Dynamic aggregation and optimal scheduling method and system for urban regional integrated energy system

Abstract

The invention discloses a dynamic aggregation and optimal scheduling method and system for an urban regional integrated energy system, and the method comprises the steps: aggregating different original models and constraint conditions of devices of the same type into an aggregation model and an aggregation constraint condition of the devices of the type based on a polyhedral model; generating an original scene based on the aggregation model of each type of device, and filtering the original scene based on the aggregation constraint condition of each type of device; according to similarity indexes and representative indexes of the scenes, reducing the filtered scenes to obtain a typical scene set; establishing a target function of day-ahead optimization scheduling and a target function of intra-day rolling scheduling based on typical scene operation parameters generated by an aggregation model and an aggregation constraint condition; the objective function of day-ahead optimal scheduling and the objective function of intra-day rolling scheduling are solved to obtain a system scheduling scheme under the condition that the operation constraints of various types of devices and the power balance constraints of the integrated energy system are met, and the optimal scheduling effect of the integrated energy system is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system optimization and scheduling, and particularly relates to a dynamic aggregation and optimal scheduling method and system for an urban area integrated energy system considering source-load uncertainty. Background Art

[0002] An integrated energy system (IES) is a system that organically combines and optimally allocates various energy forms such as electricity, heat, and gas, enabling the conversion and distribution of energy between different energy systems. Its multi-energy synergy effect can significantly improve the energy efficiency and stability of the system, and can also greatly promote the application of renewable energy.

[0003] With the rapid development of distributed energy, there are a large number and variety of devices in the IES, resulting in a significant increase in computational complexity. In addition, renewable energy and loads have certain uncertainties, and this volatility makes the energy supply-demand relationship within the system more complex, posing challenges to the optimal operation of the IES.

[0004] In the prior art, for the operation optimization and scheduling of an integrated energy system, real-time data monitoring of the integrated energy system is carried out to obtain integrated energy monitoring data, which includes power monitoring data, heat energy monitoring data, renewable energy monitoring data, and stored energy monitoring data; energy demand analysis is performed on the integrated energy monitoring data to generate energy demand data; based on the energy demand data, energy nodes of the integrated energy system are divided to construct an energy node set, and the energy node set includes power nodes, heat energy nodes, renewable energy nodes, and stored energy nodes; operation constraint analysis is performed on the energy node set to generate operation constraint data; and energy load calculation is performed on the energy node set according to the operation constraint data to generate energy node load data. Although efficient and accurate energy optimization scheduling is achieved, in the face of the complex coupling relationship between multiple energies in the IES, the models of the same type of energy devices are not aggregated, and these resources cannot be efficiently optimized and scheduled, resulting in a large computational burden and low system operation efficiency. Moreover, in the face of the randomness and volatility of renewable energy, there is a lack of effective and intuitive scheduling optimization methods. Summary of the Invention

[0005] To address the deficiencies in the existing technologies, the present invention provides a method and system for dynamic aggregation and optimal scheduling of an integrated energy system in urban areas. By considering the numerous and diverse technical parameters of the same type of equipment in actual engineering, as well as the uncertainty of the source and load, the models of the same type of equipment are aggregated, and the scenario analysis method is used to consider this uncertainty, aiming to effectively optimize the multi-time scale scheduling of the IES; by aggregating the models of the same type of energy equipment, the computational burden can be effectively reduced, thereby improving the system operation efficiency. In addition, the scenario analysis method can better describe the randomness and volatility of renewable energy. Considering these factors comprehensively can significantly improve the optimization scheduling effect of the IES.

[0006] The present invention adopts the following technical solutions.

[0007] The present invention proposes a method for dynamic aggregation and optimal scheduling of an integrated energy system in urban areas, including:

[0008] Obtain the operation parameters of various types of devices in the integrated energy system under different working conditions to establish different original models and constraint conditions for various types of devices; based on the polyhedron model, aggregate the different original models and constraint conditions of the same type of device into the aggregated model and aggregated constraint conditions of this type of device; generate the original scenarios based on the aggregated models of various types of devices, and filter the original scenarios based on the aggregated constraint conditions of various types of devices; according to the similarity index and representativeness index of the scenarios, reduce the filtered scenarios to obtain a set of typical scenarios; use the sum of the energy purchase cost, operation and maintenance cost, and penalty cost of all typical scenarios as the objective function for day-ahead optimal scheduling, and use the sum of the energy purchase cost, change in operation and maintenance cost, and penalty cost of all typical scenarios as the objective function for intraday rolling scheduling; solve the objective function of day-ahead optimal scheduling and the objective function of intraday rolling scheduling under the satisfaction of the operation constraints of various types of devices and the power balance constraint of the integrated energy system to obtain the system scheduling plan.

[0009] Preferably, the different types of devices in the integrated energy system include: wind power generation devices, photovoltaic power generation devices, electrical energy storage devices, gas energy storage devices, thermal energy storage devices, power-to-gas units, CHP units, and electric boilers.

[0010] The original model includes: a power balance model; the constraint conditions include but are not limited to: capacity constraint, power constraint, and state constraint.

[0011] Preferably, use the different original models and constraint conditions of various types of devices and the power balance constraint of the integrated energy system as the input data of the compiler; use the compiler to output the original polyhedron model set Among them, is the constraint matrix of the i-th original polyhedron model of the m-th type of device, is the constraint vector of the i-th original polyhedron model of the m-th type of device, x is the operating parameter under different working conditions, m = 1, 2, …, M, where M is the total number of device types in the integrated energy system, and i = 1, 2, …, N, where N is the total number of original polyhedron models; the original polyhedron model corresponds to the i-th original model and constraint conditions of the m-th type of device.

[0012] Preferably, the mean value is calculated for the set of original polyhedron models of the same type of device to obtain the basic polyhedron model of each type of device which satisfies the following relational expression:

[0013]

[0014] In the formula, is the constraint matrix of the basic polyhedron model of the m-th type of device, is the constraint vector of the basic polyhedron model of the m-th type of device, is the average value of the operating parameters under different working conditions;

[0015] The basic polyhedron model corresponds to the basic model and basic constraint conditions of the m-th type of device.

[0016] Preferably, in the same type of device, with the optimization goal that the scaled and translated basic polyhedron model contains the original polyhedron model and the scaling factor is the smallest, the scaling factor and translation vector of each original polyhedron model are determined;

[0017] Among them, the scaled and translated basic polyhedron model satisfies the following relational expression:

[0018]

[0019] In the formula, is the scaling factor of the i-th original polyhedron model of the m-th type of device, is the translation vector of the i-th original polyhedron model of the m-th type of device.

[0020] Preferably, in the same type of device, a mapping model and constraint conditions are established between the constraint matrix of the original polyhedron model and the constraint matrix of the basic polyhedron model, which satisfy the following relational expression:

[0021]

[0022] In the formula, U m is the mapping matrix between the constraint matrix of the original polyhedron model and the constraint matrix of the basic polyhedron model in the m-th type of device.

[0023] Preferably, in the same type of device, when the mapping model and the constraint conditions are satisfied, the average value of the scaling factors of all the original polyhedron models is calculated as the scaling factor of this type of device; the constraint vector of the basic polyhedron model is updated by using the scaling factor of this type of device and the total number of the original polyhedron models;

[0024] The scaling factors of each type of device satisfy the following relational expression:

[0025]

[0026] In the formula, is the scaling factor of the m-th type of device;

[0027] The updated constraint vector of the basic polyhedron model satisfies the following relational expression:

[0028]

[0029] In the formula, is the updated constraint vector of the basic polyhedron model.

[0030] Preferably, in the same type of device, the aggregated polyhedron model of this type of device is constructed by using the constraint matrix and the updated constraint vector of the basic polyhedron model The set of the aggregated polyhedron models of the integrated energy system is M is the total number of the device types in the integrated energy system;

[0031] The aggregated model and the aggregated constraint conditions of the aggregated polyhedron model corresponding to the m-th type of device.

[0032] Preferably, the prediction data is generated by using the aggregated models of each type of device; based on the probability distribution of the prediction data, the sampling method is adopted to obtain the prediction samples to construct different original scenarios; the original scenarios are filtered by using the aggregated constraint conditions of each type of device; according to the similarity index and the representativeness index of the scenarios, the filtered scenarios are reduced, including:

[0033] 1), The similarity index of two scenarios satisfies the following relational expression:

[0034] x(i,j) = ∑|Ws(:,i) - Ws(:,j)|

[0035] In the formula, x(i, j) is the distance between scenario i and the typical scenario j, and Ws(:,i) and Ws(:,j) are the data of the i-th scenario and the j-th scenario in the original scenario set Ws respectively;

[0036] 2), Calculate the average value y0(i) of the similarity indexes of each scenario with the remaining scenarios as the representativeness index of each scenario, and it satisfies the following relational expression:

[0037]

[0038] Wherein, x(:, i) is the similarity index of scenario i and the remaining scenarios;

[0039] 3), taking the scenario with the smallest representative index as the first scenario, and the scenario with the smallest similarity index with the first scenario as the second scenario, and taking the sum of the probability of the first scenario and the probability of the second scenario as the updated probability of the second scenario, satisfying the following relational expression:

[0040] p′ r = p r + p d

[0041] Wherein, p′ r is the updated probability of the second scenario, p r is the probability of the second scenario, p d is the probability of the first scenario;

[0042] 4), cutting the first scenario from the scenarios after the first cut to obtain an updated set of scenarios; for the updated set of scenarios, repeat steps 1) to 4) until the total number of scenarios in the updated set of scenarios reaches the set value, and output the updated set of scenarios as the typical scenario set.

[0043] Preferably, the objective function of the day-ahead optimal scheduling satisfies the following relational expression:

[0044]

[0045] Wherein, F ahead is the objective function of the day-ahead optimal scheduling, P a represents the probability of the typical scenario a, n represents the total number of typical scenarios, EPC is the energy purchase cost, OMC is the operation and maintenance cost, and WCP is the penalty cost.

[0046] Preferably, based on the aggregation model and aggregation constraint conditions of each type of device, determine the power purchase amount and gas purchase amount in each period, and calculate the energy purchase cost with the following relational expression:

[0047]

[0048] Wherein, λ e,t , λ g,t are the time-of-use electricity price and gas price in period t respectively, P g,t is the power purchase power of the system in period t, G g,t is the natural gas amount purchased by the system in period t, and T is the total number of periods;

[0049] Based on the aggregation models and aggregation constraint conditions of various types of devices, determine the charging and discharging power of the energy storage device and the operating power of the energy conversion device in each period, and calculate the operation and maintenance cost with the following relational expression:

[0050]

[0051] In the formula, β k is the unit charging and discharging cost of the k-th type of energy storage device, and β l is the unit operating cost of the j-th type of energy conversion device, are the charging and discharging powers of the k-th type of energy storage device in the t-th period respectively, and P j，t is the operating power of the j-th type of energy conversion device in the t-th period, N S is the total number of energy storage device types, and N C is the total number of energy conversion device types;

[0052] The penalty cost includes the penalty cost for wind curtailment and satisfies the following relational expression:

[0053]

[0054] In the formula, k w is the wind curtailment penalty coefficient, ΔP c，t is the wind curtailment power in the t-th period, and T is the total number of periods.

[0055] Preferably, the objective function of the intra-day rolling dispatch satisfies the following relational expression:

[0056]

[0057] In the formula, F inday is the objective function of the intra-day rolling dispatch, P s represents the probability of the typical scenario s, n represents the total number of typical scenarios, EPC is the energy purchase cost, ΔOMC is the change in the operation and maintenance cost,

[0058] and WCP is the penalty cost.

[0059] Preferably, based on the aggregation models and aggregation constraint conditions of various types of devices, determine the electricity purchase quantity and gas purchase quantity in each period, and calculate the energy purchase cost with the following relational expression:

[0060]

[0061] In the formula, λ e，t and λ g，t are the time-of-use electricity price and gas price in the t-th period respectively, P g,t is the electricity purchase power of the system in the t-th period, G g,t is the natural gas quantity purchased by the system in the t-th period, and Tinday is the total number of intra-day rolling periods;

[0062] Based on the aggregation models and aggregation constraint conditions of various types of devices, determine the charging and discharging power change amounts of energy storage devices and the operating power change amounts of energy conversion devices in each time period, and calculate the change amount of operation and maintenance costs according to the following relational expression:

[0063]

[0064] In the formula, γ k and γ J are respectively the power change penalty cost coefficients of the k-th type of energy storage device and the j-th type of energy conversion device. are respectively the charging and discharging power change amounts of the k-th type of energy storage device in the t-th time period, and ΔP j,t is the operating power change amount of the j-th type of energy conversion device in the t-th time period. N S is the total number of energy storage device types, and N C is the total number of energy conversion device types;

[0065] The penalty cost includes the penalty cost for abandoned wind and satisfies the following relational expression:

[0066]

[0067] In the formula, k w is the abandoned wind penalty coefficient, and ΔP c,t is the abandoned wind power in the t-th time period, and Tinday is the total number of intraday rolling time periods.

[0068] The present invention also proposes a dynamic aggregation and optimal scheduling system for an urban area integrated energy system, including:

[0069] A dynamic aggregation module, which is used to obtain the operating parameters of various types of devices in the integrated energy system under different working conditions, so as to establish different original models and constraint conditions of various types of devices; based on the polyhedron model, aggregate the different original models and constraint conditions of the same type of device into the aggregation model and aggregation constraint conditions of this type of device;

[0070] An optimal scheduling module, which is used to generate an original scenario based on the aggregation model of various types of devices, and filter the original scenario based on the aggregation constraint conditions of various types of devices; according to the similarity index and representativeness index of the scenarios, reduce the filtered scenarios to obtain a set of typical scenarios; based on the operating parameters of the typical scenarios generated by the aggregation model and aggregation constraint conditions, take the sum of the energy purchase cost, operation and maintenance cost, and penalty cost of all typical scenarios as the objective function of day-ahead optimal scheduling, and take the sum of the energy purchase cost, operation and maintenance cost change amount, and penalty cost of all typical scenarios as the objective function of intraday rolling scheduling; solve the objective function of day-ahead optimal scheduling and the objective function of intraday rolling scheduling under the satisfaction of the operation constraints of various types of devices and the power balance constraint of the integrated energy system to obtain the system scheduling plan.

[0071] The present invention is also a terminal, including a processor and a storage medium; the storage medium is used for storing instructions; the processor is used for operating according to the instructions to execute the steps of the method.

[0072] The present invention is also a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method are implemented.

[0073] The beneficial effects of the present invention are at least as follows compared with the prior art. The method proposed by the present invention effectively reduces the complexity of system modeling and calculation by aggregating different models and constraint conditions of the same type of devices, while retaining key dynamic characteristics, and has strong practicability; by adopting Latin hypercube sampling and scenario reduction, the uncertainty of renewable energy and load can be reasonably considered to ensure the representativeness of the scheduling scheme and the robustness of the system; through the multi-time scale optimal scheduling method, the short-term and long-term operation goals can be balanced, and while ensuring the safe and stable operation of the system, the economic cost is minimized. Description of the Drawings

[0074] Figure 1 is a flowchart of the dynamic aggregation and optimal scheduling method for the urban area integrated energy system proposed by the present invention;

[0075] Figure 2 is an architecture diagram of the integrated energy system adopted in the embodiment of the present invention;

[0076] Figure 3 is the predicted data of the wind power and photovoltaic output for the day-ahead in the embodiment of the present invention;

[0077] Figure 4 is the predicted data of various loads for the day-ahead in the embodiment of the present invention;

[0078] Figure 5 is the day-ahead and intra-day scheduling output curves of the electrical energy storage device and the gas energy storage device in the embodiment of the present invention;

[0079] Figure 6 is the day-ahead and intra-day scheduling output curves of the thermal energy storage device and the P2G device in the embodiment of the present invention;

[0080] Figure 7 is the day-ahead and intra-day scheduling output curves of the CHP unit and the electric boiler in the embodiment of the present invention. Detailed Embodiments

[0081] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only a part of the embodiments of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0082] The present invention proposes a dynamic aggregation and optimal scheduling method for an urban regional integrated energy system considering source-load uncertainty, which includes three parts: system modeling and model aggregation, scenario analysis, and optimal scheduling. The adaptive aggregation of different models in the integrated energy system is achieved through the scaling and translation method of the polyhedron model; the construction of typical scenarios is optimized by Latin hypercube sampling and K-means clustering; through multi-time scale optimal scheduling, the coordination of long-term planning and short-term dynamic optimization is realized.

[0083] As Figure 1 shown, the method includes:

[0084] Step 1: Obtain the operating parameters of various types of devices in the integrated energy system under different working conditions to establish different original models and constraint conditions for various types of devices; based on the polyhedron model, aggregate different original models of the same type of device into an aggregated model of this type of device.

[0085] Specifically, Step 1 includes:

[0086] Step 1.1: Obtain the operating parameters of various types of devices in the integrated energy system under different working conditions;

[0087] Specifically, different types of devices in the integrated energy system include but are not limited to: wind power generation devices, photovoltaic power generation devices, electrical energy storage devices, gas energy storage devices, thermal energy storage devices, power-to-gas (P2G) units, CHP units, and electric boilers (EB); devices of the same type refer to devices with the same function but different operating parameters, models, or manufacturers. For example, various electrical energy storage devices with different operating parameters are devices of the same type, and various gas energy storage devices with different models are also devices of the same type.

[0088] Step 1.2: Use the operating parameters under different working conditions to establish different original models and constraint conditions for various types of devices; use the different original models and constraint conditions of various types of devices and the power balance constraint of the integrated energy system as the input data of the compiler; use the compiler to output the original polyhedron model set;

[0089] The original models of various types of devices include, but are not limited to: power balance models; the constraint conditions of various types of devices include, but are not limited to: capacity constraints, power constraints, and state constraints; in the embodiments, models of electrical energy storage devices, gas energy storage devices, and thermal energy storage devices are established respectively, and considering the limitations of actual hardware conditions, the charging and discharging efficiency, charging and discharging power, and the state of the energy storage device are constrained.

[0090] Specifically, the original model and constraint conditions of the electrical energy storage device satisfy the following relational expressions:

[0091]

[0092] In the formula, represents the energy storage capacity of the e-th electrical energy storage device at time t, respectively represent the charging and discharging powers of the e-th energy storage device at time t, respectively represent the charging and discharging efficiencies of the e-th electrical energy storage device, respectively represent the lower and upper limit values of the energy storage capacity of the e-th electrical energy storage device, is a 0-1 variable, respectively representing the charging and discharging states of the e-th electrical energy storage device at time t, respectively represent the minimum and maximum values of the charging power of the e-th electrical energy storage device, respectively represent the minimum and maximum values of the discharging power of the e-th electrical energy storage device, respectively represent the final and initial energy storage capacities of the e-th electrical energy storage device within the scheduling period.

[0093] Similarly, the original models and constraint conditions of the gas energy storage device and the thermal energy storage device are established.

[0094] In the embodiments, the original model and constraint conditions of the energy coupling device are established, including:

[0095] The P2G unit is a device that converts electrical energy into synthetic natural gas. During the peak period of renewable energy power generation, the surplus electrical energy can be converted into natural gas, stored and used, which can effectively improve the economic benefits of the integrated energy system operation and promote the consumption of wind power; the original model and constraint conditions of the P2G unit satisfy the following relational expressions:

[0096]

[0097] In the formula, respectively represent the gas power generated and the electrical power consumed by the i-th P2G unit at time t, represents the electrical conversion efficiency of the i-th P2G unit, respectively represent the lower and upper limit values of the power consumption of the i-th P2G unit, respectively represent the maximum downward ramp rate and the maximum upward ramp rate of the i-th P2G unit;

[0099] The CHP unit is a coupled device that converts natural gas into electric power and heat. The original model and constraint conditions of the CHP unit satisfy the following relational expressions:

[0100]

[0101] In the formula, respectively represent the electric power, heat power, and gas power consumed by the c-th CHP unit at time t; respectively represent the electric-to-heat power ratio and the gas-to-electric conversion efficiency of the c-th CHP unit;

[0102] The electric boiler converts electric energy into heat energy by means of water heating, with the characteristics of no pollution and high efficiency, and is safe and reliable. The original model and constraint conditions of the electric boiler satisfy the following relational expressions:

[0103]

[0104] In the formula, represents the heat power generated by the i-th EB unit at time t, represents the electrical conversion efficiency of the i-th EB unit, μ Loss represents the heat loss efficiency of the electric boiler,

[0105] respectively represent the lower limit value and the upper limit value of the power consumption of the i-th EB unit, respectively represent the maximum downward ramp rate and the maximum upward ramp rate of the i-th EB unit.

[0106] The polyhedral model in the compiler is an efficient program optimization technology. It maps complex loop dependencies to a high-dimensional geometric space, thereby achieving parallelization and locality optimization of computational tasks at the compilation stage. By constructing and operating polyhedral representations, instructions and data access can be effectively scheduled to reduce resource contention and cache misses, thereby improving the performance of program execution. Therefore, the different original models and constraint conditions of various types of devices, and the power balance constraints of the integrated energy system are used as the input data of the compiler; the compiler outputs the set of original polyhedral models Among them, is the constraint matrix of the i-th original polyhedral model of the m-th type of device, is the constraint vector of the $i$-th original polyhedron model of the $m$-th type of device, $x$ is the operating parameter under different working conditions, $m = 1, 2, \ldots, M$, where $M$ is the total number of device types in the integrated energy system, and $i = 1, 2, \ldots, N$, where $N$ is the total number of original polyhedron models; the original polyhedron model corresponds to the $i$-th original model and constraint conditions of the $m$-th type of device. Therefore, the obtained set of polyhedron models includes all the original models of various types of devices in the integrated energy system and has a unified format.

[0107] Step 1.3: Calculate the mean value of the set of original polyhedron models of the same type of device to obtain the basic polyhedron model of each type of device Satisfy the following relational expression:

[0108]

[0109] In the formula, is the constraint matrix of the basic polyhedron model of the $m$-th type of device, is the constraint vector of the basic polyhedron model of the $m$-th type of device, is the average value of the operating parameters under different working conditions;

[0110] The basic polyhedron model corresponds to the basic model and basic constraint conditions of the $m$-th type of device;

[0111] Since all the original models of various types of devices in the integrated energy system are compiled into polyhedron models with the same format, and the mean value calculation of the constraint matrix and constraint vector is used to replace the process of establishing the basic model for each type of device by using the average value of the operating parameters under different working conditions, the establishment of the basic model can be achieved more simply and quickly.

[0112] Step 1.4: In the same type of device, with the goal of minimizing the scaling factor while ensuring that the scaled and translated basic polyhedron model contains the original polyhedron model, determine the scaling factor and translation vector of each original polyhedron model;

[0113] Among them, the scaled and translated basic polyhedron model satisfies the following relational expression:

[0114]

[0115] In the formula, is the scaling factor of the $i$-th original polyhedron model of the $m$-th type of device, is the translation vector of the $i$-th original polyhedron model of the $m$-th type of device;

[0116] The translation vector is used to adjust the position of the polyhedron model to match the dynamic characteristics of the operating states of different types of devices changing over time.

[0117] Step 1.5. In the devices of the same type, establish a mapping model and constraint conditions between the constraint matrix of the original polyhedron model and the constraint matrix of the basic polyhedron model, satisfying the following relational expressions:

[0118]

[0119] In the formula, U m is the mapping matrix between the constraint matrix of the original polyhedron model and the constraint matrix of the basic polyhedron model in the m-th type of device;

[0120] By establishing the above mapping model and constraint conditions, ensure the linear mapping relationship between the original polyhedron model and the basic polyhedron model, and ensure that the finally obtained aggregated model can cover all the original models.

[0121] Step 1.6. In the devices of the same type, when the mapping model and constraint conditions are satisfied, calculate the average value of the scaling factors of all the original polyhedron models as the scaling factor of this type of device; use the scaling factor of this type of device and the total number of the original polyhedron models to update the constraint vector of the basic polyhedron model;

[0122] The scaling factors of each type of device satisfy the following relational expressions:

[0123]

[0124] In the formula, is the scaling factor of the m-th type of device;

[0125] The updated constraint vector of the basic polyhedron model satisfies the following relational expressions:

[0126]

[0127] In the formula, is the updated constraint vector of the basic polyhedron model;

[0128] Step 1.7. In the devices of the same type, use the constraint matrix and the updated constraint vector of the basic polyhedron model to construct the aggregated polyhedron model of this type of device The aggregated model and aggregated constraint conditions corresponding to the aggregated polyhedron model of the m-th type of device; The set of aggregated polyhedron models of the integrated energy system is M is the total number of device types in the integrated energy system.

[0129] In the embodiment, taking the electrical energy storage device as an example, convert the original model and constraint conditions of the electrical energy storage device into the original polyhedron model where the variable represents the energy storage capacity of the e-th electrical energy storage device at time t, respectively represent the charging and discharging power of the e-th energy storage device in the t-th period, is a 0-1 variable, respectively representing the charging and discharging states of the e-th electrical energy storage device in the t-th period;

[0130] The original polyhedron model of the capacity is expressed as:

[0131]

[0132] The original polyhedron model of the charging and discharging power is expressed as:

[0133]

[0134] The original polyhedron model of the mutual exclusion between charging and discharging is expressed as:

[0135]

[0136] Then the original polyhedron model of the electrical energy storage device satisfies the following relational expressions:

[0137]

[0138] The set of aggregated polyhedron models of the integrated energy system includes the dynamic aggregation models of various types of devices in the integrated energy system, realizes the aggregation of the original models of the same type of devices, realizes the dynamic aggregation of the integrated energy system, and facilitates the development of scheduling optimization based on the dynamic aggregation models. In the prior art, the aggregation of multiple models in the integrated energy system is to strategically combine the prediction results of multiple models. However, due to the possible differences in the models at different management levels in the integrated energy system, the method of aggregating the prediction results will affect the prediction accuracy. In order to avoid the influence of model differences on the prediction accuracy of the integrated energy system, the present invention proposes to realize the dynamic aggregation of the original models of the same type of devices based on the polyhedron model compilation technology, which can be more widely docked and aggregated with the models at different management levels, thereby significantly improving the prediction accuracy of the system.

[0139] Step 2: Generate an original scenario based on the aggregated model of each type of device, and filter the original scenario based on the aggregated constraint conditions of each type of device; according to the similarity index and representativeness index of the scenarios, further reduce the filtered scenarios to obtain a set of typical scenarios.

[0140] There is a high proportion of renewable energy in the integrated energy system. Due to its clean and sustainable characteristics, especially the increasing proportion of wind energy in the total energy in recent years. However, the output of wind power has great randomness and intermittency, and the fluctuation range is also large, which poses challenges to the safety and reliability of the power grid, especially with the rapid development of distributed energy. Therefore, considering the uncertainty of renewable energy output has great practical significance. The present invention proposes an aggregation model and aggregation constraint conditions based on various types of devices, and uses the Latin hypercube sampling method and scenario filtering and reduction methods to obtain a representative scenario set for considering the uncertainty of renewable energy and load.

[0141] Specifically, step 2 includes:

[0142] Step 2.1, generating prediction data by using the aggregation model of various types of devices; based on the probability distribution of the prediction data, using the sampling method to obtain prediction samples to construct different original scenarios; filtering the original scenarios by using the aggregation constraint conditions of various types of devices;

[0143] In the embodiment, the aggregation model of the wind power generation device is used to predict the wind power generation. Preferably, the prediction data of the wind power generation obeys the normal distribution. Taking the prediction data of the wind power generation as the mean value and the predicted value of one-tenth of the fluctuation characteristic as the standard deviation σ. To ensure that the generated scenarios conform to the energy storage regulation ability of the system and avoid excessive fluctuations affecting the system stability, combined with the dispatchable ability of the aggregation model, dynamically correct the standard deviation of the wind power output prediction to make the energy storage and load regulation ability match the wind power fluctuation. The adjustment formula is: Where N agg 、N chldis are respectively the average alternating charge and discharge times and standard values of the electrical energy storage device in the aggregation model. Taking the adjusted standard deviation σ′ as the standard deviation to construct the probability distribution function of the wind power output, so as to characterize the uncertainty of the wind power generation; this modeling method based on the normal distribution can better reflect the random characteristics of the wind power output and provides a basis for subsequent scenario simulation and optimization.

[0144] Latin Hypercube Sampling (LHS) is an efficient numerical experimental design method that can evenly cover the probability distribution interval of random variables. By dividing the distribution of each variable into several sub-intervals and randomly sampling points within each sub-interval, LHS avoids the phenomenon of sample aggregation, significantly reduces variance, and decreases sampling error. Compared with the traditional Monte Carlo method, LHS can obtain higher simulation accuracy with fewer samples and is applicable to any distribution type and multi-dimensional random variable modeling. After obtaining the probability distribution of wind power output, a large number of samples that conform to the rules are generated through LHS. The original scenarios obtained in this way can effectively reflect the overall characteristics. The set of original scenarios is Ws (each column is a scenario, and each row is a predicted value), and the probability of each scenario is the same, satisfying the following relationship:

[0145]

[0146] where p w is the probability of scenario w, and W is the total number of original scenarios.

[0147] After obtaining the probability distribution of wind power output, a large number of samples that conform to the rules are generated through LHS. However, considering that there may be invalid scenarios that exceed the regulation capacity range among the samples, after generating a large number of samples, the invalid data is filtered according to the aggregation constraint conditions to achieve the filtering of invalid scenarios;

[0148] In the embodiment, the wind power generation device is filtered using the following aggregation constraint conditions:

[0149] where ΔP wind (s, t) is the wind power output fluctuation power at time t in scenario s, is the maximum upward ramp rate in the aggregation constraint conditions, Δt is the time interval of dispatch optimization, is the maximum instantaneous regulation power in the aggregation constraint conditions. The aggregation constraint conditions fully reflect the dynamic behavior of the equipment, including capacity dynamics, ramp limitations, charge state exclusions, etc., so they can effectively filter invalid scenarios.

[0150] Step 2.2, reducing the filtered scenarios according to the similarity index and representativeness index of the scenarios;

[0151] Specifically, Step 2.2 includes:

[0152] 1), The similarity index of two scenarios satisfies the following relationship:

[0153] x(i, j) = ∑|Ws(i) - Ws(:, j)]

[0154] Where x(i, j) is the distance between scenario i and typical scenario j, and Ws(:, i) and Ws(:, j) are the data of the i-th scenario and the j-th scenario in the original scenario set Ws, respectively;

[0155] The closer the distances between two scenarios are, the smaller the similarity index between the two scenarios;

[0156] 2), Calculate the average value y0(i) of the similarity indices between each scenario and the remaining scenarios as the representative index of each scenario, satisfying the following relational expression:

[0157]

[0158] Where x(:, i) is the similarity index between scenario i and the remaining scenarios;

[0159] The magnitude of y0(i) reflects the representativeness of scenario i to the remaining scenarios. The smaller its value, the worse the representativeness of scenario i to the remaining scenarios;

[0160] 3), Take the scenario with the smallest representative index as the first scenario, take the scenario with the smallest similarity index with the first scenario as the second scenario, and take the sum of the probability of the first scenario and the probability of the second scenario as the updated probability of the second scenario, satisfying the following relational expression:

[0161] p′ r = p r + p d

[0162] Where p′ r is the updated probability of the second scenario, p r is the probability of the second scenario, p d is the probability of the first scenario;

[0163] 4), Cut the first scenario from the scenarios after the first cut to obtain the updated scenario set; for the updated scenario set, repeat steps 1) to 4) until the total number of scenarios in the updated scenario set reaches the set value, and output the updated scenario set as the typical scenario set.

[0164] Continuously iterate the above steps until the number of remaining scenarios is equal to the target number. The scenarios obtained in this way have good representativeness, can well consider the uncertainty of new energy output, and the probability distribution will be readjusted every time a scenario is cut, ensuring that the sum of the probabilities of the cut scenarios remains unchanged.

[0165] Step 3: Based on the operating parameters of the typical scenarios generated from the aggregation model and aggregation constraint conditions, taking the minimum sum of the energy purchase cost, operation and maintenance cost, and penalty cost of all typical scenarios as the objective function for day-ahead optimal scheduling, and taking the minimum sum of the change in energy purchase cost, operation and maintenance cost, and penalty cost of all typical scenarios as the objective function for intraday rolling scheduling; solve the objective functions of day-ahead optimal scheduling and intraday rolling scheduling under the operation constraints of various types of devices and the power balance constraint of the integrated energy system to obtain the system scheduling plan.

[0166] Specifically, Step 3 includes:

[0167] Step 3.1: Based on the operating parameters of each typical scenario generated from the aggregation model and aggregation constraint conditions, taking the minimum sum of the energy purchase cost, operation and maintenance cost, and penalty cost of all typical scenarios as the objective function for day-ahead optimal scheduling;

[0168] After modeling and aggregating each device of the integrated energy system, based on the aggregation model, start the day-ahead scale optimal scheduling. The scheduling period is 24 hours, and the time interval is 1 hour. With the goal of optimal daily operation economy, combined with the typical scenario set to depict the uncertainty of wind power output and load demand, the objective function satisfies the following relationship:

[0169]

[0170] In the formula, F ahead is the objective function for day-ahead optimal scheduling, P a represents the probability of typical scenario a, n represents the total number of typical scenarios, EOC is the energy purchase cost, OMC is the operation and maintenance cost, WCP is the penalty cost, and in the embodiment, it is the penalty cost for abandoned wind;

[0171] Based on the aggregation model and aggregation constraint conditions of various types of devices, determine the electricity purchase volume and gas purchase volume for each time period, and calculate the energy purchase cost with the following relationship:

[0172]

[0173] In the formula, λ e，t , λ g，t are the time-of-use electricity price and gas price for time period t respectively, P g,t is the electricity purchase power of the system for time period t, G g,t is the natural gas volume purchased by the system for time period t, and T is the total number of time periods;

[0174] Based on the aggregation model and aggregation constraint conditions of various types of devices, determine the charge and discharge power of the energy storage device and the operating power of the energy conversion device for each time period, and calculate the operation and maintenance cost with the following relationship:

[0175]

[0176] In the formula, β k is the unit charge-discharge energy cost of the k-th type of energy storage device, and β l is the unit operation cost of the j-th type of energy conversion device. are the charge-discharge power of the k-th type of energy storage device at time t, respectively, and P i,t is the operation power of the j-th type of energy conversion device at time t. N S is the total number of energy storage device types, and N C is the total number of energy conversion device types;

[0177] The penalty cost includes the penalty cost for curtailed wind and satisfies the following relational expression:

[0178]

[0179] In the formula, k w is the penalty coefficient for curtailed wind, and ΔP c,t is the curtailed wind power at time t.

[0180] Step 3.2: Based on the operation parameters under each typical scenario generated by the aggregation model and aggregation constraint conditions, taking the minimum sum of the energy purchase cost, the change in operation and maintenance cost, and the penalty cost under all typical scenarios as the objective function of the intraday rolling scheduling;

[0181] The intraday optimization model characterizes the wind power uncertainty and load demand uncertainty at the short-term intraday time scale through the intraday typical scenario set. With the goal of minimizing the intraday energy purchase cost, power adjustment cost, and curtailment cost, and using the operation constraints of each device in the integrated energy system, power balance constraint, curtailment constraint, and energy purchase constraint as constraint conditions, solving the scheduling plan of each device with a 4h control time domain and a 15min time scale, and performing rolling optimization to obtain the intraday optimal scheduling plan.

[0182] The objective function of the intraday rolling scheduling satisfies the following relational expression:

[0183]

[0184] In the formula, F inday is the objective function of the intraday rolling scheduling, P s represents the probability of the typical scenario s, n represents the total number of typical scenarios, EPC is the energy purchase cost, ΔOMC is the change in operation and maintenance cost,

[0185] WCP is the penalty cost, which is the penalty cost for curtailed wind in the embodiment;

[0186] Based on the aggregation model and aggregation constraint conditions of each type of device, determine the electricity purchase volume and gas purchase volume for each period, and calculate the energy purchase cost with the following relational expression:

[0187]

[0188] Wherein, λ e，t and λ g，t are the time-of-use electricity price and gas price during period t respectively, P g，t is the electricity purchase power of the system during period t, G g，t is the natural gas volume purchased by the system during period t, and Tinday is the total number of intraday rolling periods.

[0189] Based on the aggregation models and aggregation constraint conditions of various types of devices, determine the charging and discharging power change amount of the energy storage device and the operating power change amount of the energy conversion device during each period, and calculate the change amount of operation and maintenance cost with the following relational expression:

[0190]

[0191] Wherein, γ k and γ l are the power change penalty cost coefficients of the k-th type of energy storage device and the j-th type of energy conversion device respectively, are the charging and discharging power change amounts of the k-th type of energy storage device during period t respectively, ΔP l，t is the operating power change amount of the j-th type of energy conversion device during period t, N S is the total number of energy storage device types, N C is the total number of energy conversion device types, and Tinday is the total number of intraday rolling periods.

[0192]

[0193] Wherein, k w is the wind curtailment penalty coefficient, ΔP c，t is the wind curtailment power during period t, and Tinday is the total number of intraday rolling periods.

[0194] Step 3.2, use the operating constraints of various types of devices and the power balance constraint of the integrated energy system as the constraints for day-ahead optimal scheduling and intraday rolling scheduling;

[0195] Among them, the power balance constraint of the integrated energy system includes:

[0196] 1), Electric power balance constraint:

[0197] P g，t + P chp，t + P wind，t + P pv,t = P p2g，t + P load，t + P es,t + ·P eb,t

[0198]

[0199]

[0200] 2) Gas power balance constraint

[0201]

[0202] 3) Thermal power balance constraint

[0203] η eb (1 - μ Loss )P eb,t +P chp,t / r chp =P HL,t +H es,t

[0204] In the formula, P g，t is the power purchased from the power grid at time t, G g，t is the amount of natural gas purchased by the system at time t, H ng is the calorific value of natural gas, P chp,t is the discharge power of the aggregated CHP unit at time t, P wind，t , P pv，t are the wind power and photovoltaic power generation at time t respectively; P p2g，t is the power consumption of the aggregated P2G unit at time t, P load，t , P GL，t , P HL，t are the electrical, gas, and thermal power load demands at time t respectively, P eb，t is the power consumption of the aggregated electric - thermal boiler at time t, P es，t , G es，t , II es，t are the power of electrical, gas, and thermal energy storage and release at time t respectively; are the lower and upper limit values of the power consumption of the aggregated P2G unit respectively; are the maximum downward and upward ramp rates of the aggregated P2G unit respectively; are the lower and upper limit values of the power consumption of the aggregated EB unit respectively; are the maximum downward and upward ramp rates of the aggregated EB unit respectively; η p2g is the efficiency of the aggregated P2G unit, η chp is the gas - electricity conversion efficiency of the aggregated CHP unit, η eb is the efficiency of the aggregated electric - thermal boiler, μ Loss is the heat transfer loss rate, r chp is the electric - thermal power ratio of the aggregated CHP unit.

[0205] Step 3.3, solve the objective function of day-ahead optimal scheduling and the objective function of intra-day rolling scheduling under the operation constraints of each type of device and the power balance constraint of the integrated energy system to obtain the system scheduling plan;

[0206] In the embodiment, based on the results of day-ahead optimal scheduling and the dynamic parameters of the aggregation model, the intra-day various loads and powers are updated and predicted. The unified polyhedron constraints generated in the aggregation model are directly used for the intra-day optimization problem to replace the independent constraints of the original dispersed devices. The typical scenarios obtained through the above-mentioned Latin hypercube sampling and scenario reduction are used to characterize the uncertainties of wind power and load. Model aggregation reduces the scale of the optimization problem and at the same time retains the characteristics of the devices, making the intra-day scheduling optimization more efficient. Through the intra-day stochastic optimization model, the optimal scheduling plans of all devices within the day can be obtained.

[0207] This embodiment provides a specific numerical example, and simulation analysis is carried out through the integrated energy system architecture with the structure shown in Figure 2 . The predicted data of new energy output and various loads for the day-ahead are shown in Figure 3 and Figure 4 . The time-of-use electricity price is shown in Table 1, and the natural gas purchase price is 5 yuan / m³.

[0208] Table 1 Time-of-use electricity price

[0209] Time period / h Electricity price Peak 11.00-15.00；18.00-21.00 1.35 Flat 8.00-10.00；16.00-17.00；22.00-24.00 0.87 Valley 0.00-7.00 0.42

[0210] The parameters of the aggregation models of various types of devices in the numerical example are shown in Tables 2 and 3.

[0211] Table 2 Parameters of the aggregation models of various coupling devices

[0212]

[0213] Table 3 Parameters of the aggregation models of various energy storage devices

[0214] Energy storage device Capacity (MW) Maximum charge / discharge power (MW) Charge / discharge energy efficiency (MW) Battery 200-1000 100 / 100 0.85 / 0.75 Gas storage tank 200-1000 100 / 100 0.98 / 0.98 Heat storage tank 200-1000 100 / 100 0.98 / 0.98

[0215] In addition, initial scenarios are generated through Latin hypercube sampling, and typical scenarios are extracted using scenario reduction technology. Their distribution characteristics provide key references for subsequent scheduling optimization. Based on these typical scenarios, Figures 5 to 7 the day-ahead and intra-day scheduling output curves of each energy device are shown. In the optimal scheduling, conditions such as the device operation constraint, power balance constraint, curtailment constraint, and energy purchase constraint of the integrated energy system need to be satisfied simultaneously. Among them, the day-ahead scheduling aims at the optimal operation economy and optimizes the overall power distribution of energy devices within a day. The intra-day scheduling aims at minimizing the intra-day energy purchase cost, power adjustment cost, and curtailment cost, and dynamically optimizes the device power output.

[0216] The present invention also provides a dynamic aggregation and optimal scheduling system for an urban area integrated energy system, including:

[0217] A dynamic aggregation module, configured to obtain the operation parameters of various types of devices in the integrated energy system under different working conditions, so as to establish different original models and constraint conditions for various types of devices; based on the polyhedron model, aggregate the different original models and constraint conditions of the same type of device into the aggregation model and aggregation constraint conditions of this type of device;

[0218] An optimal scheduling module, configured to generate original scenarios based on the aggregation models of various types of devices, and filter the original scenarios based on the aggregation constraint conditions of various types of devices; according to the similarity index and representativeness index of the scenarios, reduce the filtered scenarios to obtain a set of typical scenarios; use the sum of the energy purchase cost, operation and maintenance cost, and penalty cost of all typical scenarios as the objective function for day-ahead optimal scheduling, and use the sum of the energy purchase cost, change in operation and maintenance cost, and penalty cost of all typical scenarios as the objective function for intra-day rolling scheduling; solve the objective function of day-ahead optimal scheduling and the objective function of intra-day rolling scheduling under the premise of meeting the operation constraints of various types of devices and the power balance constraint of the integrated energy system to obtain the system scheduling plan.

[0219] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0220] The computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium used herein is not construed as an instantaneous signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0221] The computer-readable program instructions described herein can be downloaded to various computing / processing devices from a computer-readable storage medium or downloaded to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0222] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0223] 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 them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A dynamic aggregation and optimal scheduling method for an urban area integrated energy system, characterized in that, Including: Obtain the operation parameters of various types of devices in the integrated energy system under different working conditions, and establish different original models and constraint conditions for various types of devices; Based on the polyhedron model, aggregate the different original models and constraint conditions of the same type of device into the aggregated model and aggregated constraint conditions of this type of device; generate the original scenarios based on the aggregated models of various types of devices, and filter the original scenarios based on the aggregated constraint conditions of various types of devices; according to the similarity index and representativeness index of the scenarios, reduce the filtered scenarios to obtain a set of typical scenarios; take the minimum sum of the energy purchase cost, operation and maintenance cost, and penalty cost of all typical scenarios as the objective function of the day-ahead optimal scheduling, and take the minimum sum of the energy purchase cost, change in operation and maintenance cost, and penalty cost of all typical scenarios as the objective function of the intraday rolling scheduling; solve the objective functions of the day-ahead optimal scheduling and the intraday rolling scheduling under the satisfaction of the operation constraints of various types of devices and the power balance constraint of the integrated energy system to obtain the system scheduling plan.

2. The dynamic aggregation and optimal scheduling method for the urban area integrated energy system according to claim 1, characterized in that The different types of devices in the integrated energy system include: wind power generation devices, photovoltaic power generation devices, electrical energy storage devices, gas energy storage devices, thermal energy storage devices, power-to-gas units, CHP units, and electric heating boilers; The original model includes: a power balance model; the constraint conditions include but are not limited to: capacity constraint, power constraint, and state constraint.

3. The dynamic aggregation and optimal scheduling method for the urban area integrated energy system according to claim 1, characterized in that Taking the different original models and constraint conditions of various types of devices and the power balance constraint of the integrated energy system as the input data of the compiler; using the compiler to output the set of original polyhedron models Among them, is the constraint matrix of the i-th original polyhedron model of the m-th type of device, is the constraint vector of the i-th original polyhedron model of the m-th type of device, x is the operating parameter under different working conditions, m = 1, 2, …, M, M is the total number of device types in the integrated energy system, i = 1, 2, …, N, N is the total number of original polyhedron models; the original polyhedron model corresponds to the i-th original model and constraint conditions of the m-th type of device.

4. The dynamic aggregation and optimal scheduling method for the urban area integrated energy system according to claim 3, characterized in that Calculate the mean value of the original polyhedron model set of the same type of device to obtain the basic polyhedron model of each type of device Satisfy the following relational expression: In the formula, is the constraint matrix of the basic polyhedron model of the m-th type of device, is the constraint vector of the basic polyhedron model of the m-th type of device, is the average value of the operating parameters under different working conditions; Basic polyhedron model The basic model and basic constraint conditions corresponding to the m-th type of device.

5. The dynamic aggregation and optimal scheduling method for the urban area integrated energy system according to claim 4, characterized in that In the same type of device, with the optimized objective that the scaled and translated basic polyhedron model contains the original polyhedron model and the scaling coefficient is the smallest, determine the scaling coefficient and translation vector of each original polyhedron model; Among them, the scaled and translated basic polyhedron model satisfies the following relational expression: Wherein, is the scaling factor of the i-th original polyhedron model of the m-th type of device, is the translation vector of the i-th original polyhedron model of the m-th type of device.

6. The dynamic aggregation and optimal scheduling method for the urban area integrated energy system according to claim 5, characterized in that In the same type of device, establish a mapping model and constraint conditions between the constraint matrix of the original polyhedron model and the constraint matrix of the basic polyhedron model, satisfying the following relational expression: where U m is the mapping matrix between the constraint matrix of the original polyhedron model and the constraint matrix of the basic polyhedron model in the m-th type of device.

7. The dynamic aggregation and optimal scheduling method for the urban area integrated energy system according to claim 6, characterized in that In the same type of device, when the mapping model and constraint conditions are satisfied, calculate the average value of the scaling coefficients of all original polyhedron models as the scaling coefficient of this type of device; use the scaling coefficient of this type of device and the total number of original polyhedron models to update the constraint vector of the basic polyhedron model; The scaling coefficients of various types of devices satisfy the following relational expression: In the formula, is the scaling factor of the m-th type of device; The updated constraint vector of the basic polyhedron model satisfies the following relational expression: In the formula, is the updated constraint vector of the basic polyhedron model.

8. The dynamic aggregation and optimal scheduling method for the urban area integrated energy system according to claim 7, characterized in that In the same type of device, using the constraint matrix of the basic polyhedron model and the updated constraint vector, construct the aggregated polyhedron model of this type of device The set of aggregated polyhedron models of the integrated energy system is M is the total number of device types in the integrated energy system; The aggregated polyhedron model corresponds to the aggregated model and aggregated constraint conditions of the m-th type of device.

9. The dynamic aggregation and optimal scheduling method for the urban regional integrated energy system according to claim 1, characterized in that Predictive data is generated using the aggregated models of various types of devices; based on the probability distribution of the predictive data, sampling is used to obtain predictive samples to construct different original scenarios; the original scenarios are filtered using the aggregated constraint conditions of various types of devices; According to the similarity index and representativeness index of the scenarios, the filtered scenarios are reduced, including: 1), The similarity index of two scenarios satisfies the following relational expression: x(i,j)=∑|Ws(:,i)-Ws(:,j)| In the formula, x(i,j) is the distance between scenario i and typical scenario j, and Ws(:,i) and Ws(:,j) are the data of the i-th scenario and the j-th scenario in the original scenario set Ws, respectively; 2), Calculate the average value y0(i) of the similarity indexes of each scenario and the remaining scenarios as the representativeness index of each scenario, which satisfies the following relational expression: In the formula, x(:,i) is the similarity index of scenario i and the remaining scenarios; 3), Take the scenario with the smallest representativeness index as the first scenario, and take the scenario with the smallest similarity index with the first scenario as the second scenario, and take the sum of the probability of the first scenario and the probability of the second scenario as the updated probability of the second scenario, which satisfies the following relational expression: p′ r = p r + p d where p' r is the updated probability of the second scenario, p r is the probability of the second scenario, and p d is the probability of the first scenario; 4), Reduce the first scenario from the scenarios after the first reduction to obtain the updated scenario set; for the updated scenario set, repeat steps 1) to 4) until the total number of scenarios in the updated scenario set reaches the set value, and output the updated scenario set as the typical scenario set.

10. The dynamic aggregation and optimal scheduling method for the urban regional integrated energy system according to claim 1, characterized in that The objective function of the day-ahead optimal scheduling satisfies the following relational expression: In the formula, F abead is the objective function of the day-ahead optimal scheduling, P a represents the probability of typical scenario a, n represents the total number of typical scenarios, EPC represents the energy purchase cost, OMC represents the operation and maintenance cost, and WCP represents the penalty cost.

11. The dynamic aggregation and optimal scheduling method for the urban regional integrated energy system according to claim 10, characterized in that Based on the aggregated models and aggregated constraint conditions of various types of devices, determine the electricity purchase volume and gas purchase volume for each time period, and calculate the energy purchase cost according to the following relational expression: where λ e，t and λ g，t are the time-of-use electricity price and gas price in period t respectively, P g,t is the purchased electricity power of the system in period t, G g,t is the amount of natural gas purchased by the system in period t, and T is the total number of periods; Based on the aggregated models and aggregated constraint conditions of various types of devices, determine the charging and discharging power of the energy storage device and the operating power of the energy conversion device for each time period, and calculate the operation and maintenance cost according to the following relational expression: where β k is the unit charge-discharge energy cost of the k-th type of energy storage device, and β j is the unit operation cost of the j-th type of energy conversion device. are the charge-discharge power of the k-th type of energy storage device at time t, respectively, and P j，t is the operation power of the j-th type of energy conversion device at time t. N S is the total number of types of energy storage devices, and N C is the total number of types of energy conversion devices; The penalty cost includes the penalty cost for wind curtailment, which satisfies the following relational expression: where k w is the wind curtailment penalty coefficient, and ΔP c，t is the wind curtailment power at time period t, and T is the total number of time periods.

12. The dynamic aggregation and optimal scheduling method for the urban regional integrated energy system according to claim 1, characterized in that The objective function of the intraday rolling scheduling satisfies the following relational expression: where F inday is the objective function of intraday rolling scheduling, P s represents the probability of the typical scenario s, n represents the total number of typical scenarios, EPC is the power purchase cost, and ΔOMC is the change in operation and maintenance cost WCP is the penalty cost.

13. The dynamic aggregation and optimal scheduling method for the urban regional integrated energy system according to claim 12, characterized in that Based on the aggregated models and aggregated constraint conditions of various types of devices, determine the electricity purchase volume and gas purchase volume for each time period, and calculate the energy purchase cost according to the following relational expression: where λ e，t , λ g，t are the time-of-use electricity price and gas price in period t respectively, P g,t is the electricity purchase power of the system in period t, G g，t is the natural gas volume purchased by the system in period t, and Tinday is the total number of intraday rolling periods; Based on the aggregation models and aggregation constraint conditions of various types of devices, determine the changes in the charging and discharging power of energy storage devices and the changes in the operating power of energy conversion devices during each period, and calculate the change in operation and maintenance cost using the following relational expression: where γ k and γ J are the power change penalty cost coefficients of the k-th type of energy storage device and the j-th type of energy conversion device respectively, are the charging and discharging power change amounts of the k-th type of energy storage device at time t, ΔP J，t is the operating power change amount of the j-th type of energy conversion device at time t, N S is the total number of energy storage device types, N C is the total number of energy conversion device types; The penalty cost includes the penalty cost for wind curtailment and satisfies the following relational expression: where k w is the wind curtailment penalty coefficient, and ΔP c，t is the wind curtailment power at time period t, and Tinday is the total number of intraday rolling time periods.

14. A dynamic aggregation and optimal scheduling system for an integrated urban area energy system, characterized in that, including: A dynamic aggregation module, configured to obtain the operating parameters of various types of devices in the integrated energy system under different working conditions, so as to establish different original models and constraint conditions for various types of devices; Based on the polyhedron model, aggregate the different original models and constraint conditions of the same type of device into the aggregation model and aggregation constraint conditions of this type of device; An optimal scheduling module, configured to generate an original scenario based on the aggregation model of various types of devices, and filter the original scenario based on the aggregation constraint conditions of various types of devices; according to the similarity index and representativeness index of the scenarios, reduce the filtered scenarios to obtain a set of typical scenarios; based on the operating parameters of the typical scenarios generated by the aggregation model and aggregation constraint conditions, take the minimum value of the sum of the energy purchase cost, operation and maintenance cost, and penalty cost of all typical scenarios as the objective function of day-ahead optimal scheduling, and take the minimum value of the sum of the energy purchase cost, change in operation and maintenance cost, and penalty cost of all typical scenarios as the objective function of intraday rolling scheduling; solve the objective function of day-ahead optimal scheduling and the objective function of intraday rolling scheduling under the satisfaction of the operating constraints of various types of devices and the power balance constraint of the integrated energy system to obtain the system scheduling plan.

15. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used for storing instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-13.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, the steps of the method according to any one of claims 1-13 are implemented.

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