CN114282708B - Cross-region comprehensive energy system optimization operation method and system considering multi-scale demand response - Google Patents
Cross-region comprehensive energy system optimization operation method and system considering multi-scale demand response Download PDFInfo
- Publication number
- CN114282708B CN114282708B CN202111401539.4A CN202111401539A CN114282708B CN 114282708 B CN114282708 B CN 114282708B CN 202111401539 A CN202111401539 A CN 202111401539A CN 114282708 B CN114282708 B CN 114282708B
- Authority
- CN
- China
- Prior art keywords
- load
- demand response
- time
- response
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000004044 response Effects 0.000 title claims abstract description 213
- 238000000034 method Methods 0.000 title claims abstract description 60
- 238000005457 optimization Methods 0.000 title claims description 33
- 238000005265 energy consumption Methods 0.000 claims abstract description 12
- 230000003993 interaction Effects 0.000 claims abstract description 10
- 230000002776 aggregation Effects 0.000 claims abstract description 6
- 238000004220 aggregation Methods 0.000 claims abstract description 6
- 230000008569 process Effects 0.000 claims description 24
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 23
- 238000013178 mathematical model Methods 0.000 claims description 15
- 238000012546 transfer Methods 0.000 claims description 14
- 230000002452 interceptive effect Effects 0.000 claims description 9
- 238000006467 substitution reaction Methods 0.000 claims description 8
- 101100007917 Mus musculus Cst9 gene Proteins 0.000 claims description 7
- 238000001816 cooling Methods 0.000 claims description 7
- 230000006870 function Effects 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 claims description 6
- 230000017525 heat dissipation Effects 0.000 claims description 3
- 238000005057 refrigeration Methods 0.000 claims description 3
- 238000010977 unit operation Methods 0.000 claims description 3
- 230000005611 electricity Effects 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 239000007789 gas Substances 0.000 description 7
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 6
- 238000012545 processing Methods 0.000 description 5
- 238000003860 storage Methods 0.000 description 5
- 239000003345 natural gas Substances 0.000 description 3
- 238000010438 heat treatment Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000011017 operating method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
- Y04S50/16—Energy services, e.g. dispersed generation or demand or load or energy savings aggregation
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a cross-regional comprehensive energy system optimizing operation method and system considering multi-scale demand response, firstly establishing multi-scale demand response aiming at response time of demand side load, wherein the multi-scale demand response comprises an offer demand response and a real-time demand response, the offer is divided into 24 hours of offer response before day and 4 hours of offer response in day, and real-time 15 minutes of real-time response in day is adopted in real time; secondly, considering different load characteristics of electricity, heat, cold and gas, and considering different response proportions under different time scales, establishing a multi-scale variable proportion load demand response model. And finally, establishing an operation model for issuing a demand response message by the cross-regional energy service end, applying for participation in demand response by the load aggregation end, and adjusting the time and mode of energy consumption, wherein the cross-regional energy service end adjusts the output of each unit and the power interaction between the systems by using the optimized load curve. The invention can effectively improve the profit of the comprehensive energy system in each region and the comprehensive benefit of the load aggregation end.
Description
Technical Field
The invention relates to a cross-regional comprehensive energy system optimization operation method considering multi-scale demand response, in particular to multi-scale demand response.
Background
Along with the increasing exhaustion of fossil energy and the increasing severity of environmental problems, the state greatly advocates energy conservation and emission reduction, wind and light and other clean energy rapid development, and meanwhile, the comprehensive energy system can effectively integrate multiple kinds of energy, fully utilize the complementary characteristics of different energy sources, realize the step utilization of the energy and improve the utilization rate of the energy. Different areas are provided with different types of comprehensive energy systems, such as combined cooling heating power, combined cooling power, power supply systems and the like, and the comprehensive energy systems in different areas are connected by using energy connecting lines to form a trans-regional comprehensive energy system (Cross regional integrated energy system, CIES), and interaction of electric energy, heat energy and gas energy exists among the CIES, so that the operation economy of the CIES and each IES can be effectively improved, and the grid-connected consumption of wind power is promoted. The autonomous behavior of the user at the Demand side, namely Demand Response (DR), and the adjustment capability of the DR to the load can effectively configure the load of the user, relieve the energy supply pressure of the system, and reduce the energy purchasing cost at the user side. Therefore, CIES considering power interaction and demand response can further reduce the running cost of the system and promote the wind power consumption.
However, wind power and photovoltaic power generation have the characteristic of output randomness, meanwhile, load on a demand side also has the characteristic of fluctuation, the load is related to the prediction precision of renewable energy sources and the time scale, the closer the time scale is, the higher the prediction precision is, and the more accurate the operation method of the system can be formulated. Most of the existing operation methods at present are operation optimization methods for making a day-ahead system, neglecting daily load and fluctuation of renewable energy sources, and along with the wide application of intelligent electric meters and communication systems, users are more and more convenient to participate in markets as main bodies, so that the phenomena of wind and light discarding and the like can be effectively inhibited by utilizing the user to respond to demands.
Most of the current demand response aims at minimizing the running cost of the system, and a comprehensive demand response method of the multi-energy system is formulated, so that the running reliability of the system is improved. The ability of the load side response to adjust for renewable energy fluctuations is ignored without taking into account the multiscale model of demand response.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: in the operation process of the comprehensive energy system, the system can effectively reduce the operation cost of the system and improve the level of the system for the renewable energy.
In order to solve the technical problems, the invention adopts the following technical scheme:
a cross-regional comprehensive energy system optimization operation method considering multi-scale demand response comprises the following steps:
step 1: establishing a demand response model of different loads according to response attributes, energy consumption comfort level and response degree of the demand side loads;
step 2: the method comprises the steps of utilizing a demand response model established in the step 1, dividing demand response into offer demand response and real-time demand response according to different response time of a demand side load in an operation process, establishing a demand response model considering multiple time scales, and simultaneously establishing a target optimization model of cross-region comprehensive energy system operation;
step 3: according to the demand response model established in the step 1 and the demand response model established in the step 2 and considering multiple time scales, and according to different time and proportion of the load participating in demand response, establishing a multi-scale variable proportion load demand response model, namely respectively setting different compensation price proportionality coefficients and response coefficients for 24-hour offer, 4-hour real-time and 15-minute real-time load shedding, load transferability and alternative load response compensation before the day;
step 4: and the transregional energy service end utilizes an optimization algorithm to optimize and solve the established target optimization model to obtain an optimized load curve, and adjusts the output of each unit in the comprehensive energy system and the power interactive operation data among the systems according to the optimized load curve.
An optimization operation system of a cross-regional comprehensive energy system considering multi-scale demand response comprises the following program modules:
the initial demand response model building module: establishing a demand response model of different loads according to response attributes, energy consumption comfort level and response degree of the demand side loads;
a multi-time scale demand response model building module: dividing the demand response into an offer demand response and a real-time demand response according to different response time of the load on the demand side in the operation process by using the established demand response model, establishing a demand response model considering multiple time scales, and simultaneously establishing a target optimization model of the operation of the cross-region comprehensive energy system;
the multi-scale variable proportion load demand response model building module comprises: according to the established demand response model and the established demand response model considering multiple time scales, and according to different time and proportion of the load participating in demand response, establishing a multi-scale variable proportion load demand response model, namely respectively setting different compensation price proportionality coefficients and response coefficients for 24 hours offer, 4 hours real time and 15 minutes real time load shedding, load transferability and alternative load response compensation before the day;
and (3) optimizing an operation module: and the transregional energy service end utilizes an optimization algorithm to optimize and solve the established target optimization model to obtain an optimized load curve, and adjusts the output of each unit in the comprehensive energy system and the power interactive operation data among the systems according to the optimized load curve.
The invention has the beneficial effects that: according to the method, a cross-regional comprehensive energy system optimization operation model under the consideration of multi-scale demand response is established, the demand response is divided into an offer response and a real-time response in the future, the minimum cost of system operation is taken as a target in the future, the randomness of renewable energy sources and the fluctuation of loads are considered in the future, and the response condition of regulating the loads is taken as a target for maximizing wind power consumption. The method for optimizing the operation of the trans-regional comprehensive energy system by utilizing the multi-scale demand response is used for correcting the load in real time, optimizing a load graph, optimizing the output of a unit and the purchase energy of an upper-level network, and improving the level of the consumption of renewable energy while reducing the daily cost of the system. The invention can effectively solve the wind and light discarding phenomenon caused by the fluctuation of renewable energy sources, effectively realize the effective configuration of the energy sources, and simultaneously, the cost of the system can not be greatly changed.
Drawings
FIG. 1 is a schematic diagram of a cross-regional integrated energy system optimization operating method that accounts for multi-scale demand response according to the present invention;
FIG. 2 is a schematic diagram of a CIES system architecture in accordance with the present invention;
FIG. 3 is a diagram of a region architecture according to the present invention;
FIG. 4 is a solution flow chart of a cross-regional integrated energy system optimization run method that accounts for multi-scale demand response according to the present invention;
FIG. 5 is a graph of load response taking into account the demand response before date;
FIG. 6 is a graph of load response taking into account demand response during the day;
fig. 7 is a graph of the output results of the system.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings and embodiments.
A cross-regional comprehensive energy system optimization operation method considering multi-scale demand response comprises the following steps:
step 1: according to the response attribute, energy consumption comfort and response degree of the load at the demand side, a demand response model of different loads is established, and the specific steps comprise:
(11) The loads are classified into 6 categories of fixed load, load shedding, load transferring, alternative load, flexible hot load and cold load.
The base load is a fixed load and does not participate in demand response;
the load can be reduced to be the running period is unchanged, but the power can be partially reduced under the condition of meeting the user requirement, such as the use quantity of the lighting equipment and the like;
the transferable load has no constraint of continuity, the used time period can be adjusted, the operation flexibility is higher, but the total load amount required to be maintained in the whole scheduling time period is kept unchanged;
the replaceable load is the energy consumption requirement of a user on hot water, kitchen ware, heating equipment and the like, and the user can utilize an electric water heater to supply heat or utilize an electromagnetic oven to replace by comparing the power supply price with the air supply price;
the heat load is a hot water load, the hot water load has certain elasticity, the heat supply temperature is reduced in a proper range, and the influence on the use of a user is small;
the cooling load is similar to the heat load, and the cooling temperature is reduced within a certain range in consideration of the acceptance range of the user to the ambient temperature, so that the influence on the user experience is small.
And respectively establishing a demand response model of each load according to the replaceable and transferable overlapping characteristics in the load response process.
(12) The built mathematical model capable of reducing load is as follows:
wherein a epsilon (e, g) and epsilon represent E or g, e and g are respectively electric load and gas load, load-reducible power for the t period, and load-reducible minimum and maximum values, respectively. />The response ratio of (2) was 0.8, 0.9 and 1 at 24 hours before day, 4 hours during day and 15 minutes at real time, respectively.
(13) The mathematical model of the transferable load is established as follows:
in the method, in the process of the invention,load power transferable after load transfer and load power transferable before load transfer in t time periods respectively; />0-1 variable for load transfer; />For a variable 0-1 for load transfer, +.>A value of 1 indicates that the load is transferred to another period, a value of 0 indicates that the load is not transferred to another period,/for example>A value of 1 indicates that another period load is transferred to the load, and a value of 0 indicates that no another period load is transferred to the load; />Is the actual transfer amount of the load. />The response ratio of (2) was 0.85,0.95,1 at 24 hours before day, 4 hours in day and 15 minutes in real time.
(14) The mathematical model of the established alternative load is:
in the method, in the process of the invention,load post-substitution load power and pre-substitution replaceable negative sum power for t time periods respectively; />0-1 variable for the replacement load, < ->1 for the load is replaced, 0 for the load is not replaced,/for the load>A value of 1 indicates that the load replaces another load, and a value of 0 indicates that the load does not replace another load; />Is the actual substitute of the load. />The response ratio of (2) was 0.75,0.85,0.9 at 24 hours before day, 4 hours in day and 15 minutes in real time.
(15) The mathematical model of the established flexible heat load is as follows:
H(t)＝C w ρ w V w (T g -T h )Δt (4)
wherein C is w Specific heat capacity of water ρ w For density of water, V w To return water volume, T g For the temperature of water supply, T h The temperature of the backwater is H (T) is the load power of hot water at the moment T, wherein T g ∈[T g,min ,T g,max ]，T g,min 、T g,max Respectively minimum and maximum allowable water supply temperatures.
(16) The mathematical model of the established flexible cooling load is as follows:
wherein T is in (t+1)、T in (t) building indoor temperatures for the t+1 and t time periods, respectively; r is R s Is the thermal resistance of the building; c (t) is the refrigeration load of the building in the period t; t (T) out (t) is t time period outdoor temperature; k=e -Δt/τ E is a natural constant, Δt is the scheduled time period duration, τ=r s C air ，C air Is the indoor specific heat capacity, Q c And (t) is the heat dissipation capacity of the building fresh air system.
Step 2: the method comprises the steps of utilizing a demand response model established in the step 1, dividing demand response into offer demand response and real-time demand response according to different response time of a demand side load in an operation process, establishing a demand response model considering multiple time scales, and simultaneously establishing a target optimization model of cross-region comprehensive energy system operation;
the solicited demand response is solicited for 24 hours before the day, the predicted load and the renewable energy output before the day are utilized, the minimum running cost of the system is taken as the target, the demand response requirement is issued, and the demand response model in the step 1 is utilized to complete the response;
the real-time demand response is divided into 4-hour real-time demand response and 15-minute real-time demand response, wherein the 4-hour real-time demand response utilizes a 4-hour load value predicted in a day and renewable energy output, takes maximum consumption of renewable energy as a target, completes the demand response, and corrects the result of the daily demand response; 15 minutes of real-time demand response is carried out by predicting a 1 hour load value and renewable energy output in the day, taking maximum consumption of renewable energy as a target, completing the demand response, and adjusting the result of the 4 hours of real-time demand response;
the target optimization model function of the cross-region comprehensive energy system operation is shown in a formula (6):
wherein F is the day-ahead operating cost of CIES;c i 、c c,ex 、c p 、/>the system comprises power purchase, gas purchase, unit operation and maintenance, CIES tie line maintenance, wind and light discarding punishment and CO2 treatment price; p (P) t buy 、/>P t i 、P t c,ex 、P t loss And->Respectively purchasing power, purchasing gas, a unit, CIES interaction, discarding wind and light and outputting CO2 power; f (F) t com Giving the system usersThe compensation cost of (2); wherein the wind-discarding and light-discarding punishment price is adjusted according to different time scales in the day, wherein the day cost coefficient<Cost coefficient in day.
In the running process of the system, the CIES of the cross-region comprehensive energy system is regarded as a main body, each IES is connected with each other by utilizing a power line, a hot water pipeline and a gas pipeline, and the CIES has power interaction with an external power grid and a natural gas network at the same time; the price of electricity purchased/sold by the IES to the IES is smaller than the price of electricity purchased to the electric part power grid, so the total system operation cost comprises electricity purchased to the power grid, gas purchased to the gas grid, unit operation maintenance cost, network power interaction maintenance cost, abandoned wind and abandoned light cost, and compensation cost and CO2 processing cost given to a user.
By utilizing the cross-regional comprehensive energy system optimization operation method of the multi-scale demand response, the load curve is adjusted in 3 stages in front of the day, in the day and in real time through the multi-scale demand response, so that the system operation cost is reduced, and meanwhile, the capacity of absorbing renewable energy is effectively improved.
Step 3: according to the demand response model established in the step 1 and the demand response model established in the step 2 and considering multiple time scales, combining and analyzing the two models, and establishing a multi-scale variable-proportion load demand response model according to different time and proportion of the load participating in demand response, namely respectively setting different compensation price proportionality coefficients and response coefficients for 24 hours of offer, 4 hours of real time and 15 minutes of real time load shedding, load transferability and alternative load response compensation before the day.
In the process of LA completing demand response in different time scales, the CRESP can give certain economic compensation, the compensation cost is different according to the difference of response time and response quantity, the compensation unit price given is different, in the process of multi-scale demand response, the specific compensation price is that the day-ahead offer demand response < the day-in offer demand response < the real-time demand response, meanwhile, the larger the LA response proportion is, the larger the compensation unit price given by the CRESP to the LA is, the specific compensation cost is as shown in formula (13)
Wherein F is t com The compensation costs to the system are given to the user,compensating prices for heat supply and cold supply to a user respectively; Δh (t), Δc (t) are the responses of LA to the heat load and the cold load, respectively; /> Compensation prices for load shedding, load transferability and load replacement in the future; /> And->Compensating prices for load shedding, load transferring and load replacing in the day respectively; />The compensation prices for the load reducible, transferable, and alternative loads, respectively, in real time.
Step 4: the transregional energy service end issues demand response information according to predicted load and output of renewable energy, each load aggregation end applies to participate in demand response, adjusts time and mode of energy consumption, optimizes a target optimization model established by solving formula (6) by using CPLEX nonlinear quadratic programming algorithm to obtain an optimized load curve, and adjusts output of each unit in the comprehensive energy system and power interaction operation data among the systems according to the optimized load curve, wherein the method comprises the following specific steps:
after the LA demand response mode is determined, firstly, a trans-regional energy service end firstly issues demand response information according to predicted load and output conditions of renewable energy sources, each load aggregation end applies to participate in demand response, adjusts the time and mode of energy consumption, and adjusts unit output in a system and power interaction operation data among the systems by using an optimized load curve;
secondly, a load predicted value and a renewable energy predicted value of 4 hours are utilized in the day to release demand response information, LA completes demand response again, and CRESP adjusts unit output and power interactive operation data;
and finally, issuing demand response information by using the load predicted value and the renewable energy predicted value for 1 hour, and completing the final demand response information by LA, and adjusting the output and power interactive operation data of the unit by CRESP.
Fig. 4 and 5 are graphs of the load of the system in the day-ahead demand response and the day-in demand response, respectively, and as can be seen from fig. 4, the load of different classes is transferred to the transition at night in different degrees in the process of completing the demand response, so as to maximize the wind power consumption at night, and reduce the pressure of the system for supplying energy and the cost for purchasing energy to the outside in the peak period. As can be seen from fig. 5, along with the fluctuation of real-time wind power in the day, the load also responds in real time, and the system aims at maximizing the consumption of renewable energy sources, so that the load is adjusted in real time.
Fig. 6 is a graph of output results of the system, in which during the running process of the system, during the period of high wind power generation at night, the electricity purchasing price of the system is in the period of low valley, the system preferentially converts electric energy into cold energy and heat energy to output preferentially through an electric boiler and an electric refrigerator, meanwhile, the electric energy converting unit is led to convert the electric energy into natural gas for supplying energy, meanwhile, the storage battery is charged, and the system is insufficient for purchasing to the upper power grid. In the electricity price peak period, the electric load is also in the peak period, the system guides the storage battery to discharge to meet the user demand, meanwhile, the CCHP unit starts to work, and the natural gas is utilized to simultaneously meet the demands of the electric, heat and cold triple loads of the system. In the whole time period, the system can obtain the support of electric power from other areas, so that the energy supply of the system is relieved, and the whole energy purchasing cost of the system is reduced. An optimization operation system of a cross-regional comprehensive energy system considering multi-scale demand response comprises the following program modules:
the initial demand response model building module: establishing a demand response model of different loads according to response attributes, energy consumption comfort level and response degree of the demand side loads;
a multi-time scale demand response model building module: dividing the demand response into an offer demand response and a real-time demand response according to different response time of the load on the demand side in the operation process by using the established demand response model, establishing a demand response model considering multiple time scales, and simultaneously establishing a target optimization model of the operation of the cross-region comprehensive energy system;
the multi-scale variable proportion load demand response model building module comprises: according to the established demand response model and the established demand response model considering multiple time scales, combining and analyzing the two models, and establishing a multi-scale variable proportion load demand response model according to different time and proportion of load participation demand response, namely respectively setting different compensation price proportionality coefficients and response coefficients for 24 hours of offer, 4 hours of real time and 15 minutes of real time reducible load, transferable load and substitutable load response compensation;
and (3) optimizing an operation module: and issuing demand response information by the transregional energy service end according to predicted loads and the output of renewable energy sources, applying for participation in demand response by each load aggregation end, adjusting the time and mode of energy consumption, optimizing and solving the established target optimization model by the transregional energy service end by using a CPLEX nonlinear quadratic programming algorithm to obtain an optimized load curve, and adjusting the output of each unit in the comprehensive energy system and the power interactive operation data among the systems according to the optimized load curve.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (8)
1. The cross-regional comprehensive energy system optimization operation method considering the multi-scale demand response is characterized by comprising the following steps of:
step 1: establishing a demand response model of different loads according to response attributes, energy consumption comfort level and response degree of the demand side loads;
step 2: the method comprises the steps of utilizing a demand response model established in the step 1, dividing demand response into offer demand response and real-time demand response according to different response time of a demand side load in an operation process, establishing a demand response model considering multiple time scales, and simultaneously establishing a target optimization model of cross-region comprehensive energy system operation;
step 3: according to the demand response model established in the step 1 and the demand response model established in the step 2 and considering multiple time scales, and according to different time and proportion of the load participating in demand response, establishing a multi-scale variable proportion load demand response model, namely respectively setting different compensation price proportionality coefficients and response coefficients for 24-hour offer, 4-hour real-time and 15-minute real-time load shedding, load transferability and alternative load response compensation before the day;
step 4: and the transregional energy service end utilizes an optimization algorithm to optimize and solve the established target optimization model to obtain an optimized load curve, and adjusts the output of each unit in the comprehensive energy system and the power interactive operation data among the systems according to the optimized load curve.
2. The method for optimizing operation of a trans-regional integrated energy system taking into account multi-scale demand response of claim 1, wherein the method comprises the steps of: in the step (1) of the process,
the loads are classified into 6 categories of fixed load, load reducible, load transferable, alternative load, flexible hot load and cold load;
the built mathematical model capable of reducing load is as follows:
wherein a epsilon (e, g) and epsilon represent E or g, e and g are respectively electric load and gas load, load-reducible power for t time periods, respectively, minimum and maximum load-reducible values;
the mathematical model of the transferable load is established as follows:
in the method, in the process of the invention,load power transferable after load transfer and load power transferable before load transfer in t time periods respectively;0-1 variable for load transfer; />For a variable 0-1 for load transfer, +.>A value of 1 indicates that the load is transferred to another period, a value of 0 indicates that the load is not transferred to another period,/for example>A value of 1 indicates that the load is transferred to the load for another period,a value of 0 indicates that no other period of time load is transferred to the load; />Is the actual transfer amount of the load;
the mathematical model of the established alternative load is:
in the method, in the process of the invention,load post-substitution load power and pre-substitution replaceable negative sum power for t time periods respectively;0-1 variable for the replacement load, < ->1 for the load is replaced, 0 for the load is not replaced,/for the load>A value of 1 indicates that the load replaces another load, and a value of 0 indicates that the load does not replace another load; />Actual substitution amount for load;
the mathematical model of the established flexible heat load is as follows:
H(t)＝C w ρ w V w (T g -T h )Δt (4)
wherein C is w Specific heat capacity of water ρ w For density of water, V w To return water volume, T g For the temperature of water supply, T h For the temperature of backwater, H (t) is the time tLoad power of hot water, T g ∈[T g,min ,T g,max ]，T g,min 、T g,max Respectively minimum and maximum allowable water supply temperature;
the mathematical model of the established flexible cooling load is as follows:
wherein T is in (t+1)、T in (t) building indoor temperatures for the t+1 and t time periods, respectively; r is R s Is the thermal resistance of the building; c (t) is the refrigeration load of the building in the period t; t (T) out (t) is t time period outdoor temperature; k=e -Δt/τ E is a natural constant, Δt is the scheduled time period duration, τ=r s C air ，C air Is the indoor specific heat capacity, Q c And (t) is the heat dissipation capacity of the building fresh air system.
3. The method for optimizing operation of a trans-regional integrated energy system taking into account multi-scale demand response of claim 1, wherein the method comprises the steps of: in the step 2 of the process, the process is carried out,
the solicited demand response is solicited for 24 hours before the day, the predicted load and the renewable energy output before the day are utilized, the minimum running cost of the system is taken as the target, the demand response requirement is issued, and the demand response model in the step 1 is utilized to complete the response;
the real-time demand response is divided into 4-hour real-time demand response and 15-minute real-time demand response, wherein the 4-hour real-time demand response utilizes a 4-hour load value predicted in a day and renewable energy output, takes maximum consumption of renewable energy as a target, completes the demand response, and corrects the result of the daily demand response; the 15-minute real-time demand response is carried out by predicting a 1-hour load value and renewable energy output in the day, taking maximum consumption of renewable energy as a target, completing the demand response, and adjusting the result of the 4-hour real-time demand response.
4. The method for optimized operation of a trans-regional integrated energy system taking into account multi-scale demand response of claim 1, wherein, in step 2,
the target optimization model function of the cross-region comprehensive energy system operation is shown as a formula (6):
wherein F is the day-ahead operating cost of CIES;c i 、c c,ex 、c p 、/>the system comprises power purchase, gas purchase, unit operation and maintenance, CIES tie line maintenance, wind and light discarding punishment and CO2 treatment price; p (P) t buy 、/>P t i 、P t c,ex 、P t loss Andrespectively purchasing power, purchasing gas, a unit, CIES interaction, discarding wind and light and outputting CO2 power; f (F) t com The system is given a compensating cost to the user.
5. The method for optimizing operation of a trans-regional integrated energy system taking into account multi-scale demand response of claim 1, wherein the method comprises the steps of: in step 3, the CRESP gives a certain economic compensation in the process of completing the demand response by LA in different time scales, and the specific compensation cost is shown as formula (7)
Wherein F is t com The compensation costs to the system are given to the user,compensating prices for heat supply and cold supply to a user respectively; Δh (t), Δc (t) are the responses of LA to the heat load and the cold load, respectively; /> Compensation prices for load shedding, load transferability and load replacement in the future; />And->Compensating prices for load shedding, load transferring and load replacing in the day respectively; /> The compensation prices for the load reducible, transferable, and alternative loads, respectively, in real time.
6. The method for optimizing operation of a trans-regional integrated energy system taking into account multi-scale demand response of claim 1, wherein the method comprises the steps of: in step 4, the specific steps are as follows:
after the LA demand response mode is determined, firstly, a trans-regional energy service end firstly issues demand response information according to predicted load and output conditions of renewable energy sources, each load aggregation end applies to participate in demand response, adjusts the time and mode of energy consumption, and adjusts unit output in a system and power interaction operation data among the systems by using an optimized load curve;
secondly, a load predicted value and a renewable energy predicted value of 4 hours are utilized in the day to release demand response information, LA completes demand response again, and CRESP adjusts unit output and power interactive operation data;
and finally, issuing demand response information by using the load predicted value and the renewable energy predicted value for 1 hour, and completing the final demand response information by LA, and adjusting the output and power interactive operation data of the unit by CRESP.
7. An optimization operation system of a cross-regional comprehensive energy system considering multi-scale demand response comprises the following program modules:
the initial demand response model building module: establishing a demand response model of different loads according to response attributes, energy consumption comfort level and response degree of the demand side loads;
a multi-time scale demand response model building module: dividing the demand response into an offer demand response and a real-time demand response according to different response time of the load on the demand side in the operation process by using the established demand response model, establishing a demand response model considering multiple time scales, and simultaneously establishing a target optimization model of the operation of the cross-region comprehensive energy system;
the multi-scale variable proportion load demand response model building module comprises: according to the established demand response model and the established demand response model considering multiple time scales, and according to different time and proportion of the load participating in demand response, establishing a multi-scale variable proportion load demand response model, namely respectively setting different compensation price proportionality coefficients and response coefficients for 24 hours offer, 4 hours real time and 15 minutes real time load shedding, load transferability and alternative load response compensation before the day;
and (3) optimizing an operation module: and the transregional energy service end utilizes an optimization algorithm to optimize and solve the established target optimization model to obtain an optimized load curve, and adjusts the output of each unit in the comprehensive energy system and the power interactive operation data among the systems according to the optimized load curve.
8. The cross-regional integrated energy system optimization operating system taking into account multi-scale demand response of claim 7, wherein: in the initial demand response model building module,
the loads are classified into 6 categories of fixed load, load reducible, load transferable, alternative load, flexible hot load and cold load;
the built mathematical model capable of reducing load is as follows:
wherein a epsilon (e, g) and epsilon represent E or g, e and g are respectively electric load and gas load, load-reducible power for t time periods, respectively, minimum and maximum load-reducible values;
the mathematical model of the transferable load is established as follows:
in the method, in the process of the invention,load power transferable after load transfer and load power transferable before load transfer in t time periods respectively;for a variable 0-1 for load transfer, +.>A value of 1 indicates that the load is transferred to another period, a value of 0 indicates that the load is not transferred to another period,/for example>A value of 1 indicates that another period load is transferred to the load, and a value of 0 indicates that no another period load is transferred to the load; />Is the actual transfer amount of the load;
the mathematical model of the established alternative load is:
in the method, in the process of the invention,load post-substitution load power and pre-substitution replaceable negative sum power for t time periods respectively;0-1 variable for the replacement load, < ->1 for the load is replaced, 0 for the load is not replaced,/for the load>A value of 1 indicates that the load replaces another load, and a value of 0 indicates that the load does not replace another load; />Actual substitution amount for load;
the mathematical model of the established flexible heat load is as follows:
H(t)＝C w ρ w V w (T g -T h )Δt (4)
wherein C is w Specific heat capacity of water ρ w For density of water, V w To return water volume, T g For the temperature of water supply, T h The temperature of the backwater is H (T) is the load power of hot water at the moment T, wherein T g ∈[T g,min ,T g,max ]，T g,min 、T g,max Respectively minimum and maximum allowable water supply temperature;
the mathematical model of the established flexible cooling load is as follows:
wherein T is in (t+1)、T in (t) building indoor temperatures for the t+1 and t time periods, respectively; r is R s Is the thermal resistance of the building; c (t) is the refrigeration load of the building in the period t; t (T) out (t) is t time period outdoor temperature; k=e -Δt/τ E is a natural constant, Δt is the scheduled time period duration, τ=r s C air ，C air Is the indoor specific heat capacity, Q c And (t) is the heat dissipation capacity of the building fresh air system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111401539.4A CN114282708B (en) | 2021-11-19 | 2021-11-19 | Cross-region comprehensive energy system optimization operation method and system considering multi-scale demand response |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111401539.4A CN114282708B (en) | 2021-11-19 | 2021-11-19 | Cross-region comprehensive energy system optimization operation method and system considering multi-scale demand response |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114282708A CN114282708A (en) | 2022-04-05 |
| CN114282708B true CN114282708B (en) | 2024-04-09 |
Family
ID=80869912
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111401539.4A Active CN114282708B (en) | 2021-11-19 | 2021-11-19 | Cross-region comprehensive energy system optimization operation method and system considering multi-scale demand response |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114282708B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116995659A (en) * | 2023-07-25 | 2023-11-03 | 中国建筑科学研究院有限公司 | Flexible operation method of heat pump system considering renewable energy source consumption |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111860965A (en) * | 2020-06-24 | 2020-10-30 | 东南大学 | User comprehensive energy system optimal scheduling method considering energy storage multi-type services |
| CN111952980A (en) * | 2020-08-11 | 2020-11-17 | 合肥工业大学 | Comprehensive energy system optimization method and system considering response uncertainty of demand side |
| CN112467730A (en) * | 2020-11-19 | 2021-03-09 | 国网四川省电力公司经济技术研究院 | Power system optimal scheduling method considering wind-solar output prediction error and demand response flexibility |
| CN112583021A (en) * | 2020-11-23 | 2021-03-30 | 国家电网有限公司 | Comprehensive energy system optimal scheduling method and device considering comprehensive demand response |
-
2021
- 2021-11-19 CN CN202111401539.4A patent/CN114282708B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111860965A (en) * | 2020-06-24 | 2020-10-30 | 东南大学 | User comprehensive energy system optimal scheduling method considering energy storage multi-type services |
| CN111952980A (en) * | 2020-08-11 | 2020-11-17 | 合肥工业大学 | Comprehensive energy system optimization method and system considering response uncertainty of demand side |
| CN112467730A (en) * | 2020-11-19 | 2021-03-09 | 国网四川省电力公司经济技术研究院 | Power system optimal scheduling method considering wind-solar output prediction error and demand response flexibility |
| CN112583021A (en) * | 2020-11-23 | 2021-03-30 | 国家电网有限公司 | Comprehensive energy system optimal scheduling method and device considering comprehensive demand response |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114282708A (en) | 2022-04-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109523052B (en) | Virtual power plant optimal scheduling method considering demand response and carbon transaction | |
| CN107767074B (en) | Energy hub planning method considering comprehensive demand response resources | |
| CN110288152B (en) | Regional comprehensive energy system energy storage configuration method considering electric/thermal flexible load | |
| CN110163411B (en) | Operation optimization method for regional comprehensive energy system | |
| CN110163443B (en) | Natural gas pressure regulating station micro-energy network optimization scheduling method considering electricity-gas comprehensive demand response | |
| CN112583021A (en) | Comprehensive energy system optimal scheduling method and device considering comprehensive demand response | |
| CN110807588B (en) | Optimized scheduling method of multi-energy coupling comprehensive energy system | |
| CN110209135B (en) | Family energy optimization scheduling method based on micro cogeneration multi-time scale | |
| CN112398164A (en) | Micro-energy-source network group optimization operation and cost distribution method containing shared energy storage system | |
| CN112202201B (en) | Combined micro-grid operation strategy considering demand response and electric automobile | |
| CN114462724B (en) | Park aggregator double-layer optimization scheduling method considering multi-energy price incentive comprehensive demand response model | |
| CN112488363B (en) | Generalized energy storage based optimal scheduling method for multi-energy power system | |
| CN112465236B (en) | Community comprehensive energy system scheduling method considering comprehensive satisfaction degree | |
| CN115857348A (en) | Distributed energy system capacity optimization method considering comfortable energy supply of two-combined heat pump | |
| CN108023348B (en) | Method for determining optimal peak shaving installed scale of wind power based on outgoing channel | |
| CN114282708B (en) | Cross-region comprehensive energy system optimization operation method and system considering multi-scale demand response | |
| CN116663820A (en) | Comprehensive energy system energy management method under demand response | |
| CN116050637A (en) | Comprehensive energy virtual power plant optimal scheduling method and system based on time-of-use electricity price | |
| CN113128868B (en) | Regional comprehensive energy system scheduling optimization method and device | |
| CN117611382A (en) | Rural comprehensive energy system optimization method considering multilayer cooperation and demand response | |
| Zhou et al. | Flexible load optimal regulation strategy of commercial buildings under the environment of electricity market | |
| Gan-yun et al. | Optimal scheduling of regional integrated energy system considering integrated demand response | |
| CN113128799A (en) | Energy management and control method and device, electronic equipment and computer storage medium | |
| CN115470564A (en) | Public building energy system coordination control method and control assembly thereof | |
| CN113394792A (en) | New energy magnesite load virtual power plant source grid load storage intelligent control device and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |