Multi-dimensional and multi-level flexibility assessment method and system for multi-energy system

Abstract

The invention provides a multi-dimensional and multi-level flexibility assessment method and system for a multi-energy system. The method comprises the following steps: determining a multi-energy system operation baseline based on peak-valley characteristics of a load; calculating the digital characteristics of the comprehensive cloud model based on the parameters of the multi-energy system in combination with a pre-constructed flexibility quantitative index system; comparing the comprehensive cloud model digital features with standard cloud model digital features to obtain a multi-energy system flexibility evaluation result; wherein the pre-constructed flexibility quantitative index system is constructed on the basis that an instant flexibility index and a process flexibility index serve as first-level indexes, a second-level index influencing each first-level index and a third-level index influencing each second-level index. According to the method, a flexible quantitative index system is adopted, the dynamic operation interaction characteristics of the electricity-heat-gas-storage system of the urban multi-energy system containing the high-proportion new energy are effectively reflected, the flexibility of the multi-energy system under random fluctuation of the high-proportion new energy is improved, and safe, economical and efficient operation of the multi-energy system is effectively supported.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-energy system flexibility assessment, and in particular to a multi-dimensional and multi-level flexibility assessment method and system for a multi-energy system. Background Art

[0002] As the key to the transformation of the energy industry, renewable energy has made great progress. Low-carbon technologies represented by the use of renewable energy are driving the low-carbon transformation of the energy system, while its distributed and highly uncertain characteristics also pose severe challenges to the safe and reliable operation of the energy system. The lack of regulation capacity of urban power grids at the power transmission end and the great pressure on the absorption and transmission of new energy have further put forward unprecedented high requirements on the flexibility of the energy system.

[0003] However, the supply side of the urban multi-energy system with a high proportion of new energy is highly volatile, the multi-energy load on the energy consumption side is highly time-varying and the multi-energy demand is deeply coupled. The system flexibility is interdependent in time and space dimensions, making it difficult to accurately evaluate and tap potential. Existing research on power system safety and regulation has focused on the safety and stability of the power system, and multi-energy system research is mainly aimed at small and medium-sized systems such as parks and towns. However, for urban-level electricity-gas-heat-storage multi-energy systems, related research on improving regulation capabilities is still insufficient, and it is urgent to carry out research on key technologies related to quantitative evaluation and improvement of flexibility. Summary of the invention

[0004] In order to solve the problem that the existing technology has a lack of regulation capacity of the urban power grid at the power sending end, the pressure of new energy consumption and transmission is high, and the flexibility of the energy system has been put forward unprecedentedly high requirements, the present invention proposes a multi-dimensional and multi-level flexibility evaluation method for a multi-energy system, including:

[0005] Determine the operating baseline of the multi-energy system based on the peak and valley characteristics of the load;

[0006] The digital characteristics of the comprehensive cloud model are calculated based on the parameters of the multi-energy system combined with the pre-built flexibility quantitative index system;

[0007] Based on the comparison between the digital features of the comprehensive cloud model and the digital features of the standard cloud model, the flexibility assessment results of the multi-energy system are obtained;

[0008] Among them, the pre-constructed flexibility quantitative index system is constructed based on the instantaneous flexibility index and the process flexibility index as the first-level index, the second-level index affecting each first-level index, and the third-level index affecting each second-level index.

[0009] Optionally, the step of calculating the uncertainty interval and determining the system operation baseline based on the load valley-peak fluctuation characteristics includes:

[0010] The normal distribution model is used to simulate the uncertainty of the electric load, gas load and heat load errors, and the probability distribution function of the uncertain variables is obtained;

[0011] The probability distribution function of the uncertain variable is discretized, and based on the discretized probability density function, a random sampling method is used to generate N s scenes;

[0012] K-means clustering is used to cluster the N randomly sampled s The number of scenes is reduced to N0;

[0013] The N0 scenario operating ranges are used as the system operating baseline.

[0014] Optionally, the K-means clustering is used to randomly sample N ss The number of scenarios is reduced from 1 to N0, including:

[0015] Step 1: From N s In each scene, N0 objects are randomly selected as the initial cluster centers;

[0016] Step 2: According to the mean of each cluster center scene, calculate the distance between each scene and the mean of the cluster center scene, and re-divide the scene according to the minimum distance, and update N0 according to the number of re-divided scenes;

[0017] Step 3: Calculate the mean of the re-divided cluster center scenes;

[0018] Step 4: Based on the mean of the re-divided cluster center scenes, the standard measurement function is calculated using the clustering criterion function. If the standard measurement function converges, the algorithm terminates; otherwise, return to Step 2.

[0019] Optionally, the construction of the flexibility quantitative index system includes:

[0020] The instantaneous flexibility reflecting the power-type regulation capability and the process flexibility reflecting the energy-type regulation capability are used as the primary indicators;

[0021] The load fluctuation rate, short-term power balance capability, and flexibility supply capability that affect the instantaneous flexibility of the first-level indicator are used as second-level indicators;

[0022] The system supply and demand balance capability, multi-energy complementary effect, and system energy regulation capability that affect the flexibility of the first-level indicator process are used as second-level indicators;

[0023] The parameters that affect the secondary indicators are used as the tertiary indicators;

[0024] The indicators at all levels are combined and weighted by the AHP-entropy weight method to obtain the weights of the indicators at all levels;

[0025] A flexibility quantitative index system is constructed by first-level indicators, second-level indicators, third-level indicators, and weights of indicators at each level.

[0026] Optionally, the weighting of indicators at all levels is combined by the AHP-entropy weight method to obtain the weights of indicators at all levels, including:

[0027] Step 1: Determine the weight of the flexibility quantitative index system and the flexibility level division interval through expert opinions and questionnaire surveys;

[0028] Step 2: Determine whether the indicators at all levels belong to the category of “the larger the better”. If so, the actual multi-energy system operation history data is normalized by using the “the larger the better” indicator calculation formula based on the flexibility level division interval. Otherwise, the actual multi-energy system operation history data is normalized by using the “the smaller the better” indicator calculation formula based on the flexibility level division interval.

[0029] Step 3: Based on the physical meaning and characteristics of the indicators themselves, combined with the normalized historical data of actual multi-energy system operation, the objective weight of the indicator system is calculated using the entropy weight method;

[0030] Step 4: By combining subjective and objective weights, weight the three-level indicators in the flexibility quantitative index system;

[0031] Step 5: Calculate the weights of the secondary and primary indicators based on the structure of the indicator system and the weights of the third-level indicators.

[0032] Optionally, the larger the better type indicator calculation formula is as follows:

[0033]

[0034] The calculation formula of the smaller the better index is as follows:

[0035]

[0036] Where y is the normalized actual multi-energy system operation history data, x is the actual multi-energy system operation history data, and x min is the minimum value in the actual multi-energy system operation history data, x max is the maximum value in the actual multi-energy system operation history data, ω max is the maximum value of the index system weight and flexibility level division interval, ω min It is the minimum value of the index system weight and flexibility level division interval.

[0037] Optionally, the three-level indicators in the flexibility quantitative indicator system are weighted according to the following formula, including: The weight evaluation calculation method of the AHP-entropy weight method is:

[0038] η=aη1+bη2

[0039] In the formula, η represents the combined weight vector, a and b represent the weight coefficients, η1 and η2 represent the weight vectors calculated by AHP and entropy weight method respectively;

[0040] The weight coefficients a and b satisfy:

[0041] a,b>0; a+b=1.

[0042] Optionally, the weights of the secondary and primary indicators are calculated as follows:

[0043]

[0044] Where η u is the weight of the first-level index u, η ν is the weight of the secondary index v, η w is the weight of the third-level indicator w, tw is the number of second-level indicators corresponding to the first-level indicator u, and th is the number of third-level indicators corresponding to the second-level indicator v.

[0045] Optionally, the step of comparing the digital features of the comprehensive cloud model with the digital features of the standard cloud model to obtain a multi-energy system flexibility assessment result includes:

[0046] Step 1: Based on the flexibility evaluation level, the evaluation score [ω min ,ω max ] to classify and generate standard cloud model digital features according to the classification;

[0047] Step 2: Collect the actual operation data of the system multiple times to form samples. According to the allowable range of indicators in the actual operation of the multi-energy system, the samples are forward-normalized and linearly mapped so that the scores fall within the flexibility assessment level division interval, and the mean and variance of each three-level indicator are calculated;

[0048] Step 3: Calculate the digital characteristics of the three-level indicators in the comprehensive cloud model based on the mean and variance of each three-level indicator;

[0049] Step 4: Calculate the digital characteristics of the comprehensive cloud model of the secondary indicators and the primary indicators in the comprehensive cloud model through the digital characteristics and weights of the third-level indicators;

[0050] Step 5: Compare the digital features of the comprehensive cloud model with the digital features of the standard cloud model to obtain the flexibility evaluation results of the multi-energy system.

[0051] Optionally, the digital characteristics of the three-level indicators in the cloud model are calculated as follows:

[0052]

[0053] Where, Ex j 、En j and He j are the evaluation mean of the flexibility of indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. is the evaluation mean, n is the total number of evaluations, i is the number of evaluations, x j,i is the i-th evaluation value of indicator j, S j is the variance of the evaluation value of indicator j, and j is the type of the third-level indicator.

[0054] Optionally, the digital features of the comprehensive cloud model of the secondary index and the primary index are calculated as follows:

[0055]

[0056] Where, Ex j,k-1 、En j,k-1 、He j,k-1 are the evaluation mean of the flexibility of k-1 level indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. j,k 、En、He j,k are the evaluation mean of the flexibility of level k indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. j,k Represents the combined weight of the k-th level indicator j, where k represents the indicator level.

[0057] On the other hand, the present application also provides a multi-dimensional and multi-level flexibility assessment system for a multi-energy system, comprising:

[0058] A baseline determination module, used to determine the operation baseline of the multi-energy system based on the peak and valley characteristics of the load;

[0059] A digital feature calculation module is used to calculate the digital features of the comprehensive cloud model based on the parameters of the multi-energy system combined with a pre-built flexibility quantitative index system;

[0060] An evaluation module, used for obtaining a flexibility evaluation result of a multi-energy system based on a comparison between the digital features of the comprehensive cloud model and the digital features of a standard cloud model;

[0061] Among them, the pre-constructed flexibility quantitative index system is constructed based on the instantaneous flexibility index and the process flexibility index as the first-level index, the second-level index affecting each first-level index, and the third-level index affecting each second-level index.

[0062] Optionally, the baseline determination module includes:

[0063] The probability distribution calculation submodule is used to simulate the uncertainty of the errors of electric load, gas load and heat load using the normal distribution model to obtain the probability distribution function of the uncertain variables;

[0064] The initial scene selection submodule is used to discretize the probability distribution function of the uncertain variable and generate N random sampling methods based on the discretized probability density function. s scenes;

[0065] The baseline determination submodule is used to use K-means clustering to cluster the N randomly sampled s The number of scenes is reduced from 0 to N0.

[0066] Optionally, the baseline determination submodule is specifically used for:

[0067] Step 1: From N s In each scene, N0 objects are randomly selected as the initial cluster centers;

[0068] Step 2: According to the mean of each cluster center scene, calculate the distance between each scene and the mean of the cluster center scene, and re-divide the scenes according to the minimum distance;

[0069] Step 3: Calculate the mean of the re-divided cluster center scenes;

[0070] Step 4: Based on the mean of the re-divided cluster center scenes, the standard measurement function is calculated using the clustering criterion function. If the standard measurement function converges, the algorithm terminates; otherwise, return to Step 2.

[0071] Optionally, it further includes: a system building module for:

[0072] The instantaneous flexibility reflecting the power-type regulation capability and the process flexibility reflecting the energy-type regulation capability are used as the primary indicators;

[0073] The load fluctuation rate, short-term power balance capability, and flexibility supply capability that affect the instantaneous flexibility of the first-level indicator are used as second-level indicators;

[0074] The system supply and demand balance capability, multi-energy complementary effect, and system energy regulation capability that affect the flexibility of the first-level indicator process are used as second-level indicators;

[0075] The parameters that affect the secondary indicators are used as the tertiary indicators;

[0076] The indicators at all levels are combined and weighted by the AHP-entropy weight method to obtain the weights of the indicators at all levels;

[0077] A flexibility quantitative index system is constructed by first-level indicators, second-level indicators, third-level indicators, and weights of indicators at each level.

[0078] Optionally, the steps of combining and weighting the indicators at all levels by the AHP-entropy weight method in the system building module to obtain the weights of the indicators at all levels include:

[0079] Step 1: Determine the weight of the flexibility quantitative index system and the flexibility level division interval through expert opinions and questionnaire surveys;

[0080] Step 2: Determine whether the indicators at all levels belong to the category of “the larger the better”. If so, the actual multi-energy system operation history data is normalized by using the “the larger the better” indicator calculation formula based on the flexibility level division interval. Otherwise, the actual multi-energy system operation history data is normalized by using the “the smaller the better” indicator calculation formula based on the flexibility level division interval.

[0081] Step 3: Based on the physical meaning and characteristics of the indicators themselves, combined with the normalized historical data of actual multi-energy system operation, the objective weight of the indicator system is calculated using the entropy weight method;

[0082] Step 4: By combining subjective and objective weights, weight the three-level indicators in the flexibility quantitative index system;

[0083] Step 5: Calculate the weights of the secondary and primary indicators based on the structure of the indicator system and the weights of the third-level indicators.

[0084] Optionally, the larger the better type indicator calculation formula is as follows:

[0085]

[0086] The calculation formula of the smaller the better index is as follows:

[0087]

[0088] Where y is the normalized actual multi-energy system operation history data, x is the actual multi-energy system operation history data, and x min is the minimum value in the actual multi-energy system operation history data, x max is the maximum value in the actual multi-energy system operation history data, ω max is the maximum value of the index system weight and flexibility level division interval, ω min It is the minimum value of the index system weight and flexibility level division interval.

[0089] Optionally, the three-level indicators in the flexibility quantitative indicator system are weighted according to the following formula, including: The weight evaluation calculation method of the AHP-entropy weight method is:

[0090] η=aη1+bη2

[0091] In the formula, η represents the combined weight vector, a and b represent the weight coefficients, η1 and η2 represent the weight vectors calculated by AHP and entropy weight method respectively;

[0092] The weight coefficients a and b satisfy:

[0093] a,b>0; a+b=1.

[0094] Optionally, the weights of the secondary and primary indicators are calculated as follows:

[0095]

[0096] Where η u is the weight of the first-level index u, η ν is the weight of the secondary index v, η w is the weight of the third-level indicator w, tw is the number of second-level indicators corresponding to the first-level indicator u, and th is the number of third-level indicators corresponding to the second-level indicator v.

[0097] Optionally, the evaluation module is specifically used for:

[0098] Step 1: Based on the flexibility evaluation level, the evaluation score [ω min ,ω max ] to classify, and generate standard cloud images and cloud digital features according to the classification;

[0099] Step 2: Collect the actual operation data of the system multiple times to form a sample of a certain size. According to the allowable range of indicators in the actual operation of the multi-energy system, the collected samples are positively normalized and linearly mapped so that the scores fall within the flexibility level division interval, and the mean and variance of each three-level indicator are calculated;

[0100] Step 3: Calculate the digital features of the three-level indicators in the cloud model based on the mean and variance of each three-level indicator;

[0101] Step 4: Calculate the digital characteristics of the comprehensive cloud model of the secondary and primary indicators through the digital characteristics and assigned weights of the tertiary indicators.

[0102] Optionally, the digital characteristics of the three-level indicators in the cloud model are calculated as follows:

[0103]

[0104] Where, Ex j 、En j and He j are the evaluation mean of the flexibility of indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. is the evaluation mean, n is the total number of evaluations, i is the number of evaluations, x j,i is the i-th evaluation value of indicator j, S j is the variance of the evaluation value of indicator j, and j is the type of the third-level indicator.

[0105] Optionally, the digital features of the comprehensive cloud model of the secondary index and the primary index are calculated as follows:

[0106]

[0107] Where, Ex j,k-1 、En j,k-1 、He j,k-1 are the evaluation mean of the flexibility of k-1 level indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. j,k 、En、He j,k are the evaluation mean of the flexibility of level k indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. j,k Represents the combined weight of the k-th level indicator j, where k represents the indicator level.

[0108] In another aspect, the present application further provides a computing device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0109] The memory is used to store one or more programs;

[0110] When the one or more programs are executed by the at least one processor, a multi-dimensional and multi-level flexibility assessment method for a multi-energy system as described above is implemented.

[0111] On the other hand, the present application also provides a readable storage medium having an execution program stored thereon, which, when executed, implements a multi-dimensional and multi-level flexibility assessment method for a multi-energy system as described above.

[0112] Compared with the prior art, the present invention has the following beneficial effects:

[0113] The present invention provides a multi-dimensional and multi-level flexibility evaluation method for a multi-energy system, including: determining the operating baseline of the multi-energy system based on the peak and valley characteristics of the load; calculating the digital features of the comprehensive cloud model based on the parameters of the multi-energy system in combination with a pre-constructed flexibility quantitative index system; obtaining the flexibility evaluation results of the multi-energy system based on the comparison of the digital features of the comprehensive cloud model with the digital features of the standard cloud model; wherein the pre-constructed flexibility quantitative index system is based on the instantaneous flexibility index and the process flexibility index as the first-level index, the second-level index affecting each first-level index, and the third-level index affecting each second-level index. The present invention adopts a flexibility quantitative index system to effectively reflect the dynamic operation interaction characteristics of the electric-heat-gas-storage system of the urban-level multi-energy system containing a high proportion of new energy, realize the improvement of the flexibility of the multi-energy system under the random fluctuation of a high proportion of new energy, and effectively support the safe, economical and efficient operation of the multi-energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] Figure 1 A flow chart of a multi-dimensional and multi-level flexibility evaluation method for a multi-energy system of the present invention;

[0115] Figure 2 A schematic diagram of the flexibility quantification index system architecture of the present invention;

[0116] Figure 3 A flow chart of subjective and objective combination weighting of the present invention;

[0117] Figure 4 A flow chart of flexibility assessment of a multi-energy system of the present invention;

[0118] Figure 5 The figure is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0119] The present invention proposes a multi-dimensional and multi-level flexibility evaluation method and system for a multi-energy system, determines the operating baseline of the multi-energy system based on the peak and valley characteristics of the load, fully considers the interactive support characteristics of different energy sources, different spaces, and different times, establishes a multi-dimensional and multi-level flexibility evaluation index system for the multi-energy system, and evaluates the flexible response feasible domain of the system from the two perspectives of instantaneous flexibility and process flexibility. .

[0120] The present invention explores and adjusts the huge flexible adjustment capacity contained in the electricity-gas-heat-storage coupling system to support the high proportion of new energy consumption, taking into account the planning level and focusing on exploring the flexibility potential of the system from the operation level, so as to achieve the improvement of the flexibility of the multi-energy system under the random fluctuation of high proportion of new energy, and effectively support the safe, economical and efficient operation of the multi-energy system. At the same time, the invention results will fully explore and utilize the huge flexibility contained in the urban multi-energy system at the power sending end, improve the adjustment capacity of the power system and the utilization rate of new energy, ensure the safety of comprehensive energy supply, and help improve the ecological environment and sustainable development.

[0121] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0122] Embodiment 1:

[0123] A multi-dimensional and multi-level flexibility assessment method for multi-energy systems, such as Figure 1 As shown, including:

[0124] Step 1: Determine the operating baseline of the multi-energy system based on the peak and valley characteristics of the load;

[0125] Step 2: Calculate the digital characteristics of the comprehensive cloud model based on the parameters of the multi-energy system combined with the pre-built flexibility quantitative index system;

[0126] Step 3: Based on the comparison between the digital features of the comprehensive cloud model and the digital features of the standard cloud model, the flexibility assessment result of the multi-energy system is obtained;

[0127] Among them, the pre-constructed flexibility quantitative index system is constructed based on the instantaneous flexibility index and the process flexibility index as the first-level index, the second-level index affecting each first-level index, and the third-level index affecting each second-level index.

[0128] The present invention proposes a multi-dimensional and multi-level flexibility evaluation method for a multi-energy system, which includes the following contents:

[0129] S1: Determine the operating baseline of the multi-energy system by considering the peak and valley characteristics of the load;

[0130] S2: Establish a multi-dimensional and multi-level flexibility quantitative index system for multi-energy systems;

[0131] S3: Multi-dimensional and multi-level flexibility evaluation method based on cloud model.

[0132] Step 1: Determine the operating baseline of the multi-energy system based on the peak and valley characteristics of the load, including:

[0133] The normal distribution model is used to simulate the uncertainty of the electric load, gas load and heat load errors, and the probability distribution function of the uncertain variables is obtained;

[0134] The probability distribution function of the uncertain variable is discretized, and based on the discretized probability density function, a random sampling method is used to generate N s scenes;

[0135] K-means clustering is used to cluster the N randomly sampled s The number of scenes is reduced from 0 to N0.

[0136] Optionally, the K-means clustering is used to randomly sample N s The number of scenarios is reduced from 1 to N0, including:

[0137] Step 1: From N s In each scene, N0 objects are randomly selected as the initial cluster centers;

[0138] Step 2: According to the mean of each cluster center scene, calculate the distance between each scene and the mean of the cluster center scene, and re-divide the scenes according to the minimum distance;

[0139] Step 3: Calculate the mean of the re-divided cluster center scenes;

[0140] Step 4: Based on the mean of the re-divided cluster center scenes, the standard measurement function is calculated using the clustering criterion function. If the standard measurement function converges, the algorithm terminates; otherwise, return to Step 2.

[0141] Step 1 specifically includes:

[0142] S1 considers the peak and valley characteristics of the load to determine the operating baseline of the multi-energy system, including the following steps:

[0143] S101: Analyze the physical structure and energy characteristics of multi-energy systems

[0144] (1) From the perspective of energy types, the multi-energy coupling system mainly includes energy sub-networks such as electricity, natural gas and heat. In the power network, in addition to some conventional electrical equipment (such as power transmission lines and different levels of power loads), it also includes distributed renewable energy generation equipment (such as wind power, photovoltaics) and electric energy storage devices; in the natural gas network, natural gas pipelines, gas energy storage devices and pneumatic valves have become the core of the network; the thermal network is mainly composed of thermal pipelines, thermal valves and thermal loads.

[0145] (2) From the perspective of system functions, the integrated energy system includes three major functions: energy supply, energy interaction and storage, and load demand response.

[0146] (3) On the energy supply side: Each energy subsystem in the multi-energy system obtains energy supply of electricity, natural gas and heat from the upper-level energy network, and realizes the interaction and storage of electric energy, gas energy and thermal energy through different energy conversion equipment, thereby meeting different types of load demands on the user demand side.

[0147] (4) On the user demand side: Electricity, natural gas, and thermal resources obtained from the upper-level energy network can meet the needs of users of different scales through various energy conversion equipment.

[0148] S102: Analyze the uncertainty of new energy output on the energy supply side

[0149] (1) Wind energy resources have a strong temporal distribution in time and space. There is a direct relationship between wind speed and wind turbines. The uncertainty of wind turbine output can be equivalent to the uncertainty of wind speed. It is generally believed that wind speed follows the Weibull probability distribution, and its probability density function is:

[0150]

[0151] Where f(ν) is the probability density function of the wind speed obeying the Weibull probability distribution, v is the wind speed, and c and k1 are the shape parameter and scale parameter of the Weibull probability distribution, respectively.

[0152] (2) Unlike wind resources, photovoltaic resources have a smaller local effect, and usually the irradiance changes in a certain area are not much different. However, the easternmost part of my country is at 135° east longitude, and the westernmost part is around 75° east longitude. The east-west distance exceeds 5,000 kilometers, which is about a 4-hour difference. Even in the northwest region, such as Qinghai Province and Xinjiang Uygur Autonomous Region, the maximum east-west distance exceeds 2,000 kilometers. Therefore, there will be a sequence in seeing the sun, and there will be a time delay in photovoltaic output in the eastern and western provinces, which is complementary to a certain extent.

[0153] S103: Establishing multiple uncertain operation ranges based on multi-energy conversion and energy storage characteristics

[0154] (1) Determine the upper and lower bounds of the output power uncertainty range at the maximum input power;

[0155] (2) Determine the upper and lower bounds of the output power uncertainty range at the minimum input power;

[0156] (3) Determine the maximum and minimum output power of the multi-energy coupling device, which indicates that the output power of the device will deviate from the scheduling level. It also reflects that the efficiency of energy conversion is not fixed. The more the working state of the device deviates from the rated state, the greater the uncertainty of the output power of the device.

[0157] S104: Calculate the uncertainty interval based on the load valley-peak fluctuation characteristics and determine the system operation baseline

[0158] (1) The normal distribution model is used to simulate the uncertainty of the electric load, gas load and heat load errors, and its probability density function is:

[0159]

[0160] Where f(L) is the probability density function of the load obeying the Weibull probability distribution, L is the load error; μ is the location parameter of the normal distribution, which is the expectation of the load error; σ is the shape parameter of the normal distribution, which is the standard deviation of the load error.

[0161] (2) Discretize the probability distribution function of the uncertain variable and use random sampling to generate N based on the discretized probability density function. s Then K-means clustering is used to cluster the N randomly sampled s The number of scenarios is reduced from 0 to N0. The specific steps include the following 4 steps:

[0162] Step 1: From N s In each scene, N0 objects are randomly selected as the initial cluster centers;

[0163] Step 2: According to the mean value of each cluster scene (central scene), calculate the distance between each scene and these central scenes, and re-divide the corresponding scenes according to the minimum distance;

[0164] Step 3: Recalculate the mean of each (changed) cluster (central scene);

[0165] Step 4: Calculate the standard measure function. When certain conditions are met, such as function convergence, the algorithm terminates; if the conditions are not met, return to Step 2.

[0166] Before introducing step 2, the flexibility quantitative indicator system involved in this article is further introduced:

[0167] Optionally, the construction of the flexibility quantitative index system includes:

[0168] The instantaneous flexibility reflecting the power-type regulation capability and the process flexibility reflecting the energy-type regulation capability are used as the primary indicators;

[0169] The load fluctuation rate, short-term power balance capability, and flexibility supply capability that affect the instantaneous flexibility of the first-level indicator are used as second-level indicators;

[0170] The system supply and demand balance capability, multi-energy complementary effect, and system energy regulation capability that affect the flexibility of the first-level indicator process are used as second-level indicators;

[0171] The parameters that affect the secondary indicators are used as the tertiary indicators;

[0172] The indicators at all levels are combined and weighted by the AHP-entropy weight method to obtain the weights of the indicators at all levels;

[0173] A flexibility quantitative index system is constructed by first-level indicators, second-level indicators, third-level indicators, and weights of indicators at each level.

[0174] S2: Establish a multi-dimensional and multi-level flexibility quantitative indicator system for multi-energy systems, including the following steps:

[0175] S201: Based on the regulation capacity, response time and duration, clarify the multi-dimensional and multi-level flexibility evaluation principles of the multi-energy system, and establish the overall goal of the evaluation index system. Combined with the pertinence, objectivity, systematicness, feasibility, scientificity and advancement of the development of the multi-energy system, establish a set of micro-evaluation indicators in each dimension. Micro-indicators are specific to various aspects such as system safety and reliability, energy network structure, energy supply capacity, utilization efficiency, development benefits, technical equipment, intelligence level, and clean energy.

[0176] S202: Based on the principles of flexibility system construction, fully consider the interactive support characteristics of different energy sources, different spaces, and different times, and establish a multi-dimensional and multi-level flexibility evaluation index system for multi-energy systems:

[0177] Flexibility assessment system such as Figure 2 As shown in the figure, since the evaluation of multi-energy system flexibility needs to consider the response of system flexibility in both "rate" and "quantity", the first-level indicator U considers two aspects, namely, instantaneous flexibility u2 reflecting power-type regulation capability and process flexibility u1 reflecting energy-type regulation capability; among them, instantaneous flexibility can be evaluated from three aspects: load fluctuation rate, short-term power balance capability, and flexibility supply capability, and process flexibility can be evaluated from three aspects: system supply and demand balance capability, multi-energy complementary effect, and system energy regulation capability. Then, according to the significance and influencing factors of each second-level indicator, the corresponding third-level indicators are formulated.

[0178] (1) Load fluctuation rate: represents the instantaneous ramping / slipping demand generated by the net load of the system, including load fluctuation, wind power fluctuation, and photovoltaic fluctuation.

[0179] (2) Short-term power balancing capability: Since electric energy cannot be stored in large quantities, the power system has the characteristic of being used immediately after it is generated. This characteristic requires the power grid to meet the balance between power generation and power consumption in real time, that is, power balance.

[0180] (3) Flexibility supply capability: Provide auxiliary services through interruptible loads, virtual power plants, load aggregators, etc., actively participate in grid regulation interaction, and realize "source-load interaction". On the energy storage side, actively deploy pumped storage and other new energy storage, and give full play to the flexible regulation capabilities of multiple types of energy storage resources.

[0181] (4) System supply and demand balance capability: including operating capacity, response time, and response rate.

[0182] (5) Multi-energy complementary effect: including energy conversion efficiency, energy storage capacity, and dispatchable capacity.

[0183] (6) System energy regulation capability: including load fluctuation range, response cost, and operational safety.

[0184] S203: Calculation of quantitative evaluation indicators:

[0185] (1) Calculation of instantaneous flexibility evaluation index

[0186] 1. Define the flexibility requirements of multi-energy systems as:

[0187] F(t)=L p (t+1)-L p (t) (3)

[0188] In the formula, F represents the flexibility requirement, L p (t+1) and L p (t) represents the net load at time t+1 and time t respectively, and t represents the time. By decomposing the flexibility demand into positive and negative sequences, we can get the flexibility demand sequence in two directions, namely:

[0189]

[0190] In the formula, F u (t) is the upward flexibility range, F d (t) is the downward flexibility range.

[0191] 2. Define the net load fluctuation rate of the multi-energy system and the maximum allowable fluctuation rate of the distribution system as:

[0192]

[0193] Where v1(t) is the maximum allowable fluctuation rate of load, ν2(t) is the maximum allowable fluctuation rate of distribution system, s a

[0194] is the type of flexible resource, S A is the number of flexible resources, m a is the type of new energy unit, M A is the number of new energy units, R is the ramp rate of power purchased by the multi-energy system from the main grid; Indicates the s a The ramp rate of the flexibility resource; For m a The climbing rate of new energy units.

[0195] The physical meaning of formula (5) and formula (6) is whether the net load change rate of the current distribution system is within the range of the maximum climbing / slipping power that the distribution system can provide. When the net load fluctuation rate of the system is less than the maximum allowable fluctuation rate, it means that the current system climbing rate and slipping rate are within the allowable range of the system climbing rate, and there will be no insufficient flexibility accident; otherwise, it means that the net load fluctuation rate exceeds the adjustable range of the system, and corresponding measures need to be taken to limit the net load fluctuation rate. Therefore, the calculation method of the first-level indicator is:

[0196]

[0197] Where u1 is the instantaneous flexibility, ν1 is the load fluctuation range, ν1>0 indicates an increase in load, and ν2 is the system climbing range.

[0198] (2) Calculation of process flexibility evaluation indicators

[0199] There are many types of flexibility resources in the distribution network with different operating characteristics and flexibility supply potential.

[0200] In the economic dispatch involving flexible resources, changes in the adjustment potential, response cost and response rate of flexible resources will affect the response of flexible resources and thus affect the dispatch results. Therefore, the evaluation of the flexibility supply capacity of flexible resources should take into account several inherent indicators before participating in the flexibility response.

[0201] 1. Dispatchy capacity: It is used to quantify the actual remaining transmission capacity of the network under the current state of the system and characterize the real-time operation of the system. The calculation method is:

[0202]

[0203] In the formula, p b,t is the output of unit b during period t; The wind farm f in period t o The predicted output of The load f in period t u The predicted value of l,t is the actual remaining transmission capacity of the network at time t, is the actual transmission capacity of the network at time t, b is the serial number of the thermal power unit, B is the total number of thermal power units, and f o

[0204] is the wind turbine unit serial number, F o is the total amount of wind turbines, f u is the load number, F U is the total load; They represent the power transmission distribution factors of thermal power units, wind power units and loads for the lines respectively.

[0205] 2. Energy storage capacity: represents the remaining available transmission capacity of the network after considering the current current carrying capacity and the margin reserved due to system operation uncertainty (including: random shutdown of components and load and new energy uncertainty).

[0206] The method is:

[0207] ATC=TTC-TRM-CBM-ETC (9)

[0208] In the formula, ATC is the available transmission capacity of the network; ETC is the transmission capacity currently used by the system; TRM refers to the reserved necessary transmission capacity to ensure the safe and stable operation of the system, and its value is related to the time section and scale;

[0209] CBM is the reliability capacity margin; TTC is the maximum transmission capacity of the line.

[0210] 3. Operating capacity: When the unit / energy storage is in normal operation, the stable adjustable range of the unit / energy storage output power is calculated as follows:

[0211] S=P max -P min (10)

[0212] Where S is the stable adjustable range of the unit / energy storage output power, P max and P min Indicates the maximum and minimum values ​​of the flexible resource output power.

[0213] S204: Empower the evaluation index system:

[0214] The existing evaluation system weighting methods are usually divided into subjective weighting methods and objective weighting methods. The subjective weighting method is to collect opinions from relevant experts and unify the expert opinions through multiple rounds of scoring to achieve a subjective evaluation of a certain evaluation system. Its advantage is that the conclusions drawn are usually intuitive and consistent with the basic experience of system operation. The objective weighting method evaluates the operating status of the system through the relevant digital characteristics of the system operation indicators. The advantage is that it is usually more in line with the actual operation of the system. This paper combines the evaluation system with the AHP-entropy weight method to give weights, in order to complement the advantages and disadvantages of the subjective-objective evaluation method and make the weight of the evaluation system more in line with engineering practice.

[0215] The subjective and objective combination empowerment process is as follows Figure 3 As shown in the figure, the weighting steps of the AHP-entropy weight method are as follows:

[0216] Step 1: Determine the weight of the indicator system and the flexibility level classification through multiple rounds of solicitation of relevant expert opinions and questionnaire surveys [ω max ,ω min ] relatively unified opinion;

[0217] Step 2: According to the formulation of relevant indicators, normalize the indicators with larger values ​​and smaller values ​​according to the indicator data conversion, and convert the indicator scores to flexibility levels through linear mapping.

[0218] Within the scope:

[0219] Normalization of the larger the better type of indicators:

[0220]

[0221] Normalization of the smaller the better type of indicators:

[0222]

[0223] Step 3: Based on the physical meaning and characteristics of the indicator itself and combined with the actual multi-energy system operation historical data, the entropy weight method is used to calculate the objective weight of the indicator system;

[0224] Step 4: Through the combination of subjective and objective weights, the weights of the three-level indicators in the indicator system are assigned. Among them, the weight evaluation calculation method of the AHP-entropy weight method is:

[0225] η=aη1+bη2 (13)

[0226] In the formula, η1 and η2 represent the weight vectors calculated by AHP and entropy weight method respectively, and a and b satisfy:

[0227] a,b>0;a+b=1 (14)

[0228] Step 5: According to the structure of the indicator system and the weights of the three-level indicators determined in Step 4, calculate the weights of the second-level and first-level indicators. The calculation method is as follows:

[0229]

[0230] Where η u is the weight of the first-level index u, η ν is the weight of the secondary index v, η w is the weight of the third-level indicator w, tw is the number of second-level indicators corresponding to the first-level indicator u, and th is the number of third-level indicators corresponding to the second-level indicator v.

[0231] Step 2: Calculate the digital characteristics of the comprehensive cloud model based on the parameters of the multi-energy system combined with the pre-built flexibility quantitative indicator system.

[0232] Step 3: Based on the comparison between the digital features of the comprehensive cloud model and the digital features of the standard cloud model, the flexibility assessment results of the multi-energy system are obtained, including:

[0233] Step 1: Based on the flexibility evaluation level, the evaluation score [ω min ,ω max ] to classify, and generate standard cloud images and cloud digital features according to the classification;

[0234] Step 2: Collect the actual operation data of the system multiple times to form a sample of a certain size. According to the allowable range of indicators in the actual operation of the multi-energy system, the collected samples are positively normalized and linearly mapped so that the scores fall within the flexibility level division interval, and the mean and variance of each three-level indicator are calculated;

[0235] Step 3: Calculate the digital features of the three-level indicators in the cloud model based on the mean and variance of each three-level indicator;

[0236] Step 4: Calculate the digital characteristics of the comprehensive cloud model of the secondary and primary indicators through the digital characteristics and assigned weights of the tertiary indicators.

[0237] Optionally, the digital characteristics of the three-level indicators in the cloud model are calculated as follows:

[0238]

[0239] Where, Ex j 、En j and He j are the evaluation mean of the flexibility of indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. is the evaluation mean, n is the total number of evaluations, i is the number of evaluations, x j,i is the i-th evaluation value of indicator j, S j is the variance of the evaluation value of indicator j, and j is the type of the third-level indicator.

[0240] Optionally, the digital features of the comprehensive cloud model of the secondary index and the primary index are calculated as follows:

[0241]

[0242] Where, Ex j,k-1 、En j,k-1 、He j,k-1 are the evaluation mean of the flexibility of k-1 level indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. j,k 、En、He j,k are the evaluation mean of the flexibility of level k indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. j,k Represents the combined weight of the k-th level indicator j, where k represents the indicator level.

[0243] S3: A multi-dimensional and multi-level flexibility assessment method based on a cloud model, including the following steps:

[0244] S301: Considering the complex spatiotemporal interaction characteristics of multi-energy systems, analyze the flexibility supply and demand of the system in a hierarchical and graded manner, estimate the feasible domain of system flexible response from the perspectives of instantaneous flexibility and process flexibility, and realize accurate flexibility evaluation based on cloud models. The evaluation process is as follows: Figure 4 shown.

[0245] S302: Using the reverse cloud generator, the digital features of the cloud model are calculated according to the flexibility evaluation index established in the previous article, and then the cloud evaluation results are obtained. The flexibility evaluation process of the multi-energy system combined with the reverse cloud generator is as follows:

[0246] Step 1: Based on the flexibility evaluation level, the evaluation score [ω min ,ωmax ] to classify, and generate standard cloud images and cloud digital features according to the classification;

[0247] Step 2: Collect the actual operation data of the system multiple times to form a sample of a certain size. According to the allowable range of indicators in the actual operation of the multi-energy system, the collected samples are normalized and linearly mapped so that the score falls within [ω min ,ω max ] interval, and calculate the mean and variance of each third-level indicator. The calculation method is as follows:

[0248]

[0249] Where, Ex j is the evaluation mean of the flexibility of indicator j, is the evaluation mean, n is the total number of evaluations, i is the number of evaluations, x j,i is the i-th evaluation value of indicator j, S j is the variance of the evaluation value of indicator j, and j is the type of the third-level indicator.

[0250] Step 3: Calculate the digital characteristics of the three-level indicators in the cloud model. The calculation method is as follows:

[0251]

[0252] Step 4: Calculate the digital features of the comprehensive cloud model of the secondary and primary indicators through the digital features and weights of the third-level indicators. The calculation method is as follows:

[0253]

[0254] In the formula, θ j Represents the combined weight of the indicators.

[0255] Step 5: Compare the calculated digital features of the comprehensive cloud model with the digital features of the standard cloud model to form the current multi-energy system flexibility assessment results.

[0256] Unify the flexibility evaluation principles of subsystems such as electricity, heat, and gas in terms of adjustment capacity, response time, and duration, fully consider the interactive support characteristics of different energy sources, different spaces, and different times, and establish a multi-dimensional and multi-level flexibility evaluation index system for multi-energy systems. Consider the complex spatiotemporal interaction characteristics of multi-energy systems, analyze the flexibility supply and demand of the system in a hierarchical and graded manner, estimate the feasible domain of system flexible response from the perspectives of instantaneous flexibility and process flexibility, and achieve accurate flexibility evaluation.

[0257] Unifying the flexibility assessment principles of subsystems such as electricity, heat, and gas in terms of regulation capacity, response time, and duration, and establishing a multi-dimensional assessment index for the flexibility of multi-energy systems can effectively reflect the dynamic operation and interaction characteristics of the electricity-heat-gas-storage system of urban-level multi-energy systems containing a high proportion of new energy. At the same time, through flexibility assessment, the huge flexible regulation capacity contained in the electricity-gas-heat-storage coupling system can be explored to support the consumption of a high proportion of new energy. Taking into account the planning level and focusing on exploring the flexibility potential of the system from the operation level, the flexibility of the multi-energy system under the random fluctuation of a high proportion of new energy can be improved, which effectively supports the safe, economical and efficient operation of the multi-energy system.

[0258] Embodiment 2:

[0259] The present invention based on the same inventive concept also provides a multi-dimensional and multi-level flexibility evaluation system for a multi-energy system, comprising:

[0260] A baseline determination module, used to determine the operation baseline of the multi-energy system based on the peak and valley characteristics of the load;

[0261] A digital feature calculation module is used to calculate the digital features of the comprehensive cloud model based on the parameters of the multi-energy system combined with a pre-built flexibility quantitative index system;

[0262] An evaluation module, used for obtaining a flexibility evaluation result of a multi-energy system based on a comparison between the digital features of the comprehensive cloud model and the digital features of a standard cloud model;

[0263] Among them, the pre-constructed flexibility quantitative index system is constructed based on the instantaneous flexibility index and the process flexibility index as the first-level index, the second-level index affecting each first-level index, and the third-level index affecting each second-level index.

[0264] Optionally, the baseline determination module includes:

[0265] The probability distribution calculation submodule is used to simulate the uncertainty of the errors of electric load, gas load and heat load using the normal distribution model to obtain the probability distribution function of the uncertain variables;

[0266] The initial scene selection submodule is used to discretize the probability distribution function of the uncertain variable and generate N random sampling methods based on the discretized probability density function. s scenes;

[0267] The baseline determination submodule is used to use K-means clustering to cluster the N randomly sampled s The number of scenes is reduced from 0 to N0.

[0268] Optionally, the baseline determination submodule is specifically used for:

[0269] Step 1: From N sIn each scene, N0 objects are randomly selected as the initial cluster centers;

[0270] Step 2: According to the mean of each cluster center scene, calculate the distance between each scene and the mean of the cluster center scene, and re-divide the scenes according to the minimum distance;

[0271] Step 3: Calculate the mean of the re-divided cluster center scenes;

[0272] Step 4: Based on the mean of the re-divided cluster center scenes, the standard measurement function is calculated using the clustering criterion function. If the standard measurement function converges, the algorithm terminates; otherwise, return to Step 2.

[0273] Optionally, it further includes: a system building module for:

[0274] The instantaneous flexibility reflecting the power-type regulation capability and the process flexibility reflecting the energy-type regulation capability are used as the primary indicators;

[0275] The load fluctuation rate, short-term power balance capability, and flexibility supply capability that affect the instantaneous flexibility of the first-level indicator are used as second-level indicators;

[0276] The system supply and demand balance capability, multi-energy complementary effect, and system energy regulation capability that affect the flexibility of the first-level indicator process are used as second-level indicators;

[0277] The parameters that affect the secondary indicators are used as the tertiary indicators;

[0278] The indicators at all levels are combined and weighted by the AHP-entropy weight method to obtain the weights of the indicators at all levels;

[0279] A flexibility quantitative index system is constructed by first-level indicators, second-level indicators, third-level indicators, and weights of indicators at each level.

[0280] Optionally, the steps of combining and weighting the indicators at all levels by the AHP-entropy weight method in the system building module to obtain the weights of the indicators at all levels include:

[0281] Step 1: Determine the weight of the flexibility quantitative index system and the flexibility level division interval through expert opinions and questionnaire surveys;

[0282] Step 2: Determine whether the indicators at all levels belong to the category of “the larger the better”. If so, the actual multi-energy system operation history data is normalized by using the “the larger the better” indicator calculation formula based on the flexibility level division interval. Otherwise, the actual multi-energy system operation history data is normalized by using the “the smaller the better” indicator calculation formula based on the flexibility level division interval.

[0283] Step 3: Based on the physical meaning and characteristics of the indicators themselves, combined with the normalized historical data of actual multi-energy system operation, the objective weight of the indicator system is calculated using the entropy weight method;

[0284] Step 4: By combining subjective and objective weights, weight the three-level indicators in the flexibility quantitative index system;

[0285] Step 5: Calculate the weights of the secondary and primary indicators based on the structure of the indicator system and the weights of the third-level indicators.

[0286] Optionally, the larger the better type indicator calculation formula is as follows:

[0287]

[0288] The calculation formula of the smaller the better index is as follows:

[0289]

[0290] Where y is the normalized actual multi-energy system operation history data, x is the actual multi-energy system operation history data, and x min is the minimum value in the actual multi-energy system operation history data, x max is the maximum value in the actual multi-energy system operation history data, ω max is the maximum value of the index system weight and flexibility level division interval, ω min It is the minimum value of the index system weight and flexibility level division interval.

[0291] Optionally, the three-level indicators in the flexibility quantitative indicator system are weighted according to the following formula, including: The weight evaluation calculation method of the AHP-entropy weight method is:

[0292] η=aη1+bη2

[0293] In the formula, η represents the combined weight vector, a and b represent the weight coefficients, η1 and η2 represent the weight vectors calculated by AHP and entropy weight method respectively;

[0294] The a and b satisfy:

[0295] a,b>0; a+b=1.

[0296] Optionally, the weights of the secondary and primary indicators are calculated as follows:

[0297]

[0298] Where η u is the weight of the first-level index u, η ν is the weight of the secondary index v, η wis the weight of the third-level indicator w, tw is the number of second-level indicators corresponding to the first-level indicator u, and th is the number of third-level indicators corresponding to the second-level indicator v.

[0299] Optionally, the evaluation module is specifically used for:

[0300] Step 1: Based on the flexibility evaluation level, the evaluation score [ω min ,ω max ] to classify, and generate standard cloud images and cloud digital features according to the classification;

[0301] Step 2: Collect the actual operation data of the system multiple times to form a sample of a certain size. According to the allowable range of indicators in the actual operation of the multi-energy system, the collected samples are positively normalized and linearly mapped so that the scores fall within the flexibility level division interval, and the mean and variance of each three-level indicator are calculated;

[0302] Step 3: Calculate the digital features of the three-level indicators in the cloud model based on the mean and variance of each three-level indicator;

[0303] Step 4: Calculate the digital characteristics of the comprehensive cloud model of the secondary and primary indicators through the digital characteristics and assigned weights of the tertiary indicators.

[0304] Optionally, the digital characteristics of the three-level indicators in the cloud model are calculated as follows:

[0305]

[0306] Where, Ex j 、En j and He j are the evaluation mean of the flexibility of indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. is the evaluation mean, n is the total number of evaluations, i is the number of evaluations, x j,i is the i-th evaluation value of indicator j, S j is the variance of the evaluation value of indicator j, and j is the type of the third-level indicator.

[0307] Optionally, the digital features of the comprehensive cloud model of the secondary index and the primary index are calculated as follows:

[0308]

[0309] Where, Ex j,k-1 、En j,k-1 、He j,k-1 are the evaluation mean of the flexibility of k-1 level indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. j,k 、En、He j,kare the evaluation mean of the flexibility of level k indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. j,k Represents the combined weight of the k-th level indicator j, where k represents the indicator level.

[0310] Example 3

[0311] like Figure 5 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0312] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method flows or corresponding functions, so as to implement the steps of a multi-dimensional and multi-level flexibility evaluation method for a multi-energy system in the above-mentioned embodiment.

[0313] Example 4

[0314] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in the electronic device for storing programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and the extended storage medium supported by the electronic device. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a multi-dimensional and multi-level flexibility evaluation method for a multi-energy system in the above embodiment.

[0315] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take 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.) containing computer-usable program code.

[0316] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 processes in the flowchart and / or block diagram. 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.

[0317] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0318] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0319] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A multi-dimensional and multi-level flexibility assessment method for a multi-energy system, characterized in that: include: Determine the operating baseline of the multi-energy system based on the peak and valley characteristics of the load; Calculating digital features of the comprehensive cloud model based on parameters of the multi-energy system and an operating baseline of the multi-energy system in combination with a pre-constructed flexibility quantitative index system; Based on the comparison between the digital features of the comprehensive cloud model and the digital features of the standard cloud model, the flexibility assessment results of the multi-energy system are obtained; Among them, the pre-constructed flexibility quantitative index system is constructed based on the instantaneous flexibility index and the process flexibility index as the first-level index, the second-level index affecting each first-level index, and the third-level index affecting each second-level index.

2. The method according to claim 1, characterized in that The calculation of the uncertainty interval based on the load valley-peak fluctuation characteristics and determination of the system operation baseline include: The normal distribution model is used to simulate the uncertainty of the electric load, gas load and heat load errors, and the probability distribution function of the uncertain variables is obtained; The probability distribution function of the uncertain variable is discretized, and based on the discretized probability density function, a random sampling method is used to generate N s scenes; K-means clustering is used to cluster the N randomly sampled s The number of scenes is reduced to N0; The N0 scenario operating ranges are used as the system operating baseline.

3. The method according to claim 2, characterized in that The K-means clustering method is used to randomly generate N s The number of scenarios is reduced from 1 to N0, including: Step 1: From N s In each scene, N0 objects are randomly selected as the initial cluster centers; Step 2: According to the mean of each cluster center scene, calculate the distance between each scene and the mean of the cluster center scene, and re-divide the scene according to the minimum distance, and update N0 according to the number of re-divided scenes; Step 3: Calculate the mean of the re-divided cluster center scenes; Step 4: Based on the mean of the re-divided cluster center scenes, the clustering criterion function is used to calculate the standard measurement function. If the standard measurement function converges, the algorithm terminates and outputs N0 scenes; otherwise, return to Step 2.

4. The method according to claim 1, characterized in that The construction of the flexibility quantitative index system includes: The instantaneous flexibility reflecting the power-type regulation capability and the process flexibility reflecting the energy-type regulation capability are used as the primary indicators; The load fluctuation rate, short-term power balancing capability, and flexibility supply capability that affect the instantaneous flexibility of the first-level indicator are used as second-level indicators; The system supply and demand balance capability, multi-energy complementary effect, and system energy regulation capability that affect the flexibility of the first-level indicator process are used as second-level indicators; The parameters that affect the secondary indicators are used as the tertiary indicators; The indicators at all levels are combined and weighted by the AHP-entropy weight method to obtain the weights of the indicators at all levels; A flexibility quantitative index system is constructed by first-level indicators, second-level indicators, third-level indicators, and weights of indicators at each level.

5. The method according to claim 4, characterized in that The AHP-entropy weight method is used to combine and weight the indicators at all levels to obtain the weights of the indicators at all levels, including: Step 1: Determine the weight of the flexibility quantitative index system and the flexibility level division interval through expert opinions and questionnaire surveys; Step 2: Determine whether the indicators at all levels belong to the category of “the larger the better”. If so, the actual multi-energy system operation history data is normalized by using the “the larger the better” indicator calculation formula based on the flexibility level division interval. Otherwise, the actual multi-energy system operation history data is normalized by using the “the smaller the better” indicator calculation formula based on the flexibility level division interval. Step 3: Based on the physical meaning and characteristics of the indicators themselves, combined with the normalized historical data of actual multi-energy system operation, the objective weight of the indicator system is calculated using the entropy weight method; Step 4: By combining subjective and objective weights, weight the three-level indicators in the flexibility quantitative index system; Step 5: Calculate the weights of the secondary and primary indicators based on the structure of the indicator system and the weights of the third-level indicators.

6. The method according to claim 5, characterized in that The calculation formula of the larger the better index is as follows: The calculation formula of the smaller the better index is as follows: Where y is the normalized actual multi-energy system operation history data, x is the actual multi-energy system operation history data, and x min is the minimum value in the actual multi-energy system operation history data, x max is the maximum value in the actual multi-energy system operation history data, ω max is the maximum value of the index system weight and flexibility level division interval, ω min It is the minimum value of the index system weight and flexibility level division interval.

7. The method according to claim 5, characterized in that The three-level indicators in the flexibility quantitative indicator system are weighted according to the following formula, including: η=aη1+bη2 In the formula, η represents the combined weight vector, a and b represent the weight coefficients, η1 and η2 represent the weight vectors calculated by AHP and entropy weight method respectively; The weight coefficients a and b satisfy: a,b>0; a+b=1.

8. The method according to claim 7, characterized in that The weights of the secondary and primary indicators are calculated as follows: Where η u is the weight of the first-level index u, η ν is the weight of the secondary index v, η w is the weight of the third-level indicator w, tw is the number of second-level indicators corresponding to the first-level indicator u, and th is the number of third-level indicators corresponding to the second-level indicator v.

9. The method according to claim 1, characterized in that The multi-energy system flexibility assessment result is obtained by comparing the digital features of the comprehensive cloud model with the digital features of the standard cloud model, including: Step 1: Based on the flexibility evaluation level, the evaluation score [ω min ,ω max ] to classify and generate standard cloud model digital features according to the classification; Step 2: Collect the actual operation data of the system multiple times to form samples. According to the allowable range of indicators in the actual operation of the multi-energy system, the samples are forward-normalized and linearly mapped so that the scores fall within the flexibility assessment level division interval, and the mean and variance of each three-level indicator are calculated; Step 3: Calculate the digital characteristics of the three-level indicators in the comprehensive cloud model based on the mean and variance of each three-level indicator; Step 4: Calculate the digital characteristics of the comprehensive cloud model of the secondary indicators and the primary indicators in the comprehensive cloud model through the digital characteristics and weights of the third-level indicators; Step 5: Compare the digital features of the comprehensive cloud model with the digital features of the standard cloud model to obtain the flexibility evaluation results of the multi-energy system.

10. The method according to claim 9, characterized in that The digital characteristics of the three-level indicators in the comprehensive cloud model are calculated as follows: Where, Ex j 、En j and He j are the evaluation mean of the flexibility of indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. is the evaluation mean, n is the total number of evaluations, i is the number of evaluations, x j,i is the i-th evaluation value of indicator j, S j is the variance of the evaluation value of indicator j, and j is the type of the third-level indicator.

11. The method according to claim 10, characterized in that The digital characteristics of the comprehensive cloud model of the secondary indicators and primary indicators are calculated as follows: Where, Ex j,k-1 、En j,k-1 、He j,k-1 are the evaluation mean of the flexibility of k-1 level indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. j,k 、En、He j,k are the evaluation mean of the flexibility of level k indicator j, the reliability of the evaluation result, and the measure of the stability of the evaluation result, respectively. j,k Represents the combined weight of the k-th level indicator j, where k represents the indicator level.

12. A multi-dimensional and multi-level flexibility evaluation system for a multi-energy system, characterized in that: include: A baseline determination module, used to determine the operation baseline of the multi-energy system based on the peak and valley characteristics of the load; A digital feature calculation module is used to calculate the digital features of the comprehensive cloud model based on the parameters of the multi-energy system combined with a pre-built flexibility quantitative index system; An evaluation module, used for obtaining a flexibility evaluation result of a multi-energy system based on a comparison between the digital features of the comprehensive cloud model and the digital features of a standard cloud model; Among them, the pre-constructed flexibility quantitative index system is constructed based on the instantaneous flexibility index and the process flexibility index as the first-level index, the second-level index affecting each first-level index, and the third-level index affecting each second-level index.

13. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a multi-dimensional and multi-level flexibility assessment method for a multi-energy system as described in any one of claims 1 to 11 is implemented.

14. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a multi-dimensional and multi-level flexibility assessment method for a multi-energy system as described in any one of claims 1 to 11 is implemented.

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