve should be considered to match the daily load characteristics of the receiving end, while also taking into account the anti-peaking characteristics of renewable energy sources in the receiving area to reduce the pressure on the receiving end to absorb power. By calculating the typical daily load curve of the receiving grid, the optimal transmission weights at different times of the typical day are obtained. Combined with the peak-shaving balance of the sending grid, a sequence of peak-shaving variation patterns is established to obtain the DC transmission upper limit for each period of the typical day that takes into account the peak-shaving needs of the receiving end. Combining the optimal transmission weights and transmission upper limit at each time, the optimal daily DC transmission curve for the typical day is obtained.

[0157] The method of the present invention can effectively take into account the needs of the sending and receiving ends and optimize the DC transmission curve in power generation systems with a high proportion of clean electricity, such as large-scale hydro-wind-solar, wind-solar-thermal and storage power generation systems, and can provide a decision-making basis for regional new energy development and construction and DC transmission planning applied to actual power grids.

[0158] In another embodiment, a multi-energy complementary integrated transmission base DC transmission curve optimization system includes:

[0159] The module for determining the optimal annual power transmission curve is configured to determine the optimal annual power transmission curve based on the annual DC power transmission curve based on resource matching and load matching under the same DC utilization hours, combined with the clean energy curtailment rate and the receiving end load matching degree;

[0160] an optimal daily power transmission curve determination module, configured to determine an optimal daily power transmission curve that matches the power demand of the receiving end based on the optimal annual power transmission curve and a typical daily load curve of the receiving end power grid;

[0161] A peak shaving mode sequence determination module is configured to calculate a peak shaving balance sequence of the receiving-end power grid based on a typical daily load curve of the receiving-end power grid, and obtain a peak shaving slope time sequence sequence by calculating the slope of the peak shaving balance sequence; and obtain a peak shaving mode sequence based on the peak shaving slope time sequence sequence in combination with a slope sequence and a mode sequence comparison table;

[0162] The optimal daily power transmission curve correction module is configured to calculate the peak-shaving time sequence power transmission upper limit based on the peak-shaving time sequence power transmission upper limit, and correct the optimal daily power transmission curve based on the peak-shaving time sequence power transmission upper limit to obtain the optimal daily power transmission curve that takes into account the receiving end power demand and the peak-shaving demand.

[0163] The present invention has been disclosed above with preferred embodiments, which are not intended to limit the present invention. Any technical solutions obtained by adopting equivalent replacement or equivalent transformation solutions fall within the protection scope of the present invention.

Claims

1. A method for optimizing the DC transmission curve of a multi-energy complementary integrated transmission base, characterized in that: include: Based on the annual DC transmission curve based on resource matching and load matching under the same DC utilization hours, the optimal annual transmission curve is determined by combining the clean energy curtailment rate and the receiving-end load matching degree; Determining an optimal daily power transmission curve that matches the power demand of the receiving end based on the optimal annual power transmission curve and the typical daily load curve of the receiving end power grid; Based on the typical daily load curve of the receiving power grid, a peak-shaving balance sequence of the receiving power grid is calculated, and a peak-shaving slope time sequence is obtained by calculating the slope of the peak-shaving balance sequence; according to the peak-shaving slope time sequence, a peak-shaving mode sequence is obtained by combining the slope sequence with a mode sequence comparison table; Calculating a peak-shaving time sequence power transmission upper limit based on the peak-shaving mode sequence; Based on the peak-shaving time sequence power transmission upper limit, the optimal daily power transmission curve is modified to obtain the optimal daily power transmission curve that takes into account the power demand of the receiving end and the peak-shaving demand; The optimal annual power transmission curve is determined based on the annual DC power transmission curve based on resource matching and load matching under the same DC utilization hours, combined with the clean energy curtailment rate and the receiving end load matching, including: Establish an objective function that includes the clean energy curtailment rate and the matching degree of the receiving load: （1） Where, is the weight factor, is the clean energy curtailment rate in the kth month, is the receiving end load matching degree in the kth month; The constraints of the objective function are: （2） （3） （4） （5） Where, 、 are the power transmission amount based on the resource distribution of the sending end and the power transmission amount based on the load demand of the receiving end in the kth month under the same DC utilization hours, is the DC transmission capacity in the kth month after optimization, is the resource curtailment indicator function for the kth month, is the estimated electricity space of the sending grid in the kth month, is the limit value of the clean energy curtailment rate in the kth month, For the same DC utilization hours, is the rated capacity of the DC transmission channel; Based on the constraints, the objective function is solved to obtain the optimal annual power transmission curve. .

2. The method for optimizing the DC transmission curve of a multi-energy complementary integrated transmission base according to claim 1 is characterized in that: The power space of the sending power grid in the kth month Estimated according to the following method: The power balance profit or loss of the sending power grid at time t in month k is calculated according to the following formula : （6） Where, 、 They represent the power output and load of the sending-end power grid at time t in month k; Select The minimum value is taken as the power profit or loss at the kth month installed control moment , then the electricity space of the kth month in this area is , estimated by the following formula: （7） Where, represents the number of days in the kth month, is the duration of the time period, Calculate the total duration for a day.

3. The method for optimizing the DC transmission curve of a multi-energy complementary integrated transmission base according to claim 1 is characterized in that: Determining the optimal daily power transmission curve that matches the power demand of the receiving end based on the optimal annual power transmission curve and the typical daily load curve of the receiving end power grid includes: Determine the DC daily power transmission curve for the kth month based on the optimal annual power transmission curve ,in Calculate the total duration for a day; According to the typical daily load curve of the receiving power grid in the kth month , determine the power transmission weight for each period of the day : （8） Where, represents the load level of the receiving power grid at time t in the kth month, 、 They are The maximum and minimum values ​​in ; Based on the typical daily load curve of the receiving power grid in the kth month and the power transmission weights of each period of the day, the objective function is established: （9） Where, is the DC power transmission quantity at time t in the kth month, is the rated capacity of the DC transmission channel, is the number of days in the kth month; The constraints of the objective function are: （10） Where, is the duration of the time period, is the DC power transmission quantity in the kth month; Based on the constraints, the objective function is iteratively solved to obtain the optimal daily power transmission curve that matches the power demand of the receiving end. .

4. The method for optimizing the DC transmission curve of a multi-energy complementary integrated transmission base according to claim 3 is characterized in that: The receiving-end power grid peak balancing sequence Calculated according to the following formula: （11） Where, is the minimum output of the conventional power supply unit of the receiving grid at time t, 、 are the wind power and photovoltaic output of the receiving power grid at time t respectively.

5. The method for optimizing the DC transmission curve of a multi-energy complementary integrated transmission base according to claim 4 is characterized in that: The peak slope timing sequence is calculated according to the following formula: （12） Where, is the peak slope timing sequence, for The slope of change, For sequence The absolute maximum value, is the compression factor.

6. The method for optimizing the DC transmission curve of a multi-energy complementary integrated transmission base according to claim 5 is characterized in that: Peak shaving mode sequence According to the following table: Slope sequence and pattern sequence comparison table ； in, is the lower threshold for the slope pattern sequence change, is the middle threshold of the slope pattern sequence change, is the high threshold value of the slope pattern sequence change, is the unit change of the peak-shaving mode sequence.

7. The method for optimizing the DC transmission curve of a multi-energy complementary integrated transmission base according to claim 6 is characterized in that: The peak load timing upper limit is calculated according to the following formula: （13） Where, is the upper limit of peak-shaving power transmission at time t.

8. The method for optimizing DC transmission curves of a multi-energy complementary integrated transmission base according to claim 3 is characterized in that: The modifying of the optimal daily power transmission curve based on the peak-shaving time sequence power transmission upper limit includes: Daily power transmission curve Peak load transmission constraints should be met: （14） Where, is the upper limit of peak load transmission at time t; Combining equations (9) and (14) to calculate the daily power transmission curve Perform iterative optimization to obtain the optimal daily power transmission curve that takes into account both the power demand of the receiving end and the peak load demand .

9. A multi-energy complementary integrated transmission base DC transmission curve optimization system, characterized in that: include: The module for determining the optimal annual power transmission curve is configured to determine the optimal annual power transmission curve based on the annual DC power transmission curve based on resource matching and load matching under the same DC utilization hours, combined with the clean energy curtailment rate and the receiving end load matching degree; an optimal daily power transmission curve determination module, configured to determine an optimal daily power transmission curve that matches the power demand of the receiving end based on the optimal annual power transmission curve and a typical daily load curve of the receiving end power grid; A peak shaving mode sequence determination module is configured to calculate a peak shaving balance sequence of the receiving-end power grid based on a typical daily load curve of the receiving-end power grid, and obtain a peak shaving slope time sequence sequence by calculating the slope of the peak shaving balance sequence; and obtain a peak shaving mode sequence based on the peak shaving slope time sequence sequence in combination with a slope sequence and a mode sequence comparison table; an optimal daily power transmission curve correction module, configured to calculate a peak-shaving time sequence power transmission upper limit based on the peak-shaving mode sequence; Based on the peak-shaving time sequence power transmission upper limit, the optimal daily power transmission curve is modified to obtain the optimal daily power transmission curve that takes into account the power demand of the receiving end and the peak-shaving demand; The optimal annual power transmission curve is determined based on the annual DC power transmission curve based on resource matching and load matching under the same DC utilization hours, combined with the clean energy curtailment rate and the receiving end load matching, including: Establish an objective function that includes the clean energy curtailment rate and the matching degree of the receiving load: （1） Where, is the weight factor, is the clean energy curtailment rate in the kth month, is the receiving end load matching degree in the kth month; The constraints of the objective function are: （2） （3） （4） （5） Where, 、 are the power transmission amount based on the resource distribution of the sending end and the power transmission amount based on the load demand of the receiving end in the kth month under the same DC utilization hours, is the DC transmission capacity in the kth month after optimization, is the resource curtailment indicator function for the kth month, is the estimated electricity space of the sending grid in the kth month, is the limit value of the clean energy curtailment rate in the kth month, For the same DC utilization hours, is the rated capacity of the DC transmission channel; Based on the constraints, the objective function is solved to obtain the optimal annual power transmission curve. .

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