Source and storage collaborative optimization scheduling method and device considering frequency stability constraint

Abstract

The application discloses a source and storage collaborative optimization scheduling method and device considering frequency stability constraints, and relates to the field of power dispatching.The method comprises the following steps: a power system source and storage collaborative scheduling basic model containing thermal power units, wind power units, pumped storage units, battery energy storage units and flywheel energy storage units is constructed; based on a simplified frequency dynamic response model of the power system, combined with the frequency change characteristics of the power system after disturbance, the minimum frequency constraint and the maximum frequency change rate constraint of the power system are established; according to the power system source and storage collaborative scheduling basic model and the minimum frequency constraint and the maximum frequency change rate constraint of the power system, a source and storage collaborative optimization scheduling model is constructed; and the source and storage collaborative optimization scheduling model is solved based on the source and storage collaborative optimization scheduling model, so that a source and storage collaborative optimization scheduling scheme is obtained. The minimum frequency constraint and the maximum frequency change rate constraint of the power system are introduced, and the problem of frequency safety and stability of the power system under the condition of large-scale access of new energy units can be solved.

Description

Technical Field

[0001] This application relates to the field of power dispatching, and in particular to a source-storage collaborative optimization dispatching method and apparatus that considers frequency stability constraints. Background Technology

[0002] With the large-scale integration of wind turbines, some thermal power units in traditional power systems will gradually be replaced. On the one hand, unlike traditional fossil fuel power generation, wind power generation is strongly correlated with local weather conditions, exhibiting randomness and volatility in output. In relatively harsh environments, this can lead to increased power fluctuations in the power system, reducing system stability. On the other hand, wind turbines have low inertia, and their integration into the grid through power electronic devices significantly reduces the overall inertia level of the system. When a low-inertia power system is subjected to power disturbances, the system frequency changes drastically. If the frequency drop is significant, it will trigger the activation of low-frequency load shedding devices, leading to large-scale load shedding or even regional power outages on the load side. Summary of the Invention

[0003] The purpose of this application is to provide a source-storage collaborative optimization scheduling method and device that considers frequency stability constraints, which can ensure the frequency security and stability of the power system under the large-scale access of new energy units.

[0004] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a source-storage cooperative optimization scheduling method considering frequency stability constraints, including: Construct a basic model for the coordinated dispatch of power system energy sources and storage, including thermal power units, wind power units, pumped storage units, battery energy storage units, and flywheel energy storage units; Based on a simplified frequency dynamic response model of the power system, and combined with the frequency change characteristics of the power system after disturbance, minimum frequency constraints and maximum frequency change rate constraints of the power system are established. Based on the basic model of power system source-storage coordinated scheduling and the minimum frequency constraint and maximum frequency change rate constraint of power system, a source-storage coordinated optimization scheduling model is constructed. Based on the source-storage collaborative optimization scheduling model, an optimization solution for source-storage collaborative scheduling is obtained.

[0005] Secondly, this application 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 above-described source-storage collaborative optimization scheduling method considering frequency stability constraints.

[0006] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned source-storage cooperative optimization scheduling method considering frequency stability constraints.

[0007] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned source-storage collaborative optimization scheduling method considering frequency stability constraints.

[0008] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a source-storage coordinated optimization scheduling method and apparatus considering frequency stability constraints. The method includes: constructing a basic model for source-storage coordinated scheduling of a power system, comprising thermal power units, wind power units, pumped storage units, battery energy storage units, and flywheel energy storage units; establishing minimum frequency constraints and maximum frequency change rate constraints for the power system based on a simplified frequency dynamic response model and considering the frequency change characteristics after disturbances; constructing a source-storage coordinated optimization scheduling model based on the basic model and the minimum and maximum frequency change rate constraints; and optimizing the source-storage coordinated optimization scheduling model to obtain a source-storage coordinated optimization scheduling scheme. This application introduces minimum frequency constraints and maximum frequency change rate constraints for the power system, simultaneously incorporating both into the source-storage coordinated optimization scheduling model. This ensures that the scheduling results not only meet cost operation requirements but also consider frequency security requirements after disturbances, thereby improving the safe and stable operation capability of the power system under scenarios with a high proportion of new energy integration and solving the problem of power system frequency security and stability under large-scale integration of new energy units. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is an application environment diagram of a source-storage collaborative optimization scheduling method considering frequency stability constraints in one embodiment of this application; Figure 2 A flowchart illustrating a source-storage collaborative optimization scheduling method considering frequency stability constraints, provided as an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0013] The source-storage collaborative optimization scheduling method considering frequency stability constraints provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send the status information (such as power status) of the power system, including thermal power units, wind power units, pumped storage units, battery energy storage units, and flywheel energy storage units, to server 104. After receiving the power system status information, server 104 constructs a basic model for source-storage coordinated scheduling of the power system, including thermal power units, wind power units, pumped storage units, battery energy storage units, and flywheel energy storage units. Based on the simplified frequency dynamic response model of the power system and combined with the frequency change characteristics after power system disturbances, it establishes minimum frequency constraints and maximum frequency change rate constraints for the power system. Based on the basic model for source-storage coordinated scheduling and the minimum frequency constraints and maximum frequency change rate constraints of the power system, it constructs a source-storage coordinated optimization scheduling model. Based on the source-storage coordinated optimization scheduling model, it performs optimization and solves the problem to obtain a source-storage coordinated optimization scheduling scheme. Server 104 can feed back the obtained source-storage collaborative optimization scheduling scheme to terminal 102. Furthermore, in some embodiments, the source-storage collaborative optimization scheduling method considering frequency stability constraints can also be implemented independently by server 104 or terminal 102. For example, terminal 102 can directly perform source-storage collaborative optimization scheduling considering frequency stability constraints based on the power system's state information, or server 104 can obtain the power system's state information from the data storage system and perform source-storage collaborative optimization scheduling considering frequency stability constraints.

[0014] Among them, terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices and portable wearable devices, and server 104 can be implemented by independent servers or server clusters composed of multiple servers, or it can be a cloud server.

[0015] In one exemplary embodiment, such as Figure 2 As shown, a source-storage collaborative optimization scheduling method considering frequency stability constraints is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 204.

[0016] Step 201: Construct a basic model for the coordinated dispatch of power system energy sources and storage, including thermal power units, wind power units, pumped storage units, battery energy storage units, and flywheel energy storage units.

[0017] Step 202: Based on the simplified frequency dynamic response model of the power system and combined with the frequency change characteristics of the power system after disturbance, establish the minimum frequency constraint and the maximum frequency change rate constraint of the power system.

[0018] Step 203: Based on the basic model of power system source-storage coordinated scheduling and the minimum frequency constraint and maximum frequency change rate constraint of the power system, construct the source-storage coordinated optimization scheduling model.

[0019] Step 204: Based on the source-storage collaborative optimization scheduling model, perform optimization solution to obtain the source-storage collaborative optimization scheduling scheme.

[0020] By implementing steps 201 to 204 above, the minimum frequency constraint and the maximum frequency change rate constraint of the system are simultaneously incorporated into the source-storage collaborative optimization scheduling model, so that the scheduling result not only meets the cost operation requirements, but also takes into account the frequency security requirements after disturbance, thereby improving the safe and stable operation capability of the power system in the scenario of high proportion of new energy access.

[0021] In another exemplary embodiment of this application, step 201, which involves constructing a basic model for the coordinated dispatch of power system energy sources and storage, including thermal power units, wind power units, pumped storage units, battery energy storage units, and flywheel energy storage units, specifically includes: (1) Construct an objective function with the goal of minimizing the total operating cost of the power system. The total operating cost of the power system includes: fuel cost of thermal power units, start-up and shutdown cost of thermal power units, operating cost of pumped storage, operating and attenuation cost of battery energy storage, operating cost of flywheel energy storage, and frequency deviation penalty cost.

[0022] The objective function is expressed as follows: In the formula, The total operating cost of the power system; Fuel cost for thermal power units; Costs associated with the start-up and shutdown of thermal power units; The operating cost of pumped storage; Costs associated with battery energy storage operation and degradation; For flywheel energy storage operating costs; The cost of penalizing frequency deviation.

[0023] To prevent the system frequency from remaining near the lower limit of the frequency constraint for a long time during the optimization scheduling process, a frequency deviation penalty term is introduced into the objective function to guide the system to maintain a certain frequency safety margin while meeting the frequency constraints.

[0024] The specific cost calculations are as follows: 1) The expression for the fuel cost of thermal power is: In the formula, , , thermal power units Secondary fuel cost coefficient, primary fuel cost coefficient, and fixed operating cost coefficient; For thermal power units At any moment Those who have made meritorious contributions; For thermal power units At any moment The power-on status variable is set to 1 when the power is on and 0 when the power is off. , indicating the scheduling period; This represents a set of thermal power units.

[0025] 2) The expression for the start-up and shutdown cost of thermal power units is: In the formula, For thermal power units Startup costs; For thermal power units Downtime costs; For thermal power units At any moment The startup indicator variable is set to 1 when startup occurs and 0 otherwise. For thermal power units At any moment The shutdown indicator variable is set to 1 when the system stops, and 0 otherwise.

[0026] 3) The expression for the operating cost of pumped storage is: In the formula, Pumped storage unit The unit operating cost coefficient; Pumped storage unit At any moment Pumping power; Pumped storage unit At any moment The power generation capacity; The scheduling time interval; This refers to a collection of pumped storage units.

[0027] 4) The expression for the operating and degradation costs of battery energy storage is: In the formula, Battery energy storage unit The unit operating cost coefficient; Battery energy storage unit The unit attenuation cost coefficient; Battery energy storage unit At any moment The charging power; Battery energy storage unit At any moment The discharge power; This represents a collection of battery energy storage devices.

[0028] 5) The expression for the operating cost of flywheel energy storage is: In the formula, flywheel energy storage unit The unit operating cost coefficient; flywheel energy storage unit At any moment The charging power; flywheel energy storage unit At any moment The discharge power; This represents a flywheel energy storage collection.

[0029] 6) The expression for the frequency deviation penalty cost is: In the formula, This is the frequency deviation penalty coefficient; This is the penalty coefficient for the deviation of the rate of change of frequency; For a moment The deviation of the system's lowest frequency from its rated frequency; For a moment The deviation of the system's maximum frequency change rate from the ideal frequency change rate.

[0030] (2) Establish power system dispatch constraints; power system dispatch constraints include: power system power balance constraints, thermal power unit output and start-up / shutdown constraints, wind power unit output constraints, multi-energy storage system constraints, and multi-energy storage collaborative reserve and frequency regulation capacity constraints; multi-energy storage system constraints include multi-type energy storage charging and discharging power constraints, multi-type energy storage energy state constraints, battery energy storage state of charge constraints, battery energy storage net output ramping constraints, and flywheel energy storage response constraints.

[0031] The specific calculation formulas for each constraint are as follows: 1) System power balance constraints Under normal operating conditions, the system's power generation and load demand need to be balanced in real time. Considering the charging and discharging power of thermal power units, wind power units, and energy storage systems, the system power balance constraint can be expressed as: In the formula, for The output of the wind turbine at any given moment; for The system load demand at any given time; the meanings of the other variables are the same as before.

[0032] 2) Output and start-stop constraints of thermal power units ① Upper and lower limits of thermal power unit output constraints At any given time, the output of each unit must be within its allowable range. The specific expression for this constraint is: In the formula: For thermal power units The lower limit of output; For thermal power units The upper limit of output.

[0033] ②Climbing constraints for thermal power units Under normal circumstances, the output power of a steam turbine cannot be changed immediately, so its output is limited over a period of time. The specific expression for this constraint is: In the formula: For thermal power units Uphill climbing limit; For thermal power units Downhill limit.

[0034] ③ Start-up and shutdown logic constraints for thermal power units To accurately represent the start-up and shutdown actions of thermal power units, binary variables are introduced. and They represent thermal power units At any moment The specific expressions for the power-on and power-off actions are as follows: In the formula: For thermal power units exist The running status at any given moment; , thermal power units exist Startup and shutdown variables at specific times.

[0035] ④ Minimum start-up and shutdown time constraints for thermal power units Considering the safe operation and operating costs of the unit, it is required that the unit can only be shut down after being in the powered-on state for a certain period of time, and can only be powered on after being in the powered-off state for a certain period of time. The specific expression of the constraint is as follows: In the formula: For thermal power units Minimum continuous power-on time; For thermal power units The minimum continuous downtime.

[0036] ⑤ Adjusting the reserve capacity constraint for thermal power units During normal operation, to ensure the safe and stable operation of the system, a portion of reserve capacity should be reserved for primary frequency regulation. When the system is disturbed, each unit generates additional active power to suppress frequency drops. The specific expression for the constraint is as follows: In the formula, For thermal power units At any moment Reserved standby capacity.

[0037] 3) Wind turbine output constraints Given that wind turbine output is significantly affected by weather conditions, its power generation is volatile and uncertain. During the day-ahead optimization scheduling phase, the predicted day-ahead output of wind turbines is typically considered as the maximum usable output for the corresponding time period. Therefore, the following constraints apply: In the formula, For a moment The predicted output of wind turbine units.

[0038] 4) Constraints of Multiple Energy Storage Systems ①Charging and discharging power constraints of pumped storage units To rationally schedule the charging and discharging behavior of pumped storage units, constraints need to be imposed on their pumping power and power generation at any given time. The specific expression for the constraints is as follows: In the formula, Pumped storage unit Maximum pumping power; Pumped storage unit Maximum power generation capacity; Pumped storage unit At any moment The pumping state variable is set to 1 when pumping is in progress and 0 otherwise. Pumped storage unit At any moment The power generation state variable is set to 1 when power generation occurs, and 0 otherwise.

[0039] ② Energy state constraints of pumped-storage units In the formula, Pumped storage unit At any moment The energy storage status; This represents the energy storage state at the previous moment; and Pumped storage units The minimum and maximum energy storage states; Pumped storage unit The pumping efficiency; Pumped storage unit Power generation efficiency.

[0040] ③ Battery energy storage charging and discharging power constraints In the formula, Battery energy storage unit Maximum charging power; Battery energy storage unit Maximum discharge power; Battery energy storage unit At any moment The charging state variables; Battery energy storage unit At any moment The discharge state variables.

[0041] ④ Battery energy storage state of energy constraints In the formula, Battery energy storage unit At any moment Energy storage; Battery energy storage unit The charging efficiency; Battery energy storage unit The discharge efficiency; Battery energy storage unit The minimum stored energy; Battery energy storage unit The maximum energy storage capacity.

[0042] ⑤ Battery energy storage SOC constraint To ensure that the state of charge (SOC) of energy storage devices remains within a reasonable range and to prevent overcharging or over-discharging, SOC constraints must be set for battery energy storage. In the formula, Battery energy storage unit At any moment The state of charge; Battery energy storage unit Rated energy capacity; Battery energy storage unit The lower limit of SOC; Battery energy storage unit The upper limit of SOC.

[0043] ⑥ Battery energy storage net output ramp-up constraints To prevent the net output of battery energy storage devices from changing too rapidly in response to grid demand within a short period of time, a ramp constraint is introduced to limit the rate of change of power in the battery energy storage unit. The specific expression of the constraint is as follows: In the formula, The ramp-up limit for the net output of the battery energy storage unit; This represents the ramp-up limit for the net output of the battery energy storage unit.

[0044] ⑦ Flywheel energy storage charging and discharging power constraints In the formula, flywheel energy storage unit Maximum charging power; flywheel energy storage unit Maximum discharge power; flywheel energy storage unit At any moment The charging state variables; flywheel energy storage unit At any moment The discharge state variables.

[0045] ⑧ Flywheel energy storage energy state constraints In the formula, flywheel energy storage unit At any moment Energy storage; flywheel energy storage unit The minimum stored energy; flywheel energy storage unit Maximum stored energy; flywheel energy storage unit The charging efficiency; flywheel energy storage unit The discharge efficiency.

[0046] ⑨ Flywheel energy storage fast response constraints To avoid grid frequency fluctuations caused by the flywheel energy storage unit adjusting its power too quickly, the power change rate of the flywheel energy storage must be constrained. The specific expression for the constraint is as follows: In the formula, flywheel energy storage unit Net output ramp-up limit; flywheel energy storage unit The net output climbing limit.

[0047] 5) Multi-energy storage collaborative backup and frequency regulation capacity constraints To reflect the complementary characteristics of pumped hydro storage, battery energy storage, and flywheel energy storage at different time scales, a multi-energy storage collaborative backup and frequency regulation capacity constraint is further constructed, specifically including the following: ① The energy storage system has increased its reserve capacity constraint. In the formula, Pumped storage unit At any moment Reserved up-frequency modulation capacity; Battery energy storage unit At any moment Reserved up-frequency modulation capacity; flywheel energy storage unit At any moment Reserved up-frequency modulation capacity.

[0048] ② Total system reserve constraint In the formula, For a moment The system reserve factor.

[0049] ③ Energy constraints for adjusting backup energy from energy storage devices Since the increased reserve capacity provided by energy storage devices will not exceed their current releaseable energy limit, the following constraint exists, the specific expression of which is: In the formula, The duration window that provides frequency support for pumped storage; The duration window that provides frequency support for the battery; The duration window that provides frequency support for the flywheel.

[0050] Furthermore, considering the significant differences in response speed and energy capacity among different types of energy storage devices, their functions in supporting system frequency also differ. Flywheel energy storage features fast response speed and high power density but relatively small energy capacity, making it suitable for ultra-short-term rapid response at the initial stage of disturbances; battery energy storage has a relatively fast response speed and a certain energy capacity, making it suitable for short-term rapid frequency regulation support; pumped hydro storage, although relatively slow to start up, has a large energy capacity and continuous output capability, making it suitable for providing continuous power support for longer periods. Therefore, the following constraints also exist, the specific expressions of which are as follows: In the formula, The response time for flywheel energy storage; The response time for battery energy storage; This refers to the response time of pumped storage.

[0051] (3) Based on the objective function and the power system scheduling constraints, construct a basic model for power system source-storage coordinated scheduling.

[0052] In another exemplary embodiment of this application, step 202 involves constructing frequency stability constraints, including minimum frequency constraints and maximum frequency change rate constraints. For the minimum frequency constraint: the relationship between the minimum frequency after disturbance and the frequency regulation support capabilities of thermal power units, pumped storage, battery energy storage, and flywheel energy storage is described using an equivalent primary frequency regulation capability model, thus forming the minimum frequency constraint. The specific construction process is as follows: (1) Maximum power disturbance setting To characterize potential power deficit events that the system may experience during operation, the expected maximum disturbance power is introduced. In power systems with a high proportion of renewable energy integration, the randomness and volatility of renewable energy output, coupled with uncertain changes in load demand, can both cause short-term power deficits in the system, leading to deviations from the system frequency. Therefore, the model considers two typical disturbance scenarios: sudden drops in wind turbine output and short-term surges in load. The expression for the expected maximum disturbance power is: In the formula, For a moment The expected maximum disturbance power; This is the wind power sudden drop ratio coefficient; This is the load surge ratio coefficient.

[0053] (2) Construct the minimum frequency constraint The system at any time When the expected maximum power disturbance occurs, the frequency will drop instantaneously. To ensure that the system's minimum frequency does not exceed the safety lower limit, the system needs to have sufficient equivalent primary frequency regulation capability within the allowable maximum frequency deviation. The minimum frequency constraint of the power system is characterized by the constraint of the equivalent primary frequency regulation capability of the power system. Therefore, the constraint is constructed as follows: In the formula, Pumped storage unit The effective participation factor of frequency modulation; Battery energy storage unit The effective participation factor of frequency modulation; flywheel energy storage unit The effective participation factor of frequency modulation; The equivalent load damping coefficient of the system; For a moment The maximum allowable frequency deviation of the system; For a moment The lowest frequency of the system; This is the lowest frequency security threshold value allowed by the system.

[0054] For the maximum frequency change rate constraint: the relationship between the initial frequency change rate of the disturbance and the inertial response capabilities of various units and energy storage is described by the system equivalent inertia model, forming the maximum frequency change rate constraint. The specific construction process is as follows: To characterize the system's ability to withstand rapid frequency changes, the total equivalent inertial energy of the system is defined as: In the formula, For a moment The total equivalent inertial energy of the system; For thermal power units The inertia constant; For thermal power units Rated capacity; For pumped storage units The inertia constant; For pumped storage units Rated capacity; flywheel energy storage unit The equivalent virtual inertia constant; flywheel energy storage unit Rated capacity; Battery energy storage unit The equivalent virtual inertia constant; Battery energy storage unit Rated capacity.

[0055] Based on the simplified swing equation, the system at time... When subjected to the expected maximum disturbance power, its maximum rate of frequency change can be approximately expressed as: To ensure that the frequency change rate of the system does not exceed the allowable range in the initial stage of a disturbance, a constraint should be set on the maximum frequency change rate: In the formula, This is the threshold for the maximum allowed rate of change of frequency in the system.

[0056] Substituting the total equivalent inertial energy of the system, we obtain the total equivalent inertial energy constraint of the system. This constraint is then used to characterize the maximum rate of frequency change constraint of the power system. In the formula, Indicates the rated frequency.

[0057] In another exemplary embodiment of this application, in step 204, regarding the solution of the source-storage collaborative optimization scheduling model, since the model includes quadratic costs and integer variables, and all constraints are linear constraints, the source-storage collaborative optimization scheduling model is a mixed integer quadratic programming problem. The Gurobi solver is called to optimize and solve the model. When solving this scheduling problem, the Gurobi solver uses a branch-and-bound method, first decomposing the original problem into multiple subproblems with continuous variables, and then using the active set method to solve the subproblems. During the solution process, heuristic algorithms are used to quickly find feasible solutions for each problem, and the solution process is optimized through pruning, preprocessing, and conflict analysis. Finally, the Gurobi solver returns the optimal solution or a near-optimal solution, thus yielding the source-storage collaborative optimization scheduling scheme. The source-storage coordinated optimization scheduling scheme includes: (1) the start-stop status and output of each thermal power unit within 24 hours; (2) the coordinated charging and discharging strategy of the energy storage system: namely, the charging and discharging status, charging and discharging power, and state of charge of the three types of energy storage systems in each time period; (3) the reserve capacity reserved by each thermal power unit and the three types of energy storage systems in each time period.

[0058] Compared to existing scheduling methods that only consider operating costs and conventional frequency constraints, this application has the following technical advantages: (1) The minimum frequency constraint and the maximum frequency change rate constraint of the system are simultaneously incorporated into the source-storage collaborative optimization scheduling model so that the scheduling results not only meet the cost operation requirements, but also take into account the frequency security requirements after the disturbance, thereby improving the safe and stable operation capability of the power system in the scenario of high proportion of new energy access.

[0059] (2) By taking advantage of the different characteristics of pumped hydro storage, battery energy storage and flywheel energy storage in terms of response speed and support duration, the constraints take into account the multi-energy storage collaborative backup and frequency regulation capacity constraints, and a multi-time scale collaborative support mechanism is constructed. This realizes the hierarchical collaborative operation mode in which flywheel energy storage undertakes ultra-short-time rapid response, battery energy storage undertakes short-time rapid frequency regulation, and pumped hydro storage undertakes continuous power support, thereby improving the comprehensive utilization efficiency of multiple types of energy storage resources.

[0060] (3) The optimization objective comprehensively considers the cost of thermal power fuel, start-up and shutdown costs, energy storage operation costs and frequency deviation penalty costs, so that the scheduling model can make overall trade-offs between system operation costs and frequency security, and avoid the problem of insufficient frequency security margin caused by simply pursuing the optimal cost.

[0061] To verify the effectiveness of the scheduling method in this application, "based on the IEEE 10-unit 39-node system, we compared the results of the lowest frequency point and the rate of change of frequency (ROCOF) under two scenarios, considering and not considering frequency stability constraints."

[0062] (1) Comparison of the lowest frequency points: Without considering frequency stability constraints, focusing solely on minimizing operating costs results in extremely severe frequency drops after system disturbances. During periods of high wind power penetration and insufficient thermal power reserves, the system's equivalent primary frequency regulation capacity is severely inadequate. For example, at 3:00 AM, the initial load demand is 3500MW, with wind power output at 1900MW. However, only four conventional turbines are online at this time, and simulations show the lowest frequency drop to 49.52 Hz, below the safe lower limit of 49.8 Hz. Throughout the 24 time periods of the day, the lowest frequency drops below the safe lower limit of 49.8 Hz in several periods with high wind power penetration (such as periods 2-6 and 22-24), posing a risk of widespread power outages.

[0063] Considering frequency stability constraints and incorporating three types of energy storage, while activating more thermal power units to provide basic backup, pumped hydro storage, battery energy storage, and flywheel energy storage work together to form a layered frequency regulation support system. Taking the worst-case scenario of 3:00 AM as an example, flywheel energy storage and battery energy storage respond rapidly after the disturbance occurs, followed closely by pumped hydro storage and thermal power units, effectively raising the lowest frequency point for that period to 49.84 Hz. Throughout the 24 time periods of the day, the lowest frequency point remained within the safe range of 49.84~49.96 Hz, with no frequency exceeding the limit.

[0064] (2) Comparison of Rate of Change of Frequency (ROCOF): Without considering frequency stability constraints, the system lacks sufficient inertia support, resulting in a high frequency change rate in the initial stages of disturbances. Taking 3:00 AM as an example, the system's total equivalent inertial energy drops to its lowest point of the day, and simulations show a maximum frequency change rate of 0.8 Hz / s, exceeding the safety threshold of 0.5 Hz / s. Throughout the 24 time periods of the day, the frequency change rate during the early morning windy period mostly exceeds the limit, posing a serious threat to power grid safety.

[0065] Considering frequency stability constraints and introducing three types of energy storage, the total equivalent inertial energy constraint of the system incorporates the inertial resources of various generating units and energy storage into unified management. Flywheel energy storage and battery energy storage, with their rapid response characteristics, can curb the initial rate of frequency drop with extremely fast response at the beginning of a disturbance, and are subsequently supported by thermal power units and pumped storage.

[0066] Taking 3:00 as an example again, the simulation showed that the system's frequency change rate decreased from 0.8 Hz / s to 0.45 Hz / s during this period, which is below the safety threshold. The maximum RoCoF value for all 24 time periods of the day was 0.45 Hz / s, with no time periods exceeding the limit, ensuring the system's frequency stability.

[0067] The above comparison results show that: First, the source-storage collaborative optimization scheduling model proposed in this application can effectively ensure the frequency safety of the system under typical disturbance scenarios with a reasonable increase in cost. The lowest frequency of the system remains above 49.8 Hz within 24 hours, and the maximum frequency change rate does not exceed 0.5 Hz / s, meeting the set constraints. Second, flywheel energy storage, battery energy storage, and pumped hydro storage achieve the expected multi-timescale hierarchical collaboration in response timing and power division: flywheel energy storage responds first after the disturbance occurs, undertaking the ultra-short-term rapid inertia support in the early stage of the disturbance, effectively suppressing the initial frequency drop rate; battery energy storage responds later, undertaking the short-term rapid frequency adjustment support, compensating for power deficit and supporting the gradual recovery of frequency; pumped hydro storage starts slightly later, but with its continuous output capacity, it undertakes long-term power support, ensuring that the system frequency stably recovers to near the rated value, verifying the effectiveness of multi-energy storage collaborative backup.

[0068] This application also provides an application scenario in which the above-mentioned source-storage coordinated optimization scheduling method considering frequency stability constraints is applied. Specifically, the source-storage coordinated optimization scheduling method considering frequency stability constraints provided in this embodiment can be applied in the source-storage coordinated optimization scheduling scenario of a power system. This scenario includes a data analysis stage and a scheduling stage; the data analysis stage is used to construct a basic model of source-storage coordinated scheduling of the power system, including thermal power units, wind power units, pumped storage units, battery energy storage units, and flywheel energy storage units; establish the minimum frequency constraint and maximum frequency change rate constraint of the power system; construct a source-storage coordinated optimization scheduling model based on the basic model of source-storage coordinated scheduling of the power system and the minimum frequency constraint and maximum frequency change rate constraint of the power system, and perform optimization solution to obtain the source-storage coordinated optimization scheduling scheme; the scheduling stage is used to regulate the start-up time and output of thermal power units, wind power units, pumped storage units, battery energy storage units, and flywheel energy storage units in the power system according to the source-storage coordinated optimization scheduling scheme. The source-storage coordinated optimization scheduling method considering frequency stability constraints provided in this embodiment belongs to the data analysis stage.

[0069] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores source-storage cooperative optimization scheduling data considering frequency stability constraints. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a source-storage cooperative optimization scheduling method considering frequency stability constraints.

[0070] Figure 3 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0071] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0072] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0073] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations and be authorized by the owner of the corresponding device.

[0074] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0075] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0076] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0077] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A source-storage collaborative optimization scheduling method considering frequency stability constraints, characterized in that, include: Construct a basic model for the coordinated dispatch of power system energy sources and storage, including thermal power units, wind power units, pumped storage units, battery energy storage units, and flywheel energy storage units; Based on a simplified frequency dynamic response model of the power system, and combined with the frequency change characteristics of the power system after disturbance, minimum frequency constraints and maximum frequency change rate constraints of the power system are established. Based on the basic model of power system source-storage coordinated scheduling and the minimum frequency constraint and maximum frequency change rate constraint of power system, a source-storage coordinated optimization scheduling model is constructed. Based on the source-storage collaborative optimization scheduling model, an optimization solution for source-storage collaborative scheduling is obtained.

2. The source-storage collaborative optimization scheduling method considering frequency stability constraints according to claim 1, characterized in that, The minimum frequency constraint of a power system is characterized by the equivalent primary frequency regulation capability constraint of the power system. The expression for the equivalent primary frequency modulation capability constraint is as follows: in, ; ; In the formula, For thermal power units At any moment Reserved standby capacity for future adjustments; Pumped storage unit At any moment Reserved up-frequency modulation capacity; Battery energy storage unit At any moment Reserved up-frequency modulation capacity; flywheel energy storage unit At any moment Reserved up-frequency modulation capacity; Pumped storage unit The effective participation factor of frequency modulation; Battery energy storage unit The effective participation factor of frequency modulation; flywheel energy storage unit The effective participation factor of frequency modulation; The equivalent load damping coefficient of the system; For a moment The maximum allowable frequency deviation of the system; Indicates the rated frequency; For a moment The lowest frequency of the system; This is the lowest frequency security threshold value allowed by the system. For a moment The expected maximum disturbance power.

3. The source-storage collaborative optimization scheduling method considering frequency stability constraints according to claim 1, characterized in that, The maximum rate of frequency change constraint of a power system is characterized by the total equivalent inertial energy constraint; the expression for the total equivalent inertial energy constraint is: In the formula, For a moment The total equivalent inertial energy of the system; For a moment The expected maximum disturbance power; The maximum allowable rate of change threshold for the system; Indicates the rated frequency.

4. The source-storage collaborative optimization scheduling method considering frequency stability constraints according to claim 1, characterized in that, Construct a basic model for the coordinated dispatch of power system energy sources and storage, including thermal power units, wind power units, pumped storage units, battery energy storage units, and flywheel energy storage units. Specifically, this includes: The objective function is constructed with the goal of minimizing the total operating cost of the power system. The total operating cost of the power system includes: fuel cost of thermal power units, start-up and shutdown cost of thermal power units, operating cost of pumped storage, operating and degradation cost of battery energy storage, operating cost of flywheel energy storage, and frequency deviation penalty cost. Establish power system dispatch constraints; power system dispatch constraints include: power system power balance constraints, thermal power unit output and start-up / shutdown constraints, wind power unit output constraints, multi-energy storage system constraints, and multi-energy storage collaborative reserve and frequency regulation capacity constraints; multi-energy storage system constraints include multi-type energy storage charging and discharging power constraints, multi-type energy storage energy state constraints, battery energy storage state of charge constraints, battery energy storage net output ramping constraints, and flywheel energy storage response constraints; Based on the objective function and power system scheduling constraints, a basic model for power system source-storage coordinated scheduling is constructed.

5. The source-storage collaborative optimization scheduling method considering frequency stability constraints according to claim 4, characterized in that, The objective function expression is: In the formula, The total operating cost of the power system; Fuel cost for thermal power units; Costs associated with the start-up and shutdown of thermal power units; The operating cost of pumped storage; Costs associated with battery energy storage operation and degradation; For flywheel energy storage operating costs; The cost of penalizing frequency deviation.

6. The source-storage collaborative optimization scheduling method considering frequency stability constraints according to claim 5, characterized in that, The expression for the fuel cost of thermal power is: In the formula, , , thermal power units Secondary fuel cost coefficient, primary fuel cost coefficient, and fixed operating cost coefficient; For thermal power units At any moment Those who have made meritorious contributions; For thermal power units At any moment The power-on status variables; Indicates the scheduling period; Represents a set of thermal power units; The expression for the start-up and shutdown cost of thermal power units is: In the formula, For thermal power units Startup costs; For thermal power units Downtime costs; For thermal power units At any moment The start indicator variable; For thermal power units At any moment The shutdown indication variable; The expression for the operating cost of pumped storage is: In the formula, Pumped storage unit The unit operating cost coefficient; Pumped storage unit At any moment Pumping power; Pumped storage unit At any moment The power generation capacity; The scheduling time interval; This refers to a collection of pumped storage units.

7. The source-storage collaborative optimization scheduling method considering frequency stability constraints according to claim 6, characterized in that, The expression for the operating and degradation costs of battery energy storage is as follows: In the formula, Battery energy storage unit The unit operating cost coefficient; Battery energy storage unit The unit attenuation cost coefficient; Battery energy storage unit At any moment The charging power; Battery energy storage unit At any moment The discharge power; Represents a collection of battery energy storage devices; The expression for the operating cost of flywheel energy storage is: In the formula, flywheel energy storage unit The unit operating cost coefficient; flywheel energy storage unit At any moment The charging power; flywheel energy storage unit At any moment The discharge power; Represents a flywheel energy storage collection; The expression for the frequency deviation penalty cost is: In the formula, This is the frequency deviation penalty coefficient; This is the penalty coefficient for the deviation of the rate of change of frequency; For a moment The deviation of the system's lowest frequency from its rated frequency; For a moment The deviation of the system's maximum frequency change rate from the ideal frequency change rate.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the source-storage collaborative optimization scheduling method considering frequency stability constraints as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the source-storage collaborative optimization scheduling method considering frequency stability constraints as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the source-storage collaborative optimization scheduling method considering frequency stability constraints as described in any one of claims 1-7.

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