Collaborative optimization scheduling method and system considering energy storage frequency modulation reserve

Abstract

The invention discloses a collaborative optimization scheduling method and system considering energy storage frequency modulation reserve. The method comprises the following steps: acquiring basic data of a power system; based on the basic data, a collaborative optimization scheduling model considering energy storage frequency modulation reserve is established, and the collaborative optimization scheduling model takes the minimum sum of the coal consumption cost, the unit start-stop cost and the wind curtailment cost of the thermal power generating unit as a scheduling target under multiple constraint conditions; the multiple constraint conditions comprise a lithium battery power constraint, a charging and discharging state constraint, an SOC constraint, a frequency modulation response constraint, a frequency safety constraint, a frequency modulation standby constraint, a flywheel energy recovery constraint, a thermal power generating unit operation constraint and a power balance constraint; solving the collaborative optimization scheduling model to obtain an optimal scheduling scheme of the power system; by comprehensively utilizing the advantages of a traditional unit, flywheel energy storage and lithium battery energy storage, the safety and operation economy of the system during frequency disturbance are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly relates to a collaborative optimization scheduling method and system considering energy storage frequency modulation reserve. Background Art

[0002] With the continuous expansion of the scale of the power system and the significant increase in the proportion of new energy power generation, the inherent intermittency and volatility characteristics of new energy pose a severe challenge to the frequency stability of the power system. Therefore, in order to meet the frequency stability requirements of a high-proportion new energy power system, it is necessary to make full use of various frequency modulation resources to improve the frequency modulation ability of the power grid, enhance the anti-interference ability of the power grid, effectively promote the stable operation of the power system, and help prevent large-scale power outages caused by frequency amplitude fluctuations in the power system under power deficit disturbance faults.

[0003] The main technical bottleneck faced by the current power system frequency modulation lies in that a single type of frequency modulation resource is difficult to meet the complex and changeable frequency modulation requirements of the system, and there is a lack of an effective collaborative cooperation mechanism among various frequency modulation resources, resulting in the advantages of frequency modulation resources such as traditional power sources, flywheel energy storage, and lithium battery energy storage not being fully utilized, restricting the improvement of the system resource utilization efficiency and the optimization of the economic operation level.

[0004] In related technologies, the solution proposed in the patent application document with publication number CN115907271A is essentially a configuration method of energy storage rather than a scheduling method, and its objective function includes the configuration cost of energy storage, obtaining an energy storage configuration scheme under different wind power outputs rather than the system economic optimal scheduling under frequency modulation reserve. In addition, the solution proposed in the patent application document with publication number CN118944194A is for the uncertainty of new energy in the integrated energy system to perform reserve and optimization, aiming at the lowest total system cost, and obtaining the optimal output and reserve capacity under the reserve constraint conditions established between the upper and lower reserve capacities and the ramping power rather than the collaborative cooperation of various frequency modulation resources to achieve the economic optimization of the system. Summary of the Invention

[0005] The technical problem to be solved by the present invention is how to comprehensively utilize the advantages of traditional units, flywheel energy storage, and lithium battery energy storage to improve the safety and operation economy of the system during frequency disturbances.

[0006] The present invention solves the above technical problems through the following technical means:

[0007] A collaborative optimization scheduling method considering energy storage frequency modulation reserve is proposed, including:

[0008] Obtain the basic data of the power system;

[0009] Based on the above basic data, a collaborative optimization scheduling model considering energy storage frequency regulation reserve is established. The collaborative optimization scheduling model aims to minimize the sum of the coal consumption cost of thermal power units, the unit start-stop cost, and the wind curtailment cost under multiple constraint conditions. The multiple constraint conditions include the power constraint of lithium batteries, the charge-discharge state constraint, the SOC constraint, the frequency response constraint, the frequency security constraint, the frequency regulation reserve constraint, the flywheel energy recovery constraint, the operation constraint of thermal power units, and the power balance constraint;

[0010] Solve the collaborative optimization scheduling model to obtain the optimal scheduling plan for the power system.

[0011] Furthermore, the above basic data includes power source data, line data, load data, and energy storage-related data. The power source data includes the conventional parameters of thermal power units and the conventional parameters of wind turbines.

[0012] Furthermore, the formula of the scheduling objective function in the collaborative optimization scheduling model is expressed as:

[0013]

[0014] In the formula, is the coal consumption cost of thermal power units, is the unit start-stop cost, is the wind curtailment cost, c cur is the penalty for wind power curtailment per unit power, is the wind power curtailment rate in the k-th period, is the maximum output of wind turbines, Δk is the time interval, and k, i, j represent the time period, the number of thermal power units, and the number of wind turbines respectively.

[0015] Furthermore, the power constraint of lithium batteries is:

[0016]

[0017] In the formula, P es,t is the power of lithium battery energy storage in the t-th period, and are the maximum and minimum values of the lithium battery power respectively;

[0018] The charge-discharge state constraint is:

[0019]

[0020] In the formula, t1, t2, t3 are the total time periods of charging, discharging, and static states respectively, T is the total daily working time of lithium battery energy storage, P cha,t is the charging power of lithium battery energy storage, P dis,t is the discharging power of lithium battery energy storage;

[0021] The SOC constraint is as follows:

[0022]

[0023] In the formula, SOC t is the state of charge value of the lithium - battery energy storage at time t, SOC min , SOC max are its lower and upper limits respectively; SOC0 is the initial state of charge value of the lithium - battery energy storage, SOC t＝T is the state of charge value of the lithium - battery energy storage at the end state; η cha , η dis are the charging efficiency and discharging efficiency of the lithium - battery energy storage respectively; E is the capacity of the lithium - battery energy storage.

[0024] Furthermore, the frequency - modulation response constraint is as follows:

[0025]

[0026] In the formula, ΔP0(t) is the active power disturbance received by the system at time t, are the inertial response and primary frequency - modulation response of the thermal power unit respectively, are the virtual inertial response and virtual primary frequency - modulation response of the wind power respectively, are the sum of the virtual inertial responses and the sum of the virtual primary frequency - modulation responses of the flywheel and the lithium - battery energy storage respectively, ΔP D is the active power of the load damping characteristic, ΔP Dv is the active power of the energy - storage damping characteristic, H sys is the total inertia time constant of the system, K Wj is the virtual primary frequency - modulation power gain coefficient, K v is the energy - storage virtual primary frequency - modulation power gain coefficient, K D is the system load damping coefficient, K Dv is the energy - storage damping coefficient, Δf(t) is the frequency deviation at time t.

[0027] Furthermore, the frequency - safety constraint includes the maximum rate of change of frequency sub - constraint, the quasi - steady - state frequency sub - constraint and the minimum - frequency point sub - constraint, where:

[0028] The maximum rate of change of frequency sub - constraint is as follows:

[0029]

[0030] In the formula, is the allowable value of the maximum RoCoF of the system, U Gi characterizes the operating state of thermal power unit i, H Gi is the inertia time constant, H Wj is the virtual inertia time constant, H vis the virtual inertia time constant of energy storage, and ΔP0 is the active power disturbance received by the system;

[0031] The quasi-steady-state frequency sub-constraint is:

[0032]

[0033] In the formula, represents the allowable value of the quasi-steady-state maximum frequency deviation, and K Gi is the primary frequency regulation power gain coefficient, and K Wj is the virtual primary frequency regulation power gain coefficient, and K v is the virtual primary frequency regulation gain coefficient of energy storage, and K D is the load damping coefficient, and K Dv is the damping coefficient of energy storage;

[0034] The frequency lowest point sub-constraint is:

[0035]

[0036] In the formula, Δf max is the allowable value of the maximum frequency deviation of the system, is the primary frequency regulation response power of thermal power units.

[0037] Further, the frequency regulation reserve constraint is:

[0038]

[0039] In the formula, is the reserve power at the lowest frequency point of thermal power units, is the primary frequency regulation power of thermal power units, is the maximum value of the active power output of thermal power unit i, and P Gi (k) is the active power output of thermal power unit i; is the reserve power at the lowest frequency point of wind power units, and K Wj (k) is the virtual primary frequency regulation power gain coefficient, is the curtailment rate of wind power, is the maximum output of wind power units, and Δf max is the allowable value of the maximum frequency deviation of the system; is the reserve power at the lowest frequency point of lithium-ion energy storage, and K fw (k) is the primary frequency regulation power gain coefficient of flywheel energy storage, is the maximum output of flywheel energy storage; is the reserve power at the lowest frequency point of lithium-ion energy storage, and K bs (k) is the primary frequency regulation power gain coefficient of lithium-ion energy storage, is the maximum output of lithium battery energy storage, and P es (k) is the output of lithium-ion energy storage at time k; is the quasi-steady state frequency reserve power of the thermal power unit, U Gi (k) is the start-up and shutdown status of the thermal power unit in period k, K Gi is the primary frequency regulation power gain coefficient of the thermal power unit, is the maximum quasi-steady state frequency deviation; is the quasi-steady state frequency reserve power of the wind power unit, is the quasi-steady state frequency reserve power of the flywheel, is the quasi-steady state frequency reserve power of the flywheel, is the quasi-steady state frequency reserve power of the lithium battery, is the maximum power of the lithium battery; ΔP0(k) is the active power disturbance received by the system, K D (k) is the system load damping response coefficient, K Dv (k) is the energy storage damping response coefficient.

[0040] Furthermore, the flywheel energy recovery constraint is:

[0041]

[0042] P cf (k) × t cf ≥ ΔE fw

[0043] In the formula, P cf (k) is the reserve power for the flywheel energy storage frequency modulation energy recovery of the thermal power unit, is the reserve power at the lowest point of the thermal power unit frequency, P Gi (k) is the output of the thermal power unit, is the maximum value of the active power output of the thermal power unit i, t cf is the charging time of the flywheel by the thermal power unit after the frequency modulation ends, ΔE fw is the energy consumed by the flywheel energy storage participating in the frequency modulation.

[0044] Furthermore, the operating constraint of the thermal power unit is:

[0045]

[0046] In the formula, and are respectively the minimum and maximum values of the active power output of the thermal power unit i, U Gi characterizes the operating status of the thermal power unit i, and represent the up-ramp rate and down-ramp rate of the thermal power unit i, P Gi,t 、P Gi,t-1 are respectively the outputs of the thermal power unit at times t and t - 1.

[0047] Furthermore, the power balance constraint is:

[0048]

[0049] Wherein, P Gi,t is the active power of thermal power unit i at time period t, P Wj,t is the predicted power of wind farm j at time period t, L d,t is the predicted load power of node d at time period t, P cha,t is the charging power of lithium battery energy storage, P dis,t is the discharging power of lithium battery energy storage, N G is the number of thermal power units, N W is the number of wind turbines, N d is the number of nodes.

[0050] In addition, the present invention also proposes a collaborative optimal scheduling system considering energy storage frequency modulation reserve, including:

[0051] A data acquisition module for acquiring the basic data of the power system;

[0052] A collaborative optimal scheduling model construction module for establishing a collaborative optimal scheduling model considering energy storage frequency modulation reserve based on the basic data, wherein the collaborative optimal scheduling model takes the minimum sum of the coal consumption cost, unit start-stop cost and wind curtailment cost of thermal power units as the scheduling objective under multiple constraint conditions, and the multiple constraint conditions include lithium battery power constraint, charge and discharge state constraint, SOC constraint, frequency modulation response constraint, frequency safety constraint, frequency modulation reserve constraint, flywheel energy recovery constraint, thermal power unit operation constraint and power balance constraint;

[0053] A model solving module for solving the collaborative optimal scheduling model to obtain the optimal scheduling scheme of the power system.

[0054] Furthermore, the basic data includes power source data, line data, load data and energy storage related data, wherein the power source data includes the conventional parameters of thermal power units and the conventional parameters of wind turbines.

[0055] Furthermore, the formula of the scheduling objective function in the collaborative optimal scheduling model is expressed as:

[0056]

[0057] Wherein, is the coal consumption cost of thermal power units, is the unit start-stop cost, is the wind curtailment cost, c cur is the penalty for wind power curtailment per unit power, is the wind power curtailment rate at time period k, $P_{wind,max}$ is the maximum output of the wind turbine, $\Delta k$ is the time interval, and $k$, $i$, and $j$ represent the time period, the number of thermal power units, and the number of wind turbines, respectively.

[0058] Further, the power constraint of the lithium battery is:

[0059]

[0060] In the formula, $P$ es,t is the power of the lithium battery energy storage at time $t$, and are the maximum and minimum values of the lithium battery power, respectively;

[0061] The charge and discharge state constraint is:

[0062]

[0063] In the formula, $t_1$, $t_2$, and $t_3$ are the total time periods of charging, discharging, and standing states, respectively, $T$ is the total daily working time period of the lithium battery energy storage, $P$ cha,t is the charging power of the lithium battery energy storage, and $P$ dis,t is the discharging power of the lithium battery energy storage;

[0064] The SOC constraint is:

[0065]

[0066] In the formula, $SOC$ t is the state of charge value of the lithium battery energy storage at time $t$, $SOC$ min , $SOC$ max are its lower and upper limits, respectively; $SOC_0$ is the initial state of charge value of the lithium battery energy storage, and $SOC$ t＝T is the state of charge value at the end state of the lithium battery energy storage; $\eta$ cha , $\eta$ dis are the charging efficiency and discharging efficiency of the lithium battery energy storage, respectively; $E$ is the capacity of the lithium battery energy storage.

[0067] Further, the frequency modulation response constraint is:

[0068]

[0069] In the formula, $\Delta P_0(t)$ is the active power disturbance received by the system at time $t$, are the inertial response and primary frequency modulation response of the thermal power unit, respectively, are the virtual inertial response and virtual primary frequency modulation response of the wind power, respectively, are the sum of the virtual inertial responses and the sum of the virtual primary frequency modulation responses of the flywheel and the lithium battery energy storage, respectively, $\Delta P$ D is the active power of the load damping characteristic, and $\Delta P$ DvThe active power for energy storage damping characteristics, H sys The total inertia time constant of the system, K Wj The virtual primary frequency regulation power gain coefficient, K v The energy storage virtual primary frequency regulation power gain coefficient, K D The system load damping coefficient, K Dv The energy storage damping coefficient, Δf(t) is the frequency deviation at time t.

[0070] Furthermore, the frequency security constraints include the maximum rate of change of frequency sub-constraint, the quasi-steady state frequency sub-constraint, and the minimum frequency point sub-constraint, where:

[0071] The maximum rate of change of frequency sub-constraint is:

[0072]

[0073] In the formula, The allowable value of the system's maximum RoCoF, U Gi Characterizes the operating state of thermal power unit i, H Gi The inertia time constant, H Wj The virtual inertia time constant, H v The energy storage virtual inertia time constant, ΔP0 is the active power disturbance received by the system;

[0074] The quasi-steady state frequency sub-constraint is:

[0075]

[0076] In the formula, Represents the allowable value of the quasi-steady state maximum frequency deviation, K Gi The primary frequency regulation power gain coefficient, K Wj The virtual primary frequency regulation power gain coefficient, K v The energy storage virtual primary frequency regulation gain coefficient, K D The load damping coefficient, K Dv The energy storage damping coefficient;

[0077] The minimum frequency point sub-constraint is:

[0078]

[0079] In the formula, Δf max The allowable value of the system's maximum frequency deviation, The primary frequency regulation response power of the thermal power unit.

[0080] Furthermore, the frequency regulation reserve constraint is:

[0081]

[0082] Wherein, is the reserve power at the lowest frequency point of the thermal power unit, is the primary frequency regulation power of the thermal power unit, is the maximum active power output of thermal power unit i, P Gi (k) is the active power output of thermal power unit i; is the reserve power at the lowest frequency point of the wind power unit, K Wj (k) is the virtual primary frequency regulation power gain coefficient, is the wind curtailment rate, is the maximum output of the wind power unit, Δf max is the allowable value of the maximum system frequency deviation; is the reserve power at the lowest frequency point of the lithium - ion energy storage, K fw (k) is the primary frequency regulation power gain coefficient of the flywheel energy storage, is the maximum output of the flywheel energy storage; is the reserve power at the lowest frequency point of the lithium - ion energy storage, K bs (k) is the primary frequency regulation power gain coefficient of the lithium - ion energy storage, is the maximum output of the lithium - battery energy storage, P es (k) is the output of the lithium - ion energy storage at time k; is the quasi - steady - state frequency reserve power of the thermal power unit, U Gi (k) is the start - up and shut - down status of the thermal power unit at time k, K Gi is the primary frequency regulation power gain coefficient of the thermal power unit, is the maximum quasi - steady - state frequency deviation; is the quasi - steady - state frequency reserve power of the wind power unit, is the quasi - steady - state frequency reserve power of the flywheel, is the quasi - steady - state frequency reserve power of the flywheel, is the quasi - steady - state frequency reserve power of the lithium - battery, is the maximum power of the lithium - battery; ΔP0(k) is the active power disturbance received by the system, K D (k) is the system load damping response coefficient, K Dv (k) is the energy storage damping response coefficient.

[0083] Furthermore, the flywheel energy recovery constraint is:

[0084]

[0085] P cf (k)×t cf ≥ΔE fw

[0086] Wherein, P cf (k) is the reserve power for the flywheel energy storage to recover energy after frequency regulation by the thermal power unit, The reserve power at the lowest frequency point of the thermal power unit is P Gi (k) is the output of the thermal power unit, is the maximum value of the active power output of thermal power unit i, t cf is the charging time of the flywheel by the thermal power unit after the frequency regulation ends, ΔE fw is the energy consumed by the flywheel energy storage participating in frequency regulation.

[0087] Furthermore, the operating constraints of the thermal power unit are:

[0088]

[0089] In the formula, and are the minimum and maximum values of the active power output of thermal power unit i respectively, U Gi represents the operating state of thermal power unit i, and represent the up-ramp rate and down-ramp rate of thermal power unit i, P Gi,t 、P Gi,t-1 are the outputs of the thermal power unit at time t and t - 1 respectively.

[0090] Furthermore, the power balance constraint is:

[0091]

[0092] In the formula, P Gi,t is the active power of thermal power unit i at time t, P Wj,t is the predicted power of wind farm j at time t, L d,t is the predicted load power of node d at time t, P cha,t is the charging power of the lithium battery energy storage, P dis,t is the discharging power of the lithium battery energy storage, N G is the number of thermal power units, N W is the number of wind turbines, N d is the number of nodes.

[0093] In addition, the present invention also proposes a computer device, including: one or more processors;

[0094] The processor is used to store one or more programs;

[0095] When the one or more programs are executed by the one or more processors, the collaborative optimization scheduling method considering energy storage frequency regulation reserve as described above is implemented.

[0096] In addition, the present invention also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, the collaborative optimization scheduling method considering energy storage frequency regulation reserve as described above is implemented.

[0097] The advantages of the present invention are as follows:

[0098] (1) Under multiple constraint conditions, the present invention takes the minimum of the sum of the coal consumption cost, start-stop cost and wind abandonment cost of a thermal power unit as the scheduling objective, and establishes a collaborative optimization scheduling model considering energy storage frequency regulation reserve; then, by using a solver to solve the model, an optimal scheduling plan for the power system is obtained. Since the constraint conditions comprehensively consider the characteristics of various types of energy storage such as traditional thermal power units, flywheel energy storage, and lithium battery energy storage, the advantages of traditional units, flywheel energy storage, and lithium battery energy storage can be comprehensively utilized. In addition to the conventional unit operation constraints and energy storage operation constraints, the set constraint conditions also consider the constraint conditions of system frequency safety and frequency regulation reserve, ensuring the realization of the optimal scheduling objective of the wind power, energy storage, and thermal power unit system economically on the basis of system frequency safety, meeting the requirements of the system for frequency regulation reserve and new energy consumption, and improving the safety and operation economy of the system during frequency disturbances.

[0099] (2) The present invention considers the frequency safety constraints after the system is disturbed, including the sub-constraint of the maximum rate of change of frequency (RoCoF), the sub-constraint of the quasi-steady-state frequency, and the sub-constraint of the frequency minimum point. Among them, at the moment when the maximum RoCoF of the system appears, only the inertial response of synchronous units, wind farms, and energy storage provides power support for active disturbances, and the maximum RoCoF sub-constraint is set when the RoCoF of the system reaches the maximum value; when the system enters the quasi-steady state, the quasi-steady-state frequency sub-constraint is designed considering the primary frequency regulation response power of synchronous units, wind farms, and energy storage; and the frequency minimum point sub-constraint is designed considering the safe operation when the system frequency drops to the lowest allowable value. Therefore, the frequency safety constraints set by the present invention can measure the contribution of various resources to the primary frequency regulation power of the system, and ensure that the frequency change of the system is always within the safe range during the entire primary frequency regulation process.

[0100] (3) The frequency safety constraint is the reserve that the system should set aside based on the principle of dynamic frequency response. The present invention combines the power derived from the frequency safety constraint and the output of each energy storage resource in the current system, and sets the frequency minimum point safety constraint and quasi-steady-state frequency safety constraint considering the characteristics of system inertia changing with unit start-stop and the primary frequency regulation reserve capacity in the frequency regulation reserve constraint, further ensuring that the system adjusts the frequency within the safe range when encountering frequency amplitude fluctuations.

[0101] (4) The objective function constructed by the present invention considers the wind abandonment penalty. Through optimal scheduling, renewable energy such as wind power is utilized as much as possible, the phenomenon of wind abandonment is reduced, and the resource waste and economic losses caused by wind abandonment are reduced.

[0102] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned by practice of the present invention. Description of the Drawings

[0103] Figure 1 is a schematic flowchart of a collaborative optimal scheduling method considering energy storage frequency regulation reserve proposed in an embodiment of the present invention;

[0104] Figure 2 is a schematic diagram of an equivalent model of system frequency response in an embodiment of the present invention;

[0105] Figure 3 is a schematic diagram of a modified IEEE 14-node system provided in an embodiment of the present invention;

[0106] Figure 4 is a schematic diagram of the optimal start-up mode of synchronous units in an embodiment of the present invention, where (a) is the start-stop state of the units under 5% system disturbance, and (b) is the start-stop state of the units under 10% system disturbance;

[0107] Figure 5 is a schematic diagram of the frequency regulation reserve capacity of flywheel energy storage and lithium battery energy storage under system disturbance in an embodiment of the present invention, where (a) is the frequency regulation reserve capacity under 5% system disturbance; (b) is the frequency regulation reserve capacity under 10% system disturbance;

[0108] Figure 6 is a schematic structural diagram of a collaborative optimal scheduling system considering energy storage frequency regulation reserve proposed in an embodiment of the present invention. Detailed Embodiment

[0109] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0110] As Figure 1 shown, an embodiment of the present invention proposes a collaborative optimal scheduling method considering energy storage frequency regulation reserve, and the method includes the following steps:

[0111] S10. Obtain the basic data of the power system;

[0112] S20. Establish a collaborative optimal scheduling model considering energy storage frequency regulation reserve based on the basic data. The collaborative optimal scheduling model aims to minimize the sum of the coal consumption cost of thermal power units, the unit start-stop cost, and the wind curtailment cost under multiple constraint conditions. The constraint conditions include lithium battery power constraint, charge-discharge state constraint, SOC constraint, frequency regulation response constraint, frequency security constraint, frequency regulation reserve constraint, flywheel energy recovery constraint, thermal power unit operation constraint, and power balance constraint.

[0113] It should be noted that in this embodiment, aiming to achieve the optimal economic scheduling goal of the wind power, energy storage, and thermal power unit system on the basis of ensuring system frequency security, the constructed objective function is to minimize the sum of the coal consumption cost of thermal power units, the start-stop cost, and the wind power penalty cost. The set constraint conditions, in addition to the conventional unit operation constraints and energy storage operation constraints, mainly consider the system frequency security constraint conditions and the frequency regulation reserve constraint. Moreover, the frequency regulation reserve constraint is based on the principle of the dynamic response of the power system frequency and introduces a frequency security index. Finally, the optimal economic scheduling of the system is realized under the condition of fully utilizing lithium battery energy storage and flywheel energy storage for frequency regulation reserve.

[0114] S30. Solve the collaborative optimal scheduling model to obtain the optimal scheduling plan for the power system.

[0115] It should be noted that in this embodiment, under multiple constraint conditions, with the goal of minimizing the sum of the coal consumption cost, start-stop cost, and wind curtailment cost of thermal power units, a collaborative optimal scheduling model considering energy storage frequency regulation reserve is established. Then, by using a solver to solve the model, the optimal scheduling plan for the power system is obtained. Since the constraint conditions include lithium battery power constraint, charge-discharge state constraint, SOC constraint, frequency regulation response constraint, frequency security constraint, frequency regulation reserve constraint, flywheel energy recovery constraint, thermal power unit operation constraint, and power balance constraint, considering the characteristics of various types of energy storage, the respective advantages of traditional units, flywheel energy storage, and lithium battery energy storage can be comprehensively utilized. And considering the system frequency security constraint conditions and the frequency regulation reserve constraint conditions, it ensures the realization of the optimal economic scheduling goal of the wind power, energy storage, and thermal power unit system on the basis of system frequency security, meeting the requirements of the system for frequency regulation reserve and new energy consumption.

[0116] In practical applications, lithium battery energy storage has high power and high energy and can be used for frequency regulation reserve. However, the flywheel energy storage has a small capacity, and its capacity limitation will affect its response to frequency fluctuations when considering reserve. Based on this consideration, it is considered that after primary frequency regulation, the flywheel is charged for standby by the thermal power unit, so that it can ensure that frequency regulation power can be provided at all times. Of course, this part of the charging standby power is much smaller than the thermal power unit itself bearing the frequency regulation power without the flywheel.

[0117] As a further preferred technical solution, the basic data includes power supply data, line data, load data, and energy storage related data. The power supply data includes the conventional parameters of thermal power units and the conventional parameters of wind power units, where:

[0118] The conventional parameters of the thermal power unit include: the minimum active power output of thermal power unit i The maximum active power output of thermal power unit i The maximum upward ramp rate The maximum downward ramp rate The maximum start-up ramp rate The maximum shutdown ramp rate The minimum on-time T i on and the minimum off-time T i off and the inertia time constant H Gi and the primary frequency regulation power gain coefficient K Gi and the primary frequency regulation response time constant T Gi .

[0119] The conventional parameters of the wind power unit include: the wind farm capacity the virtual inertia time constant H wj and the virtual primary frequency regulation power gain coefficient K wj .

[0120] Furthermore, the energy storage related data includes: the minimum output of lithium battery energy storage the maximum output the inertia time response coefficient H bs and the primary frequency regulation power gain coefficient K bs and the damping coefficient k bs ; the minimum output of flywheel energy storage the maximum output the inertia time response coefficient H fw and the primary frequency regulation power gain coefficient K fw and the damping coefficient k fw .

[0121] Furthermore, the line data and the load data include the active power of each node and the load rate of each time period.

[0122] As a further preferred technical solution, in S20, a collaborative optimization scheduling model considering energy storage frequency regulation reserve is established based on the basic data, specifically including:

[0123] (1) Establish the constraint conditions of the collaborative optimization scheduling model considering energy storage frequency regulation reserve

[0124] Specifically, in this embodiment, for the characteristics of various types of energy storage, for example, flywheel energy storage can participate in inertial response and primary frequency regulation response, but is not suitable for participating in hourly-level scheduling; lithium battery energy storage can participate in inertial response, primary frequency regulation response, and can also participate in hourly-level power and energy balance. The model considers that flywheel energy storage can complete full charge and discharge within 1 minute, and it only participates in frequency regulation reserve; lithium battery energy storage can both perform frequency regulation reserve and day-ahead economic dispatch. By comprehensively considering the characteristics of various types of energy storage, the constraint conditions for establishing a coordinated optimal scheduling model considering energy storage frequency regulation reserve include lithium battery power constraint, charge and discharge state constraint, SOC constraint, frequency regulation response constraint, frequency safety constraint, frequency regulation reserve constraint, flywheel energy recovery constraint, thermal power unit operation constraint, and power balance constraint, where:

[0125] 1) Lithium battery power constraint

[0126] The change range of the lithium battery power needs to be between its lower limit and upper limit to maintain its own safety and stability.

[0127]

[0128] In the formula, P es,t is the power of the lithium battery energy storage at time t; and are the maximum and minimum values of the lithium battery power respectively.

[0129] 2) Charge and discharge state constraint

[0130] The lithium battery energy storage can be in a charging state, a discharging state, or a stationary state, and it is necessary to constrain the output power of the energy storage in each state.

[0131]

[0132] In the formula, t1, t2, and t3 are the total time periods of charging, discharging, and stationary states respectively, T is the total daily working time period of the lithium battery energy storage, P cha,t is the charging power of the lithium battery energy storage, and P dis,t is the discharging power of the lithium battery energy storage.

[0133] 3) SOC constraint

[0134] The state of charge (SOC) of the lithium battery energy storage is crucial for ensuring the safety and effectiveness of the battery. It directly affects the safety, performance, and lifespan of the battery. By managing the SOC, overcharging or over-discharging of the battery can be prevented. The SOC constraint conditions set in this embodiment are as follows:

[0135]

[0136] In the formula, SOCt is the state of charge value of the lithium - battery energy storage at time t, SOC min and SOC max are its lower and upper limits respectively; SOC0 is the initial state of charge value of the lithium - battery energy storage, SOC t＝T is the state of charge value at the end state of the lithium - battery energy storage; η cha and η dis are the charging efficiency and discharging efficiency of the lithium - battery energy storage respectively; E is the capacity of the lithium - battery energy storage.

[0137] It should be noted that the lithium - battery power constraint, charge - discharge state constraint and SOC constraint are factors that need to be considered to ensure its safe and reliable operation in dispatching, which can avoid over - charging and over - discharging and affect its long - term use.

[0138] 4) Frequency - modulation response model constraint

[0139] At t = 0, the power system with energy storage is disturbed by ΔP0(t), and its transient frequency dynamic characteristics can be modeled by the swing equation. After the system is disturbed, the active power deficit is supported by the inertial response and primary frequency - modulation response of thermal power units, the virtual inertial response, virtual primary frequency - modulation response and load damping response of the wind farm, the virtual inertial response, virtual damping response and virtual primary frequency - modulation response of energy storage. The equivalent model of the system frequency response is as Figure 2 shown. It should be noted that this dispatching model is established based on the principle of the dynamic frequency response of the power system, and through this model, the power borne by various resources during the frequency - modulation process can be analyzed.

[0140] After the system receives the disturbance, the active disturbance is supported by the inertial response and primary frequency - modulation response of thermal power units, the virtual inertial response, virtual primary frequency - modulation response and load damping response of the wind farm, the virtual inertial response, virtual damping response and virtual primary frequency - modulation response of energy storage. Therefore, the frequency - modulation response constraint is set as:

[0141]

[0142] where, ΔP0(t) is the active disturbance received by the system at time t, are the inertial response and primary frequency - modulation response of thermal power units respectively, are the virtual inertial response and virtual primary frequency - modulation response of wind power respectively, are the sum of the virtual inertial responses and the sum of the virtual primary frequency - modulation responses of flywheel and lithium - battery energy storage respectively, ΔP D is the active power of the load damping characteristic, ΔP Dv is the active power of the energy - storage damping characteristic, H sys is the total inertia time constant of the system, K Wj is the virtual primary frequency - modulation power gain coefficient, K vis the virtual primary frequency regulation response coefficient of energy storage, K D is the load damping coefficient, K Dv is the energy storage damping coefficient, and Δf(t) is the frequency deviation.

[0143] Furthermore, H sys is the total inertia time constant of the system, which is expressed as:

[0144]

[0145] where, U Gi represents the start-stop state of synchronous machine i, with a value of 0 indicating shutdown and 1 indicating startup; H Gi is the inertia time constant, H wj is the virtual inertia time constant, H bs is the lithium battery inertia time response coefficient, H fw is the flywheel inertia time response coefficient.

[0146] It should be noted that the overall inertia time constant can measure the response speed of a system to disturbances. The total inertia time constant designed in this embodiment can reflect the responses of thermal power units, wind power, lithium battery energy storage, and flywheel energy storage to disturbances.

[0147] 5) Frequency safety constraints

[0148] Considering the frequency safety constraints after the system is disturbed, including: the rate of change of frequency (RoCoF) sub-constraint, the quasi-steady state frequency sub-constraint, and the frequency minimum point sub-constraint.

[0149] 5-1) The maximum RoCoF of the system appears at t = 0 + moment. At this time, the system frequency deviation Δf = 0, and the primary frequency regulation response powers of thermal power units, wind farms, and energy storage and the active load damping response ΔP D and the active power of the energy storage damping response ΔP Dv are all 0. At this time, only the inertia responses of synchronous units, wind farms, and energy storage provide power support for active disturbances, and the system RoCoF reaches the maximum value. The maximum RoCoF sub-constraint is expressed as:

[0150]

[0151] where, is the allowable value of the system maximum RoCoF. It can be equivalently converted into a linear constraint, that is, the maximum RoCoF constraint, as shown in formula (7):

[0152]

[0153] When the primary frequency regulation response of the system enters the quasi-steady state (t = ∞), the system frequency deviation stabilizes at Δf ss , at this time, the RoCoF of the system is expressed as:

[0154]

[0155] 5-2) When the system enters the quasi-steady state, the primary frequency regulation response powers of the synchronous generator sets, wind farms, and energy storage and are respectively expressed as:

[0156]

[0157] Substituting them into the system perturbation equation, the quasi-steady state frequency safety sub-constraint of the system can be obtained as:

[0158]

[0159] where represents the allowable value of the maximum frequency deviation in the quasi-steady state. It can be equivalently converted into a linear constraint, as shown in Equation (11).

[0160]

[0161] 5-3) Let the allowable value of the maximum frequency deviation of the system be Δf max . Assume that at a certain moment, the system frequency just drops to the lowest allowable value (f0 - Δf max ), at this moment, there is:

[0162]

[0163] The safety sub-constraint at the lowest frequency point can be expressed as:

[0164]

[0165] It should be noted that the frequency safety constraints designed in this embodiment can measure the contributions of various resources to the primary frequency regulation power of the system, and ensure that the frequency change of the system is always within the safe range during the entire primary frequency regulation process.

[0166] 6) Frequency regulation reserve constraint

[0167] This embodiment considers the characteristics of the system inertia changing with the start-stop of generator sets and the frequency lowest point safety constraint and quasi-steady state frequency safety constraint of the primary frequency regulation reserve capacity:

[0168]

[0169] It should be noted here that 5) the frequency security constraint is the reserve that the system should set aside based on the principle of dynamic frequency response; 6) in the frequency regulation reserve constraint, the actual reserve capacity is determined by combining the power derived in 5) and the output of each resource in the current system. The frequency nadir security constraint formula (22), the quasi-steady-state frequency security constraint formula (23), (14) and (21) are the reserve constraints for various resources separately.

[0170] 7) Flywheel energy recovery constraint

[0171] Due to the time scale relationship of flywheel energy storage, it does not participate in the day-ahead economic dispatch; however, after it participates in frequency regulation, its energy needs to be recovered. Considering that the thermal power unit charges it for standby, that is, after the frequency regulation ends, the thermal power unit charges the flywheel. The set flywheel energy recovery constraint is:

[0172]

[0173] P cf (k)×t cf ≥ΔE fw (25)

[0174] In the formula, P cf (k) is the standby power for the thermal power unit to recover the energy after flywheel energy storage frequency regulation, is the standby power of the thermal power unit at the frequency nadir, P Gi (k) is the output of the thermal power unit, is the maximum value of the active power output of the thermal power unit i, t cf is the time for the thermal power unit to charge the flywheel after the frequency regulation ends, ΔE fw is the energy consumed by the flywheel energy storage participating in frequency regulation.

[0175] It should be noted that due to the small capacity of flywheel energy storage, the limitation of its capacity will affect its response to frequency fluctuations when considering standby. Based on this, it is considered that after the primary frequency regulation ends, the thermal power unit charges the flywheel for standby, so that it can ensure that frequency regulation power can be provided at all times.

[0176] 8) Thermal power unit operation constraint

[0177]

[0178] In the formula, and are the minimum and maximum values of the active power output of the thermal power unit i respectively; U Gi represents the operating state of the thermal power unit i, 0 indicates that the unit is in the shutdown state, and 1 indicates that the unit is in the operating state.

[0179]

[0180] In the formula, and represent the upward and downward ramping rates of thermal power unit i, and P Gi,t , P Gi,t-1 are the outputs of the thermal power unit at time t and t - 1 respectively.

[0181] It should be noted that in this embodiment, by restricting the operating range of the unit, the unit is prevented from operating under extreme conditions, thus extending the service life of the equipment.

[0182] 9) Power balance constraint

[0183]

[0184] In the formula, P Gi,t is the active power of thermal power unit i at time t; P Wj,t is the predicted power of wind farm j at time t; L d,t is the predicted load power of node d at time t, P cha,t is the charging power of the lithium - battery energy storage, P dis,t is the discharging power of the lithium - battery energy storage, N G is the number of thermal power units, N W is the number of wind turbines, N d is the number of nodes.

[0185] In this embodiment, the power balance constraint reflects the role of energy storage in system balance.

[0186] (2) Establish the objective function of the collaborative optimal scheduling model considering energy storage frequency regulation reserve

[0187] Since the main objective of energy storage collaborative optimization is to minimize the daily operating cost of the system, the daily operating cost specifically includes the coal consumption of thermal power units, the unit start - stop cost, and the penalty for wind curtailment. Taking the sum of the coal consumption cost of thermal power units, the unit start - stop cost, and the wind curtailment cost as the scheduling objective, the total objective function is constructed as:

[0188]

[0189] In the formula, is the coal consumption cost of thermal power units, is the unit start - stop cost, is the wind curtailment cost, c cur is the penalty cost per unit power for wind curtailment, is the wind curtailment rate, is the maximum wind power output, Δk is the time interval, and k, i, j represent the time period, the i - th thermal power unit, and the j - th wind power respectively.

[0190] The objective function constructed in this embodiment takes into account the penalty for wind curtailment. Through optimal scheduling, renewable energy such as wind power is utilized as much as possible to reduce wind curtailment and the resulting waste of resources and economic losses.

[0191] As a further preferred technical solution, S30, solving the collaborative optimal scheduling model to obtain the optimal scheduling plan for the power system, specifically includes:

[0192] By calling the solver in Matlab to solve the collaborative optimal scheduling model considering energy storage frequency regulation reserve, the optimal scheduling plan for the power system is obtained.

[0193] It should be noted that since the collaborative optimal scheduling model considering energy storage frequency regulation reserve is a linear model, the solver in Matlab is directly used for solving in this embodiment, and the detailed process is not specifically described in this embodiment.

[0194] Specifically, taking Equation (29) as the optimization objective and considering Constraints (1) to (28), it is the collaborative optimal scheduling model considering energy storage frequency regulation reserve proposed in this embodiment. This model can be solved through the commercial software GUROBI based on the MATLAB platform. For the Figure 3 power system shown, its common topological structure in the actual power system is simulated by the IEEE 14-node system. It has a moderate scale, can reflect the basic characteristics of the power system, is convenient for calculation and analysis. In this embodiment, wind power and energy storage are added for improvement, making the system closer to the characteristics of modern power systems, and the optimal scheduling problems of various resources can be studied. Using the collaborative optimal scheduling method considering energy storage frequency regulation reserve provided by the present invention, the operating results obtained under different system disturbances are compared as shown in Table 1, and the starting modes are compared as Figure 4 shown, and the reserve power of the energy storage in each period is as Figure 5 shown.

[0195] Table 1 System Scheduling Results

[0196]

[0197]

[0198] It should be noted that compared with the traditional economic scheduling scheme that does not consider frequency security, the system of this scheme can effectively ensure the frequency security of the system, and the model compares the effects of the system under different disturbances.

[0199] In addition, as Figure 6 shown, another embodiment of the present invention also proposes a collaborative optimal scheduling system considering energy storage frequency regulation reserve. The system includes:

[0200] The data acquisition module 10 is used to acquire the basic data of the power system;

[0201] The collaborative optimization scheduling model construction module 20 is used to establish a collaborative optimization scheduling model considering energy storage frequency regulation reserve based on the basic data, where the collaborative optimization scheduling model takes the minimum sum of the coal consumption cost of thermal power units, the unit start-stop cost, and the wind curtailment cost as the scheduling objective under multiple constraint conditions. The multiple constraint conditions include lithium battery power constraint, charge and discharge state constraint, SOC constraint, frequency regulation response constraint, frequency safety constraint, frequency regulation reserve constraint, flywheel energy recovery constraint, thermal power unit operation constraint, and power balance constraint;

[0202] The model solving module 30 is used to solve the collaborative optimization scheduling model to obtain the optimal scheduling plan of the power system.

[0203] As a further preferred technical solution, the basic data acquired by the data acquisition module 10 includes power source data, line data, load data, and energy storage related data, where the power source data includes the conventional parameters of thermal power units and wind turbines.

[0204] As a further preferred technical solution, the formula of the scheduling objective function in the collaborative optimization scheduling model is expressed as:

[0205]

[0206] In the formula, is the coal consumption cost of thermal power units, is the unit start-stop cost, is the wind curtailment cost, c cur is the penalty for wind curtailment per unit power of wind power, is the wind curtailment rate of wind power in the k-th period, is the maximum output of wind turbines, Δk is the time interval, and k, i, j represent the time period, the number of thermal power units, and the number of wind turbines respectively.

[0207] As a further preferred technical solution, the multiple constraint conditions include lithium battery power constraint, charge and discharge state constraint, SOC constraint, frequency regulation response constraint, frequency safety constraint, frequency regulation reserve constraint, flywheel energy recovery constraint, thermal power unit operation constraint, and power balance constraint. Each constraint condition is specifically expressed as:

[0208] (1) The lithium battery power constraint is:

[0209]

[0210] In the formula, P es,t is the power of the lithium battery energy storage at time t, and are the maximum and minimum values of the lithium battery power respectively;

[0211] (2) The charge-discharge state constraint is as follows:

[0212]

[0213] In the formula, t1, t2, and t3 are the total time periods of the charging, discharging, and standby states respectively, T is the total daily working time period of the lithium battery energy storage, P cha,t is the charging power of the lithium battery energy storage, P dis,t is the discharging power of the lithium battery energy storage;

[0214] (3) The SOC constraint is as follows:

[0215]

[0216] In the formula, SOC t is the state of charge value of the lithium battery energy storage at time t, SOC min , SOC max are its lower and upper limits respectively; SOC0 is the initial state of charge value of the lithium battery energy storage, SOC t＝T is the state of charge value of the lithium battery energy storage at the final state; η cha , η dis are the charging efficiency and discharging efficiency of the lithium battery energy storage respectively; E is the capacity of the lithium battery energy storage.

[0217] (4) The frequency regulation response constraint is as follows:

[0218]

[0219] In the formula, ΔP0(t) is the active power disturbance received by the system at time t, are the inertial response and primary frequency regulation response of the thermal power unit respectively, are the virtual inertial response and virtual primary frequency regulation response of the wind power respectively, are the sum of the virtual inertial responses and the sum of the virtual primary frequency regulation responses of the flywheel and the lithium battery energy storage respectively, ΔP D is the active power of the load damping characteristic, ΔP Dv is the active power of the energy storage damping characteristic, H sys is the total inertia time constant of the system, K Wj is the virtual primary frequency regulation power gain coefficient, K v is the energy storage virtual primary frequency regulation power gain coefficient, K D is the system load damping coefficient, K Dv is the energy storage damping coefficient, Δf(t) is the frequency deviation at time t.

[0220] (5) The frequency safety constraint includes the maximum frequency change rate sub-constraint, the quasi-steady state frequency sub-constraint, and the frequency minimum point sub-constraint, where:

[0221] The maximum rate of change of frequency sub - constraint is as follows:

[0222]

[0223] In the formula, is the maximum allowable value of the system RoCoF, U Gi characterizes the operating state of thermal power unit i, H Gi is the inertia time constant, H Wj is the virtual inertia time constant, H v is the virtual inertia time constant of energy storage, and ΔP0 is the active power disturbance received by the system;

[0224] The quasi - steady - state frequency sub - constraint is as follows:

[0225]

[0226] In the formula, represents the allowable value of the maximum quasi - steady - state frequency deviation, K Gi is the primary frequency regulation power gain coefficient, K Wj is the virtual primary frequency regulation power gain coefficient, K v is the virtual primary frequency regulation gain coefficient of energy storage, K D is the load damping coefficient, K Dv is the damping coefficient of energy storage;

[0227] The frequency lowest point sub - constraint is as follows:

[0228]

[0229] In the formula, Δf max is the allowable value of the maximum system frequency deviation, is the primary frequency regulation response power of the thermal power unit.

[0230] (6) The frequency regulation reserve constraint is as follows:

[0231]

[0232]

[0233] In the formula, is the reserve power at the lowest frequency point of the thermal power unit, is the primary frequency regulation power of the thermal power unit, is the maximum active power output of thermal power unit i, P Gi (k) is the active power output of thermal power unit i; is the reserve power at the lowest frequency point of the wind power unit, K Wj (k) is the virtual primary frequency regulation power gain coefficient, is the curtailment rate of wind power, is the maximum output of the wind turbine, Δf max is the allowable value of the maximum system frequency deviation; is the minimum standby power of the lithium - ion energy storage, K fw (k) is the primary frequency regulation power gain coefficient of the flywheel energy storage, is the maximum output of the flywheel energy storage; is the minimum standby power of the lithium - ion energy storage, K bs (k) is the primary frequency regulation power gain coefficient of the lithium - ion energy storage, is the maximum output of the lithium - battery energy storage, P es (k) is the output of the lithium - ion energy storage at time k; is the quasi - steady - state frequency standby power of the thermal power unit, U Gi (k) is the start - up and shut - down condition of the thermal power unit at time k, K Gi is the primary frequency regulation power gain coefficient of the thermal power unit, is the maximum quasi - steady - state frequency deviation; is the quasi - steady - state frequency standby power of the wind turbine, is the quasi - steady - state frequency standby power of the flywheel, is the quasi - steady - state frequency standby power of the flywheel, is the quasi - steady - state frequency standby power of the lithium - battery, is the maximum power of the lithium - battery; ΔP0(k) is the active power disturbance received by the system, K D (k) is the system load damping response coefficient, K Dv (k) is the energy storage damping response coefficient.

[0234] (7) The flywheel energy recovery constraint is:

[0235]

[0236] P cf (k)×t cf ≥ΔE fw

[0237] In the formula, P cf (k) is the standby power for the flywheel energy storage to recover energy after frequency regulation by the thermal power unit, is the minimum frequency standby power of the thermal power unit, P Gi (k) is the output of the thermal power unit, is the maximum value of the active power output of the thermal power unit i, t cf is the time for the thermal power unit to charge the flywheel after the frequency regulation ends, ΔE fw is the energy consumed by the flywheel energy storage participating in frequency regulation.

[0238] (8) The thermal power unit operation constraint is:

[0239]

[0240] In the formula, and are respectively the minimum and maximum active power outputs of thermal power unit i, and U Gi characterizes the operating state of thermal power unit i, and represent the upward and downward ramping rates of thermal power unit i, and P Gi,t , P Gi,t-1 are respectively the outputs of thermal power units at time t and t - 1.

[0241] (9) The power balance constraint is:

[0242]

[0243] In the formula, P Gi,t is the active power of thermal power unit i at time t, P Wj,t is the predicted power of wind farm j at time t, L d,t is the predicted load power of node d at time t, P cha,t is the charging power of lithium - battery energy storage, P dis,t is the discharging power of lithium - battery energy storage, N G is the number of thermal power units, N W is the number of wind turbines, N d is the number of nodes.

[0244] As a further preferred technical solution, the model solving module 30 is specifically configured to call a solver in matlab to solve the collaborative optimization scheduling model considering energy storage frequency regulation reserve, and obtain the optimal scheduling plan of the power system.

[0245] It should be noted that other embodiments or specific implementation methods of the collaborative optimization scheduling system considering energy storage frequency regulation reserve according to the present invention can refer to the above - mentioned method embodiments, and will not be elaborated here.

[0246] In addition, another embodiment of the present invention also proposes a computer device, including: one or more processors;

[0247] The processor is used to store one or more programs;

[0248] When the one or more programs are executed by the one or more processors, the collaborative optimization scheduling method considering energy storage frequency regulation reserve as described in the previous embodiment is implemented.

[0249] In addition, another embodiment of the present invention also proposes a computer - readable storage medium, on which a computer program is stored. When the computer program is executed, the collaborative optimization scheduling method considering energy storage frequency regulation reserve as described in the previous embodiment is implemented.

[0250] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0251] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0252] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript, etc.

[0253] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be realized 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 devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0254] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that realizes the functions in the flowFigure 1 one process or multiple processes and / or blocks Figure 1 the functions specified in one block or multiple blocks

[0255] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing the steps of the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 the steps of the functions specified in one block or multiple blocks

[0256] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention

Claims

1. A collaborative optimization scheduling method considering energy storage frequency regulation and standby, characterized in that: include: Obtain basic data of the power system; Based on the basic data, a collaborative optimization scheduling model considering energy storage, frequency regulation and standby is established, wherein the collaborative optimization scheduling model takes the sum of coal consumption cost, unit start-up and shutdown cost and wind abandonment cost of thermal power units as the minimum scheduling target under multiple constraints, and the multiple constraints include lithium battery power constraint, charge and discharge state constraint, SOC constraint, frequency regulation response constraint, frequency safety constraint, frequency regulation standby constraint, flywheel energy recovery constraint, thermal power unit operation constraint and power balance constraint; The collaborative optimization scheduling model is solved to obtain the optimal scheduling plan for the power system.

2. The collaborative optimization scheduling method considering energy storage and frequency regulation as claimed in claim 1 is characterized in that: The basic data includes power supply data, line data, load data and energy storage related data, wherein the power supply data includes conventional parameters of thermal power units and conventional parameters of wind power units.

3. The collaborative optimization scheduling method considering energy storage and frequency regulation as claimed in claim 1 is characterized in that: The formula of the scheduling objective function in the collaborative optimization scheduling model is expressed as: In the formula, is the coal consumption cost of thermal power units, is the unit start-up and shutdown cost, is the wind curtailment cost, c cur The penalty for wind power abandonment per unit power is: is the wind power abandonment rate in period k, is the maximum output of the wind turbine, Δk is the time interval, k, i, j represent the time period, the number of thermal power units, and the number of wind turbines, respectively.

4. The collaborative optimization scheduling method considering energy storage and frequency regulation as claimed in claim 1 is characterized in that: The lithium battery power constraint is: Where P es,t is the power of lithium battery energy storage in time period t, and They are the maximum and minimum values ​​of lithium battery power respectively; The charge and discharge state constraints are: Where t1, t2, and t3 are the total time periods of charging, discharging, and static states, respectively; T is the total working time of the lithium battery energy storage day; P cha,t is the charging power of lithium battery energy storage, P dis,t The discharge power of lithium battery energy storage; The SOC constraint is: In the formula, SOC t is the state of charge value of the lithium battery energy storage at time t, SOC min , SOC max are the lower and upper limits respectively; SOC 0 is the initial state of charge value of the lithium battery energy storage, SOC t＝T is the state of charge value of the lithium battery at the end of energy storage; η cha , η dis They are respectively the charging efficiency and discharging efficiency of lithium battery energy storage; E is the energy storage capacity of lithium battery.

5. The collaborative optimization scheduling method considering energy storage and frequency regulation as claimed in claim 1 is characterized in that: The frequency modulation response constraint is: Where ΔP0(t) is the active disturbance received by the system at time t, They are the inertial response and primary frequency modulation response of thermal power units. They are the virtual inertial response and virtual primary frequency regulation response of wind power respectively. are the sum of the virtual inertial response of the flywheel and lithium battery energy storage, and the sum of the virtual primary frequency modulation response, ΔP D is the load damping characteristic active power, ΔP Dv is the active power of energy storage damping characteristics, H sys is the total inertia time constant of the system, K Wj is the virtual primary frequency modulation power gain coefficient, K v K is the energy storage virtual primary frequency modulation power gain coefficient, D is the system load damping coefficient, K Dv is the energy storage damping coefficient, and Δf(t) is the frequency deviation at time t.

6. The collaborative optimization scheduling method considering energy storage and frequency regulation as claimed in claim 1 is characterized in that: The frequency safety constraint includes a maximum frequency change rate sub-constraint, a quasi-steady-state frequency sub-constraint and a frequency minimum point sub-constraint, wherein: The maximum frequency change rate sub-constraint is: In the formula, is the maximum RoCoF allowed value of the system, U Gi Characterizes the operating status of thermal power unit i, H Gi is the inertia time constant, H Wj is the virtual inertia time constant, H v is the virtual inertia time constant of energy storage, ΔP0 is the active disturbance received by the system; The quasi-steady-state frequency sub-constraint is: In the formula, Indicates the maximum allowable frequency deviation in the quasi-steady state, K Gi is the primary frequency modulation power gain coefficient, K Wj is the virtual primary frequency modulation power gain coefficient, K v K is the energy storage virtual primary frequency modulation gain coefficient, D is the load damping coefficient, K Dv is the energy storage damping coefficient; The frequency minimum point constraint is: Where Δf max is the maximum allowable frequency deviation of the system, It is the primary frequency regulation response power of the thermal power unit.

7. The collaborative optimization scheduling method considering energy storage and frequency regulation as claimed in claim 1 is characterized in that: The frequency regulation reserve constraint is: In the formula, The reserve power at the lowest frequency point of the thermal power unit. is the primary frequency modulation power of the thermal power unit, is the maximum active output of thermal power unit i, P Gi (k) is the active output of thermal power unit i; is the standby power at the lowest frequency of the wind turbine, K Wj (k) is the virtual primary frequency modulation power gain coefficient, is the wind abandonment rate, is the maximum output of the wind turbine, Δf max is the maximum allowable frequency deviation of the system; is the lowest standby power of lithium battery energy storage, K fw (k) is the primary frequency modulation power gain coefficient of flywheel energy storage, Maximum output for flywheel energy storage; is the lowest standby power of lithium battery energy storage, K bs (k) is the primary frequency modulation power gain coefficient of lithium battery energy storage, is the maximum output of lithium battery energy storage, P es (k) is the lithium battery energy storage output during time period k; is the quasi-steady-state frequency reserve power of thermal power units, U Gi (k) is the start and stop status of thermal power units in period k, K Gi is the primary frequency regulation power gain coefficient of the thermal power unit, is the maximum quasi-steady-state frequency deviation; is the quasi-steady-state frequency reserve power of the wind turbine, is the flywheel quasi-steady-state frequency reserve power, is the flywheel quasi-steady-state frequency reserve power, It is the quasi-steady-state frequency backup power of lithium battery. is the maximum power of the lithium battery; ΔP0(k) is the active disturbance of the system, K D (k) is the system load damping response coefficient, K Dv (k) is the energy storage damping response coefficient.

8. The collaborative optimization scheduling method considering energy storage and frequency regulation as claimed in claim 1 is characterized in that: The flywheel energy recovery constraint is: Where P cf (k) is the standby power of the thermal power unit after energy recovery from flywheel energy storage frequency modulation, is the reserve power at the lowest frequency point of the thermal power unit, P Gi (k) is the output of the thermal power unit, is the maximum active output of thermal power unit i, t cf ΔE is the time it takes for the flywheel to charge after the frequency modulation is completed. fw The energy consumed by the flywheel energy storage and frequency regulation.

9. The collaborative optimization scheduling method considering energy storage and frequency regulation as claimed in claim 1, characterized in that: The operating constraints of the thermal power unit are: In the formula, and are the minimum and maximum active output of thermal power unit i, U Gi Characterizes the operating status of thermal power unit i, and represents the ramp-up rate and ramp-down rate of thermal power unit i, P Gi,t , P Gi,t-1 are the outputs of thermal power units at time periods t and t-1 respectively.

10. The collaborative optimization scheduling method considering energy storage and frequency regulation as claimed in claim 1, characterized in that: The power balance constraint is: Where P Gi,t is the active power of thermal power unit i in period t, P Wj,t is the predicted power of wind farm j in period t, L d,t is the predicted load power of node d in period t, P cha,t is the charging power of lithium battery energy storage, P dis,t is the discharge power of lithium battery energy storage, N G is the number of thermal power units, N W is the number of wind turbines, N d is the number of nodes.

11. A collaborative optimization dispatching system considering energy storage and frequency regulation, characterized in that: include: A data acquisition module is used to obtain basic data of the power system; A collaborative optimization scheduling model building module is used to establish a collaborative optimization scheduling model considering energy storage, frequency regulation and standby based on the basic data, wherein the collaborative optimization scheduling model takes the sum of coal consumption cost, unit start-up and shutdown cost and wind abandonment cost of thermal power units as the minimum scheduling target under multiple constraints, and the multiple constraints include lithium battery power constraint, charge and discharge state constraint, SOC constraint, frequency regulation response constraint, frequency safety constraint, frequency regulation standby constraint, flywheel energy recovery constraint, thermal power unit operation constraint and power balance constraint; The model solving module is used to solve the collaborative optimization scheduling model to obtain the optimal scheduling plan for the power system.

12. The coordinated optimization dispatching system considering energy storage and frequency regulation as claimed in claim 11, characterized in that: The basic data includes power supply data, line data, load data and energy storage related data, wherein the power supply data includes conventional parameters of thermal power units and conventional parameters of wind power units.

13. The coordinated optimization dispatching system considering energy storage and frequency regulation as claimed in claim 11, characterized in that: The formula of the scheduling objective function in the collaborative optimization scheduling model is expressed as: In the formula, is the coal consumption cost of thermal power units, is the unit start-up and shutdown cost, is the wind curtailment cost, c cur The penalty for wind power abandonment per unit power is: is the wind power abandonment rate in period k, is the maximum output of the wind turbine, Δk is the time interval, k, i, j represent the time period, the number of thermal power units, and the number of wind turbines, respectively.

14. The coordinated optimization dispatching system considering energy storage and frequency regulation as claimed in claim 11, characterized in that: The lithium battery power constraint is: Where P es,t is the power of lithium battery energy storage in time period t, and They are the maximum and minimum values ​​of lithium battery power respectively; The charge and discharge state constraints are: Where t1, t2, and t3 are the total time periods of charging, discharging, and static states, respectively; T is the total working time of the lithium battery energy storage day; P cha,t is the charging power of lithium battery energy storage, P dis,t The discharge power of lithium battery energy storage; The SOC constraint is: In the formula, SOC t is the state of charge value of the lithium battery energy storage at time t, SOC min , SOC max are the lower and upper limits respectively; SOC 0 is the initial state of charge value of the lithium battery energy storage, SOC t＝T is the state of charge value of the lithium battery at the end of energy storage; η cha , η dis They are respectively the charging efficiency and discharging efficiency of lithium battery energy storage; E is the energy storage capacity of lithium battery.

15. The coordinated optimization dispatching system considering energy storage and frequency regulation as claimed in claim 11, characterized in that: The frequency modulation response constraint is: Where ΔP0(t) is the active disturbance received by the system at time t, They are the inertial response and primary frequency modulation response of thermal power units. They are the virtual inertial response and virtual primary frequency regulation response of wind power respectively. are the sum of the virtual inertial response of the flywheel and lithium battery energy storage, and the sum of the virtual primary frequency modulation response, ΔP D is the load damping characteristic active power, ΔP Dv is the active power of energy storage damping characteristics, H sys is the total inertia time constant of the system, K Wj is the virtual primary frequency modulation power gain coefficient, K v K is the energy storage virtual primary frequency modulation power gain coefficient, D is the system load damping coefficient, K Dv is the energy storage damping coefficient, and Δf(t) is the frequency deviation at time t.

16. The coordinated optimization dispatching system considering energy storage and frequency regulation as claimed in claim 11, characterized in that: The frequency safety constraint includes a maximum frequency change rate sub-constraint, a quasi-steady-state frequency sub-constraint and a frequency minimum point sub-constraint, wherein: The maximum frequency change rate sub-constraint is: In the formula, is the maximum RoCoF allowed value of the system, U Gi Characterizes the operating status of thermal power unit i, H Gi is the inertia time constant, H Wj is the virtual inertia time constant, H v is the virtual inertia time constant of energy storage, ΔP0 is the active disturbance received by the system; The quasi-steady-state frequency sub-constraint is: In the formula, Indicates the maximum allowable frequency deviation in the quasi-steady state, K Gi is the primary frequency modulation power gain coefficient, K Wj is the virtual primary frequency modulation power gain coefficient, K v K is the energy storage virtual primary frequency modulation gain coefficient, D is the load damping coefficient, K Dv is the energy storage damping coefficient; The frequency minimum point constraint is: Where Δf max is the maximum allowable frequency deviation of the system, It is the primary frequency regulation response power of the thermal power unit.

17. The coordinated optimization dispatching system considering energy storage and frequency regulation as claimed in claim 11, characterized in that: The frequency regulation reserve constraint is: In the formula, The reserve power at the lowest frequency point of the thermal power unit. is the primary frequency modulation power of the thermal power unit, is the maximum active output of thermal power unit i, P Gi (k) is the active output of thermal power unit i; is the standby power at the lowest frequency of the wind turbine, K Wj (k) is the virtual primary frequency modulation power gain coefficient, is the wind abandonment rate, is the maximum output of the wind turbine, Δf max is the maximum allowable frequency deviation of the system; is the lowest standby power of lithium battery energy storage, K fw (k) is the primary frequency modulation power gain coefficient of flywheel energy storage, Maximum output for flywheel energy storage; is the lowest standby power of lithium battery energy storage, K bs (k) is the primary frequency modulation power gain coefficient of lithium battery energy storage, is the maximum output of lithium battery energy storage, P es (k) is the lithium battery energy storage output during time period k; is the quasi-steady-state frequency reserve power of thermal power units, U Gi (k) is the start and stop status of thermal power units in period k, K Gi is the primary frequency regulation power gain coefficient of the thermal power unit, is the maximum quasi-steady-state frequency deviation; is the quasi-steady-state frequency reserve power of the wind turbine, is the flywheel quasi-steady-state frequency reserve power, is the flywheel quasi-steady-state frequency reserve power, It is the quasi-steady-state frequency backup power of lithium battery. is the maximum power of the lithium battery; ΔP0(k) is the active disturbance of the system, K D (k) is the system load damping response coefficient, K Dv (k) is the energy storage damping response coefficient.

18. The coordinated optimization dispatching system considering energy storage and frequency regulation as claimed in claim 11, characterized in that: The flywheel energy recovery constraint is: P cf (k)×t cf ≥ΔE fw Where P cf (k) is the standby power of the thermal power unit after energy recovery from flywheel energy storage frequency modulation, is the reserve power at the lowest frequency point of the thermal power unit, P Gi (k) is the output of the thermal power unit, is the maximum active output of thermal power unit i, t cf ΔE is the time it takes for the flywheel to charge after the frequency modulation is completed. fw The energy consumed by the flywheel energy storage and frequency regulation.

19. The coordinated optimization dispatching system considering energy storage and frequency regulation as claimed in claim 11, characterized in that: The operating constraints of the thermal power unit are: In the formula, and are the minimum and maximum active output of thermal power unit i, U Gi Characterizes the operating status of thermal power unit i, and represents the ramp-up rate and ramp-down rate of thermal power unit i, P Gi,t , P Gi,t-1 are the outputs of thermal power units at time periods t and t-1 respectively.

20. The coordinated optimization dispatching system considering energy storage and frequency regulation as claimed in claim 11, characterized in that: The power balance constraint is: Where P Gi,t is the active power of thermal power unit i in period t, P Wj,t is the predicted power of wind farm j in period t, L d,t is the predicted load power of node d in period t, P cha,t is the charging power of lithium battery energy storage, P dis,t is the discharge power of lithium battery energy storage, N G is the number of thermal power units, N W is the number of wind turbines, N d is the number of nodes.

21. A computer device, characterized in that: include: one or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, the collaborative optimization scheduling method considering energy storage, frequency regulation and reserve as described in any one of claims 1 to 10 is implemented.

22. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the collaborative optimization scheduling method considering energy storage, frequency regulation and standby as described in any one of claims 1 to 10 is implemented.

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