Receiving end regional power grid-oriented wind-light-water-fire nuclear multi-energy complementary long-term scheduling method

Abstract

The invention belongs to the field of power system dispatching, and provides a wind-solar-water-fire nuclear multi-energy complementary long-term dispatching method for a receiving end regional power grid. The invention provides a dispatching optimization model based on the total power shortage square sum minimization of a power grid by fully considering network-provincial coordinated dispatching and inter-provincial mutual aid and taking into account the dual goals of guaranteeing the power supply safety and promoting the clean energy consumption. The model can determine monthly power transmission quantity and inter-provincial power interaction quantity of various power supplies. Through instance analysis of the East China power grid, the result shows that the problem of power shortage of each province in the East China region can be remarkably relieved in different research scenes, and the phenomenon of power abandoning is effectively reduced. A new energy power generation mode has an important influence on power balance and power shortage conditions of each province, a direct current monthly loose season strict power transmission operation mode is adopted to achieve a remarkable effect, and compared with a traditional mode of distributing electric quantity in proportion, the power shortage expectation of the whole power grid is reduced by about 0.2 billion kWh.

Description

Technical Field

[0001] The present invention belongs to the field of power system dispatching, and relates to a long-term dispatching method for multi-energy complementarity of wind, light, water, fire and nuclear power for a receiving-end regional power grid. Background Art

[0002] New energy power generation is significantly affected by meteorological conditions, showing obvious seasonal and random fluctuations. With the rapid grid connection of new energy sources such as wind and light, the power grid is facing increasing pressure for consumption. By coordinating the grid-provincial coordinated dispatching of wind, light, water, fire and nuclear power, it is possible to effectively promote cross-regional energy complementarity, reduce the phenomenon of wind and light curtailment, and improve the flexibility of the entire regional power grid and the reliability of power supply. However, due to multiple uncertainties such as runoff and wind and light power generation capabilities, as well as the widely different load characteristics and resource endowments of each province, how to formulate effective dispatching principles to achieve grid-provincial coordinated dispatching is a key issue and practical challenge that needs to be solved urgently at present. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a long-term dispatching method for multi-energy complementarity of wind, light, water, fire and nuclear power for a receiving-end regional power grid. By deeply analyzing the dispatching output and load characteristics of each provincial dispatching, and statistically analyzing the available power sources that can be dispatched by the East China Power Grid dispatching, a long-term dispatching model with the minimum expected sum of power shortage squares of the East China Power Grid is constructed around the fundamental principles of ensuring the safe power supply of the power grid and promoting the consumption of new energy, and the monthly power transmission plan and the results of inter-provincial mutual assistance of the East China Power Grid are determined.

[0004] On the one hand, the present invention provides a long-term dispatching method for multi-energy complementarity of wind, light, water, fire and nuclear power for a receiving-end regional power grid, including the following steps:

[0005] S1) Regarding the grid-regulated hydropower, external DC power, thermal power, nuclear power, and pumped storage as dispatchable power sources, the provincial dispatch power sources are given typical operation modes according to historical actual data and do not participate in the optimization calculation;

[0006] S2) Generating new energy scenarios for the first mode, the second mode, and the third mode;

[0007] S3) Setting scenarios for the inter-provincial distribution ratio of DC hydropower in the mode of loose in months and strict in seasons;

[0008] S4) Taking the minimum expected sum of power shortage squares of the overall East China Power Grid as the goal, where the power shortage amount is obtained by subtracting the total energy output of the provincial dispatch and the grid dispatch from the load under a certain scenario at a certain time of each province, and establishing a long-term dispatching model for multi-energy complementarity of wind, light, water, fire and nuclear power for a receiving-end regional power grid;

[0009] S5) On the specified solver platform, using the specified program to solve the long-term dispatching model for multi-energy complementarity of wind, light, water, fire and nuclear power for a receiving-end regional power grid, and optimizing the dispatching of the grid dispatch power sources including grid-regulated hydropower, DC hydropower, thermal power, nuclear power, and pumped storage to achieve monthly power balance for each province.

[0010] In a possible implementation, step S2) generates new energy scenarios of the first mode, the second mode, and the third mode, and the specific steps are as follows:

[0011] A1. According to the monthly new energy output data P of each provincial power grid i,m , calculate the annual new energy utilization hours H of each provincial power grid i ;

[0012] A2. Sort the annual new energy utilization hours H of each province i from large to small to obtain the sorted result H i,1 , H i,2 , …H i,N ;

[0013] A3. Select the corresponding years as typical scenarios of different modes, and take the monthly output data of the years where the empirical frequencies are 0-20%, 40%-60%, and 80%-100% as the scenarios of the first mode, the second mode, and the third mode respectively;

[0014] A4. According to the scenarios of different modes, extract the monthly output data P of each scenario corresponding year i,m .

[0015] In a possible implementation, step S3) sets the scenario of the inter-provincial distribution ratio of direct current hydropower in the monthly loose and quarterly strict mode, and the steps are as follows:

[0016] B1. According to formula (1), calculate the fixed power E transmitted by the k-th direct current hydropower to each province under the condition of a fixed ratio k,m ;

[0017] E k,m = E k × P k,s (1)

[0018] In the formula: E k is the total power of the k-th direct current hydropower; P k,s is the power transmission ratio of the k-th direct current hydropower to Province S;

[0019] B2. According to formula (2), adjust the power of each DC line to the quarterly quota Q k,q ;

[0020]

[0021] In the formula: Q k,q is the power of the k-th DC in the q-th quarter; M q is the set of months corresponding to quarter q, where q = 1, 2, 3, 4 correspond to the four seasons of spring, summer, autumn, and winter;

[0022]

[0023] B3, verify whether the adjusted power quantity meets the total power balance.

[0024] In a possible implementation, step S4) aims to minimize the sum of squares of power shortages in the overall East China Power Grid. The power shortage is obtained by subtracting the total output of energy from the provincial dispatching and grid dispatching from the load of each province in a certain scenario at a certain time period. The long-term scheduling model of multi-energy complementarity of wind, light, water, fire, and nuclear for the receiving-end regional power grid is established as follows:

[0025] The objective function is to minimize the sum of squares of power shortages in the overall East China Power Grid;

[0026]

[0027] loe s,t,i = max(0, [L s,t - ph s,t,i - pn s,t,i - pf s,t,i - po s,t,i - pc s,t,i ·T t ) (5)

[0028]

[0029] po s,t,i = pw s,t,i + ps s,t,i + pz s,t,i (7)

[0030] In the formula: loe s,t,i is the total power shortage in the sth province in the ith scenario at the tth time period, MWh; P i is the occurrence probability of the ith scenario; T t is the total number of hours in the tth time period, h; I is the total number of scenarios; T is the total number of time periods in the long-term scale; S is the total number of provinces and cities in the East China region; L s,t is the load of the sth province at the tth time period, MW; ph s,t,i is the actual output of the grid dispatching hydropower in the sth province in the ith scenario at the tth time period, MW; pn s,t,i is the actual output of the grid dispatching nuclear power in the sth province in the ith scenario at the tth time period, MW; pf s,t,i is the actual output of the grid dispatching thermal power in the sth province in the ith scenario at the tth time period, MW; po s,t,i is the actual output of the sth province in the ith scenario at the tth time period, MW; ph p,s,t,i is the actual output of the hydropower of the p power station in the sth province in the ith scenario at the tth time period, MW; pc s,t,i is the actual output of the pumped storage in the sth province in the ith scenario at the tth time period, MW; pw s,t,iis the actual wind power output in the s province during the t period of the i scenario, MW; ps s,t,i is the actual photovoltaic output in the s province during the t period of the i scenario, MW; pz s,t,i is the actual output of other energy sources in the s province during the t period of the i scenario, MW;

[0031] The constraint conditions include water balance constraints, head calculation, power station output characteristics, thermal power output constraints, nuclear power output constraints, and pumped storage output constraints, as follows;

[0032]

[0033] In the formula: qc p,t,i is the outflow of the hydropower station p during the t period of the i scenario, m 3 / s; qm p,t,i is the inflow of the hydropower station p during the t period of the i scenario, m 3 / s; v p,t+1 is the final reservoir capacity of the p hydropower station at the t period, m 3 ; v p,t is the initial reservoir capacity of the hydropower station p at the t period, m 3 , and the reservoir capacity is unique in each scenario and is the output decision of the proposed model, aiming to improve the adaptability of the scheduling decision in each scenario; H p,t,i and are the head and head loss of the hydropower station p during the t period of the i scenario, m; z p,t is the initial upstream water level of the hydropower station p at the t period, m; z p,t+1 is the upstream water level of the hydropower station p at the t+1 period, m; is the average tail water level of the hydropower station p during the t period of the i scenario, m; ph p,t,i is the actual hydropower output of the p power station during the t period of the i scenario, MW; qd p,t,i is the power generation flow of the hydropower station p during the t period of the i scenario, m 3 / s; is the output characteristic curve of the hydropower station p; pf all is the total thermal power output of the East China Grid Dispatching, MW; pn t is the total nuclear power output of the East China Grid Dispatching at the t period, MW; pc t is the total pumped storage output of the East China Grid Dispatching at the t period, MW.

[0034] Second, a computing device is provided. The computing device includes a processor and a memory. Among them, a computer program is stored in the memory, and the processor is configured to run the computer program to execute the long-term scheduling method for multi-energy complementation of wind, light, water, and nuclear power for the receiving-end regional power grid described in any one of the above.

[0035] In a third aspect, a storage medium is provided, which stores a computer program. Wherein, the computer program is configured to execute the above-mentioned long-term dispatching method for multi-energy complementarity of wind, light, water, fire and nuclear power for the receiving-end regional power grid when running.

[0036] Compared with the existing methods, the beneficial effects of the present invention are as follows: considering the power demand fluctuations and resource endowment differences among provinces, giving play to the ability of the regional power grid to make up for shortages and surpluses, through network-province coordinated dispatching and inter-provincial mutual assistance, significantly reducing the power shortage problems in each province of the regional power grid, effectively alleviating the phenomenon of abandoned electricity, and improving the new energy consumption capacity. Description of the Drawings

[0037] Figure 1 is a flowchart of the long-term dispatching method for multi-energy complementarity of wind, light, water, fire and nuclear power for the receiving-end regional power grid;

[0038] Power balance of each province in the conventional new energy mode in Figure 2(a);

[0039] Power shortage of each province in the conventional new energy mode in Figure 2(b);

[0040] Figure 3 Power balance of each province in the large new energy mode;

[0041] Figure 4 Power shortage of each province in the large new energy mode;

[0042] Figure 5 Power balance of each province in the small new energy mode;

[0043] Figure 6 Power shortage of each province in the small new energy mode;

[0044] Figure 7 Power balance of each province during the DC "loose in months and strict in seasons" power transmission operation;

[0045] Figure 8 Power shortage of each province during the DC "loose in months and strict in seasons" power transmission operation. Detailed Embodiments

[0046] The following further describes the detailed embodiments of the present invention in conjunction with the drawings and technical solutions.

[0047] At present, under the situation of the continuous growth of new energy in large receiving-end power systems, domestic and foreign research mainly focuses on giving play to the coordinated dispatching of various power sources in provincial power grids to achieve the optimal allocation of resources. For high-proportion renewable energy power systems, some scholars have proposed a hierarchical distributed multi-source coordinated optimal dispatching system for transmission and distribution grids, which fully respects and utilizes the autonomous operation characteristics of each stakeholder to achieve decentralized autonomy and collaborative optimization; considering the spatio-temporal correlation of power sources and loads, some scholars have proposed a multi-objective optimization method for medium- and long-term power supply and demand balance, which reduces the investment, operation and environmental costs of power sources while meeting the premise of power supply conservation, and also reduces system power curtailment and cross-regional transmission, promoting the local consumption of renewable energy; some scholars have also proposed a medium- and long-term optimal operation strategy for hydropower planning based on the complementary characteristics of wind and light, which optimizes the coordinated operation of new energy and hydropower by correcting the predicted output and energy storage fluctuations. These methods provide effective ideas for the multi-energy complementary dispatching of large receiving-end power grids, but there is less research on the inter-provincial mutual assistance ability. How to consider the power demand fluctuations and resource endowment differences between provinces, meet the "ensure safe power supply" and "promote clean consumption" of regional power grids, and give full play to the surplus and deficit mutual assistance ability of regional power grids still requires further research.

[0048] In view of the above problems, the present invention proposes a long-term multi-energy complementary dispatching method for wind, light, water, fire and nuclear power for the receiving-end regional power grid, and takes the East China Power Grid as an application test platform. The results show that in different research scenarios, the present invention can significantly alleviate the power shortage problems in each province of the East China region and effectively alleviate the power curtailment phenomenon through inter-provincial mutual assistance and network-province coordinated dispatching. The new energy power generation method has an important impact on the power balance and power shortage situation in each province. The effect of adopting the DC "monthly delivery and quarterly strict" power transmission operation mode is significant. Compared with the traditional method of allocating power according to proportion, the total power shortage expectation of the whole network is reduced by about 200 million kWh.

[0049] The present invention provides a long-term multi-energy complementary dispatching method for wind, light, water, fire and nuclear power for the receiving-end regional power grid. See Figure 1 , and the specific implementation steps are as follows:

[0050] S1) Regarding the hydropower of the network dispatching, external DC, thermal power, nuclear power, and pumped storage as dispatchable power sources, the power sources of the provincial dispatching are given typical operation modes according to historical actual data and do not participate in the optimization calculation;

[0051] S2) Generate new energy scenarios for the first mode, the second mode, and the third mode;

[0052] The first mode, the second mode, and the third mode here can also be referred to as the large mode, the conventional mode, and the small mode. The large mode here generally refers to the operation mode of the power grid or new energy power generation system under large-scale, high-capacity, and complex conditions. In this mode, there are many types of power sources connected to the system (such as various energy sources like wind, light, water, fire, and nuclear), the equipment scale is large, the transmission line connections are close, and the requirements for the optimal allocation and regulation of power resources are relatively high. The conventional mode refers to the operation mode of the power grid or new energy power generation based on existing mature technologies and equipment according to the traditional operation mode. This mode usually takes stability and reliability as the core, the operating conditions are relatively fixed, and it is applicable to conventional energy sources (such as thermal power and hydropower) or new energy sources with mature technologies (such as large-scale wind power and photovoltaic power stations). The small mode refers to the operation mode of the power grid or new energy power generation system under small-scale, low-capacity, and simple conditions. In this mode, the system may only connect a small number of power sources (such as small distributed photovoltaics and small wind turbines), the number of equipment is small, the transmission lines are simple, and the requirements for the reuse and flexibility of power resources are relatively high.

[0053] S3) Set the scenario of the inter-provincial distribution ratio of direct current hydropower in the monthly loose and quarterly strict mode;

[0054] S4) Taking the minimum expected sum of squares of power shortages in the East China Power Grid as the goal, the power shortage is obtained by subtracting the total power output of the provincial dispatching and grid dispatching energy sources from the load of each province in a certain scenario at a certain time period, and establish a long-term scheduling model for multi-energy complementarity of wind, light, water, fire, and nuclear for the receiving-end regional power grid;

[0055] S5) On the specified solver platform, use the specified program to solve the long-term scheduling model for multi-energy complementarity of wind, light, water, fire, and nuclear for the receiving-end regional power grid, and optimize the scheduling of the grid dispatching power sources including grid dispatching hydropower, direct current hydropower, thermal power, nuclear power, and pumped storage to achieve monthly power balance for each province.

[0056] Here, the specified solver platform can be, for example, the Gurobi (a kind of optimization solver) solver platform, and the specified program can be a Python (a programming language) program. This embodiment does not limit this.

[0057] In an embodiment of the present application, a possible implementation method is provided. In step S2), generate new energy scenarios for the first mode, the second mode, and the third mode. The specific steps are as follows:

[0058] A1. According to the monthly new energy output data P of each provincial power grid i,m , calculate the annual utilization hours H of new energy for each provincial power grid i ;

[0059] A2. Sort the annual utilization hours H of new energy for each province from large to small to obtain the sorted result H i , H i,1 , Hi,2 ,...H i,N ;

[0060] A3. Select the corresponding year as the typical scenario for different modes, and take the monthly output data of the years where the empirical frequencies are 0 - 20%, 40% - 60%, and 80% - 100% as the scenario data for the first mode, the second mode, and the third mode respectively;

[0061] A4. According to the scenarios of different modes, extract the monthly output data P of each scenario for the corresponding year i,m .

[0062] In the embodiment of the present application, a possible implementation method is provided. In step S3), set the scenario of the inter-provincial allocation ratio of the direct current hydropower under the monthly loose and quarterly strict mode. The steps are as follows:

[0063] B1. According to formula (1), calculate the fixed power E transmitted from the k-th direct current hydropower to each province under the condition of a fixed ratio k,m ;

[0064] E k,m =E k ×P k,s (1)

[0065] In the formula: E k is the total power of the k-th direct current hydropower; P k,s is the power transmission ratio of the k-th direct current hydropower to Province S;

[0066] B2. According to formula (2), adjust the power of each DC line to the quarterly quota Q k,q ;

[0067]

[0068] In the formula: Q k,q is the power of the k-th DC in the q-th quarter; M q is the set of months corresponding to the q-th quarter, where q = 1, 2, 3, 4 correspond to the four seasons of spring, summer, autumn, and winter;

[0069]

[0070] B3. Verify whether the adjusted power meets the total power balance.

[0071] In the embodiment of the present application, a possible implementation method is provided. In step S4), with the goal of minimizing the expected square sum of the overall power shortage in the East China Power Grid, the power shortage is obtained by subtracting the total energy output of the provincial dispatching and the grid dispatching from the load of each province under a certain scenario at a certain time period. Establish a long-term multi-energy complementary scheduling model for the wind-solar-hydro-nuclear power in the receiving regional power grid as follows:

[0072] The objective function is to minimize the overall square sum of the power shortage in the East China Power Grid;

[0073]

[0074] loe s,t,i = max(0, [L s,t - ph s,t,i - pn s,t,i - pf s,t,i - po s,t,i - pc s,t,i ) · T t ) (5)

[0075]

[0076] po s,t,i = pw s,t,i + ps s,t,i + pz s,t,i (7)

[0077] In the formula: loe s,t,i is the total power shortage in s province during the t - period of the i - scenario, MWh (megawatt - hour); P i is the occurrence probability of the i - scenario; T t is the total number of hours in the t - period, h; I is the total number of scenarios; T is the total number of periods on a long - term scale; S is the total number of provinces and cities in East China; L s,t is the load of s province during the t - period, MW (megawatt); ph s,t,i is the actual output of the grid - regulated hydropower in s province during the t - period of the i - scenario, MW; pn s,t,i is the actual output of the grid - regulated nuclear power in s province during the t - period of the i - scenario, MW; pf s,t,i is the actual output of the grid - regulated thermal power in s province during the t - period of the i - scenario, MW; po s,t,i is the actual output of s province during the t - period of the i - scenario, MW; ph p,s,t,i is the actual output of the hydropower of p power station in s province during the t - period of the i - scenario, MW; pc s,t,i is the actual output of the pumped - storage in s province during the t - period of the i - scenario, MW; pw s,t,i is the actual output of the wind power in s province during the t - period of the i - scenario, MW; ps s,t,i is the actual output of the photovoltaic power in s province during the t - period of the i - scenario, MW; pz s,t,i is the actual output of other energy sources in s province during the t - period of the i - scenario;

[0078] Next, the constraint conditions can be set:

[0079] ① Water balance constraint:

[0080] qc p,t,i +(v p,t+1 - v p,t ) / Tt = qm p,t,i (8 - 1)

[0081] Where: qc p,t,i is the discharge flow of hydropower station p in scenario i at time period t, m 3 / s; qm p,t,i is the inflow flow of hydropower station p in scenario i at time period t, m 3 / s; v p,t+1 is the end reservoir storage of hydropower station p at time period t, m 3 ; v p,t is the initial reservoir storage of hydropower station p at time period t, m 3 , and the reservoir storage is unique under each scenario and is the output decision of the proposed model, aiming to improve the adaptability of the scheduling decision under each scenario.

[0082] ② Reservoir flow relationship:

[0083] qc p,t,i = qd p,t,i + qz p,t,i (9)

[0084] Where: qd p,t,i is the power generation flow of hydropower station p in scenario i at time period t, m 3 / s; qz p,t,i is the spill flow of hydropower station p in scenario i at time period t, m 3 / s.

[0085] ③ Discharge flow upper limit constraint:

[0086]

[0087] Where: is the maximum allowable discharge flow of hydropower station p at time period t, m 3 / s.

[0088] ④ Power generation flow constraint:

[0089]

[0090] Where: and are the maximum allowable power generation flow and the minimum allowable power generation flow of hydropower station h at time period t, m 3 / s.

[0091] ⑤ Power station output range limit:

[0092]

[0093] Where: and They are the installed capacity and the minimum technical output of Hydropower Station p, in MW.

[0094] ⑥ Water level - storage capacity relationship:

[0095]

[0096] In the formula: z p,t is the initial water level above the dam of Hydropower Station p at time t, in m; is the water level - storage capacity function relationship of Hydropower Station p, which is a non - linear function.

[0097] ⑦ Tail water level - discharge relationship:

[0098]

[0099] In the formula: is the average tail water level of Hydropower Station p during time t in scenario i, in m; is the tail water level - discharge function relationship of Hydropower Station p, which is a non - linear function.

[0100] ⑧ Head calculation:

[0101]

[0102] In the formula: H p,t,i and are the head and head loss of Hydropower Station p during time t in scenario i, in m.

[0103] ⑨ Head loss constraint:

[0104]

[0105] In the formula: is the head loss - discharge correlation function of Hydropower Station p, which is a non - linear function.

[0106] ⑩ Power output characteristics of the power station:

[0107]

[0108] In the formula: is the power output characteristic curve of Hydropower Station p.

[0109] Water level upper and lower limit constraints:

[0110]

[0111] In the formula: and are the maximum allowable water level and the minimum allowable water level of Hydropower Station p at time t, in m.

[0112] Initial and final water level constraints of the power station:

[0113]

[0114] Where: and are the initial water level and the final water level of the dispatching of hydropower station p, respectively, in m.

[0115] Thermal power output constraint:

[0116]

[0117] For each time period of each scenario, it is necessary to satisfy:

[0118]

[0119] Where: pf all is the total thermal power output of the East China Grid Dispatching, in MW, and pf max is the maximum thermal power output of the East China Grid Dispatching in a time period.

[0120] Nuclear power output constraint:

[0121]

[0122] Where: pn t is the total nuclear power output of the East China Grid Dispatching at time t, in MW.

[0123] Pumped storage power output constraint:

[0124]

[0125] Where: pc t is the total pumped storage power output of the East China Grid Dispatching at time t, in MW.

[0126] Then, on the Gurobi solver platform, a Python program can be used to solve the long-term scheduling model of wind-solar-hydro-thermal-nuclear multi-energy complementarity for the receiving-end regional power grid, optimize the dispatching of grid dispatching power sources (including grid dispatching hydropower, DC hydropower, thermal power, nuclear power, pumped storage), and achieve monthly power balance for each province.

[0127] The East China Power Grid is the largest regional power grid in China and also the largest receiving-end power grid for "West-East Power Transmission", covering four provinces and one municipality of Shanghai, Jiangsu, Zhejiang, Anhui, and Fujian. The installed capacity of new energy in the whole grid exceeds 180 million kW, with a growth of 170 million kW in the past decade, a 17-fold increase. The proportion of the installed capacity of new energy in the whole grid has increased from 3% to 29%, becoming a highly representative large receiving-end high-proportion new energy power grid.

[0128] The present invention conducts a combined dispatching analysis on the grid-regulated power sources (hydropower, external DC power, thermal power, nuclear power, pumped storage), realizes the monthly power balance of each province, where the grid-regulated power sources are used for optimization calculation, and the provincial-regulated power sources are given typical operation modes according to the actual data in 2023. Among the grid-regulated power sources, the annual total power generation of thermal power is given, the monthly power generation of nuclear power is given, the runoff and operation data of hydropower stations are given for hydropower, the monthly power and the inter-provincial distribution ratio are given for external DC power, the monthly power is given for pumped storage, and the grid-regulated power sources are used for optimization calculation. Among them, the power distribution ratios of DC power and grid-regulated power plants are shown in the following table:

[0129] Table 1 DC Power Distribution Ratio

[0130]

[0131] Table 2 Grid-Regulated Power Plant Power Distribution Ratio

[0132]

[0133]

[0134] Among the provincial-regulated power sources, the monthly power consumption is given for the load data, the monthly power generation scenarios are given for wind power and photovoltaic power, the monthly power generation is given for hydropower, thermal power and nuclear power, the monthly power is given for pumped storage, and they participate in the balance calculation in the typical operation mode. At the same time, to consider different situations of new energy and different distribution situations of DC power, the power shortage situations and the operation modes of grid-regulated power sources under different new energy scenarios (including large mode, conventional mode, small mode) and the "loose month and strict season" mode are studied.

[0135] Sort the annual utilization hours of new energy in East China, and take the monthly output data of the years where the top 20%, 40%-60%, and the bottom 20% are located as the input data for generating scenarios of large mode, conventional mode, and small mode respectively. Among them, the annual power generation of the conventional mode is 212 billion kWh, the large mode is 2.5% more than the conventional mode, and the small mode is 3.3% less than the conventional mode.

[0136] Figures 2(a) and 2(b) respectively show the power balance situation and power shortage situation of each province under the conventional mode of new energy. From the calculation results of the conventional mode of new energy, it can be seen that there are power shortages in Jiangsu, Zhejiang, and Anhui provinces in spring, summer, and winter. Among them, the power shortage situation in Anhui Province is more obvious, with an average power shortage of 299 MW. Jiangsu has more power shortages in summer (July and August), with an average power shortage of 376 MW; Zhejiang has a more serious power shortage in July, with a power shortage of 543 MW. Therefore, overall, the power shortage risk in Shanghai is relatively small, and the three provinces have the risk of power curtailment in January, February, and October.

[0137] Figure 3 and Figure 4The power balance and power shortage situations of each province under the large-scale new energy mode are respectively given. It can be seen from the calculation results of the large-scale new energy mode that due to the uneven distribution of power generation capacity throughout the year, there is still a risk of power shortage in May, July, August, and December. The total expected power shortage is 2.615 billion kWh, a 37% reduction compared to the conventional mode. Therefore, it is recommended to coordinate and increase the DC power reception in July and August, and appropriately reduce the power reception in September to November.

[0138] Figure 5 and Figure 6 The power balance and power shortage situations of each province under the small-scale new energy mode are respectively given. It can be seen from the calculation results of the small-scale new energy mode that the annual expected power shortage of the entire network reaches 10.2 billion kWh. Among them, Anhui has the largest gap, which is 4.45 billion kWh, mainly distributed in summer and winter. Compared with the operation of the new energy conventional mode, the power shortage risk under the small-scale new energy mode has increased significantly, with an increase of 146%, highlighting the difficulty of power dispatching in the small-scale scenario.

[0139] Figure 7 and Figure 8 The power balance and power shortage situations of each province under the "loose in months and strict in seasons" power transmission operation of DC are respectively given. It can be seen from the calculation results of the "loose in months and strict in seasons" power transmission operation mode of DC that compared with the power distribution control mode by proportion in each month, the amount of abandoned electricity is reduced by 50 million kWh, a 3% reduction; the total expected power shortage of the entire network is reduced by 200 million kWh, a 5% reduction. The "loose in months and strict in seasons" power transmission operation mode of DC has good results.

[0140] Based on the same inventive concept, an embodiment of the present application further provides a computing device, including a processor and a memory. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the long-term scheduling method for multi-energy complementarity of wind, light, water, and nuclear power for the receiving-end regional power grid in any one of the above embodiments.

[0141] Based on the same inventive concept, an embodiment of the present application further provides a storage medium, in which a computer program is stored. The computer program is configured to execute the long-term scheduling method for multi-energy complementarity of wind, light, water, and nuclear power for the receiving-end regional power grid in any one of the above embodiments when running.

[0142] Those skilled in the art can clearly understand that the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments. For the sake of brevity, they will not be described in detail here.

[0143] Those of ordinary skill in the art can understand that the technical solution of this application can be essentially embodied in the form of a software product, and this computer software product is stored in a storage medium, which includes a number of program instructions for causing an electronic device (such as a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of this application when the program instructions are running. The aforementioned storage medium includes various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0144] Alternatively, all or part of the steps of implementing the foregoing method embodiments can be completed by hardware related to program instructions (such as an electronic device like a personal computer, a server, or a network device), and the program instructions can be stored in a computer-readable storage medium. When the program instructions are executed by the processor of the electronic device, the electronic device executes all or part of the steps of the methods described in the embodiments of this application.

[0145] The above embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that within the spirit and principles of this application, they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the corresponding technical solutions to deviate from the protection scope of this application.

Claims

1. A long-term dispatching method for multi-energy complementarity of wind, light, water, and nuclear power in a receiving-end regional power grid, characterized in that It includes the following steps: S1) Regarding the grid-regulated hydropower, external DC power, thermal power, nuclear power, and pumped storage as dispatchable power sources, the provincial-regulated power sources are given typical operation modes according to historical actual data and do not participate in the optimization calculation; S2) Generate new energy scenarios for the first mode, the second mode, and the third mode; S3) Set the scenarios of the inter-provincial distribution ratio of DC hydropower under the monthly loose and quarterly strict mode; S4) With the goal of minimizing the expected sum of squares of power shortages in the East China Power Grid, the power shortage is obtained by subtracting the total output of provincial-regulated and grid-regulated energy from the load under a certain scenario in a certain period of each province, and establish a long-term scheduling model for multi-energy complementarity of wind, light, water, fire, and nuclear power for the receiving-end regional power grid; S5) On the specified solver platform, use the specified program to solve the long-term scheduling model for multi-energy complementarity of wind, light, water, fire, and nuclear power for the receiving-end regional power grid, and optimize the dispatch of the grid-regulated power sources including grid-regulated hydropower, DC hydropower, thermal power, nuclear power, and pumped storage to achieve monthly power balance for each province.

2. The method according to claim 1, characterized in that Step S2) Generate new energy scenarios for the first mode, the second mode, and the third mode. The specific steps are as follows: A1. Calculate the annual utilization hours H of new energy for each provincial power grid according to the monthly new energy output data P of each provincial power grid i,m i ;​ A2, the annual utilization hours H of new energy in each province i are sorted from large to small to obtain the sorted result H i,1 , H i,2 , …H i,N ; A3. Select the corresponding year as the typical scenario for different modes, and take the monthly output data of the years where the empirical frequencies are 0-20%, 40%-60%, and 80%-100% as the scenarios for the first mode, the second mode, and the third mode respectively; A4, Extract the monthly output data P for each scenario corresponding to the year according to scenarios of different methods i,m 。 3. The method according to claim 2, wherein Step S3) Set the scenarios of the inter-provincial distribution ratio of DC hydropower under the monthly loose and quarterly strict mode. The steps are as follows: B1. According to formula (1), calculate the fixed power quantity \(E\) transmitted from the \(k\)-th through-flow hydroelectric power to each province under the condition of a fixed ratio k,m ; E k,m = E k × P k,s (1) where: E k is the total hydroelectric power of the k-th DC power; P k,s is the power transmission ratio of the k-th DC power to Province S; B2. According to formula (2), adjust the electricity quantity of each DC line to the quarterly quota Q k,q ; Where: Q k,q The electricity consumption of the k-th DC in the q-th quarter; M q Is the set of months corresponding to the quarter q, where q = 1, 2, 3, 4 correspond to the four seasons of spring, summer, autumn and winter; B3. According to formula (3), verify whether the adjusted power meets the total power balance.

4. The method according to claim 3, wherein Step S4) With the goal of minimizing the expected sum of squares of power shortages in the East China Power Grid, the power shortage is obtained by subtracting the total output of provincial-regulated and grid-regulated energy from the load under a certain scenario in a certain period of each province, and establish the following long-term scheduling model for multi-energy complementarity of wind, light, water, fire, and nuclear power for the receiving-end regional power grid: The objective function is to minimize the total sum of squares of power shortages in the East China Power Grid; loe s,t,i = max(0, [L s,t - ph s,t,i - pn s,t,i - pf s,t,i - po s,t,i - pc s,t,i · T t ) (5) po s,t,i = pw s,t,i + ps s,t,i + pz s,t,i (7) where: loe s,t,i is the total power shortage in s province during the i scenario and t period, MWh; P i is the occurrence probability of the i scenario; T t is the total number of hours in the t period, h; I is the total number of scenarios; T is the total number of long-term scale periods; S is the total number of provinces and cities in East China; L s,t is the load of s province during the t period, MW; ph s,t,i Actual output of the dispatching center for hydropower in the s province i scenario during the t period, MW; pn s,t,i Actual output of nuclear power for internal grid dispatching in scenario i and time period t in Province s, MW; pf s,t,i Actual output of thermal power for internal grid dispatching in scenario i and time period t in Province s, MW; po s,t,i Actual output in Province s in scenario i and time period t, MW; ph p,s,t,i Actual output of hydropower during the t time period in the i scenario of the p power station in s province, MW; pc s,t,i Actual pumped-storage output in Province s during period t of Scenario i, MW; pw s,t,i Actual wind power output in Province s during period t of Scenario i, MW; ps s,t,i Actual PV output in Province s during period t of scenario i, MW; pz s,t,i Actual output of other energy sources in Province s during period t of scenario i, MW; The constraint conditions include water balance constraint, head calculation, power station output characteristics, thermal power output constraint, nuclear power output constraint, and pumped storage output constraint, which are specifically as follows; where: qc p,t,i is the discharge flow of hydropower station p during the t-th period in the i-th scenario, m 3 / s; qm p,t,i is the inflow of hydropower station p during the t-th period in the i-th scenario, m 3 / s; v p,t+1 is the final reservoir storage of hydropower station p at the t-th period, m 3 ; v p,t Initial reservoir storage of hydropower station p at the beginning of period t, m 3 , the reservoir storage is unique under each scenario and is the output decision of the proposed model, aiming to improve the adaptability of the scheduling decision under each scenario; H p,t,i and Head and head loss of hydropower station p during period t in scenario i, m; z p,t Initial water level above the dam of hydropower station p at period t, m; z p,t+1 Water level above the dam of hydropower station p at period t + 1, m; Average tail water level of hydropower station p during period t in scenario i, m; ph p,t,i Actual output of hydropower during the t period in the i scenario of the p power station, MW; qd p,t,i is the power generation flow rate of hydropower station p during time period t in scenario i, m 3 / s; is the output characteristic curve of hydropower station p; pf all is the total thermal power output of East China Grid Dispatching, MW; pn t Total nuclear power output of East China Grid Dispatching during period t, MW; pc t Total pumped storage output of East China Grid Dispatching during period t, MW.

5. A computing device, characterized in that, It includes a processor and a memory. Among them, a computer program is stored in the memory, and the processor is configured to run the computer program to execute the long-term scheduling method for multi-energy complementarity of wind, light, water, fire, and nuclear power for the receiving-end regional power grid according to any one of claims 1 to 4.

6. A storage medium, characterized in that, A computer program is stored in the storage medium, and the computer program is configured to execute the long-term scheduling method for multi-energy complementarity of wind, light, water, fire, and nuclear power for the receiving-end regional power grid according to any one of claims 1 to 4 when running.

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