Power dispatching balance optimization method and system based on risk planning constraint

Abstract

The invention relates to a power dispatching balance optimization method and system based on risk planning constraint, and the method comprises the steps: carrying out the calculation of the real-time settlement price scene according to the day-ahead planned bidding cost, the set of real-time settlement price scenes, the occurrence probability of the scenes, and the real-time power purchase cost, the total penalty cost and the natural gas purchase cost under each scene; constructing a scheduling optimization objective function model of the energy system; the scheduling optimization objective function model aims at realizing the minimum system cost through power supply and demand optimization; constructing a thermoelectric supply and demand balance constraint condition of the energy system; converting the thermoelectric supply and demand balance constraint condition into an opportunity constraint framework and an energy supply and demand net value; and equivalently converting the opportunity constraint framework into a processable deterministic constraint condition according to a cumulative distribution function. According to the technical scheme, the opportunity constraint planning technology is adopted to solve the uncertainty in the thermoelectric supply and demand balance constraint condition, so that the energy supply reliability of the energy system is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of power dispatch optimization, and in particular to a method and system for optimizing power dispatch balance based on risk planning constraints. Background Art

[0002] In recent years, efficient and low-carbon energy supply has received great attention worldwide. Regional integrated energy systems integrate different forms of energy systems (such as heating systems, electrical systems, natural gas systems, etc.), which have excellent economic and environmental performance and are considered to be a promising way to improve energy flexibility and efficiency. Grid-connected integrated energy systems can purchase electricity from power companies to improve the reliability of energy supply.

[0003] However, the uncertainty of renewable energy poses a challenge to stable and economic operation. Since the power deviation between the day-ahead plan and the real-time output may lead to additional penalty costs, the power supply and demand behavior must be optimized. It is difficult to solve the impact of renewable energy uncertainty on power dispatch balance with existing technologies. Summary of the invention

[0004] In order to at least to some extent overcome the problem in related technologies that it is difficult to resolve the impact of uncertainty of renewable energy on power dispatching balance, the present application provides a power dispatching balance optimization method and system based on risk planning constraints.

[0005] The scheme of this application is as follows:

[0006] According to a first aspect of an embodiment of the present application, a method for optimizing power dispatching balance based on risk planning constraints is provided, comprising:

[0007] According to the day-ahead planned bidding cost, the set of real-time settlement price scenarios, the probability of occurrence of the scenarios, and the real-time electricity purchase cost, the total amount of penalty fees, and the natural gas purchase cost under each scenario, a scheduling optimization objective function model of the energy system is constructed; the scheduling optimization objective function model aims to minimize the system cost by optimizing the supply and demand of electricity;

[0008] Construct the constraints of heat and electricity supply and demand balance in energy system;

[0009] Convert the thermal power supply and demand balance constraints into an opportunity constraint framework and a net value of energy supply and demand;

[0010] The chance constraint framework is equivalently converted into tractable deterministic constraints according to the cumulative distribution function.

[0011] Preferably, constructing a dispatch optimization objective function model for an energy system includes:

[0012]

[0013] Where F represents the objective function; C DA represents the planned bidding cost on the day before; N s represents the set of real-time settlement price scenarios; π s represents the probability of scenario s occurring; represents the real-time electricity purchase cost under scenario s; represents the total amount of penalty fees incurred due to deviations under scenario s; represents the natural gas purchase cost under scenario s.

[0014] Preferably, the method further comprises:

[0015] Based on the electricity consumption and the day-ahead electricity price, the bidding cost of the day-ahead plan is calculated as:

[0016]

[0017] in, Indicates the real-time power consumption at time l; It represents the electricity price at the lth time the day before.

[0018] Preferably, the method further comprises:

[0019] According to the transmission power between the system and the power grid and the interval of each operation decision time slot, the planned power consumption of the day is calculated:

[0020]

[0021] in, represents the planned electricity consumption at the lth hour the day before; represents the transmission power between the system and the grid; Δt represents the interval of each operation decision time slot;

[0022] Calculate the difference between the planned power consumption and the real-time power consumption:

[0023]

[0024] Where, ΔE s,l Indicates the difference between the planned power consumption and the real-time power consumption on the previous day;

[0025] The real-time electricity purchase cost is calculated based on the difference between the planned electricity consumption and the real-time electricity consumption, as well as the real-time supply electricity price:

[0026]

[0027] in, represents the real-time supply electricity price at time l;

[0028] According to the difference between the planned power consumption and the real-time power consumption, as well as the penalty cost and the allowable deviation coefficient, the total penalty fee caused by the deviation at time l is determined:

[0029]

[0030] in, represents the total penalty fee caused by deviation at time l under scenario s; p represents the penalty cost; r represents the allowable deviation coefficient;

[0031] Determine the total amount of penalty fees due for deviations:

[0032]

[0033] Calculate the natural gas purchase cost based on natural gas price, CHP unit power, gas boiler thermal power, CHP unit efficiency and gas boiler efficiency:

[0034]

[0035] Among them, v gas represents the price of natural gas; Indicates the electrical power of the cogeneration unit; Indicates the heating power of the gas boiler; η CHP represents the efficiency of the combined heat and power unit; η GB Indicates gas boiler efficiency.

[0036] Preferably, the constraints for balancing the thermal power supply and demand of the energy system include:

[0037] The constraints for balancing electricity supply and demand in constructing energy systems are:

[0038]

[0039] Among them, P t PV Represents photovoltaic power generation; P t WT represents wind power generation; Indicates the battery charging power; Indicates battery discharge power; Indicates the electric power of the electric boiler; P t load Indicates electrical load;

[0040] Constructing the heat supply and demand balance constraints of the energy system:

[0041]

[0042] in, is the heat production power of the cogeneration unit; Indicates the heating power of electric boiler; Indicates the charging power of the heat storage system; Indicates the heat release power of the heat storage system; Indicates heat load.

[0043] Preferably, the constraints of the electric power of the cogeneration unit are:

[0044]

[0045] in, and Indicates the upper and lower limits of the combined heat and power unit power;

[0046] The relationship between the electrical power and heat generation power of the cogeneration unit is:

[0047]

[0048] Wherein, b represents the thermoelectric ratio;

[0049] The constraints of the heating power of the electric boiler are:

[0050]

[0051] in, and They represent the upper and lower limits of the heating power of the electric boiler respectively;

[0052] The relationship between the electric power and heat generation power of an electric boiler is:

[0053]

[0054] Among them, η EB Indicates the efficiency of electric boiler;

[0055] The constraints of the heating power of the gas boiler are:

[0056]

[0057] in, and They respectively represent the upper and lower limits of the heating power of the gas boiler.

[0058] Preferably, the heat and power supply and demand balance constraint condition is converted into an opportunity constraint framework and a net value of energy supply and demand, including:

[0059] Taking the renewable energy generation as an uncertain factor, the opportunity constraint framework is used to solve the uncertain factors in the thermal power supply and demand balance constraint conditions. The converted opportunity constraint framework is:

[0060]

[0061] The net value of energy supply and demand is:

[0062]

[0063] P t net =P t load -P t PV -P t WT ;

[0064]

[0065] Where Pr(·) represents the probability of an event; η and β represent the confidence levels of the power and heat balance constraints, respectively; and Represent the supply of electricity and heat respectively; P t net Represents the net electricity demand.

[0066] Preferably, the opportunity constraint framework is equivalently converted into a processable deterministic constraint condition according to the cumulative distribution function, including:

[0067]

[0068] in, and Represent the uncertain variables P t net and The inverse function of the CDF.

[0069] Preferably, the method further comprises:

[0070] Will and As quantiles with nominal probabilities, quantile predictions are made using the Pinball loss function:

[0071]

[0072] Among them, y i and Represents the actual value y and predicted value of the i-th sample n samples is the total number of samples; α represents the nominal probability.

[0073] According to a second aspect of an embodiment of the present application, a power dispatching balance optimization system based on risk planning constraints is provided, comprising:

[0074] Processor and memory;

[0075] The processor and the memory are connected via a communication bus:

[0076] Wherein, the processor is used to call and execute the program stored in the memory;

[0077] The memory is used to store a program, and the program is used to execute at least one of the above-mentioned methods for optimizing power dispatching balance based on risk planning constraints.

[0078] The technical solution provided by the present application may include the following beneficial effects: The power dispatch balance optimization method based on risk planning constraints in the present application includes: constructing a dispatch optimization objective function model of the energy system according to the day-ahead planned bidding cost, a set of real-time settlement price scenarios, the probability of occurrence of the scenarios, and the real-time electricity purchase cost, total penalty fee, and natural gas purchase cost under each scenario; the dispatch optimization objective function model aims to minimize the system cost through power supply and demand optimization; constructing the thermal power supply and demand balance constraints of the energy system; converting the thermal power supply and demand balance constraints into an opportunity constraint framework and a net value of energy supply and demand; and converting the opportunity constraint framework into a processable deterministic constraint based on the cumulative distribution function.

[0079] In this technical solution, it is considered that renewable energy generation exhibits intermittency and dynamics, while power and heat loads vary with the random energy consumption behavior of users. They need to be fully considered as uncertain factors in the day-ahead scheduling of the energy system. Therefore, the chance constraint programming technology is used to solve the uncertainty in the constraints of thermal power supply and demand balance to ensure the energy supply reliability of the energy system. By using chance constraints, uncertainty is effectively resolved, and the chance constraints can be equivalently restated as tractable deterministic constraints without any prior knowledge or probability distribution assumptions.

[0080] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0082] Figure 1 It is a flowchart of a method for optimizing power dispatching balance based on risk planning constraints provided by an embodiment of the present application;

[0083] Figure 2 It is a structural diagram of a power dispatching balance optimization system based on risk planning constraints provided by an embodiment of the present application.

[0084] Reference numerals: processor-21; memory-22. DETAILED DESCRIPTION

[0085] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0086] Embodiment 1

[0087] A power dispatching balance optimization method based on risk planning constraints, comprising:

[0088] S11: Based on the day-ahead planned bidding cost, the set of real-time settlement price scenarios, the probability of occurrence of the scenarios, and the real-time electricity purchase cost, the total penalty fee, and the natural gas purchase cost under each scenario, a dispatch optimization objective function model of the energy system is constructed; the dispatch optimization objective function model aims to minimize the system cost by optimizing the supply and demand of electricity;

[0089] S12: Constructing the constraints of thermal power supply and demand balance in energy systems;

[0090] S13: Convert the thermal power supply and demand balance constraints into an opportunity constraint framework and energy supply and demand net value;

[0091] S14: The chance constraint framework is equivalently converted into tractable deterministic constraints based on the cumulative distribution function.

[0092] It should be noted that the dispatch optimization objective function model is to achieve the minimum system cost by optimizing the power supply and demand, and to build a dispatch optimization objective function model for the energy system, including:

[0093]

[0094] Where F represents the objective function; C DA represents the planned bidding cost on the day before; N s represents the set of real-time settlement price scenarios; π s represents the probability of scenario s occurring; represents the real-time electricity purchase cost under scenario s; represents the total amount of penalty fees incurred due to deviations under scenario s; represents the natural gas purchase cost under scenario s.

[0095] The various parameters in the scheduling optimization objective function model are calculated as follows:

[0096] According to the electricity consumption and the day-ahead electricity price, the day-ahead bidding cost is calculated:

[0097]

[0098] in, Indicates the real-time power consumption at time l; It represents the electricity price at the lth time the day before.

[0099] According to the transmission power between the system and the power grid and the interval of each operation decision time slot, the planned power consumption of the day is calculated:

[0100]

[0101] in, represents the planned electricity consumption at the lth hour the day before; represents the transmission power between the system and the grid; Δt represents the interval of each operation decision time slot;

[0102] Calculate the difference between the planned power consumption and the real-time power consumption:

[0103]

[0104] Where, ΔE s,l Indicates the difference between the planned power consumption and the real-time power consumption on the previous day;

[0105] The real-time electricity purchase cost is calculated based on the difference between the planned electricity consumption and the real-time electricity consumption, as well as the real-time supply electricity price:

[0106]

[0107] in, represents the real-time supply electricity price at time l;

[0108] According to the difference between the planned power consumption and the real-time power consumption, as well as the penalty cost and the allowable deviation coefficient, the total penalty fee caused by the deviation at time l is determined:

[0109]

[0110] in, represents the total penalty fee caused by deviation at time l under scenario s; p represents the penalty cost; r represents the allowable deviation coefficient;

[0111] Determine the total amount of penalty fees due for deviations:

[0112]

[0113] The regional integrated energy system usually purchases natural gas at a predetermined price, so the natural gas purchase cost is calculated based on the natural gas price, the power of the cogeneration unit, the thermal power of the gas boiler, the efficiency of the cogeneration unit, and the efficiency of the gas boiler:

[0114]

[0115] Among them, v gas represents the price of natural gas; Indicates the electrical power of the cogeneration unit; Indicates the heating power of the gas boiler; η CHP represents the efficiency of the combined heat and power unit; η GB Indicates gas boiler efficiency.

[0116] Construct the heat and power supply and demand balance constraints of the energy system, including:

[0117] The constraints for balancing electricity supply and demand in constructing energy systems are:

[0118]

[0119] Among them, P t PV Represents photovoltaic power generation; P t WT represents wind power generation; Indicates the battery charging power; Indicates battery discharge power; Indicates the electric power of the electric boiler; P t load Indicates electrical load;

[0120] Constructing the heat supply and demand balance constraints of the energy system:

[0121]

[0122] in, is the heat production power of the cogeneration unit; Indicates the heating power of electric boiler; Indicates the charging power of the heat storage system; Indicates the heat release power of the heat storage system; Indicates heat load.

[0123] In the International Energy System (IES), the balance between supply and demand of electricity and heat is crucial. The production and consumption of electricity and heat need to be precisely matched to ensure a stable supply and efficient use of energy. If the supply and demand are out of balance, it may lead to power system instability, frequent power outages or excess energy waste.

[0124] The parameters in the heat and power supply and demand balance constraints of the energy system must also ensure the following constraints:

[0125] Combined heat and power can generate electricity by consuming natural gas and generate thermal energy by using the waste heat of the micro-turbine in the combined heat and power. The power output of the combined heat and power is limited to a certain range and coupled with the thermal power. The constraints on the power output of the combined heat and power unit are:

[0126]

[0127] in, and Indicates the upper and lower limits of the combined heat and power unit power;

[0128] The relationship between the electrical power and heat generation power of the cogeneration unit is:

[0129]

[0130] Wherein, b represents the thermoelectric ratio;

[0131] The electric boiler EB uses electricity to generate heat energy. The constraints on the heat generation power of the electric boiler are:

[0132]

[0133] in, and They represent the upper and lower limits of the heating power of the electric boiler respectively;

[0134] The relationship between the electric power and heat generation power of an electric boiler is:

[0135]

[0136] Among them, η EB Indicates the efficiency of electric boiler;

[0137] The gas boiler GB generates heat energy by consuming natural gas. The constraints of the heat generation power of the gas boiler are:

[0138]

[0139] in, and They respectively represent the upper and lower limits of the heating power of the gas boiler.

[0140] Renewable energy generation exhibits intermittency and dynamics, while power and heat loads vary with the random energy consumption behavior of users. Therefore, they need to be fully considered as uncertain factors in the day-ahead supply strategy of the energy system. This technical solution assumes that the probability of satisfying the constraints is not less than a certain confidence level to incorporate uncertain factors. Therefore, chance constrained programming techniques are used to address the uncertainties in the supply model to ensure the reliability of energy supply in the energy system. Here, the renewable energy generation is subtracted from the power load, and the net power load is directly considered, thereby capturing both supply and demand uncertainties.

[0141] Based on this, the constraints of thermal power supply and demand balance are converted into an opportunity constraint framework and the net value of energy supply and demand, including:

[0142] Taking renewable energy generation as an uncertain factor, the opportunity constraint framework is used to solve the uncertain factors in the thermal power supply and demand balance constraints. The converted opportunity constraint framework is:

[0143]

[0144] The net value of energy supply and demand is:

[0145]

[0146] P t net =P t load -P t PV -P t WT ;

[0147]

[0148] Where Pr(·) represents the probability of an event; η and β represent the confidence levels of the power and heat balance constraints, respectively; and Represent the supply of electricity and heat respectively; P t net Represents the net electricity demand.

[0149] In order to effectively solve the optimization problem with chance constraints, the implicit form of chance constraints needs to be equivalently converted into tractable deterministic constraints. Therefore, the chance constraint framework is equivalently converted into tractable deterministic constraints according to the cumulative distribution function:

[0150]

[0151] in, and Represent the uncertain variables P tnet and The inverse function of the CDF.

[0152] According to the concept of quantile of uncertain variables, and It can be interpreted as a quantile with a nominal probability (i.e., confidence level). Since it is not easy to obtain the exact probability distribution of uncertain variables in practice, this technical solution uses the Pinball loss function to perform quantile prediction:

[0153]

[0154] Among them, y i and Represents the actual value y and predicted value of the i-th sample n samples is the total number of samples; α represents the nominal probability.

[0155] Based on the constructed data-driven quantile prediction method, quantile and It can be derived without any prior knowledge or assumptions about probability distribution, thus converting the chance constraint equivalent into a tractable form.

[0156] In this technical solution, it is considered that renewable energy generation exhibits intermittency and dynamics, while power and heat loads vary with the random energy consumption behavior of users. They need to be fully considered as uncertain factors in the day-ahead scheduling of the energy system. Therefore, the chance constraint programming technology is used to solve the uncertainty in the constraints of thermal power supply and demand balance to ensure the energy supply reliability of the energy system. By using chance constraints, uncertainty is effectively resolved, and the chance constraints can be equivalently restated as tractable deterministic constraints without any prior knowledge or probability distribution assumptions.

[0157] Embodiment 2

[0158] A power dispatch balance optimization system based on risk planning constraints, referring to Figure 2 ,include:

[0159] Processor 21 and memory 22;

[0160] The processor 21 and the memory 22 are connected via a communication bus:

[0161] The processor 21 is used to call and execute the program stored in the memory 22;

[0162] The memory 22 is used to store a program, and the program is used to execute at least one of the power dispatching balance optimization methods based on risk planning constraints in the above embodiments.

[0163] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0164] It should be noted that, in the description of this application, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0165] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0166] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0167] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0168] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0169] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0170] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0171] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for optimizing power dispatching balance based on risk planning constraints, characterized in that: include: According to the day-ahead planned bidding cost, the set of real-time settlement price scenarios, the probability of occurrence of the scenarios, and the real-time electricity purchase cost, the total amount of penalty fees, and the natural gas purchase cost under each scenario, a scheduling optimization objective function model of the energy system is constructed; the scheduling optimization objective function model aims to minimize the system cost by optimizing the supply and demand of electricity; Construct the constraints of heat and electricity supply and demand balance in energy system; Convert the thermal power supply and demand balance constraints into an opportunity constraint framework and a net value of energy supply and demand; The chance constraint framework is equivalently converted into tractable deterministic constraints according to the cumulative distribution function.

2. The method according to claim 1, characterized in that Construct the dispatch optimization objective function model of the energy system, including: Where F represents the objective function; C DA represents the planned bidding cost on the day before; N s represents the set of real-time settlement price scenarios; π s represents the probability of scenario s occurring; represents the real-time electricity purchase cost under scenario s; represents the total amount of penalty fees incurred due to deviations under scenario s; represents the natural gas purchase cost under scenario s.

3. The method according to claim 2, characterized in that The method further comprises: Based on the electricity consumption and the day-ahead electricity price, the bidding cost of the day-ahead plan is calculated as: in, Indicates the real-time power consumption at time l; It represents the electricity price at the lth time the day before.

4. The method according to claim 3, characterized in that The method further comprises: According to the transmission power between the system and the power grid and the interval of each operation decision time slot, the planned power consumption of the day is calculated: in, represents the planned electricity consumption at the lth hour the day before; represents the transmission power between the system and the grid; Δt represents the interval of each operation decision time slot; Calculate the difference between the planned power consumption and the real-time power consumption: Where, ΔE s,l Indicates the difference between the planned power consumption and the real-time power consumption on the previous day; The real-time electricity purchase cost is calculated based on the difference between the planned electricity consumption and the real-time electricity consumption, as well as the real-time supply electricity price: in, represents the real-time supply electricity price at time l; According to the difference between the planned power consumption and the real-time power consumption, as well as the penalty cost and the allowable deviation coefficient, the total penalty fee caused by the deviation at time l is determined: in, represents the total penalty fee caused by deviation at time l under scenario s; p represents the penalty cost; r represents the allowed deviation coefficient; Determine the total amount of penalty fees due for deviations: Calculate the natural gas purchase cost based on natural gas price, CHP unit power, gas boiler thermal power, CHP unit efficiency and gas boiler efficiency: Among them, v gas represents the price of natural gas; Indicates the electrical power of the cogeneration unit; Indicates the heating power of the gas boiler; η CHP represents the efficiency of the combined heat and power unit; η GB Indicates gas boiler efficiency.

5. The method according to claim 4, characterized in that Construct the heat and power supply and demand balance constraints of the energy system, including: The constraints for balancing electricity supply and demand in constructing energy systems are: Among them, P t PV Represents photovoltaic power generation; P t WT represents wind power generation; Indicates the battery charging power; Indicates battery discharge power; Indicates the electric power of the electric boiler; P t load Indicates electrical load; Constructing the heat supply and demand balance constraints of the energy system: in, is the heat production power of the cogeneration unit; Indicates the heating power of electric boiler; Indicates the charging power of the heat storage system; Indicates the heat release power of the heat storage system; Indicates heat load.

6. The method according to claim 5, characterized in that The constraints of the electric power of the cogeneration unit are: in, and Indicates the upper and lower limits of the combined heat and power unit power; The relationship between the electrical power and heat generation power of a cogeneration unit is: Wherein, b represents the thermoelectric ratio; The constraints of the heating power of the electric boiler are: in, and They represent the upper and lower limits of the heating power of the electric boiler respectively; The relationship between the electric power and heat generation power of an electric boiler is: Among them, η EB Indicates the efficiency of electric boiler; The constraints of the heating power of the gas boiler are: in, and They respectively represent the upper and lower limits of the heating power of the gas boiler.

7. The method according to claim 6, characterized in that The thermal power supply and demand balance constraints are converted into an opportunity constraint framework and energy supply and demand net value, including: Taking the renewable energy generation as an uncertain factor, the opportunity constraint framework is used to solve the uncertain factors in the thermal power supply and demand balance constraint conditions. The converted opportunity constraint framework is: The net value of energy supply and demand is: P t net =P t load -P t PV -P t WT ; Where Pr(·) represents the probability of an event; η and β represent the confidence levels of the power and heat balance constraints, respectively; and Represent the supply of electricity and heat respectively; P t net Represents the net electricity demand.

8. The method according to claim 7, characterized in that The opportunity constraint framework is equivalently converted into tractable deterministic constraints based on the cumulative distribution function, including: in, and Represent the uncertain variables P t net and The inverse function of the CDF.

9. The method according to claim 8, characterized in that The method further comprises: Will and As quantiles with nominal probabilities, quantile predictions are made using the Pinball loss function: Among them, y i and Represents the actual value y and predicted value of the i-th sample n samples is the total number of samples; α represents the nominal probability.

10. A power dispatching balance optimization system based on risk planning constraints, characterized in that: include: Processor and memory; The processor and the memory are connected via a communication bus: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store a program, and the program is used to execute at least a method for optimizing power dispatching balance based on risk planning constraints as described in any one of claims 1 to 9.

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