A long-term dispatching method for wind, solar, hydro, thermal, and nuclear energy complementary energy sources for receiving area power grids

Abstract

The present invention belongs to the field of power system dispatching, and proposes a long-term dispatching method for wind, solar, water, fire, and nuclear multi-energy complementarity for the receiving area power grid. The present invention fully considers the coordinated dispatching of the grid and provinces and the mutual assistance between provinces, adheres to the dual goals of ensuring the security of power supply and promoting the consumption of clean energy, and proposes a dispatching optimization model based on the square sum minimization of the overall power shortage of the power grid. The model can determine the monthly power transmission volume of various power sources and the amount of inter-provincial power interaction. Through the example analysis of the East China Power Grid, the results show that the present invention can significantly alleviate the power shortage problem in various provinces in the East China region under different research scenarios and effectively reduce the phenomenon of power abandonment. The new energy power generation method has an important impact on the power balance and power shortage situation of each province. The adoption of the DC monthly loose and seasonal strict power transmission operation mode has achieved remarkable results. Compared with the traditional proportional power distribution method, the overall power shortage of the power grid is expected to be reduced by about 200 million 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 wind, solar, water, fire and nuclear multi-energy complementarity for a receiving area power grid. Background Art

[0002] Renewable energy generation is significantly affected by meteorological conditions, exhibiting distinct seasonal and random fluctuations. With the rapid integration of renewable energy sources such as wind and solar, the power grid faces increasing pressure to accommodate these resources. Coordinating the dispatch of wind, solar, hydro, thermal, and nuclear power across the grid and provinces can effectively promote cross-regional energy complementarity, reduce wind and solar curtailment, and enhance the flexibility of the regional power grid and the reliability of power supply. However, due to multiple uncertainties such as runoff and wind and solar power generation capacity, as well as the diverse load characteristics and resource endowments across provinces, developing effective dispatching principles to achieve coordinated dispatch across the grid and provinces remains a key issue and practical challenge that needs to be addressed. Summary of the Invention

[0003] The technical problem addressed by this invention is to provide a long-term dispatching method for wind, solar, hydro, thermal, and nuclear energy, targeting the receiving region's power grid. By thoroughly analyzing the output and load characteristics of each province and analyzing the available power sources for dispatch in the East China Power Grid, and focusing on the fundamental principles of ensuring grid security and promoting the integration of renewable energy, a long-term dispatching model that minimizes power shortages and minimizes expected power shortages was constructed. This model then determined the East China Power Grid's monthly power transmission plans and inter-provincial coordination results.

[0004] In one aspect of the present invention, a method for long-term dispatching of wind, solar, water, thermal, and nuclear multi-energy complementarity for a receiving area power grid is provided, comprising the following steps:

[0005] S1) Grid-dispatched hydropower, off-region DC power, thermal power, nuclear power, and pumped storage are considered dispatchable power sources, while provincial-dispatched power sources are given a typical operating mode based on historical actual data and are not involved in the optimization calculation;

[0006] S2) generating first mode, second mode, and third mode new energy scenarios;

[0007] S3) setting the inter-provincial allocation ratio of DC hydropower in the scenario of monthly loose and seasonal strict mode;

[0008] S4) With the goal of minimizing the overall power shortage square and expectation of the East China power grid, the power shortage is calculated by subtracting the total output of provincial and grid dispatch energy from the load in a certain scenario in a certain period of time in each province. A long-term dispatch model for wind, solar, hydro, thermal, and nuclear multi-energy complementarity is established for the receiving area power grid;

[0009] S5) On the designated solver platform, use the designated program to solve the long-term dispatch model of wind, solar, hydro, thermal, and nuclear multi-energy complementarity for the receiving area power grid, optimize the dispatch of grid-dispatched power sources including grid-dispatched hydropower, DC hydropower, thermal power, nuclear power, and pumped storage, and achieve monthly power balance in each province.

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

[0011] A1, based on the monthly renewable energy output data of each provincial power grid P i,m Calculate the annual utilization hours of renewable energy in each provincial power grid H i ;

[0012] A2, calculate the annual utilization hours of renewable energy in each province H i Sort from large to small and get the sorted result H i,1 , H i,2 ,…H i,N ;

[0013] A3: Select corresponding years as typical scenarios for different methods, and take the monthly output data of the years with empirical frequencies of 0-20%, 40%-60%, and 80%-100% as the first, second, and third method scenarios respectively;

[0014] A4, according to different scenarios, extract the monthly output data P for each scenario i,m .

[0015] In one possible implementation, step S3) sets the inter-provincial allocation ratio of DC hydropower in the scenario of the loose monthly and strict seasonal mode, and the steps are as follows:

[0016] B1, according to formula (1), calculate the fixed amount of electricity E transmitted to each province by the kth DC hydropower under the condition of a fixed ratio k,m ;

[0017] E k,m =E k ×P k,s (1)

[0018] Where: E k is the total amount of DC hydropower in the kth line; P k,s is the proportion of electricity delivered to Province S by the kth DC hydropower line;

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

[0020]

[0021] Where: Q k,q The kth DC power in the qth 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 meets the total power balance.

[0024] In one possible implementation, step S4) aims to minimize the overall power shortage square and expectation of the East China power grid. The power shortage is calculated by subtracting the total output of provincial and grid dispatch energy from the load in a certain scenario in a certain period of time in each province. A long-term dispatch model for wind, solar, hydro, thermal, and nuclear multi-energy complementarity is established for the receiving area power grid as follows:

[0025] The objective function is to minimize the sum of the overall power shortage squares in the 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] Where: loe s,t,i is the total power shortage in scenario i in province s during period t, MWh; P i is the probability of occurrence of scenario i; T t is the total number of hours in period t, h; I is the total number of scenarios; T is the total number of long-term time periods; S is the total number of provinces and cities in East China; L s,t is the load of province s during period t, MW; ph s,t,i is the actual output of hydropower dispatched by the grid in time period t in scenario i in province s, MW; pn s,t,i is the actual output of nuclear power dispatched by the grid in time period t in scenario i in province s, MW; pf s,t,i is the actual output of thermal power dispatched by the grid in time period t in scenario i in province s, MW; s,t,i is the actual output of province s during period t in scenario i, MW; ph p,s,t,i is the actual hydropower output of power station p in province i during period t, MW; pc s,t,i is the actual output of pumped storage in province s during period t in scenario i, MW; pw s,t,iis the actual wind power output of province s during period t in scenario i, MW; ps s,t,i is the actual photovoltaic output of province s during period t in scenario i, MW; pz s,t,i is the actual output of other energy sources in province s during period t in scenario i, MW;

[0031] The constraints include water balance constraints, head calculation, power plant 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 hydropower station p in scenario i during period t, m 3 / s;qm p,t,i is the inflow flow of hydropower station p in scenario i during period t, m 3 / s;v p,t+1 is the final storage capacity of hydropower station p in period t, m 3 ;v p,t is the initial storage capacity of hydropower station p at time t, m 3 , the storage capacity is unique in each scenario and is the output decision of the proposed model, the purpose of which is to improve the adaptability of scheduling decisions in various scenarios; H p,t,i and are the hydraulic head and hydraulic head loss of hydropower station p in scenario i during period t, m; z p,t is the initial water level above the dam of hydropower station p in period t, m; z p,t+1 is the water level above the dam of hydropower station p at time t+1, m; is the average tailwater level of hydropower station p in scenario i during period t, m; ph p,t,i is the actual hydropower output of power station p in scenario i during period t, MW; qd p,t,i is the power generation flow of hydropower station p in scenario i during period t, m 3 / s; is the output characteristic curve of the hydropower station p; pf all Total thermal power output for East China Grid, MW; pn t is the total nuclear power output of East China Power Grid during period t, MW; pc t is the total output of pumped storage in East China power grid during period t, MW.

[0034] In a second aspect, a computing device is provided, which includes a processor and a memory, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute any of the above-mentioned methods for long-term scheduling of wind, solar, water, fire and nuclear multi-energy complementarity for the receiving area power grid.

[0035] In a third aspect, a storage medium is provided, which stores a computer program, wherein the computer program is configured to execute any of the above-mentioned long-term scheduling methods for wind, solar, water, fire and nuclear multi-energy complementarity for the receiving area power grid when running.

[0036] Compared with the existing methods, the beneficial effects of the present invention are: taking into account the fluctuations in electricity demand and differences in resource endowments among provinces, giving full play to the surplus and shortage mutual assistance capabilities of the regional power grid, and through coordinated scheduling between grid provinces and inter-provincial mutual assistance, significantly reducing the power shortage problem in various provinces of the regional power grid, effectively alleviating the phenomenon of power abandonment, and improving the new energy absorption capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of a long-term dispatching method for wind, solar, water, thermal, and nuclear energy complementary energy sources for the receiving area power grid;

[0038] Figure 2 (a) Electricity balance in each province using new energy and conventional methods;

[0039] Figure 2(b) Power shortage in each province using new energy and conventional methods;

[0040] Figure 3 Electricity balance in various provinces with large new energy sources ;

[0041] Figure 4: Power shortage in various provinces with large new energy sources ;

[0042] Figure 5 Electricity balance of new energy and small-scale power generation in various provinces ;

[0043] Figure 6: Power shortage of new energy and small-scale power generation in various provinces ;

[0044] <a href="javascript:;" class="see-img-anchor" img-id="HDA0005281128250000051" img-title="图7直流"月松季严”送电运行各省电力平衡情况"> Figure 7: Power balance in various provinces during the "loose in the month, strict in the season" DC power transmission period ;

[0045] <a href="javascript:;" class="see-img-anchor" img-id="HDA0005281128250000052" img-title="图8直流"月松季严”送电运行各省缺电情况"> Figure 8: Power shortages in various provinces during the "loose in the month, strict in the season" DC power transmission operation . DETAILED DESCRIPTION

[0046] The specific implementation of the present invention is further described below in conjunction with the accompanying drawings and technical solutions.

[0047] Currently, with the continued growth of renewable energy in large-scale receiving power systems, research both domestically and internationally focuses on leveraging the coordinated dispatch of various power sources in provincial power grids to achieve optimal resource allocation. For power systems with a high proportion of renewable energy, some researchers have proposed a hierarchical, distributed, multi-source coordinated optimization dispatch system for transmission and distribution networks. This fully respects and leverages the autonomous operational characteristics of various stakeholders, achieving decentralized autonomy and collaborative optimization. Taking into account the spatiotemporal correlation between sources and loads, some researchers have proposed a multi-objective optimization method for medium- and long-term power supply and demand balance. This method, while ensuring power supply conservation, reduces the investment, operating, and environmental costs of power sources, reduces system curtailment and cross-regional transmission, and promotes local renewable energy consumption. Other researchers have proposed a medium- and long-term optimal operation strategy for hydropower planning based on the complementary characteristics of wind and solar power. By correcting for fluctuations in predicted output and energy storage, this strategy optimizes the coordinated operation of renewable energy and hydropower. These methods provide effective approaches to multi-energy complementary dispatch in large-scale receiving power grids, but research on inter-provincial mutual assistance is limited. Further research is needed to address inter-provincial power demand fluctuations and resource endowment differences, ensure the regional power grid's ability to "ensure secure supply," promote clean consumption, and fully leverage its surplus-shortage mutual assistance capabilities.

[0048] In response to the above problems, the present invention proposes a long-term dispatching method for wind, solar, water, fire and nuclear multi-energy complementarity for the receiving area power grid, and uses the East China Power Grid as an application test platform. The results show that under different research scenarios, the present invention can significantly alleviate the power shortage problem in various provinces in East China through inter-provincial mutual assistance and coordinated dispatching between the grid and provinces, and effectively alleviate the phenomenon of power abandonment. Renewable energy generation methods have an important impact on the power balance and power shortage situation in various provinces. The use of the DC "monthly transmission and seasonal strict" power transmission operation mode has a significant effect. Compared with the traditional method of allocating electricity proportionally, the total power shortage of the entire network is expected to be reduced by about 200 million kWh.

[0049] The present invention provides a long-term dispatching method for wind, solar, water, fire and nuclear multi-energy complementarity for the receiving area power grid, see Figure 1 The specific implementation steps are as follows:

[0050] S1) Grid-dispatched hydropower, off-region DC power, thermal power, nuclear power, and pumped storage are considered dispatchable power sources, while provincial-dispatched power sources are given a typical operating mode based on historical actual data and are not involved in the optimization calculation;

[0051] S2) generating first mode, second mode, and third mode new energy scenarios;

[0052] The first, second, and third modes here can also be referred to as the large mode, conventional mode, and small mode. The large mode here usually refers to the operating mode of the power grid or new energy power generation system under large-scale, high-capacity, and complex conditions. In this mode, the system accesses multiple types of power sources (such as wind, solar, water, fire, and nuclear energy), the equipment is large-scale, the transmission lines are closely connected, and the optimization configuration and regulation capabilities of power resources are highly required. The conventional mode refers to the operation mode of the power grid or new energy power generation based on existing mature technologies and equipment and carried out in accordance with the traditional operating mode. This mode usually focuses on stability and reliability, with relatively fixed operating conditions, and is suitable for conventional energy (such as thermal power and hydropower) or technologically mature new energy (such as large wind power and photovoltaic power stations). The small mode refers to the operating 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 access a small number of power sources (such as small distributed photovoltaic and small wind turbines), the number of equipment is small, the transmission lines are simple, and the reuse and flexibility of power resources are highly required.

[0053] S3) setting the inter-provincial allocation ratio of DC hydropower in the scenario of monthly loose and seasonal strict mode;

[0054] S4) With the goal of minimizing the overall power shortage square and expectation of the East China power grid, the power shortage is calculated by subtracting the total output of provincial and grid dispatch energy from the load in a certain scenario in a certain period of time in each province. A long-term dispatch model for wind, solar, hydro, thermal, and nuclear multi-energy complementarity is established for the receiving area power grid;

[0055] S5) On the designated solver platform, use the designated program to solve the long-term dispatch model of wind, solar, hydro, thermal, and nuclear multi-energy complementarity for the receiving area power grid, optimize the dispatch of grid-dispatched power sources including grid-dispatched hydropower, DC hydropower, thermal power, nuclear power, and pumped storage, and achieve monthly power balance in each province.

[0056] Here, the designated solver platform may be a Gurobi (an optimization solver) solver platform, and the designated program may be a Python (a programming language) program, which is not limited in this embodiment.

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

[0058] A1, based on the monthly renewable energy output data of each provincial power grid P i,m Calculate the annual utilization hours of renewable energy in each provincial power grid H i ;

[0059] A2, calculate the annual utilization hours of renewable energy in each province H i Sort from large to small and get the sorted result H i,1 , Hi,2 ,…H i,N ;

[0060] A3: Select corresponding years as typical scenarios for different methods, and take the monthly output data of the years with empirical frequencies of 0-20%, 40%-60%, and 80%-100% as the first, second, and third method scenarios respectively;

[0061] A4, according to different scenarios, extract the monthly output data P for each scenario i,m .

[0062] The present application provides a possible implementation method, in which step S3) sets the inter-provincial allocation ratio of DC hydropower in the scenario of monthly loose and seasonal strict mode, and the steps are as follows:

[0063] B1, according to formula (1), calculate the fixed amount of electricity E transmitted to each province by the kth DC hydropower under the condition of a fixed ratio k,m ;

[0064] E k,m =E k ×P k,s (1)

[0065] Where: E k is the total amount of DC hydropower in the kth line; P k,s is the proportion of electricity delivered to Province S by the kth DC hydropower line;

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

[0067]

[0068] Where: Q k,q The kth DC power in the qth 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;

[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), the overall power shortage square and expected minimum of the East China power grid are taken as the goal. The power shortage is obtained by subtracting the total output of provincial and network dispatching energy from the load in a certain scenario in a certain period of time in each province. A long-term dispatch model of wind, solar, water, fire and nuclear multi-energy complementarity for the receiving area power grid is established as follows:

[0072] The objective function is to minimize the sum of the overall power shortage squares 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] Where: loe s,t,i is the total power shortage in scenario i in province s during period t, MWh (megawatt-hours); P i is the probability of occurrence of scenario i; T t is the total number of hours in period t, h; I is the total number of scenarios; T is the total number of long-term time periods; S is the total number of provinces and cities in East China; L s,t is the load of province s during period t, MW (megawatt); ph s,t,i is the actual output of hydropower dispatched by the grid in time period t in scenario i in province s, MW; pn s,t,i is the actual output of nuclear power dispatched by the grid in time period t in scenario i in province s, MW; pf s,t,i is the actual output of thermal power dispatched by the grid in time period t in scenario i in province s, MW; s,t,i is the actual output of province s during period t in scenario i, MW; ph p,s,t,i is the actual hydropower output of power station p in province i during period t, MW; pc s,t,i is the actual output of pumped storage in province s during period t in scenario i, MW; pw s,t,i is the actual wind power output of province s during period t in scenario i, MW; ps s,t,i is the actual photovoltaic output of province s during period t in scenario i, MW; pz s,t,i is the actual output of other energy sources in province s during period t in scenario i, MW;

[0078] Next, you can set the constraints:

[0079] ① Water balance constraints:

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

[0081] In the formula: qc p,t,i is the outflow of hydropower station p in scenario i during period t, m 3 / s;qm p,t,i is the inflow flow of hydropower station p in scenario i during period t, m 3 / s;v p,t+1 is the final storage capacity of hydropower station p in period t, m 3 ;v p,t is the initial storage capacity of hydropower station p at time t, m 3 ,The storage capacity is unique in each scenario and is the output decision of the proposed model, ,which aims to improve the adaptability of scheduling decisions in various scenarios.

[0082] ②Reservoir 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 during period t, m 3 / s;qz p,t,i is the water discharge of hydropower station p in scenario i during period t, m 3 / s.

[0085] ③Limit of outbound traffic:

[0086]

[0087] Where: is the maximum outflow allowed by the hydropower station p during the period t, m 3 / s.

[0088] ④Power generation flow constraints:

[0089]

[0090] Where: and are the maximum and minimum power generation flows allowed by hydropower station h during period t, m 3 / s.

[0091] ⑤Power station output range limit:

[0092]

[0093] Where: and are the installed capacity and minimum technical output of hydropower station p, MW respectively.

[0094] ⑥ Water level-reservoir capacity relationship:

[0095]

[0096] Where: z p,t is the initial water level above the dam of hydropower station p in period t, m; is the water level-reservoir capacity function of the hydropower station p, which is a nonlinear function.

[0097] ⑦ Tailwater level-outflow flow relationship:

[0098]

[0099] Where: is the average tailwater level of hydropower station p in scenario i during period t, m; is the tailwater level-outflow function of the hydropower station p, which is a nonlinear function.

[0100] ⑧Head calculation:

[0101]

[0102] Where: H p,t,i and are the hydraulic head and hydraulic head loss of hydropower station p in scenario i during period t, m respectively.

[0103] ⑨Head loss constraint:

[0104]

[0105] Where: It is the head loss-outflow correlation function of the hydropower station p, which is a nonlinear function.

[0106] ⑩Power station output characteristics:

[0107]

[0108] Where: is the output characteristic curve of the hydropower station p.

[0109] Water level upper and lower limit constraints:

[0110]

[0111] Where: and are the maximum and minimum allowable water levels of the hydropower station p in time period t, m respectively.

[0112] Power station initial and final water level constraints:

[0113]

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

[0115] Thermal power output constraints:

[0116]

[0117] For each time period of each scenario, the following conditions must be met:

[0118]

[0119] Where: pf all Total thermal power output for East China Grid, MW, pf max It is the maximum output during the thermal power period of the East China Grid.

[0120] Nuclear power output constraints:

[0121]

[0122] Where: pn t is the total nuclear power output of East China Power Grid during period t, MW.

[0123] Pumped storage output constraints:

[0124]

[0125] Where: pc t is the total output of pumped storage in East China power grid during period t, MW.

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

[0127] The East China Grid is my country's largest regional power grid and the largest recipient of the "West-to-East Power Transmission" initiative, covering four provinces and one municipality: Shanghai, Jiangsu, Zhejiang, Anhui, and Fujian. The grid boasts over 180 gigawatts of installed renewable energy capacity, a 170 gigawatt increase over the past decade, a 17-fold increase. Its share of total installed capacity has increased from 3% to 29%, making it a highly representative grid with a high proportion of renewable energy.

[0128] The present invention conducts a joint dispatch analysis of grid-regulated power sources (hydropower, off-region DC, thermal power, nuclear power, and pumped storage) to achieve monthly power balance in each province, where the grid-regulated power sources are used for optimization calculations, and the provincial power sources are given a typical operating mode based on actual data in 2023. Among the grid-regulated power sources, thermal power is given an annual total power generation, nuclear power is given a monthly power generation, hydropower is given runoff and hydropower station operation data, off-region DC is given a monthly power generation and inter-provincial distribution ratio, and pumped storage is given a monthly power generation, and the grid-regulated power sources are used for optimization calculations. Among them, the power distribution ratio of DC and grid-regulated power plants is shown in the following table:

[0129] Table 1 DC power distribution ratio

[0130]

[0131] Table 2 Power distribution ratio of grid dispatching power plants

[0132]

[0133]

[0134] For provincial power supply, load data is used to represent monthly electricity consumption, wind power and photovoltaic power generation scenarios are used to represent monthly power generation, hydropower, thermal power, and nuclear power are used to represent monthly power generation, and pumped storage is used to represent monthly power generation. These calculations are performed using typical operating modes. To account for the varying scenarios of renewable energy and DC distribution, we also study power shortages and grid-regulated power supply operation under various renewable energy scenarios (including large-scale, conventional, and small-scale) and the "monthly loose seasonal tight" model.

[0135] The annual utilization hours of East China's renewable energy resources were ranked, and monthly output data for the top 20%, 40%-60%, and bottom 20% of the years were used as input data for the large-scale, conventional, and small-scale scenarios, respectively. The conventional scenario generated 212 billion kWh of electricity annually, with the large-scale scenario generating 2.5% more electricity and the small-scale scenario generating 3.3% less.

[0136] Figures 2(a) and 2(b) show the power balance and power shortage situation in each province under the conventional renewable energy model, respectively. The results from the conventional renewable energy model show that Jiangsu, Zhejiang, and Anhui provinces all experience power shortages in spring, summer, and winter. Anhui Province experiences a more pronounced power shortage, with an average power shortage of 299 MW. Jiangsu experiences a more severe power shortage in the summer (July and August), with an average power shortage of 376 MW. Zhejiang experiences a more severe power shortage in July, with a power shortage of 543 MW. Overall, Shanghai faces a lower risk of power shortages, while the three provinces face the risk of power curtailment in January, February, and October.

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

[0138] Figures 5 and 6 show the power balance and power shortage in each province under the new energy small-scale mode, respectively. The calculation results of the new energy small-scale mode show that the annual power shortage of the entire network is expected to reach 10.2 billion kWh, of which Anhui has the largest gap of 4.45 billion kWh, mainly distributed in the summer and winter seasons; the operation of new energy small-scale mode has a significantly greater risk of power shortage than that of conventional new energy mode, with an increase of 146%, highlighting the difficulty of power dispatch under the small-scale mode scenario.

[0139] Figures 7 and 8 show the power balance and power shortage situations in each province under the DC "monthly loose and seasonal strict" power transmission mode. Calculation results for this mode show that compared with the monthly proportional power distribution control method, this mode reduces wasted power by 50 million kWh, a 3% reduction. The total power shortage in the entire network is expected to be reduced by 200 million kWh, a 5% reduction. The DC "monthly loose and seasonal strict" power transmission mode has shown good results.

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

[0141] Based on the same inventive concept, an embodiment of the present application also provides a storage medium, which stores a computer program, wherein the computer program is configured to execute any one of the above-mentioned embodiments of the long-term scheduling method for wind, solar, water, fire and nuclear multi-energy complementarity for the receiving area power grid when running.

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

[0143] Those skilled in the art will appreciate that the technical solution of the present application, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes a number of program instructions for causing an electronic device (e.g., a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application when the program instructions are executed. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0144] Alternatively, all or part of the steps of implementing the aforementioned method embodiments may be accomplished by hardware related to program instructions (such as electronic devices such as personal computers, servers, or network devices), and the program instructions may be stored in a computer-readable storage medium. When the program instructions are executed by a processor of an electronic device, the electronic device executes all or part of the steps of the methods described in the various embodiments of the present application.

[0145] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that, within the spirit and principles of the present application, they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate from the protection scope of the present application.

Claims

1. A long-term dispatching method for wind, solar, water, fire and nuclear multi-energy complementarity for the receiving area power grid, characterized by: The steps include: S1) Grid-dispatched hydropower, off-region DC power, thermal power, nuclear power, and pumped storage are considered dispatchable power sources. Provincial dispatched power sources are given a typical operating mode based on historical actual data and are not involved in the optimization calculation; S2) Generate the first, second, and third new energy scenarios using monthly output data from different years with different experience frequencies; S3) Setting the scenario of DC hydropower inter-provincial allocation ratio; S4) With the goal of minimizing the overall power shortage square and expectation of the East China power grid, the power shortage is calculated by subtracting the total energy output of provincial and grid dispatch from the load in a certain scenario in each province during a certain period of time. A long-term dispatch model for wind, solar, hydro, thermal, and nuclear energy complementarity is established for the receiving area power grid. S5) On a designated solver platform, use a designated program to solve a long-term dispatch model for wind, solar, hydro, thermal, and nuclear energy for the receiving region's power grid. This model optimizes the dispatch of grid-dispatched power sources, including grid-dispatched hydropower, DC hydropower, thermal power, nuclear power, and pumped storage, to achieve monthly power balance in each province. Among them, step S4) takes the overall power shortage square and expectation of the East China power grid as the goal, and the power shortage is obtained by subtracting the total output of provincial and grid dispatch energy from the load in a certain scenario in a certain period of time in each province. A long-term dispatch model of wind, solar, hydro, thermal, and nuclear multi-energy complementarity for the receiving area power grid is established as follows: The objective function is to minimize the sum of the overall power shortage squares in the East China power grid; (4) (5) (6) (7) Where: for s Province i Scenario t Total power shortage during the period, MWh; for i The probability of the scenario occurring; for t The total number of hours in the period, h; I is the total number of scenes; T is the total number of long-term time periods; S This is the total number of provinces and cities in East China; for s Province t Load during the period, MW; for s Province i Scenario t The actual output of hydropower dispatched by the grid during the period, MW; for s Province i Scenario t The actual output of nuclear power dispatched by the grid during the period, MW; for s Province i Scenario t The actual output of thermal power dispatched by the grid during the period, MW; for i Scenario t During the period s Actual output of the province, MW; for p power station s Province i Scenario t Actual hydropower output during the period, MW; for i Scenario t During the period s Actual output of pumped storage in the province, MW; for i Scenario t During the period s Actual wind power output of the province, MW; for i Scenario t During the period s The actual photovoltaic output of the province, MW; for i Scenario t During the period s Actual output of other energy sources in the province, MW; The constraints include water balance constraints, head calculation, power plant output characteristics, thermal power output constraints, nuclear power output constraints, and pumped storage output constraints, as follows; (8) Where: For hydropower stations p exist i Scenario t Outbound flow within the period, ; For hydropower stations p exist i Scenario t Inbound traffic during the period, ; for p Hydropower station in t The final storage capacity of the time period, ; For hydropower stations p exist t Initial storage capacity of the period, ,The storage capacity is unique in each scenario and is the output decision of the proposed model, ,which aims to improve the adaptability of scheduling decisions in various scenarios; and Hydropower Station p exist i Scenario t Head and head loss during the period, m; For hydropower stations p exist t Initial water level above the dam during the time period, m; For hydropower stations p exist t+ Water level above the dam in period 1, m; For hydropower stations p exist i Scenario t Average tailwater level during the period, m; for p power station i Scenario t Actual hydropower output during the period, MW; For hydropower stations p exist i Scenario t The power generation flow during the period, ; For hydropower stations p Output characteristic curve; Total thermal power output for East China Grid, MW; for t Total nuclear power output of East China Network during the period, MW; for t The total output of pumped storage in East China Power Grid during this period is MW.

2. The method according to claim 1, characterized in that Step S2) Generate the first, second, and third new energy scenarios of the monthly output data of the years with different experience frequencies. The specific steps are as follows: A1, based on the monthly renewable energy output data of each provincial power grid Calculate the annual utilization hours of renewable energy in each provincial power grid ; A2: Calculate the annual utilization hours of renewable energy in each province Sort from large to small and get the sorted results , ,… ; A3: Select corresponding years as typical scenarios for different methods, and use the monthly output data of the years with empirical frequencies of 0-20%, 40%-60%, and 80%-100% as the first, second, and third method scenarios respectively; A4: Extract the monthly output data for each scenario based on different scenarios. .

3. The method according to claim 2, characterized in that Step S3) Set the scenario of DC hydropower inter-provincial allocation ratio, the steps are as follows: B1, according to formula (1), calculate the ratio of the The fixed amount of electricity delivered to each province by DC hydropower ; (1) Where: For the Total electricity consumption of DC hydropower lines; For the DC hydropower lines The proportion of electricity delivered to the province; B2, according to formula (2), adjust the power consumption of each DC line to the quarterly quota ; (2) Where: No. DC Quarterly electricity consumption; It is quarterly The corresponding month set, where Corresponding to the four seasons of spring, summer, autumn and winter; (3) B3, according to formula (3), verify whether the adjusted power meets the total power balance.

4. A computing device, characterized in that It includes a processor and a memory, wherein 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 of wind, solar, water, fire and nuclear multi-energy complementarity for the receiving area power grid as described in any one of claims 1 to 3.

5. A storage medium, characterized in that The storage medium stores a computer program, wherein the computer program is configured to execute the long-term dispatching method for wind, solar, water, fire and nuclear multi-energy complementarity for the receiving area power grid according to any one of claims 1 to 3 when running.

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