CN109190988B - Demand side response game method for realizing optimal coordination of temperature control load - Google Patents

Demand side response game method for realizing optimal coordination of temperature control load Download PDF

Info

Publication number
CN109190988B
CN109190988B CN201811057764.9A CN201811057764A CN109190988B CN 109190988 B CN109190988 B CN 109190988B CN 201811057764 A CN201811057764 A CN 201811057764A CN 109190988 B CN109190988 B CN 109190988B
Authority
CN
China
Prior art keywords
temperature control
control load
temperature
power
kth
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
Application number
CN201811057764.9A
Other languages
Chinese (zh)
Other versions
CN109190988A (en
Inventor
丁一
谢敦见
惠红勋
梅峰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University ZJU
State Grid Zhejiang Electric Power Co Ltd
Original Assignee
Zhejiang University ZJU
State Grid Zhejiang Electric Power Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Zhejiang University ZJU, State Grid Zhejiang Electric Power Co Ltd filed Critical Zhejiang University ZJU
Priority to CN201811057764.9A priority Critical patent/CN109190988B/en
Publication of CN109190988A publication Critical patent/CN109190988A/en
Application granted granted Critical
Publication of CN109190988B publication Critical patent/CN109190988B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Abstract

The invention discloses a demand side response game method for realizing optimal coordination of temperature control loads, which establishes a demand side temperature control load game method, fully excavates and utilizes the energy storage characteristic of the temperature control loads, aims at minimizing the power expenditure of individual loads, reasonably distributes the load power of each time period and can improve the efficiency of demand side electric energy supply. The method can reduce the power consumption expenditure of each user and improve the flexibility of power supply of the demand side.

Description

Demand side response game method for realizing optimal coordination of temperature control load
Technical Field
The invention belongs to the technical field of power systems and smart power grids, and relates to a demand side response game method for realizing optimal coordination of temperature control loads.
Background
With the progress of power grid intellectualization, demand side response becomes a hot spot in the field of future smart power grid research. The existing demand side response project is used for mining the scheduling potential of temperature control loads (such as air conditioners, heat pumps and other loads) with energy storage characteristics, so that certain auxiliary service can be provided for a power grid on the premise of not influencing the power utilization satisfaction of users, and the power grid is helped to operate safely and stably in an efficient manner. However, the complex user behaviors faced by the demand side response and the reward distribution among user individuals to the user side response effect bring uncertain factors, the existing demand response method cannot meet the dispatching demand of the power grid, and a demand side response method for realizing optimal coordination of temperature control load is urgently needed.
Disclosure of Invention
The invention aims to provide a demand side response game method for realizing optimal coordination of temperature control loads.
In order to achieve the purpose, the invention adopts the technical scheme that:
a demand side response game method for realizing optimal coordination of temperature control loads comprises the following steps:
step 1, initializing temperature initial values of all temperature control loads
Figure BDA0001796256190000011
Power utilization policy set
Figure BDA0001796256190000012
And corresponding
Figure BDA0001796256190000013
Step 2, solving the multi-time scale minimum power utilization cost optimization problem of the temperature control load to obtain a new power utilization strategy set
Figure BDA0001796256190000014
Step 3, judgment
Figure BDA0001796256190000015
If yes, go to step 4, if not, update
Figure BDA0001796256190000016
And sending a new strategy to other temperature control loads;
step 4, judging whether other temperature control load power utilization strategies are updated, if so, turning to the step 5, otherwise, outputting
Figure BDA0001796256190000017
Finishing;
step 5, receiving and updating the power utilization strategies after updating other temperature control loads
Figure BDA0001796256190000021
Turning to step 2 until output
Figure BDA0001796256190000022
And (6) ending.
In the demand side response game method for realizing the optimal coordination of the temperature control load, in step 1:
for the kth temperature control load, the initial value of the problem is optimized
Figure BDA0001796256190000023
The current temperature is taken as a standard;
for the k-th temperature controlled load,
Figure BDA0001796256190000024
representing the minimum electricity cost electricity utilization strategy set of a single temperature control load in N optimization stages:
Figure BDA0001796256190000025
(1) in the formula
Figure BDA0001796256190000026
The power value of the kth temperature-controlled load in the nth optimization stage is shown.
Figure BDA0001796256190000027
The total power of the other temperature control loads except the kth temperature control load is expressed as follows:
Figure BDA0001796256190000028
(2) wherein K represents the total temperature control load number.
In the demand side response game method for realizing the optimal coordination of the temperature control load, the step 2 comprises the following steps:
step 2.1, determining an objective function of the minimum electricity cost of the single temperature control load multi-time scale minimum electricity cost optimization problem:
Figure BDA0001796256190000029
(3) in the formula
Figure BDA00017962561900000210
Representing the total power of other temperature control loads except the kth temperature control load; cn(. is) a total cost function of demand side loads, typically generator fuel cost function, in the nth optimization stage;
Figure BDA00017962561900000211
is the electricity cost of the kth temperature control load in the nth optimization stage; h is the actual duration of the optimization time per stage.
Step 2.2, determining the constraint conditions of the single temperature control load multi-time scale minimum electricity consumption cost optimization problem:
Figure BDA00017962561900000212
Figure BDA00017962561900000213
(4) in the formula
Figure BDA00017962561900000214
Is the initial temperature value of the kth temperature control load in the nth optimization stage;
Figure BDA00017962561900000215
Figure BDA00017962561900000216
respectively indicating that the temperature control load meets the minimum and maximum electric power consumption of a comfortable temperature interval required by a user under the temperature initial value;
Figure BDA00017962561900000217
representing the maximum power of the kth temperature controlled load. In summary, the power value of the kth temperature control load in the nth optimization stage expressed by the formula (2)
Figure BDA0001796256190000031
The requirements of the current comfortable temperature range and the electrical characteristics of the load should be met at the same time.
(5) Wherein G (-) represents the temperature control load transfer function and can pass through the temperature initial value in the nth optimization stage
Figure BDA0001796256190000032
And power value
Figure BDA0001796256190000033
Deducing the initial temperature value of the next optimization stage
Figure BDA0001796256190000034
Is represented as follows:
Figure BDA0001796256190000035
(6) wherein C represents the heat capacity of the object on which the temperature control load acts, and if the air conditioner acts to adjust the indoor temperature, C corresponds to the total heat capacity of the indoor air; h represents the thermal resistance of the heat exchange between the temperature control load acting object and the external environment, the energy of the heat exchange is in direct proportion to the difference value of the internal temperature and the external temperature, and the thermal resistance H is a direct proportion coefficient; t isextRepresents the ambient temperature; kEThe energy efficiency ratio of the temperature control load is represented and is the ratio of electric power to cooling/heating utility power.
Step 2.3, solving the multi-time scale minimum electricity consumption cost optimization problem of a single temperature control load:
the multi-time scale minimum electricity consumption cost optimization problem of a single temperature control load can be quickly solved by a dynamic programming method. The bellman equation for dynamic programming is as follows:
Figure BDA0001796256190000036
(7) in the formula
Figure BDA0001796256190000037
The equation represents the total cost function from the beginning of the nth optimization phase to the end of the nth optimization phase. Simultaneously assuming the initial value of the reverse calculation of the dynamic programming
Figure BDA0001796256190000038
The invention has the following beneficial effects: the method is a demand side response game method which can give full play to the potential of temperature control load in the background of an intelligent power grid; the demand side response game method fully excavates and utilizes the energy storage characteristic of the temperature control load; the method not only reduces the electricity consumption expenditure of each user, but also improves the flexibility of the electric energy supply of the demand side.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
As shown in fig. 1, the demand side response game method for realizing optimal coordination of temperature controlled loads of the present invention includes the following steps:
step 1, initializing temperature initial values of all temperature control loads
Figure BDA0001796256190000041
Power utilization policy set
Figure BDA0001796256190000042
And corresponding
Figure BDA0001796256190000043
Step 2, solving the multi-time scale minimum power utilization cost optimization problem of the temperature control load to obtain a new power utilization strategy set
Figure BDA0001796256190000044
Step 3, judgment
Figure BDA0001796256190000045
If yes, go to step 4, if not, update
Figure BDA0001796256190000046
And sending a new strategy to other temperature control loads;
step 4, judging whether other temperature control load power utilization strategies are updated, if so, turning to the step 5, otherwise, outputting
Figure BDA0001796256190000047
Finishing;
step 5, receiving and updating the power utilization strategies after updating other temperature control loads
Figure BDA0001796256190000048
Turning to step 2 until output
Figure BDA0001796256190000049
And (6) ending.
In the demand side response game method for realizing the optimal coordination of the temperature control load, in step 1:
for the kth temperature control load, the initial value of the problem is optimized
Figure BDA00017962561900000410
The current temperature is taken as a standard;
for the k-th temperature controlled load,
Figure BDA00017962561900000411
representing the minimum electricity cost electricity utilization strategy set of a single temperature control load in N optimization stages:
Figure BDA00017962561900000412
(1) in the formula
Figure BDA00017962561900000413
The power value of the kth temperature-controlled load in the nth optimization stage is shown.
Figure BDA00017962561900000414
The total power of the other temperature control loads except the kth temperature control load is expressed as follows:
Figure BDA00017962561900000415
(2) wherein K represents the total temperature control load number.
In the demand side response game method for realizing the optimal coordination of the temperature control load, the step 2 comprises the following steps:
step 2.1, determining an objective function of the minimum electricity cost of the single temperature control load multi-time scale minimum electricity cost optimization problem:
Figure BDA00017962561900000416
(3) in the formula
Figure BDA00017962561900000417
Representing the total power of other temperature control loads except the kth temperature control load; cn(. is) a total cost function of demand side loads, typically generator fuel cost function, in the nth optimization stage;
Figure BDA00017962561900000418
is the electricity cost of the kth temperature control load in the nth optimization stage; h is the actual duration of the optimization time per stage.
Step 2.2, determining the constraint conditions of the single temperature control load multi-time scale minimum electricity consumption cost optimization problem:
Figure BDA0001796256190000051
Figure BDA0001796256190000052
(4) in the formula
Figure BDA0001796256190000053
Is the initial temperature value of the kth temperature control load in the nth optimization stage;
Figure BDA0001796256190000054
Figure BDA0001796256190000055
respectively indicating that the temperature control load meets the minimum and maximum electric power consumption of a comfortable temperature interval required by a user under the temperature initial value;
Figure BDA0001796256190000056
representing the maximum power of the kth temperature controlled load. In summary, the power value of the kth temperature control load in the nth optimization stage expressed by the formula (2)
Figure BDA0001796256190000057
The requirements of the current comfortable temperature range and the electrical characteristics of the load should be met at the same time.
(5) Wherein G (-) represents the temperature control load transfer function and can pass through the temperature initial value in the nth optimization stage
Figure BDA0001796256190000058
And power value
Figure BDA0001796256190000059
Deducing the initial temperature value of the next optimization stage
Figure BDA00017962561900000510
Is represented as follows:
Figure BDA00017962561900000511
(6) wherein C represents the heat capacity of the object on which the temperature control load acts, and if the air conditioner acts to adjust the indoor temperature, C corresponds to the total heat capacity of the indoor air; h represents the thermal resistance of the heat exchange between the temperature control load acting object and the external environment, the energy of the heat exchange is in direct proportion to the difference value of the internal temperature and the external temperature, and the thermal resistance H is a direct proportion coefficient; t isextRepresents the ambient temperature; kEThe energy efficiency ratio of the temperature control load is represented and is the ratio of electric power to cooling/heating utility power.
Step 2.3, solving the multi-time scale minimum electricity consumption cost optimization problem of a single temperature control load:
the multi-time scale minimum electricity consumption cost optimization problem of a single temperature control load can be quickly solved by a dynamic programming method. The bellman equation for dynamic programming is as follows:
Figure BDA00017962561900000512
(7) in the formula
Figure BDA00017962561900000513
The equation represents the total cost function from the beginning of the nth optimization phase to the end of the nth optimization phase. Simultaneously assuming the initial value of the reverse calculation of the dynamic programming
Figure BDA00017962561900000514

Claims (3)

1. A demand side response game method for realizing optimal coordination of temperature control loads is characterized by comprising the following steps:
step 1, initializing temperature initial values of all temperature control loads
Figure FDA0003155337120000011
Power utilization policy set
Figure FDA0003155337120000012
And corresponding
Figure FDA0003155337120000013
Figure FDA0003155337120000014
Represents the minimum electricity cost electricity utilization strategy set in N optimization stages for the kth temperature control load,
Figure FDA0003155337120000015
representing the total power of other temperature control loads except the kth temperature control load;
step 2, solving the multi-time scale minimum power utilization cost optimization problem of the temperature control load to obtain a new power utilization strategy set
Figure FDA0003155337120000016
Step 3, judgment
Figure FDA0003155337120000017
If yes, go to step 4, if not, update
Figure FDA0003155337120000018
And sending a new strategy to other temperature control loads;
step 4, judging whether other temperature control load power utilization strategies are updated, if so, turning to the step 5, otherwise, outputting
Figure FDA0003155337120000019
Finishing;
step 5, receiving and updating the power utilization strategies after updating other temperature control loads
Figure FDA00031553371200000110
Turning to step 2 until output
Figure FDA00031553371200000111
End up；
The step 2 is realized as follows:
step 2.1, determining an objective function of the minimum electricity cost of the single temperature control load multi-time scale minimum electricity cost optimization problem:
Figure FDA00031553371200000112
(1) in the formula
Figure FDA00031553371200000113
Representing the total power of other temperature control loads except the kth temperature control load; cn(. h) is a total cost function of demand side loads in the nth optimization stage, which is a generator fuel cost function;
Figure FDA00031553371200000114
is the electricity cost of the kth temperature control load in the nth optimization stage; h is the actual duration of each stage of optimization time;
step 2.2, determining the constraint conditions of the single temperature control load multi-time scale minimum electricity consumption cost optimization problem:
Figure FDA00031553371200000115
Figure FDA00031553371200000116
(2) in the formula
Figure FDA00031553371200000117
Is the initial temperature value of the kth temperature control load in the nth optimization stage;
Figure FDA00031553371200000118
Figure FDA0003155337120000021
respectively indicating that the temperature control load meets the minimum and maximum electric power consumption of a comfortable temperature interval required by a user under the temperature initial value;
Figure FDA0003155337120000022
represents the maximum power of the kth temperature-controlled load;
(3) wherein G (-) represents the temperature controlled load transfer function by the temperature initial value in the nth optimization stage
Figure FDA0003155337120000023
And power value
Figure FDA0003155337120000024
Deducing the initial temperature value of the next optimization stage
Figure FDA0003155337120000025
Is represented as follows:
Figure FDA0003155337120000026
(4) wherein C represents the heat capacity of the object on which the temperature control load acts, and C corresponds to the total heat capacity of the indoor air when the air conditioner acts to adjust the indoor temperature; h represents the thermal resistance of the heat exchange between the temperature control load acting object and the external environment, the energy of the heat exchange is in direct proportion to the difference value of the internal temperature and the external temperature, and the thermal resistance H is a direct proportion coefficient; t isextRepresents the ambient temperature; kERepresenting the energy efficiency ratio of the temperature control load, which is the ratio of electric power to refrigeration/heating utility power;
step 2.3, solving the multi-time scale minimum electricity consumption cost optimization problem of a single temperature control load:
the multi-time scale minimum electricity consumption cost optimization problem of a single temperature control load can be quickly solved by a dynamic programming method, and the Bellman equation of the dynamic programming is as follows:
Figure FDA0003155337120000027
(5) in the formula
Figure FDA0003155337120000028
The formula represents the total cost function from the beginning of the nth optimization phase to the end of the nth optimization phase; simultaneously assuming the initial value of the reverse calculation of the dynamic programming
Figure FDA0003155337120000029
2. The demand-side response gaming method for achieving optimal coordination of temperature controlled loads according to claim 1, wherein in step 1:
for the kth temperature control load, the initial value of the problem is optimized
Figure FDA00031553371200000210
Subject to the current temperature.
3. The demand-side responsive gaming method for effecting optimal coordination of temperature controlled loads according to claim 1, wherein for a kth temperature controlled load,
Figure FDA00031553371200000211
expressed as:
Figure FDA00031553371200000212
(6) in the formula
Figure FDA00031553371200000213
Representing the power value of the kth temperature control load in the nth optimization stage;
Figure FDA00031553371200000214
the total power of the other temperature control loads except the kth temperature control load is expressed as follows:
Figure FDA00031553371200000215
(7) wherein K represents the total temperature control load number.
CN201811057764.9A 2018-09-11 2018-09-11 Demand side response game method for realizing optimal coordination of temperature control load Active CN109190988B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811057764.9A CN109190988B (en) 2018-09-11 2018-09-11 Demand side response game method for realizing optimal coordination of temperature control load

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811057764.9A CN109190988B (en) 2018-09-11 2018-09-11 Demand side response game method for realizing optimal coordination of temperature control load

Publications (2)

Publication Number Publication Date
CN109190988A CN109190988A (en) 2019-01-11
CN109190988B true CN109190988B (en) 2021-10-22

Family

ID=64910248

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811057764.9A Active CN109190988B (en) 2018-09-11 2018-09-11 Demand side response game method for realizing optimal coordination of temperature control load

Country Status (1)

Country Link
CN (1) CN109190988B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7248907B2 (en) * 2019-08-14 2023-03-30 富士通株式会社 Optimizer and method of controlling the optimizer

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105116942B (en) * 2015-06-26 2017-03-15 国网山西省电力公司 A kind of thermostatic control intelligence load combined optimization method
CN105550806A (en) * 2015-12-09 2016-05-04 国家电网公司 Typical load ordered power utilization control method and device
CN106487011B (en) * 2016-11-28 2019-06-25 东南大学 A kind of family microgrid energy optimization method based on Q study

Also Published As

Publication number Publication date
CN109190988A (en) 2019-01-11

Similar Documents

Publication Publication Date Title
CN109063925B (en) Optimized operation method for regional comprehensive energy system considering load aggregators
CN111400641A (en) Day-ahead optimal scheduling method for comprehensive energy system containing heat accumulation type electric heating
Cole et al. Use of model predictive control to enhance the flexibility of thermal energy storage cooling systems
CN108280556B (en) Power supply quantity two-stage optimization scheduling method based on thermal load adjustment
CN110209135B (en) Family energy optimization scheduling method based on micro cogeneration multi-time scale
CN109685396B (en) Power distribution network energy management method considering public building demand response resources
GR20190100088A (en) Method for improving the energy management of a nearly zero energy building
CN111047097B (en) Daily rolling optimization method of comprehensive energy system
CN108494012A (en) A kind of meter and the electric regional complex energy resource system method for on-line optimization for turning gas technology
CN111027747A (en) Household energy control method considering user comfort risk preference
CN115857348A (en) Distributed energy system capacity optimization method considering comfortable energy supply of two-combined heat pump
CN109190988B (en) Demand side response game method for realizing optimal coordination of temperature control load
CN111271824B (en) Centralized and decentralized control method for demand response of central air conditioner
CN111600381B (en) Power consumption metering control air conditioning system and power grid peak shaving frequency modulation method
CN107563547B (en) Comprehensive energy management and control method for optimizing depth of energy consumption of user side
CN113420413B (en) Flexible load adjustability quantification method and system based on load plasticity
CN115470963A (en) Optimized operation method for virtual energy storage of load based on electricity price
CN115641209A (en) Comprehensive energy optimization model construction method considering carbon transaction and demand response
CN113869775A (en) Park operation strategy generation method for multi-type user comprehensive demand response
CN112629072A (en) Energy-saving control device of air source heat pump for coal-to-electricity users
CN112310969A (en) Regional user power supply method
Jiajia et al. Coordinated heat and power dispatch of industrial park microgrid considering distributed heating and indoor comfort constraint
Lujie et al. The Load Aggregation Strategy of Central Air-conditioning for Smoothing Wind Power Fluctuation
CN112967152B (en) Water-loop heat pump load aggregation modeling and control method
Zhihan et al. Research on the implementation architecture and demand response controlling strategy for adjustable load

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