Adjustable potential analysis method and equipment for adjustable load resources in building park

Abstract

The invention relates to an adjustable potential analysis method for adjustable load resources in a building park. The method comprises the following steps: constructing an adjustable load resource adjustment power model in the building park; calculating the dynamic carbon emission of the adjustable load resource; establishing an adjustable load resource multi-target aggregation model; and calculating the comprehensive adjustment potential of the adjustable load resources in the building park. The low-carbon contribution of each adjustment behavior of the park is accurately quantified, and the load resources are promoted to be upgraded from one-way economic response to multi-dimensional collaborative response; the accuracy and credibility of potential evaluation are remarkably improved, the problem of deviation caused by extensive aggregation is solved, and a reliable data basis is provided for power grid demand response and marketization transaction; a key decision basis is provided for the park to participate in power grid interaction, the transformation of the building park to a flexible resource polymer is powerfully promoted on the premise of ensuring the safety and comfort of energy consumption, and a solid support is provided for the construction of a novel power system and the digital transformation of park energy.

Description

Technical Field

[0001] This invention relates to the field of power systems and energy management technology, and in particular to a method and equipment for analyzing the adjustable potential of adjustable load resources in building parks. Background Technology

[0002] Energy consumption in building industrial parks is shifting from a rigid and uncontrollable model to a flexible and adjustable one. These parks contain a large amount of adjustable load resources with potential for temporal and spatial shifts, such as electric vehicles, electric heating, and air conditioning and lighting systems in smart buildings. Effectively tapping into the adjustable potential of these resources is crucial for participating in grid demand response, reducing energy costs in the parks, and promoting the integration of renewable energy.

[0003] Currently, the analysis of the adjustment potential of adjustable load resources has two main shortcomings:

[0004] First, the contribution of adjustable load resources to carbon emission reduction was not fully considered, the carbon emission reduction benefits of adjustment behavior could not be accurately assessed, and synergistic optimization was not carried out among multiple objectives such as carbon emissions, economic benefits and adjustment stability.

[0005] Second, the aggregation method is extensive, lacking a refined distinction and integrated calculation method for the adjustment potential of aggregated and dispersed resources, resulting in low credibility of potential assessment results and difficulty in guiding actual scheduling. Summary of the Invention

[0006] To address the problems in existing technologies, such as neglecting the carbon emission reduction benefits of load and the lack of reliability of results due to the crude methods of load adjustability potential assessment, the primary objective of this invention is to provide a method for analyzing the adjustability potential of adjustable load resources in building parks that incorporates dynamic carbon emission calculations into the load adjustment potential analysis system, thereby significantly improving the accuracy and reliability of potential assessment.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for analyzing the adjustable potential of adjustable load resources within a building park, the method comprising the following sequential steps:

[0008] (1) Construct a power model for adjustable load resources in a building park, which includes an adjustable load model for electric vehicles, an adjustable load model for direct-heating electric heating, and an adjustable load model for intelligent buildings;

[0009] (2) Calculate the dynamic carbon emissions of adjustable load resources based on the adjustable load resource adjustment power model in the building park;

[0010] (3) Based on the adjustable load resource adjustment power model and the dynamic carbon emission of adjustable load resources in the building park, establish a multi-objective aggregation model of adjustable load resources;

[0011] (4) Calculate the comprehensive adjustment potential of adjustable load resources in the building park based on the dynamic carbon emissions of adjustable load resources and the multi-objective aggregation model of adjustable load resources.

[0012] In step (1), the formula for the adjustable load model of the electric vehicle is as follows:

[0013]

[0014] In the formula: Let i be the load value of electric vehicle i during the adjustment period t; The charging power of electric vehicle i during the adjustment period t; , These represent the start and end times of charging for electric vehicle i, respectively. Let represent the operating state of electric vehicle i. If electric vehicle i is put into operation during the adjustment period t, then =1, if electric vehicle i stops running during the adjustment period t, then =0;

[0015] according to Calculate the response power of electric vehicle i during the adjustment period t. :

[0016] ;

[0017] In the formula, The baseline charging load value for electric vehicle i during the adjustment period t; A positive value indicates that the power is adjusted downwards. A negative value indicates that the power is adjusted upwards.

[0018] The formula for the adjustable load model of the direct-heating electric heating is as follows:

[0019] ;

[0020] In the formula: The load value of the direct-heating electric heating load j during the adjustment period t; To meet the heat load requirements at higher temperatures; For heat load requirements at lower temperatures; The heat load requirement at the real-time temperature; , , Each corresponds to a specific period of daily electric heating load: off-peak, normal, and peak.

[0021] Calculate the response power of a direct-heating electric heating load j during the adjustment period t. :

[0022] ;

[0023] In the formula, This represents the baseline heating load value for direct-heating electric heating load j during the adjustment period t. A positive value indicates that the power is adjusted downwards. A negative value indicates that the power is adjusted upwards.

[0024] The intelligent building load regulation model specifically refers to:

[0025] The relationship between air conditioner operating power and temperature is as follows:

[0026] ;

[0027] In the formula: The load value of air conditioning load k during the adjustment period t; This refers to the rated power of the air conditioner. , These represent the upper and lower limits of indoor temperature, respectively. , With set temperature The relationship is as follows:

[0028] ;

[0029] In the formula, The width of the indoor temperature variation;

[0030] Average power of air conditioning load for:

[0031] ;

[0032] In the formula, Outdoor temperature This represents the average indoor temperature. R is the energy efficiency ratio of the air conditioner, and R is the equivalent thermal resistance of the air conditioner.

[0033] The relationship between the power of the lighting system and the required brightness is as follows:

[0034] ;

[0035] In the formula: , These refer to the power and brightness of the lighting system during the adjustment period t, respectively. The power required per unit of lighting brightness; Indoor lighting area;

[0036] The response power of the intelligent building's load regulation during the regulation period t is calculated as follows:

[0037] ;

[0038] ;

[0039] In the formula, Let n be the load value of lighting load during the adjustment period t; , These are the baseline load values ​​for air conditioning load k and lighting load n during the adjustment period t, respectively. This represents the response power of the intelligent building air conditioning load k during the adjustment period t. A positive value indicates downward adjustment power, and a negative value indicates upward adjustment power. This represents the response power of the lighting load n during the adjustment period t. A positive value indicates a downward adjustment power, while a negative value indicates an upward adjustment power.

[0040] Step (2) specifically refers to the change in dynamic carbon emissions of adjustable load resources during the adjustment period t as follows:

[0041] ;

[0042] In the formula, The carbon emissions of adjustable load resource m within the building park during the adjustment period t; The response power of adjustable load resource m within the building park during the adjustment period t varies depending on the type of adjustable load resource. This represents the carbon emission intensity value of the power grid node where the building park is located during the regulation period t. The value varies with time and the total response power of all adjustable load resources in the park:

[0043] ;

[0044] In the formula, This is a function relating the load changes at power grid nodes to the carbon emission intensity at power grid nodes. Let be the response power of electric vehicle i during the adjustment period t; The response power of the direct-heating electric heating load j during the adjustment period t; The response power of the intelligent building air conditioning load k during the adjustment period t; Let n be the response power of the lighting load n during the adjustment period t.

[0045] In step (3), the objective function maxC of the adjustable load resource multi-objective aggregation model is:

[0046] ;

[0047] In the formula, The objective function is the carbon emission reduction benefit; The objective function is the economic benefit. The objective function for adjusting power deviation penalty; , , All are weighting coefficients, satisfying ;

[0048] Among them, the objective function for carbon emission reduction benefits for:

[0049] ;

[0050] In the formula, To adjust the incentive price per unit of carbon emission reduction within time period t; The decision variable for whether the adjustable load resource m should be included in the aggregate response subject is... A value of 1 indicates that the adjustable load resource m is included in the aggregate response body. A value of 0 indicates that the adjustable load resource m is not included in the aggregate response body; A positive value indicates that load adjustment reduces carbon emissions, resulting in a positive carbon reduction benefit. A negative value indicates that load adjustment leads to an increase in carbon emissions, resulting in a negative carbon reduction benefit. The carbon emissions of adjustable load resource m within the building park during the adjustment period t;

[0051] Economic benefit objective function for:

[0052] ;

[0053] In the formula, To adjust the electricity market clearing price at the building park node within time period t; This represents the market-based benefits brought about by the adjustment of adjustable load resources; The response power of adjustable load resource m within the building park during the adjustment period t varies depending on the type of adjustable load resource.

[0054] Adjusting the power deviation penalty objective function for:

[0055] ;

[0056] In the formula, The response power deviation of adjustable load resource m during the adjustment period t; To adjust for deviations in time period t, a penalty price is imposed. The value is negative, and the larger the value, the smaller the penalty.

[0057] In step (3), the constraints of the adjustable load resource multi-objective aggregation model include charging power constraints, battery capacity constraints, indoor temperature constraints, and light intensity constraints.

[0058] The charging power constraint is:

[0059] ;

[0060] In the formula, and These are the upper and lower limits of the charging power of electric vehicles (i). Let i be the load value of electric vehicle i during the adjustment period t;

[0061] The battery capacity constraint is:

[0062] ;

[0063] In the formula, Let be the battery capacity value of electric vehicle i during the adjustment period t. and These represent the upper and lower limits of the battery capacity for electric vehicles, respectively. Take 0.2, Take 0.9;

[0064] The indoor temperature constraint is:

[0065] ;

[0066] In the formula, Indoor temperature; , These represent the upper and lower limits of indoor temperature, respectively.

[0067] The light intensity constraint is:

[0068] ;

[0069] In the formula: , These are the minimum and maximum permissible lighting luminance, respectively; To adjust the outdoor light intensity during time period t; To adjust the indoor light intensity during time period t.

[0070] Step (4) specifically includes the following steps:

[0071] (4a) Calculate the adjustment potential of aggregated adjustable load resources:

[0072] ;

[0073] ;

[0074] In the formula, and These represent the upward and downward adjustment potentials after the aggregation of various adjustable load resources; The upward adjustment potential of the adjustable load resource m included in the aggregation entity; The downward adjustment potential of the adjustable load resource m included in the aggregation entity; The response power of adjustable load resource m within the building park during the adjustment period t; The decision variable for whether the adjustable load resource m should be included in the aggregation entity;

[0075] (4b) Calculate the adjustment potential of distributed adjustable load resources:

[0076] ;

[0077] ;

[0078] In the formula, and These represent the upward and downward adjustment potential of various adjustable load resources not included in the aggregation entity; The upward adjustment potential of adjustable load resource m that is not included in the aggregation entity; The downward adjustment potential of adjustable load resource m that is not included in the aggregation entity; The demand response participation of adjustable load resource m that is not included in the aggregation entity;

[0079] (4c) Calculate the comprehensive adjustment potential of adjustable load resources:

[0080] ;

[0081] ;

[0082] In the formula, , These represent the combined upward and downward adjustment potential of the building park during the adjustment period t.

[0083] Another object of the present invention is to provide an electronic device comprising:

[0084] Processor; and

[0085] A memory storing computer program instructions that, when executed by the processor, cause the processor to perform the adjustable load resource adjustable potential analysis method within the building park as described above.

[0086] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the adjustable load resource adjustable potential analysis method in a building park as described above.

[0087] As can be seen from the above technical solution, the beneficial effects of this invention are as follows: First, it breaks through the traditional model and constructs a multi-objective aggregation model for adjustable load resources, incorporating dynamic carbon emission calculation into the load regulation potential analysis system. This achieves comprehensive optimization of carbon-electricity synergy in load potential analysis, enabling precise quantification of the low-carbon contribution of each regulation behavior in the industrial park, and promoting the upgrade of load resources from unidirectional economic response to multi-dimensional collaborative response. Second, by establishing an adjustable load resource regulation power model within the industrial park and differentiating the characteristics of aggregated and decentralized resources for differentiated calculation, it significantly improves the accuracy and reliability of potential assessment, overcomes the deviation problem caused by extensive aggregation, and provides a reliable data foundation for grid demand response and market-based transactions. Third, it provides key decision-making basis for the industrial park's participation in grid interaction, supporting it in scientifically formulating dispatch strategies while ensuring energy safety and comfort, thereby simultaneously achieving the comprehensive goals of cost reduction and efficiency improvement, promoting renewable energy consumption, and reducing carbon footprint. This powerfully promotes the transformation of industrial parks into flexible resource aggregates and provides solid support for the construction of new power systems and the digital transformation of energy in industrial parks. Attached Figure Description

[0088] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0089] like Figure 1 As shown, a method for analyzing the adjustable load potential within a building park is described. This method includes the following sequential steps:

[0090] (1) Construct a power model for adjustable load resources in a building park, which includes an adjustable load model for electric vehicles, an adjustable load model for direct-heating electric heating, and an adjustable load model for intelligent buildings;

[0091] (2) Calculate the dynamic carbon emissions of adjustable load resources based on the adjustable load resource adjustment power model in the building park;

[0092] (3) Based on the adjustable load resource adjustment power model and the dynamic carbon emission of adjustable load resources in the building park, establish a multi-objective aggregation model of adjustable load resources;

[0093] (4) Calculate the comprehensive adjustment potential of adjustable load resources in the building park based on the dynamic carbon emissions of adjustable load resources and the multi-objective aggregation model of adjustable load resources.

[0094] In step (1), the formula for the adjustable load model of the electric vehicle is as follows:

[0095]

[0096] In the formula: Let i be the load value of electric vehicle i during the adjustment period t; The charging power of electric vehicle i during the adjustment period t; , These represent the start and end times of charging for electric vehicle i, respectively. Let represent the operating state of electric vehicle i. If electric vehicle i is put into operation during the adjustment period t, then =1, if electric vehicle i stops running during the adjustment period t, then =0;

[0097] according to Calculate the response power of electric vehicle i during the adjustment period t. :

[0098] ;

[0099] In the formula, The baseline charging load value for electric vehicle i during the adjustment period t; A positive value indicates that the power is adjusted downwards. A negative value indicates that the power is adjusted upwards.

[0100] The load characteristics of direct-heating electric heating follow the principle of "more use during off-peak hours and less use during peak hours." Specifically, this means high-load heating during off-peak hours, low-load operation during peak hours, and flexible adjustment based on actual demand during normal hours. The formula for the adjustable load model of direct-heating electric heating is as follows:

[0101] ;

[0102] In the formula: The load value of the direct-heating electric heating load j during the adjustment period t; To meet the heat load requirements at higher temperatures; For heat load requirements at lower temperatures; The heat load requirement at the real-time temperature; , , Each corresponds to a specific period of daily electric heating load: off-peak, normal, and peak.

[0103] Calculate the response power of a direct-heating electric heating load j during the adjustment period t. :

[0104] ;

[0105] In the formula, This represents the baseline heating load value for direct-heating electric heating load j during the adjustment period t. A positive value indicates that the power is adjusted downwards. A negative value indicates that the power is adjusted upwards.

[0106] The intelligent building load regulation model specifically refers to:

[0107] As a major energy consumer, smart buildings concentrate their energy consumption on their environmental control systems. Air conditioning is the core load, possessing rapid response and flexible adjustment capabilities, enabling it to participate in demand-side management while ensuring accurate temperature control. Furthermore, intelligent lighting systems can dynamically adjust based on real-time natural illuminance, effectively reducing energy consumption.

[0108] The relationship between air conditioner operating power and temperature is as follows:

[0109] ;

[0110] In the formula: The load value of air conditioning load k during the adjustment period t; This refers to the rated power of the air conditioner. , These represent the upper and lower limits of indoor temperature, respectively. , With set temperature The relationship is as follows:

[0111] ;

[0112] In the formula, The width of the indoor temperature variation;

[0113] Without considering the instantaneous power consumption when the air conditioner starts, the indoor temperature needs to be maintained at [ ] during one start-stop cycle. , Within the range, let its average temperature be... According to the law of conservation of energy, the average cooling power of an air conditioning load is equal to the average rate of heat loss. for:

[0114] ;

[0115] In the formula, Outdoor temperature This represents the average indoor temperature. R is the energy efficiency ratio of the air conditioner, and R is the equivalent thermal resistance of the air conditioner.

[0116] The lighting system and sunlight intensity work together to maintain indoor illumination. When electricity prices are high during the day, the output of the lighting equipment can be appropriately reduced. The relationship between the power of the lighting system and the required brightness is as follows:

[0117] ;

[0118] In the formula: , These refer to the power and brightness of the lighting system during the adjustment period t, respectively. The power required per unit of lighting brightness; Indoor lighting area;

[0119] The response power of the intelligent building's load regulation during the regulation period t is calculated as follows:

[0120] ;

[0121] ;

[0122] In the formula, Let n be the load value of lighting load during the adjustment period t; , These are the baseline load values ​​for air conditioning load k and lighting load n during the adjustment period t, respectively. This represents the response power of the intelligent building air conditioning load k during the adjustment period t. A positive value indicates downward adjustment power, and a negative value indicates upward adjustment power. This represents the response power of the lighting load n during the adjustment period t. A positive value indicates a downward adjustment power, while a negative value indicates an upward adjustment power.

[0123] Step (2) specifically refers to the change in dynamic carbon emissions of adjustable load resources during the adjustment period t as follows:

[0124] ;

[0125] In the formula, The carbon emissions of adjustable load resource m within the building park during the adjustment period t; The response power of adjustable load resource m within the building park during the adjustment period t varies depending on the type of adjustable load resource. This represents the carbon emission intensity value of the power grid node where the building park is located during the regulation period t. The value varies with time and the total response power of all adjustable load resources in the park:

[0126] ;

[0127] In the formula, This is a function relating the load changes at power grid nodes to the carbon emission intensity at power grid nodes. Let be the response power of electric vehicle i during the adjustment period t; The response power of the direct-heating electric heating load j during the adjustment period t; The response power of the intelligent building air conditioning load k during the adjustment period t; Let n be the response power of the lighting load n during the adjustment period t.

[0128] In step (3), the objective function maxC of the adjustable load resource multi-objective aggregation model is:

[0129] ;

[0130] In the formula, The objective function is the carbon emission reduction benefit; The objective function is the economic benefit. The objective function for adjusting power deviation penalty; , , All are weighting coefficients, satisfying ;

[0131] Among them, the objective function for carbon emission reduction benefits for:

[0132] ;

[0133] In the formula, To adjust the incentive price per unit of carbon emission reduction within time period t; The decision variable for whether the adjustable load resource m should be included in the aggregate response subject is... A value of 1 indicates that the adjustable load resource m is included in the aggregate response body. A value of 0 indicates that the adjustable load resource m is not included in the aggregate response body; A positive value indicates that load adjustment reduces carbon emissions, resulting in a positive carbon reduction benefit. A negative value indicates that load adjustment leads to an increase in carbon emissions, resulting in a negative carbon reduction benefit. The carbon emissions of adjustable load resource m within the building park during the adjustment period t;

[0134] Economic benefit objective function for:

[0135] ;

[0136] In the formula, To adjust the electricity market clearing price at the building park node within time period t; This represents the market-based benefits brought about by the adjustment of adjustable load resources; The response power of adjustable load resource m within the building park during the adjustment period t varies depending on the type of adjustable load resource.

[0137] Adjusting the power deviation penalty objective function for:

[0138] ;

[0139] In the formula, The response power deviation of adjustable load resource m during the adjustment period t; To adjust for deviations in time period t, a penalty price is imposed. The value is negative, and the larger the value, the smaller the penalty.

[0140] In step (3), the constraints of the adjustable load resource multi-objective aggregation model include charging power constraints, battery capacity constraints, indoor temperature constraints, and light intensity constraints.

[0141] The charging power constraint is:

[0142] ;

[0143] In the formula, and These are the upper and lower limits of the charging power of electric vehicles (i). Let i be the load value of electric vehicle i during the adjustment period t;

[0144] The battery capacity constraint is:

[0145] ;

[0146] In the formula, Let be the battery capacity value of electric vehicle i during the adjustment period t. and These represent the upper and lower limits of the battery capacity for electric vehicles, respectively. Take 0.2, Take 0.9;

[0147] The indoor temperature constraint is:

[0148] ;

[0149] In the formula, Indoor temperature; , These represent the upper and lower limits of indoor temperature, respectively.

[0150] The light intensity constraint is:

[0151] ;

[0152] In the formula: , These are the minimum and maximum permissible lighting luminance, respectively; To adjust the outdoor light intensity during time period t; To adjust the indoor light intensity during time period t;

[0153] The constraints of the adjustable load resource multi-objective aggregation model also include adjustable load resource adjustment response rate constraints and adjustable load resource adjustment response deviation constraints.

[0154] The adjustable load resource adjustment response rate constraint is:

[0155] ;

[0156] In the formula, The response rate of the adjustable load resource m; To achieve the maximum demand response rate; The decision variable for whether the adjustable load resource m should be included in the aggregate response subject;

[0157] The adjustable load resource adjustment response deviation constraint is:

[0158] ;

[0159] In the formula, To adjust the maximum permissible positive deviation of the response during the time period t; To adjust the maximum permissible negative deviation of the response during the time period t; This is to adjust the response power deviation of all adjustable load resources within the adjustment period t.

[0160] Step (4) specifically includes the following steps:

[0161] (4a) Calculate the adjustment potential of aggregated adjustable load resources:

[0162] ;

[0163] ;

[0164] In the formula, and These represent the upward and downward adjustment potentials after the aggregation of various adjustable load resources; The upward adjustment potential of the adjustable load resource m included in the aggregation entity; The downward adjustment potential of the adjustable load resource m included in the aggregation entity; The response power of adjustable load resource m within the building park during the adjustment period t; The decision variable for whether the adjustable load resource m should be included in the aggregation entity;

[0165] (4b) Calculate the regulation potential of dispersed adjustable load resources: For scattered, small-capacity loads not included in the aggregation entity, the response participation of different types of load resources is obtained using statistical evaluation methods, and then their regulation potential is obtained:

[0166] ;

[0167] ;

[0168] In the formula, and These represent the upward and downward adjustment potential of various adjustable load resources not included in the aggregation entity; The upward adjustment potential of adjustable load resource m that is not included in the aggregation entity; The downward adjustment potential of adjustable load resource m that is not included in the aggregation entity; The demand response participation of adjustable load resource m that is not included in the aggregation entity;

[0169] (4c) Calculate the comprehensive adjustment potential of adjustable load resources:

[0170] ;

[0171] ;

[0172] In the formula, , These represent the combined upward and downward adjustment potential of the building park during the adjustment period t.

[0173] In summary, this invention breaks through traditional models by constructing a multi-objective aggregation model for adjustable load resources. It incorporates dynamic carbon emission calculations into the load regulation potential analysis system, achieving comprehensive optimization of carbon-electricity synergy in load potential analysis. This allows for the precise quantification of the low-carbon contribution of each regulation behavior within the industrial park, promoting the upgrade of load resources from unidirectional economic response to multi-dimensional collaborative response. By establishing an adjustable load resource regulation power model within the industrial park and differentiating calculations based on the characteristics of aggregated and decentralized resources, the accuracy and reliability of potential assessment are significantly improved, overcoming the bias problems caused by extensive aggregation. This provides a reliable data foundation for grid demand response and market-based transactions. Furthermore, it provides key decision-making basis for the industrial park's participation in grid interaction, supporting it in scientifically formulating dispatch strategies while ensuring energy safety and comfort. This simultaneously achieves the comprehensive goals of cost reduction and efficiency improvement, promoting renewable energy consumption, and reducing carbon footprint, powerfully driving the transformation of industrial parks into flexible resource aggregates and providing solid support for the construction of new power systems and the digital transformation of industrial park energy.

[0174] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for analyzing the adjustable potential of adjustable load resources within a building park, characterized in that: The method includes the following steps in sequence: (1) Construct a power model for adjustable load resources in a building park, which includes an adjustable load model for electric vehicles, an adjustable load model for direct-heating electric heating, and an adjustable load model for intelligent buildings; (2) Calculate the dynamic carbon emissions of adjustable load resources based on the adjustable load resource adjustment power model in the building park; (3) Based on the adjustable load resource adjustment power model and the dynamic carbon emission of adjustable load resources in the building park, establish a multi-objective aggregation model of adjustable load resources; (4) Calculate the comprehensive adjustment potential of adjustable load resources in the building park based on the dynamic carbon emissions of adjustable load resources and the multi-objective aggregation model of adjustable load resources.

2. The method for analyzing the adjustable potential of adjustable load resources within a building park according to claim 1, characterized in that: In step (1), the formula for the adjustable load model of the electric vehicle is as follows: ； In the formula: Let i be the load value of electric vehicle i during the adjustment period t; The charging power of electric vehicle i during the adjustment period t; , These represent the start and end times of charging for electric vehicle i, respectively. Let represent the operating state of electric vehicle i. If electric vehicle i is put into operation during the adjustment period t, then =1, if electric vehicle i stops running during the adjustment period t, then =0; according to Calculate the response power of electric vehicle i during the adjustment period t. : ； In the formula, The baseline charging load value for electric vehicle i during the adjustment period t; A positive value indicates that the power is adjusted downwards. A negative value indicates an upward adjustment of power; The formula for the adjustable load model of the direct-heating electric heating is as follows: ； In the formula: The load value of the direct-heating electric heating load j during the adjustment period t; To meet the heat load requirements at higher temperatures; For heat load requirements at lower temperatures; The heat load requirement at the real-time temperature; , , Each corresponds to a specific period of daily electric heating load: off-peak, normal, and peak. Calculate the response power of a direct-heating electric heating load j during the adjustment period t. : ； In the formula, This represents the baseline heating load value for direct-heating electric heating load j during the adjustment period t. A positive value indicates that the power is adjusted downwards. A negative value indicates an upward adjustment of power; The intelligent building load regulation model specifically refers to: The relationship between air conditioner operating power and temperature is as follows: ； In the formula: The load value of air conditioning load k during the adjustment period t; This refers to the rated power of the air conditioner. , These represent the upper and lower limits of indoor temperature, respectively. , With set temperature The relationship is as follows: ； In the formula, The width of the indoor temperature variation; Average power of air conditioning load for: ； In the formula, Outdoor temperature This represents the average indoor temperature. R is the energy efficiency ratio of the air conditioner, and R is the equivalent thermal resistance of the air conditioner. The relationship between the power of the lighting system and the required brightness is as follows: ； In the formula: , These refer to the power and brightness of the lighting system during the adjustment period t, respectively. The power required per unit of lighting brightness; Indoor lighting area; The response power of the intelligent building's load regulation during the regulation period t is calculated as follows: ； ； In the formula, Let n be the load value of lighting load during the adjustment period t; , These are the baseline load values ​​for air conditioning load k and lighting load n during the adjustment period t, respectively. This represents the response power of the intelligent building air conditioning load k during the adjustment period t. A positive value indicates downward adjustment power, and a negative value indicates upward adjustment power. This represents the response power of the lighting load n during the adjustment period t. A positive value indicates a downward adjustment power, while a negative value indicates an upward adjustment power.

3. The method for analyzing the adjustable potential of adjustable load resources within a building park according to claim 1, characterized in that: Step (2) specifically refers to the change in dynamic carbon emissions of adjustable load resources during the adjustment period t as follows: ； In the formula, The carbon emissions of adjustable load resource m within the building park during the adjustment period t; The response power of adjustable load resource m within the building park during the adjustment period t varies depending on the type of adjustable load resource. This represents the carbon emission intensity value of the power grid node where the building park is located during the regulation period t. The value varies with time and the total response power of all adjustable load resources in the park: ； In the formula, This is a function relating the load changes at power grid nodes to the carbon emission intensity at power grid nodes. Let be the response power of electric vehicle i during the adjustment period t; The response power of the direct-heating electric heating load j during the adjustment period t; The response power of the intelligent building air conditioning load k during the adjustment period t; Let n be the response power of the lighting load n during the adjustment period t.

4. The method for analyzing the adjustable potential of adjustable load resources within a building park according to claim 1, characterized in that: In step (3), the objective function maxC of the adjustable load resource multi-objective aggregation model is: ； In the formula, The objective function is the carbon emission reduction benefit; The objective function is the economic benefit. The objective function for adjusting power deviation penalty; , , All are weighting coefficients, satisfying ; Among them, the objective function for carbon emission reduction benefits for: ； In the formula, To adjust the incentive price per unit of carbon emission reduction within time period t; For the decision variable of whether the adjustable load resource m should be included in the aggregate response subject, A value of 1 indicates that the adjustable load resource m is included in the aggregate response body. A value of 0 indicates that the adjustable load resource m is not included in the aggregate response body; A positive value indicates that load adjustment reduces carbon emissions, resulting in a positive carbon reduction benefit. A negative value indicates that load adjustment leads to an increase in carbon emissions, resulting in a negative carbon reduction benefit. The carbon emissions of adjustable load resource m within the building park during the adjustment period t; Economic benefit objective function for: ； In the formula, To adjust the electricity market clearing price at the building park node within time period t; This represents the market-based benefits brought about by the adjustment of adjustable load resources; The response power of adjustable load resource m within the building park during the adjustment period t varies depending on the type of adjustable load resource. Adjusting the power deviation penalty objective function for: ； In the formula, The response power deviation of adjustable load resource m during the adjustment period t; To adjust for deviations in time period t, a penalty price is imposed. The value is negative, and the larger the value, the smaller the penalty.

5. The method for analyzing the adjustable potential of adjustable load resources within a building park according to claim 1, characterized in that: In step (3), the constraints of the adjustable load resource multi-objective aggregation model include charging power constraints, battery capacity constraints, indoor temperature constraints, and light intensity constraints. The charging power constraint is: ； In the formula, and These are the upper and lower limits of the charging power of electric vehicles (i). Let i be the load value of electric vehicle i during the adjustment period t; The battery capacity constraint is: ； In the formula, Let be the battery capacity value of electric vehicle i during the adjustment period t. and These represent the upper and lower limits of the battery capacity for electric vehicles, respectively. Take 0.2, Take 0.9; The indoor temperature constraint is: ； In the formula, Indoor temperature; , These represent the upper and lower limits of indoor temperature, respectively. The light intensity constraint is: ； In the formula: , These are the minimum and maximum permissible lighting luminance, respectively; To adjust the outdoor light intensity during time period t; To adjust the indoor light intensity during time period t.

6. The method for analyzing the adjustable potential of adjustable load resources within a building park according to claim 1, characterized in that: Step (4) specifically includes the following steps: (4a) Calculate the adjustment potential of aggregated adjustable load resources: ； ； In the formula, and These represent the upward and downward adjustment potentials after the aggregation of various adjustable load resources; The upward adjustment potential of the adjustable load resource m included in the aggregation entity; The downward adjustment potential of the adjustable load resource m included in the aggregation entity; The response power of adjustable load resource m within the building park during the adjustment period t; The decision variable for whether the adjustable load resource m should be included in the aggregation entity; (4b) Calculate the adjustment potential of distributed adjustable load resources: ； ； In the formula, and These represent the upward and downward adjustment potential of various adjustable load resources not included in the aggregation entity; The upward adjustment potential of adjustable load resource m that is not included in the aggregation entity; The downward adjustment potential of adjustable load resource m that is not included in the aggregation entity; The demand response participation of adjustable load resource m that is not included in the aggregation entity; (4c) Calculate the comprehensive adjustment potential of adjustable load resources: ； ； In the formula, , These represent the combined upward and downward adjustment potential of the building park during the adjustment period t.

7. An electronic device, comprising: processor; as well as A memory storing computer program instructions that, when executed by the processor, cause the processor to perform the method for analyzing the adjustable potential of adjustable load resources within a building park as described in any one of claims 1-6.

8. A computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the method for analyzing the adjustable potential of adjustable load resources in a building park as described in any one of claims 1-6.

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