A novel multi-source multi-time scale scheduling method for power systems, device and medium

Abstract

The application provides a novel power system multi-source multi-time scale scheduling method, device and medium. The method comprises the following steps: performing long-term scheduling or short-term scheduling according to the predicted intermittent duration of new energy output, performing long-term scheduling or short-term scheduling by using a pre-constructed long-term scheduling model and a short-term scheduling model, and obtaining long-term scheduling or short-term scheduling results; taking the thermal power unit output result and the energy storage state result of the last day in the long-term scheduling or short-term scheduling result as a boundary condition, performing day-ahead scheduling by using a pre-constructed day-ahead scheduling model, and obtaining long-term / short-term-day-ahead coupled scheduling results; and arranging a power generation plan and an energy storage device state according to the long-term / short-term-day-ahead coupled scheduling results, so as to realize the final optimization scheduling purpose. The application solves the power supply and demand balance problem of the novel power system caused by the long-period intermittency of new energy output, improves the economy and power supply reliability of the novel power system, and has high engineering application value.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new power systems, in particular to a new power system multi-source multi-time scale scheduling method, device and medium. BACKGROUND

[0002] In recent years, with the rapid development of power systems, the structure and operation characteristics are becoming increasingly complex, and the power system is facing the problem of reduced resilience. The supply and demand of new power systems presents high uncertainty, and the system balance changes from "deterministic power generation tracking uncertain load" to "uncertain power generation and uncertain load two-way matching". With the increasing popularity of renewable energy, the complexity of power grid scheduling also increases. Due to the intermittent nature of new energy at different time scales, the amount of new energy generated during the intermittent period is greatly reduced, and the system power supply capacity is severely insufficient, threatening the safe operation of the power grid.

[0003] Due to the increasing amount of renewable energy, the new power system has a new feature - Dunkelflaute, which refers to the situation where the sky is overcast and the wind is very small, at which time the output of wind power and photovoltaic power is sharply reduced, causing serious problems in the balance between power supply and demand. Therefore, the new power system needs to fully tap the potential of various adjustable resources, accurately manage power load, carry out coordinated optimization scheduling of source, network, load and storage, and improve the interaction level of source, network, load and storage. At present, flexible resources are included in the selection of power systems in a variety of ways, such as transforming coal-fired power plants, demand response resources, and combining with wind energy, solar energy, coal, natural gas, hydropower and other energy resources.

[0004] In the face of the increasingly complex power supply and demand situation and the challenge of 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 various adjustable resources and achieve optimal allocation of resources through scientific scheduling strategies. For example, transforming traditional coal-fired power plants into flexible dispatchable power sources not only helps to improve power generation efficiency, but also quickly provides backup power when new energy power 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 relieving peak load pressure and improving the overall adjustment capability of the system. Through joint scheduling with renewable energy sources such as wind energy and solar energy, as well as flexible scheduling of natural gas, hydrogen energy and hydropower, diversified utilization of energy can be achieved, the adaptability of the power grid can be enhanced, and the risks caused by renewable energy fluctuations can be reduced.

[0005] At present, many scholars have done a lot of research on the dispatching problem of high proportion of new energy power system. Literature (Tan Qi, Sun Chenhao, Tang Hao, et al. Day-ahead dispatching method for power system with elastic 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 source and load elastic resources in new type power system and improve the consumption rate of new energy power generation, a day-ahead dispatching method based on Seq2Seq model is proposed to ensure safety and economy; Literature (Lei Xingyu, Yang Linze, Chen Yongtao, et al. Chance constrained economic dispatch method considering fine response action of automatic generation control [J / OL]. Power System Automation, 1-11 [2024-12-23]) builds an AGC fine particle time scale response model considering the uncertainty of net load, and proposes a chance constrained economic dispatch method with explicit embedded AGC fine particle time scale response model; Literature (Zhang Ye, Li Fengting, Zhang Gaohang, et al. Optimization dispatching method of wind power high penetration power system considering wind power climbing reserve demand [J]) for the optimization dispatching problem of wind power high penetration system, a prediction error distribution model is established based on wind power climbing-power characteristics to determine the system reserve demand, and a continuous time optimization dispatching method of wind power climbing reserve demand is proposed; Literature (Peng Chunhua, Lou Yujie, Fan Guozhu, et al. Robust optimization dispatching of new type power system based on node carbon potential response [J / OL]. Power Grid Technology, 1-15 [2024-12-23]) proposes a robust optimization dispatching method of new type power system based on node carbon potential response, which improves the economy and low carbon of the system, and processes the uncertainty of wind and light through confidence gap decision theory to improve the robustness and flexibility of the system. The above research on the dispatching problem of new type power system mainly focuses on day-ahead-day-in, while for the new type power system dominated by new energy, the output of new energy has long-term intermittency, so the dispatching mode looking forward to only day-ahead is difficult to cope with. SUMMARY

[0006] The purpose of the present application overcomes the above-mentioned deficiencies existing in the prior art, combines long-term dispatching or short-term dispatching with day-ahead dispatching, realizes wind, light, water, fire and hydrogen multi-time scale dispatching under the premise of ensuring power system power balance during new energy intermittent output period, and improves the economy and power supply reliability of the power system.

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

[0008] A new type of power system multi-source multi-time scale dispatching method, comprising the following steps:

[0009] Step 1: according to the predicted intermittent duration of new energy output, long-term dispatching or short-term dispatching is carried out by using the pre-constructed long-term dispatching model and short-term dispatching model, and the long-term dispatching or short-term dispatching result is obtained;

[0010] The long-term scheduling model and the short-term scheduling model both take the minimum new power system operation cost and the maximum power supply reliability as the target, and the constraint conditions include node power balance constraint, thermal power unit operation constraint, new energy output constraint, electrochemical energy storage operation constraint, seasonal hydrogen storage operation constraint, pumped storage power station operation constraint, direct current flow constraint and load shedding constraint;

[0011] Step 2: taking the thermal power unit output result and the energy storage state result of the last day in the long-term scheduling or short-term scheduling result as the boundary condition, performing day-ahead scheduling by using the pre-constructed day-ahead scheduling model to obtain the long-term / short-term-day-ahead coupled scheduling result;

[0012] 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, and the constraint conditions of the day-ahead scheduling model are added to the constraint conditions of the long-term scheduling model and the short-term scheduling model, including thermal power unit coupling constraint and energy storage state coupling constraint;

[0013] Step 3: arranging the power generation plan and the energy storage device state according to the long-term / short-term-day-ahead coupled scheduling result to achieve the final optimization scheduling purpose.

[0014] Further, in step 1, the subsequent daily total output of new energy is predicted, and the daily total output of new energy is judged, when the daily total output of new energy is below the normal daily total output of new energy times, it is determined that the new energy is intermittent output day on that day;

[0015] The intermittent duration of new energy output is determined according to the start day and the end day of the intermittent output of new energy, and the intermittent duration of new energy output is short for 2-3 days and long for 7-8 days, which needs to be adjusted according to the actual prediction situation to expand the scheduling period, which is as follows:

[0016] ;

[0017] Among them, is the daily total output of the new energy on the day in the intermittent duration of the new energy output, is the normal daily total output of the new energy, is the start day of the intermittent output, is the end day of the intermittent output; is the intermittent duration of the new energy output;

[0018] When , the daily total output of each type of new energy in the days in advance is selected and input into the short-term scheduling model to perform the Short-term scheduling of the day; when At that time, choose in advance The total daily output of various renewable energy sources is used as input into the long-term scheduling model for advance scheduling. Long-term scheduling of the day; and These are the thresholds for the duration of short-term scheduling and the thresholds for the duration of long-term scheduling, respectively.

[0019] Furthermore, in step 1, the objective functions of the long-term scheduling model and the short-term scheduling model are identical in form. Therefore, the long-term scheduling model and the short-term scheduling model are established uniformly, as follows:

[0020] ;

[0021] In the formula, For the operating costs of the new power system, For the operating costs of thermal power units, For energy storage operating costs, For the cost of energy curtailment, For load shedding costs;

[0022] The operating cost of thermal power units includes the fuel cost of each unit and the start-up and shutdown costs, as detailed below:

[0023] ;

[0024] In the formula, The scheduling period; This refers to the total number of thermal power units in the new power system. , , The first Consumption characteristic coefficients of the secondary, primary, and constant terms of a thermal power unit; For the first Taiwan thermal power units The unit's output at any given moment; For the first Taiwan thermal power units The start / stop status at any given moment; For the first Start-up and shutdown costs of thermal power units in Taiwan;

[0025] Energy storage operating costs include the depreciation costs of electrochemical energy storage, the start-up costs of hydrogen storage equipment, and the start-up costs of pumped storage power stations, as detailed below:

[0026] ;

[0027] In the formula, This represents the operating cost coefficient for electrochemical energy storage. and Electrochemical energy storage The charging and discharging power at any given moment; This is the operating cost coefficient for pumped storage power stations; and Pumped storage power stations are respectively The charging and discharging power at any given moment; This represents the total start-up time of the electrolyzers within the scheduling cycle. Since the electrolyzers are not started at all times within the scheduling cycle, a new symbol is used. To correspond; For the electrolytic cell in Startup costs at any given moment;

[0028] Renewable energy sources in the new power system include wind power, photovoltaic power, and hydropower. Penalty costs for curtailment of various renewable energy sources are incorporated into the objective function to maximize their utilization, as detailed below:

[0029] ;

[0030] In the formula, , and These are the curtailment penalty coefficients for wind power, photovoltaic power, and hydropower, respectively. , and Wind power, photovoltaic power and hydropower respectively Power curtailment at any given moment;

[0031] When renewable energy sources provide intermittent power for extended periods, the new power system will experience significant load shedding to maintain power supply and demand balance. 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 detailed below:

[0032] ;

[0033] In the formula, This is the load shedding penalty factor; For new power systems Node No. The amount of load shedding at any given moment; This represents the total number of nodes in the new power system.

[0034] Furthermore, in step 1, the node power balance constraints are as follows:

[0035] At each node and at each moment, the sum of the output of thermal power, wind power, photovoltaic power, hydropower, energy storage charging and discharging power, and the inflow and outflow power of connected lines must be balanced with the difference between the node load and the load shedding amount.

[0036] ;

[0037] wherein, , , and are the thermal power, wind power, photovoltaic power and hydropower output of the th node in the new power system at the th moment; , and are the discharging power of the electrochemical energy storage, pumped storage power station and seasonal hydrogen storage of the th node in the new power system at the th moment; , and are the charging power of the electrochemical energy storage, pumped storage power station and seasonal hydrogen storage of the th node in the new power system at the th moment; is the transmission power of the line connected with the th node at the th moment; and are the line set with the th node as the terminal node and the starting node, respectively; is the load of the th node in the new power system at the th moment; is the load shedding amount of the th node in the new power system at the th moment; Thermal power unit operation constraints are as follows:

[0038] Thermal power units have upper and lower limits of output when operating, and the increase and decrease rates of output are also limited by their own physical properties:

[0039]

[0040] ;

[0041] ;

[0042] wherein, and are the lower and upper limits of the th thermal power unit; and are the maximum up-ramp rate and the maximum down-ramp rate of the th thermal power unit;

[0043] New energy output constraints are as follows:​

[0044] The output of each type of new energy at is not more than its predicted value:

[0045] ;

[0046] wherein, , and are the predicted output values of wind power, photovoltaic power and hydropower at ; , and are the outputs of wind power, photovoltaic power and hydropower at ;

[0047] The electrochemical energy storage operation constraints include charge and discharge power constraints and electric quantity constraints, and are specifically as follows:

[0048] ;

[0049] ;

[0050] wherein, and are the upper limits of charge and discharge power of the electrochemical energy storage; and are the charge and discharge states of the electrochemical energy storage at ; is the stored energy of the electrochemical energy storage at ; and are the charge and discharge efficiencies of the electrochemical energy storage;

[0051] The seasonal hydrogen storage operation constraints are specifically as follows:

[0052] The seasonal hydrogen storage adopts a low-pressure hydrogen storage mode. Due to the physical characteristics of the equipment, there is a certain self-loss in the energy release process, there is only one charge and discharge state per day, and the hydrogen energy is charged and discharged within the capacity range, and the initial state is the same in the dispatching cycle:

[0053] ;

[0054] wherein, and are the charge and discharge states of the seasonal hydrogen storage on the th day; and are the charge and discharge powers of the seasonal hydrogen storage on the th day; is the capacity of the seasonal hydrogen storage; and respectively, are the initial and final capacities of the seasonal hydrogen storage scheduling period; is the proportion coefficient of the total capacity occupied by the initial and final states of seasonal hydrogen storage; is the capacity of seasonal hydrogen storage on the day; is the self-loss coefficient; and respectively, are the charging and discharging efficiencies of seasonal hydrogen storage;

[0055] The pumped storage power station operation constraints are as follows:

[0056] To cope with the intermittent output of new energy in the scheduling period, the pumped storage only exists in the pumping state and does not exist in the power generation state during the normal output period of new energy:

[0057] ;

[0058] In the formula, is the normal output day of new energy in the scheduling period; and respectively, are the power generation state and pumping state of the pumped storage power station in the normal output day of new energy ;

[0059] The direct current flow constraint is as follows:

[0060] To ensure safety, the system needs to meet the line flow constraint during normal operation, and the line flow does not exceed the safety limit. To simplify the processing, the direct current flow model is adopted:

[0061] ;

[0062] In the formula, is the transmission power of the line at the moment; and respectively, are the voltage phase angles of the initial and final nodes of the line at the moment; is the reactance of the line ; is the upper limit of the transmission power of the line ;

[0063] The load shedding constraint is as follows:

[0064] To ensure power supply and demand balance during the intermittent output period of new energy, the system will appear load shedding. The total load shedding of each node cannot exceed the total load. To improve power supply reliability, load shedding is not allowed on the normal output day of new energy:

[0065] ;

[0066] in, The days during which new energy sources operate normally within the dispatch cycle; For new power systems Node number is at The workload of the moment; For new power systems Node number is at The load shedding amount at any given time.

[0067] Furthermore, in step 2, the day-ahead scheduling using the pre-built day-ahead scheduling model is specifically as follows:

[0068] 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 to solve the day-ahead scheduling model and obtain the long-term / short-term-day coupled scheduling results.

[0069] Furthermore, in step 2, the day-ahead scheduling model uses the output of thermal power units and the status of energy storage devices on the last day of the long-term or short-term scheduling as boundary conditions for day-ahead scheduling. Its objective function is consistent in form with the objective functions of the long-term and short-term scheduling models, only the scheduling period is different.

[0070] 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.

[0071] Furthermore, in step 2, the coupling constraints of the thermal power unit are specifically as follows:

[0072] In day-ahead dispatching, the output of thermal power units is guided by either long-term or short-term dispatching. However, because the predicted output of new energy sources differs between the long-term and short-term forecasting stages and the day-ahead stage, adjustments to the output of thermal power units are also permitted within a set range.

[0073] ;

[0074] In the formula, For the first Taiwan thermal power units The unit's output at any given moment; In long-term or short-term scheduling, the last day and the day-ahead scheduling are... The same moment in time; For the first Taiwan thermal power units in long-term or short-term dispatch Efforts made at all times; the first output adjustment deviation of the thermal power unit at the moment;

[0075] The energy storage state coupling constraint is specifically as follows:

[0076] 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 scheduling or short-term scheduling result:

[0077]

[0078] In the formula, , and are the energy states of the electrochemical energy storage, seasonal hydrogen storage and pumped storage power station at the starting moment of day-ahead scheduling; , and are the energy states of the electrochemical energy storage, seasonal hydrogen storage and pumped storage power station at the starting moment of the last day in the long-term scheduling or short-term scheduling.

[0079] Further, in step 3, the long-term / short-term-day-ahead coupling scheduling result is arranged into the power generation plans of various types of power sources in the new power system, to obtain the power generation curves of various types of power sources within a day and the planning of the energy storage device state, and each power generation unit performs corresponding power generation scheduling according to the received scheduling instruction, to ensure that each unit operates according to the plan, and to achieve the final optimization purpose.

[0080] The application further provides a computer device, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the new power system multi-source multi-time scale scheduling method when executing the computer program.

[0081] The application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the new power system multi-source multi-time scale scheduling method.

[0082] Compared with the prior art, the application has the following advantages:

[0083] The application reasonably utilizes the pumped storage power station and seasonal energy storage, reduces the new energy curtailment rate, reduces the system operation cost, reduces the load shedding, improves the power grid resilience, and under the guidance of the different time scale 2MT-S scheduling strategy, when the new energy output presents intermittent low output, the overall load shedding of the power grid can be greatly reduced at the sacrifice of a small amount of economy, the power supply and demand balance problem is relieved, and the power supply reliability is improved. ​​

[0084] The application solves the power supply and demand balance problem of the new power system caused by the long-period intermittence of new energy output, 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 DRAWINGS

[0085] Figure 1 A step flowchart of a new power system multi-source multi-time scale scheduling method in an embodiment of the application.

[0086] Figure 2 A structure topology diagram of a new power system in an embodiment of the application.

[0087] Figure 3 A scheduling result schematic diagram of scenario 1 in an embodiment of the application.

[0088] Figure 4 A scheduling result schematic diagram of scenario 2 in an embodiment of the application.

[0089] Figure 5 A scheduling result schematic diagram of scenario 3 in an embodiment of the application.

[0090] Figure 6 A scheduling result schematic diagram of scenario 4 in an embodiment of the application.

[0091] Figure 7 A scheduling result schematic diagram of scenario 5 in an embodiment of the application.

[0092] Figure 8 A scheduling result schematic diagram of scenario 6 in an embodiment of the application.

[0093] Figure 9 A scheduling result schematic diagram of scenario 7 in an embodiment of the application.

[0094] Figure 10 A scheduling result schematic diagram of scenario 8 in an embodiment of the application. DETAILED DESCRIPTION

[0095] The specific implementation of the application is further described below in combination with the drawings and examples.

[0096] A new power system multi-source multi-time scale scheduling method, as shown in Figure 1 , includes the following steps:

[0097] Step 1: According to the predicted intermittent duration of new energy output, long-term scheduling or short-term scheduling is performed by using the pre-constructed long-term scheduling model and short-term scheduling model, and long-term scheduling or short-term scheduling results are obtained;

[0098] In one embodiment, , ; and are short-term scheduling duration day threshold and long-term scheduling duration day threshold, respectively.

[0099] The daily total output of new energy in the next 10 days is predicted, and the daily total output of new energy is judged. When the daily total output of new energy is below times of the normal daily total output, it is determined that the new energy on that day is an intermittent output day;

[0100] The duration of new energy intermittent output is determined according to the start day and the end day of new energy intermittent output. The duration of new energy intermittent output is short for 2-3 days and long for 7-8 days. The time scale needs to be adjusted according to the actual prediction situation to extend the scheduling period, as follows:

[0101] ;

[0102] wherein, is the daily total output of the day in the duration of new energy intermittent output, is the normal daily total output of new energy, is the start day of intermittent output, is the end day of intermittent output; is the duration of new energy intermittent output;

[0103] When , the daily total output of each type of new energy in the previous 3 days is selected as input into the short-term scheduling model for short-term scheduling in the previous 3 days; when , the daily total output of each type of new energy in the previous 10 days is selected as input into the long-term scheduling model for long-term scheduling in the previous 10 days.

[0104] 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, with the constraint conditions including node power balance constraint, thermal power unit operation constraint, new energy output constraint, electrochemical energy storage operation constraint, seasonal hydrogen storage operation constraint, pumped storage power station operation constraint, direct current flow constraint and load shedding constraint;

[0105] 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:

[0106] ;

[0107] In the formula, is the operating cost of the new power system, is the operating cost of the thermal power unit, is the operating cost of the energy storage For the cost of energy curtailment, For load shedding costs;

[0108] The operating cost of thermal power units includes the fuel cost of each unit and the start-up and shutdown costs, as detailed below:

[0109] ;

[0110] In the formula, The scheduling period; This refers to the total number of thermal power units in the new power system. , , The first Consumption characteristic coefficients of the secondary, primary, and constant terms of a thermal power unit; For the first Taiwan thermal power units The unit's output at any given moment; For the first Taiwan thermal power units The start / stop status at any given moment; For the first Start-up and shutdown costs of thermal power units in Taiwan;

[0111] Energy storage operating costs include the depreciation costs of electrochemical energy storage, the start-up costs of hydrogen storage equipment, and the start-up costs of pumped storage power stations, as detailed below:

[0112] ;

[0113] In the formula, This represents the operating cost coefficient for electrochemical energy storage. and Electrochemical energy storage The charging and discharging power at any given moment; This is the operating cost coefficient for pumped storage power stations; and Pumped storage power stations are respectively The charging and discharging power at any given moment; This represents the total start-up time of the electrolyzers within the scheduling cycle. Since the electrolyzers are not started at all times within the scheduling cycle, a new symbol is used. To correspond; For the electrolytic cell in Startup costs at any given moment;

[0114] Renewable energy sources in the new power system include wind power, photovoltaic power, and hydropower. Penalty costs for curtailment of various renewable energy sources are incorporated into the objective function to maximize their utilization, as detailed below:

[0115] ;

[0116] wherein, , and are the abandoned energy penalty coefficients of wind power, photovoltaic power and hydropower, respectively; , and are the abandoned power of wind power, photovoltaic power and hydropower at the time of

[0117] When the new energy intermittently outputs for a long time, a large amount of load shedding will occur in the new power system to maintain the balance between power supply and demand. In order to reduce the degree of load shedding as much as possible 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, which is as follows:

[0118]

[0119] wherein, is the load shedding penalty coefficient; is the load shedding amount of the th node in the new power system at the time of is the total number of nodes in the new power system.

[0120] The node power balance constraint is as follows:

[0121] Each node at each time must ensure that the sum of the thermal power, wind power, photovoltaic power, hydropower output, energy storage charging and discharging power and the inflow and outflow power of the connected line is balanced with the difference between the node load and the load shedding amount:

[0122]

[0123] wherein, , , and are the thermal power, wind power, photovoltaic power and hydropower output of the th node in the new power system at the time of , and are the discharging power of the electrochemical energy storage, pumped storage power station and seasonal hydrogen storage of the th node in the new power system at the time of , and are the charging power of the electrochemical energy storage, pumped storage power station and seasonal hydrogen storage of the th node in the new power system at the time of ​​​​​​​To and The line connected to node number 1 exist Transmission power at any given moment; and respectively with The node is a set of lines consisting of a termination node and a start node. For new power systems Node No. The workload of the moment; For new power systems Node No. The amount of load shedding at any given moment;

[0124] The specific operating constraints for thermal power units are as follows:

[0125] Thermal power units have upper and lower limits to their output during operation, and the rate of increase and decrease in output is also limited by their own physical characteristics.

[0126] ;

[0127] ;

[0128] In the formula, and The first The lower limit of the thermal power unit; and The first The maximum uphill and downhill speeds of the thermal power units;

[0129] The constraints on new energy output are as follows:

[0130] Various new energy sources The output at any given time does not exceed its predicted value:

[0131] ;

[0132] In the formula, , and These are wind power, solar power, and hydropower, respectively. The predicted output value at any given time; , and These are wind power, solar power, and hydropower, respectively. Efforts made at all times;

[0133] The operational constraints of electrochemical energy storage include charge / discharge power constraints and energy constraints, as detailed below:

[0134] ;

[0135] ;

[0136] wherein, and are the upper limits of the charge-discharge power of the electrochemical energy storage, respectively; and are the charge-discharge state of the electrochemical energy storage at the moment; is the storage energy of the electrochemical energy storage at the moment; and are the charge-discharge efficiencies of the electrochemical energy storage, respectively;

[0137] The seasonal hydrogen storage operation constraints are as follows:

[0138] The seasonal hydrogen storage adopts a low-pressure hydrogen storage mode. Due to the physical characteristics of the equipment, there is a certain self-loss during energy release. There is only one charge-discharge state per day, and the hydrogen energy is charged and discharged within the capacity range. The initial state in the dispatching cycle is the same:

[0139] ;

[0140] wherein, and are the charge-discharge states of the seasonal hydrogen storage on the th day, respectively; and are the charge-discharge powers of the seasonal hydrogen storage on the th day, respectively; is the capacity of the seasonal hydrogen storage; and are the initial and final capacities of the seasonal hydrogen storage dispatching cycle, respectively; is the proportion coefficient of the initial and final states of the seasonal hydrogen storage in the total capacity; is the capacity of the seasonal hydrogen storage on the th day; is the self-loss coefficient; and are the charge-discharge efficiencies of the seasonal hydrogen storage, respectively;

[0141] The pumped storage power station operation constraints are as follows:

[0142] To cope with the intermittent output of new energy in the dispatching cycle, the pumped storage only exists in the pumping state and does not exist in the power generation state during the normal output of new energy:

[0143] ;

[0144] wherein, is the normal output day of new energy in the dispatching cycle; and respectively are normal new energy output days the generation state and the pumping state of the internal pumped storage power station;

[0145] The DC power flow constraint is as follows:

[0146] To ensure safety, the system needs to meet the line power flow constraint during normal operation, and the line power flow does not exceed the safety limit. To simplify the processing, the DC power flow model is adopted:

[0147] ;

[0148] wherein, is the transmission power of the line at the time t; and are the voltage phase angles of the nodes at the beginning and end of the time t; is the reactance of the line ; is the upper limit of the transmission power of the line ; is the transmission power of the line ;

[0149] The load shedding constraint is as follows:

[0150] To ensure power supply and demand balance during the intermittent output of new energy, the system will have load shedding. The load shedding of each node cannot exceed the total load. To improve power supply reliability, load shedding is not allowed on normal new energy output days:

[0151] ;

[0152] wherein, is the normal new energy output day in the dispatching period; is the load of the node at the time t; is the load shedding amount of the node at the time t. Step 2: Take the thermal power output results and energy storage state results of the last day in the long-term dispatching or short-term dispatching results as boundary conditions, and use the pre-constructed day-ahead dispatching model to perform day-ahead dispatching to obtain long-term / short-term-day-ahead coupled dispatching results, which are as follows:

[0153] Step 2: Take the thermal power output results and energy storage state results of the last day in the long-term dispatching or short-term dispatching results as boundary conditions, and use the pre-constructed day-ahead dispatching model to perform day-ahead dispatching to obtain long-term / short-term-day-ahead coupled dispatching results, which are as follows:

[0154] ​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. The day-ahead scheduling model is established in MATLAB using the yalmip toolbox or other software development platforms, and solved using the Gurobi solver or other commercial optimization software to obtain the long-term / short-term-day coupled scheduling results.

[0155] 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, with the addition of thermal power unit coupling constraints and energy storage state coupling constraints.

[0156] The day-ahead scheduling model uses the output of thermal power units and the status of energy storage devices on the last day of long-term or short-term scheduling as boundary conditions for day-ahead scheduling. Its objective function is consistent in form with the objective functions of the long-term and short-term scheduling models, except that the scheduling period is different. In one embodiment, the long-term scheduling period is 10 days, the short-term scheduling period is 3 days, and the day-ahead scheduling period is 1 day.

[0157] 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.

[0158] Furthermore, in step 2, the coupling constraints of the thermal power unit are specifically as follows:

[0159] In day-ahead dispatching, the output of thermal power units is guided by either long-term or short-term dispatching. However, because the predicted output of new energy sources differs between the long-term and short-term forecasting stages and the day-ahead stage, adjustments to the output of thermal power units are also permitted within a set range.

[0160] ;

[0161] In the formula, For the first Taiwan thermal power units The unit's output at any given moment; In long-term or short-term scheduling, the last day and the day-ahead scheduling are... The same moment in time; For the first Taiwan thermal power units in long-term or short-term dispatch Efforts made at all times; The first is caused by the error in the prediction of new energy sources. Taiwan thermal power units the output adjustment deviation at the moment;

[0162] The energy storage state coupling constraint is specifically as follows:

[0163] The initial energy state of each type of energy storage device should be the same as the initial energy state at the last day of the long-term scheduling or short-term scheduling result:

[0164]

[0165] In the formula, , and are the energy states of the electrochemical energy storage, seasonal hydrogen storage and pumped storage power station at the starting moment of the day-ahead scheduling; , and are the energy states of the electrochemical energy storage, seasonal hydrogen storage and pumped storage power station at the starting moment of the last day of the long-term scheduling or short-term scheduling.

[0166] Step 3: arranging the power generation plan and the energy storage device state according to the long-term / short-term-day-ahead coupling scheduling result to achieve the final optimization scheduling purpose;

[0167] The long-term / short-term-day-ahead coupling scheduling result is arranged into the power generation plan of each type of power source in the new type of power system to obtain the power generation curve of each type of power source within a day and the planning of the energy storage device state, and each power generation unit performs corresponding power generation scheduling according to the received scheduling instruction to ensure that each unit operates according to the plan and realizes the final optimization purpose.

[0168] In one embodiment, as shown in Figure 2 , the new type of power system includes 30 nodes, each serial number represents a node label, G represents a thermal power unit, P represents a photovoltaic power station, hpp represents a hydropower station, W represents a wind power unit, S represents an electrochemical energy storage, H represents seasonal hydrogen storage, and ps represents a pumped storage power station. The new type of 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 550 MW, a photovoltaic power station with a total capacity of 250 MW and a hydropower station with a total capacity of 500 MW. The total capacity of the electrochemical energy storage is 230 MW·h, the upper limit of the charge and discharge power is 50 MW, and the charge and discharge efficiency is 95%. The electrolytic tank start-stop cost is 500 yuan, the seasonal hydrogen storage power generation efficiency is 60%, and the power generation power upper limit is 30 MW. The total capacity of the pumped storage power station is 1000 MW, the power generation power upper limit is 60 MW, and the efficiency is 85%. The penalty coefficient of abandoned energy is 600 yuan / ( MW·h), and the penalty coefficient of load shedding is 1000 yuan / ( MW·h).

[0169] Table 1 Thermal power unit parameters

[0170] ​

[0171] In order to fully verify the effectiveness of the present application, eight scenarios are set for comparative analysis to verify the economy and power supply reliability under different scenarios. The specific settings of the scenarios are as follows:

[0172] Scenario 1: Wind, light, water and fire joint scheduling operation in long-term scheduling, only considering electrochemical energy storage.

[0173] Scenario 2: Wind, light, water and fire joint scheduling operation in long-term scheduling, considering electrochemical energy storage and pumped storage power station.

[0174] Scenario 3: Wind, light, water and fire joint scheduling operation in long-term scheduling, considering electrochemical energy storage, pumped storage power station and seasonal hydrogen storage.

[0175] Scenario 4: Traditional day-ahead economic dispatch when new energy intermittent output.

[0176] Scenario 5: 2MT-S coupled economic dispatch (new energy long-term intermittent output).

[0177] Scenario 6: 2MT-S coupled economic dispatch (new energy short-term intermittent output).

[0178] Scenario 7: Traditional day-ahead economic dispatch when high proportion of new energy system normal output.

[0179] Scenario 8: 2MT-S coupled economic dispatch when high proportion of new energy system normal output.

[0180] In one embodiment, by comparing and analyzing scenarios 1, 2 and 3, the advantages of pumped storage power station and seasonal hydrogen storage are verified on a long-term scale. The scheduling results of scenarios 1-3 correspond to Figures 3-5 , and the specific comparative analysis results are shown in Table 2.

[0181] By Figure 3 , Figure 4 and Table 2, the simulation results of scenarios 1 and 2 are analyzed. When wind, light, water and fire joint scheduling operation and only considering electrochemical energy storage access, the new energy curtailment rate is 16.6%, the system degree electricity cost is 0.34 yuan / kW·h, the total charge and discharge power of electrochemical energy storage is 410.25 MW, the total system load shedding is 281.27 MW, and the maximum system node load shedding at a single time is 89.27 MW; when the pumped storage power station is connected, the new energy curtailment rate decreases by 0.6%, the system degree electricity cost decreases to 0.3394 yuan / kW·h, which improves the economy of the system, at the same time, the electrochemical energy storage is shared by the power supply pressure, the overall system load shedding is also reduced by 104.9 MW, which greatly improves the system power supply reliability.

[0182] By Figure 4 , Figure 5and Table 2, when the wind-solar-water-fire combined dispatching system further accesses seasonal hydrogen storage, the new energy curtailment rate decreases by 0.2%, the system degree electricity cost decreases to 0.3393 yuan / kW·h, further improving the economy, at the same time, the system overall load shedding also decreases by 47.93 MW, the maximum load shedding of the system node at a single time decreases by 31.72 MW, improving the resilience of the power grid.

[0183] In summary, the introduction of flexible resources has obvious synergistic effect, especially the effect of seasonal hydrogen storage in solving the problem of long-term low output of new energy is particularly significant.

[0184] Table 2 Dispatching results

[0185]

[0186] In one embodiment, to further embody the advantages of the present application compared with the traditional day-ahead economic dispatch, scenarios 4, 5 and 6 are set for comparative analysis, and the dispatching results of scenarios 4-6 correspond to Figures 6-8 , and the specific comparative analysis results are shown in Table 3.

[0187] The comparative analysis of scenarios 4 and 5 is corresponding to the long-term intermittent output of new energy, and the comparative results are shown by Figure 6 , Figure 7 and Table 3. From the results of Figure 6 , it can be seen that in the new power system, only relying on the traditional day-ahead economic dispatch will cause a serious power gap, the system overall load shedding is as high as 12300 MW, and the total output of thermal power is only 8140 MW; when the day-ahead dispatching is guided by the short-term dispatching result, the system overall load shedding is only 52.43 MW, and the system degree electricity cost increases by 0.096 yuan / kW·h, which greatly improves the power supply reliability at the sacrifice of a small amount of economy.

[0188] The comparative analysis of scenarios 4 and 6 is corresponding to the long-term intermittent output of new energy, and the comparative results are shown by Figure 6 , Figure 8 and Table 3. When the day-ahead dispatching is guided by the short-term dispatching result, the system overall load shedding is only 98.3 MW, and the system degree electricity cost increases by 0.075 yuan / kW·h, which greatly improves the power supply reliability at the sacrifice of a small amount of economy.

[0189] Table 3 Comparative analysis results under intermittent output

[0190]

[0191] Meanwhile, when new energy sources are outputting normally, the system's power supply reliability is guaranteed, and coupled scheduling should not compromise the economic efficiency of day-ahead scheduling. In one embodiment, to further verify effectiveness, scenarios 7 and 8 are set up for comparative analysis. The scheduling results of scenarios 7 and 8 correspond to... Figures 9-10 The specific comparative analysis results are shown in Table 4.

[0192] pass Figure 9 , Figure 10 As shown in Table 4, when the high proportion of new energy systems is operating at normal capacity, traditional economic dispatch relies on only one thermal power unit for power generation, while coupled economic dispatch relies on two units generating power simultaneously. However, the overall thermal power output is not significantly different, ranging from 3.93*10 3 MW increased to 4.32*10 3 The increase in the number of generating units and the total output in MW leads to an increase in the total transmission power of the line; the system cost per kilowatt-hour remains almost unchanged, increasing by only 0.001 yuan / kW·h. The results show that when the renewable energy is outputting normally, the 2MT-S coupled economic dispatch does not lose its economic efficiency.

[0193] Table 4 Comparative Analysis Results under Normal Output Conditions

[0194]

[0195] 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 executes the computer program to implement the novel multi-source multi-timescale scheduling method for power systems.

[0196] In one embodiment, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the novel multi-source multi-timescale scheduling method for power systems.

[0197] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0198] The present application is described in reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of the flows 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 the 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 produce a device that implements the flow Figure 1 one or more flows and / or blocks. Figure 1 one or more blocks.

[0199] These computer program instructions can also be stored in a computer readable memory that can direct the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce a product including instruction devices that implement the flow Figure 1 one or more flows and / or blocks. Figure 1 one or more blocks.

[0200] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer implemented process, so that the instructions executed on the computer or other programmable device provide a process for implementing the flow Figure 1 one or more flows and / or blocks. Figure 1 one or more blocks.

[0201] It is known by common technical knowledge that the present application can be implemented by other embodiments without departing from the spirit or essential characteristics thereof. Therefore, the above disclosed embodiments are only illustrative and not restrictive. All changes within the scope of the present application or within the scope equivalent to the present application are included in the present application.

[0202] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application and not to limit it, although the present application has been described in detail with reference to the above examples, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced by equivalent, without departing from the spirit and scope of the present application. Any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered within the protection scope of the claims of the present application.

Claims

1. A novel multi-source, multi-time-scale scheduling method for power systems, characterized in that, Includes the following steps: Step 1: Based on the predicted number of days of intermittent power output from new energy sources, long-term or short-term scheduling is performed using pre-built long-term and short-term scheduling models to obtain the long-term or short-term scheduling results; both the long-term and short-term scheduling models aim to minimize the operating cost of the new power system and maximize the power supply reliability. Step 2: Using the output results of 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, perform day-ahead scheduling using the pre-built day-ahead scheduling model to obtain the long-term / short-term-day coupled scheduling results; 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, with the addition of thermal power unit coupling constraints and energy storage state coupling constraints. The day-ahead scheduling using a pre-built day-ahead scheduling model is 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 to solve the day-ahead scheduling model and obtain the long-term / short-term-day coupled scheduling results. 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. The node power balance constraint is as follows: at each node at each time, the sum of the output of thermal power, wind power, photovoltaic power, hydropower, energy storage charging and discharging power, and the inflow and outflow power of connected lines must be balanced with the difference between the node load and the load shedding amount. The operating constraints of the thermal power unit are as follows: the thermal power unit has upper and lower limits of output during operation, and the rate of increase and decrease of output is limited by its own physical characteristics. The aforementioned new energy output constraints are: various new energy sources in... The output at any given time does not exceed its predicted value; The electrochemical energy storage operation constraints include both charge / discharge power constraints and energy constraints. Seasonal hydrogen storage operation constraints: Seasonal hydrogen storage adopts low-pressure hydrogen storage method, and hydrogen energy is charged and released within the capacity range, with the initial state being the same throughout the scheduling cycle; The operational constraints of the pumped storage power station are as follows: during the normal output of new energy sources, 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 must not exceed the safety limit; The load shedding constraint is as follows: During the intermittent output of new energy sources, the new power system will experience load shedding to ensure the balance between power supply and demand. The load shedding of each node cannot exceed the total load of the node. Load shedding is not allowed on days when new energy sources are in normal output. Step 3: Arrange the power generation plan and energy storage device status according to the long-term / short-term-day coupled scheduling results to achieve the final optimized scheduling goal.

2. The novel multi-source, multi-time-scale scheduling method for power systems according to claim 1, characterized in that: In step 1, the curtailment costs of various new energy sources are added to the objective functions of the long-term and short-term scheduling models. The objective functions of the long-term and short-term scheduling models are identical in form, as follows: ； In the formula, For the operating costs of the new power system, For the operating costs of thermal power units, For energy storage operating costs, For the cost of energy curtailment, This is the cost of load shedding.

3. The novel multi-source, multi-time-scale scheduling method for power systems according to claim 1, characterized in that: In step 2, The specific coupling constraints of the thermal power units are as follows: The output of thermal power units in the daytime dispatch is guided by long-term or short-term dispatch. At the same time, since the forecast error of the power output of new energy differs between the long-term and short-term stages and the daytime stage, the output of thermal power units is allowed to be adjusted within the set range. The energy storage state coupling constraints are as follows: The initial energy state of all types of energy storage devices 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.

4. A novel multi-source, multi-time-scale scheduling method for power systems according to claim 1, characterized in that: In step 3, the long-term / short-term-day coupled scheduling results are organized into the power generation plans of various power sources in the new power system, and the power generation curves of various power sources within a day and the planning of the energy storage device status are obtained. 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 final goal of optimized scheduling.

5. A novel multi-source, multi-time-scale scheduling method for power systems according to claim 1, characterized in that: In step 1, predict the future of new energy sources. The daily total output is used to determine the daily total output of new energy sources. When the daily total output of new energy sources is within the normal range... If the output is less than twice the normal value, the day is considered an intermittent output day for the new energy source. The duration of intermittent power output from renewable energy sources is determined based on the start and end dates of these intermittent output days, as detailed below: ； in, The first during the duration of intermittent power output of new energy sources Daily total effort, To contribute to the normal daily total output of new energy sources The day marked the start of intermittent power output. The date of termination of intermittent power output; Number of intermittent continuous days for contributing to new energy sources; when At that time, choose in advance The total daily output of various new energy sources is input into the short-term scheduling model for advance scheduling. Short-term scheduling of the day; when At that time, choose in advance The total daily output of various renewable energy sources is used as input into the long-term scheduling model for advance scheduling. Long-term scheduling of days; and These are the thresholds for the duration of short-term scheduling and the thresholds for the duration of long-term scheduling, respectively.

6. 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, it implements the novel multi-source multi-timescale scheduling method for power systems as described in any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a novel multi-source, multi-time-scale scheduling method for power systems according to any one of claims 1 to 5.

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