Probabilistic power supply insurance capability assessment method considering new energy uncertainty

Abstract

The invention discloses a probabilistic power supply guarantee capability assessment method considering new energy uncertainty, which comprises the steps of obtaining historical statistical data of new energy in different time periods, constructing a new energy output probability density function based on the historical statistical data, and determining new energy probability density distribution; time sequence production simulation calculation is carried out based on the source network load storage calculation boundary in the target research area, and a conventional unit output curve, a tie line power exchange curve, a pumped storage energy output curve and a demand side response working curve are obtained; based on the conventional unit output curve, the tie line power exchange curve, the pumped storage energy output curve, the demand side response working curve and the load demand curve, constructing a power demand curve which needs to be satisfied by new energy output; and based on the power demand curve and the new energy probability density distribution which need to be satisfied by the new energy output, obtaining the power gap probability and the power shortage expectation of the target time period, and carrying out supply guarantee capability evaluation based on the power gap probability and the power shortage expectation.

Description

Technical Field

[0001] This invention relates to the field of power system planning and operation technology, and more specifically, to a probabilistic power supply capacity assessment method and system that takes into account the uncertainty of new energy sources. Background Technology

[0002] Under the background of the construction of new power systems, the traditional hydropower and thermal power supply system is gradually transforming into a new power supply system with an increasing proportion of new energy sources. The randomness, volatility, and anti-peak-shaving characteristics of new energy sources have significantly changed the supply and demand balance characteristics of the power system. The deterministic power balance calculation methods and power supply capacity assessment methods applicable to traditional power systems are no longer suitable for the calculation and analysis needs under the new situation. There is an urgent need to conduct research on probabilistic power supply capacity assessment methods that take into account the uncertainty of new energy sources.

[0003] The primary condition for conducting power supply capacity assessment is power system production simulation calculation. Current production simulation methods include time-series production simulation and stochastic production simulation based on equivalent load curves. Time-series production simulation includes simulation methods based on optimization algorithms and heuristic algorithms. The former generally aims for optimal system operating economy or maximum renewable energy utilization, constructing optimization models that consider the operating characteristics of various power sources, energy storage, and demand-side response. These models are often mixed-integer programming models, solved using precise algorithms such as branch and bound. For large-scale power systems, direct solution is time-consuming, labor-intensive, and inefficient. The latter generally extracts calculation rules from actual power system production, simulating the system operation process step-by-step, enabling rapid and efficient production simulation calculations under specific objectives. Stochastic production simulation methods are generally based on equivalent load curves, arranging generator unit production and operation in a predetermined order to simulate system dispatching, thereby determining system production indicators and system reliability indicators such as power generation, production costs, and marginal prices for each power plant under optimal operating conditions.

[0004] Power supply capacity assessment based on time-series production simulation relies on deterministic computational boundary conditions. In particular, the simulation of renewable energy operation characteristics still uses deterministic output curves, failing to reflect the uncertainty of renewable energy output at different times. Therefore, production simulation calculations based on this approach can only obtain power balance results under a single scenario, unable to assess the frequency of that renewable energy output scenario. Obtaining indicators such as Loss of Load Probability (LOLP) and Expected Energy Not Served (EENS) requires repeated calculations based on large sampling samples, resulting in enormous computational loads and significant time and effort. Power supply capacity assessment based on stochastic production simulation essentially ignores time-series characteristics, making it difficult to accurately characterize the changes in the operational status of various resources such as power generation, grid, load, and storage over time. Furthermore, stochastic production simulation methods have poor compatibility with new resources such as renewable energy, new energy storage, and demand-side response, increasingly failing to meet the needs of supply and demand balance analysis in new power systems.

[0005] In summary, for the assessment of the power supply capacity of new power systems, there is an urgent need to propose a probabilistic power supply capacity assessment method that takes into account the uncertainty of new energy output. Summary of the Invention

[0006] This invention proposes a probabilistic power supply capacity assessment method and system that considers the uncertainty of new energy sources, in order to solve the problem of how to conduct probabilistic power supply capacity assessment for systems with strong uncertainty.

[0007] To address the aforementioned problems, according to one aspect of the present invention, a probabilistic power supply capacity assessment method considering the uncertainty of new energy sources is provided, the method comprising:

[0008] Obtain historical statistical data of new energy sources at different times, and construct a probability density function of new energy output based on the historical statistical data to determine the probability density distribution of new energy sources;

[0009] Based on the source-grid-load-storage calculation boundary within the target study area, time-series production simulation calculations are performed to obtain the output curves of conventional units, the power exchange curves of tie lines, the output curves of pumped storage, and the demand-side response curves. Among these, the output curves of new energy sources participating in the simulation calculations are predicted curves.

[0010] Based on the aforementioned conventional unit output curves, tie-line power exchange curves, pumped storage output curves, demand-side response curves, and load demand curves, a power demand curve that needs to be met by renewable energy output is constructed.

[0011] Based on the electricity demand curve that needs to be met by renewable energy output and the probability density distribution of renewable energy, the probability of power shortage and the expected power shortage during the target period are obtained, so as to assess the supply guarantee capacity based on the probability of power shortage and the expected power shortage.

[0012] Preferably, the method utilizes kernel density estimation to construct a probability density function for new energy output based on the historical statistical data.

[0013] Preferably, based on the conventional unit output curve, tie-line power exchange curve, pumped storage output curve, demand-side response curve, and load demand curve, a power demand curve that needs to be met by renewable energy output is constructed, including:

[0014] P t uns =P t dem -P t nor -P t line -P t sto -P t res ,

[0015] Among them, P t uns P represents the electricity demand that needs to be met by renewable energy sources during time period t. t dem The total load for time period t has already taken into account reserve requirements; P t nor For the output of the conventional units in time period t; P t line P represents the power exchange capacity of the tie line during time period t. t sto P is the output of pumped storage energy in time period t; t res Let t be the demand-side response power during time period t.

[0016] Preferably, the method for obtaining the probability of a power shortage and the expected power insufficiency for a target period based on the power demand curve that needs to be met by renewable energy output and the probability density distribution of renewable energy includes:

[0017] For time period t, when the output of new energy source P t re The electricity demand P that needs to be met by renewable energy output is less than the demand P. t uns When the output of renewable energy is less than P, it indicates that there is a power shortage in the system; otherwise, it indicates that there is no power shortage and the power supply demand is met. When there is still a power shortage in the system after the output of renewable energy, the output of renewable energy is less than P based on the probability density distribution of renewable energy. t uns The probability α at that time t ;

[0018] The expected battery deficit is calculated using the following methods:

[0019]

[0020] Where EENS represents the expected power shortage; J represents the probability density function f of renewable energy output in time period t. t (·) After discretization The number of segments corresponding to time; α tj f is the probability density function t (·) The probability of new energy output in the j-th segment after discretization.

[0021] Preferably, the power output of the new energy source is obtained based on the probability density distribution of the new energy source, which is less than P. t uns The probability α at that time t ,include:

[0022]

[0023] Where P(X) is the probability that the output of new energy is less than a preset threshold X, and its value is α, X = P t uns When, its value is α t ;P t re For the output of new energy sources in time period t; f t (·) represents the probability density function of new energy power output.

[0024] According to another aspect of the present invention, a probabilistic power supply guarantee capability assessment system considering the uncertainty of new energy sources is provided, the system comprising:

[0025] The probability density function construction unit is used to obtain historical statistical data of new energy at different time periods, construct the probability density function of new energy output based on the historical statistical data, and determine the probability density distribution of new energy.

[0026] The simulation calculation unit is used to perform time-series production simulation calculations based on the source-grid-load-storage calculation boundary within the target study area, and to obtain the output curves of conventional units, the power exchange curves of tie lines, the output curves of pumped storage, and the demand-side response curves; among them, the output curves of new energy sources participating in the simulation calculations are predicted curves.

[0027] The electricity demand curve construction unit is used to construct an electricity demand curve that needs to be met by renewable energy output based on the conventional unit output curve, tie line power exchange curve, pumped storage output curve, demand-side response working curve, and load demand curve.

[0028] The assessment unit is used to obtain the probability of power shortage and the expected power shortage during the target period based on the power demand curve that needs to be met by renewable energy output and the probability density distribution of renewable energy, so as to assess the supply guarantee capacity based on the probability of power shortage and the expected power shortage.

[0029] Preferably, the probability density function construction unit uses a kernel density estimation method to construct a new energy output probability density function based on the historical statistical data.

[0030] Preferably, the electricity demand curve construction unit constructs an electricity demand curve that needs to be met by renewable energy output, based on the conventional unit output curve, tie-line power exchange curve, pumped storage output curve, demand-side response curve, and load demand curve, including:

[0031] P t uns =P t dem -P t nor -P t line -P t sto -P t res ,

[0032] Among them, P t uns P represents the electricity demand that needs to be met by renewable energy sources during time period t. t dem The total load for time period t has already taken into account reserve requirements; P t nor For the output of the conventional units in time period t; P t line P represents the power exchange capacity of the tie line during time period t. t sto P is the output of pumped storage energy in time period t; t res Let t be the demand-side response power during time period t.

[0033] Preferably, the evaluation unit, based on the electricity demand curve that needs to be met by renewable energy output and the probability density distribution of renewable energy, obtains the probability of a power shortage and the expected power insufficiency for the target period, including:

[0034] For time period t, when the output of new energy source P t re The electricity demand P that needs to be met by renewable energy output is less than the demand P. t unsWhen the output of renewable energy is less than P, it indicates that there is a power shortage in the system; otherwise, it indicates that there is no power shortage and the power supply demand is met. When there is still a power shortage in the system after the output of renewable energy, the output of renewable energy is less than P based on the probability density distribution of renewable energy. t uns The probability α at that time t ;

[0035] The expected battery deficit is calculated using the following methods:

[0036]

[0037] Where EENS represents the expected power shortage; J represents the probability density function f of renewable energy output in time period t. t (·) After discretization The number of segments corresponding to time; α tj f is the probability density function t (·) The probability of new energy output in the j-th segment after discretization.

[0038] Preferably, the evaluation unit obtains the new energy output less than P based on the probability density distribution of the new energy source. t uns The probability α at that time t ,include:

[0039]

[0040] Where P(X) is the probability that the output of new energy is less than a preset threshold X, and its value is α, X = P t uns When, its value is α t ;P t re For the output of new energy sources in time period t; f t (·) represents the probability density function of new energy power output.

[0041] Based on another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the steps in a probabilistic power supply capacity assessment method that takes into account the uncertainty of new energy sources.

[0042] According to another aspect of the present invention, the present invention provides an electronic device, comprising:

[0043] The aforementioned computer-readable storage medium; and

[0044] One or more processors for executing a program in the computer-readable storage medium.

[0045] This invention provides a probabilistic power supply capacity assessment method and system that considers the uncertainty of new energy sources. The method includes: acquiring historical statistical data of new energy sources at different time periods; constructing a probability density function for new energy output based on the historical statistical data to determine the probability density distribution of new energy sources; performing time-series production simulation calculations based on the source-grid-load-storage calculation boundary within the target study area to obtain the output curves of conventional generating units, the power exchange curves of tie lines, the output curves of pumped storage, and the demand-side response curves; wherein the output curves of new energy sources participating in the simulation calculations are predicted curves; constructing a power demand curve that needs to be met by new energy output based on the output curves of conventional generating units, the power exchange curves of tie lines, the output curves of pumped storage, the demand-side response curves, and the load demand curves; and obtaining the probability of a power shortage and the expected power insufficiency for the target time period based on the power demand curve and the probability density distribution of new energy sources, and then assessing the power supply capacity based on the probability of a power shortage and the expected power insufficiency. This invention assesses power supply capacity based on time-series production simulation methods. It can reflect the time-series coupled operation characteristics of various new resources such as power generation, grid, load and storage. It adopts time-segmented probabilistic characterization of the output characteristics of new energy sources, fully characterizes the random characteristics of new energy output, conducts power balance analysis from a probabilistic perspective, assesses power supply capacity, promotes the transition of power supply capacity analysis from deterministic one-sided analysis to probabilistic quantitative analysis, and improves the scientific analysis level of power supply capacity in highly uncertain power systems. Attached Figure Description

[0046] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0047] Figure 1 A flowchart of a probabilistic power supply capacity assessment method 100 considering the uncertainty of new energy sources according to an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the probability density function of new energy power output according to an embodiment of the present invention;

[0049] Figure 3 This is an example diagram showing the results of a time-series production simulation calculation according to an embodiment of the present invention;

[0050] Figure 4 This is a diagram showing the operational status of various resources during the summer peak load period from July 12 to August 4, according to an embodiment of the present invention.

[0051] Figure 5 This is a graph showing the electricity demand curve that needs to be met by renewable energy output from July 12 to August 4 according to an embodiment of the present invention.

[0052] Figure 6 This is a probability density map of new energy output at 12:00 on July 24th according to an embodiment of the present invention;

[0053] Figure 7 This is a schematic diagram of the structure of a probabilistic power supply capacity assessment system 700 that takes into account the uncertainty of new energy sources according to an embodiment of the present invention. Detailed Implementation

[0054] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0055] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0056] Figure 1 This is a flowchart of a probabilistic power supply capacity assessment method 100 that considers the uncertainty of new energy sources according to an embodiment of the present invention. Figure 1 As shown, the probabilistic power supply capacity assessment method considering the uncertainty of new energy sources provided by this invention assesses power supply capacity based on time-series production simulation. It reflects the time-series coupled operation characteristics of various types of new resources (source, grid, load, and storage), employs time-segmented probabilistic characterization of new energy output characteristics, fully depicting the stochastic characteristics of new energy output, and conducts power balance analysis from a probabilistic perspective to assess power supply capacity. This promotes the transition of power supply capacity analysis from deterministic unilateral analysis to probabilistic quantitative analysis, improving the scientific analysis level of power supply capacity in highly uncertain power systems. The probabilistic power supply capacity assessment method 100 considering the uncertainty of new energy sources provided by this invention begins at step 101. In step 101, historical statistical data of new energy sources at different time periods are obtained, and a probability density function of new energy output is constructed based on the historical statistical data to determine the probability density distribution of new energy sources.

[0057] Preferably, the method utilizes kernel density estimation to construct a probability density function for new energy output based on the historical statistical data.

[0058] In this invention, a probability density function for new energy output is constructed by combining historical statistical data of new energy sources for each time period. In order to focus on key time periods, data statistics can be carried out specifically for the midday peak load and the evening peak load.

[0059] Specifically, by combining historical statistical data of new energy sources for each period, a probability density distribution function of new energy output is constructed, and the new energy output dataset is shown in Equation (1):

[0060]

[0061] in, This is the dataset of renewable energy output during time period t. for The nth data point in the dataset has a total quantity of N.

[0062] Based on the renewable energy output dataset, the kernel density estimation method can be used to construct the renewable energy output probability density function f. t (·),like Figure 2 As shown in the figure. Therefore, the probability of the new energy output being less than X is shown in equation (2):

[0063]

[0064] Among them, P t re The output of new energy sources in time period t; α is the probability that the output of new energy sources is less than X.

[0065] This allows us to construct the probability density function of new energy sources for each time period. Generally, to study the power balance during key periods, we can focus on constructing the probability density function of new energy sources as needed for periods such as the midday peak and evening peak load. Furthermore, we can determine the probability level corresponding to the output of new energy sources.

[0066] In step 102, time-series production simulation calculations are performed based on the source-grid-load-storage calculation boundary within the target study area to obtain the output curves of conventional units, the power exchange curves of tie lines, the output curves of pumped storage, and the demand-side response curves; among them, the output curves of new energy sources participating in the simulation calculations are predicted curves.

[0067] In this invention, by combining the source-grid-load-storage calculation boundary within the study area, an 8760-hour time-series production simulation calculation is carried out to obtain the output curves of conventional units such as thermal power and hydropower, as well as the working curves of power exchange, energy storage, and demand-side response of tie lines. The output curves of new energy participating in the simulation calculation are predicted curves.

[0068] When conducting time-series production simulation calculations, the calculation boundary of the power generation, grid, load, and storage system includes the operating characteristic parameters of thermal power, hydropower, nuclear power, pumped storage, and other units, as well as energy storage; parameters such as the rated capacity and power curve of tie lines; the demand-side response ratio; and calculation parameters such as the reserve rate. The data attributes of each parameter are shown in Table 1.

[0069] Table 1. Data attributes of parameters required for time-series production simulation calculations.

[0070]

[0071]

[0072] An 8760-hour production simulation was conducted using a heuristic time-series production simulation method. New energy sources directly participated in the simulation based on their predicted output curves. This yielded 8760-hour operating curves for all resources except new energy sources under ideal conditions, including conventional unit output curves, tie-line power flow curves, pumped storage output curves, and demand-side response curves. An example is shown below. Figure 3 .

[0073] In step 103, based on the conventional unit output curve, tie line power exchange curve, pumped storage output curve, demand-side response curve, and load demand curve, a power demand curve that needs to be met by renewable energy output is constructed.

[0074] Preferably, based on the conventional unit output curve, tie-line power exchange curve, pumped storage output curve, demand-side response curve, and load demand curve, a power demand curve that needs to be met by renewable energy output is constructed, including:

[0075] P t uns =P t dem -P t nor -P t line -P t sto -P t res ,

[0076] Among them, P t uns P represents the electricity demand that needs to be met by renewable energy sources during time period t. t dem The total load for time period t has already taken into account reserve requirements; P t nor For the output of the conventional units in time period t; P t line P represents the power exchange capacity of the tie line during time period t. t sto P is the output of pumped storage energy in time period t; t res Let t be the demand-side response power during time period t.

[0077] In this invention, based on the output of conventional generating units, power exchange via interconnection lines, output of pumped storage energy storage, and demand-side response, and combined with the probability distribution of renewable energy output, the power supply and demand balance status under different renewable energy output probabilities is statistically analyzed on a time-by-time basis or for key time periods.

[0078] Among them, by combining the output of conventional generating units, the power exchange of tie lines, the output of pumped storage energy storage and the demand-side response working curves, as well as the comprehensive load curve, a power demand curve that needs to be met by the output of new energy sources can be constructed, as shown in equation (3):

[0079] P t uns =P t dem -P t nor -P t line -P t sto -P t res (3)

[0080] Among them, P t uns P represents the electricity demand that needs to be met by renewable energy sources during time period t. t dem The total load for time period t has already taken into account reserve requirements; P t nor For the output of the conventional units in time period t; P t line P represents the power exchange capacity of the tie line during time period t. t sto P is the output of pumped storage energy in time period t; t res Let t be the demand-side response power during time period t.

[0081] In step 104, based on the power demand curve that needs to be met by renewable energy output and the probability density distribution of renewable energy, the probability of power shortage and the expected power shortage for the target period are obtained, so as to assess the supply guarantee capacity based on the probability of power shortage and the expected power shortage.

[0082] Preferably, the method for obtaining the probability of a power shortage and the expected power insufficiency for a target period based on the power demand curve that needs to be met by renewable energy output and the probability density distribution of renewable energy includes:

[0083] For time period t, when the output of new energy source P t re The electricity demand P that needs to be met by renewable energy output is less than the demand P. t uns When the output of renewable energy is less than P, it indicates that there is a power shortage in the system; otherwise, it indicates that there is no power shortage and the power supply demand is met. When there is still a power shortage in the system after the output of renewable energy, the output of renewable energy is less than P based on the probability density distribution of renewable energy. t uns The probability α at that time t ;

[0084] The expected battery deficit is calculated using the following methods:

[0085]

[0086] Where EENS represents the expected power shortage; J represents the probability density function f of renewable energy output in time period t. t (·) After discretization The number of segments corresponding to time; α tj f is the probability density function t (·) The probability of new energy output in the j-th segment after discretization.

[0087] Preferably, the power output of the new energy source is obtained based on the probability density distribution of the new energy source, which is less than P. t uns The probability α at that time t ,include:

[0088]

[0089] Where P(X) is the probability that the output of new energy is less than a preset threshold X, and its value is α, X = P t uns When, its value is α t ;P t re For the output of new energy sources in time period t; f t (·) represents the probability density function of new energy power output.

[0090] In this invention, for time t, when the output P of the new energy source... t re Less than P t uns When the output of new energy sources is less than P, it indicates that there is a power shortage in the system after considering the output of new energy sources; otherwise, it indicates that there is no power shortage in the system and the power supply demand is met. When there is a power shortage in the system after the output of new energy sources, the power output of new energy sources can be calculated by combining the probability density distribution of new energy sources with equation (2). t uns Time (i.e., X = P) t uns The probability α of ) t Among these, the probability of a power shortage in the system can be calculated on a time-by-time basis or for key time periods.

[0091] Power supply capacity can be assessed using the probability of load shedding (LOLP) and the expected energy shortage (EENS), where LOLP is α. t .

[0092] The calculation method for EENS is shown in equation (4):

[0093]

[0094] Among them, P t uns and P t re These represent the electricity demand that needs to be met by renewable energy output in time period t, and the renewable energy output, respectively; f t (·) represents the probability density function of new energy power output in time period t. For ease of engineering calculations, f can generally be... t (·) By discretizing and segmenting, (4) can be transformed into (5).

[0095]

[0096] Where J is the probability density function f of the new energy output in time period t. t (·) After discretization The number of segments corresponding to time; α tj f is the probability density function t (·) The probability of new energy output in the j-th segment after discretization.

[0097] This invention proposes a probabilistic power supply capacity assessment method that considers the uncertainty of new energy sources. First, it constructs a probability distribution of new energy output based on historical statistical data. Second, it conducts power balance analysis using new energy forecast curves to obtain power curves for conventional units and energy storage. Third, it calculates the power supply capacity during peak load periods based on the probabilistic output levels of new energy sources. The method constructs a probability distribution of new energy output based on extensive statistical data, enabling a probabilistic representation of new energy output at each time point. It then conducts time-series production simulation calculations based on new energy forecast curves, obtaining 8760-hour operating curves for resources other than new energy. Combined with the load demand at each time point, it calculates the power demand that new energy output needs to meet. By combining the probability of new energy output, the probabilistic quantification of the system's power supply capacity can be achieved. This method maintains the time-series coupling characteristics of power system production simulation, employs time-segmented probabilistic representation of new energy output characteristics, reflects the stochastic nature of new energy output, and conducts power balance analysis from a probabilistic perspective to assess power supply capacity. This method organically combines mathematical statistics with current practical engineering methods, promoting the transition of power supply capacity analysis methods from deterministic one-sided analysis to probabilistic quantitative analysis, and has promising application prospects in future power system planning and operation.

[0098] The following specific examples illustrate the embodiments of the present invention.

[0099] In an embodiment of the invention, a case study verification was conducted based on a small-scale single-region power system. This power system has a peak load of 3 million kW, and its power sources include two thermal power units (each 1 million kW), one hydropower unit (250,000 kW), one nuclear power unit (300,000 kW), 500,000 kW of wind power, 600,000 kW of photovoltaic power, a total of 1 million kW of pumped storage capacity, and approximately 10,000 kW of demand-side response resources. The external power transmission capacity is approximately 500,000 kW. The hot reserve rate is set to 5%. Time-series production simulation calculations were conducted to obtain operating curves for conventional unit output, tie-line power exchange, pumped storage output, and demand-side response. Figure 4 This displays the operational status of various resources during the peak summer load period from July 12th to August 4th; furthermore, it can construct electricity demand curves that require renewable energy output to meet demand, such as... Figure 5 As shown.

[0100] by Figure 5 Taking the 326th moment (i.e., 12:00 on July 24th) as an example, the electricity demand that needs to be met by renewable energy sources at this time is 564,200 kW. Combined with... Figure 6 The probability density function shown indicates that, with a 99.11% probability, the output of new energy sources will be less than 564,200 kW, which will be insufficient to meet electricity demand in most cases. This means that the probability of load shedding during this period is as high as 99.11%. The cumulative time-by-time calculation shows that the expected electricity shortage during this period (July 12 to August 4) is approximately 30 million kWh.

[0101] Figure 7 This is a schematic diagram of the structure of a probabilistic power supply capacity assessment system 700 that considers the uncertainty of new energy sources according to an embodiment of the present invention. Figure 7 As shown, the probabilistic power supply capacity assessment system 700 considering the uncertainty of new energy sources provided by the embodiments of the present invention includes: a probability density function construction unit 701, a simulation calculation unit 702, a power demand curve construction unit 703, and an assessment unit 704.

[0102] Preferably, the probability density function construction unit 701 is used to obtain historical statistical data of new energy sources at different times, construct a probability density function of new energy output based on the historical statistical data, and determine the probability density distribution of new energy sources.

[0103] Preferably, the probability density function construction unit 701 uses the kernel density estimation method to construct the probability density function of new energy output based on the historical statistical data.

[0104] Preferably, the simulation calculation unit 702 is used to perform time-series production simulation calculations based on the source-grid-load-storage calculation boundary within the target study area, and to obtain the output curves of conventional units, the power exchange curves of tie lines, the output curves of pumped storage energy, and the demand-side response curves; wherein, the output curves of new energy sources participating in the simulation calculations are predicted curves.

[0105] Preferably, the power demand curve construction unit 703 is used to construct a power demand curve that needs to be met by renewable energy output based on the conventional unit output curve, tie line power exchange curve, pumped storage output curve, demand-side response working curve, and load demand curve.

[0106] Preferably, the electricity demand curve construction unit 703 constructs an electricity demand curve that needs to be met by renewable energy output based on the conventional unit output curve, tie-line power exchange curve, pumped storage output curve, demand-side response curve, and load demand curve, including:

[0107] P t uns =P t dem -P t nor -P t line -P t sto -P t res ,

[0108] Among them, P t uns P represents the electricity demand that needs to be met by renewable energy sources during time period t. t dem The total load for time period t has already taken into account reserve requirements; P t nor For the output of the conventional units in time period t; P t line P represents the power exchange capacity of the tie line during time period t. t sto P is the output of pumped storage energy in time period t; t res Let t be the demand-side response power during time period t.

[0109] Preferably, the evaluation unit 704 is used to obtain the probability of power shortage and the expected power shortage during the target period based on the power demand curve that needs to be met by the output of new energy sources and the probability density distribution of new energy sources, so as to evaluate the supply guarantee capacity based on the probability of power shortage and the expected power shortage.

[0110] Preferably, the evaluation unit 704, based on the electricity demand curve that needs to be met by renewable energy output and the probability density distribution of renewable energy, obtains the probability of a power shortage and the expected power insufficiency for a target period, including:

[0111] For time period t, when the output of new energy source P t re The electricity demand P that needs to be met by renewable energy output is less than the demand P. t uns When the output of renewable energy is less than P, it indicates that there is a power shortage in the system; otherwise, it indicates that there is no power shortage and the power supply demand is met. When there is still a power shortage in the system after the output of renewable energy, the output of renewable energy is less than P based on the probability density distribution of renewable energy. t uns The probability α at that time t ;

[0112] The expected battery deficit is calculated using the following methods:

[0113]

[0114] Where EENS represents the expected power shortage; J represents the probability density function f of renewable energy output in time period t. t (·) After discretization The number of segments corresponding to time; α tj f is the probability density function t (·) The probability of new energy output in the j-th segment after discretization.

[0115] Preferably, the evaluation unit 704 obtains the new energy output less than P based on the probability density distribution of the new energy source. t uns The probability α at that time t ,include:

[0116]

[0117] Where P(X) is the probability that the output of new energy is less than a preset threshold X, and its value is α, X = P t uns When, its value is α t ;P t re For the output of new energy sources in time period t; f t (·) represents the probability density function of new energy power output.

[0118] The probabilistic power supply capacity assessment system 700 considering the uncertainty of new energy sources in this embodiment of the present invention corresponds to the probabilistic power supply capacity assessment method 100 considering the uncertainty of new energy sources in another embodiment of the present invention, and will not be described again here.

[0119] Based on another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the steps in a probabilistic power supply capacity assessment method that considers the uncertainty of new energy sources.

[0120] According to another aspect of the present invention, the present invention provides an electronic device, comprising:

[0121] The aforementioned computer-readable storage medium; and

[0122] One or more processors for executing a program in the computer-readable storage medium.

[0123] The present invention has been described with reference to a few embodiments. However, it will be apparent to those skilled in the art that other embodiments besides those disclosed above fall equivalently within the scope of the present invention.

[0124] Generally, all terms used in this invention are interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed unless explicitly stated otherwise.

[0125] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0126] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0127] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0128] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for probabilistic power supply capability evaluation considering uncertainty of new energy, characterized in that, The method comprises: acquiring historical statistical data of new energy in different time periods, constructing a new energy output probability density function based on the historical statistical data, and determining a new energy probability density distribution; based on the source network load storage calculation boundary in the target research area, performing time sequence production simulation calculation to acquire a conventional unit output curve, a tie-line power exchange curve, a pumped storage energy output curve, and a demand side response working curve; wherein the output curve of the new energy participating in the simulation calculation adopts a prediction curve; based on the conventional unit output curve, the tie-line power exchange curve, the pumped storage energy output curve, the demand side response working curve, and the load demand curve, constructing a power demand curve to be met by the new energy output; based on the power demand curve to be met by the new energy output and the new energy probability density distribution, acquiring a power gap probability and an energy deficiency expectation of a target time period, and performing power supply capacity evaluation based on the power gap probability and the energy deficiency expectation.

2. The method of claim 1, wherein, The method utilizes a kernel density estimation method to construct the new energy output probability density function based on the historical statistical data.

3. The method of claim 1, wherein, based on the conventional unit output curve, the tie-line power exchange curve, the pumped storage energy output curve, the demand side response working curve, and the load demand curve, constructing a power demand curve to be met by the new energy output, comprising: P t uns = P t dem - P t nor - P t line - P t sto - P t res , P t uns P t dem P t nor P t line P t sto P t res P 4. The method of claim 1, wherein, based on the power demand curve to be met by the new energy output and the new energy probability density distribution, acquiring a power gap probability and an energy deficiency expectation of a target time period, comprising: For time period t, when the output of new energy source P t re The electricity demand P that needs to be met by renewable energy output is less than the demand P. t uns When the output of renewable energy is less than P, it indicates that there is a power shortage in the system; otherwise, it indicates that there is no power shortage and the power supply demand is met. When there is still a power shortage in the system after the output of renewable energy, the output of renewable energy is less than P based on the probability density distribution of renewable energy. t uns The probability α at that time t ; calculating the energy deficiency expectation by using the following method, comprising: wherein EENS is the expected energy not supplied; J is the probability density function f of the new energy output in the t period t (·) after discretization, the number of segments corresponding to the t period; a tj is the probability density function f t (·) the probability of new energy output in the j segment after discretization.

5. The method of claim 4, wherein, Based on the probability density distribution of new energy, the probability α that the output of new energy is less than P t uns t ,​ comprising: Wherein, P(X) is the probability of the new energy output being less than the preset threshold X, the value of which is a, and X=P t uns the value of which is a t ; P t re is the output of the new energy in the t period; f t is the probability density function of the new energy output.

6. A probabilistic power supply capability evaluation system considering uncertainty of new energy, characterized in that, The system comprises: a probability density function construction unit configured to acquire historical statistical data of new energy in different time periods, construct a new energy output probability density function based on the historical statistical data, and determine a new energy probability density distribution; a probability distribution determination unit configured to acquire historical statistical data of new energy in different time periods, construct a new energy output probability density function based on the historical statistical data, and determine a new energy probability density distribution; a simulation calculation unit configured to perform time sequence production simulation calculation based on a source network load storage calculation boundary in a target research area to acquire a conventional unit output curve, a tie-line power exchange curve, a pumped storage energy output curve, and a demand side response working curve; wherein the output curve of the new energy participating in the simulation calculation adopts a prediction curve; a power demand curve construction unit configured to construct a power demand curve to be met by the new energy output based on the conventional unit output curve, the tie-line power exchange curve, the pumped storage energy output curve, the demand side response working curve, and the load demand curve; an evaluation unit configured to acquire a power gap probability and an energy deficiency expectation of a target time period based on the power demand curve to be met by the new energy output and the new energy probability density distribution, and perform power supply capacity evaluation based on the power gap probability and the energy deficiency expectation.

7. The system of claim 6, wherein, The probability density function construction unit utilizes a kernel density estimation method to construct the new energy output probability density function based on the historical statistical data.

8. The system of claim 6, wherein, The power demand curve construction unit constructs a power demand curve to be met by new energy output based on the conventional unit output curve, the tie-line power exchange curve, the pumped storage energy output curve, the demand side response work curve, and the load demand curve, and includes: P t uns = P t dem - P t nor - P t line - P t sto - P t res , P t uns P is the power demand that needs to be met by new energy output in the tth period; t dem P is the comprehensive load in the tth period, which has considered the reserve demand; t nor P is the conventional unit output in the tth period; t line P is the power exchange power of the tie line in the tth period; t sto P is the pumped storage energy output in the tth period; t res P is the demand side response power in the tth period.

9. The system of claim 6, wherein, The evaluation unit obtains the power gap probability and the power shortage expectation of the target period based on the power demand curve to be met by new energy output and the new energy probability density distribution, and includes: For time period t, when the output of new energy source P t re The electricity demand P that needs to be met by renewable energy output is less than the demand P. t uns When the output of renewable energy is less than P, it indicates that there is a power shortage in the system; otherwise, it indicates that there is no power shortage and the power supply demand is met. When there is still a power shortage in the system after the output of renewable energy, the output of renewable energy is less than P based on the probability density distribution of renewable energy. t uns The probability α at that time t ; The power shortage expectation is calculated in the following manner, and includes: wherein EENS is the expected energy not supplied; J is the probability density function f of the new energy output in the t period t (·) after discretization, the number of segments corresponding to the t period; a tj is the probability density function f t (·) the probability of new energy output in the j segment after discretization.

10. The system of claim 9, wherein, The evaluation unit obtains a probability a that the new energy output is less than P based on a new energy probability density distribution t uns when the new energy output is less than P t , The program is executed by the processor to implement the steps of the method in any one of claims 1-5. Wherein, P(X) is the probability of the new energy output being less than the preset threshold X, the value of which is a, and X=P t uns the value of which is a t ; P t re is the output of the new energy in the t period; f t is the probability density function of the new energy output.

11. A computer readable storage medium having stored thereon a computer program, characterized in that The program is executed by the processor to implement the steps of the method in any one of claims 1-5.

12. An electronic device, comprising: The computer readable storage medium in claim 11; And One or more processors for executing the program in the computer readable storage medium. ​

Images