CN111987716A - Multi-class heat storage electric heating user load group combined response scheduling method - Google Patents
Multi-class heat storage electric heating user load group combined response scheduling method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
- F24D13/00—Electric heating systems
- F24D13/02—Electric heating systems solely using resistance heating, e.g. underfloor heating
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
- F24D15/00—Other domestic- or space-heating systems
- F24D15/02—Other domestic- or space-heating systems consisting of self-contained heating units, e.g. storage heaters
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
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- F24D19/1096—Arrangement or mounting of control or safety devices for electric heating systems
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/52—The controlling of the operation of the load not being the total disconnection of the load, i.e. entering a degraded mode or in current limitation
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/58—The condition being electrical
- H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
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Abstract
The invention discloses the technical field of power dispatching, and particularly relates to a multi-class heat accumulation electric heating user load group combined response dispatching method, which is used for modeling an energy consumption thermal process of a user and acquiring typical heat load curves and power load curves of a full heat accumulation type user and a half heat accumulation type user; clustering user loads with similar thermodynamic characteristics and energy consumption characteristics; classifying users into working users, home users and random users according to the user behavior characteristics and the clustering result; according to the capacity characteristics and the energy utilization characteristics of users of multiple types, the load group peak regulation priority order of the users of the multiple types of heat storage and electric heating is formulated; and decomposing the load value to be reduced and sending the load value to the terminal control system of each user load group to perform user load group combined response under the emergency condition of the power grid. The system can ensure that the user load heating is not influenced on the premise of the safety of the power grid, and realizes that the user does not stop heating in the power grid critical safe operation scene.
Description
Technical Field
The invention belongs to the technical field of power dispatching, and particularly relates to a multi-class heat-storage electric heating user load group combined response dispatching method.
Background
The heat accumulation electric heating user accumulates heat at the load valley moment, releases heat at the peak moment, fully utilizes the peak valley electricity price difference to obtain the income, and different users are different to the power demand, the heat demand and the power consumption behavior, are influenced by various factors, and have the characteristics of continuity and periodicity. The operation optimization is carried out on the heat accumulation electric heating under the condition of ensuring the real-time load balance of the system, the system can be operated safely and stably for an electric power system, and the benefit of each degree of electric energy can be fully exerted for heat accumulation electric heating users for heat supply. In the power system, because the characteristics and the properties of the user power loads are different, different types of user loads show different load characteristics, but the user energy data has wide sources, various types of data do not exist independently, and the user energy data has strong correlation, including load characteristics, installed capacity, user heat condition, working day and holiday information, and the user energy requirements are analyzed in a targeted manner, so that the user energy requirements can be better served for the individual requirements of the energy market. When the electric wire netting meets emergency such as low voltage problem, in order to reduce the impact of heat accumulation electric heating system to the electric wire netting, the electric wire netting can issue the load reduction instruction to many kinds of heat accumulation electric heating system, and the electric wire netting is adjusted the in-process and is inevitably can cause the influence to heat accumulation electric heating user's power demand.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a multi-class heat accumulation electric heating user load group combined response scheduling method, which enables heat accumulation electric heating users to participate in peak regulation according to the formulated peak regulation priority sequence of the multi-class heat accumulation electric heating user load group, ensures the stability of a power grid and can reduce the influence on the heating requirements of the users to the maximum extent.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a multi-class heat storage electric heating user load group combined response scheduling method comprises the following steps: a. dividing users into full heat accumulation type users and half heat accumulation type users according to the equipment capacity of the heat accumulation electric heating users; modeling an energy utilization heat process of a user, analyzing the heat supply and heat dissipation processes of the user, and respectively obtaining a typical heat load curve and a typical power load curve of a full heat accumulation type user and a half heat accumulation type user; b. according to typical heat load curves and typical electricity load curves of full heat storage type users and half heat storage type users, user loads with similar thermodynamic characteristics and energy consumption characteristics are clustered; classifying users into working users, home users and random users according to the user behavior characteristics and the clustering result; c. setting the load group peak regulation priority order of the multi-class heat accumulation electric heating users according to the capacity characteristics and the user energy utilization characteristics of the full heat accumulation type users, the half heat accumulation type users, the working type users, the in-home type users and the random type users; d. and decomposing the load values to be reduced according to the number of the user load groups and the current power grid voltage value and issuing the load values to the terminal control system of each user load group to perform user load group combined response under the emergency condition of the power grid under the critical safe operation scene of the power grid on the basis of the formulated peak regulation priority sequence of the multi-class heat storage and heat power heating user load groups.
Further, in the step a, the total heat storage type user takes the minimum electric heating charge used by the user as an objective function:
in the formula: j represents the electricity fee generated by the user for using the electric heating system one day; t is tg0The starting time of the electricity price valley; t is tf0The starting time when the electricity price is at the peak; t is tgThe ending time of the electricity price valley; t is tcEnd time for heat storage of the heat storage device; t is tfThe end time when the electricity price is at the peak; pzHeating electric power for the direct heating device; pxCHeating electric power for the heat storage device; pxFDischarging the electrical power for the thermal storage device; c. Cg(t) is the electricity price at the time of the valley of t; c. Cf(t) is the peak time electricity price at time t;
wherein t iscThe expression of (a) is:
in the formula: qmaxIs the maximum capacity of the thermal storage device; etaxThe heating efficiency of the heat storage device is improved;
the objective function of the energy process optimization of the semi-regenerative user is as follows:
in the formula: t is tFThe time for using the heat in the heat storage device completely at the peak value of the electricity price; t is tz0Restarting the starting time of heating of the direct heating equipment when the electricity price is at the peak value; t is tzAnd restarting the end time of the heating of the direct heating equipment when the electricity price is peak.
Further, in the step b, the load characteristics of all users are subjected to clustering analysis according to a fuzzy C-means clustering algorithm.
Further, the peak regulation priority order of the multi-class heat accumulation electric heating user load group is as follows: the full heat accumulation type user is superior to the half heat accumulation type user, and the working type user is superior to the random type user in the half heat accumulation mode user, and the home type user is superior.
Further, in the step d, the parameter issued to the terminal control system of each user load group further includes a time for limiting the electric heating access.
And further, the terminal control system of the user load group performs coordination control and combined response on the managed multi-class user electric heating load group, after the user load group responds, the terminal control system detects whether the peak shaving amount meets the requirement, when the peak shaving total amount meets the requirement, the control layer does not control the user load group any more and performs next round of detection, otherwise, the user load group responds according to the set peak shaving sequence.
Compared with the prior art, the invention has the following beneficial effects: the heat accumulation type electric heating multi-class user load group is subjected to aggregate classification through different characteristics of the multi-class user load group, including capacity characteristics and user energy consumption characteristics, and unified management and scheduling are performed on the same type of user load group; the method comprises the steps of establishing a corresponding peak shifting regulation priority order, giving a coordination combination response strategy of the multi-class user load group under the power grid critical safe operation scene according to the regulation order, and executing the coordination combination response strategy of the multi-class user load group under the power grid critical safe operation scene to ensure the stability of the power grid, reduce the influence on the heating demand of the user to the maximum extent and realize the power failure non-stop heating of the user under the power grid critical safe operation scene.
Drawings
FIG. 1 is a schematic diagram of energy usage characteristics of a thermal process affecting a user;
FIG. 2 is a typical consumer semi-thermal mode time profile;
fig. 3 is a multi-class heat accumulating type electric heating load group combined response framework;
FIG. 4 is a graph of outdoor temperature change for a typical user;
FIG. 5 is a graph of the temperature change of the inner surface of a typical customer wall;
FIG. 6 is a graph of typical user clinical room temperature changes;
FIG. 7 is a graph of the thermal load demand of a typical user;
FIG. 8 is a typical user peak to valley electricity rate curve;
fig. 9 is a typical heat load characteristic curve of a full heat accumulating type electric heating user;
fig. 10 is an electric load characteristic curve for a typical full heat accumulating type electric heating user;
fig. 11 is a heat load characteristic curve of a typical semi-regenerative electric heating user;
fig. 12 is an electric load characteristic curve for a typical semi-regenerative electric heating user;
FIG. 13 is a load characteristic curve for different user behaviors of a small dwelling size;
FIG. 14 is a load characteristic curve for different user behaviors of a middle house type;
FIG. 15 is a load characteristic curve for different user behaviors of a large dwelling size;
FIG. 16 is a sample expanded electrical load curve for each exemplary user;
FIG. 17 is an optimal scheduling load curve of a typical total heat storage user in a power grid security scene;
FIG. 18 is an optimal scheduling load curve of a typical semi-thermal storage user in a power grid safety scene;
FIG. 19 is a typical total thermal storage consumer electrical load day-ahead scheduling curve and response strategy;
FIG. 20 is a typical semi-thermal storage operational customer electrical load day-ahead planned scheduling curve;
FIG. 21 illustrates a typical semi-regenerative operating user power response strategy under critical safety of the grid;
FIG. 22 is a typical semi-thermal random-type customer electrical load day-ahead planned scheduling curve;
FIG. 23 illustrates a typical semi-thermal random user power response strategy under critical safety of a power grid;
FIG. 24 is a typical semi-thermal storage at home consumer electrical load day-ahead planned scheduling curve;
fig. 25 is a typical semi-regenerative home consumer power response strategy under grid critical safety.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
A multi-class heat storage electric heating user load group combined response scheduling method comprises the following steps: a. dividing users into full heat accumulation type users and half heat accumulation type users according to the equipment capacity of the heat accumulation electric heating users; modeling an energy utilization heat process of a user, analyzing the heat supply and heat dissipation processes of the user, and respectively obtaining a typical heat load curve and a typical power load curve of a full heat accumulation type user and a half heat accumulation type user; b. according to typical heat load curves and typical electricity load curves of full heat storage type users and half heat storage type users, user loads with similar thermodynamic characteristics and energy consumption characteristics are clustered; classifying users into working users, home users and random users according to the user behavior characteristics and the clustering result; c. setting the load group peak regulation priority order of the multi-class heat accumulation electric heating users according to the capacity characteristics and the user energy utilization characteristics of the full heat accumulation type users, the half heat accumulation type users, the working type users, the in-home type users and the random type users; d. and decomposing the load values to be reduced according to the number of the user load groups and the current power grid voltage value and issuing the load values to the terminal control system of each user load group to perform user load group combined response under the emergency condition of the power grid under the critical safe operation scene of the power grid on the basis of the formulated peak regulation priority sequence of the multi-class heat storage and heat power heating user load groups.
Dividing users into full heat accumulation type users and half heat accumulation type users according to the equipment capacity of the heat accumulation electric heating users; modeling the energy utilization heat process of a user, analyzing the heat supply and heat dissipation processes of the user, and respectively obtaining a typical heat load curve and a typical electricity load curve of a full heat storage type user and a half heat storage type user
For heat accumulating type electric heating, when heat supply is completely provided by a heat accumulating device, if the heat stored in the heat accumulating device is exhausted, then the direct heating device needs to be started to meet the requirement of comfort. In order to consider the economy, when the electricity price is at the valley value, the electric heating generates heat through the direct-heating equipment, and meanwhile, an energy charging switch of the heat storage device is started to supplement heat for the heat storage device; and when the electricity price is at the peak value, the direct heating equipment is closed, and the energy release switch of the heat storage device is opened. If the heat stored in the heat storage device can meet the requirement of a user until the next electricity price valley value appears, the electric heating is defined as full heat storage type electric heating; if when the heat that heat accumulation device stored satisfies user's demand inadequately, in order to consider the economic nature, need reach user's comfort level lower limit soon at the temperature and open the heat production of directly-heated equipment again, can guarantee like this that economic nature can satisfy the comfort level again, define electric heating this moment and be half heat accumulation formula electric heating. The time between the half heat accumulation type electric heating heat accumulation device and the direct-heating device is turned on again after the heat energy of the half heat accumulation type electric heating heat accumulation device is used up is different from the electric heat conversion in other time intervals, the time is the heat energy dissipation time, the ambient temperature can not be immediately reduced to the lower limit of the comfortable temperature of a human body due to the stop work of the electric heating device, and the direct-heating device does not need to be turned on immediately due to the fact that the building has certain heat accumulation capacity. When the temperature is reduced to the lower limit of the comfortable temperature of the human body through the building envelope and then the direct heating part is restarted, the balance between the economy and the comfort degree can be kept to the maximum extent. The time-varying relationship between the indoor temperature and the heat is analyzed by considering thermodynamic factors related to the building. Meanwhile, the thermal load requirement of the user can be inversely solved by establishing a thermodynamic model to set the initial temperature under the premise of considering the comfortable temperature of the human body.
In the process of using energy by a user, various factors affect the energy using characteristics of the user, and the factors can be divided into external factors and internal factors, wherein the external factors are outdoor meteorological conditions and temperature conditions, and generally affect the thermal process through a wall, a window or air in a heat transfer mode; and the internal factors are sensible heat dissipation of home appliances, lighting devices or human bodies, and heat generation of electric heating devices, the former being generally in the form of convection or radiation.
According to the heat balance theory, when the indoor heat supply of the building is larger than the heat dissipation capacity, the indoor temperature rises, otherwise, the indoor temperature falls. When the heat supply amount is equal to the heat dissipation amount, the temperature is kept unchanged, and the state at this time is called a heat balance state. The states of the influencing factors in the thermal process are shown in fig. 1, and these factors are explained below:
(1) heat quantity Q of heating equipmentin
Here especially the heat Q generated by electric heatingin；
(2) Heat transfer between indoor air and inner surface of wall
In the formula: qwall(τ) heat dissipated by heat transfer through the wall surface at time τ; xi is the heat transfer coefficient between the indoor air and the inner surface of the wall body;
the surface area of the jth wall surface; t is tj(τ) is the surface temperature of the jth wall at time τ; t is tin(τ) is the indoor temperature at time τ;
(3) heat transfer quantity considering window shading coefficient and cold load coefficient
In the formula: qwin(τ) is the solar radiant heat penetrating the window at τ;
is the area of the jth window; djSolar heat factor for the jth window; cjIs the cold load factor of the window; zjThe shading coefficient of the window;
(4) heat transfer Q through air to the outside and adjacent roomsatm(τ)
Qatm(τ)＝Qout(τ)+Qadj(τ)＝cpρGout(τ)[tout(τ)-tin(τ)]+cpρGadj(τ)[tadj(τ)-tin(τ)] (3)
In the formula: qout(τ) is the heat transfer amount between the indoor air and the outdoor air at τ; qadj(tau) is the heat transfer quantity between the air passing through the room and the adjacent room at the time of tau; c. CpIs a constant pressure molar heat capacity; ρ is the air density; gout(tau) is the indoor and outdoor ventilation volume at the time of tau; gadj(tau) is the ventilation volume of the indoor and the adjacent room at the time of tau; t is tout(τ) is outdoor temperature at time τ; t is tadj(τ) is the critical chamber temperature at time τ;
(5) total heat Q of sensible heat radiation of indoor other household equipment, lighting device and human bodyad(τ)
In the formula: qe(τ) is the heat dissipation capacity of the household appliance at time τ; ql(τ) is the heat dissipation capacity of the lighting device at time τ; qp(tau) is the heat dissipation capacity of the human body at the time of tau; a. therIs the floor area of the building interior; ceIs the cold load factor of the household appliance; clIs the cold load factor of the lighting device; cpxIs the cold load coefficient of the human body; q. q.seHeat dissipation per unit area of the household appliance; q. q.slHeat dissipation per unit area of the lighting device; n is the number of people per unit area; q. q.spxHeat dissipation capacity for human body sensible heat; q. q.spqHeat dissipation capacity of latent heat of human body;
is the clustering coefficient;
by combining the above-mentioned influencing factors in the thermal process, the expression of the change of the indoor air temperature of the building with time is obtained as follows:
in the formula: v is the air volume in the room.
Because the difference of all kinds of users heat accumulation formula electric heating heat storage capacity, this patent divides heat accumulation formula electric heating user into two kinds of classification: a full heat storage mode and a half heat storage mode. When the electricity price is at the valley value, the direct-heating device supplies heat in the two modes, and meanwhile, the heat storage switch is started to supplement heat for the heat storage device. However, when the electricity price is at a peak, the two modes are different. The full heat storage mode is that the heat storage capacity is sufficient, and the heat supply at the electricity price peak value is completely provided by the heat storage device, while the half heat storage mode is that the heat of the heat storage device is completely used at the electricity price peak value, and the direct heating device is turned on again to supply heat for ensuring the comfort.
When the heat storage capacity of the user heat storage type electric heating is sufficient:
the minimum electric heating charge of the user is taken as an objective function:
in the formula: j represents the electricity fee generated by the user for using the electric heating system one day; t is tg0The starting time of the electricity price valley; t is tf0The starting time when the electricity price is at the peak; t is tgThe ending time of the electricity price valley; t is tcEnd time for heat storage of the heat storage device; t is tfThe end time when the electricity price is at the peak; pzHeating electric power for the direct heating device; pxCHeating electric power for the heat storage device; pxFDischarging the electrical power for the thermal storage device; c. Cg(t) is the electricity price at the time of the valley of t; c. Cf(t) is the peak time electricity price at time t;
wherein, tcThe expression of (a) is:
in the formula: qmaxIs the maximum capacity of the thermal storage device; etaxThe heating efficiency of the heat storage device is improved;
constraint conditions are as follows:
(1) meet the load demand
Pzηz≥L(t) (8)
tg0≤t≤tg (9)
PxFηx≥L(t) (10)
tf0≤t≤tf (11)
In the formula: etazThe heating efficiency of the direct heating device is improved; l (t) is the user heat load demand at time t.
(2) Upper and lower limits of electric power of heater of direct heating equipment
In the formula:
the upper limit value of the electric power for heating the direct heating device.
(3) Upper and lower limits of electric power for heat storage and heating device of heat storage equipment
In the formula:
storing an upper limit value of electric power for the heat storage device;
the upper limit value of the direct thermoelectric power of the heat storage device.
When the heat storage capacity of the heat storage type electric heating of the user is insufficient, namely the objective function of the energy process optimization of the half heat storage type user is as follows:
in the formula: t is tFThe time for using the heat in the heat storage device completely at the peak value of the electricity price; t is tz0Restarting the starting time of heating of the direct heating equipment when the electricity price is at the peak value; t is tzRestarting the direct heating equipment for heating when the electricity price is peakThe end time of (d); the time distribution of the phases of the pattern is shown in fig. 2.
The half heat storage mode is not limited to the full heat storage mode.
The electric load curves of typical semi-heat accumulation type electric heating and full heat accumulation type electric heating can be obtained by solving the model.
Secondly, according to typical heat load curves and typical electricity load curves of a full heat accumulation type user and a half heat accumulation type user, clustering user loads with similar thermodynamic characteristics and energy consumption characteristics; classifying users into working type users, home type users and random type users according to user behavior characteristics and clustering results
The change rule of the load is different according to the types of the load, and it is impractical to measure and analyze the huge user loads one by one, so that the loads are classified and integrated according to the characteristics of the loads. For the load groups of the regenerative electric heating users studied in the embodiment, comprehensive cluster analysis is indispensable, and although there is still a certain difference in load characteristics among the loads classified into the same class, since the relative change of the loads is stable and regular, the difference can be ignored for such classification, especially under the condition of large load quantity. With the development of the system and the increase of electric heating users, the cluster analysis can save research time, maintain the precision of the load model and enhance the accuracy of the research of the load characteristics. According to typical heat load curves and typical electricity load curves of full heat storage type users and half heat storage type users, user loads with similar thermodynamic characteristics and energy consumption characteristics are clustered; according to the user behavior characteristics and the clustering result, the users are classified into working users, home users and random users, and the clustering method comprises the following steps:
(1) and clustering the users with similar thermodynamic characteristics and energy consumption characteristics according to the above, and classifying the users into n classes.
(2) Calling the thermodynamic model of the heat storage capacity of the building and the load model (formula (1) -formula (15)) of the heat storage electric heating, and solving the load characteristics of various users to obtain the usage of each typeLoad characteristic curve of household, denoted as Kj(j＝1,2,…，n)。
(3) And carrying out clustering analysis on the load characteristics of all users according to a fuzzy C-means clustering algorithm, and obtaining the optimal clustering center number C.
(4) Selecting typical users in each class of users, calculating the center of gravity of each class of users according to the following formula, thereby taking the users closest to the center of gravity as the typical users, and expressing the load curve of the typical users as Kts(1≤s≤c)：
In the formula: wiIs the center of gravity of the ith class user; qjThe specific weight of the jth user in the ith user is obtained.
(5) According to the load curve K of the selected typical usertsAnd their corresponding ratios are substituted into the following formula to obtain the polymeric load curve K:
in the formula: lambda [ alpha ]sThe s-th typical user accounts for the total of all the typical users.
The fuzzy C-means clustering algorithm divides the samples into n classes, and describes the degree of each sample belonging to each class, and the degree is called the degree of membership. The optimal distribution of membership is found by calculating the clustering center number c that minimizes the objective function J, the expression of J being as follows:
in the formula: fuzzy weighting coefficient m ∈ [1, ∞); c is the number of cluster centers; dij＝‖xj-ciII denotes sample xjTo ciJ ═ 1,2, …, n; c. CiClustering for class iCenter, i ═ 1,2, …, c; u. ofijIt represents the sample x as degree of membershipjDegree of membership to class i; j represents the weighted sum of squares of distances from each type of sample to its corresponding cluster center, corresponding to the objective function J (electricity charge generated by the user using electric heating every day) in formula (6) and formula (15).
Membership matrix U ═ Uij]X n, each element in the matrix having the following constraints:
the specific clustering algorithm is as follows:
(1) determining a clustering center number c, initializing a membership degree matrix U, namely randomly generating U under the condition of satisfying constraint conditionsij。
(2) Calculating a membership matrix and a clustering center by the following formula:
wherein r is 1,2, …, c; sample xjThe distance to the i-th class center is divided by the distance to the class centers, and the function of the ratio is the sample xjMembership to class i.
(3) Given the accuracy of convergence (>0), after k iterations, if
| (k) | J (k +1) -J | stops the iteration, otherwise let k ═ k +1 continue the iteration.
The user load groups of the same type are managed and dispatched in a unified way according to the classification result by classifying the user load groups of the multiple types.
Thirdly, setting the load group peak regulation priority order of the multi-class heat accumulation electric heating users according to the capacity characteristics and the user energy utilization characteristics of the full heat accumulation type users, the half heat accumulation type users, the working type users, the in-home type users and the random type users
The power grid does not have the low voltage problem under the normal operating condition, the access of heat accumulation formula electric heating can not threaten the power grid operation safety, therefore the needs and the scheduling plan day before of many kinds of user's load according to self, carry out and fill the operation strategy day by day night, heat accumulation formula electric heating is at load trough time heat accumulation, the peak is exothermic constantly, reduce peak valley load difference, make full use of peak valley price difference simultaneously, obtain the profit, balanced regional load simultaneously reduces peak valley difference, also can obtain the supplementary service income of peak regulation, improve entire system operational benefits.
In a critical safe operation scene of a power grid, after electric heating is implemented, voltage drop can occur when a load group of multiple types of electric heating users is connected to an adjacent node, and the grid loss has a tendency of slightly rising. The load of the power system is changed continuously along with the time and is influenced by various factors, the power system has the characteristics of continuity and periodicity, and when the power grid has an emergency situation, such as a low voltage problem, the access scale and the power of the heat accumulating type electric heating system are limited to prevent the voltage of the power grid from exceeding the limit.
Therefore, in order to ensure the safety of the power grid, the electric heating load with higher flexibility of the part is disconnected according to the running state of the power grid and the characteristics of different load groups at the user side, so that the shortage power of the power grid is compensated. However, the safety of the power grid is ensured, and meanwhile, heating is also required to be ensured, so that the temporarily disconnected electric heating load also needs to carry out heat load peak shifting. The peak shifting sequence is that the heat accumulation electric heating with large adjustable capacity is preferred, namely, the electric heating user adopts a full heat accumulation mode; then, the user group working out in the semi-heat storage mode can easily shift the peak and reserve a certain adjustable margin because the user group does not need heat load in the daytime, and simultaneously the heat supply requirement of the user is considered; the second is a random class user, and the last is a user class at home, and the coordination group and the response are carried out according to the scheduling sequence. Namely, the full heat accumulation type user is superior to the half heat accumulation type user, and the working type user is superior to the random type user in the half heat accumulation mode user is superior to the home type user.
Fourthly, based on the formulated peak regulation priority sequence of the multi-class heat storage and power heating user load groups, under the critical safe operation scene of the power grid, decomposing the load values to be reduced according to the number of the user load groups and the current voltage value of the power grid, and sending the load values to the terminal control system of each user load group to perform user load group combined response under the emergency condition of the power grid
Based on the theory, the basic principle and strategy of the coordinated combination response technology of the multi-class user load group under the critical safe operation scene of the power grid are provided in the embodiment, and the core idea is that firstly, a multi-class heat accumulating type electric heating load group combined response framework is utilized to bring the multi-class electric heating load at the user side into a framework for responding the power grid requirement, a coordinated combination control strategy is adopted among the user load groups, a load group coordinated control system sets the moment and the specific load value of electric heating access to be limited according to the system parameters such as the number of the user load groups, the power grid voltage condition and the like, and the data are sent to a terminal control system of each user load group.
Under the condition that the basic heating of the user load is not influenced, the load group control strategy provided by the embodiment is adopted to carry out coordinated control on the managed multi-class user electric heating load groups, so that the system can run without power failure and continuous heating in the critical safe operation scene of a power grid, and a multi-class heat accumulating type electric heating load group combined response framework is shown in fig. 3 and shows the basic principle of multi-class heat accumulating type electric heating load group combined response. It can be seen that the multi-class heat accumulating type electric heating load group combined response depends on a load group coordination control system, the system sets the time and specific load value of electric heating access limitation according to system parameters such as the number of user load groups, the power grid voltage condition and the like, and sends the data to the terminal control system of each user load group, the specific response priority sequence is as shown in the foregoing, and after the load group response, when the detected peak shifting total amount meets the requirement, the control layer does not control the load group any more and carries out the next round of detection.
Through the analysis, two key links of the combined response of the multi-class heat accumulating type electric heating load group are as follows:
1) the system parameter setting of the combined response of the electric heating load groups comprises the number of the user load groups, the time when the electric heating access is required to be limited and a specific load value, and only if the parameter setting is reasonable, the load groups can sufficiently and sensitively respond to accidents and disturbances, respond to the power grid demand in time, and simultaneously avoid the impact of excessive response on the power grid and insufficient heat supply to users.
2) The response control strategy of the multi-class load group requires not only that the load group effectively maintains the safety and stability of the power grid in time, but also that the heating demand and experience of users are not influenced when the load group responds. Therefore, how to reasonably and effectively control and shift the peak based on the comfort level and heating without influencing the user according to the characteristics of the load and the load group.
Based on the energy consumption characteristic analysis of the multi-class user load groups in the previous steps, including capacity characteristics and user energy consumption characteristic characteristics, the embodiment formulates a corresponding peak regulation priority order, and formulates a coordination combination response strategy of the multi-class user load groups in a power grid critical safety operation scene based on the regulation priority order. Load values needing to be reduced at the critical moment of the power grid are decomposed and issued to each load group terminal control system, and then a coordination combination response strategy of multi-class user load groups under the critical safe operation scene of the power grid is executed.
The multi-class heat accumulating type electric heating user load group combined response method provided by the embodiment provides a heat accumulating type electric heating user load group combined response strategy in a critical safety state of a power grid, can disconnect partial electric heating loads with higher flexibility according to the response strategy in the critical safety state of the power grid, reduces the shortage power of the power grid, coordinates groups and responds according to a set scheduling sequence, can ensure that the user load heating under the premise of the safety of the power grid is not influenced, can avoid the impact of excessive response on the power grid, and realizes the uninterrupted heating operation of a system in the power grid critical safety operation scene when the user has power failure.
The multi-class heat-storage electric heating user load group combined response scheduling method of the invention is further explained by combining with a specific application example.
It was mentioned above that the heat load demand of the user can be calculated if the thermodynamic model, i.e. the time-varying equation for the temperature of the air in the building room, is initialized and the human comfort temperature is taken into account. We assume that the initial temperature in the building room is 18 c and the optimal comfortable temperature for the human body is 22 c. The parameters of the building indoor and outdoor (with the default settings of the household equipment, lighting and body parameters) obtained by information acquisition are shown in table 1:
TABLE 1 various parameters of the interior and exterior of a building
| Inputting parameters | Numerical value |
| Room volume V (m)3) | 240 |
| Area of room Ar(m2) | 78 |
| Indoor wall area Mwall(m2) | 86 |
| Heat transfer coefficient between indoor air and inner surface of wall | 3.5 |
| Window area Mwin(m2) | 17.8 |
| Solar heat factor D of window | 0.513 |
| Coefficient of cold load of window C | 23.3 |
| Shading coefficient of window | 0.59 |
| Indoor and outdoor ventilation Gout(m3/h) | 284 |
| Indoor and critical ventilation volume Gadj(m3/h) | 172 |
| Constant pressure molar heat capacity cp(J/kg.K) | 1.003 |
| Air Density ρ (kg/m)3) | 1.270 |
According to investigation, the change curves of the outdoor temperature, the inner surface of the wall and the adjacent room temperature of a typical user are shown in fig. 4-6.
The initial temperature, the optimal comfortable temperature of human body and the building parameters are substituted into the time-varying equation of the indoor temperature of the building, so that the heat load demand of the user can be obtained, and the curve is shown in fig. 7.
In order to highlight the group load characteristics, the peak-valley electricity price is set into two sections to more clearly show the group load characteristics, as shown in fig. 8, the heating rated power of the selected typical single-user heat accumulating type electric heating direct heating equipment is 6kW, the heating rated power of the heat accumulating equipment is 6.7kW, the energy efficiency ratio is set to 3, the energy consumption of the heat accumulating equipment in the heat releasing process is much smaller than that in the heating process, the rated power is set to 1kW, and the capacity of the heat accumulating tank is 200 kWh. The capacity of the heat storage tank in the full heat storage mode is fully enough to meet the heat load demand of the peak electricity price, and it can be assumed that the load characteristics of the previous day are similar to those of today, so that the heat released by the heat storage tank on the previous day is equal to the heat required by the peak electricity price of this day.
Assuming that the optimal comfortable temperature of the user is 22 ℃, the load characteristics of the electric heating in the full heat storage mode can be obtained as shown in fig. 9 and 10 by introducing parameter information and satisfying the constraint conditions.
For half heat accumulating type equipment, the capacity of a heat accumulating tank is set to be 120kWh, other conditions are the same as the setting of full heat accumulating type equipment, at the moment, the heat load requirement of the peak electricity price cannot be met even if the heat in the heat accumulating tank is fully stored, the electric heating equipment is closed after the heat in the heat accumulating tank is completely released during the peak electricity price, and the direct heating equipment is restarted to supply heat when the indoor temperature is about to fall to the lower limit of the comfortable temperature of a human body through the heat process of the energy storage characteristic of the building. The load characteristics of the semi-regenerative electric heating can be obtained by setting the comfortable temperature range of human body to 18-25 ℃, as shown in fig. 11 and 12:
in both cases, the user load characteristic has a shape with a high end and a low middle, and the reason can be easily understood from the objective function, so that the equipment can work at the valley of the electricity price in order to realize the lowest total electricity price. The direct-heating electric heating load characteristic should correspond to load demand, and the difference between the heat accumulation type and the heat accumulation type is that the electricity consumed in the electricity price peak value heating process is transferred to the electricity price valley, while the electricity consumed in the heat release process is much smaller than that in the heating process, so the load peak valley difference is larger, and the electricity price is reduced. From the power load characteristic curve, the full heat accumulation type can be regarded as a special case of the half heat accumulation type, namely the heat accumulation device prolongs the heat release process of the heat accumulation device to the whole electricity price peak period, and the overall characteristic is relatively simple.
Then, the load groups of the heat accumulating type electric heating users participating in load response are classified, one cell of the Tianjin area is selected as a research scene, and 100 heat accumulating type electric heating users are selected as the research sceneFor the study subjects, their room size was divided into large dwelling size (> 144 m)2) Middle house type (90-144 m)2) Small house type (< 90 m)2)。
The information sampling is performed on different house types, and the obtained building parameters are shown in table 2:
TABLE 2 building parameters for different dwelling types
The typical load characteristic curve 13, fig. 14 and fig. 15 of the electric heating of each grouping category are obtained by clustering according to the load characteristics of the single distributed heat storage electric heating:
according to the clustering curve, the heat accumulating type electric heating system can be divided into a full heat accumulating type user and a half heat accumulating type user according to the capacity of the heat accumulating type electric heating system; according to the survey of the user using behaviors, the users are classified into working users (users who do not have heat load at home in the daytime at night during working day), home users (users who are old people and children who do not go out almost) and random users (users who do not have regular traveling at ordinary times). During regulation, the capacity of the full heat accumulation type user equipment is large, the regulation is most suitable, and in the half heat accumulation type users, as the house type users have certain heat load in the daytime and the heat load curve in the whole day is large, the regulation priority is the lowest, the priority of the working users is the highest, and the random user is between the two. The specific classification according to room size and user behavior and the overall proportion of each category is shown in table 3:
TABLE 3 electric heating user Classification
| Categories | Size of room | User behavior classification | Ratio of occupation of | |
| 1 | Big | Worker | 12％ | |
| 2 | Big house type | |
14％ | |
| 3 | Big house type | Is random | 8％ | |
| 4 | Middle | Worker | 11％ | |
| 5 | Middle house type | |
12％ | |
| 6 | Middle house type | Is random | 6％ | |
| 7 | Small | Worker | 22％ | |
| 8 | Small house type | |
10％ | |
| 9 | Small house type | Is random | 5％ |
Expanding the samples to 200 groups, clustering the power loads of the users according to a fuzzy C-means algorithm, calculating to obtain an optimal clustering number C which is 5, and selecting typical users from the clustering number as shown in Table 4:
TABLE 4 clustered subscriber groups and their ratios
| Clustering user groupings | Typical user | Ratio of occupation of |
| |
1 | 24％ |
| |
2 | 27％ |
| |
3 | 15％ |
| |
9 | 24％ |
| |
5 | 10％ |
The simulation results show that the characteristic curve of the electric load of a typical user is shown in fig. 16:
in fig. 16, the electric load characteristics for electric heating of each typical user are basically high at both ends and low at the middle. The typical users are in peak electricity consumption at the valley of electricity prices from eight o 'clock in the evening to six o' clock in the next morning, and since the users are at home in this period, the factors influencing the load characteristics are the thermal characteristics of the building and the electricity consumption in the daytime. And when the electricity price is at the peak value from six times in the morning to eight times in the evening, great difference occurs among the load characteristics of each typical user. About six o 'clock to thirteen o' clock, the difference is still small because the electric heating device is turned off, the heat load is fully satisfied by the stored energy, and the energy consumption in this period is much smaller than that when the heating device is turned on. After twelve o' clock at noon, the heat stored in the electric heating heat storage tank is completely used, and due to the influence of the thermal characteristics of buildings, the time for restarting the electric heating equipment of different house types is different, and the action change of users causes the result shown in the figure. From the perspective of regulation, the full heat accumulation type user equipment has large capacity and is most suitable for regulation, and in the semi-heat accumulation type user, because the house type user has a certain heat load in the daytime and the heat load curve in the whole day is large, the regulation priority is the lowest, the priority of the working user is the highest, and the regulation priority of the random user is between the two.
And (3) combining and regulating multiple types of users in a power grid safety scene:
the power grid does not have the low voltage problem under the safe operation condition, the access of heat accumulation type electric heating can not threaten the power grid operation safety, therefore the load of multiple classes of users is according to self needs and scheduling plan day before, fill the operation strategy of putting day by day night, heat accumulation type electric heating is carried out at load trough time instant, the peak moment is exothermic, reduce peak valley load difference, make full use of peak valley price difference simultaneously, obtain the income, balance regional load simultaneously, reduce peak valley difference, also can obtain the supplementary service income of adjusting peak, improve the operational benefits of entire system.
The optimal comfortable temperature of the user is set to 22 ℃, and the day-ahead economic optimal scheduling load curve meeting the constraint condition is shown in fig. 17.
When the semi-heat storage mode is adopted, the heat load requirement of the peak-time electricity price cannot be met even if the heat in the heat storage tank is fully stored, the electric heating equipment is closed after the heat in the heat storage tank is completely released when the peak-time electricity price is reached, and the direct heating equipment is restarted to supply heat when the indoor temperature is about to fall to the lower limit of the comfortable temperature of the human body through the heat process of the energy storage characteristic of the building. Fig. 18 shows a typical user semi-thermal storage mode scheduling curve when the comfortable temperature range of the human body is set to 18 to 25 ℃.
As can be seen from fig. 17 and 18, the user load characteristic in both cases takes a shape of high at both ends and low at the middle, and the reason for this is easily understood from the objective function, and in order to achieve the lowest total electricity price, the apparatus is made to operate at the bottom of the electricity price as much as possible. The direct-heating electric heating load characteristic should correspond to load demand, and the difference between the heat accumulation type and the heat accumulation type is that the electricity consumed in the electricity price peak value heating process is transferred to the electricity price valley, while the electricity consumed in the heat release process is much smaller than that in the heating process, so the load peak valley difference is larger, and the electricity price is reduced. From the power load dispatching curve, the power grid has no low voltage problem under the normal operation condition, and the access of the heat accumulation type electric heating does not threaten the operation safety of the power grid, so that the loads of multiple classes of users execute a night charging and day discharging operation strategy according to the self needs and a day-ahead dispatching plan, and the overall characteristic is relatively simple.
And (3) combining and regulating multiple types of users under the critical safety scene of the power grid:
based on the classification, the loads with high power utilization flexibility and universality of typical full heat accumulation type distributed electric heating, semi heat accumulation type working electric heating, semi heat accumulation type random electric heating and semi heat accumulation type household electric heating are selected for combined response, and a corresponding load group control strategy is adopted to assist a power grid to maintain a normal operation state under the conditions of disturbance and critical safety. On the basis of the foregoing, assuming that the critical safety problem of the power grid exists in the multi-class user access area power grid at two moments 5 and 6, a large amount of accesses of the heat accumulating type electric heating needs to be limited, a coordinated combined response strategy of a multi-class user load group in the critical safety operation scene of the power grid is executed, and the response result is shown in fig. 19-25.
In fig. 19, the capacity of the all-heat-storage type user equipment is large, the all-heat-storage type user equipment is optimally controlled, the all-heat-storage type user is limited to be connected with electric heating within 5 and 6 hours of the emergency time of the power grid, the normal work of the heat storage part is kept only by low-power operation, and the peak shifting is changed to the night low-electricity-price time due to the fact that the heat storage capacity is sufficient, and the operation economy of the whole system is guaranteed.
In the semi-heat accumulating type user group, the priority of a working user is highest, and because the working user does not need heat load in the daytime, the peak shifting can be easily carried out, a certain adjustable margin is reserved, and meanwhile, the heating requirement of the user is considered. Fig. 20 shows a planned scheduling curve of a semi-thermal storage working type user load group in the day ahead, and fig. 21 shows a power regulation strategy of the semi-thermal storage working type user load group participation response under the critical safety of the power grid.
In fig. 21, the electric load characteristics for electric heating of the active user group are basically high at both ends and low at the middle. When the electricity price is low from eight hours at night to six hours in the morning, all the users are in electricity consumption peak, because the power grid is in emergency for 5 hours and 6 hours, the semi-heat-accumulation type working user group stops the heating power of the heat accumulation type electric heating, only the low-power operation keeps the normal work of the heat accumulation part to maintain the heat accumulation and heating, at the moment, the electric heating equipment is closed, and the heat load is completely satisfied by the stored energy. After sixteen hours in the afternoon, heat storage and energy charging are started, due to the influence of the thermal characteristics of the building, the time for restarting the electric heating equipment with different capacities is different, and the action change of the user causes the result shown in the figure.
When the adjustment of the working type user is not enough to meet the requirement, the random type user group needs to be adjusted, a typical semi-thermal storage random type user electrical load day-ahead planned scheduling curve is shown in fig. 22, and a power adjustment strategy for the semi-thermal storage random type user load group to participate in response under the critical safety of the power grid is shown in fig. 23.
In fig. 23, the random type user has a higher heat load demand than the working type user group, and thus the energy storage capacity is exhausted and charged in the afternoon. When the combined regulation and response is participated, the peak shift is advanced to about 13-14 hours, the electric heating equipment of different house types is restarted, users with different capacities and loads have different time, and the action change of the users causes the result of the graphic representation, the general trend is that the load charging curve moves forward, thereby ensuring that the random users are not heated continuously when the power failure occurs.
When the random user regulation is not enough to meet the demand, the household user group needs to be regulated, a typical semi-heat-storage household user electric load day-ahead planned scheduling curve is shown in fig. 24, and a power regulation strategy for semi-heat-storage household user load group participation response under the critical safety of a power grid is shown in fig. 25.
In fig. 25, the family type user group has a higher heat load demand than the working type user group and the random type user group, and the heat load is larger in the daytime, so that the regulation priority is the lowest. The home subscriber group in this example, having participated in the combined regulation, has exhausted its energy storage capacity and is charged at the morning. When the combined regulation and response is participated, the peak shift is advanced to about 10-12 hours, the electric heating equipment of different house types is restarted, users with different capacities and loads have different time, and the behavior change of the users causes the result of the graphic representation, the general trend is that the load charging curve is advanced, thereby ensuring that the users at home are not heated continuously when the power failure occurs.
In conclusion, by executing the coordination combination response strategy of the multi-class user load groups in the power grid critical safety operation scene and performing coordination group and response according to the established scheduling sequence, the basic heating of the user loads under the premise of power grid safety can be guaranteed not to be affected, and the system can operate without stopping heating in power failure in the power grid critical safety operation scene.
From the above description, it can be known that the multi-class user load group combined response scheduling method provided by the patent can effectively maintain the safety and stability of the power grid by controlling and shifting the operation modes of the heat accumulating type electric heating multi-class users in the critical safe operation state of the power grid, does not influence the heating requirements and experiences of the users, and realizes the uninterrupted heating in power failure under extreme conditions. The multi-class user load group combined response scheduling method can provide a practical and effective operation scheme for the aspects of solving the cleanliness of a heating system, improving the safety of a power grid after the heat storage electric heating system is connected and the like.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (6)
1. A multi-class heat storage electric heating user load group combined response scheduling method is characterized by comprising the following steps:
a. dividing users into full heat accumulation type users and half heat accumulation type users according to the equipment capacity of the heat accumulation electric heating users; modeling an energy utilization heat process of a user, analyzing the heat supply and heat dissipation processes of the user, and respectively obtaining a typical heat load curve and a typical power load curve of a full heat accumulation type user and a half heat accumulation type user;
b. according to typical heat load curves and typical electricity load curves of full heat storage type users and half heat storage type users, user loads with similar thermodynamic characteristics and energy consumption characteristics are clustered; classifying users into working users, home users and random users according to the user behavior characteristics and the clustering result;
c. setting the load group peak regulation priority order of the multi-class heat accumulation electric heating users according to the capacity characteristics and the user energy utilization characteristics of the full heat accumulation type users, the half heat accumulation type users, the working type users, the in-home type users and the random type users;
d. and decomposing the load values to be reduced according to the number of the user load groups and the current power grid voltage value and issuing the load values to the terminal control system of each user load group to perform user load group combined response under the emergency condition of the power grid under the critical safe operation scene of the power grid on the basis of the formulated peak regulation priority sequence of the multi-class heat storage and heat power heating user load groups.
2. The multi-category heat-storage electric heating user load group combined response scheduling method of claim 1, wherein in the step a, the total heat-storage type users use the minimum electric heating power cost of the users as an objective function:
in the formula: j represents the electricity fee generated by the user for using the electric heating system one day; t is tg0The starting time of the electricity price valley; t is tf0The starting time when the electricity price is at the peak; t is tgThe ending time of the electricity price valley; t is tcEnd time for heat storage of the heat storage device; t is tfThe end time when the electricity price is at the peak; pzHeating electric power for the direct heating device; pxCHeating electric power for the heat storage device; pxFDischarging the electrical power for the thermal storage device; c. Cg(t) is the electricity price at the time of the valley of t; c. Cf(t) is the peak time electricity price at time t;
wherein t iscThe expression of (a) is:
in the formula: qmaxIs the maximum capacity of the thermal storage device; etaxThe heating efficiency of the heat storage device is improved;
the objective function of the energy process optimization of the semi-regenerative user is as follows:
in the formula: t is tFThe time for using the heat in the heat storage device completely at the peak value of the electricity price; t is tz0Restarting the starting time of heating of the direct heating equipment when the electricity price is at the peak value; t is tzAnd restarting the end time of the heating of the direct heating equipment when the electricity price is peak.
3. The multi-class thermal storage electric heating user load group combined response scheduling method of claim 1, wherein in the step b, the load characteristics of all users are subjected to cluster analysis according to a fuzzy C-means clustering algorithm.
4. The multi-class thermal storage electric heating user load group combined response scheduling method of claim 1, wherein the multi-class thermal storage electric heating user load group peak regulation priority order is: the full heat accumulation type user is superior to the half heat accumulation type user, and the working type user is superior to the random type user in the half heat accumulation mode user, and the home type user is superior.
5. The combined response scheduling method of multi-class thermal storage electric heating user load groups according to claim 1, wherein in the step d, the parameters issued to the terminal control system of each user load group further include a time for limiting electric heating access.
6. The multi-class heat-storage electric heating user load group combined response scheduling method of claim 1, wherein a terminal control system of a user load group performs coordinated control and combined response on the managed multi-class user electric heating load group, after the user load group responds, the terminal control system detects whether the peak shaving amount meets the requirement, when the peak shaving amount meets the requirement, a control layer does not control the user load group any more and performs next round of detection, otherwise, the user load group responds according to a set peak shaving sequence.
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| CN113569415A (en) * | 2021-08-02 | 2021-10-29 | 国网山东省电力公司营销服务中心(计量中心) | Distributed electric heating equipment operation optimization algorithm based on user load demand |
| CN113606633A (en) * | 2021-06-29 | 2021-11-05 | 国网天津市电力公司电力科学研究院 | Heating system with double-glass double-faced PV/T assembly and heat pump coupled and control method thereof |
| CN115218270A (en) * | 2022-08-18 | 2022-10-21 | 建科环能科技有限公司 | Intelligent group control method and system for distributed electric heating of terminal substation level |
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| CN115218270B (en) * | 2022-08-18 | 2023-09-29 | 建科环能科技有限公司 | Distributed electric heating intelligent group control method and system for terminal substation level |
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