A method and system for scheduling electric heating of a group of thermal storage electric boilers taking into account user heat demand

Abstract

The present invention relates to a method and system for scheduling electric heating for a group of thermal storage electric boilers that takes into account user heat demand, and relates to the field of energy scheduling. The method comprises: constructing a distributed photovoltaic output prediction model based on historical meteorological data and distributed photovoltaic output data; predicting photovoltaic power generation data for a set prediction day based on the distributed photovoltaic output prediction model; determining a heat load demand model based on user heat demand information and temperature information for the set prediction day; constructing a thermal storage electric boiler operation model based on the heat load model; and determining a scheduling plan for the thermal storage electric boilers and distributed photovoltaic output for the set prediction day based on the photovoltaic power generation data for the set prediction day, the heat load demand model, and the thermal storage electric boiler operation model, with the objective functions of minimizing abandoned solar power, maximizing line surplus capacity during peak load periods, and minimizing line peak-to-valley difference. The present invention improves the flexibility of thermal storage electric boiler-photovoltaic scheduling and resource utilization.

Description

Technical Field

[0001] The present invention relates to the technical field of energy planning, and in particular to a method and system for scheduling electric heating of a thermal storage electric boiler group taking into account user heat demand. Background Art

[0002] In recent years, electric heating has been widely adopted in counties across northern China as they transition to electric energy substitution. Thermal storage boilers, due to their pollution-free operation and high flexibility in both electricity and heat, have led to their widespread adoption. Meanwhile, distributed photovoltaic systems, covering entire counties in northern China, have also been widely promoted in recent years. Their fluctuating output has placed significant pressure on the regional power grid's peak regulation, particularly during peak heating periods, as heating units often operate in a "heat-based electricity" mode, significantly limiting their regulation capabilities.

[0003] Currently, thermal storage electric boilers operate independently under the control of heating providers, typically starting up in the late night to store enough heat for the next day's heating, then suspending daytime electricity use. This approach presents the following major issues: First, the centralized nighttime startup causes severe overloads on certain lines during peak loads; second, the design of heat storage and heating power is relatively crude, failing to carefully quantify the differentiated thermal comfort needs of heating users and the heat dissipation characteristics of the heating areas; and third, the synergistic effect of thermal storage electric boilers on both the electric and thermal sides is not fully exploited, resulting in significant waste of excess capacity. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for scheduling electric heating of a group of thermal storage electric boilers taking into account the heat demand of users, thereby improving the flexibility of scheduling and the utilization rate of resources.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for scheduling electric heating of a group of thermal storage electric boilers taking into account user heat demand, comprising:

[0007] Acquire historical meteorological data and distributed photovoltaic output data of the area to be dispatched, and construct a distributed photovoltaic output prediction model based on the historical meteorological data and the distributed photovoltaic output data;

[0008] Predicting photovoltaic power generation data for a set forecast day in the area to be dispatched according to the distributed photovoltaic output forecast model;

[0009] Determining a heat load demand model based on user heat demand information in the area to be scheduled and temperature information on a set forecast day;

[0010] Constructing a thermal storage electric boiler operation model based on the heat load model;

[0011] According to the photovoltaic power generation data of the set forecast day, the heat load demand model and the thermal storage electric boiler operation model, the scheduling plan for each thermal storage electric boiler and distributed photovoltaic output on the set forecast day in the area to be scheduled is determined with the objective function of minimizing the amount of abandoned light, maximizing the line surplus capacity during peak load and minimizing the line peak-to-valley difference rate.

[0012] Optionally, the acquiring of historical meteorological data and photovoltaic output data, and constructing a photovoltaic output prediction model based on the historical meteorological data and the photovoltaic output data, specifically includes:

[0013] Preprocessing the historical meteorological data, clustering the preprocessed historical meteorological data to obtain k types of meteorological scene data;

[0014] The distributed photovoltaic output prediction model is determined based on k types of meteorological scene data and the corresponding distributed photovoltaic output data.

[0015] Optionally, preprocessing the historical meteorological data and clustering the preprocessed historical meteorological data to obtain k types of meteorological scene data specifically includes:

[0016] The historical meteorological data is preprocessed, and the preprocessed historical meteorological data is clustered based on a k-means clustering method to obtain k types of meteorological scene data.

[0017] Optionally, the heat load demand model is expressed as:

[0018]

[0019] Among them, Q heat，t is the heat load required by the building at time t, Q HT,t is the heat conducted by the enclosure structure at time t, T is the response time; Q INF,t Q is the heat consumption of air infiltration at time t; IH,t is the heat output of the indoor heat source at time t.

[0020] Optionally, the constraint condition of the heat load demand model includes that the average prediction index of thermal sensation is within a preset range;

[0021] The thermal sensation average prediction index is expressed as:

[0022]

[0023] Among them, λ PMV,t represents the average prediction index of thermal sensation of the user during period t, M represents the energy metabolism rate of the human body, and I cl Indicates thermal resistance of clothing; T s Indicates the set temperature; T in,t is the indoor air temperature during period t;

[0024] The constraints of the user thermal comfort model are:

[0025] in, λ PMV,t Indicates the minimum, Indicates the maximum limit.

[0026] Optionally, the thermal storage electric boiler operation model includes a thermal storage electric boiler electric heat conversion model and a heat load balance model;

[0027] The heat storage electric boiler electric heat conversion model is expressed as: PH n,t =PE n *η 1,n ;

[0028] Among them, PH n,t is the thermal power of the nth thermal storage electric boiler at time t; PE n is the electric power of the nth thermal storage electric boiler at time t, η 1,n is the heat generation efficiency of the nth thermal storage electric boiler;

[0029] The heat load balance model is expressed as:

[0030]

[0031] PG n,t =BG n,t +SG n,t ;

[0032] Among them, PG n,t is the heating power of the nth thermal storage electric boiler at time t, η2 is the heat network loss coefficient; HL n,t P is the sum of the heat load and heat power of the nth thermal storage electric boiler at time t; heat,n,i,t BG is the thermal power of the i-th building within the heating range I of the n-th thermal storage electric boiler at time t; n,t is the partial thermal power of the nth thermal storage electric boiler at time t, SG n,t is the partial thermal power of the thermal storage tank of the nth thermal storage electric boiler at time t;

[0033] The constraints of the thermal storage electric boiler operation model include:

[0034] 0≤PE n,t ≤PE n,max ;

[0035] 0≤BG n,t ≤PH n,t ;

[0036] 0≤SG n,t ≤SG n,max ;

[0037]

[0038]

[0039] Among them, PE n,max represents the rated power of the nth thermal storage electric boiler, Q BM,n Indicates the maximum heat storage capacity of the nth thermal storage electric boiler.

[0040] Optionally, the objective function is expressed as:

[0041]

[0042] Among them, T represents the response time, K represents the number of lines, and P pv,t Indicates the actual distributed photovoltaic power generation capacity at time t, represents the predicted distributed photovoltaic power generation power at time t output by the distributed photovoltaic output prediction model, is the maximum active power limit of the kth line, is the maximum load of the kth line, is the minimum load of the kth line, λ1 and λ2 are weight factors;

[0043] The constraints of the objective function are:

[0044]

[0045] Among them, P k,t is the active power of the thermal storage electric boiler and distributed photovoltaic at time t of the kth line, Q k,t is the reactive power of the kth line where the thermal storage electric boiler and distributed photovoltaic are located at time t, is the transmission capacity limit allowed for the kth line.

[0046] The present invention also discloses an electric heating scheduling system for a group of thermal storage electric boilers taking into account user heat demand, comprising:

[0047] A distributed photovoltaic output prediction model building module is used to obtain historical meteorological data and distributed photovoltaic output data of the area to be dispatched, and build a distributed photovoltaic output prediction model based on the historical meteorological data and the distributed photovoltaic output data;

[0048] A photovoltaic power generation data prediction module is used to predict the photovoltaic power generation data of the to-be-scheduled area on a set prediction day based on the distributed photovoltaic output prediction model;

[0049] a heat load demand model determination module, configured to determine a heat load demand model based on user heat demand information in the area to be scheduled and temperature information on a set forecast day;

[0050] a thermal storage electric boiler operation model determination module, configured to construct a thermal storage electric boiler operation model based on the heat load model;

[0051] The scheduling scheme determination module is used to determine the scheduling scheme for each thermal storage electric boiler and distributed photovoltaic output on the set forecast day in the area to be scheduled based on the photovoltaic power generation data on the set forecast day, the heat load demand model and the thermal storage electric boiler operation model, with the goals of minimizing the amount of abandoned light, maximizing the line surplus capacity during peak load and minimizing the line peak-to-valley difference rate.

[0052] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0053] The present invention determines a heat load demand model based on user heat demand information and temperature information on a set forecast day. According to the photovoltaic power generation data on the set forecast day, the heat load demand model and the thermal storage electric boiler operation model, the present invention takes minimizing the amount of abandoned light, maximizing the line surplus capacity at peak load and minimizing the line peak-to-valley difference rate as objective functions, and determines the scheduling plan for the thermal storage electric boiler and distributed photovoltaic output on the set forecast day, thereby improving the scheduling flexibility and resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 This is a schematic diagram of the process of the electric heating scheduling method of the thermal storage electric boiler group considering the user's heat demand of the present invention Figure 1 ;

[0056] Figure 2 This is a schematic diagram of the process of the electric heating scheduling method of the thermal storage electric boiler group considering the user's heat demand of the present invention Figure 2 ;

[0057] Figure 3 This is a flow chart of photovoltaic output prediction according to the present invention;

[0058] Figure 4 This is a schematic diagram of setting the average prediction index of thermal sensation of the present invention;

[0059] Figure 5 This is a schematic diagram of the joint scheduling of thermal storage electric boilers and distributed photovoltaic systems according to the present invention;

[0060] Figure 6 Schematic diagram of the energy flow relationship between the power grid, the thermal storage electric boiler and the user in the present invention;

[0061] Figure 7 This is a structural schematic diagram of an electric heating scheduling system for a group of thermal storage electric boilers that takes into account user heat demand in the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0063] The purpose of the present invention is to provide a method and system for scheduling electric heating of a group of thermal storage electric boilers taking into account the heat demand of users, thereby improving the flexibility of scheduling and the utilization rate of resources.

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0065] Figure 1 This is a schematic diagram of the process of the electric heating scheduling method of the thermal storage electric boiler group considering the user's heat demand of the present invention Figure 1 ; Figure 2 This is a schematic diagram of the process of the electric heating scheduling method of the thermal storage electric boiler group considering the user's heat demand of the present invention Figure 2 ,like Figure 1-2 As shown, a method for scheduling electric heating of a group of thermal storage electric boilers taking into account user heat demand includes the following steps:

[0066] Step 101: Obtain historical meteorological data and distributed photovoltaic output data of the area to be dispatched, and build a distributed photovoltaic output prediction model based on the historical meteorological data and the distributed photovoltaic output data.

[0067] Among them, Figure 3 As shown, step 101 specifically includes:

[0068] Obtain historical meteorological data and distributed photovoltaic output data (historical distributed photovoltaic output data) of the area to be dispatched, analyze the impact of various meteorological factors on photovoltaic output, and build a distributed photovoltaic output prediction model based on the historical meteorological data and the distributed photovoltaic output data.

[0069] The historical meteorological data is preprocessed, and the preprocessed historical meteorological data is clustered to obtain k types of meteorological scene data.

[0070] The preprocessing of the historical meteorological data includes preprocessing the historical meteorological data according to seasons (spring, summer, autumn, winter).

[0071] The distributed photovoltaic output prediction model is determined based on k types of meteorological scene data and the corresponding distributed photovoltaic output data.

[0072] The preprocessing of the historical meteorological data and clustering of the preprocessed historical meteorological data to obtain k types of meteorological scene data specifically includes:

[0073] The historical meteorological data is preprocessed, and the preprocessed historical meteorological data is clustered based on a k-means clustering method to obtain k types of meteorological scene data.

[0074] The sample data of historical meteorological data is the characteristic vector X of meteorological conditions;

[0075] X=[R hor ,R obl ,S hor ,S obl ,T per ,T max ,T min ,T mean ,t max ,t min ,H max ,H min ,H mean ];

[0076] Among them, R hor 、R obl 、S hor 、S obl are horizontal irradiance, oblique irradiance, horizontal scattering, oblique scattering, T per is the illumination time, T max 、T min 、T mean , t max , t min are the highest, lowest and average ambient temperatures and the time when the highest and lowest temperatures occur, respectively. max 、H min 、H mean are the maximum, minimum and average relative humidity respectively, and the dimension of X is m=13.

[0077] Cluster the pre-processed historical meteorological data to obtain k types of meteorological scene data, including:

[0078] The Person correlation coefficient method is used to calculate the correlation coefficient γ between each meteorological factor and photovoltaic output s , s=1,2,3,……,m. Define the weighted Euclidean distance between samples as:

[0079]

[0080] Among them, d ij Represents sample X i and sample X j The weighted Euclidean distance, x i,s and x j,s They are samples X i and sample X j The sth element of γ s represents the correlation coefficient between the sth meteorological factor and photovoltaic output.

[0081] The preprocessed historical meteorological data are finally clustered based on the k-means clustering method. The principle is that the smaller the Euclidean distance between two samples, the greater the similarity. The loss function is defined as the sum of the distances between the sample and the center of the cluster to which it belongs, that is, formula (1). The clustering criterion is to minimize the loss function value W(C), that is, formula (2), and finally obtain the k-class clustering result.

[0082]

[0083]

[0084] in, is the center of the l-th class sample, X i is a sample belonging to the lth class, n l is the number of samples in the lth class, l = 1, 2, 3, ..., k.

[0085] Based on the clustering results and the predicted meteorological conditions, a regression forecast is performed on the photovoltaic output, with the forecast day set as the next day. The specific steps are as follows:

[0086] Collect the next day's weather forecast information and obtain the input sample X p ;

[0087] Calculate the input sample X p The distance between each class in the k-class aggregation is taken as the class with the minimum value as the sample X p Class. Input sample X p The minimum distance between each class can be calculated by the following formula:

[0088]

[0089] is the center of the lth class sample l=1,2,3,……,k; x p,s 、 X p 、 The sth element of .

[0090] Use nonlinear mapping to transform the sample X pMapping from the original space to the high-dimensional feature space, using the least squares method to p The prediction curve (distributed photovoltaic output prediction model) is obtained by fitting the samples of the corresponding class:

[0091]

[0092] Among them, f(X) represents the predicted output, K(X,X i ) is the kernel function, X is the sample to be predicted, sa represents the number of samples in the category to which the sample X belongs, X i represents the i-th sample in the category to which sample X belongs, α i and b are coefficients determined using the least squares method.

[0093] Step 102: Predicting photovoltaic power generation data of a predetermined forecast day in the area to be dispatched according to the distributed photovoltaic output forecast model.

[0094] According to formula (5), input the sample vector X to predict the distributed photovoltaic power generation

[0095] Distributed photovoltaics report the next day's photovoltaic output forecast to the regional smart energy platform And adjustable output ratio, including:

[0096] 1) Actual power generation capacity of distributed photovoltaics P pv,t It is divided into self-consumption electricity, clean heating electricity and online electricity, which can be calculated by the following formula:

[0097] P pv,t =P pv-res,t +P pv-heat,t +P pv-grid,t (6)

[0098] P pv-res,t =α res,t ·P pv,t (7)

[0099] P pv-heat,t =α heat,t ·P pv,t (8)

[0100] Among them, P pv-res,t is the self-consumed electricity of distributed photovoltaics, P pv-heat,t The electricity for clean heating (i.e. the electricity supplied to thermal storage boiler users through contracts signed with thermal storage boiler users), P pv-grid,t is the amount of electricity consumed by the Internet. res,t is the proportion of self-consumption electricity at time t, α heat,t is the clean heating ratio at time t.

[0101] 2) Constraints on distributed photovoltaic output

[0102] The actual power generation of distributed photovoltaics should be less than its predicted power, that is, The proportion of electricity used for self-use and clean heating in each period should be limited to a certain range:

[0103]

[0104] Step 103: Determine a heat load demand model based on the heat demand information of users in the area to be scheduled and the temperature information of the set forecast day.

[0105] The user's thermal demand information includes the indoor temperature set by the user, that is, the present invention constructs a thermal load demand model based on the user's thermal comfort.

[0106] Among them, step 103 specifically includes: obtaining relevant information of heating users and temperature forecast information for the next day, and constructing a heat load demand model based on factors such as user thermal comfort and building heat dissipation.

[0107] The predicted mean vote (PMV) indicator of indoor thermal comfort is introduced to quantify the user's thermal comfort. The constraints of the heat load demand model include that the predicted mean vote is within a preset range;

[0108] The thermal sensation average prediction index is expressed as:

[0109]

[0110] Among them, λ PMV,t represents the average prediction index of thermal sensation of the user during period t, M represents the energy metabolism rate of the human body, and I cl Indicates thermal resistance of clothing; T s Indicates the set temperature; T in,t is the indoor air temperature during period t;

[0111] Considering that different types of users have different requirements for thermal comfort at different times, the PMV index of users can be limited by time and user type. The PMV index should be kept between ±0.5 and ±1. For example, the PMV limit of a user throughout the day can be set as Figure 4 In a period of time, the PMV index should be limited to the minimum allowed in that period of time. λ PMV,t With the maximum limit Between, that is:

[0112] The constraints of the user thermal comfort model are:

[0113]

[0114] in, λ PMV,t Indicates the minimum, Indicates the maximum limit.

[0115] The heat load demand model is expressed as:

[0116]

[0117] Q iNF,in =C air ρ air NSH(T in,t -T out,t ) (13)

[0118] Q IH,t =Q ine,t +Q inh,t (14)

[0119]

[0120] Among them, Q heat，t is the heat load required by the building at time t, Q HT,t is the heat conducted by the enclosure structure at time t, T' is the response time; Q INF,t Q is the heat consumption of air infiltration at time t; IH,t is the heat output of the indoor heat source at time t.

[0121] K is the temperature difference correction coefficient of the enclosure structure; a is the heat transfer coefficient of the enclosure structure; A is the area of the enclosure structure; T in,t is the indoor temperature, T out,t is the outdoor temperature at time t, i.e. the predicted temperature (the temperature of the set prediction day); C air is the specific heat capacity of air; ρ air is the air density; N is the number of air changes; S is the building area; H is the indoor height of the building; Q ine Heat generated by electrical equipment; Q inh Generates heat for the human body.

[0122] Step 104: Constructing a thermal storage electric boiler operation model based on the heat load model.

[0123] The thermal storage electric boiler operation model includes a thermal storage electric boiler electric heat conversion model and a heat load balance model;

[0124] The electric heat conversion model of the thermal storage electric boiler is expressed as:

[0125] PH n,t =PE n *η 1,n (16)

[0126] Among them, PH n,t is the thermal power of the nth thermal storage electric boiler at time t; PE n is the electric power of the nth thermal storage electric boiler at time t, η 1,n is the heat generation efficiency of the nth thermal storage electric boiler;

[0127] The heat load balance model is expressed as:

[0128]

[0129] PG n,t =BG n,t +SG n,t (18)

[0130] Among them, PG n,t is the heating power of the nth thermal storage electric boiler at time t, η2 is the heat network loss coefficient; HL n,t P is the sum of the heat load and heat power of the nth thermal storage electric boiler at time t; heat,n,i,t BG is the thermal power of the i-th building within the heating range I of the n-th thermal storage electric boiler at time t; n,t is the partial thermal power of the nth thermal storage electric boiler at time t, SG n,t is the partial thermal power of the heat storage tank of the nth heat storage electric boiler at time t.

[0131] The energy flow relationship between the thermal storage electric boiler, distributed photovoltaic (grid) and users is as follows: Figure 6 shown.

[0132] The constraints of the thermal storage electric boiler operation model include:

[0133] Electric power constraint: The real-time electric power of each thermal storage electric boiler shall not exceed its rated value;

[0134] 0≤PE n,t ≤PE n,max (19)

[0135] Electric boiler heating constraints: The real-time heating power of each electric boiler is less than its real-time heat production power;

[0136] 0≤BG n,t ≤PH n,t (20)

[0137] Thermal storage tank heat storage and release power constraint: The real-time heating power of the thermal storage tank is less than its maximum heating power;

[0138] 0≤SG n,t ≤SG n,max (twenty one)

[0139] Initial heat storage constraint: The net heat storage of each heat storage tank in one cycle (24 hours) is 0; that is, after a complete operation cycle, the heat storage is the initial heat storage Q0;

[0140]

[0141] Maximum heat storage constraint: The real-time heat storage of the heat storage tank is less than its maximum heat storage Q BM,n ;

[0142]

[0143] Among them, PE n,max represents the rated power of the nth thermal storage electric boiler, Q BM,n Indicates the maximum heat storage capacity of the nth thermal storage electric boiler.

[0144] Step 105: Based on the photovoltaic power generation data of the set forecast day, the heat load demand model and the thermal storage electric boiler operation model, the scheduling plan for each thermal storage electric boiler and distributed photovoltaic output on the set forecast day in the area to be scheduled is determined with the objective function of minimizing the amount of abandoned light, maximizing the line surplus capacity during peak load and minimizing the line peak-to-valley difference.

[0145] The scheduling plan includes the start and stop status and power consumption plan of each thermal storage electric boiler within the total scheduling period, as well as the output plan of distributed photovoltaics.

[0146] like Figure 5 As shown in the figure, the heating supplier collects the heat demand information and building information of heating users, evaluates the heat load demand based on the user thermal comfort model and the building heat dissipation model, and transmits the parameters of the thermal storage electric boiler to the regional smart energy platform.

[0147] The regional smart energy platform combines grid-side information, distributed photovoltaic predicted output, thermal storage electric boiler parameters and other information to build a thermal storage electric boiler-distributed photovoltaic day-ahead joint scheduling model.

[0148] The scheduling strategy is: on the premise of meeting the basic thermal comfort of users, by controlling the start and stop status of the thermal storage electric boiler and the power consumption plan, the amount of abandoned light of distributed photovoltaic is minimized; on the premise of ensuring that the line flow does not exceed the line, during the peak load period, the line has the largest surplus capacity and the smallest peak-to-valley rate variance.

[0149] Among them, step 105 specifically includes: constructing a day-ahead joint scheduling model of the thermal storage electric boiler and distributed photovoltaics based on the photovoltaic power generation data of the set forecast day, the heat load demand model and the thermal storage electric boiler operation model, and determining the scheduling plan for the thermal storage electric boiler and distributed photovoltaic output on the set forecast day with the objective functions of minimizing the amount of abandoned light, maximizing the line surplus capacity during peak load and minimizing the line peak-to-valley difference rate.

[0150] Objective function:

[0151] The amount of abandoned light in distributed photovoltaic is the smallest, and the amount of abandoned light in period t can be expressed as:

[0152]

[0153] The surplus capacity of the line is the largest during peak load period. The average surplus capacity of K lines during peak load period can be calculated as follows:

[0154]

[0155] The variance of the peak-to-valley difference rate of the line is the smallest. The variance of the peak-to-valley difference of K lines can be calculated by the following formula:

[0156]

[0157] The objective function is expressed as:

[0158]

[0159] Among them, T represents the total time period of scheduling, K represents the number of lines, and P pv,t Indicates the actual distributed photovoltaic power generation capacity at time t, represents the predicted distributed photovoltaic power generation power at time t output by the distributed photovoltaic output prediction model, is the maximum active power limit of the kth line, is the maximum load of the kth line, is the minimum load of the kth line, λ1 and λ2 are weight factors;

[0160] The constraints of the objective function are:

[0161] Power flow constraints for thermal storage electric boilers and photovoltaic lines:

[0162]

[0163] Voltage Constraints:

[0164]

[0165] Among them, P k,t is the active power of the thermal storage electric boiler and distributed photovoltaic at time t of the kth line, Q k,t is the reactive power of the kth line where the thermal storage electric boiler and distributed photovoltaic are located at time t, is the transmission capacity limit allowed for the kth line, is the maximum voltage limit allowed for the i-th node, are the minimum voltage limits allowed for the i-th node, U i,tis the voltage of the i-th node at time t.

[0166] The constraints of the objective function also include the constraints of distributed photovoltaic output and the constraints of the thermal storage electric boiler operation model.

[0167] The objective function is solved and the optimal output dispatching scheme of thermal storage electric boiler and distributed photovoltaic day-ahead output is obtained.

[0168] The regional smart energy platform reports the electricity consumption plans of distributed photovoltaic and thermal storage electric boiler enterprise users to the dispatching center, and distributes them to each distributed photovoltaic and thermal storage electric boiler user.

[0169] The above objective function is solved by using Matlab optimization software in conjunction with the CPLEX solver.

[0170] The method disclosed in the present invention is a day-ahead electric heating scheduling method for the joint operation of thermal storage electric boilers and distributed photovoltaics that takes into account flexible heat demand. On the one hand, the flexible regulation of the thermal storage electric boilers can increase the local consumption ratio of distributed photovoltaics in counties and reduce the proportion of abandoned light. On the other hand, relying on the diversified operation modes of the thermal storage electric boilers and the flexible quantification of user heat demand, the demand response potential of the thermal storage electric boilers can be further explored, the utilization rate of their capacity can be improved, and the flexible peak-shaving operation of the thermal storage electric boilers can be realized while meeting the flow constraints of the distribution network, thereby reducing the peak-shaving pressure of the power grid.

[0171] Figure 7 This is a schematic diagram of the structure of a heat storage electric boiler group electric heating scheduling system that takes into account user heat demand. Figure 7 As shown, a thermal storage electric boiler group electric heating scheduling system considering user heat demand includes:

[0172] A distributed photovoltaic output prediction model building module 201 is used to obtain historical meteorological data and distributed photovoltaic output data of the area to be dispatched, and build a distributed photovoltaic output prediction model based on the historical meteorological data and the distributed photovoltaic output data;

[0173] The photovoltaic power generation data prediction module 202 is used to predict the photovoltaic power generation data of the scheduled area on a set prediction day according to the distributed photovoltaic output prediction model;

[0174] A heat load demand model determination module 203 is used to determine a heat load demand model based on user heat demand information in the area to be scheduled and temperature information on a set forecast day;

[0175] A thermal storage electric boiler operation model determination module 204 is configured to construct a thermal storage electric boiler operation model based on the heat load model;

[0176] The scheduling scheme determination module 205 is used to determine the scheduling scheme for each thermal storage electric boiler and distributed photovoltaic output on the set forecast day in the area to be scheduled based on the photovoltaic power generation data on the set forecast day, the thermal load demand model and the thermal storage electric boiler operation model, with the goals of minimizing the amount of abandoned light, maximizing the line surplus capacity during peak loads, and minimizing the line peak-to-valley difference rate.

[0177] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0178] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for scheduling electric heating of a group of thermal storage electric boilers taking into account user heat demand, characterized in that: include: Acquire historical meteorological data and distributed photovoltaic output data of the area to be dispatched, and construct a distributed photovoltaic output prediction model based on the historical meteorological data and the distributed photovoltaic output data; Predicting photovoltaic power generation data for a set forecast day in the area to be dispatched according to the distributed photovoltaic output forecast model; Determining a heat load demand model based on user heat demand information in the area to be scheduled and temperature information on a set forecast day; Constructing a thermal storage electric boiler operation model based on the heat load model; Based on the photovoltaic power generation data for the set forecast day, the heat load demand model, and the thermal storage electric boiler operation model, a scheduling plan for each thermal storage electric boiler and distributed photovoltaic output for the set forecast day in the area to be scheduled is determined, with the objective function of minimizing the amount of abandoned solar power, maximizing the line surplus capacity during peak load periods, and minimizing the line peak-to-valley difference rate; The heat load demand model is expressed as: Among them, Q heat，t is the heat load required by the building at time t, Q HT,t is the heat conducted by the enclosure structure at time t, T is the response time; Q INF,t Q is the heat consumption of air infiltration at time t; IH,t is the heat output of the indoor heat source at time t; The constraints of the heat load demand model include that the average prediction index of thermal sensation is within a preset range; The thermal sensation average prediction index is expressed as: Among them, λ PMV,t represents the average prediction index of thermal sensation of the user during period t, M represents the energy metabolism rate of the human body, and I cl Indicates thermal resistance of clothing; T s Indicates the set temperature; T in,t is the indoor air temperature during period t; The constraints of the user thermal comfort model are: in, λ PMV,t Indicates the minimum, Indicates the maximum limit; The objective function is expressed as: Among them, T represents the response time, K represents the number of lines, and P pv,t Indicates the actual distributed photovoltaic power generation capacity at time t, represents the predicted distributed photovoltaic power generation power at time t output by the distributed photovoltaic output prediction model, is the maximum active power limit of the kth line, is the maximum load of the kth line, is the minimum load of the kth line, λ1 and λ2 are weight factors; The constraints of the objective function are: Among them, P k,t is the active power of the thermal storage electric boiler and distributed photovoltaic at time t of the kth line, Q k,t is the reactive power of the kth line where the thermal storage electric boiler and distributed photovoltaic are located at time t, is the transmission capacity limit allowed for the kth line.

2. The method for scheduling electric heating of a group of thermal storage electric boilers considering user heat demand according to claim 1, characterized in that: The acquiring of historical meteorological data and photovoltaic output data, and constructing a photovoltaic output prediction model based on the historical meteorological data and the photovoltaic output data, specifically includes: Preprocessing the historical meteorological data, clustering the preprocessed historical meteorological data to obtain k types of meteorological scene data; The distributed photovoltaic output prediction model is determined based on k types of meteorological scene data and the corresponding distributed photovoltaic output data.

3. The method for scheduling electric heating of a group of thermal storage electric boilers considering user heat demand according to claim 2, characterized in that: The preprocessing of the historical meteorological data and clustering of the preprocessed historical meteorological data to obtain k types of meteorological scene data specifically includes: The historical meteorological data is preprocessed, and the preprocessed historical meteorological data is clustered based on a k-means clustering method to obtain k types of meteorological scene data.

4. The method for scheduling electric heating of a group of thermal storage electric boilers considering user heat demand according to claim 1, characterized in that: The thermal storage electric boiler operation model includes a thermal storage electric boiler electric heat conversion model and a heat load balance model; The heat storage electric boiler electric heat conversion model is expressed as: PH n,t =PE n *η 1,n ; Among them, PH n,t is the thermal power of the nth thermal storage electric boiler at time t; PE n is the electric power of the nth thermal storage electric boiler at time t, η 1,n is the heat generation efficiency of the nth thermal storage electric boiler; The heat load balance model is expressed as: PG n,t =BG n,t +SG n,t ; Among them, PG n,t is the heating power of the nth thermal storage electric boiler at time t, η2 is the heat network loss coefficient; HL n,t P is the sum of the heat load and heat power of the nth thermal storage electric boiler at time t; heat,n,i,t BG is the thermal power of the i-th building within the heating range I of the n-th thermal storage electric boiler at time t; n,t is the partial thermal power of the nth thermal storage electric boiler at time t, SG n,t is the partial thermal power of the thermal storage tank of the nth thermal storage electric boiler at time t; The constraints of the thermal storage electric boiler operation model include: 0≤PE n,t ≤PE n,max ; 0≤BG n,t ≤PH n,t ; 0≤SG n,t ≤SG n,max ; Among them, PE n,max represents the rated power of the nth thermal storage electric boiler, Q BM,n Indicates the maximum heat storage capacity of the nth thermal storage electric boiler.

5. A heat storage electric boiler group electric heating scheduling system considering user heat demand, characterized in that: include: A distributed photovoltaic output prediction model building module is used to obtain historical meteorological data and distributed photovoltaic output data of the area to be dispatched, and build a distributed photovoltaic output prediction model based on the historical meteorological data and the distributed photovoltaic output data; A photovoltaic power generation data prediction module is used to predict the photovoltaic power generation data of the to-be-scheduled area on a set prediction day based on the distributed photovoltaic output prediction model; a heat load demand model determination module, configured to determine a heat load demand model based on user heat demand information in the area to be scheduled and temperature information on a set forecast day; a thermal storage electric boiler operation model determination module, configured to construct a thermal storage electric boiler operation model based on the heat load model; a scheduling scheme determination module, configured to determine, based on the photovoltaic power generation data for the set forecast day, the heat load demand model, and the thermal storage electric boiler operation model, a scheduling scheme for each thermal storage electric boiler and distributed photovoltaic output in the area to be scheduled for the set forecast day, with the objectives of minimizing the amount of abandoned solar power, maximizing the line surplus capacity during peak load periods, and minimizing the line peak-to-valley difference rate; The heat load demand model is expressed as: Among them, Q heat，t is the heat load required by the building at time t, Q HT,t is the heat conducted by the enclosure structure at time t, T is the response time; Q INF,t Q is the heat consumption of air infiltration at time t; IH,t is the heat output of the indoor heat source at time t; The constraints of the heat load demand model include that the average prediction index of thermal sensation is within a preset range; The thermal sensation average prediction index is expressed as: Among them, λ PMV,t represents the average prediction index of thermal sensation of the user during period t, M represents the energy metabolism rate of the human body, and I cl Indicates thermal resistance of clothing; T s Indicates the set temperature; T in,t is the indoor air temperature during period t; The constraints of the user thermal comfort model are: in, λ PMV,t Indicates the minimum, Indicates the maximum limit; The objective function is expressed as: Among them, T represents the response time, K represents the number of lines, and P pv,t Indicates the actual distributed photovoltaic power generation capacity at time t, represents the predicted distributed photovoltaic power generation power at time t output by the distributed photovoltaic output prediction model, is the maximum active power limit of the kth line, is the maximum load of the kth line, is the minimum load of the kth line, λ1 and λ2 are weight factors; The constraints of the objective function are: Among them, P k,t is the active power of the thermal storage electric boiler and distributed photovoltaic at time t of the kth line, Q k,t is the reactive power of the kth line where the thermal storage electric boiler and distributed photovoltaic are located at time t, is the transmission capacity limit allowed for the kth line.

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