Novel power system multi-source multi-time scale scheduling method, device and medium

Abstract

The invention provides a novel power system multi-source multi-time scale scheduling method, equipment and a medium. The method comprises the following steps: performing long-term scheduling or short-term scheduling according to predicted intermittent continuous days of new energy output, and performing long-term scheduling or short-term scheduling by using a pre-constructed long-term scheduling model and short-term scheduling model to obtain a long-term scheduling or short-term scheduling result; taking the output result and the energy storage state result of the thermal power generating unit of the last day in the long-term scheduling or short-term scheduling results as boundary conditions, and performing day-ahead scheduling by using a pre-constructed day-ahead scheduling model to obtain a long-term / short-term-day-ahead coupling scheduling result; and according to a long-term / short-term-day-ahead coupling scheduling result, a power generation plan and an energy storage device state are arranged, and a final optimization scheduling purpose is realized. The method solves the problem of power supply and demand balance of the novel power system caused by long-period intermittency of new energy output, improves the economical efficiency and power supply reliability of the novel power system, and has very high engineering application value.

Description

Technical Field

[0001] The present invention relates to the field of novel power systems, and in particular to a novel power system multi-source multi-time scale scheduling method, equipment and medium. Background Art

[0002] In recent years, with the rapid development of power systems, the structure and operating characteristics have become increasingly complex, and the power system is facing the problem of reduced resilience. The new power system is highly uncertain on both the supply and demand sides, and the system balance has shifted from "deterministic power generation tracking uncertain load" to "two-way matching of uncertain power generation and uncertain load". With the increasing popularity of renewable energy, the dispatch complexity of the power grid has also increased. Since new energy is intermittent at different time scales, the power generation of new energy is greatly reduced during the intermittent period, and the system's power supply capacity is seriously insufficient, threatening the safe operation of the power grid.

[0003] Due to the increasing number of renewable energy sources, the new power system has a new feature - Dunkelflaute, which refers specifically to the situation where the sky is overcast and the wind is very light. At this time, the output of wind power and photovoltaic power drops sharply, which brings serious problems to the balance of supply and demand of electricity. Therefore, the new power system needs to fully tap the potential of various adjustable resources, accurately manage the power load, carry out coordinated optimization of source, grid, load and storage, and improve the level of interaction between source, grid, load and storage. At present, there are many options for incorporating flexible resources into the power system, such as transforming coal-fired power plants, demand response resources, and combining with multiple energy resources such as wind power, solar energy, coal, natural gas, and hydropower.

[0004] In the face of the increasingly complex electricity supply and demand situation and the challenges of the Dunkelflaute phenomenon, the flexibility and resilience of the power system are particularly important. In order to effectively respond to these challenges, the power system needs to integrate a variety of adjustable resources and achieve the optimal allocation of resources through scientific scheduling strategies. For example, transforming traditional coal-fired power plants into flexible power sources not only helps to improve power generation efficiency, but also can quickly provide backup power when new energy generation is insufficient. In addition, the introduction of demand response resources enables users to adjust their power consumption strategies according to power supply conditions, thereby alleviating peak load pressure and improving the overall regulation capacity of the system. Through joint scheduling with renewable energy sources such as wind power and solar energy, as well as flexible scheduling of natural gas, hydrogen energy and hydropower, it is possible to achieve diversified utilization of energy, enhance the adaptability of the power grid, and reduce the risks caused by fluctuations in renewable energy.

[0005] At present, many scholars have done a lot of research on the dispatching problem of high-proportion renewable energy power systems. Literature (Tan Qi, Sun Chenhao, Tang Hao, et al. Day-ahead dispatching method for power systems with flexible resources based on Conv-Seq2Seq model [J / OL]. Control and Decision, 1-9 [2024-12-23]) In order to make full use of the dispatching potential of flexible resources on both sides of the source and load in the new power system and improve the consumption rate of renewable energy power generation, a day-ahead dispatching method based on the Seq2Seq model is proposed to ensure safety and economy; Literature (Lei Xingyu, Yang Linze, Chen Yongtao, et al. Opportunity-constrained economic dispatching method considering fine response action of automatic generation control [J / OL]. Automation of Electric Power Systems, 1-11 [2024-12-23]) constructs an AGC considering net load uncertainty. Fine-grained time scale response model, and proposed an opportunity-constrained economic dispatch method with an explicit embedded AGC fine-grained time scale response model; Literature (Zhang Ye, Li Fengting, Zhang Gaohang, et al. Optimal dispatch method for wind power high penetration power system considering wind power ramping reserve demand [J]) For the optimal dispatch problem of wind power high penetration system, a prediction error distribution model was established based on wind power ramping-power characteristics to determine the system reserve demand, and a continuous time optimal dispatch method for wind power ramping reserve demand was proposed; Literature (Peng Chunhua, Lou Yujie, Fan Guozhu, et al. Robust optimal dispatch of a new power system based on node carbon potential response [J / OL]. Power System Technology, 1-15 [2024-12-23]) proposed a new robust optimal dispatch method for power system based on node carbon potential response, which improved the economy and low-carbon nature of the system, and improved the robustness and flexibility of the system by handling wind and solar uncertainties through confidence gap decision theory. The above-mentioned research on the dispatching problem of the new power system mainly focuses on the day-ahead and intra-day levels. However, for the new power system with new energy as the main body, the output of new energy has long-term intermittent nature, so the dispatching method that only looks forward to the day-ahead at most is difficult to cope with. Summary of the invention

[0006] The purpose of the present invention is to overcome the above-mentioned shortcomings of the prior art, combine long-term scheduling or short-term scheduling with day-ahead scheduling, and realize multi-time scale scheduling of wind, solar, water, fire and hydrogen on the premise of ensuring the power balance of the power system during the intermittent output of new energy, so as to improve the economy and power supply reliability of the power system.

[0007] The purpose of the present invention is achieved by at least one of the following technical solutions.

[0008] A novel multi-source and multi-time scale dispatching method for power system includes the following steps: Step 1: According to the predicted duration of intermittent renewable energy output, long-term scheduling or short-term scheduling is performed using the pre-built long-term scheduling model and short-term scheduling model to obtain long-term scheduling or short-term scheduling results; The long-term dispatch model and the short-term dispatch model both aim to minimize the operating cost of the new power system and maximize the power supply reliability. The constraints include node power balance constraints, thermal power unit operation constraints, new energy output constraints, electrochemical energy storage operation constraints, seasonal hydrogen storage operation constraints, pumped storage power station operation constraints, DC power flow constraints and load shedding constraints. Step 2: Using the last day’s thermal power unit output and energy storage status results in the long-term or short-term scheduling results as boundary conditions, use the pre-built day-ahead scheduling model to perform day-ahead scheduling and obtain the long-term / short-term day-ahead coupled scheduling results; The objective function of the day-ahead scheduling model is consistent with the objective functions of the long-term scheduling model and the short-term scheduling model. The constraints of the day-ahead scheduling model are based on the constraints of the long-term scheduling model and the short-term scheduling model, and the coupling constraints of the thermal power units and the energy storage state are newly added. Step 3: Arrange the power generation plan and the status of the energy storage device according to the long-term / short-day-ahead coupled scheduling results to achieve the ultimate optimization scheduling goal.

[0009] Furthermore, in step 1, the subsequent development of new energy is predicted The total daily output of the new energy is judged. When the total daily output of the new energy is higher than the normal daily output, times or less, the renewable energy source is judged as an intermittent output day; The duration of intermittent output of new energy is determined according to the start and end dates of the intermittent output of new energy. The duration of intermittent output of new energy can be as short as 2-3 days or as long as 7-8 days. The time scale needs to be adjusted to expand the scheduling cycle according to the actual forecast situation. The details are as follows: ; in, The duration of intermittent power generation of renewable energy The total daily output of the day, The normal daily output of new energy. The starting date of intermittent power generation. The day when intermittent power output ends; The number of days that intermittent power generation from renewable energy sources lasts; when When selecting The total daily output of various types of renewable energy is input into the short-term dispatch model to conduct advance Short-term scheduling of the day; When selecting The total daily output of various types of renewable energy is used as input to the long-term scheduling model for advance scheduling. Long-term scheduling of the day; and They are the short-term scheduling duration threshold and the long-term scheduling duration threshold respectively.

[0010] Furthermore, in step 1, the objective functions of the long-term scheduling model and the short-term scheduling model are consistent in form, so the long-term scheduling model and the short-term scheduling model are established uniformly, as follows: ; In the formula, The operating cost of the new power system is is the operating cost of thermal power units, is the energy storage operating cost, is the cost of abandoned energy, is the load shedding cost; The operating costs of thermal power units include the fuel costs and start-up and shutdown costs of each unit, as follows: ; In the formula, is the scheduling period; is the total number of thermal power units in the new power system; , , Respectively The consumption characteristic coefficients of the quadratic, linear and constant terms of the thermal power units; For the Thermal power units in The unit output at the time; For the Thermal power units in The start and stop status at all times; For the The start-up and shutdown costs of thermal power units; The energy storage operating costs include the depreciation cost of electrochemical energy storage, the startup cost of hydrogen storage equipment, and the startup cost of pumped storage power stations, as follows: ; In the formula, is the operating cost coefficient of electrochemical energy storage; and Electrochemical energy storage The charging and discharging power at the moment; is the operating cost coefficient of the pumped storage power station; and They are pumped storage power stations in The charging and discharging power at the moment; is the total start-up time of the electrolyzer during the scheduling period. Since the electrolyzer is not started at all times during the scheduling period, a new symbol is used to correspond; For electrolytic cell Start-up costs at the moment; Renewable energy in the new power system includes wind power, photovoltaic power generation and hydropower. The penalty costs of abandoning various types of new energy are added to the objective function to maximize the use of various types of renewable energy, as follows: ; In the formula, , and are the energy abandonment penalty coefficients for wind power, photovoltaic power generation and hydropower respectively; , and Wind power, photovoltaic power generation and hydropower The power abandonment at the moment; When renewable energy is intermittent for a long time, the new power system will experience a large amount of load shedding to maintain the balance between power supply and demand. In order to minimize the degree of load shedding and improve the power supply reliability of the new power system, a load shedding penalty term is added to the objective function as the load shedding cost, as follows: ; In the formula, is the load shedding penalty coefficient; For new power systems Node No. The amount of load shedding at the moment; is the total number of nodes in the new power system.

[0011] Furthermore, in step 1, the node power balance constraint is as follows: At each node and at each time, the sum of the thermal power, wind power, photovoltaic, hydropower output, energy storage charging and discharging power, and the inflow and outflow power of the connected lines must be balanced with the difference between the node load and the load shedding amount: ; In the formula, , , and They are respectively Node No. The output of thermal power, wind power, photovoltaic power and hydropower at the moment; , and They are respectively Node No. Discharge power of electrochemical energy storage, pumped storage power stations and seasonal hydrogen storage at all times; , and They are respectively Node No. Charging power of electrochemical energy storage, pumped storage power stations and seasonal hydrogen storage at all times; For Lines connected to nodes exist The transmission power at the moment; and Respectively The node No. is the line set of the end node and the start node; For new power systems Node No. The load of the moment; For new power systems Node No. The amount of load shedding at the moment; The operating constraints of thermal power units are as follows: Thermal power units have upper and lower limits on output during operation, and the rate of increase and decrease in output is also limited by their own physical properties: ; ; In the formula, and Respectively The lower limit of the number of thermal power units; and Respectively Maximum ramp-up and ramp-down rates of thermal power units; The output constraints of new energy sources are as follows: Various new energy sources The output at any time does not exceed its predicted value: ; In the formula, , and Wind power, photovoltaic power and hydropower Output forecast value at the time; , and Wind power, photovoltaic power and hydropower The effort of the moment; The operation constraints of electrochemical energy storage include charge and discharge power constraints and power constraints, as follows: ; ; In the formula, and They are the upper limits of charge and discharge power of electrochemical energy storage; and They are The charge and discharge state of electrochemical energy storage at all times; for The stored energy of electrochemical energy storage at all times; and They are the charge and discharge efficiency of electrochemical energy storage; Seasonal hydrogen storage operation constraints are as follows: Seasonal hydrogen storage uses low-pressure hydrogen storage. Due to the physical characteristics of the equipment, there is a certain amount of self-loss during the energy release process. There is only one charging and discharging state every day, and hydrogen energy is charged and discharged within the capacity range. The initial state within the scheduling cycle is the same: ; In the formula, and Respectively The charging and discharging status of seasonal hydrogen storage; and Respectively Daily seasonal hydrogen storage charging and discharging power; is the seasonal hydrogen storage capacity; and are the start and end capacities of the seasonal hydrogen storage scheduling cycle, respectively; is the ratio coefficient of the initial and final states of seasonal hydrogen storage to the total capacity; For the Daily seasonal hydrogen storage capacity; is the self-loss coefficient; and They are seasonal hydrogen storage charge and discharge efficiency; The operating constraints of pumped storage power stations are as follows: In order to cope with the intermittent output of renewable energy during the dispatching period, pumped storage only exists in the pumping state during the normal output of renewable energy, and does not exist in the power generation state: ; In the formula, It is the normal output day of renewable energy within the dispatch cycle; and The normal output day of new energy The power generation and pumping status of the internal pumped storage power station; The DC power flow constraints are as follows: To ensure safety, the system must meet the line flow constraints during normal operation, and the line flow does not exceed the safety limit. To simplify the process, the DC flow model is adopted: ; In the formula, For line exist The transmission power at the moment; and Line exist The voltage phase angle of the nodes at the beginning and end of the time; For line Reactance; For line The upper limit of transmission power; Load shedding constraints are as follows: During the intermittent output of renewable energy, the system will experience load shedding to ensure the balance of power supply and demand. The load shedding of each node cannot exceed the total load of the node. To improve power supply reliability, load shedding is not allowed on the normal output days of renewable energy: ; in, It is the normal output day of renewable energy within the dispatch cycle; For new power systems Node No. The load of the moment; For new power systems Node No. The load shedding at the moment.

[0012] Furthermore, in step 2, the pre-built day-ahead scheduling model is used to perform day-ahead scheduling, specifically as follows: The thermal power output and energy storage status results of the last day in the obtained long-term or short-term scheduling results are substituted as known quantities into the thermal power unit coupling constraints and energy storage status coupling constraints of the day-ahead scheduling model, and the day-ahead scheduling model is solved to obtain the long-term / short-day-ahead coupled scheduling results.

[0013] Furthermore, in step 2, the day-ahead scheduling model uses the output of the thermal power units and the state of the energy storage device on the last day of the long-term scheduling or short-term scheduling as boundary conditions for day-ahead scheduling, and its objective function is consistent with the objective functions of the long-term scheduling model and the short-term scheduling model in form, except that the scheduling period is different; The constraints of the day-ahead dispatch model include node power balance constraints, thermal power unit operation constraints, new energy output constraints, electrochemical energy storage operation constraints, seasonal hydrogen storage operation constraints, pumped storage power station operation constraints, DC power flow constraints, load shedding constraints, thermal power unit coupling constraints and energy storage state coupling constraints.

[0014] Furthermore, in step 2, the coupling constraints of the thermal power generation units are specifically as follows: The output of thermal power units in day-ahead dispatch is guided by long-term dispatch or short-term dispatch. At the same time, since the forecast output of new energy sources has different forecast errors in the long-term and short-term stages and the day-ahead stage, the output of thermal power units is also allowed to be adjusted within the set range: ; In the formula, For the Thermal power units in The unit output at the time; The last day in long-term or short-term scheduling and the day before scheduling The same moment of the moment; For the The thermal power units are in long-term or short-term scheduling. The effort of the moment; The first Thermal power units in Output adjustment deviation at any time; The energy storage state coupling constraints are specifically as follows: The initial energy state of each type of energy storage device should be the same as the energy state at the initial moment of the last day in the long-term or short-term scheduling results: ; In the formula, , and are the energy states of electrochemical energy storage, seasonal hydrogen storage and pumped storage power station at the start of day-ahead dispatch; , and They are the energy states of electrochemical energy storage, seasonal hydrogen storage and pumped storage power stations at the beginning of the last day in long-term or short-term scheduling.

[0015] Furthermore, in step 3, the long-term / short-day-ahead coupled scheduling results are organized into power generation plans for various power sources in the new power system, and the power generation curves of various power sources within a day and the status of the energy storage device are planned. Each power generation unit performs corresponding power generation scheduling according to the received scheduling instructions to ensure that each unit operates as planned and achieve the ultimate optimization goal.

[0016] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned novel multi-source and multi-time-scale scheduling method for a power system when executing the computer program.

[0017] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned novel multi-source and multi-time scale scheduling method for a power system.

[0018] Compared with the prior art, the advantages of the present invention are: The present invention rationally utilizes pumped-storage power stations and seasonal energy storage, reduces the abandonment rate of renewable energy, reduces system operating costs, reduces load shedding, and improves grid resilience; at the same time, under the guidance of the 2MT-S scheduling strategy at different time scales, when the output of renewable energy presents intermittent low output, the overall load shedding of the grid can be greatly reduced at the expense of a small degree of economic efficiency, thereby alleviating the problem of power balance and improving power supply reliability.

[0019] The present invention solves the problem of power supply and demand balance in the new power system caused by the long-term intermittent output of new energy, improves the economy and power supply reliability of the new power system, is easy to implement, and has high engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention is a flowchart of the steps of a novel multi-source and multi-time scale scheduling method for a power system in an embodiment of the present invention.

[0021] Figure 2 It is a structural topology diagram of a novel power system in an embodiment of the present invention.

[0022] Figure 3 Schematic diagram of scheduling results of scenario 1 in an embodiment of the present invention.

[0023] Figure 4 Schematic diagram of scheduling results of scenario 2 in an embodiment of the present invention.

[0024] Figure 5 Schematic diagram of scheduling results of scenario 3 in an embodiment of the present invention.

[0025] Figure 6 Schematic diagram of scheduling results of scenario 4 in an embodiment of the present invention.

[0026] Figure 7 Schematic diagram of scheduling results of scenario 5 in an embodiment of the present invention.

[0027] Figure 8 Schematic diagram of scheduling results of scenario 6 in an embodiment of the present invention.

[0028] Fig. 9 Schematic diagram of the scheduling result of scenario 7 in an embodiment of the present invention.

[0029] Fig.10 Schematic diagram of scheduling results of scenario 8 in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The specific implementation of the present invention is further described below with reference to the accompanying drawings and examples.

[0031] A new multi-source and multi-time scale dispatching method for power systems, such as Figure 1 As shown, the following steps are included: Step 1: According to the predicted duration of intermittent renewable energy output, long-term scheduling or short-term scheduling is performed using the pre-built long-term scheduling model and short-term scheduling model to obtain long-term scheduling or short-term scheduling results; In one embodiment, , ; and They are the short-term scheduling duration threshold and the long-term scheduling duration threshold respectively.

[0032] Predict the total daily output of new energy for the next 10 days, and make a judgment on the total daily output of new energy. times or less, the renewable energy source is judged as an intermittent output day; The duration of intermittent output of new energy is determined according to the start and end dates of the intermittent output of new energy. The duration of intermittent output of new energy can be as short as 2-3 days or as long as 7-8 days. The time scale needs to be adjusted to expand the scheduling cycle according to the actual forecast situation. The details are as follows: ; in, The duration of intermittent power generation of renewable energy The total daily output of the day, The normal daily output of new energy. The starting date of intermittent power generation. The day when intermittent power output ends; The number of days that intermittent power generation from renewable energy sources lasts; when When , the total daily output of various types of renewable energy sources 3 days in advance is selected and input into the short-term dispatch model for 3-day-in-advance short-term dispatch; When selecting the total daily output of various types of renewable energy sources 10 days in advance, the total daily output is input into the long-term scheduling model for long-term scheduling 10 days in advance.

[0033] The long-term dispatch model and the short-term dispatch model both aim to minimize the operating cost of the new power system and maximize the power supply reliability. The constraints include node power balance constraints, thermal power unit operation constraints, new energy output constraints, electrochemical energy storage operation constraints, seasonal hydrogen storage operation constraints, pumped storage power station operation constraints, DC power flow constraints and load shedding constraints. The objective functions of the long-term scheduling model and the short-term scheduling model are consistent in form, so the long-term scheduling model and the short-term scheduling model are established uniformly, as follows: ; In the formula, The operating cost of the new power system is is the operating cost of thermal power units, is the energy storage operating cost, is the cost of abandoned energy, is the load shedding cost; The operating costs of thermal power units include the fuel costs and start-up and shutdown costs of each unit, as follows: ; In the formula, is the scheduling period; is the total number of thermal power units in the new power system; , , Respectively The consumption characteristic coefficients of the quadratic, linear and constant terms of the thermal power units; For the Thermal power units in The unit output at the time; For the Thermal power units in The start and stop status at all times; For the The start-up and shutdown costs of thermal power units; The energy storage operating costs include the depreciation cost of electrochemical energy storage, the startup cost of hydrogen storage equipment, and the startup cost of pumped storage power stations, as follows: ; In the formula, is the operating cost coefficient of electrochemical energy storage; and Electrochemical energy storage The charging and discharging power at the moment; is the operating cost coefficient of the pumped storage power station; and They are pumped storage power stations in The charging and discharging power at the moment; is the total start-up time of the electrolyzer during the scheduling period. Since the electrolyzer is not started at all times during the scheduling period, a new symbol is used to correspond; For electrolytic cell Start-up costs at the moment; Renewable energy in the new power system includes wind power, photovoltaic power generation and hydropower. The penalty costs of abandoning various types of new energy are added to the objective function to maximize the use of various types of renewable energy, as follows: ; In the formula, , and are the energy abandonment penalty coefficients for wind power, photovoltaic power generation and hydropower respectively; , and Wind power, photovoltaic power generation and hydropower The power abandonment at the moment; When renewable energy is intermittent for a long time, the new power system will experience a large amount of load shedding to maintain the balance between power supply and demand. In order to minimize the degree of load shedding and improve the power supply reliability of the new power system, a load shedding penalty term is added to the objective function as the load shedding cost, as follows: ; In the formula, is the load shedding penalty coefficient; For new power systems Node No. The amount of load shedding at the moment; is the total number of nodes in the new power system.

[0034] Node power balance constraints are as follows: At each node and at each time, the sum of the thermal power, wind power, photovoltaic, hydropower output, energy storage charging and discharging power, and the inflow and outflow power of the connected lines must be balanced with the difference between the node load and the load shedding amount: ; In the formula, , , and They are respectively Node No. The output of thermal power, wind power, photovoltaic power and hydropower at the moment; , and They are respectively Node No. Discharge power of electrochemical energy storage, pumped storage power stations and seasonal hydrogen storage at all times; , and They are respectively Node No. Charging power of electrochemical energy storage, pumped storage power stations and seasonal hydrogen storage at all times; For Lines connected to nodes exist The transmission power at the moment; and Respectively The node No. is the line set of the end node and the start node; For new power systems Node No. The load of the moment; For new power systems Node No. The amount of load shedding at the moment; The operating constraints of thermal power units are as follows: Thermal power units have upper and lower limits on output during operation, and the rate of increase and decrease in output is also limited by their own physical properties: ; ; In the formula, and Respectively The lower limit of the number of thermal power units; and Respectively Maximum ramp-up and ramp-down rates of thermal power units; The output constraints of new energy sources are as follows: Various new energy sources The output at any time does not exceed its predicted value: ; In the formula, , and Wind power, photovoltaic power and hydropower Output forecast value at the time; , and Wind power, photovoltaic power and hydropower The effort of the moment; The operation constraints of electrochemical energy storage include charge and discharge power constraints and power constraints, as follows: ; ; In the formula, and They are the upper limits of charge and discharge power of electrochemical energy storage; and They are The charge and discharge state of electrochemical energy storage at all times; for The stored energy of electrochemical energy storage at all times; and They are the charge and discharge efficiency of electrochemical energy storage; Seasonal hydrogen storage operation constraints are as follows: Seasonal hydrogen storage uses low-pressure hydrogen storage. Due to the physical characteristics of the equipment, there is a certain amount of self-loss during the energy release process. There is only one charging and discharging state every day, and hydrogen energy is charged and discharged within the capacity range. The initial state within the scheduling cycle is the same: ; In the formula, and Respectively The charging and discharging status of seasonal hydrogen storage; and Respectively Daily seasonal hydrogen storage charging and discharging power; is the seasonal hydrogen storage capacity; and are the start and end capacities of the seasonal hydrogen storage scheduling cycle, respectively; is the ratio coefficient of the initial and final states of seasonal hydrogen storage to the total capacity; For the Daily seasonal hydrogen storage capacity; is the self-loss coefficient; and They are seasonal hydrogen storage charge and discharge efficiency; The operating constraints of pumped storage power stations are as follows: In order to cope with the intermittent output of renewable energy during the dispatching period, pumped storage only exists in the pumping state during the normal output of renewable energy, and does not exist in the power generation state: ; In the formula, It is the normal output day of renewable energy within the dispatch cycle; and The normal output day of new energy The power generation and pumping status of the internal pumped storage power station; The DC power flow constraints are as follows: To ensure safety, the system must meet the line flow constraints during normal operation, and the line flow does not exceed the safety limit. To simplify the process, the DC flow model is adopted: ; In the formula, For line exist The transmission power at the moment; and Line exist The voltage phase angle of the nodes at the beginning and end of the time; For line Reactance; For line The upper limit of transmission power; Load shedding constraints are as follows: During the intermittent output of renewable energy, the system will experience load shedding to ensure the balance of power supply and demand. The load shedding of each node cannot exceed the total load of the node. To improve power supply reliability, load shedding is not allowed on the normal output days of renewable energy: ; in, It is the normal output day of renewable energy within the dispatch cycle; For new power systems Node No. The load of the moment; For new power systems Node No. The load shedding at the moment.

[0035] Step 2: Take the output results of the thermal power units and the energy storage status results of the last day in the long-term or short-term scheduling results as boundary conditions, use the pre-built day-ahead scheduling model to perform day-ahead scheduling, and obtain the long-term / short-term day-ahead coupled scheduling results, as follows: The thermal power output and energy storage status results of the last day in the obtained long-term or short-term scheduling results are substituted as known quantities into the thermal power unit coupling constraints and energy storage state coupling constraints of the day-ahead scheduling model. The day-ahead scheduling model is established in MATLAB using the yalmip toolbox or other software development platforms, and is solved using the Gurobi solver or other commercial optimization solving software to obtain the long-term / short-day-ahead coupled scheduling results.

[0036] The objective function of the day-ahead scheduling model is consistent with the objective functions of the long-term scheduling model and the short-term scheduling model. The constraints of the day-ahead scheduling model are based on the constraints of the long-term scheduling model and the short-term scheduling model, and the coupling constraints of the thermal power units and the energy storage state are newly added. The day-ahead scheduling model uses the output of the thermal power unit and the state of the energy storage device on the last day of long-term scheduling or short-term scheduling as boundary conditions for day-ahead scheduling. Its objective function is consistent with the objective functions of the long-term scheduling model and the short-term scheduling model in form, except that the scheduling period is different. In one embodiment, the period of long-term scheduling is 10 days, the period of short-term scheduling is 3 days, and the period of day-ahead scheduling is 1 day. The constraints of the day-ahead dispatch model include node power balance constraints, thermal power unit operation constraints, new energy output constraints, electrochemical energy storage operation constraints, seasonal hydrogen storage operation constraints, pumped storage power station operation constraints, DC power flow constraints, load shedding constraints, thermal power unit coupling constraints and energy storage state coupling constraints.

[0037] Furthermore, in step 2, the coupling constraints of the thermal power generation units are specifically as follows: The output of thermal power units in day-ahead dispatch is guided by long-term dispatch or short-term dispatch. At the same time, since the forecast output of new energy sources has different forecast errors in the long-term and short-term stages and the day-ahead stage, the output of thermal power units is also allowed to be adjusted within the set range: ; In the formula, For the Thermal power units in The unit output at the time; The last day in long-term or short-term scheduling and the day before scheduling The same moment of the moment; For the The thermal power units are in long-term or short-term scheduling. The effort of the moment; The first Thermal power units in Output adjustment deviation at any time; The energy storage state coupling constraints are specifically as follows: The initial energy state of each type of energy storage device should be the same as the energy state at the initial moment of the last day in the long-term or short-term scheduling results: ; In the formula, , and are the energy states of electrochemical energy storage, seasonal hydrogen storage and pumped storage power station at the start of day-ahead dispatch; , and They are the energy states of electrochemical energy storage, seasonal hydrogen storage and pumped storage power stations at the beginning of the last day in long-term or short-term scheduling.

[0038] Step 3: Arrange the power generation plan and the status of the energy storage device according to the long-term / short-day-ahead coupled scheduling results to achieve the ultimate optimization scheduling purpose; The long-term / short-day-ahead coupled scheduling results are organized into power generation plans for various power sources in the new power system, and the power generation curves of various power sources within a day and the status of energy storage devices are planned. Each power generation unit performs corresponding power generation scheduling based on the received scheduling instructions to ensure that each unit operates as planned and achieve the ultimate optimization goal.

[0039] In one embodiment, Figure 2 As shown in Figure 1, the new power system includes 30 nodes in total. Each serial number represents the node number. G represents thermal power units, P represents photovoltaic power stations, hpp represents hydropower stations, W represents wind turbines, S represents electrochemical energy storage, H represents seasonal hydrogen storage, and ps represents pumped storage power stations. The new power system includes 5 thermal power units, and the specific parameters are shown in Table 1. It includes a wind farm with a total capacity of 550MW, a photovoltaic power station with a total capacity of 250MW, and a hydropower station with a total capacity of 500MW. The total capacity of electrochemical energy storage is 230MW·h, the upper limit of charging and discharging power is 50MW, and the charging and discharging efficiency is 95%. The start-up and shutdown cost of the electrolyzer is 500 yuan, the seasonal hydrogen storage power generation efficiency is 60%, and the upper limit of power generation is 30MW. The total capacity of the pumped storage power station is 1000MW, the upper limit of power generation is 60MW, and the efficiency is 85%. The penalty coefficient for energy abandonment is 600 yuan / (MW·h), and the penalty coefficient for load shedding is 1000 yuan / (MW·h).

[0040] Table 1 Thermal power unit parameters

[0041] In order to fully verify the effectiveness of the present invention, 8 scenarios are set for comparative analysis to verify the economy and power supply reliability under different scenarios. The specific scenarios are set as follows: Scenario 1: Combined operation of wind, solar, water and fire in long-term dispatch, only considering electrochemical energy storage.

[0042] Scenario 2: Combined operation of wind, solar, water and fire in long-term dispatch, taking into account electrochemical energy storage and pumped storage power stations.

[0043] Scenario 3: Combined operation of wind, solar, water and fire in long-term dispatch, taking into account electrochemical energy storage, pumped storage power stations and seasonal hydrogen storage.

[0044] Scenario 4: Traditional day-ahead economic dispatch when renewable energy output is intermittent.

[0045] Scenario 5: 2MT-S coupled economic dispatch (long-term intermittent output of new energy).

[0046] Scenario 6: 2MT-S coupled economic dispatch (short-term intermittent output of new energy).

[0047] Scenario 7: Traditional day-ahead economic dispatch when a high proportion of new energy systems are operating normally.

[0048] Scenario 8: 2MT-S coupled economic dispatch when a high proportion of new energy systems are operating normally.

[0049] In one embodiment, by comparing and analyzing scenarios 1, 2, and 3, the advantages of pumped storage power stations and seasonal hydrogen storage are verified on a long-term scale. The scheduling results of scenarios 1 to 3 correspond to Figures 3-5 The specific comparative analysis results are shown in Table 2.

[0050] pass Figure 3 , Figure 4 As shown in Table 2, when wind, solar, water and fire are jointly dispatched and only electrochemical energy storage is connected, the new energy abandonment rate is 16.6%, the system cost of electricity is 0.34 yuan / kW·h, the total charging and discharging power of electrochemical energy storage is 410.25MW, the total system load shedding is 281.27MW, and the maximum load shedding of the system node at a single moment is 89.27MW; when pumped storage is connected, the new energy abandonment rate drops by 0.6%, and the system cost of electricity is reduced to 0.3394 yuan / kW·h, which improves the economy of the system. At the same time, the power supply pressure is shared for electrochemical energy storage, and the overall system load shedding is also reduced by 104.9MW, which greatly improves the power supply reliability of the system.

[0051] pass Figure 4 , Figure 5 As shown in Table 2, the simulation results of scenarios 2 and 3 show that when the wind, solar, water and fire joint dispatching system is further connected to seasonal hydrogen storage, the new energy power abandonment rate decreases by 0.2%, and the system cost per kilowatt-hour is reduced to 0.3393 yuan / kW·h, further improving the economy. At the same time, the overall load shedding of the system is also reduced by 47.93MW, and the maximum load shedding of the system node at a single moment is reduced by 31.72MW, which improves the resilience of the power grid.

[0052] On the whole, the introduction of flexible resources has obvious synergistic effects, especially seasonal hydrogen storage, which is particularly effective in solving the problem of long-term low output of new energy.

[0053] Table 2 Scheduling results

[0054] In one embodiment, in order to further demonstrate the advantages of the present invention over traditional day-ahead economic dispatch, scenarios 4, 5, and 6 are set for comparative analysis. The dispatch results of scenarios 4 to 6 correspond to Figures 6-8 The specific comparative analysis results are shown in Table 3.

[0055] The comparative analysis of scenarios 4 and 5 corresponds to the situation of long-term intermittent output of renewable energy. Figure 6 , Figure 7The comparison results are shown in Table 3. Figure 6 The results show that in the new power system, relying solely on traditional day-ahead economic dispatch will result in a serious power gap, with the overall system load shedding reaching 12,300 MW and the total thermal power output being only 8,140 MW. When day-ahead dispatch is guided by long-term dispatch results, the overall system load shedding is only 52.43 MW, and the system cost per kilowatt-hour increases by 0.096 yuan / kW·h. This greatly improves power supply reliability at the expense of a small amount of economy.

[0056] The comparative analysis of scenarios 4 and 6 corresponds to the situation of long-term intermittent output of renewable energy. Figure 6 , Figure 8 The results are compared with those in Table 3. When the day-ahead dispatch was guided by the short-term dispatch results, the overall system load shedding was only 98.3MW, and the system electricity cost increased by 0.075 yuan / kW·h. Similarly, the power supply reliability was greatly improved at the expense of a small amount of economy.

[0057] Table 3 Comparative analysis results under intermittent output

[0058] At the same time, when the new energy is outputting normally, the reliability of the system power supply is guaranteed, and the coupling scheduling should not lose the economy of the day-ahead scheduling. In one embodiment, to further verify the effectiveness, scenarios 7 and 8 are set for comparative analysis. The scheduling results of scenarios 7 and 8 correspond to Figures 9 and 10 The specific comparative analysis results are shown in Table 4.

[0059] pass Fig. 9 , Fig.10 As can be seen from the results in Table 4, when the high-proportion new energy system is operating normally, the traditional economic dispatch relies on only one thermal power unit to generate electricity, while the coupled economic dispatch relies on two units to generate electricity at the same time, but the overall thermal power output is not much different, from 3.93*10 3 MW increased to 4.32*10 3 MW, the increase in the number of output units and the increase in total output led to an increase in the total transmission power of the line; the system electricity cost remained almost unchanged, only increasing by 0.001 yuan / kW·h. The results show that when the new energy is normally output, the 2MT-S coupled economic dispatch does not lose economic efficiency.

[0060] Table 4 Comparative analysis results under normal output conditions

[0061] In one embodiment, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the novel multi-source and multi-time-scale scheduling method for a power system when executing the computer program.

[0062] In one embodiment, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the novel multi-source and multi-time-scale scheduling method for a power system is implemented.

[0063] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0064] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0067] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A novel multi-source and multi-time scale dispatching method for power system, characterized in that: The following steps are involved: Step 1: According to the predicted number of days of intermittent output of renewable energy, long-term scheduling or short-term scheduling is performed using the pre-built long-term scheduling model and short-term scheduling model to obtain long-term scheduling or short-term scheduling results; the long-term scheduling model and the short-term scheduling model both aim to minimize the operating cost of the new power system and maximize the power supply reliability; Step 2: Using the last day’s thermal power unit output and energy storage status results in the long-term or short-term scheduling results as boundary conditions, use the pre-built day-ahead scheduling model to perform day-ahead scheduling and obtain the long-term / short-term day-ahead coupled scheduling results; The constraints of the day-ahead dispatch model include the addition of thermal power unit coupling constraints and energy storage state coupling constraints based on the constraints of the long-term dispatch model and the short-term dispatch model. Step 3: Arrange the power generation plan and the status of the energy storage device according to the long-term / short-day-ahead coupled scheduling results to achieve the ultimate optimization scheduling goal.

2. A novel multi-source and multi-time scale dispatching method for power system according to claim 1, characterized in that: In step 1, the abandoned energy costs of various types of renewable energy are added to the objective functions of the long-term scheduling model and the short-term scheduling model. The objective functions of the long-term scheduling model and the short-term scheduling model are consistent in form, as follows: ； In the formula, The operating cost of the new power system is is the operating cost of thermal power units, is the energy storage operating cost, is the cost of abandoned energy, is the load shedding cost.

3. A novel multi-source and multi-time scale dispatching method for power system according to claim 1, characterized in that: In step 2, the pre-built day-ahead scheduling model is used to perform day-ahead scheduling, as follows: The thermal power output and energy storage status results of the last day in the obtained long-term or short-term scheduling results are substituted as known quantities into the thermal power unit coupling constraints and energy storage status coupling constraints of the day-ahead scheduling model, and the day-ahead scheduling model is solved to obtain the long-term / short-day-ahead coupled scheduling results.

4. A novel multi-source and multi-time scale dispatching method for power system according to claim 3, characterized in that: In step 2, the constraints of the day-ahead scheduling model include: node power balance constraints, thermal power unit operation constraints, new energy output constraints, electrochemical energy storage operation constraints, seasonal hydrogen storage operation constraints, pumped storage power station operation constraints, DC power flow constraints, load shedding constraints, thermal power unit coupling constraints and energy storage state coupling constraints.

5. A novel multi-source and multi-time scale dispatching method for power system according to claim 4, characterized in that: The node power balance constraint is: each node at each time must ensure that the sum of thermal power, wind power, photovoltaic, hydropower output, energy storage charging and discharging power, and the inflow and outflow power of the connected lines is balanced with the difference between the node load and the load shedding amount; The operation constraints of the thermal power unit are as follows: the thermal power unit has an upper and lower output limit during operation, and the rate of increase and decrease of the output is limited by its own physical characteristics; The new energy output constraints are: The output at any time does not exceed its predicted value; The electrochemical energy storage operation constraints include: charge and discharge power constraints and power constraints: Seasonal hydrogen storage operation constraints: Seasonal hydrogen storage uses low-pressure hydrogen storage to charge and discharge hydrogen energy within the capacity range, and the initial state is the same during the scheduling cycle; The operation constraint of the pumped storage power station is: during the normal output period of new energy, the pumped storage power station only exists in the pumping state, and does not exist in the power generation state; The DC power flow constraint is: the new power system must meet the line power flow constraint during normal operation, and the line power flow does not exceed the safety limit; The load shedding constraint is: the new power system will shear load during the intermittent output of renewable energy to ensure the balance of power supply and demand. The load shedding of each node cannot exceed the total load of the node. Load shedding is not allowed on the day when renewable energy has normal output.

6. A novel multi-source and multi-time scale dispatching method for power system according to claim 4, characterized in that: In step 2, The coupling constraints of the thermal power units are as follows: The output of thermal power units in day-ahead dispatch is guided by long-term dispatch or short-term dispatch. At the same time, since the forecast output of new energy sources has different forecast errors in the long-term and short-term stages and the day-ahead stage, the output of thermal power units is allowed to be adjusted within the set range. The energy storage state coupling constraints are specifically as follows: The initial energy state of each type of energy storage device should be the same as the energy state at the initial moment of the last day in the long-term or short-term scheduling results.

7. A novel multi-source and multi-time scale dispatching method for power system according to claim 1, characterized in that: In step 3, the long-term / short-day-ahead coupled scheduling results are organized into power generation plans for various power sources in the new power system, and the power generation curves of various power sources within a day and the status of the energy storage device are planned. Each power generation unit performs corresponding power generation scheduling according to the received scheduling instructions, so that each unit operates according to the plan and achieves the ultimate optimization scheduling goal.

8. A novel multi-source and multi-time scale dispatching method for power system according to claim 1, characterized in that: In step 1, predict the follow-up of new energy The total daily output of the new energy is judged. When the total daily output of the new energy is higher than the normal daily output, times or less, the renewable energy source is judged as an intermittent output day; The duration of intermittent output of renewable energy is determined based on the start and end dates of the intermittent output of renewable energy, as follows: ； in, The duration of intermittent power generation of renewable energy The total daily output of the day, The normal daily output of new energy. The starting date of intermittent power generation. The day when intermittent power output ends; The number of days that intermittent power generation from renewable energy sources lasts; when When selecting The total daily output of various types of renewable energy is input into the short-term dispatch model to conduct advance Short-term scheduling of the day; When selecting The total daily output of various types of renewable energy is used as input to the long-term scheduling model for advance scheduling. Long-term scheduling of the day; and They are the short-term scheduling duration threshold and the long-term scheduling duration threshold respectively.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, a novel multi-source and multi-time-scale scheduling method for a power system as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a novel multi-source and multi-time scale scheduling method for a power system according to any one of claims 1 to 8 is implemented.

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