Power supply planning method, system, equipment and medium

Abstract

The invention relates to the technical field of power supply planning of an electric power system, and discloses a power supply planning method, system and device and a medium, and the method comprises the steps: obtaining key parameters of the electric power system, and generating a plurality of source-load random scenes; based on these scenarios, an uncertain set of component faults is constructed, thereby establishing power disturbance and system frequency response equations, including frequency balance constraints. And constructing a power supply planning model including an objective function and constraint conditions, and solving the model to optimize power supply configuration. According to the method, the reliability, the stability and the adaptability of the power system are improved by considering the uncertainty and the frequency stability. Stable operation of the system under various conditions is ensured, the optimal power supply configuration is rapidly determined through the planning model, the planning efficiency is improved, resource waste is reduced, and sustainable development is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power system power planning, and in particular to a power planning method, system, equipment and medium. Background Art

[0002] In modern power grids, disturbances caused by various random factors have a significant impact on the frequency stability of the power system. With the increasing penetration of renewable energy sources such as wind and solar power, the scale of the power system is also expanding, and its components are becoming more complex and diverse. Therefore, during the planning stage, it is imperative to consider the randomness of renewable energy output and the potential failure of power system components, as these factors can have a significant impact on the frequency stability of the power system.

[0003] While existing literature has established frequency security constraints and corresponding planning models that account for the randomness of renewable energy output, these studies have not fully considered the potential impact of component failures on frequency security. As the proportion of renewable energy in the energy mix increases, system inertia continues to decrease, and the volatility between sources and loads intensifies, frequency stability issues caused by random disturbances are becoming increasingly serious and cannot be ignored.

[0004] Therefore, it is necessary to ensure that the system has the ability to cope with frequency fluctuations caused by various random disturbances during the planning stage of the power system to ensure the stability and reliability of power supply. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a power planning method, system, device and medium, which can solve the problem that the existing power planning method does not fully consider the potential impact of component failure on frequency safety.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a power supply planning method, comprising:

[0009] obtaining a first parameter of a target power system;

[0010] The first parameter includes historical output data and historical load data of renewable energy units;

[0011] generating a plurality of source-load random scenarios according to the first parameter;

[0012] Based on the plurality of source-load random scenarios, establishing an uncertainty set for describing component failures;

[0013] The uncertain set is used to determine the number of fault scenarios under several source-load random scenarios;

[0014] Establishing a power disturbance based on the uncertainty set, and establishing a system frequency response equation and a frequency balance constraint for the system frequency response equation according to the power disturbance;

[0015] Establishing a power supply planning model, wherein the power supply planning model includes a first objective function and a first constraint condition, wherein the first constraint condition includes a frequency balance constraint;

[0016] The power planning model is solved, and power optimization is performed based on the solution results.

[0017] As a preferred solution of the power supply planning method of the present invention, the step of establishing an uncertainty set for describing component failures includes:

[0018] Preset fault status variables;

[0019] obtaining a fault state variable of a target component in a target power system;

[0020] The target elements include conventional units, renewable energy units and target power system lines;

[0021] Obtain the total number of components in the target power system and the maximum acceptable number of faulty components;

[0022] All the components include conventional units, renewable energy units, and power system lines that have already been established, as well as conventional units, renewable energy units, and power system lines that are yet to be established;

[0023] An inequality based on the fault state variable of the target component, the total number of components and the maximum acceptable number of faulty components is established, and the inequality is used as an uncertain set.

[0024] As a preferred solution of the power supply planning method of the present invention, wherein: the establishing of the power disturbance based on the uncertainty set includes:

[0025] Obtain the source and load fluctuations of the target power system and the fault state variables of the target components under the uncertainty set;

[0026] A power disturbance based on source-load fluctuations of the target power system and a target component fault state variable is established.

[0027] This optimized solution more comprehensively considers uncertainties in the power system, such as source-load fluctuations and component failures, thereby improving the accuracy and reliability of power planning. By accurately simulating the power disturbances caused by these uncertainties, it can provide more effective guarantees for the stable operation of the power system.

[0028] As a preferred embodiment of the power planning method of the present invention, the first objective function of the power planning model is to minimize the investment cost of various power sources and the annual operating cost;

[0029] The annual operating cost in the first objective function includes the following cost functions:

[0030] Conventional unit operating costs, renewable energy unit energy curtailment costs and load shedding costs.

[0031] As a preferred solution of the power supply planning method described in the present invention, the system frequency response equation is determined by the inertia level under different scenarios, the differentiated frequency response time of different units and the power disturbance of the uncertain set.

[0032] As a preferred solution of the power supply planning method described in the present invention, the first constraint condition also includes investment and construction logic constraints and unit combination constraints.

[0033] As a preferred solution of the power planning method of the present invention, the solving of the power planning model includes:

[0034] Solving the power planning model in two stages;

[0035] The first stage is used to determine the minimum investment cost of various power sources;

[0036] The second stage is used to determine the minimum annual operating cost.

[0037] In a second aspect, the present invention provides a power supply planning system, comprising:

[0038] A data acquisition module, configured to acquire a first parameter of a target power system;

[0039] The first parameter includes historical output data and historical load data of renewable energy units;

[0040] A scenario generation module, configured to generate a plurality of source-load random scenarios according to the first parameters;

[0041] An uncertain set determination module, configured to establish an uncertain set for describing component failures based on the plurality of source-load random scenarios;

[0042] The uncertain set is used to determine the number of fault scenarios under several source-load random scenarios;

[0043] A constraint establishing module, configured to establish a power disturbance based on the uncertainty set, and establish a system frequency response equation and a frequency balance constraint for the system frequency response equation according to the power disturbance;

[0044] A model building module, configured to build a power planning model, wherein the power planning model includes a first objective function and a first constraint condition, wherein the first constraint condition includes a frequency balance constraint;

[0045] The model solving module is used to solve the power planning model and optimize the power supply according to the solution results.

[0046] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described above when executing the computer program.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described above when the computer program is executed by a processor.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention proposes a power supply planning method to obtain a first parameter of a target power system; generate several source-load random scenarios based on the first parameter; establish an uncertain set for describing component failures based on the several source-load random scenarios; establish a power disturbance based on the uncertain set, and establish a system frequency response equation and a frequency balance constraint for the system frequency response equation based on the power disturbance; establish a power supply planning model, the power supply planning model includes a first objective function and a first constraint condition, the first constraint condition includes a frequency balance constraint; solve the power supply planning model, and perform power supply optimization based on the solution result.

[0049] By considering the uncertain set of component failures, the present invention can more accurately simulate various fault conditions that may occur in actual power systems, thereby improving the overall reliability of the system. By establishing system frequency response equations and frequency balance constraints, the present invention can ensure the stability of system frequency under various disturbance conditions and avoid system crashes caused by too low frequency. By generating multiple source-load random scenarios, the present invention can comprehensively consider the system performance under different load and power generation conditions, making power supply planning more comprehensive and adaptable. By establishing a power supply planning model and solving it, the present invention can quickly find the optimal power supply configuration solution, improve planning efficiency, and reduce unnecessary waste of resources. The power supply planning method of the present invention can adapt to the ever-changing power demand and power generation capacity, ensuring the long-term stable operation of the power system. By optimizing the power supply configuration, the present invention helps to reduce the impact on the environment and promote the sustainable development of the power system.

[0050] The power planning method proposed in this paper effectively improves the reliability, stability, and adaptability of power systems by considering their uncertainty and frequency stability. By comprehensively generating random source-load scenarios and establishing system frequency response equations, the method ensures stable system operation under various circumstances. Furthermore, by establishing and solving a power planning model, the method can quickly identify the optimal power configuration, improve planning efficiency, reduce resource waste, and promote the sustainable development of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0052] Figure 1 A flow chart of a power planning method provided by one embodiment of the present invention.

[0053] Figure 2 A schematic diagram of a typical wind-load scenario of a power supply planning method provided by an embodiment of the present invention.

[0054] Figure 3 A model solving framework diagram of a power supply planning method provided by one embodiment of the present invention.

[0055] Figure 4 A schematic diagram of a system topology of an improved IEEE RTS-79 example of a power planning method provided by an embodiment of the present invention.

[0056] Figure 5 A diagram comparing system frequency response performance when a power planning method provided by one embodiment of the present invention is embedded with and without frequency safety constraints.

[0057] Figure 6 An internal structure diagram of an electronic device providing a power planning method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0059] Example 1, reference Figures 1-6 , which is a first embodiment of the present invention, provides a power supply planning method, comprising:

[0060] There are some problems in the existing related technologies, such as low power planning efficiency, inaccurate planning results, and inability to adapt to changing power demand.

[0061] This application provides a method that can effectively solve the above-mentioned problems. Next, we will explain in detail how to implement the power planning method in combination with multiple embodiments.

[0062] Figure 1 A flow chart of a power supply planning method is shown, comprising:

[0063] S101, obtaining a first parameter of a target power system;

[0064] In the embodiment of the present application, the first parameter includes historical output data and historical load data of renewable energy units;

[0065] In an optional embodiment, the historical output data of renewable energy units includes historical data on wind power generation, solar power generation, and hydropower generation. Specifically, this historical data may include historical records of wind speed, wind direction, irradiance, temperature, rainfall, water level, flow, and other parameters. This data is collected in real time by sensors, pre-processed, and stored in a database for analysis and calculation by the power planning system.

[0066] In an optional embodiment, historical load data includes industrial, commercial, and residential load data, covering load variations over different time periods (e.g., daily, weekly, monthly, and annual). This data is also collected in real time using advanced metering devices. After pre-processing steps such as cleaning and integration, it is stored in a dedicated load database, providing strong support for load forecasting and supply-demand balance analysis in power planning systems.

[0067] In the embodiments of this application, the historical output data and load data of renewable energy units are used to establish subsequent source-load randomization scenarios. Therefore, the historical output data and load data obtained by this application must at least include the key information required for constructing source-load randomization scenarios. For example, detailed records of meteorological parameters such as wind speed, wind direction, and irradiance, as well as the specific values of various types of loads at different time periods, ensure the accuracy and comprehensiveness of scenario construction.

[0068] It should be noted that obtaining the target power system's primary parameters can provide a solid data foundation for subsequent power planning, ensuring accurate analysis and scientific decision-making, thereby effectively improving planning efficiency and the credibility of the results. Furthermore, in-depth analysis of the primary parameters can reveal the inherent laws of power system operation, providing strong support for optimizing the power structure and improving energy efficiency, thereby achieving sustainable development of the power system.

[0069] S102, generating a plurality of source-load random scenarios according to the first parameters;

[0070] like Figure 2 The figure shows a schematic diagram of typical wind load scenarios for a power planning method, in which wind turbines are considered as renewable energy units, and typical wind load scenarios under four typical scenarios are established.

[0071] In an optional embodiment, the four scenarios can be high wind speed and high load, low wind speed and high load, high wind speed and low load, and low wind speed and low load. Each scenario accurately reflects the actual operating conditions of the power system by simulating different meteorological and load conditions, providing comprehensive data support for subsequent power supply planning and optimization.

[0072] It should be noted that the establishment of several source-load random scenarios is for the purpose of subsequently establishing fault scenarios under different scenarios or obtaining fault state variables under different scenarios, so as to more accurately evaluate the stability and reliability of the power system under different operating conditions.

[0073] S103, establishing an uncertainty set for describing component failures based on the multiple source-load random scenarios;

[0074] In an embodiment of the present application, the uncertainty set is used to determine the number of fault scenarios under several source-load random scenarios.

[0075] In an optional embodiment, the uncertainty set can be established by different methods. For example, historical data and statistical analysis methods can be used to construct an uncertainty set containing multiple possibilities based on the load changes of the power supply and the uncertainty of the power supply demand.

[0076] In an optional embodiment, expert experience or simulation can be used to quantitatively analyze the uncertainties in power planning, thereby forming a more accurate and comprehensive uncertainty set and providing more reliable data support for subsequent power planning decisions.

[0077] It should be noted that the uncertainty set established using the above method can cover a variety of possible power planning scenarios, including but not limited to the uncertainty of load growth, the volatility of renewable energy generation, and changes in the grid structure. However, it cannot be closely aligned with the subsequently designed power planning model, resulting in potential deviations in practical applications. Therefore, the present invention further proposes an optimization scheme that closely integrates the uncertainty set with the power planning model to improve the accuracy and practicality of power planning.

[0078] In the embodiment of the present application, establishing an uncertainty set for describing component failures includes:

[0079] Preset fault status variables;

[0080] obtaining a fault state variable of a target component in a target power system;

[0081] The target elements include conventional units, renewable energy units and target power system lines;

[0082] Obtain the total number of components in the target power system and the maximum acceptable number of faulty components;

[0083] All the components include conventional units, renewable energy units, and power system lines that have already been established, as well as conventional units, renewable energy units, and power system lines that are yet to be established;

[0084] An inequality based on the fault state variable of the target component, the total number of components and the maximum acceptable number of faulty components is established, and the inequality is used as an uncertain set.

[0085] In an optional embodiment, the fault state variable can be designed according to the specific situation. For example, when there are three different fault states, the numbers 1, 2, and 3 are used to represent the fault state variable, where 1 represents a short circuit fault, 2 represents a component fault, and 3 represents an open circuit fault. When the fault has only two states, non-fault or fault, the numbers 0 and 1 are used to represent it, where 0 represents a fault and 1 represents normal operation.

[0086] In an embodiment of the present application, the fault state variable is set to two states, represented by the numbers 0 and 1, where 0 represents a fault and 1 represents normal operation, and the fault state variables of the conventional unit, wind turbine unit and line at time t are obtained (0 represents a fault and 1 represents normal operation).

[0087] For example, the following uncertainty set is established:

[0088]

[0089] Among them, z g,t , z w,t , zl,t Respectively represent the fault state variables of conventional units, wind turbines and lines at time t (0 for fault, 1 for normal operation); N E is the number of all components in the system, including conventional and wind turbines and lines that have been built and are to be built; K is the maximum number of faulty components, G is the number of conventional units, W is the number of wind turbines, L is the number of lines in the target power system, and T is the time

[0090] It should be noted that establishing an uncertainty set to describe component failures based on the aforementioned random source-load scenarios can improve the robustness and adaptability of the power planning system. By accounting for the uncertainty of component failures, the system can plan and optimize under a variety of failure scenarios, ensuring that the power system can maintain stable operation and meet load demands under various possible failure scenarios. Furthermore, establishing this uncertainty set facilitates a more comprehensive assessment of the risk and reliability of power planning solutions, providing decision makers with more accurate and comprehensive information support.

[0091] S104, establishing a power disturbance based on the uncertainty set, and establishing a system frequency response equation and a frequency balance constraint for the system frequency response equation according to the power disturbance;

[0092] In an optional embodiment, the power disturbance is established according to the uncertainty set in order to determine the magnitude of the frequency disturbance that may affect the subsequent power planning module under different fault scenarios in different scenarios, thereby better performing power planning and design.

[0093] In an embodiment of the present application, establishing the power disturbance based on the uncertainty set includes:

[0094] Obtain the source and load fluctuations of the target power system and the fault state variables of the target components under the uncertainty set;

[0095] A power disturbance based on source-load fluctuations of the target power system and a target component fault state variable is established.

[0096] Specifically, there are S different scenarios in this application, and the unbalanced power ΔP at time t in each scenario s is s,t The power disturbance caused by source load fluctuation and component failure is described as shown in Equation (2).

[0097]

[0098] Among them, p w,s,t , p b,s,t are the predicted output of wind turbine w and the predicted load value of node b at time t under scenario s; B represents the number of nodes in the target power system, σ W , σ Lare wind power and load variation coefficients, respectively;

[0099] In an optional embodiment, it is assumed that the wind power output and load forecast errors obey normal distribution respectively: w,s,t ~N(0,σ W p w,s,t ), e b,s,t ~N(0,σ L p b,s,t The unbalanced power defined by equation (2) assumes that the power fluctuation direction of all units and node loads is consistent, and considers the power disturbance in the worst case.

[0100] In an optional embodiment, the system frequency response equation may be established in different ways. For example, a classical model or a data-driven model may be used to construct the system frequency response equation.

[0101] It should be noted that the classical model is usually based on the physical characteristics and dynamic behavior of the power system, and describes the frequency response of the system through a series of differential equations.

[0102] It should be noted that data-driven models utilize extensive historical data and machine learning algorithms to predict the system's frequency response, offering greater flexibility and adaptability. By training neural networks, data-driven models can more accurately capture frequency trends in complex scenarios, improving system stability.

[0103] It should also be noted that the above two methods of establishing the system frequency response equation have their own advantages and disadvantages, and in practical applications, the choice should be based on the specific scenario and needs. In order to improve the accuracy and reliability of the system frequency response, this application designs a system frequency response equation based on the power disturbance of the above uncertainty set.

[0104] In an embodiment of the present application, the system frequency response equation is determined by the inertia levels in different scenarios, the differentiated frequency response times of different units, and the power disturbance of the uncertain set.

[0105] Specifically, by simplifying the swing equation, the system frequency response equation can be obtained as shown in equation (3).

[0106]

[0107] in, is the inertia level of the system at time t under scenario s;

[0108] f0 is the nominal frequency; H g , H w are the inertia time constants of conventional unit g and wind turbine unit w respectively; P g,maxis the maximum output of conventional unit g. To obtain the analytical expression of frequency deviation Δf(t), it is assumed that the frequency response power P of the generator set is R (t) linear growth, and considering the different frequency response times of different units:

[0109]

[0110] Among them, R i,s,t For all units with the fastest frequency response (the frequency response time is T i ) The sum of the maximum frequency response powers at time t in scenario s; N p is the number of different frequency response times.

[0111] It should be noted that the swing equation is an important tool for describing the dynamic behavior of power systems. It takes into account factors such as inertia, damping, and the dynamic characteristics of generators in the power system. By solving the swing equation, the frequency response of the system under different fault scenarios can be obtained, thereby evaluating the effectiveness of the power planning scheme. In the embodiment of the present application, by simplifying and processing the swing equation, an analytical expression for the system frequency response equation is obtained, which provides an important theoretical basis for subsequent power planning decisions.

[0112] It should be noted that, from formula (4), it can be seen that the frequency response power is piecewise linear, and the lowest frequency point occurs at time t nadir Its frequency response time interval (T n-1 ,T n ] is related. If t∈(T n-1 ,T n ],n=1,...,N p (T0=0), the following constraints must obviously be satisfied:

[0113]

[0114] In order to obtain the lowest frequency f nadir The analytical expression of , substitute equation (4) into equation (3) and integrate with respect to t to obtain:

[0115]

[0116] Considering the maximum frequency change rate of the system after the unbalanced power appears ROCOF max Cannot exceed a certain limit And the lowest frequency f nadir Must be above a given threshold Construct the maximum frequency change rate and the minimum frequency drop constraint:

[0117]

[0118] Based on equations (3), (6) to (7), the frequency minimum point constraint (i.e., power balance constraint) (9) can be transformed into a second-order cone constraint:

[0119]

[0120] Since formula (10) is only in (T n-1 ,T n ] period, according to the economic rule of configuring the unit with the smallest possible standby power, let n = N p , relax constraint (10) so that it holds for all frequency response periods.

[0121] It should be noted that establishing a power perturbation based on the uncertainty set, and then establishing a system frequency response equation based on the power perturbation and frequency balance constraints for the system frequency response equation can fully account for the impact of various uncertainties on the system frequency response, thereby improving the robustness and adaptability of power planning solutions. By accurately modeling the system frequency response and transforming the constraints, the performance of different power planning solutions in complex scenarios can be more effectively evaluated, thereby selecting the optimal power configuration strategy.

[0122] S105: Establish a power planning model, where the power planning model includes a first objective function and a first constraint condition, where the first constraint condition includes a frequency balance constraint;

[0123] In an optional embodiment, the power planning model can use a decomposition and coordination method, which is a method that decomposes the power planning problem into several sub-problems and then uses specific techniques (such as Bender's decomposition and coordination technique) to iteratively solve them. This method can reduce the dimensionality of each sub-problem, making the model easier to handle, thereby significantly reducing the computational workload of the entire solution process. For example, in JASP (Jiaotong Automati System Planning Package), the power investment decision model is the core, which optimizes the power investment and construction plan, and coordinates and iterates with the transmission cost correction model, the typical day operation mode model, and the production optimization model.

[0124] In an optional embodiment, the power planning model may also employ heuristic methods, which are practical algorithms whose convergence or optimality cannot be rigorously proven mathematically. Due to the complexity of power planning problems, finding an effective convergent algorithm is sometimes difficult, making heuristic methods a viable option. Heuristic methods can solve power planning problems by simplifying rigorous algorithms or designing practical algorithms that achieve near-optimal solutions.

[0125] However, the above two methods cannot fully meet the solution purpose and power supply planning goals of this application, so a power supply planning model is designed.

[0126] In the embodiment of the present application, the first objective function of the power planning model is to minimize the investment costs of various power sources and the annual operating costs;

[0127] The annual operating cost in the first objective function includes the following cost functions:

[0128] Conventional unit operating costs, renewable energy unit energy curtailment costs and load shedding costs.

[0129] In an embodiment of the present application, the first constraint condition also includes investment and construction logic constraints and unit combination constraints.

[0130] In an optional embodiment, the investment and construction logic constraints may include but are not limited to: resource availability constraints, i.e. ensuring that the required resources are available during the investment and construction period; time window constraints, specifying that the investment and construction activities must be completed within a specific time period; spatial layout constraints, taking into account the physical layout and space requirements of the facilities; cost budget constraints, ensuring that the investment and construction costs do not exceed the budget limit, etc.

[0131] In an optional embodiment, the unit combination constraints may include but are not limited to: unit output upper and lower limit constraints, that is, the output power of each unit must be within the preset upper and lower limits when it is in operation; unit start and stop time constraints, which stipulate the minimum time interval required for the unit to start from start to stop or from stop to start; ramp rate constraints, which limit the increase or decrease of the unit output power per unit time to ensure that the unit can smoothly adjust the output power; minimum start and stop time constraints, which ensure that the unit maintains a certain period of time in continuous operation or shutdown state to meet the stable operation and maintenance requirements of the unit; and network flow constraints, which take into account the power balance between nodes in the power system and the line transmission capacity limitations to ensure the safe and stable operation of the power grid.

[0132] Specifically, the objective function of the stochastic adaptive robust power planning model considering frequency security is to minimize the sum of the investment cost of each type of power source and the annual operating cost. The operating cost includes the operating cost of conventional units, the energy curtailment cost of wind turbines, and the load shedding cost, namely:

[0133]

[0134] Among them, G C , G, W C , W are the set of all conventional units to be built and all wind turbine units to be built respectively; C g , are the investment cost of conventional unit g, the investment cost of wind turbine w, the operating cost of conventional unit g, the wind curtailment penalty of wind turbine w, and the load shedding penalty of node b; x g , x wThe decision variables for the investment and construction of conventional units g and wind turbine units w; p g,s,t , are the output of conventional unit g, the abandoned power of wind turbine w and the load shedding power of node b at time t under scenario s; N D is the number of days in the planning level year; π s is the probability of scenario s occurring.

[0135] It should be noted that establishing a power planning model ensures that the power planning solution achieves the optimal balance between economy and reliability while ensuring frequency safety. By comprehensively considering the investment costs and annual operating costs of various power sources, and strictly adhering to frequency balance constraints, the selected power configuration strategy can be guaranteed to operate stably in a complex and changing power system environment, while reducing operating costs and improving economic benefits. Furthermore, the power planning model can be flexibly adjusted according to specific scenarios and needs to adapt to different power system planning and operation requirements.

[0136] S106: Solve the power planning model and optimize the power supply according to the solution result.

[0137] In an embodiment of the present application, solving the power planning model includes:

[0138] Solving the power planning model in two stages;

[0139] The first stage is used to determine the minimum investment cost of various power sources;

[0140] The second stage is used to determine the minimum annual operating cost.

[0141] like Figure 3 The figure shows a schematic diagram of the power planning model used in this application. The model is divided into two stages. The first stage, "min," minimizes investment costs and determines the investment and construction decisions for conventional units and wind turbines to meet the growing load demand. The second stage, "max," seeks the worst-case Nk fault scenario that maximizes operating costs. Subsequently, "min," under the worst-case Nk fault scenario, minimizes the system's operating costs under all typical scenarios in the planned year, solving the problem using the C&CG algorithm.

[0142] In summary, the present invention proposes a power supply planning method, which obtains a first parameter of a target power system; generates several source-load random scenarios based on the first parameter; establishes an uncertain set for describing component failures based on the several source-load random scenarios; establishes a power disturbance based on the uncertain set, and establishes a system frequency response equation and a frequency balance constraint for the system frequency response equation based on the power disturbance; establishes a power supply planning model, the power supply planning model includes a first objective function and a first constraint condition, the first constraint condition includes a frequency balance constraint; solves the power supply planning model, and performs power supply optimization based on the solution result.

[0143] Example 2, refer to Table 1, Table 2, Figure 4 、 Figure 5 , which is a first embodiment of the present invention, wherein:

[0144] Table 1: Power planning results under different fault conditions

[0145]

[0146]

[0147] Among them, B1(76,300) represents the construction of one 76MW conventional unit and one 300MW wind turbine unit at node 1.

[0148] Table 2: Power planning results for different unbalanced power representations

[0149]

[0150] Case 1 refers to expressing the unbalanced power according to formula (2), and case 2 refers to setting the unbalanced power to a constant value of 400MW.

[0151] exist Figure 4 This application is implemented on the basis of Figure 5 This figure compares the system frequency response performance with and without frequency safety constraints. The blue curve represents the lowest frequency drop with the frequency safety constraint. The red curve represents the lowest frequency drop without the frequency safety constraint.

[0152] At most time points, the minimum frequency drop value of the system with embedded frequency safety constraints (blue curve) is significantly higher than that of the system without embedded frequency safety constraints (red curve), indicating that embedding frequency safety constraints helps improve the frequency stability of the system.

[0153] Especially in the 6th hour, when a significant disturbance occurred, the minimum frequency drop of the system with embedded frequency safety constraints was much higher than that without embedded frequency safety constraints, showing stronger anti-disturbance capability.

[0154] The light blue bar graph shows the system inertia level when the frequency safety constraint is embedded. The pink bar graph shows the system inertia level when the frequency safety constraint is not embedded.

[0155] The system inertia level with frequency safety constraints embedded (light blue bar graph) is generally higher than that without frequency safety constraints embedded (pink bar graph), and the difference is particularly obvious at the disturbance moment of the 6th hour.

[0156] Higher system inertia levels help slow the rate of frequency change, thereby improving the frequency stability and recovery capability of the system.

[0157] Embedding frequency safety constraints can effectively reduce the magnitude of frequency drops, especially in the face of large disturbances. Embedding frequency safety constraints increases the system's inertia level and enhances the system's ability to resist frequency fluctuations.

[0158] Overall, embedding frequency security constraints can significantly improve the frequency response performance of the power system and ensure more stable and reliable grid operation.

[0159] Embodiment 3: This embodiment further provides a power supply planning system, including:

[0160] A data acquisition module, configured to acquire a first parameter of a target power system;

[0161] The first parameter includes historical output data and historical load data of renewable energy units;

[0162] A scenario generation module, configured to generate a plurality of source-load random scenarios according to the first parameters;

[0163] An uncertain set determination module, configured to establish an uncertain set for describing component failures based on the plurality of source-load random scenarios;

[0164] The uncertain set is used to determine the number of fault scenarios under several source-load random scenarios;

[0165] A constraint establishing module, configured to establish a power disturbance based on the uncertainty set, and establish a system frequency response equation and a frequency balance constraint for the system frequency response equation according to the power disturbance;

[0166] A model building module, configured to build a power planning model, wherein the power planning model includes a first objective function and a first constraint condition, wherein the first constraint condition includes a frequency balance constraint;

[0167] The model solving module is used to solve the power planning model and optimize the power supply according to the solution results.

[0168] The above-mentioned unit modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0169] This embodiment also provides an electronic device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The electronic device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a power planning method is implemented. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the electronic device, or an external keyboard, touchpad or mouse.

[0170] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the following steps are implemented:

[0171] obtaining a first parameter of a target power system;

[0172] The first parameter includes historical output data and historical load data of renewable energy units;

[0173] generating a plurality of source-load random scenarios according to the first parameter;

[0174] Based on the plurality of source-load random scenarios, establishing an uncertainty set for describing component failures;

[0175] The uncertain set is used to determine the number of fault scenarios under several source-load random scenarios;

[0176] Establishing a power disturbance based on the uncertainty set, and establishing a system frequency response equation and a frequency balance constraint for the system frequency response equation according to the power disturbance;

[0177] Establishing a power supply planning model, wherein the power supply planning model includes a first objective function and a first constraint condition, wherein the first constraint condition includes a frequency balance constraint;

[0178] The power planning model is solved, and power optimization is performed based on the solution results.

[0179] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0180] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages.

[0181] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0182] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0183] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0184] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0185] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A power supply planning method, characterized in that: include: obtaining a first parameter of a target power system; The first parameter includes historical output data and historical load data of renewable energy units; generating a plurality of source-load random scenarios according to the first parameter; Based on the plurality of source-load random scenarios, establishing an uncertainty set for describing component failures; The uncertain set is used to determine the number of fault scenarios under several source-load random scenarios; Establishing a power disturbance based on the uncertainty set, and establishing a system frequency response equation and a frequency balance constraint for the system frequency response equation according to the power disturbance; Establishing a power supply planning model, wherein the power supply planning model includes a first objective function and a first constraint condition, wherein the first constraint condition includes a frequency balance constraint; The power planning model is solved, and power optimization is performed based on the solution results.

2. A power supply planning method according to claim 1, characterized in that: The establishing of an uncertainty set for describing component failures includes: Preset fault status variables; obtaining a fault state variable of a target component in a target power system; The target elements include conventional units, renewable energy units and target power system lines; Obtain the total number of components in the target power system and the maximum acceptable number of faulty components; All the components include conventional units, renewable energy units, and power system lines that have already been established, as well as conventional units, renewable energy units, and power system lines that are yet to be established; An inequality based on the fault state variable of the target component, the total number of components and the maximum acceptable number of faulty components is established, and the inequality is used as an uncertain set.

3. A power supply planning method according to claim 2, characterized in that: The establishing of the power disturbance based on the uncertainty set includes: Obtain the source and load fluctuations of the target power system and the fault state variables of the target components under the uncertainty set; A power disturbance based on source-load fluctuations of the target power system and a target component fault state variable is established.

4. A power supply planning method according to claim 3, characterized in that: The first objective function of the power planning model is to minimize the investment cost of various power sources and the annual operating cost; The annual operating cost in the first objective function includes the following cost functions: Conventional unit operating costs, renewable energy unit energy curtailment costs and load shedding costs.

5. A power supply planning method according to claim 4, characterized in that: The system frequency response equation is determined by the inertia levels under different scenarios, the differentiated frequency response times of different units, and the power disturbance of the uncertain set.

6. A power supply planning method according to claim 5, characterized in that: The first constraint condition also includes investment and construction logic constraints and unit combination constraints.

7. A power supply planning method according to claim 6, characterized in that: Solving the power planning model includes: Solving the power planning model in two stages; The first stage is used to determine the minimum investment cost of various power sources; The second stage is used to determine the minimum annual operating cost.

8. A power supply planning system, applying the method according to any one of claims 1 to 7, characterized in that: include: A data acquisition module, configured to acquire a first parameter of a target power system; The first parameter includes historical output data and historical load data of renewable energy units; A scenario generation module, configured to generate a plurality of source-load random scenarios according to the first parameters; An uncertain set determination module, configured to establish an uncertain set for describing component failures based on the plurality of source-load random scenarios; The uncertain set is used to determine the number of fault scenarios under several source-load random scenarios; A constraint establishing module, configured to establish a power disturbance based on the uncertainty set, and establish a system frequency response equation and a frequency balance constraint for the system frequency response equation according to the power disturbance; A model building module, configured to build a power planning model, wherein the power planning model includes a first objective function and a first constraint condition, wherein the first constraint condition includes a frequency balance constraint; The model solving module is used to solve the power planning model and optimize the power supply according to the solution results.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a power planning method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a power planning method according to any one of claims 1 to 7 are implemented.

Images