Integrated energy system scheduling method, device and equipment considering power grid bearing capacity, storage medium and program product

Abstract

The invention relates to the technical field of integrated energy system scheduling, provides an integrated energy system scheduling method, device and equipment considering power grid bearing capacity, a storage medium and a program product, and can improve the robustness of a scheduling decision. The method comprises the following steps: acquiring a wind and light output prediction error fuzzy set under different confidence levels; evaluating the wind and light output prediction error fuzzy set under different confidence levels, determining a target confidence level to obtain a wind and light output actual value expression, and determining an electrothermal gas hydrogen power balance constraint of the integrated energy system; according to the opportunity constraint distribution robust model, determining a robust operation interval upper bound accumulation probability and a robust operation interval lower bound accumulation probability at each moment, and constructing a robustness objective function of the integrated energy system; obtaining a power grid bearing capacity constraint of the integrated energy system; and solving according to the electrothermal gas-hydrogen power balance constraint, the power grid bearing capacity constraint and the robustness objective function of the integrated energy system to obtain a scheduling result of the integrated energy system.

Description

Technical Field

[0001] The present application relates to the technical field of integrated energy system scheduling, and in particular to an integrated energy system scheduling method, apparatus, computer equipment, storage medium, and computer program product taking into account the carrying capacity of a power grid. Background Art

[0002] Energy systems are shifting from a focus on fossil fuels to a focus on renewable energy to reduce greenhouse gas emissions. With the integration of wind and photovoltaic power, microgrids are facing increasing challenges. The uncertainty of wind and solar power needs to be accounted for when scheduling integrated energy systems (IES) to ensure stable operation.

[0003] When optimizing the scheduling of integrated energy systems, wind and solar power output has great uncertainty and randomness, resulting in poor robustness of scheduling decisions. Summary of the Invention

[0004] Based on this, it is necessary to provide a comprehensive energy system scheduling method, device, computer equipment, storage medium and computer program product that takes into account the carrying capacity of the power grid to address the above technical problems.

[0005] The present application provides a method for dispatching an integrated energy system taking into account the carrying capacity of a power grid, the method comprising:

[0006] Obtain wind and solar power output prediction error fuzzy sets at different confidence levels; the wind and solar power output prediction error fuzzy set at each confidence level is obtained by the following steps: determining the prediction error sample that is smaller than the i-th reference sample point in the i-th prediction error sample set of wind and solar power output, and obtaining the i-th corresponding sample number; determining the i-th target true probability interval at the confidence level based on the relative size between the i-th corresponding sample number and the total number of samples included in the i-th prediction error sample set; obtaining the wind and solar power output prediction error fuzzy set at the confidence level based on the true probability intervals of each target at the confidence level;

[0007] Evaluate the fuzzy set of wind and solar power output prediction errors at different confidence levels, determine the target confidence level, obtain the actual value expression of wind and solar power output, and determine the power balance constraints of electricity, heat, gas and hydrogen in the integrated energy system;

[0008] According to the chance-constrained distributed robust model, the upper bound cumulative probability of the robust operation interval and the lower bound cumulative probability of the robust operation interval at each moment are determined to construct the robustness objective function of the integrated energy system.

[0009] Obtaining a grid carrying capacity constraint of the integrated energy system;

[0010] The scheduling result of the integrated energy system is obtained by solving the electric, thermal, gas and hydrogen power balance constraints, the grid carrying capacity constraints and the robustness objective function of the integrated energy system.

[0011] The present application provides a comprehensive energy system scheduling device taking into account the carrying capacity of the power grid, the device comprising:

[0012] A fuzzy set acquisition module is used to obtain fuzzy sets of wind and solar power output prediction errors at different confidence levels; the fuzzy set of wind and solar power output prediction errors at each confidence level is obtained by the following steps: determining the prediction error sample that is smaller than the i-th reference sample point in the i-th prediction error sample set of wind and solar power output, and obtaining the i-th corresponding sample number; determining the i-th target true probability interval at the confidence level based on the relative size between the i-th corresponding sample number and the total number of samples included in the i-th prediction error sample set; and obtaining the wind and solar power output prediction error fuzzy set at the confidence level based on the true probability intervals of each target at the confidence level;

[0013] The power balance constraint construction module is used to evaluate the fuzzy set of wind and solar power output prediction errors at different confidence levels, determine the target confidence level, obtain the actual value expression of wind and solar power output, and determine the power balance constraints of electricity, heat, gas and hydrogen in the integrated energy system;

[0014] The objective function construction module is used to divide the robust model according to the chance constraint, determine the cumulative probability of the upper bound of the robust operation interval and the cumulative probability of the lower bound of the robust operation interval at each moment, and thus construct the robustness objective function of the integrated energy system;

[0015] A grid carrying capacity constraint building module, used for obtaining the grid carrying capacity constraint of the integrated energy system;

[0016] The solution module is used to solve the electric, thermal, gas and hydrogen power balance constraints, grid carrying capacity constraints and robustness objective functions of the integrated energy system to obtain the scheduling results of the integrated energy system.

[0017] The present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the above method.

[0018] The present application provides a computer-readable storage medium having a computer program stored thereon, and the computer program is used by a processor to execute the above method.

[0019] The present application provides a computer program product having a computer program stored thereon, wherein the computer program is used by a processor to execute the above method.

[0020] The above-mentioned integrated energy system scheduling method, device, computer equipment, storage medium and computer program product taking into account the carrying capacity of the power grid obtain the fuzzy set of wind and solar power output prediction errors at different confidence levels; the fuzzy set of wind and solar power output prediction errors at each confidence level is obtained by the following steps: determining the prediction error sample that is smaller than the i-th reference sample point in the i-th prediction error sample set of wind and solar power output, and obtaining the i-th corresponding sample number; determining the i-th target true probability interval at the confidence level based on the relative size between the i-th corresponding sample number and the total number of samples included in the i-th prediction error sample set; and obtaining the i-th target true probability interval based on the true probability intervals of each target at the confidence level. The fuzzy set of wind and solar power output prediction errors at this confidence level; the fuzzy set of wind and solar power output prediction errors at different confidence levels is evaluated to determine the target confidence level to obtain the actual value expression of wind and solar power output and determine the power balance constraint of the electric, thermal, gas and hydrogen of the integrated energy system; according to the opportunity constraint distributed robust model, the upper bound cumulative probability of the robust operation interval and the lower bound cumulative probability of the robust operation interval at each moment are determined to construct the robustness objective function of the integrated energy system; the grid carrying capacity constraint of the integrated energy system is obtained; according to the power balance constraint of the electric, thermal, gas and hydrogen of the integrated energy system, the grid carrying capacity constraint and the robustness objective function are solved to obtain the scheduling result of the integrated energy system. The solution provided in this application establishes a distributed robust optimization model, including a fuzzy set of wind and solar power output prediction errors and a chance constraint distributed robust model, which fully considers the uncertainty of wind and solar power output and improves the robustness of the scheduling result. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of a method for scheduling an integrated energy system taking into account the carrying capacity of a power grid in one embodiment;

[0022] Figure 2 A structural diagram of an integrated energy system in one embodiment;

[0023] Figure 3 (a) is a flow chart of the green certificate-carbon exchange coordination mechanism in one embodiment;

[0024] Figure 3 (b) is a solution flow chart based on the NSGA-II-WPA algorithm in one embodiment;

[0025] Figure 4 A structural block diagram of an integrated energy system dispatching device taking into account the carrying capacity of the power grid in one embodiment;

[0026] Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0028] An Integrated Energy System (IES) is an energy system that integrates multiple energy forms and their associated equipment and technologies to achieve efficient energy production, transmission, conversion, and utilization through optimized scheduling and coordinated control. Combined Heat and Power (CHP) is a highly efficient energy utilization method that produces both electricity and heat in the same process. It significantly improves energy efficiency by utilizing the waste heat generated by fuel during power generation for heating, cooling, or other industrial processes. The Kalina Cycle (KC) is an improved thermodynamic cycle designed to increase power generation efficiency and reduce environmental impact. A Waste Heat Boiler (WHB) utilizes waste heat or exhaust gases from industrial processes to generate steam or hot water. It is widely used to improve energy efficiency and reduce energy waste. A Hydrogen Fuel Cell (HFC) is a device that directly converts the chemical energy of hydrogen and oxygen into electricity through an electrochemical reaction. Distributionally Robust Optimization (DRO): Enhances model robustness by considering all possible data distributions, ensuring that the optimized solution maintains good performance in the face of actual data deviations. Imprecise Dirichlet Model (IDM): A statistical model used to handle data uncertainty and model multiple possible distributions. NSGA-II-WPA: An extended version of a multi-objective optimization algorithm that combines the non-dominated sorting genetic algorithm II (NSGA-II) and the weighted perturbation and adaptation strategy (WPA). Green Certificate Trading (GCT) and Carbon Emission Trading (CET) Joint Mechanism: A comprehensive tool to promote emission reduction and renewable energy development. Grid Hosting Capacity: Refers to the maximum renewable energy installed capacity or load demand that the power grid can accommodate while meeting safe operating conditions. It is usually limited by factors such as line capacity, voltage stability, and protective device operation characteristics.

[0029] The integrated energy system scheduling method provided in this application taking into account the grid carrying capacity can be executed by a computer device, including Figure 1Steps shown.

[0030] Step S101, obtaining fuzzy sets of wind and solar power output prediction errors at different confidence levels;

[0031] The fuzzy set of wind and solar power output prediction errors at each confidence level is obtained through the following steps: determining the prediction error samples that are smaller than the ith reference sample point in the ith prediction error sample set of wind and solar power output, and obtaining the ith corresponding sample number; determining the ith target true probability interval at the confidence level based on the relative size between the ith corresponding sample number and the total number of samples included in the ith prediction error sample set; and obtaining the fuzzy set of wind and solar power output prediction errors at the confidence level based on the true probability intervals of each target at the confidence level.

[0032] The structure of the integrated energy system containing hydrogen energy storage is as follows: Figure 2 As shown in the figure, this system primarily consists of an energy supply unit, an energy coupling unit, an energy storage unit, and various loads. Clean energy is provided by photovoltaic and wind turbines, while the power grid and natural gas network are responsible for energy transmission. The system's multi-energy coupling breaks down barriers between energy sources and facilitates the absorption of renewable energy. The comprehensive utilization of hydrogen energy can adjust the output of individual units, thereby improving the stability of the integrated energy system. The electrolyzer (EL) in the hydrogen energy utilization model converts electricity into hydrogen. The generated hydrogen is absorbed by the methane reactor (MR) and converted into natural gas. The generated natural gas can be fed into the gas grid and gas-fired boilers. The hydrogen generated by the EL from excess wind and solar resources is stored in hydrogen storage tanks (HS). During peak energy demand periods, the hydrogen storage tanks are used by hydrogen fuel cells (HFCs), maximizing energy efficiency. To overcome the limitations of traditional CHPs, including gas turbines (GTs) and waste heat boilers (WHBs), and to rationally match electricity and heat load trends, electric boilers (EBs) and Kalina cycles are introduced into CHPs to decouple the heat and power bundling constraints of CHPs and achieve flexible IES source-side heat and power response. Gas boilers (GBs) are connected to the gas grid.

[0033] This application constructs a CHP thermal power flexible response model, which includes a waste heat boiler (WHB) and a gas turbine (GT). The thermal and electrical power outputs of the gas turbine are expressed as:

[0034] (1)

[0035] Where: and are the thermal power and electrical power output of the gas turbine at time t; and are the thermal and electrical power conversion efficiencies of the gas turbine, respectively; is the gas power input to the gas turbine at time t.

[0036] After adding the Kalina cycle and electric boiler model, the heat energy from the gas turbine flows to the waste heat boiler and the Kalina cycle, and the electric energy flows to the electric boiler and the electric load, which can be expressed as:

[0037] (2)

[0038] Where: is the thermal power input from the gas turbine to the waste heat boiler at time t; is the thermal power of the Kalina cycle at time t; is the electric power flowing into the electric boiler at time t; is the electric power flowing into the grid subsystem at time t.

[0039] On this basis, the improved CHP thermal power and electrical power can be rewritten as:

[0040] (3)

[0041] Where: and is the thermal and electrical output power of the flexible response model of the combined heat and power unit at time t; and are the output thermal power of the waste heat boiler and the electric boiler at time t respectively; is the electrical power of the Kalina cycle at time t.

[0042] Except for the gas turbine GT, the conversion model of the remaining CHP units can be expressed as:

[0043] (4)

[0044] Where: 、 and are the conversion efficiencies of electric boiler, waste heat boiler and Kalina cycle respectively; is the electric power of the waste heat boiler at time t.

[0045] This application also constructs a hydrogen energy comprehensive utilization model, which mainly includes hydrogen fuel cells, electrolyzers, methane reactors and hydrogen storage tanks. Through the above equipment, multi-energy complementarity can be achieved, improving the flexible mobilization capability of the integrated energy system. Hydrogen fuel cells use hydrogen energy to obtain heat energy and electrical energy. The three energies are coupled through hydrogen fuel cells, and the energy conversion relationship is as follows:

[0046] (5)

[0047] Where: and are the electrical output power and thermal output power of the hydrogen fuel cell at time t, respectively; is the hydrogen input power of the hydrogen fuel cell at time t; is the electricity generation efficiency of hydrogen fuel cells; is the heat-to-electricity ratio of a hydrogen fuel cell.

[0048] Electric energy is converted into hydrogen energy through the electrolyzer, thereby realizing energy coupling between electricity and hydrogen. The conversion relationship is expressed as:

[0049] (6)

[0050] Where: and are the input electrical energy and output hydrogen power of the electrolyzer at time t respectively; is the hydrogen production efficiency of the electrolyzer.

[0051] The methane reactor can use hydrogen to generate methane, which is then transported to the cogeneration unit through the gas network pipeline for utilization. The conversion relationship is expressed as:

[0052] (7)

[0053] Where: and are the input hydrogen energy and output natural gas power of the methane reactor at time t, respectively; is the energy conversion efficiency.

[0054] The lossy hydrogen storage model (which can be denoted as HT) can be expressed as:

[0055] (8)

[0056] Where: is the hydrogen storage capacity at time t; Energy efficiency for charging and discharging hydrogen storage tanks; and is the amount of hydrogen charged into and discharged from the hydrogen storage tank at time t.

[0057] This application involves a green certificate-carbon exchange mechanism. The green certificate exchange mechanism encourages enterprises to generate electricity through new energy by stipulating the proportion of renewable energy power generation on the electricity load side, thereby obtaining a certain number of green certificates. When the green certificates obtained are higher than the quota index, certain resources can be obtained through green certificates. Conversely, green certificates can be obtained through resources when green certificates are needed. The core of the carbon exchange mechanism is the exchange of carbon emission rights. When the carbon emissions of high-carbon units are higher than the free quota, carbon emission rights need to be obtained through resources. Conversely, resources can be obtained through carbon emission rights. In order to control carbon emissions, a step-by-step carbon emission mechanism is implemented for carbon emission sources. The step-by-step carbon exchange model is:

[0058] (9)

[0059] Where: The carbon exchange cost for the integrated energy system; The basic resource volume for carbon exchange; It is the step carbon exchange increase; is the length of the carbon emission ladder interval; Carbon emission credits are allocated free of charge; is the actual carbon emissions of the integrated energy system.

[0060] The carbon emissions of the integrated energy system mainly include purchased electricity, cogeneration, and gas boilers. Therefore, the actual carbon emissions can be expressed as:

[0061] (10)

[0062] Where: is the actual carbon emissions of the integrated energy system, The total carbon emissions from purchased electricity, combined heat and power generation, and gas boiler units. =The carbon emissions absorbed by the methane reactor. Similar to the ladder carbon exchange, the more green certificates the integrated energy system holds, the higher the value of the green certificates. Conversely, when more green certificates need to be purchased, the cost will also be higher. The ladder green certificate exchange model is:

[0063] (11)

[0064] Where: Proceeds from the sale of green certificates for integrated energy systems; The basic resource volume for green certificate exchange; The growth rate of the step-by-step resource volume when the quota indicator is exceeded; The growth rate of the step-by-step resource volume when the quota indicator is not reached; The length of the green certificate ladder interval; =Green Certificates are allocated to each sector. When these two mechanisms are combined, the low-carbon nature of the integrated energy system can be maximized, resulting in emission reduction benefits. The Green Certificate-Carbon Exchange coordination mechanism is shown in Figure 3(a).

[0065] Green electricity emission reduction can be understood as the amount of greenhouse gas emissions reduced through green electricity while maintaining the same power generation. This is compared with traditional thermal power generation to obtain the carbon emission reduction represented by green certificates. The number of green certificates is determined by the consumption of renewable energy power generation in the integrated energy system. The carbon emissions of the integrated energy system can be calculated using the carbon footprint method and then converted into carbon emission rights. The conversion coefficient is obtained by exchanging the basic resource volume. It can be expressed as:

[0066] (12)

[0067] Where: The basic resource volume for green certificate exchange; It is the basic resource amount for carbon exchange.

[0068] Therefore, the carbon emission rights and green certificate quantities can be rewritten as:

[0069] (13)

[0070] (14)

[0071] Where, is the number of green certificates after the two mechanisms are combined; The number of green certificates participating in the conversion; is the carbon emission reduction coefficient associated with the green certificate, is the carbon emission rights value after relevant calculations; Allocate green certificates to departments. is the actual carbon emissions of the integrated energy system.

[0072] The distributionally robust optimization involved in this application combines stochastic optimization and robust optimization. Taking into account the different random distribution characteristics of uncertain factors such as wind power and photovoltaics (which can be referred to as wind and solar for short), chance constraints are used to construct an adjustable uncertainty set. The wind and solar output prediction error fuzzy set and the chance-constrained distributionally robust model are based on the IDM theory. In order to ensure the robustness of the optimization results, this application constructs wind and solar output prediction error fuzzy sets at different confidence levels, evaluates the wind and solar output prediction error fuzzy sets at each confidence level, and analyzes the rationality and effectiveness of the wind and solar output prediction error fuzzy sets at each confidence level in describing the uncertainty of wind and solar output. For example, at a certain confidence level, the wind and solar output prediction error range determined from the corresponding wind and solar output prediction error fuzzy set is combined with the actual operation of the integrated energy system to determine whether it can meet specific application scenarios and requirements. If, at a certain confidence level, the wind and solar output range defined by the corresponding wind and solar output prediction error fuzzy set is too wide, resulting in the integrated energy system reserving too much backup capacity to deal with uncertainty, increasing operating costs, and exceeding the actual needs of the integrated energy system, then the confidence level may be inappropriate; if, at a certain confidence level, the wind and solar output range defined by the corresponding wind and solar output prediction error fuzzy set is too narrow, unable to effectively deal with wind and solar output fluctuations, affecting the reliability of the integrated energy system, then the confidence level may be inappropriate.

[0073] The present application obtains multiple prediction error sample sets of wind and solar power output, and each prediction error sample set may include prediction error samples; for each prediction error sample set, the prediction error sample set can be prediction error samples, and determine the corresponding reference sample point G; illustratively, the corresponding reference sample point G can be determined based on the i-th prediction error sample set. prediction error samples and determine the i-th reference sample point G.

[0074] At the confidence level The true probability of The interval can be expressed as:

[0075] (15)

[0076] Where I is the Beta distribution B(m i ,s+m i ) cumulative distribution function; H is the Beta distribution B(s+m i ,nm i )’s cumulative distribution function; for The upper bound of the distribution, for lower bound of the distribution; is the random variable of wind and solar power prediction error The number of samples that are smaller than the i-th reference sample point G (also called the i-th typical sample point), that is, the number of samples in the i-th prediction error sample set of wind and solar power output that are smaller than the i-th reference sample point G; is the total number of samples in the i-th prediction error sample set, It is also the total number of prediction error sample sets; s is the step size.

[0077] The relative size between the number of samples corresponding to the i-th prediction error sample set and the total number of samples included in the i-th prediction error sample set is determined at the confidence level The true probability interval of the i-th target under .

[0078] According to the confidence level The true probability interval of each target under , and the confidence level is obtained The fuzzy set of wind and solar power output prediction error under can be expressed as:

[0079] (16)

[0080] Where: P represents the fuzzy set of wind and solar power output prediction errors, which is a probability distribution set that meets specific conditions; is the i-th prediction error random variable The cumulative probability distribution of is used to describe the i-th forecast error random variable The probability of a value being less than or equal to a certain value; To estimate the range The basic probability set of all probability sets in can be understood as a set determined a priori and containing all possible probability distributions, which provides a probability distribution space for the construction of the fuzzy set P of wind and solar power output prediction errors; is the i-th prediction error random variable Upper limit of is the i-th prediction error random variable The lower limit of At the confidence level The true probability interval of the i-th target under . The value range of i covers each prediction error sample set of wind and solar output. For example, If it is equal to 100, then the value of i can range from 1 to 100 integers.

[0081] Formula (16) is defined at the confidence level The fuzzy set P of wind and solar output forecast error under the condition is composed of the cumulative probability distribution function F that satisfies specific conditions. Specifically, for the i-th forecast error sample set, its cumulative probability distribution is The value of The true probability interval of the i-th target under At the same time, the cumulative probability distribution function F comes from the basic probability set In an integrated energy system, considering the uncertainty of the output of new energy sources such as wind power and photovoltaic power, by constructing such a fuzzy set, the probability distribution of wind and solar power output forecast errors can be more accurately characterized, providing an important basis for subsequent uncertainty processing and the formulation of scheduling strategies.

[0082] Step S102 , evaluate the fuzzy set of wind and solar power output prediction errors at different confidence levels, determine the target confidence level, obtain the actual value expression of wind and solar power output, and determine the power balance constraints of electricity, heat, gas and hydrogen of the integrated energy system.

[0083] After constructing fuzzy sets of wind and solar power output forecast errors at different confidence levels, the fuzzy sets can be evaluated for each confidence level to analyze their rationality and effectiveness in describing wind and solar power output uncertainty. For example, at a certain confidence level, the wind and solar power output forecast error range determined by the corresponding fuzzy set can be combined with the actual operation of the integrated energy system to determine whether it can meet specific application scenarios and requirements. If, at a certain confidence level, the wind and solar power output forecast error range defined by the corresponding fuzzy set is too wide, causing the integrated energy system to reserve too much backup capacity to cope with uncertainty, increasing operating costs and exceeding the actual needs of the integrated energy system, then the confidence level may be inappropriate. If, at a certain confidence level, the wind and solar power output range defined by the fuzzy set is too narrow, unable to effectively cope with wind and solar power output fluctuations and affecting the reliability of the integrated energy system, then the confidence level may be inappropriate. If, at a certain confidence level, the wind and solar power output range defined by the corresponding wind and solar power output prediction error fuzzy set is appropriate, will not cause the integrated energy system to reserve too much backup capacity to deal with uncertainty, and will not exceed the actual needs of the integrated energy system, then the confidence level is appropriate and can be used as the target confidence level.

[0084] After obtaining the target confidence level, the actual value expression of wind and solar power output can be obtained according to the target confidence level. As the target confidence level, the actual value of wind and solar power output can be expressed as:

[0085] (17)

[0086] Where: and are the actual output values ​​of wind power and photovoltaic power at time t, which can be collectively referred to as the actual output value of wind and photovoltaic power; and are the lower limits of wind power and photovoltaic power output at time t respectively; and are the output limits of wind power and photovoltaic power at time t respectively; and is the output forecast value of wind power and photovoltaic power at time t, which can be collectively referred to as the wind and solar power output forecast value at time t.

[0087] Formula (17) describes the range of actual output values ​​of wind power and photovoltaic power at time t, considering the fuzzy set of wind and solar power output prediction errors. Specifically, the actual output value of wind power at time t is Between its lower limit and upper limit These two boundary values ​​are based on the predicted output value of wind power at time t. as well as Determined. The actual output value of photovoltaic at time t Between its lower limit and upper limit These two boundary values ​​are based on the predicted output value of photovoltaic at time t. as well as Sure.

[0088] In an integrated energy system, wind power and photovoltaic power generation have uncertainties. Formula (17) defines the range of actual wind and solar power generation output, helping system operators more accurately grasp the fluctuations in wind and solar power generation. Based on this, when formulating power dispatch plans, arranging backup capacity, and conducting power balance analysis, the uncertainty of wind and solar power generation can be more reasonably considered, thereby improving the stability and reliability of the integrated energy system. For example, once the output range of wind power and photovoltaic power generation at a certain moment is known, the output of conventional energy generation equipment can be reasonably arranged accordingly to avoid system power imbalance caused by fluctuations in wind and solar power generation output.

[0089] Based on formula (17), the power balance constraint of the integrated energy system of electricity, heat, gas and hydrogen can be constructed, which can be expressed as:

[0090] (18)

[0091] Where: and are electric load and gas load respectively; Purchasing gas power for IES; is the electrical output power of the hydrogen fuel cell at time t; is the electrical power of the Kalina cycle at time t; is the electric power flowing into the grid subsystem at time t; is the electric power of the electrolytic cell at time t; is the electric power of the electric boiler at time t.

[0092] is the output thermal power of the gas boiler at time t; is the output thermal power of the hydrogen fuel cell at time t; is the output thermal power of the waste heat boiler at time t; is the output thermal power of the electric boiler at time t; is the output thermal power of the Kalina cycle at time t.

[0093] is the gas power input to the gas turbine at time t; is the gas power input to the gas boiler at time t; is the output natural gas power of the integrated energy system at time t; is the output natural gas power of the methane reactor at time t;

[0094] is the hydrogen input power of the hydrogen fuel cell at time t; is the hydrogen input power of the methane reactor at time t; is the output hydrogen power of the electrolyzer at time t.

[0095] Step S103, according to the chance-constrained distribution robust model, determine the upper bound cumulative probability of the robust operation interval and the lower bound cumulative probability of the robust operation interval at each moment, so as to construct the robustness objective function of the integrated energy system.

[0096] To describe the robustness and tolerance of the integrated energy system to disturbances caused by wind and solar power forecast errors, a robust operation range of upper and lower reserves is constructed. The integrated energy system's preset spinning reserve can quickly respond to forecast errors within the robust operation range. If the integrated energy system cannot absorb errors outside the range, it will adopt the adjustment methods of wind and solar power abandonment and load shedding, and its risk is measured by cost loss. Combined with the joint distribution of wind and solar power forecast errors, a chance-constrained distributed robust model can be obtained. The chance-constrained distributed robust model is mainly used to describe the robustness and tolerance range of the integrated energy system under the influence of wind and solar power forecast errors. Taking into account the uncertainty of wind and solar power output, the chance-constrained distributed robust model is used to determine the integrated energy system's strategy for dealing with wind and solar power forecast errors, such as using spinning reserves to deal with errors within the range, and adopting wind and solar power abandonment and load shedding to deal with errors outside the range, and measuring the risk by cost loss.

[0097] According to the chance constraint distribution robust model, the upper bound cumulative probability of the robust operation interval and the lower bound cumulative probability of the robust operation interval at each moment can be determined. For example, the upper bound cumulative probability of the robust operation interval at time t can be obtained. and the cumulative probability of the lower bound of the robust operating interval .

[0098] According to the cumulative probability of the upper bound of the robust operation interval and the cumulative probability of the lower bound of the robust operation interval at each moment, the robustness objective function of the integrated energy system is constructed.

[0099] Step S104: obtaining the grid carrying capacity constraint of the integrated energy system.

[0100] The grid carrying capacity constraint can be expressed as:

[0101] (19)

[0102] Where, P exmin 、P exmax are the minimum and maximum interaction powers of the tie line respectively; P ex,t is the interaction power of the tie line at time t. The tie line is used to connect the integrated energy system with the large power system.

[0103] Step S105 , solving the electricity, heat, gas and hydrogen power balance constraints, grid carrying capacity constraints and robustness objective function of the integrated energy system to obtain a scheduling result of the integrated energy system.

[0104] After solving the power balance constraints of electricity, heat, gas and hydrogen, the grid carrying capacity constraints and the robustness objective function, the scheduling results of the integrated energy system are obtained.

[0105] In the above-mentioned integrated energy system dispatching method taking into account the grid carrying capacity, the wind and solar power output prediction error fuzzy sets at different confidence levels are obtained; the wind and solar power output prediction error fuzzy sets at different confidence levels are evaluated to determine the target confidence level to obtain the actual value expression of the wind and solar power output and determine the power balance constraint of the electric, thermal, gas and hydrogen of the integrated energy system; according to the opportunity constraint distributed robust model, the upper bound cumulative probability of the robust operation interval and the lower bound cumulative probability of the robust operation interval at each moment are determined to construct the robustness objective function of the integrated energy system; the grid carrying capacity constraint of the integrated energy system is obtained; according to the power balance constraint of the electric, thermal, gas and hydrogen of the integrated energy system, the grid carrying capacity constraint and the robustness objective function are solved to obtain the dispatching result of the integrated energy system. The solution provided in this application establishes a distributed robust optimization model, including a wind and solar power output prediction error fuzzy set and an opportunity constraint distributed robust model, which fully considers the uncertainty of wind and solar power and improves the robustness of the dispatching result.

[0106] In one embodiment, determining the i-th target true probability interval at the confidence level based on the relative size between the i-th corresponding sample number and the total number of samples included in the i-th prediction error sample set includes:

[0107] The first value is obtained based on half of the difference between 1 and the confidence level; the second value is obtained based on half of the sum of 1 and the confidence level; the inverse function of the first cumulative distribution function related to the number of samples corresponding to the i-th is applied to the first value to obtain the lower limit of the true probability; the inverse function of the second cumulative distribution function related to the number of samples corresponding to the i-th is applied to the second value to obtain the upper limit of the true probability; the first true probability interval is obtained based on the lower limit of the true probability and the upper limit of the true probability; the second true probability interval is obtained based on the lower limit of the true probability and 1; the second true probability interval is obtained based on 0 and the true probability The upper limit value is obtained to obtain the third true probability interval; when the number of samples corresponding to the i-th is greater than 0 and less than the total number of samples included in the i-th prediction error sample set, the first true probability interval is used as the i-th target true probability interval under the confidence level; when the number of samples corresponding to the i-th is equal to the total number of samples included in the i-th prediction error sample set, the second true probability interval is used as the i-th target true probability interval under the confidence level; when the number of samples corresponding to the i-th is equal to 0, the third true probability interval is used as the i-th target true probability interval under the confidence level.

[0108] For example, the prediction error sample set i includes prediction error samples, determine the i-th reference sample point G. At the confidence level The true probability The interval can be expressed as:

[0109] (15)

[0110] Where I is the Beta distribution B(m i ,s+m i ) cumulative distribution function; H is the Beta distribution B(s+m i ,nm i )’s cumulative distribution function; for The upper bound of the distribution, for lower bound of the distribution; is the random variable of wind and solar power prediction error The number of samples that are smaller than the i-th reference sample point G (also called the i-th typical sample point), that is, the number of samples in the i-th prediction error sample set of wind and solar power output that are smaller than the i-th reference sample point G; is the total number of samples in the i-th prediction error sample set, It is also the total number of prediction error sample sets; s is the step size.

[0111] Among them, the first value is ; The second value is The first cumulative distribution function associated with the number of samples corresponding to the i-th is I, and its inverse function is recorded as I -1 The second cumulative distribution function associated with the number of samples corresponding to the i-th is H, and its inverse function is recorded as H -1 The first true probability interval is [ , ]; the second true probability interval is [ , 1]; the third true probability interval is [0, ].

[0112] When the number of samples corresponding to the i-th is greater than 0 and less than the total number of samples included in the i-th prediction error sample set, that is, 0 < m i <n, the first true probability interval [ , ] is the true probability interval of the i-th target at this confidence level. When the number of samples corresponding to the i-th is equal to the total number of samples included in the i-th prediction error sample set, that is, m i =n, the second true probability interval [ , 1] as the true probability interval of the i-th target at this confidence level. When the number of samples corresponding to the i-th is equal to 0, that is, m i =0, the third true probability interval [0, ] as the true probability interval of the i-th target at this confidence level.

[0113] In one embodiment, the chance-constrained distribution robust model includes a robust operating interval set, where the robust operating interval set is composed of operating interval variables within the robust operating interval. Determining the upper bound cumulative probability of the robust operating interval and the lower bound cumulative probability of the robust operating interval at each moment based on the chance-constrained distribution robust model includes:

[0114] For time t, according to the positive spinning reserve operating value at time t provided by the gas turbine in the integrated energy system, the negative spinning reserve operating value at time t and the total installed capacity of the new energy units in the integrated energy system, the upper limit value of the robust operating range at time t and the lower limit value of the robust operating range at time t are obtained respectively; the upper limit value of the robust operating range at time t is substituted into the lower boundary function of the joint cumulative probability distribution of wind and solar output at time t to obtain the upper bound cumulative probability of the robust operating range at time t; the opposite number of the lower limit value of the robust operating range at time t is substituted into the upper boundary function of the joint cumulative probability distribution of wind and solar output at time t to obtain the lower bound cumulative probability of the robust operating range at time t.

[0115] To describe the robustness and tolerance of the integrated energy system to disturbances caused by wind and solar power forecast errors, a robust operating range for upper and lower reserves is constructed. The integrated energy system's preset spinning reserve can quickly respond to forecast errors within the robust operating range. If the integrated energy system cannot absorb errors outside this range, it will resort to wind and solar power curtailment and load shedding, with the risk measured by cost losses. Combined with the joint distribution of wind and solar power forecast errors, the chance-constrained distributed robust model can be expressed as:

[0116] (20)

[0117] Where: is the single-cycle robustness; is the robustness at time t; is the set of robust operating intervals; is the operating interval variable at time t; is the robust operating range at time t; is the lower limit of the robust operation range at time t; is the upper limit of the robust operation range at time t; The positive spinning reserve operating value provided to the gas turbine at time t; The negative spinning reserve operating value provided to the gas turbine at time t; for robust cumulative probability; is the cumulative probability of the upper bound of the robust operating interval at time t; is the cumulative probability of the lower bound of the robust operating interval at time t; is the lower bound function of the joint cumulative probability distribution of wind and solar power output at time t; is the upper bound function of the joint cumulative probability distribution of wind and solar power output at time t; is the prediction error of wind and solar power output at time t; is the expected load shedding value at time t; is the expected value of wind and solar power curtailment at time t; It is the total installed capacity of new energy units in the integrated energy system.

[0118] After constructing the above-mentioned chance-constrained distribution robust model, the cumulative probability of the upper bound of the robust operation interval at time t and the cumulative probability of the lower bound of the robust operation interval at time t can be determined. Specifically:

[0119] Step 1: Determine the upper and lower bounds of the robust operation range:

[0120] (1.1) Clarify the formula , which defines the form of the robust operating interval set, the operating interval variable at time t The value range of is determined by the upper limit of the robust operation interval at time t and the lower limit of the robust operating range at time t .

[0121] (1.2) According to the formula and , combined with the positive spinning reserve value provided by the gas turbine at time t , the negative spinning reserve value provided by the gas turbine at time t and the total installed capacity of new energy units , determine the upper limit of the robust operation range at time t and the lower limit of the robust operating range at time t .

[0122] Step 2: Calculate the cumulative probability of the robust operating interval boundary:

[0123] (2.1) According to the formula , combined with the lower boundary function of the joint cumulative probability distribution of wind and solar output at time t (This function is usually determined based on prior information such as the joint distribution of wind and solar forecast errors), the upper limit of the robust operation range at time t Substitution Thus, the cumulative probability of the upper bound of the robust operation interval at time t is obtained .

[0124] (2.2) According to the formula , combined with the upper boundary function of the joint cumulative probability distribution of wind and solar power output at time t (This function is usually determined based on prior information such as the joint distribution of wind and solar forecast errors), the lower limit of the robust operating range at time t is Substitute the opposite number of Thus, the cumulative probability of the lower bound of the robust operating interval at time t is obtained .

[0125] Step 2: Verification and correlation calculation:

[0126] (3.1) Through formula , calculate the robust cumulative probability ,Sure and Whether the difference is within a reasonable range (in line with the probability value characteristics) can help determine the accuracy of the calculation.

[0127] (3.2) According to the formula and On the one hand, verify Whether the probability difference value interval requirements are met; on the other hand, determine the robust gradient at time t , so that the calculation results of these two accumulated probabilities are integrated into the system robustness quantification system.

[0128] In one embodiment, based on the positive spinning reserve operation value at time t provided by the gas turbine in the integrated energy system, the negative spinning reserve operation value at time t, and the total installed capacity of the new energy units in the integrated energy system, an upper limit value of the robust operation range at time t and a lower limit value of the robust operation range at time t are obtained, respectively, including:

[0129] The upper limit of the robust operating range at time t is obtained based on the ratio of the positive spinning reserve operating value at time t provided by the gas turbine in the integrated energy system to the total installed capacity of the new energy units in the integrated energy system; the lower limit of the robust operating range at time t is obtained based on the ratio of the negative spinning reserve operating value at time t provided by the gas turbine in the integrated energy system to the total installed capacity of the new energy units in the integrated energy system.

[0130] According to the formula , determine the positive spinning reserve value provided by the gas turbine at time t Total installed capacity of new energy units The ratio is used as the upper limit of the robust operation range at time t. According to the formula , determine the negative spinning reserve operating value provided by the gas turbine at time t Total installed capacity of new energy units The ratio is used as the lower limit of the robust operation range at time t. .

[0131] In one embodiment, the integrated energy system scheduling results are obtained by solving the power balance constraints of electricity, heat, gas and hydrogen, the grid carrying capacity constraints and the robustness objective function of the integrated energy system, including:

[0132] Obtain the cost objective function, spinning reserve constraints, energy storage system constraints, and equipment operation constraints of the integrated energy system; perform multi-objective solution based on the power balance constraints of electricity, heat, gas, and hydrogen, the grid carrying capacity constraints, spinning reserve constraints, energy storage system constraints, equipment operation constraints, cost objective function, and robustness objective function to obtain the scheduling results of the integrated energy system.

[0133] The cost objective function of an integrated energy system is to minimize the total cost of the system. Low carbon can also be linked to cost indicators through a green certificate-carbon exchange mechanism. At the same time, due to the uncertainty of new energy grid integration, wind and solar power curtailment often occurs. Therefore, by incorporating wind and solar power curtailment penalty costs and spinning reserve costs into the total dispatch cost, the cost objective function for minimizing the total cost can be expressed as:

[0134] (twenty one)

[0135] Where: The cost of purchased energy; Equipment operating costs; is the spinning reserve cost; Penalty costs for curtailing wind and solar power. and The expressions of are shown in formula (9) and formula (11), and the specific expressions of the remaining costs are as follows:

[0136] (twenty two)

[0137] Where: and are the electricity and gas purchase volumes at time t respectively; and are the purchase price of electric power and natural gas power at time t respectively; and Energy supply equipment and energy storage equipment Operating cost coefficient of equipment Including methane reactor MR, hydrogen fuel cell HFC, electrolyzer EL, electric boiler EB and wind power photovoltaic equipment Mainly hydrogen storage tanks; Energy supply equipment Output power at time t; and Energy storage devices The charging and discharging power at time t; and are the positive standby cost coefficient and negative standby cost coefficient of gas turbine respectively; and are the unit cost coefficients for wind and solar curtailment respectively; and are the amount of wind and solar power curtailment at time t, respectively; T is a single cycle.

[0138] Robustness objective function of the integrated energy system: To avoid subjectively setting the robustness of the integrated energy system, fully reflect the constraint relationship between cost and robustness in the scheduling plan, and obtain a better robust optimization plan, robustness is set as the goal of collaborative optimization. Its maximization expression (i.e., robustness objective function) is:

[0139] (twenty three)

[0140] Where: is the spare robust weight coefficient; is the load shedding penalty coefficient; It is the penalty coefficient for curtailing wind and solar power. and is the cumulative probability of the upper bound of the robust operating interval at time t and the cumulative probability of the lower bound of the robust operating interval at time t determined by the chance constrained distribution robust model.

[0141] The integrated energy system solves the uncertainty problem of renewable energy output forecast error by setting positive and negative spinning reserve capacity for gas turbine units. The spinning reserve constraint constructed in this embodiment can be expressed as:

[0142] (twenty four)

[0143] Where: and are the positive spinning reserve demand and negative spinning reserve demand of wind power at time t respectively; and are the positive spinning reserve demand and negative spinning reserve demand of PV at time t respectively; and are the positive spinning reserve demand and negative spinning reserve demand caused by load forecast error at time t, respectively; and They are the unit's ascending ramp rate and descending ramp rate respectively; For transition time; and It is the maximum output and minimum output of the unit.

[0144] The energy storage system constraints constructed in this embodiment can be expressed as:

[0145] (25)

[0146] Where: for The capacity of the hydrogen storage model HT at a given moment; is the self-damage rate of the hydrogen storage model HT; and are 0-1 variables, representing the power storage flag and power release flag of the hydrogen storage model HT; and is the storage power and release power of the hydrogen storage model HT; and are the upper and lower capacity limits of the hydrogen storage model HT; is the maximum power of the hydrogen storage model HT.

[0147] The equipment operation constraints constructed in this embodiment can be expressed as:

[0148] (26)

[0149] Where: and Energy supply equipment Upper and lower limits of the climbing rate; and Energy supply equipment The upper and lower limits of the output power.

[0150] In one embodiment, a multi-objective solution is performed based on the power balance constraints of electricity, heat, gas and hydrogen, the grid carrying capacity constraints, the spinning reserve constraints, the energy storage system constraints, the equipment operation constraints, the cost objective function and the robustness objective function to obtain the scheduling results of the integrated energy system, including:

[0151] Based on a non-dominated sorting genetic algorithm with an elite strategy, a multi-objective solution is performed by combining the power balance constraints of electricity, heat, gas and hydrogen, the grid carrying capacity constraints, the spinning reserve constraints, the energy storage system constraints, the equipment operation constraints, the cost objective function and the robustness objective function to obtain the scheduling results of the integrated energy system.

[0152] The non-dominated sorting genetic algorithm with an elitist strategy (NSGA-II-WPA algorithm) first searches for the Pareto front and then optimizes using the Weighted Product Approach (WPA). This method addresses the slow convergence problem of the non-dominated sorting genetic algorithm (NSGA-II algorithm) and prevents the objective from falling into a local WPA optimum. After determining the optimization direction, it can quickly search for the optimal solution and converge in that direction, achieving efficient and deep optimization. The specific solution steps are as follows, and the solution process is shown in Figure 3(b):

[0153] (1) Input historical wind and solar power output data, wind and solar power forecast data and various equipment parameters of the integrated energy system.

[0154] (2) Generate confidence bands based on the confidence level and historical wind and solar power output data.

[0155] (3) Set the algorithm parameters, initialize the population, and perform fast non-dominated sorting.

[0156] (4) Calculate the degree of crowding of individuals in the population , the calculation formula is:

[0157] (27)

[0158] in, and The degree of congestion is much greater than other congestion degrees; where: is the crowding level of individuals corresponding to the dominance level; is the number of individuals corresponding to the dominance level; and are the system operation cost and comprehensive robustness of the latter and former bodies respectively; and are the minimum and maximum function values ​​in the corresponding dominance levels, respectively.

[0159] Random individual hybridization mutations are used to obtain offspring individual genes. The calculation formula is as follows:

[0160] (28)

[0161] (29)

[0162] Where: For future generations genes; is the hybridization parameter; is a mutagenic factor with fixed parameters; when the mutagenic factor Less than Gene mutation occurs when is the variation parameter and and They are all random numbers between 0 and 1.

[0163] (6) Combine the offspring obtained in step (5) with the parents, and after non-dominated sorting, select the N individuals with the highest fitness to form a new parent group.

[0164] (7) The general direction of the optimal Pareto frontier can be determined by the number of individuals on the Pareto frontier and the non-dominated ranks. Only the first dominant rank is a dominant rank, and only individuals within it are dominant individuals. WPA hunting is performed based on the frontier direction, and the formula used is:

[0165] (30)

[0166] Where: To search for individuals; For this step advantage individuals; To be closest disadvantaged individuals; Fixed search step size for the pareto frontier; is a free parameter in the search, which is a random number between -0.5 and 0.5.

[0167] Merge the new individuals obtained in step (7) and the dominant individuals into a new population, perform fast non-dominated sorting, and recalculate Repeat step (7) until the dominant individuals account for the majority of the population, thus obtaining the Pareto frontier.

[0168] With the integration of wind and photovoltaic power, microgrids face increasing challenges. The uncertainty of wind and solar power needs to be accounted for in the grid-connected scheduling of integrated energy systems to ensure stable operation. Hydrogen, as an efficient and clean secondary energy source, can be integrated into integrated energy systems to create a hydrogen integrated energy system (HIES). This system plays a crucial role in increasing renewable energy consumption and reducing carbon emissions. Energy systems are shifting from a fossil fuel-based to a renewable energy-based approach to reduce greenhouse gas emissions. This goal of reducing carbon emissions further drives the development of integrated energy systems. Green certificates and carbon swaps are important tools for promoting renewable energy development and reducing carbon emissions. Green certificates are used to certify and incentivize renewable energy generation, while carbon swaps reduce greenhouse gas emission costs through exchange mechanisms. Hydrogen, as a clean secondary energy source with high energy density and reproducibility, can serve as a key means of energy storage and peak load regulation in integrated energy systems. Hydrogen can be produced through water electrolysis and, when needed, used in fuel cells or combustion to generate electricity. Furthermore, the uncertainty of wind and solar power needs to be accounted for in the optimal scheduling of hydrogen integrated energy systems to ensure stable system operation. Current methods for addressing wind and solar power uncertainty include stochastic optimization and robust optimization.

[0169] The optimal scheduling of integrated energy systems is a complex and evolving research field. With growing energy demand, environmental pressures, and technological advancements, optimized scheduling technologies for integrated energy systems are constantly developing and evolving to achieve higher energy efficiency, lower operating costs, and greater system reliability. The following is a detailed introduction to the development of technologies related to optimized scheduling for integrated energy systems.

[0170] In the early stages of the development of integrated energy systems, optimized scheduling primarily focused on cost-based scheduling and generation planning. Traditional optimization methods include linear programming (LP), mixed-integer linear programming (MILP), and dynamic programming (DP). These methods primarily target conventional power sources such as thermal and hydropower, focusing on reducing fuel consumption and operating costs through optimized generation planning. However, with significant advances in renewable energy technology, the cost of wind and solar power has plummeted, and installed capacity has grown rapidly. Simultaneously, advances in energy storage technology have created new opportunities for optimized scheduling of integrated energy systems. The application of technologies such as battery storage, pumped hydro, and hydrogen storage has enabled power systems to better balance supply and demand, improving system flexibility and reliability. Energy storage systems are playing an increasingly important role in the optimized scheduling of integrated energy systems. Optimal scheduling strategies need to consider the charging and discharging schedules of energy storage systems to balance load fluctuations and the uncertainty of renewable energy sources. Advanced optimization methods such as nonlinear programming (NLP), stochastic programming (SP), and robust optimization (RO) are widely used in the scheduling optimization of energy storage systems. These methods can handle the dynamic characteristics and uncertainties of energy storage systems, improving the cost-effectiveness and reliability of system operation. The integration of distributed energy resources such as photovoltaics, wind power, gas turbines, and micro fuel cells has further complicated the system structure. Microgrids, as flexible energy management units, can achieve efficient utilization and management of local energy. In the optimal scheduling of microgrids, the coordinated optimization of local load demand and distributed energy generation is crucial. Multi-objective optimization (MOO) and multi-agent system (MAS) technologies are widely used to achieve comprehensive optimization of cost, environmental protection, and reliability. With advances in computing power and algorithmic technology, advanced optimization methods are increasingly being used in the optimal scheduling of integrated energy systems. These methods, including but not limited to evolutionary algorithms and intelligent optimization algorithms, are widely used in the optimal scheduling of integrated energy systems due to their global search capabilities and ability to handle complex nonlinear problems. These algorithms can effectively handle large-scale, multi-constraint, and multi-objective optimization problems, and improve the robustness and adaptability of scheduling strategies.Evolutionary algorithms include genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE); intelligent optimization algorithms include ant colony optimization (ACO) and artificial bee colony (ABC). Each of these evaluation methods has its own advantages and disadvantages, varying in scope and complexity.

[0171] This application proposes a comprehensive energy system scheduling strategy that takes into account grid capacity constraints. This strategy establishes a fuzzy set of wind and solar power output forecast errors and an opportunity-constrained distributed robustness model. By fully considering the constraints of robustness, cost, and low-carbon performance in the scheduling scheme, a cost-low-carbon-robust multi-objective optimization scheduling model is constructed. Through multi-energy complementarity and intelligent optimization, the grid's renewable energy absorption capacity and operational safety are enhanced, eliminating the limitations of subjective robustness settings in conventional distributed robustness optimization, ultimately achieving low-carbon cost optimization for a comprehensive energy system that balances robustness. The solution provided in this application example establishes a more sophisticated comprehensive energy system model, primarily including a CHP thermal power flexible response model, a hydrogen energy comprehensive utilization model, and a green certificate-carbon ladder exchange mechanism. Furthermore, the solution provided in this application example establishes a distributed robustness optimization model, including a fuzzy set of wind and solar power output forecast errors based on IDM and an opportunity-constrained distributed robustness model. Furthermore, the solution provided in this application example focuses on multi-objective solution algorithms. This application example focuses not only on the comprehensive rationality of optimization objectives but also on the solution and analysis of the multi-objective cost robustness model. This comprehensive consideration and analysis provides technical support for robust cost scheduling of integrated energy systems.

[0172] This application optimizes the robust cost scheduling strategy for the integrated energy system, achieving low-cost, low-carbon, and highly reliable operation of IES grid-connected systems. It has the following advantages:

[0173] (1) In model construction, hydrogen energy utilization and CHP thermal power flexible response are mainly considered; the Kalina cycle and electric boiler are introduced on the source side to transform the cogeneration unit to achieve flexible thermal power response on the supply side. In terms of energy storage, the high power density and high energy density of hydrogen can be combined to smooth the power fluctuations of wind power and photovoltaic grid connection and improve energy utilization.

[0174] (2) In terms of reducing carbon emissions, a green certificate-carbon exchange mechanism is introduced. By combining the ladder carbon and ladder green certificate exchange models, the green transformation of IES is promoted, providing a cost-effective solution for the safe and efficient operation of the power grid in scenarios with a high proportion of new energy grid connection.

[0175] (3) Apply a distributed robust optimization algorithm to handle the uncertainty of wind and photovoltaic power output. By taking robustness as one of the optimization objectives, the subjectivity of setting confidence levels is eliminated, and the robustness of the IES optimization scheduling scheme is accurately described. Co-optimization is performed considering the constraints of system cost, making the robustness setting more reasonable.

[0176] (4) NSGA-II-WPA is used to solve the multi-objective optimization model, addressing the slow convergence problem of NSGA-II and preventing the objectives from falling into the local optimum of WPA. Robust cost optimization scheduling of the integrated energy system taking into account the green certificate-carbon exchange is realized. Through simulation experiments and multi-scenario comparisons, the algorithm can improve the overall energy efficiency of IES, and the optimization speed is better than the NSGA-II algorithm.

[0177] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0178] Based on the same inventive concept, an embodiment of the present application also provides an integrated energy system scheduling device that takes into account the grid's carrying capacity. The solution provided by this device is similar to the solution described in the above method. The specific limitations of the device embodiment can be found in the above-mentioned limitations of the method and will not be repeated here.

[0179] In one embodiment, Figure 4 As shown, a comprehensive energy system scheduling device taking into account the carrying capacity of the power grid is provided, comprising:

[0180] The fuzzy set acquisition module 401 is used to obtain fuzzy sets of wind and solar power output prediction errors at different confidence levels. The fuzzy sets of wind and solar power output prediction errors at each confidence level are obtained by: determining the prediction error samples that are smaller than the i-th reference sample point in the i-th prediction error sample set of wind and solar power output, and obtaining the i-th corresponding sample number; determining the i-th target true probability interval at the confidence level based on the relative size between the i-th corresponding sample number and the total number of samples included in the i-th prediction error sample set; and obtaining the wind and solar power output prediction error fuzzy set at the confidence level based on the true probability intervals of the respective targets at the confidence level.

[0181] The power balance constraint construction module 402 is used to evaluate the fuzzy set of wind and solar power output prediction errors at different confidence levels, determine the target confidence level, obtain the actual value expression of wind and solar power output, and determine the power balance constraints of electricity, heat, gas and hydrogen of the integrated energy system;

[0182] The objective function construction module 403 is used to determine the upper bound cumulative probability of the robust operation interval and the lower bound cumulative probability of the robust operation interval at each moment according to the chance constraint distribution robust model, so as to construct the robustness objective function of the integrated energy system;

[0183] A grid carrying capacity constraint building module 404 is used to obtain the grid carrying capacity constraint of the integrated energy system;

[0184] The solution module 405 is used to solve the electric, thermal, gas and hydrogen power balance constraints, grid carrying capacity constraints and robustness objective function of the integrated energy system to obtain a scheduling result of the integrated energy system.

[0185] In one embodiment, the fuzzy set acquisition module 401 is further configured to: obtain a first value based on half of the difference between 1 and the confidence level; obtain a second value based on half of the sum of 1 and the confidence level; apply an inverse function of a first cumulative distribution function associated with the number of samples corresponding to the ith to the first value to obtain a lower limit value of the true probability; apply an inverse function of a second cumulative distribution function associated with the number of samples corresponding to the ith to the second value to obtain an upper limit value of the true probability; obtain a first true probability interval based on the lower limit value of the true probability and the upper limit value of the true probability; obtain a second true probability interval based on the lower limit value of the true probability and 1. rate interval; according to 0 and the true probability upper limit value, a third true probability interval is obtained; when the i-th corresponding sample number is greater than 0 and less than the total number of samples included in the i-th prediction error sample set, the first true probability interval is used as the i-th target true probability interval under the confidence level; when the i-th corresponding sample number is equal to the total number of samples included in the i-th prediction error sample set, the second true probability interval is used as the i-th target true probability interval under the confidence level; when the i-th corresponding sample number is equal to 0, the third true probability interval is used as the i-th target true probability interval under the confidence level.

[0186] In one embodiment, the chance-constrained distribution robust model includes a robust operating interval set, which is composed of operating interval variables in the robust operating interval; the objective function construction module 403 is also used to: for time t, according to the positive rotating reserve operating value at time t, the negative rotating reserve operating value at time t and the total installed capacity of new energy units in the integrated energy system provided by the gas turbine in the integrated energy system, respectively obtain the upper limit value of the robust operating interval at time t and the lower limit value of the robust operating interval at time t; substitute the upper limit value of the robust operating interval at time t into the lower boundary function of the joint cumulative probability distribution of wind and solar output at time t to obtain the upper limit cumulative probability of the robust operating interval at time t; substitute the opposite number of the lower limit value of the robust operating interval at time t into the upper boundary function of the joint cumulative probability distribution of wind and solar output at time t to obtain the lower limit cumulative probability of the robust operating interval at time t.

[0187] In one embodiment, the objective function construction module 403 is further used to: obtain the upper limit value of the robust operating range at time t based on the ratio of the positive spinning reserve operating value at time t provided by the gas turbine in the integrated energy system to the total installed capacity of the new energy units in the integrated energy system; and obtain the lower limit value of the robust operating range at time t based on the ratio of the negative spinning reserve operating value at time t provided by the gas turbine in the integrated energy system to the total installed capacity of the new energy units in the integrated energy system.

[0188] In one embodiment, the solution module 405 is also used to: obtain the cost objective function, rotating reserve constraints, energy storage system constraints, and equipment operation constraints of the integrated energy system; perform multi-objective solution based on the electric, thermal, gas, and hydrogen power balance constraints, grid carrying capacity constraints, rotating reserve constraints, energy storage system constraints, equipment operation constraints, cost objective function, and robustness objective function to obtain the scheduling result of the integrated energy system.

[0189] In one embodiment, the solution module 405 is also used to: perform multi-objective solution based on a non-dominated sorting genetic algorithm with an elite strategy, combined with the electric, thermal, gas and hydrogen power balance constraints, grid carrying capacity constraints, rotating reserve constraints, energy storage system constraints, equipment operation constraints, cost objective functions and robustness objective functions to obtain a scheduling result of the integrated energy system.

[0190] Each module in the aforementioned integrated energy system dispatching device taking into account grid carrying capacity can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0191] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program. The internal structure diagram of the computer device can be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data involved in the above method. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes a comprehensive energy system scheduling method taking into account the carrying capacity of the power grid. Those skilled in the art can understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0192] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0193] In one embodiment, a computer program product is provided, on which a computer program is stored. The computer program is used by a processor to execute the steps in the above-mentioned various method embodiments.

[0194] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0195] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0196] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for dispatching an integrated energy system taking into account the carrying capacity of a power grid, characterized in that: The method comprises: Obtain wind and solar power output prediction error fuzzy sets at different confidence levels; the wind and solar power output prediction error fuzzy set at each confidence level is obtained by the following steps: determining the prediction error sample that is smaller than the i-th reference sample point in the i-th prediction error sample set of wind and solar power output, and obtaining the i-th corresponding sample number; determining the i-th target true probability interval at the confidence level based on the relative size between the i-th corresponding sample number and the total number of samples included in the i-th prediction error sample set; obtaining the wind and solar power output prediction error fuzzy set at the confidence level based on the true probability intervals of each target at the confidence level; Evaluate the fuzzy set of wind and solar power output prediction errors at different confidence levels, determine the target confidence level, obtain the actual value expression of wind and solar power output, and determine the power balance constraints of electricity, heat, gas and hydrogen in the integrated energy system; According to the chance-constrained distributed robust model, the upper bound cumulative probability of the robust operation interval and the lower bound cumulative probability of the robust operation interval at each moment are determined to construct the robustness objective function of the integrated energy system. Obtaining a grid carrying capacity constraint of the integrated energy system; The scheduling result of the integrated energy system is obtained by solving the electric, thermal, gas and hydrogen power balance constraints, the grid carrying capacity constraints and the robustness objective function of the integrated energy system.

2. The method according to claim 1, characterized in that According to the relative size between the number of samples corresponding to the ith and the total number of samples included in the ith prediction error sample set, the true probability interval of the ith target at the confidence level is determined, including: The first value is obtained based on half the difference between 1 and the confidence level; The second value is obtained by adding half of the sum of 1 and the confidence level; Applying the inverse function of the first cumulative distribution function associated with the number of samples corresponding to the i-th value to the first value to obtain a lower limit value of the true probability; Applying the inverse function of the second cumulative distribution function associated with the number of samples corresponding to the i-th value to the second value to obtain the true probability upper limit value; Obtaining a first true probability interval according to the true probability lower limit value and the true probability upper limit value; According to the true probability lower limit value and 1, a second true probability interval is obtained; According to 0 and the true probability upper limit value, a third true probability interval is obtained; When the number of samples corresponding to the i-th prediction error sample set is greater than 0 and less than the total number of samples included in the i-th prediction error sample set, the first true probability interval is used as the i-th target true probability interval at the confidence level; When the number of samples corresponding to the i-th is equal to the total number of samples included in the i-th prediction error sample set, the second true probability interval is used as the i-th target true probability interval at the confidence level; When the number of samples corresponding to the i-th value is equal to 0, the third true probability interval is used as the i-th target true probability interval at the confidence level.

3. The method according to claim 1, characterized in that The chance-constrained distribution robust model includes a robust operating interval set, where the robust operating interval set is composed of operating interval variables within the robust operating interval. According to the chance-constrained distribution robust model, determining the upper bound cumulative probability of the robust operating interval and the lower bound cumulative probability of the robust operating interval at each moment includes: At time t, based on the positive spinning reserve operation value at time t provided by the gas turbine in the integrated energy system, the negative spinning reserve operation value at time t, and the total installed capacity of the new energy units in the integrated energy system, the upper limit value of the robust operation range at time t and the lower limit value of the robust operation range at time t are obtained respectively; Substituting the upper limit value of the robust operation interval at time t into the lower boundary function of the joint cumulative probability distribution of wind and solar power output at time t, the upper bound cumulative probability of the robust operation interval at time t is obtained; The opposite number of the lower limit value of the robust operation range at time t is substituted into the upper boundary function of the joint cumulative probability distribution of wind and solar power output at time t to obtain the lower limit cumulative probability of the robust operation range at time t.

4. The method according to claim 3, characterized in that According to the positive spinning reserve operation value at time t provided by the gas turbine in the integrated energy system, the negative spinning reserve operation value at time t, and the total installed capacity of the new energy units in the integrated energy system, the upper limit value of the robust operation range at time t and the lower limit value of the robust operation range at time t are obtained respectively, including: The upper limit of the robust operation range at time t is obtained based on the ratio of the positive spinning reserve operation value provided by the gas turbine in the integrated energy system at time t to the total installed capacity of the new energy units in the integrated energy system; According to the ratio of the negative spinning reserve operation value provided by the gas turbine in the integrated energy system at time t to the total installed capacity of the new energy units in the integrated energy system, the lower limit of the robust operation range at time t is obtained.

5. The method according to claim 1, wherein The scheduling results of the integrated energy system are obtained by solving the power balance constraints of electricity, heat, gas and hydrogen, the grid carrying capacity constraints and the robustness objective function of the integrated energy system, including: Obtaining a cost objective function, spinning reserve constraints, energy storage system constraints, and equipment operation constraints of the integrated energy system; A multi-objective solution is performed based on the electric, thermal, gas and hydrogen power balance constraints, grid carrying capacity constraints, spinning reserve constraints, energy storage system constraints, equipment operation constraints, cost objective function and robustness objective function to obtain the scheduling result of the integrated energy system.

6. The method according to claim 5, characterized in that A multi-objective solution is performed based on the power balance constraints of electricity, heat, gas and hydrogen, grid carrying capacity constraints, spinning reserve constraints, energy storage system constraints, equipment operation constraints, cost objective function and robustness objective function to obtain the scheduling results of the integrated energy system, including: Based on the non-dominated sorting genetic algorithm with elite strategy, a multi-objective solution is performed in combination with the power balance constraints of electricity, heat, gas and hydrogen, grid carrying capacity constraints, spinning reserve constraints, energy storage system constraints, equipment operation constraints, cost objective function and robustness objective function to obtain the scheduling result of the integrated energy system.

7. A comprehensive energy system dispatching device taking into account the carrying capacity of the power grid, characterized in that: The device comprises: A fuzzy set acquisition module is used to obtain fuzzy sets of wind and solar power output prediction errors at different confidence levels; the fuzzy set of wind and solar power output prediction errors at each confidence level is obtained by the following steps: determining the prediction error sample that is smaller than the i-th reference sample point in the i-th prediction error sample set of wind and solar power output, and obtaining the i-th corresponding sample number; determining the i-th target true probability interval at the confidence level based on the relative size between the i-th corresponding sample number and the total number of samples included in the i-th prediction error sample set; and obtaining the wind and solar power output prediction error fuzzy set at the confidence level based on the true probability intervals of each target at the confidence level; The power balance constraint construction module is used to evaluate the fuzzy set of wind and solar power output prediction errors at different confidence levels, determine the target confidence level, obtain the actual value expression of wind and solar power output, and determine the power balance constraints of electricity, heat, gas and hydrogen in the integrated energy system; The objective function construction module is used to divide the robust model according to the chance constraint, determine the cumulative probability of the upper bound of the robust operation interval and the cumulative probability of the lower bound of the robust operation interval at each moment, and thus construct the robustness objective function of the integrated energy system; A grid carrying capacity constraint building module, used for obtaining the grid carrying capacity constraint of the integrated energy system; The solution module is used to solve the electric, thermal, gas and hydrogen power balance constraints, grid carrying capacity constraints and robustness objective functions of the integrated energy system to obtain the scheduling results of the integrated energy system.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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