Multi-resource joint scheduling and frequency control method, system, equipment and medium

Abstract

The invention discloses a multi-resource joint scheduling and frequency control method and system, equipment and a medium. The method comprises the following steps: acquiring related data of each piece of equipment of a power system in real time to construct a unified scheduling model; obtaining scheduling cycles based on the unified scheduling model, and updating the operation data of each device of the power system in real time in each scheduling cycle; according to the updated equipment operation data and the power system frequency constraint, an optimal scheduling model with the lowest economic operation cost as a target is constructed, a mixed integer linear programming method and a Benders decomposition technology are adopted, and the optimal scheduling model is iteratively solved to obtain a scheduling scheme; and according to the scheduling scheme, generating equipment control instructions of the power system, and controlling the frequency of the power system according to the control instructions. According to the method, the optimal scheduling model is constructed to obtain the scheduling scheme, stable operation of the system can be rapidly and effectively dealt with and maintained when the power system has complex conditions, and the frequency stability of the power system is ensured while the goal of lowest economic operation cost is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system control, and in particular to a multi-resource joint scheduling and frequency control method, system, device and medium. BACKGROUND

[0002] With the growing global demand for clean energy, the proportion of renewable energy in the power system continues to rise. The scale of renewable energy power generation such as wind and solar energy is expanding, and power electronic devices are also widely used in the power grid. This has promoted energy transformation, but also brought new challenges to the power system. The traditional power system mainly relies on synchronous generators to provide inertia response to maintain stable operation, while renewable energy generation itself lacks mechanical inertia, and the use of power electronic devices further reduces the overall inertia of the system. In this case, once the power system encounters a sudden disturbance such as a large load fluctuation, generator unit failure or accidental island switching, the system frequency is prone to dramatic changes, which may cause safety accidents or even power grid collapse.

[0003] Existing scheduling methods rely mainly on traditional unit commitment optimization and economic dispatch strategies, focusing on steady-state economic optimization, but ignoring the dynamic frequency response process. Even though some studies introduce a primary frequency dynamic model, the nonlinearity of the dynamic response equation is strong, and the error is large when discretized and linearized, making it difficult to strictly control the system frequency change rate and frequency deviation within the safe range. Although microgrid technology has been widely promoted, many controllable resources can improve system dynamic performance, but existing microgrid scheduling technology lacks joint scheduling and coordination of energy storage systems and renewable energy, and cannot achieve dual optimization of economy and frequency safety. Some studies use mathematical methods such as mixed integer linear programming to solve the problem, but in the modeling of system transient response and frequency safety constraints, a preset capacity or fixed proportion method is used, which cannot adaptively adjust the control strategy according to the real-time operating state and dynamic response characteristics, resulting in a scheduling scheme that is difficult to cope with complex disturbance scenarios. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a multi-resource joint scheduling and frequency control method to solve the problem that traditional scheduling and control methods cannot balance economy and safety, and cannot effectively control the primary frequency dynamic response and frequency change rate within the safe range.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a multi-resource joint scheduling and frequency control method, comprising:

[0008] Real-time acquisition of power system equipment related data, according to the equipment related data, construct a unified scheduling model;

[0009] Based on the unified scheduling model, obtain the scheduling period, and in each scheduling period, real-time update of power system equipment operation data;

[0010] According to the updated equipment operation data and the power system frequency constraint, an optimization scheduling model with the lowest economic operation cost as the target is constructed, and a mixed integer linear programming method and Benders decomposition technology are used to iteratively solve the optimization scheduling model to obtain a scheduling scheme;

[0011] According to the scheduling scheme, generate power system equipment control instructions, and control the power system frequency according to the control instructions.

[0012] As a preferred scheme of the multi-resource joint scheduling and frequency control method of the application, wherein: real-time acquisition of power system equipment related data, according to the equipment related data, construct a unified scheduling model, including:

[0013] Get the data of the generator set, the energy storage system, the renewable energy power generation unit, the load and the power exchange equipment with the main network, model each equipment based on the constraint condition, and construct a unified scheduling model;

[0014] The unified scheduling model is constrained by the discretized and linearized primary frequency dynamic response equation.

[0015] As a preferred scheme of the multi-resource joint scheduling and frequency control method of the application, wherein: real-time update of power system equipment operation data, including:

[0016] Based on the preset processing instructions and the deviation data, obtain the actual output data of the generator set and the actual output data of the renewable energy power generation unit;

[0017] The energy state data of the energy storage unit is obtained through the energy storage system charge and discharge power calculation;

[0018] The load data of the current and future time period is updated in real time through the load prediction model and the historical load data.

[0019] As a preferred scheme of the multi-resource joint scheduling and frequency control method of the application, wherein: according to the updated equipment operation data and the power system frequency constraint, an optimization scheduling model with the lowest economic operation cost as the target is constructed, including:

[0020] The power system frequency constraint includes power system power balance constraint, generator set output constraint, energy storage system charge and discharge constraint, and power system frequency change rate constraint;

[0021] The economic operation cost includes a generator set power generation cost, a start-stop cost, an energy storage operation cost, and a purchase and sale cost of power exchange with a main grid.

[0022] The preferred technical solution has the beneficial effect that various constraints are set, and the costs of generator set power generation, start-stop, energy storage operation, and purchase and sale of power exchange with the main grid are comprehensively considered.

[0023] As a preferred solution of the multi-resource joint scheduling and frequency control method, the mixed integer linear programming method and the Benders decomposition technique are used to iteratively solve the optimization scheduling model to obtain a scheduling scheme, including:

[0024] The Benders decomposition technique is used to decompose the overall optimization problem of the optimization scheduling model into a main problem and a sub-problem;

[0025] The main problem is responsible for solving the unit start-stop decision, the energy storage state, and the basic power allocation to obtain a candidate solution;

[0026] The sub-problem checks the primary frequency modulation dynamic response and frequency safety constraints according to the candidate solution output by the main problem;

[0027] If the sub-problem detects that the frequency deviation or the frequency change rate of a certain period exceeds the preset safety threshold, a corresponding feasibility or optimality cut plane is generated and output to the main problem, so that the scheduling scheme is corrected in the next iteration.

[0028] The preferred technical solution has the beneficial effect that the Benders decomposition technique is used to split the complex optimization scheduling model problem into a main problem and a sub-problem, and the solution is achieved by division of labor and cooperation, which reduces the difficulty of solving, improves the calculation efficiency, quickly obtains a scheduling scheme that meets the dual requirements of economy and frequency safety, and enhances the adaptability of the system to different operating conditions.

[0029] As a preferred solution of the multi-resource joint scheduling and frequency control method, the Benders decomposition technique is used to split the complex optimization scheduling model problem into a main problem and a sub-problem, and the solution is achieved by division of labor and cooperation, which reduces the difficulty of solving, improves the calculation efficiency, quickly obtains a scheduling scheme that meets the dual requirements of economy and frequency safety, and enhances the adaptability of the system to different operating conditions.

[0030] When the power system detects that the grid connection line with the main grid is disconnected, the generator set and the energy storage system that can quickly participate in frequency modulation are identified according to the scheduling scheme, and control instructions are issued to provide or absorb the necessary power in the shortest time according to the pre-allocated capacity or virtual inertia response strategy, thereby suppressing the instantaneous change of frequency.

[0031] When the power system enters the island mode, the generator set, the energy storage system and the renewable energy output unit in the power system are integrated into a new balance mode based on the scheduling scheme to maintain power supply to the load in the island range and keep the frequency stable.

[0032] The beneficial effects of the preferred technical scheme are as follows: when the power system monitors that the grid-connected line is disconnected, the generator set and the energy storage system capable of rapid frequency regulation can be quickly identified and enabled to suppress frequency sudden change according to a preset strategy; if the power system enters the island mode, the resources in the system can be integrated to maintain power supply and frequency stability; the stable operation of the power system in different working conditions such as grid connection abnormality, island operation and grid connection restoration can be ensured, and the reliability and anti-interference ability of the system are improved.

[0033] As a preferred scheme of the multi-resource joint scheduling and frequency control method, the method further comprises the following steps.

[0034] During the period when the power system enters the island mode, if the energy storage capacity of the energy storage system is insufficient or the generator set cannot meet the load demand, load management or emergency scheduling measures are performed according to a priority strategy to avoid larger range fluctuations of the system frequency or voltage;

[0035] If the main grid fault is eliminated and the power system has grid connection conditions, the synchronization of the power system and the main grid in voltage, frequency and phase is evaluated, and after the technical conditions for grid connection closing are met, the grid connection operation is gradually restored according to the grid connection switching program.

[0036] In a second aspect, the application provides a multi-resource joint scheduling and frequency control system, comprising: a data acquisition and update module, configured to acquire relevant data of each device of a power system in real time, and construct a unified scheduling model according to the device-related data; a scheduling period is obtained based on the unified scheduling model, and in each scheduling period, the running data of each device of the power system is updated in real time;

[0037] A scheduling model construction module is configured to construct an optimal scheduling model with the lowest economic operation cost as the target according to the updated device running data and the frequency constraint of the power system;

[0038] A scheduling scheme generation module is configured to obtain a scheduling scheme by iteratively solving the optimal scheduling model using a mixed integer linear programming method and a Benders decomposition technique;

[0039] A control adjustment module is configured to generate control instructions of each device of the power system according to the scheduling scheme, and control the frequency of the power system according to the control instructions.

[0040] In a third aspect, the application provides an electronic device, comprising:

[0041] a memory and a processor;

[0042] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, implement the steps of the multi-resource joint scheduling and frequency control method.

[0043] In a fourth aspect, the present application provides a computer readable storage medium storing computer executable instructions, and the computer executable instructions, when executed by a processor, implement the steps of the multi-resource joint scheduling and frequency control method.

[0044] Compared with the prior art, the present application has the following beneficial effects: the present application constructs a unified scheduling model by acquiring device data in real time, and uses a discretized and linearized first-order frequency dynamic response equation for constraint, so that the unified scheduling model can accurately reflect the system operation state. An optimal scheduling model is constructed with the lowest economic operation cost as the target and various frequency constraints are incorporated, and the optimal scheduling model is iteratively solved by combining mixed integer linear programming and Benders decomposition technology, so that the economic cost and frequency safety are effectively balanced. Control instructions are generated according to the scheduling scheme, which can quickly suppress frequency sudden change when the grid-connected line is disconnected, integrate resources to maintain power supply and frequency stability in island mode, and perform load management and other measures to avoid large fluctuations in voltage and frequency when energy storage or power generation is insufficient during islanding, so as to improve the reliability, stability and economy of the power system under different working conditions, and ensure the quality of power supply. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0046] Figure 1 The overall flowchart of the multi-resource joint scheduling and frequency control method of an embodiment of the present application is shown.

[0047] Figure 2 The maximum frequency deviation diagram of each scheduling period of the multi-resource joint scheduling and frequency control method of an embodiment of the present application without transient control is shown.

[0048] Figure 3 The maximum frequency change rate diagram of each scheduling period of the multi-resource joint scheduling and frequency control method of an embodiment of the present application without transient control is shown.

[0049] Figure 4 The maximum frequency deviation diagram of each scheduling period of the multi-resource joint scheduling and frequency control method of an embodiment of the present application with transient control is shown.

[0050] Figure 5 The maximum frequency change rate of each scheduling cycle of the method for applying transient control of the multi-resource joint scheduling and frequency control method according to an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0051] In order to make the above objectives, features and advantages of the present application more apparent, clear and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should belong to the protection scope of the present application.

[0052] Embodiment 1, Reference Figure 1 According to an embodiment of the present application, a method for multi-resource joint scheduling and frequency control is provided, comprising:

[0053] S100: Real-time acquisition of device-related data of the power system, and construction of a unified scheduling model according to the device-related data;

[0054] S102: Acquisition of a scheduling cycle based on the unified scheduling model, and real-time update of the operation data of each device of the power system in each scheduling cycle;

[0055] S104: Construction of an optimal scheduling model with the lowest economic operation cost as the target according to the updated device operation data and the frequency constraints of the power system, iterative solution of the optimal scheduling model by using the mixed integer linear programming method and the Benders decomposition technology to obtain a scheduling scheme;

[0056] S106: Generation of control instructions of each device of the power system according to the scheduling scheme, and control of the frequency of the power system according to the control instructions.

[0057] It should be noted that the application constructs a unified scheduling model by acquiring relevant data of each device of the power system in real time, constructs an optimal scheduling model based on the unified scheduling model, and adopts a mixed integer linear programming method and a Benders decomposition technique when solving the optimal scheduling model. The Benders decomposition technique skillfully decomposes the overall optimization problem into a main problem and a sub-problem, solves the main problem to obtain a candidate solution, and feeds back the frequency deviation or frequency change rate detected based on the candidate solution to the main problem, so that the main problem corrects the scheduling scheme in the next iteration. Through such cyclic iteration, a high-quality scheduling scheme can be efficiently solved. According to the scheduling scheme, device control instructions are generated, and in complex situations such as disconnection of the grid connection line and entry into the island mode, the system can quickly and effectively respond to maintain stable operation of the system, ensure the frequency stability of the power system, and improve the reliability, stability and economy of the power system while achieving the goal of minimizing the economic operation cost.

[0058] Embodiment 2, refer to Figure 1 For an embodiment of the application, based on the above embodiment, a multi-resource joint scheduling and frequency control method is provided.

[0059] In the embodiment of the application, the relevant data of each device of the power system is acquired in real time in step S100, and a unified scheduling model is constructed according to the device-related data, including sub-steps A1-A2.

[0060] A1: Obtain the data of the power exchange equipment between the generator set, the energy storage system, the renewable energy power generation unit, the load and the main grid, model each device based on the constraint condition, and construct a unified scheduling model;

[0061] A2: Use the discretized and linearized primary frequency modulation dynamic response equation to constrain the unified scheduling model.

[0062] In the embodiment of the application, a central control unit is set to realize real-time monitoring and data processing of the unified scheduling model.

[0063] In the embodiment of the application, for a conventional generator set, the unified scheduling model considers the dynamic response characteristics, start-stop cost, minimum start / stop time, ramp rate constraint and upper and lower limits of the output of the unit.

[0064] For example, by describing the limitation of the power change of the generator set per unit time, it is ensured that there will be no sharp fluctuation during the start-up or shutdown process.

[0065] In the embodiment of the application, for the energy storage system, a dynamic model of its charging and discharging power and energy state is established, and a control strategy for providing virtual inertia support when participating in primary frequency modulation is introduced, so that the energy storage system can quickly respond to frequency changes in a short time.

[0066] In the embodiments of the present application, the model of the renewable energy power generation unit introduces prediction errors and uncertainties, and reflects the volatility of the actual power generation output by setting upper and lower deviation intervals, so that the scheduling decision can be more robust.

[0067] In the embodiments of the present application, the load module describes the power system demand through a load prediction model and load damping characteristics, and the power exchange module of the main grid is modeled according to the bidirectional power transmission and power flow limit conditions, ensuring that the system safety operation requirements can be met in both grid-connected and island modes.

[0068] In the embodiments of the present application, in order to fully consider the frequency stability in scheduling optimization, the present application also embeds the discretized and linearized primary frequency modulation dynamic response equation in the unified model, which is expressed as:

[0069]

[0070] where Δf t,n is the frequency deviation at time t in the nth time step, Δt PFC is the time step of the primary frequency modulation, H s,t is the system inertia at time t, P USG,t is the power exchange of the main grid, ΔP g,ltd,i,t,n and ΔP S,ltd,t,n represent the limited output power of the ith unit and the energy storage system during the frequency modulation process, respectively, and D is the load damping coefficient. The central control unit provides accurate and timely input data for subsequent scheduling optimization and dynamic control by real-time acquisition, cleaning and storage of data of the above modules, and constitutes the basis of the entire system economic scheduling and frequency safety control.

[0071] In an alternative embodiment, the central control unit can be equipped with an intelligent early warning module. Based on the real-time collected data, combined with historical operation data and preset risk threshold, the intelligent early warning module can monitor and analyze the potential frequency abnormalities, equipment overload and other risks of the power system in real time. Once an abnormal situation is detected, an early warning signal is immediately sent out, and a corresponding risk assessment report is provided to help the operation and maintenance personnel take measures in advance to prevent system failure.

[0072] In another alternative embodiment, in view of the prediction errors and uncertainties of the renewable energy power generation unit, in addition to setting upper and lower deviation intervals, a machine learning algorithm can also be introduced for dynamic prediction optimization. By using historical power generation data, weather data, equipment operation state data and other multi-source information, the machine learning model is trained to continuously improve the prediction accuracy of renewable energy power generation. At the same time, according to the real-time weather changes and equipment feedback information, the prediction results are dynamically adjusted, so that the scheduling decision can more accurately adapt to the fluctuations of renewable energy power generation.

[0073] In the embodiment of the present application, in step S102, the scheduling period is obtained based on the unified scheduling model, and in each scheduling period, the operation data of each device of the power system is updated in real time, including sub-steps B1-B3.

[0074] B1: Based on the preset processing instructions and the deviation data, the actual output data of the generator set and the actual output data of the renewable energy power generation unit are obtained;

[0075] B2: The energy state data of the energy storage unit is obtained through the calculation of the charging and discharging power of the energy storage system;

[0076] B3: The load data of the current and future time period is updated in real time through the load prediction model and the historical load data.

[0077] In the embodiment of the present application, the central control unit obtains various key data in real time through field data acquisition of the power system and communication network in each scheduling period, to ensure that the data used in the subsequent scheduling optimization process is accurate and timely.

[0078] For example, for a conventional generator set, the central control unit acquires the actual output data of each unit at time t, and the acquisition process takes into account the possible deviation between the preset output instruction and the actual response, and the actual output data is represented as:

[0079]

[0080] Wherein, represents the preset output instruction of the i-th unit at time t, g,i,t is the deviation caused by the response characteristics of the device, measurement error or environmental factors; through continuous monitoring of these data, the power system can update the real-time operation state of each unit in time, and provide basic load information and power generation capacity for subsequent scheduling.

[0081] For example, for the energy storage system, the central control unit not only acquires the current charging and discharging power, but also monitors the energy state of the energy storage unit in real time. The state updating process of the energy storage system is represented as:

[0082]

[0083] Wherein, E t is the energy state at time t, P ch,t and P dch,t represent the charging and discharging power at time t, η ch and η dch are the charging and discharging efficiencies, and Δt is the time step of data acquisition. In this way, the central control unit can accurately master the available energy of the energy storage device and its rapid response capability, and provide necessary information basis for system frequency support.

[0084] For example, in the case of renewable energy power generation units, the central control unit not only collects its actual power generation data in real time, but also obtains power generation forecasts based on meteorological data and historical operation records. The relationship between actual output and forecast output can be expressed as:

[0085]

[0086] in, It represents the predicted output at time t based on weather and historical data, RE,t The prediction error reflects fluctuations in actual power generation due to meteorological changes and equipment characteristics. This information makes the dispatch model more robust in the face of uncertainty and can effectively compensate for and adjust for the uncertainty of renewable energy.

[0087] For example, for system load, the central control unit updates the load demand for the current and future periods in real time using the load forecasting model and historical load data. The forecasting formula is expressed as:

[0088] L t =αL t-1 +β+ò L,t

[0089] Among them, L t is the load demand at time t, α and β are the parameters of the load forecasting model, L,t The load data not only covers the overall load level, but also includes the load change rate and load damping characteristics. This information is crucial for building a system frequency dynamic response model.

[0090] For example, the central control unit collects power exchange data with the main grid in real time, including the main grid power purchase and sales information. The exchange data is represented as:

[0091]

[0092] in, represents the power imported from the main grid at time t, and This represents the power exported to the main grid, subject to system power flow and preset power exchange limits. By monitoring the main grid power exchange data in real time, the system ensures that bidirectional power transfer meets economic requirements while preventing overloads that could compromise system safety.

[0093] In an optional embodiment, the scheduling period based on the unified scheduling model can be dynamically adjusted according to the real-time operating conditions of the power system. For example, when the output of the renewable energy power generation unit in the power system fluctuates greatly or the load change rate is high, the scheduling period is appropriately shortened to update the equipment operation data more frequently, so that the scheduling decision can adapt to the system changes more timely. When the system operation is relatively stable and the operation state of each device changes little, the scheduling period can be extended to reduce the frequency of data collection and processing, and reduce the operating cost and computing burden of the system. At the same time, the adjustment of the scheduling period can also be combined with the trading period of the electricity market to ensure that the scheduling decision of the system can better meet the needs of economic operation within the market trading period.

[0094] In an optional embodiment, the step S102 of updating the operation data of each device of the power system can also adopt a distributed data collection and processing architecture. In this architecture, data collection nodes are distributed at key positions of the power system, and each node is responsible for collecting the operation data of the devices nearby and performing preliminary processing and analysis; the data processed preliminarily is transmitted to the central control unit through a high-speed communication network. The advantage of the distributed data collection and processing architecture is that it can reduce the delay and bandwidth pressure of data transmission, and improve the real-time performance and accuracy of data collection.

[0095] In another optional embodiment, the step S102 of updating the operation data of each device of the power system can also adopt an edge computing technology to perform some simple calculations and decisions directly on the data collection nodes, such as judging whether preventive maintenance of the device is needed according to the real-time operation state of the device, or optimizing and adjusting the operation of the device in a local range.

[0096] It should be noted that all the above data are subjected to data cleaning, preprocessing and storage after collection, and are uniformly stored in the database of the central control unit. The database constitutes a dynamically updated data set, which not only provides necessary basic data for the optimization scheduling in the current scheduling period, but also provides support for historical data analysis, trend prediction and parameter correction of the frequency dynamic response model. Through continuous collection and feedback of real-time data, the central control unit can ensure that the optimization decision in each scheduling period is based on the latest and accurate operation state, thereby improving the economy and safety of the entire double-high power system.

[0097] In the embodiment of the application, the step S104 of constructing an optimization scheduling model and solving to obtain a scheduling scheme includes sub-steps C1-C5.

[0098] C1: The power system frequency constraint includes power system power balance constraint, generator set output constraint, energy storage system charge and discharge constraint, and power system frequency change rate constraint.

[0099] C2: Economic operation cost includes generator set power generation cost, start-stop cost, energy storage operation cost, and purchase and sale cost of power exchange with the main grid.

[0100] C3: The overall optimization problem of the optimal scheduling model is decomposed into a master problem and a sub-problem by using Benders decomposition technology.

[0101] C4: The master problem is responsible for solving the unit start-stop decision, energy storage state and basic power allocation to obtain a candidate solution; the sub-problem checks the primary frequency modulation dynamic response and frequency safety constraint according to the candidate solution output by the master problem.

[0102] C5: If the sub-problem detects that the frequency deviation or frequency change rate of a time period exceeds the preset safety threshold, a corresponding feasibility or optimality cut plane is output to the master problem, so as to correct the scheduling scheme in the next iteration.

[0103] The mathematical expression of the minimum economic operation cost is:

[0104]

[0105] Wherein, C g,i,t is the power generation cost coefficient of the i th unit at time t, USU i,t and USD i,t are binary decision variables of unit start and stop, C USG,t represents the cost coefficient of purchasing and selling electricity from the main grid, and Δt is the scheduling period length.

[0106] In the embodiment of the application, the frequency constraint of the power system includes:

[0107] ① System power balance constraint:

[0108]

[0109] Wherein, is the actual power generation of renewable energy at time t, L t is the system load.

[0110] ② Unit output constraint and unit ramp rate constraint:

[0111]

[0112] P g,i,t -P g,i,t-1 ≤RU i ,P g,i,t-1 -P g,i,t ≤RD i

[0113] Wherein, U i,tis the binary decision variable for whether the unit is online or not; RU i and RD i represent the unit's upward and downward ramp rate limits, respectively.

[0114] ③The energy storage system needs to meet the charging and discharging power limit conditions and the upper and lower limit constraints of the energy state:

[0115]

[0116] E min ≤E t ≤E max

[0117] ④To prevent the system frequency from changing too quickly, the frequency change rate is also constrained in the optimization scheduling model:

[0118]

[0119] Δf min ≤Δf t,n ≤Δf max

[0120] In an alternative embodiment, the genetic algorithm can be used to solve the optimization scheduling model. The unit start-stop decision, energy storage state and power allocation variables are encoded to form a "chromosome" representing a set of possible scheduling schemes. Through selection, crossover and mutation operations, the optimal solution is constantly searched in the solution space. In each generation population, the fitness of each individual is evaluated according to the fitness function, and the individuals with high fitness are selected for crossover and mutation operations to generate new individuals. After several generations of evolution, the global optimal solution is gradually approached.

[0121] In another alternative embodiment, the particle swarm optimization algorithm can also be used to solve the optimization scheduling model. Each scheduling scheme is regarded as a "particle" in the solution space, and each particle has its own position and velocity. The particles constantly fly in the solution space, adjusting their speed and position by tracking their historical optimal position and the global optimal position of the group. In each iteration, the particles update their positions according to the current speed, and calculate the fitness value of the new position. Through continuous iteration, the particles gradually gather towards the global optimal position, and finally find the optimal scheduling scheme.

[0122] It should be noted that the application adopts a mixed integer linear programming method, and utilizes a Benders decomposition technique to decompose the overall optimization problem into a main problem and a sub-problem, through multiple iterations, when all frequency safety indicators meet the requirements, finally outputting an optimal scheduling scheme meeting the dual requirements of economy and safety. In this way, the central control unit can utilize the latest collected data to coordinate the joint scheduling of multiple resources such as units, energy storage and renewable energy, and ensure that the frequency of the system remains within the preset safe range under various disturbance conditions, thereby greatly improving the economy and stability of the overall power system.

[0123] In the embodiment of the application, in step S106, the device control instructions of the power system are generated according to the scheduling scheme, and the frequency of the power system is controlled according to the control instructions, including sub-steps D1-D4.

[0124] D1: When the power system monitors that the grid-connected line with the main grid is disconnected, the generator set and the energy storage system that can be quickly put into frequency modulation are identified according to the scheduling scheme, and control instructions are issued to provide or absorb the necessary power for the power system in the shortest time according to the pre-allocated capacity or virtual inertia response strategy, so as to suppress the instantaneous sudden change of frequency.

[0125] D2: When the power system enters the island mode, the generator set, the energy storage system and the renewable energy output unit in the power system are integrated into a new balance mode based on the scheduling scheme to maintain power supply to the load in the island range and keep the frequency stable.

[0126] D3: During the period when the power system enters the island mode, if the energy storage capacity of the energy storage system is insufficient or the generator set cannot meet the load demand, load management or emergency scheduling measures are performed according to the priority strategy to avoid larger fluctuations in system frequency or voltage.

[0127] D4: If the main grid is restored after the main grid failure, the power system has the grid-connected condition, the synchronization of the power system and the main grid in voltage, frequency and phase is evaluated, and after the technical conditions for grid connection are met, the grid-connected operation is gradually restored according to the grid-connected switching program.

[0128] In the embodiment of the application, when the power system enters the island mode, if the island state is expected to last for a long time or the load and the renewable energy output fluctuate significantly, the central control unit can start rolling scheduling or temporary re-optimization to adjust the output reference value of the unit and the charging and discharging strategy of the energy storage system. The rolling scheduling or re-optimization can combine the latest load prediction and renewable energy output prediction, so as to consider the economy and safety of operation in the island mode.

[0129] It should be noted that the application can make full use of the fast frequency regulation capability of the unit and the energy storage system to effectively support the frequency when an unexpected island or other serious fault occurs, inhibit the excessive expansion of the frequency deviation and the change rate in the early stage, and prevent cascading reactions or large-scale power outages. At the same time, during the island mode operation, the rolling scheduling or re-optimization can dynamically respond to the fluctuation demand of the load and the renewable energy, and quickly complete seamless reconnection with the main grid after the fault is cleared, further improving the overall reliability and frequency security of the double-high power system in the context of high proportion of renewable energy and high proportion of power electronic equipment.

[0130] Embodiment 3, refer to Figures 2 to 5 Based on the previous embodiment, the present embodiment provides an application case of a multi-resource joint scheduling and frequency control method to illustrate the implementability and beneficial effects of the scheme.

[0131] A small power grid test system is constructed to verify the effectiveness of the method of the application, which includes six conventional generating units with a total installed capacity of 25MW, a set of energy storage system with a rated power of 2MW and a capacity of 4MWh, and a photovoltaic power generation unit with a rated power of 8.3MW. The load and power generation prediction data of the test system in each scheduling period are given in hourly resolution for the optimization scheduling model of the application to call and verify.

[0132] In order to take into account the primary frequency modulation requirements of the unit and the energy storage system, the droop constant of the conventional generating unit and the energy storage unit is set to 5% and 2.5% respectively, and the time constant of the controller is uniformly set to 2s; wherein the control link of the energy storage system for providing virtual inertia support includes two time constants, which are 0.2s and 1s respectively, and the inertia gain coefficient K IR is set to -6 to ensure that power response can be quickly provided when a disturbance occurs.

[0133] The maximum allowed power exchange capacity between the test system and the main grid is 4MW, and the system nominal frequency is 50Hz. Based on the relevant literature of power system frequency safety, the frequency deviation threshold considered in the present embodiment is ±0.8Hz, and the frequency change rate threshold is ±1.25Hz / s, so as to ensure that the system operation still meets the safety requirements in the case of high proportion of renewable energy access.

[0134] Further, the primary frequency modulation maximum ramp rate of the unit in the present embodiment is set to 0.5MW / s, and the load damping coefficient is set to 2. In addition, all scheduling and control algorithms are implemented in the GAMS environment, and the Gurobi solver is used to search for the optimal solution, so as to minimize the overall operation cost of the system while meeting the above frequency safety threshold and verifying the feasibility and effectiveness of the method of the application.

[0135] In the set-contrast scheme, the traditional microgrid dispatching method is used first, and the transient frequency stability constraint and primary frequency regulation reserve requirement are not taken into account, and only the primary frequency reserve is set according to the traditional capacity proportion, i.e. 13.3% of the load demand. It is assumed that the energy storage system participates in the primary frequency regulation to provide the primary frequency response. The maximum frequency deviation and the frequency change rate obtained by this method are shown in Figs. 2 and 3, respectively. Figure 2 and Fig. 3, respectively. Figure 3 At this time, the total operation cost of the microgrid is about 8586.1 dollars.

[0136] However, the system has frequency extreme points, i.e. the minimum value, and the frequency change rate threshold is broken in several time periods.

[0137] Specifically, within 12 hours before dispatching, the microgrid mainly purchases power from the main grid due to the low market electricity price, and the purchased power is limited to the maximum power exchange capacity of 4 MW. In this case of high load purchase, if islanding or large disturbance occurs, the system frequency will drop significantly and the frequency change rate will be high. Subsequently, when the market electricity price rises from the 13th to the 19th hour, the microgrid sells power to the outside to obtain more benefits and reduce its operation cost. Since there is no mandatory frequency stability constraint, only the frequency change rate threshold is temporarily broken at the 16th and 17th hours;

[0138] The main reason is that the units have sufficient primary frequency regulation reserve, which avoids serious over-limit of the frequency and the frequency change rate. After entering the 20th to 24th hour, the microgrid again chooses to purchase power from the main grid, and the reduction of the market electricity price prompts the system to import power from the main grid, so that the frequency minimum value and the frequency change rate over-limit phenomenon occur in the subsequent 4 hours. However, since some units are put into operation at this time, the overall inertia is increased, so that the frequency change rate over-limit degree in this period is lower than that in the extreme condition of the first 12 hours.

[0139] To ensure that the system frequency is stable and the operation cost is optimal when unexpected islanding is removed or other disturbances occur, the multi-resource joint dispatching and control method based on Benders decomposition under transient frequency constraint proposed in the present application is applied to the test system, and the energy storage system is included in the primary frequency regulation process, combined with droop control and inertia response to assist the system frequency. After optimization, the maximum frequency deviation and the frequency change rate of the system in each period are maintained within the safety threshold, as shown in Figs. 4 and 5, respectively, which meets the frequency stability requirement. Figure 4 and Fig. 5, respectively. Figure 5

[0140] ​Embodiment 4, the above is a schematic solution of the multi-resource joint scheduling and frequency control method. It should be noted that the technical solution of the multi-resource joint scheduling and frequency control system belongs to the same concept as the technical solution of the multi-resource joint scheduling and frequency control method described above. The technical solution of the multi-resource joint scheduling and frequency control system in this embodiment is not described in detail, and the details can be referred to the description of the technical solution of the multi-resource joint scheduling and frequency control method.

[0141] The embodiment also provides a multi-resource joint scheduling and frequency control system, comprising:

[0142] A data acquisition and update module is configured to acquire relevant data of each device of the power system in real time, construct a unified scheduling model according to the device-related data, and acquire a scheduling period based on the unified scheduling model, and update the operation data of each device of the power system in real time in each scheduling period.

[0143] A scheduling model construction module is configured to construct an optimal scheduling model with the lowest economic operation cost as the target according to the updated device operation data and the frequency constraint of the power system.

[0144] A scheduling scheme generation module is configured to iteratively solve the optimal scheduling model to acquire a scheduling scheme by using a mixed integer linear programming method and a Benders decomposition technique.

[0145] A control adjustment module is configured to generate control instructions of each device of the power system according to the scheduling scheme, and control the frequency of the power system according to the control instructions.

[0146] The embodiment also provides an electronic device suitable for multi-resource joint scheduling and frequency control, comprising a memory and a processor. The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the multi-resource joint scheduling and frequency control method proposed in the above embodiment.

[0147] The embodiment also provides a storage medium having a computer program stored thereon. The program is executed by a processor to implement the multi-resource joint scheduling and frequency control method proposed in the above embodiment.

[0148] The storage medium proposed in the embodiment and the multi-resource joint scheduling and frequency control method proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0149] Those skilled in the art can clearly understand the present application by the above description of the embodiments, and the present application can be realized by software and necessary general hardware, and of course, can also be realized by hardware. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disc, and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0150] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A multi-resource joint scheduling and frequency control method, characterized in that: include: Obtain relevant data of each device in the power system in real time and build a unified dispatching model based on the relevant data of the equipment; Obtaining a dispatch cycle based on the unified dispatch model, and updating the operating data of each device in the power system in real time within each dispatch cycle; Based on the updated equipment operation data and power system frequency constraints, an optimization dispatch model with the goal of minimizing economic operation costs is constructed. The mixed integer linear programming method and Benders decomposition technique are used to iteratively solve the optimization dispatch model to obtain a dispatch solution. According to the scheduling plan, control instructions for each device in the power system are generated, and the power system frequency is controlled according to the control instructions.

2. The multi-resource joint scheduling and frequency control method according to claim 1, wherein: Real-time data on various devices in the power system is obtained, and a unified dispatch model is built based on the data, including: Obtain data on generator sets, energy storage systems, renewable energy generation units, loads, and power exchange equipment between the main grid, model each device based on constraints, and build a unified scheduling model; The unified scheduling model is constrained by using a discretized and linearized primary frequency modulation dynamic response equation.

3. The multi-resource joint scheduling and frequency control method according to claim 2, wherein: Real-time update of operating data of various equipment in the power system, including: Based on preset processing instructions and deviation data, obtain actual output data of the generator set and actual output data of the renewable energy power generation unit; Obtain energy status data of the energy storage unit through calculation of the energy storage system charge and discharge power; The load data for the current and future periods are updated in real time through the load forecast model and historical load data.

4. The multi-resource joint scheduling and frequency control method according to claim 3, wherein: Based on updated equipment operating data and power system frequency constraints, an optimal dispatch model is constructed with the goal of minimizing economic operating costs, including: Power system frequency constraints include power system power balance constraints, generator output constraints, energy storage system charge and discharge constraints, and power system frequency change rate constraints; The economic operating costs include the power generation costs of the generator sets, the start-up and shutdown costs, the energy storage operating costs, and the purchase and sale costs of power exchange with the main grid.

5. The multi-resource joint scheduling and frequency control method according to claim 4, wherein: The mixed integer linear programming method and Benders decomposition technique are used to iteratively solve the optimization scheduling model to obtain a scheduling solution, including: Decomposing the overall optimization problem of the optimization scheduling model into a main problem and sub-problems by using Benders decomposition technology; The main problem is responsible for solving the unit start-up and shutdown decisions, energy storage status and basic power allocation to obtain candidate solutions; The sub-problem verifies the dynamic response of the power system primary frequency regulation and frequency security constraints based on the candidate solutions output by the main problem; If the sub-problem detects that the frequency deviation or frequency change rate in a certain period exceeds the preset safety threshold, the corresponding feasibility or optimality cutting plane is generated and output to the main problem, so that the scheduling plan can be corrected in the next round of iteration.

6. The multi-resource joint scheduling and frequency control method according to claim 2, wherein: According to the dispatching plan, control instructions for each device in the power system are generated, and the power system frequency is controlled according to the control instructions, including: When the power system detects a disconnection in the grid-connected line to the main grid, it identifies generators and energy storage systems that can be quickly put into frequency regulation according to the dispatch plan, issues control instructions, and provides or absorbs the necessary power to the power system in the shortest possible time according to the pre-assigned capacity or virtual inertia response strategy, thereby suppressing instantaneous frequency fluctuations. When the power system enters island mode, based on the scheduling scheme, the generator sets, energy storage systems and renewable energy output units in the power system are integrated into a new balance mode to maintain power supply to the loads within the island range and keep the frequency stable.

7. The multi-resource joint scheduling and frequency control method according to claim 6, wherein: Also includes: During the period when the power system enters island mode, if the energy storage system's storage capacity is insufficient or the generator sets cannot meet the load demand, load management or emergency dispatch measures are implemented according to the priority strategy to avoid larger fluctuations in system frequency or voltage; If the main grid fault is eliminated and the power system is ready for grid connection, evaluate the synchronization of voltage, frequency and phase between the power system and the main grid, and after the technical conditions for grid connection and closing are met, gradually restore grid connection operation according to the grid connection switching procedure.

8. A multi-resource joint scheduling and frequency control system, applying the method according to any one of claims 1 to 7, characterized in that: include: The data acquisition and update module is used to obtain relevant data of various equipment in the power system in real time and build a unified scheduling model based on the relevant data of the equipment; Obtaining a dispatch cycle based on the unified dispatch model, and updating the operating data of each device in the power system in real time within each dispatch cycle; The dispatch model construction module is used to build an optimized dispatch model with the goal of minimizing economic operation costs based on updated equipment operation data and power system frequency constraints; A scheduling solution generation module is used to iteratively solve the optimization scheduling model to obtain a scheduling solution by using a mixed integer linear programming method and a Benders decomposition technique; The control adjustment module is used to generate control instructions for each device in the power system according to the scheduling plan, and control the power system frequency according to the control instructions.

9. An electronic device comprising: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the multi-resource joint scheduling and frequency control method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the multi-resource joint scheduling and frequency control method according to any one of claims 1 to 7.

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