Load regulation method of integrated energy system considering meteorological factors and time-of-use electricity price

Abstract

The present application relates to the comprehensive energy system load regulation method considering meteorological factors and time-of-use electricity price, including the following steps: constructing the physical model of the energy consumption power of the cold-electricity energy supply system of the comprehensive energy system under the two working conditions of electric refrigeration and electric ice making; selecting the regulation period of the load side by using the fuzzy C-means clustering method, and constructing the physical model of the cold load and the temperature and humidity index; based on the selected regulation period and the constructed physical models, constructing a multi-objective load regulation model and a multi-objective cold supply output power active control model; and solving the optimal solution of the multi-objective load regulation model and the multi-objective cold supply output power active control model by the epsilon-constraint method and the multi-dimensional preference analysis linear programming method. The present application considers the influence of meteorological factors and time-of-use electricity price, and realizes the relief of the supply and demand contradiction of the comprehensive energy system during the energy use peak period and the reduction of the energy use cost through the two-stage progressive cold load demand self-regulation and cold supply output power active control strategy.

Description

Technical Field

[0001] This invention relates to the field of integrated energy load regulation and optimization technology, and in particular to a method for integrated energy system load regulation that takes into account meteorological factors and time-of-use electricity pricing. Background Technology

[0002] Integrated energy systems are complex, coupled systems that integrate energy production, transmission, storage, and consumption. They possess unique advantages in improving energy efficiency, enabling large-scale development of clean energy, and enhancing the flexibility and security of energy supply. They are an effective way to promote the construction of a modern energy system and the transformation of the energy structure. With the continuous improvement of the electrification level of load-side energy-consuming equipment and the widespread application of electricity substitution technologies, a large number of electric refrigeration devices, such as chillers and ice makers, have emerged to meet the cooling load demands of users. Consequently, the coupling and conversion characteristics between electrical and cooling energy exacerbate the supply-demand contradictions in integrated energy systems during the high-temperature period of the cooling season, leading to increased energy costs.

[0003] To address the above issues, existing methods primarily focus on utilizing energy management strategies such as demand response to achieve economical and reliable operation of integrated energy systems. There is a lack of research on load regulation strategies for integrated energy systems that consider meteorological factors and time-of-use pricing during peak energy consumption periods. With the continuous development of regulation and control technologies and methods, optimizing system operation and employing proactive control strategies to deeply explore the load regulation potential of integrated energy systems and enhance overall operational flexibility has significant practical application value in load reduction and energy conservation.

[0004] However, existing methods, on the one hand, have become an effective way to expand the multi-energy supply and demand balance space of integrated energy systems by flexibly adjusting the demand for cooling and heating loads. But adjusting load demand based solely on the influence of a single factor (such as temperature, air quality, etc.) and failing to reflect regional environmental and climatic differences in the setting of benchmark values, have low interpretability and reliability, and the measurement of energy comfort is not comprehensive, which can easily lead to a decrease in the user experience after regulation.

[0005] On the other hand, existing methods mostly utilize the energy storage specificity of ice storage air conditioning to meet the system's supply and demand balance, but do not further explore its potential for flexible regulation under multiple operating conditions during peak loads, as well as its positive role in energy conservation and cost reduction of integrated energy systems.

[0006] Therefore, due to the inherent energy supply pattern of the integrated energy system and the influence of unpredictable meteorological factors, meteorological factors cause changes in energy comfort, which in turn leads to an increase in the supply and demand contradiction of the system during peak energy consumption periods. At the same time, the impact of time-of-use electricity pricing further increases the cost of energy consumption. Summary of the Invention

[0007] The purpose of this invention is to take into account the impact of meteorological factors and time-of-use electricity prices, and to alleviate the supply and demand contradiction of the integrated energy system during peak energy consumption periods and reduce energy costs through a two-stage progressive cooling load demand self-adjustment and active control strategy of cooling output power. This provides a load regulation method for integrated energy systems that takes into account meteorological factors and time-of-use electricity prices.

[0008] To achieve the above-mentioned objectives, the embodiments of the present invention provide the following technical solutions:

[0009] A comprehensive energy system load regulation method considering meteorological factors and time-of-use pricing includes the following steps:

[0010] Step 1: Construct a physical model of the energy consumption power of the integrated energy system's cooling-electricity power supply system under two operating conditions: electric refrigeration and electric ice making.

[0011] Step 2: Select the control period on the load side using the fuzzy C-means clustering method, and construct a physical model of cooling load and temperature and humidity index;

[0012] Step 3: Based on the selected control period and the constructed physical models, construct a multi-objective load regulation model and a multi-objective active control model for cooling output power;

[0013] Step 4: Solve for the optimal solutions of the multi-objective load regulation model and the multi-objective cooling output power active control model using the ε-constraint method and the multi-dimensional preference analysis linear programming method.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0015] Due to the complementary characteristics of heterogeneous energy conversion coupling and the operational flexibility of storage and storage units in integrated energy systems, such as ice storage air conditioning, which can generate and store ice electrically during off-peak hours and use water recirculation to melt ice during peak hours to reduce the power output of electric cooling, multi-energy supply and demand balance can be achieved, while also reducing peak electricity demand and further reducing system energy costs. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the energy supply mode of the integrated energy system of the present invention;

[0018] Figure 2 This is a schematic diagram of the composition and structure of the cold-electric power supply system of the present invention;

[0019] Figure 3 This is a flowchart of the method of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0021] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance, or suggesting any such actual relationship or order between these entities or operations. Additionally, the terms "connected," "linked," etc., can refer to a direct connection between elements or an indirect connection via other elements.

[0022] Example 1:

[0023] This invention is based on an integrated energy system containing multiple heterogeneous energy sources such as electricity, heat, and cooling. It proposes a load regulation method for this integrated energy system that considers meteorological factors and time-of-use pricing. Figure 1 As shown, the integrated energy system includes a power distribution center, distributed power sources, combined heat and power (CHP) units, and a cooling-electricity supply system (mainly ice storage air conditioning on the user side). The power distribution center provides external power through the municipal power grid; the distributed power sources provide electricity through photovoltaic power generation; the CHP units provide heat energy after conversion through the natural gas network; and the cooling-electricity supply system features electrically driven storage and storage, enabling electric-cooling coupling conversion during the cooling season. During off-peak hours, it stores cooling capacity in the form of ice, allowing for direct cooling through chillers and coordinated cooling through ice melting with recycled water during peak hours, thus reducing system operating pressure.

[0024] Please see Figure 2The cooling-electricity power supply system is divided into a cooling supply side and a user side. The system includes a cooling water circulation system, a solution circulation system, and a chilled water circulation system. The cooling water circulation system includes a chiller, a cooling tower, and a cooling water pump; the solution circulation system includes an ice storage tank and a circulating water pump; and the chilled water circulation system includes a heat exchanger and a chilled water pump.

[0025] The refrigeration-electricity power supply system (ice storage air conditioning) mainly includes two operating modes: electric refrigeration and electric ice making. It can realize multiple functions such as ice storage, refrigeration unit cooling alone, ice storage tank cooling alone, refrigeration unit and ice storage tank combined cooling, and refrigeration unit cooling while simultaneously storing ice. The refrigeration unit consumes electrical energy in both refrigeration and ice making modes, while the ice storage tank releases cold by melting ice with return water, and therefore does not consume electrical energy.

[0026] Please see Figure 3 The method includes the following steps:

[0027] Step 1: Construct a physical model of the energy consumption power of the integrated energy system's cooling-electricity power supply system under two operating conditions: electric refrigeration and electric ice making.

[0028] Since the energy consumption of the cooling-electric power supply system is mainly on the cooling supply side, the energy consumption calculation primarily considers the energy consumption of the chiller, cooling tower, cooling water pump, and circulating water pump under both electric refrigeration and electric ice-making operating conditions. Specifically, the energy consumption of the chiller is directly proportional to the unit load on the cooling supply side, while the cooling water pump and circulating water pump operate continuously. Therefore, the power consumption W of the cooling-electric power supply system at time t under both electric refrigeration and electric ice-making operating conditions is calculated. zl(zb) The physical model of (t) is:

[0029]

[0030]

[0031] Among them, W h,zl(zb) (t) represents the power consumption of the refrigerator at time t, in W. to,zl(zb) (t) represents the power consumption of the cooling tower at time t, in W. p (t) represents the power consumption of the water pump at time t, and the water pump includes a cooling water pump and a circulating water pump; Q zl(zb) (t) represents the refrigeration and ice-making power of the cold-electric power supply system at time t; Q zl(zb),R E represents the rated refrigeration and ice-making power of the cold-electric power supply system at time t. zl(zb),R P represents the rated power consumption of the cooling-electricity power supply system at time t. x (t) represents the power consumption of the circulating water pump at time t, P l (t) represents the power consumption of the cooling water pump at time t.

[0032] Combining the above two equations, we get:

[0033]

[0034] Step 2: Use the fuzzy C-means clustering method to select the control period on the load side and construct a physical model of cooling load and temperature and humidity index.

[0035] While the time-of-use (TOU) pricing mechanism for cooling season energy can effectively guide user energy consumption periods and shorten the peak-valley difference on the load side, thus alleviating supply-demand imbalances, it primarily relies on the "peak-valley-flat" pricing periods allocated by energy suppliers based on the overall situation of their supply areas. This means that peak price periods may not completely coincide with peak energy consumption periods in local systems, leading to significant supply-demand imbalances when electricity prices are high. Furthermore, during peak periods of temperature and humidity indices, there is a greater margin for load reduction by adjusting load demand itself. Therefore, this scheme further considers the cumulative effect of multiple factors and matches the peak level of TOU pricing to precisely select load control periods.

[0036] Fuzzy C-means clustering can give the probability of a sample belonging to a class based on the similarity relationship and membership degree between each sample and the cluster center, so as to achieve more flexible clustering results. Therefore, it can divide the meteorological and electricity price factors at different times of the day into three categories: peak, valley and flat.

[0037] Define the loss function for the clustering model:

[0038]

[0039] Among them, u ij Let be the membership degree of the i-th sample belonging to the j-th cluster, and construct the membership degree matrix H based on it, where n is the total number of samples, cl is the number of clusters, and b represents the b-th iteration; Let j be the cluster center; The membership factor of the clustering model. ;d ij Let be the distance from the i-th sample to the j-th cluster center.

[0040] (1) Randomly initialize the number of clusters cl to obtain the membership matrix H; let b=1, start the first iteration, and calculate the cluster centers of the first iteration:

[0041]

[0042] Where, x i This represents the i-th sample;

[0043] (2) Calculate the cluster centers in the b-th iteration:

[0044]

[0045] (3) Update the membership function H and the loss function J:

[0046]

[0047] Where i = 1, 2, ..., n, j = 1, 2, ..., cl, k = 1, 2, ..., cl; .

[0048] (4) By setting a membership termination threshold Loss function termination threshold To obtain the final classification.

[0049] (5) Selection of control period and analysis of the peak load period t after clustering. r,f Peak period t of time-of-use electricity pricing p,f The degree of agreement, and the matching peak period as the control period t. j , i.e. t j ∈t r,f ,t p,f This is for subsequent analysis.

[0050] A physical model of cooling load is constructed based on the heat balance equation. For indoor heat transfer, the instantaneous heat gain at any time period includes: the heat conducted by the building envelope at time t Q1(t), the basic cooling load demand Q3(t) (independent of meteorological factors), the cooling output Q2(t), and the change in indoor heat between two adjacent time periods. The physical model for cooling load is:

[0051]

[0052]

[0053]

[0054] Where S is the effective cooling area; C is the heat capacity per unit cooling area, taken as... ω is the heat dissipation coefficient due to the temperature difference between the inside and outside of the building, taken as... ;T in (t) represents the indoor temperature at time t, T in (t-1) represents the indoor temperature at time t-1, T out (t) represents the outdoor temperature at time t.

[0055] Then, the cooling load demand at time t, i.e., the cooling output Q2(t), can be calculated:

[0056]

[0057] Because the impact of a single meteorological factor on user experience is limited—for example, while temperature is an important standard for measuring the perception of warmth and cold in nature, it cannot fully reflect the human body's feeling of comfort when using energy—the temperature and humidity index takes into account the combined effects of meteorological factors, further enhancing or constraining the impact of a single meteorological factor. As a comprehensive meteorological factor indicator with the greatest impact on the load of an integrated energy system, the temperature and humidity index can be used to characterize the degree of comfort users feel regarding ambient temperature and humidity. The physical model of the temperature and humidity index is as follows:

[0058]

[0059] Where THI(t) is the temperature and humidity index at time t; T(t) is the temperature at time t, in units of... R(t) represents the relative humidity at time t, in percent.

[0060] Meanwhile, the optimal indoor temperature and humidity index setting is related to humidity and neutral temperature. Neutral temperature refers to the most suitable temperature for a certain season, and the neutral temperature is:

[0061]

[0062] Among them, T n The neutral temperature under a certain season; T s,ave The average monthly outdoor temperature during a given season.

[0063] Step 3: Based on the selected control period and the constructed physical models, construct a multi-objective load regulation model and a multi-objective active control model for cooling output power.

[0064] Based on the selection of control periods and the construction of various physical models, this step first establishes a multi-objective load regulation model that optimizes temperature and humidity comfort while minimizing cooling energy demand, thereby reducing cooling load demand. In the second stage, a multi-objective active control model for cooling output power that minimizes power consumption and energy costs is proposed, achieving both reduced operating energy consumption and lower energy costs.

[0065] In the first stage, the multi-objective load regulation model aims to maximize temperature and humidity comfort while minimizing cooling load demand during regulation periods. Therefore, the multi-objective load regulation model is established with the objectives of minimizing temperature and humidity index deviation and minimizing cooling load demand.

[0066]

[0067]

[0068] Wherein, F1 represents the deviation of the temperature and humidity index during the control period, and F2 represents the cooling load demand during the control period; THI in(t) represents the actual indoor temperature and humidity index at time t, THI set (t) represents the optimal temperature and humidity index at time t.

[0069] Considering the relevant constraints of the multi-objective load regulation model:

[0070] (1) Temperature and humidity index constraints

[0071] During load adjustment, the indoor temperature and humidity index needs to be controlled within an acceptable range, therefore the following must be met:

[0072]

[0073] Among them, THI min The minimum temperature and humidity index (THI) max This represents the maximum value of the temperature and humidity index.

[0074] (2) Cooling load demand constraints

[0075] The cooling load demand after adjustment should be less than the cooling load demand before adjustment.

[0076]

[0077] in, This is the adjusted cooling load demand.

[0078] In the second stage, because the electro-cooling conversion efficiency is lower under ice-making conditions compared to refrigeration conditions, and energy loss occurs due to multi-layer phase transitions and heat dissipation during ice storage, a multi-objective active control model for cooling output power is constructed to improve energy utilization efficiency and minimize the power consumption and energy costs of the cooling-electricity power supply system.

[0079]

[0080]

[0081] Where F3 represents the power consumption of the cooling-electricity power supply system, F4 represents the energy cost of the cooling-electricity power supply system, T represents one control cycle of the cooling-electricity power supply system, and W represents the power consumption of the cooling-electricity power supply system. zl (t) represents the power consumption of the cooling-electric power supply system under cooling conditions at time t, in W. zb e(t) represents the power consumption of the cold-electricity power supply system under ice-making conditions at time t; e(t) represents the time-of-use electricity price.

[0082] Considering the relevant constraints of the multi-objective active control model for cooling output power:

[0083] (1) Energy balance constraint

[0084] The sum of the cooling capacity supplied by the refrigeration unit directly and by ice melting at each moment should equal the cooling load demand of the cooling-electricity power supply system at that moment:

[0085]

[0086] Among them, Q zl (t) represents the power (Q) directly supplied by the chiller at time t. rb (t) represents the power of the refrigerator for melting ice and providing cooling at time t; Let t be the cooling load demand of the cooling-electricity power supply system at time t, which is also the adjusted cooling load demand in the multi-objective load regulation model.

[0087] (2) Restrictions on refrigeration and ice-making cooling systems

[0088] The cooling and ice-making power of the refrigeration unit should be less than its rated cooling and ice-making power.

[0089]

[0090]

[0091] Among them, Q zl (t) represents the cooling power of the refrigerator at time t, Q zl,R (t) represents the rated cooling power of the refrigerator at time t; Q zb (t) represents the ice-reducing power of the refrigeration mechanism at time t, Q. zb,R (t) represents the rated ice-making power of the refrigerator at time t.

[0092] (3) Ice storage constraints

[0093] The hourly ice storage capacity of the ice storage tank in a cold-electricity power supply system is related to the unit load:

[0094]

[0095]

[0096] Among them, Q ice (t) represents the ice storage power of the ice storage tank at time t; Q zb (t) represents the ice-making power of the ice storage tank at time t; The cold energy conversion rate during ice making in the ice storage tank is taken as 0.94; Q ice The total ice storage capacity of the ice storage tank is constrained as follows:

[0097]

[0098] Among them, Q ice,max This represents the maximum total ice storage capacity of the ice storage tank.

[0099] (4) De-icing power constraint

[0100] The maximum melting rate of the ice storage tank at any given time varies with the amount of ice remaining in the tank:

[0101]

[0102] Among them, Q rb,max (t) represents the maximum ice-melting cooling power of the ice storage tank at time t; Q rb (i) represents the ice-melting cooling power of the ice storage tank at time i, i∈t-1; a is the cold energy release coefficient, taken as 0.4. The ice-melting cooling power constraint of the ice storage tank at time t is:

[0103]

[0104] (5) Constraints on ice melting volume within the cycle

[0105] Within a cycle, the cooling-electricity power supply system should utilize the nighttime ice storage capacity as much as possible during ice melting and cooling to avoid affecting the ice melting rate.

[0106]

[0107] This represents the total power used for ice melting and cooling within one cycle.

[0108] Step 4: Solve for the optimal solutions of the multi-objective load regulation model and the multi-objective cooling output power active control model using the ε-constraint method and the multi-dimensional preference analysis linear programming method.

[0109] This step uses the ε-constraint method and multidimensional preference analysis linear programming method to find the optimal solutions for the multi-objective load regulation model and the multi-objective cooling output power active control model, obtaining the minimum F1, F2, F3, and F4.

[0110] The ε-constraint method is used to transform the multi-objective problem into a single-objective problem, thereby obtaining the Pareto optimal solution set. First, the ideal point (f1) is selected... * f2 * ) and the lowest point (f1) N f2 N A range of values ​​is defined, with a fixed step size. Gradually change By finding the value of , a series of single-objective problems can be formed and solved to obtain the Pareto front or its approximation.

[0111] After obtaining a set of Pareto optimal solutions, a multidimensional preference analysis linear programming method is used to dedimensionalize the multiple objectives. From the Pareto front, the solution closest to the common ideal solution is selected as the optimal solution. Then, the Euclidean distance from the non-dominated solution to the ideal solution is calculated. The non-dominated solution with the smallest distance is the optimal solution to the multi-objective problem.

[0112]

[0113] Where i is the number of the non-dominated solution and j is the number of the objective; The location of the non-dominated solution; The location of the ideal solution.

[0114] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A comprehensive energy system load regulation method considering meteorological factors and time-of-use pricing, characterized by: Includes the following steps: Step 1: Construct a physical model of the energy consumption power of the integrated energy system's cooling-electricity power supply system under two operating conditions: electric refrigeration and electric ice making. Step 2: Select the control period on the load side using the fuzzy C-means clustering method, and construct a physical model of cooling load and temperature and humidity index; The control period is selected, and the peak load period t after clustering is analyzed. r,f Peak period t of time-of-use electricity pricing p,f The degree of agreement, and the matching peak period as the control period t. j , i.e. t j ∈t r,f ,t p,f ; Step 3: Based on the selected control period and the constructed physical models, construct a multi-objective load regulation model and a multi-objective active control model for cooling output power; Step 3, the step of constructing a multi-objective load regulation model based on the selected control period and the constructed physical models, includes: Establish a multi-objective load regulation model: Where F1 represents the deviation of the temperature and humidity index during the control period, and F2 represents the cooling load demand during the control period; t j Indicates the period of regulation; THI in (t) represents the actual indoor temperature and humidity index at time t, THI set Q(t) represents the optimal temperature and humidity index at time t; Q2(t) represents the cooling load demand at time t; Step 3, which involves constructing a multi-objective active control model for cooling output power based on the selected control period and the constructed physical models, includes: Construct a multi-objective active control model for cooling output power: Where F3 represents the power consumption of the cooling-electricity power supply system, F4 represents the energy cost of the cooling-electricity power supply system, T represents one control cycle of the cooling-electricity power supply system, and W represents the power consumption of the cooling-electricity power supply system. zl (t) represents the power consumption of the cooling-electric power supply system under cooling conditions at time t, in W. zb e(t) represents the power consumption of the cold-electricity power supply system under ice-making conditions at time t; e(t) represents the time-of-use electricity price. Step 4: Solve for the optimal solutions of the multi-objective load regulation model and the multi-objective cooling output power active control model using the ε-constraint method and the multi-dimensional preference analysis linear programming method.

2. The integrated energy system load regulation method considering meteorological factors and time-of-use pricing according to claim 1, characterized in that: Step 1 specifically includes the following steps: The power consumption (W) of the cooling-electric power supply system at time t under both electric refrigeration and electric ice-making operating conditions. zl(zb) The physical model of (t) is: Among them, W h,zl(zb) (t) represents the power consumption of the refrigerator at time t, in W. to,zl(zb) (t) represents the power consumption of the cooling tower at time t, in W. p (t) represents the power consumption of the water pump at time t, and the water pump includes a cooling water pump and a circulating water pump; Q zl(zb) (t) represents the refrigeration and ice-making power of the cold-electric power supply system at time t; Q zl(zb),R E represents the rated refrigeration and ice-making power of the cold-electric power supply system at time t. zl(zb),R P represents the rated power consumption of the cooling-electricity power supply system at time t. x (t) represents the power consumption of the circulating water pump at time t, P l (t) represents the power consumption of the cooling water pump at time t.

3. The integrated energy system load regulation method considering meteorological factors and time-of-use pricing as described in claim 1, characterized in that: Step 2, which involves selecting the control period on the load side using the fuzzy C-means clustering method, includes: Define the loss function for the clustering model: Among them, u ij Let be the membership degree of the i-th sample belonging to the j-th cluster, and construct the membership degree matrix H based on it, where n is the total number of samples, cl is the number of clusters, and b represents the b-th iteration; Let j be the cluster center; This is the membership factor for the clustering model. ;d ij Let be the distance from the i-th sample to the j-th cluster center; (1) Randomly initialize the number of clusters cl to obtain the membership matrix H; let b=1, start the first iteration, and calculate the cluster centers in the b-th iteration: Where, x i This represents the i-th sample; (2) Update the membership function H and the loss function J: Where i = 1, 2, ..., n, j = 1, 2, ..., cl; ; (3) By setting a membership termination threshold Loss function termination threshold To obtain the final classification.

4. The integrated energy system load regulation method considering meteorological factors and time-of-use pricing according to claim 1, characterized in that: Step 2, the step of constructing a physical model of the cooling load, includes: A physical model of cooling load is constructed based on the heat balance equation. For indoor heat transfer, the instantaneous heat gain at any time period includes: the heat conducted by the building envelope at time t Q1(t), the basic cooling load demand Q3(t), the cooling load demand Q2(t), and the change in indoor heat between two adjacent time periods. The physical model for cooling load is: Where S is the effective cooling area; C is the heat capacity per unit cooling area, taken as... ω is the heat dissipation coefficient due to the temperature difference between the inside and outside of the building, taken as... ;T in (t) represents the indoor temperature at time t, T in (t-1) represents the indoor temperature at time t-1, T out (t) represents the outdoor temperature at time t; Then, the cooling load demand Q2(t) at time t can be calculated: 。 5. The integrated energy system load regulation method considering meteorological factors and time-of-use pricing according to claim 1, characterized in that: Step 2, the step of constructing the physical model of the temperature and humidity index, includes: The physical model for the temperature and humidity index is: Where THI(t) is the temperature and humidity index at time t; T(t) is the temperature at time t, in units of... R(t) represents the relative humidity at time t, in percent.

6. The integrated energy system load regulation method considering meteorological factors and time-of-use pricing according to claim 1, characterized in that: The constraints of the multi-objective load regulation model are: (1) Temperature and humidity index constraints: During load adjustment, the indoor temperature and humidity index needs to be controlled within an acceptable range, therefore the following must be met: Among them, THI min The minimum temperature and humidity index (THI) max This represents the maximum value of the temperature and humidity index. (2) Cooling load demand constraints: The cooling load demand after adjustment should be less than the cooling load demand before adjustment. in, This is the adjusted cooling load demand.

7. The integrated energy system load regulation method considering meteorological factors and time-of-use pricing according to claim 1, characterized in that: The constraints of the multi-objective cooling output power active control model are as follows: (1) Energy balance constraint: The sum of the cooling capacity supplied by the refrigeration unit directly and by ice melting at each moment should equal the cooling load demand of the cooling-electricity power supply system at that moment: Among them, Q zl (t) represents the power (Q) directly supplied by the chiller at time t. rb (t) represents the power of the refrigerator for melting ice and providing cooling at time t; Let t be the cooling load demand of the cooling-electricity power supply system at time t, which is also the adjusted cooling load demand in the multi-objective load regulation model. (2) Restrictions on refrigeration and ice-making cooling systems: The cooling and ice-making power of the refrigeration unit should be less than its rated cooling and ice-making power; Among them, Q zl (t) represents the cooling power of the refrigerator at time t, Q zl,R (t) represents the rated cooling power of the refrigerator at time t; Q zb (t) represents the ice-reducing power of the refrigeration mechanism at time t, Q. zb,R (t) represents the rated ice-making power of the refrigerator at time t; (3) Ice storage constraints: The hourly ice storage capacity of the ice storage tank in a cold-electricity power supply system is related to the unit load: Among them, Q ice (t) represents the ice storage power of the ice storage tank at time t; Q zb (t) represents the ice-making power of the ice storage tank at time t; The cold energy conversion rate during ice making in the ice storage tank is taken as 0.94; Q ice The total ice storage capacity of the ice storage tank is constrained as follows: Among them, Q ice,max This represents the maximum total ice storage capacity of the ice storage tank. (4) De-icing power constraint: The maximum melting rate of the ice storage tank at any given time varies with the amount of ice remaining in the tank: Among them, Q rb,max (t) represents the maximum ice-melting cooling power of the ice storage tank at time t; Q rb (i) represents the ice-melting cooling power of the ice storage tank at time i, i∈t-1; a is the cold release coefficient; the ice-melting cooling power constraint of the ice storage tank at time t is: (5) Limitation on ice melt volume within a cycle: Within a cycle, the cooling-electricity power supply system should utilize the nighttime ice storage capacity as much as possible during ice melting and cooling to avoid affecting the ice melting rate. This represents the total power used for ice melting and cooling within one cycle.

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