CN112184285A - Demand response complementary electricity price system for high-component flexible load - Google Patents

Demand response complementary electricity price system for high-component flexible load Download PDF

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CN112184285A
CN112184285A CN202010923284.7A CN202010923284A CN112184285A CN 112184285 A CN112184285 A CN 112184285A CN 202010923284 A CN202010923284 A CN 202010923284A CN 112184285 A CN112184285 A CN 112184285A
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module
electricity price
complementary
load
electricity
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汤瑞欣
朱伟东
符政鑫
许斯滨
许方园
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Guangdong University of Technology
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Guangdong University of Technology
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    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • 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
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Abstract

The invention discloses a demand response complementary electricity price system facing to high-component flexible loads, which comprises an optimal demand party response complementary new electricity price making system, an identification judging module, a power grid dispatching module, a high-component flexible load user group and other user groups, wherein the optimal demand party response complementary new electricity price making system comprises a demand party response complementary new electricity price making module, a high-component flexible load user group and a demand response complementary electricity price making module; wherein: the optimal demander response complementary new electricity price making system is respectively connected to the identification judgment module, the power grid dispatching module, the high-component flexible load user group and other user groups; the identification judging module is connected with the high-component flexible load user group; the power grid dispatching module is connected with the high-component flexible load user group; and the power grid dispatching module is connected with other user groups. According to the invention, through the cooperation of the optimal demander response complementary new electricity price making system, the identification judging module and the power grid scheduling module, by means of a complementary electricity price mechanism, a plurality of complementary electricity prices linear electricity prices are provided for a high-component flexible load group, off-peak electricity utilization is realized, the operation cost of the power system is reduced, and the social energy efficiency level is improved.

Description

Demand response complementary electricity price system for high-component flexible load
Technical Field
The invention relates to the technical field of power systems, in particular to a demand response complementary electricity price system for high-component flexible loads.
Background
Smart grids are a future development trend of global power systems. As one of the primary roles of smart grids, demand response promotes power consumers to use electricity in a more efficient manner through interoperability between power companies and consumers. Through demand response, the utility obtains an operating mode in demand side management that affects the characteristics of the electrical load. In network operation, demand response helps to reduce peak power demand, thereby mitigating expensive or emergently generated power demands, such as the need for spinning reserve.
Demand responses can be divided into two categories: the first type is time-based demand response; the second category is incentive based demand response. Incentive-based demand responses are time-based, and through advanced contractual rules, the customer's consumption behavior may change temporarily. The utility gives a corresponding compensation or penalty for the load change. The time-based demand response is also referred to as price-based demand response, and means that the user responsively adjusts the power demand according to the received price signal, and includes time-of-use electricity price response, real-time electricity price response, sword-shaped electricity price response, and the like.
The united states power market environment is open, is the country which implements most demand response items and has the most complete types in the world at present, and has three typical business operation modes: a government direct management mode, a grid company management mode, and an independent third party management mode. There are eight regional power markets in europe, each with different market rules and technical standards, and there is no overall demand response implementation plan, and demand response projects developed in various countries are mainly based on respective established schemes and rules. The development of Chinese demand response is mainly demand-side management, the marketization is not strong, the participation of users is low, and the multi-sided heavy and administrative means, "orderly power utilization" is the main principle in China, especially in the period of meeting the peak and summer.
According to the load change condition of a power grid, the time-of-use electricity price is divided into a plurality of time periods such as a valley period, a flat period and a peak period every day, different electricity price levels are set for the time periods respectively, so that users are encouraged to reasonably arrange electricity utilization time, peak clipping and valley filling are achieved, the utilization efficiency of power resources is improved, and the time-of-use electricity price is the most popular demand response measure at present. However, in the traditional time-of-use electricity price mode, the time-of-use electricity prices of all users in the area are the same, so that most of the users intensively transfer the load to the same time period, and the effect of 'peak clipping and valley filling' is weakened.
In the prior art, chinese patent publication No. CN105470971A discloses a flexible adaptive power load control system and a control method thereof in 2016, 04, 06, month, the system includes: the system comprises a demand response center, a secondary acquisition control device, a primary acquisition control device, a user electric device, a cloud device and a user terminal device. The scheme can support multiple demand response modes to a certain extent, and provides a power load control solution with certain flexibility, but the problem cannot be solved, so that a demand response complementary electricity price system facing high-component flexible loads is urgently needed by users.
Disclosure of Invention
The invention provides a demand response complementary type electricity price system facing to high-component flexible load, aiming at solving the problem that the 'peak clipping and valley filling' effect is weakened due to the fact that time-of-use electricity prices of all users in an area are the same in a traditional time-of-use electricity price mode.
The primary objective of the present invention is to solve the above technical problems, and the technical solution of the present invention is as follows:
a demand response complementary electricity price system facing high-component flexible loads comprises an optimal demander response complementary new electricity price making system, an identification judging module, a power grid dispatching module, a high-component flexible load user group and other user groups; wherein:
the optimal demander response complementary new electricity price making system is connected with the identification judging module;
the optimal demander response complementary new electricity price making system is connected with the power grid dispatching module;
the optimal demander response complementary new electricity price making system is connected with a high-component flexible load user group;
the optimal demander response complementary new electricity price making system is connected with other user groups;
the identification judgment module is connected with the high-component flexible load user group;
the power grid dispatching module is connected with a high-component flexible load user group;
and the power grid dispatching module is connected with other user groups.
Preferably, the optimal demander response complementary new electricity price making system comprises an objective function module, an index constraint module and an optimization algorithm module; wherein:
the input end of the target function module is connected with the input end of the high-component flexible load user group;
the output end of the objective function module is connected with the input end of the optimization algorithm module;
and the output end of the index constraint module is connected with the input end of the optimization algorithm module.
Preferably, the objective function module comprises an optimal user load calculation module and a power grid scheduling simplification optimization module; wherein:
the input end of the optimal user load calculation module is connected with the output end of the high-composition flexible load user group;
and the output end of the optimal user load calculation module is connected with the input end of the power grid dispatching simplification optimization module.
Preferably, the mathematical model established by the optimal user load calculation module includes:
an objective function: min: cost-new＝(Bill-Bill0)+(Salary-Salary0) ①；
Wherein, (Bill-Bill)0) For typical daily bill changes of electricity charges, (Salary-Salary0) Typical daily wage changes. Bill is alternative complementary type electricityDaily electricity charge at price, Bill0Salary is the wage of workers at alternative complementary electricity rates, Salary for the daily electricity rate at the traditional peak-to-valley electricity rates0The method is the wage of workers under the traditional peak-valley electricity price.
Preferably, the mathematical model established by the optimal user load calculation module further includes:
the first constraint function is:
Figure BDA0002667459780000031
the second constraint function is:
Stat，unite＝random(ON_prot，unite) ③；
Stat，k1＝Stat，k2＝…＝Stat，m＝…＝Sta t，unite ④；
Figure BDA0002667459780000032
wherein Stat，uniteA logical operating state as a device operating in parallel;
the third constraint function is:
ON_prot，k，old＝ON_prot，k，new ⑥；
Figure BDA0002667459780000033
wherein, ON _ prot，k，oldAnd ON _ prot，k，newThe switching probability of the device k at the conventional peak-to-valley electricity rate in the t period and the switching probability of the device k at the alternative complementary electricity rate in the t period are expressed.
Preferably, the first constraint function, the second constraint function, and the third constraint function are derived by combining a daily electricity rate calculation formula under the alternative complementary electricity rates, where the daily electricity rate calculation formula specifically includes:
Figure BDA0002667459780000041
Figure BDA0002667459780000042
wherein the time step is set to 1 minute, and the behavior of the device k in the period t is defined as the switching probability by (ON _ pro)tk) To represent; the logic working state of the device k in the t period is StatiFrom (ON _ prot)k) Generating a random function of (1); random () is a random function; pr (total reflection)tThe electricity price of the standby complementary electricity price in the time period t; k is the power consumption of the device K in the period t; 60 indicates 60 minutes for 1 hour and 1440 indicates 1440 minutes for 1 day.
Preferably, the first constraint function, the second constraint function, and the third constraint function are further derived by combining a worker wage calculation formula under the alternative complementary electricity price, where the worker wage calculation formula specifically is:
Figure BDA0002667459780000043
Figure BDA0002667459780000044
preferably, the mathematical model established by the power grid dispatching simplification optimization module includes:
an objective function:
Figure BDA0002667459780000045
the constraint function is:
Figure BDA0002667459780000046
Figure BDA0002667459780000047
Figure BDA0002667459780000048
wherein, Ci，tThe power generation cost at the moment t of the ith generator, Pi，tIs the power generation amount at the moment t of the ith generator, Dj，tFor the optimum time-sequential load at the jth load time t, PLmaxFor maximum tidal power limit of grid line, SF is transfer factor matrix, KP is generator correlation matrix, PtIs a unit matrix at the time t, KD is a load incidence matrix, DtFor the optimal time-series load group matrix at time t, Pmin，PmaxAnd the generator set generating capacity lower limit matrix and the generator set generating capacity upper limit matrix are obtained, NG is the number of the generator sets, and ND is the optimal time sequence conforming group number.
Preferably, the expression of the index constraint function output by the index constraint module is as follows:
Figure BDA0002667459780000051
Figure BDA0002667459780000052
among them, Cost-oldCost for traditional peak-to-valley electricity prices; cost-newFor cost at alternative complementary electricity prices, α is the minimum percentage reduction in cost at complementary electricity prices, ON _ protkRepresenting the probability of switching of device k during time t, ON _ protk，BBSFor setting the switching probability of the device k in the time period t according to the alternative complementary electricity price, ON _ protk，0The switching probability of the device k in the time period t is set according to the traditional peak-valley electricity price.
Preferably, the mathematical model established by the power grid dispatching module is as follows:
the objective function is:
Figure BDA0002667459780000053
the constraint function is:
Figure BDA0002667459780000054
Figure BDA0002667459780000055
Figure BDA0002667459780000056
Figure BDA0002667459780000061
Figure BDA0002667459780000062
Figure BDA0002667459780000063
Figure BDA0002667459780000064
wherein: NG is the number of units, NT is the number of hours, Fi(Pit) Generating cost at time t, P, of a cost function for the ith unititIs the generated power of the ith unit at the moment t, IitFor start-stop state of ith unit at time t, SUitAnd SDitFor the start-stop cost of the ith unit for t hours, Dnew_tFor the total load of the high-component flexible load user group at time t, Dold_tAt time tTotal load of other user groups, LosstIs the net rack loss at time t, URiFor the power rise slope, UP, between any two times of the ith moduleiFor the power rise slope, DR, at the initial time of the ith unitiFor the power down slope, DP, between any two times of the ith groupiFor the power down slope, P, at the initial moment of the ith unittThe generated power at the time t, KP is the generator set incidence matrix of the power plant, KDnewLoad incidence matrix, KD, for a high-component flexible load user groupoldThe load correlation matrix of other user groups, B is the node susceptance matrix,
Figure BDA0002667459780000065
is the voltage phase angle, xb is the impedance matrix, KL is the line correlation matrix, KLtFor network flow at time t, PLmin，PLmaxThe minimum value and the maximum value of the network power flow.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
according to the invention, through the cooperation of the optimal demander response complementary new electricity price making system, the identification judging module and the power grid scheduling module, by means of a complementary electricity price mechanism, a plurality of complementary electricity prices linear electricity prices are provided for a high-component flexible load group, off-peak electricity utilization is realized, the operation cost of the power system is reduced, and the social energy efficiency level is improved.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a sub-flowchart of the optimal acquirer response complementary new price making system in accordance with the present invention;
FIG. 3 is a sub-flow diagram of the objective function module of the present invention;
wherein the reference numbers in the figures represent respectively: 1, the optimal demander responds to a complementary new electricity price making system; 2 identifying a judging module; 3, a power grid dispatching module; 4 high-component flexible load user group; 5 other user groups; 11 an objective function module; 12 index constraint module; 13 an optimization algorithm module; 111 an optimal user load calculation module; 112 grid dispatch simplification optimization module.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Example 1
As shown in fig. 1, a demand response complementary electricity price system for high-component flexible loads includes an optimal demand response complementary new electricity price formulation system 1, an identification judgment module 2, a power grid scheduling module 3, a high-component flexible load user group 4, and other user groups 5; wherein:
the optimal demander response complementary new electricity price making system 1 is connected with the identification judging module 2;
the optimal demander response complementary new electricity price making system 1 is connected with the power grid dispatching module 3;
the optimal demander response complementary new electricity price making system 1 is connected with a high-component flexible load user group 4;
the optimal demander response complementary new electricity price making system 1 is connected with other user groups 5;
the identification judgment module 2 is connected with a high-component flexible load user group 4;
the power grid dispatching module 3 is connected with a high-component flexible load user group 4;
the power grid dispatching module 3 is connected with other user groups 5.
In the above scheme, the optimal demand party response complementary new electricity price making system 1 is responsible for receiving traditional peak-valley electricity prices of a power grid, unit parameters (including start-stop cost, output curve, output range, climbing constraint, start-stop time constraint and the like of each unit), grid frame parameters (including reactance, length, current constraint and the like of each grid frame line), and related equipment data of a high-component flexible load user group 4 (a group of users with large individual load capacity, small group quantity and large load transfer potential), and finally outputting an optimal complementary new electricity price set and an identifier set (the optimal complementary new electricity price set has complementarity of 'high you low I, low I high' in a low inertia area, and the identifier set is 0 and 1 identifiers); the identification judging module 2 is responsible for judging the identification combination output by the optimal demander response complementary new electricity price formulating system 1 (if the identification is 1, the high-component flexible load user group 4 will accept the optimal complementary new electricity price, if the identification is 0, the high-component flexible load user group 4 will accept the traditional peak-valley electricity price); the power grid dispatching module 3 is responsible for receiving unit parameters and grid frame parameters of a power grid, the total load of the high-component flexible load user group 4 and the loads of other user groups 5, establishing a corresponding mathematical model for optimization calculation, and finally outputting the power grid cost of optimal demander response.
As shown in fig. 2, specifically, the optimal demander response complementary new electricity price making system 1 includes an objective function module 11, an index constraint module 12 and an optimization algorithm module 13; wherein:
the input end of the objective function module 11 is connected with the input end of the high-composition flexible load user group 4;
the output end of the objective function module 11 is connected with the input end of the optimization algorithm module 13;
the output end of the index constraint module 12 is connected with the input end of the optimization algorithm module 13.
In the above scheme, the objective function module 11 is responsible for receiving the unit parameters, grid frame parameters, traditional peak-valley electricity prices, constraints of each electric device of the high-component flexible load user group 4 (including normal operation constraints, immovable constraints of start and stop of the device, linked operation constraints of a production line, shortest start time constraints of the device, maximum start probability constraints, and the like), alternative complementary electricity prices of each high-component flexible load user (i.e. electricity prices estimated by the power grid through the device constraints of the high-component flexible load user group 4), and constructing an objective function, and finally outputting an optimal power grid cost expression under the alternative complementary electricity prices; the index constraint module 12 is responsible for comparing the cost and the benefit and outputting a cost index constraint function; the optimization algorithm module 13 is responsible for receiving the optimal power grid cost expression under the alternative complementary power rates output by the objective function module 11 and the cost index constraint function output by the index constraint module 12, and finally outputting an optimal complementary new power rate set and an identification set through an optimization calculation method (such as a lagrange multiplier method and a genetic algorithm).
As shown in fig. 3, specifically, the objective function module 11 includes an optimal user load calculation module 111 and a power grid scheduling simplification optimization module 112; wherein:
the input end of the optimal user load calculation module 111 is connected with the output end of the high-component flexible load user group 4;
the output end of the optimal user load calculation module 111 is connected with the input end of the power grid dispatching simplification optimization module 112.
In the above scheme, the optimal user load calculation module 111 is responsible for receiving the constraints of each electric device of the high-component flexible load user group 4, the alternative complementary electricity prices of each high-component flexible load user, and the traditional peak-valley electricity prices of the power grid, and establishing a corresponding mathematical model, and finally calculating an optimal time sequence load group set (i.e. an optimal load group set); the power grid dispatching simplification optimization module 112 is responsible for receiving the unit parameters and the grid frame parameters of the power grid and the optimal time sequence load group set output by the optimal user load calculation module 111, establishing a simplified and optimized mathematical model, and finally calculating the optimal power grid cost under the alternative complementary power price.
Specifically, the mathematical model established by the optimal user load calculation module 111 includes:
an objective function: min: cost-new＝(Bill-Bill0)+(Salary-Salary0) ①；
Wherein, (Bill-Bill)0) For typical daily bill changes of electricity charges, (Salary-Salary0) Typical daily wage changes. Bill is the daily electricity charge at the alternative complementary electricity price, Bill0For the daily electricity charge at the conventional peak-to-valley electricity rate, Salary isSalary, a worker pay at alternative complementary electricity prices0The method is the wage of workers under the traditional peak-valley electricity price.
In the above scheme, the optimal user load calculation module 111 minimizes the potential cost by controlling the devices of the high flexible load users.
Specifically, the mathematical model established by the optimal user load calculation module 111 further includes:
the first constraint function is:
Figure BDA0002667459780000091
the second constraint function is:
Stat，unite＝random(ON_prot，unite) ③；
Stat，k1＝Stat，k2＝…＝Stat，m＝…＝Sta t，unite ④；
Figure BDA0002667459780000092
wherein Stat，uniteA logical operating state as a device operating in parallel;
the third constraint function is:
ON_prot，k，old＝ON-prot，k，new ⑥；
Figure BDA0002667459780000093
wherein, ON _ prot，k，oldAnd ON _ prot，k，newThe switching probability of the device k at the conventional peak-to-valley electricity rate in the t period and the switching probability of the device k at the alternative complementary electricity rate in the t period are expressed.
In the above scheme, the high-component flexible load user group 4 has a certain number of product manufacturing tasks per week or per household, daily productivity needs to be ensured to ensure the number of products, and the total switching time of each device should be kept consistent, so that a first constraint function can be obtained; the devices on the same assembly line will open the switches at the same time, so that different devices in an assembly line work in parallel on the same schedule, thereby obtaining a second constraint function; some equipment start-up procedures require high costs, and therefore these consumers usually remain operating for 24 hours, from which a third constraint function is derived under which the control schedule of the respective equipment remains the same at any demand response price.
Specifically, the first constraint function, the second constraint function, and the third constraint function are derived by combining a daily electricity rate calculation formula under the alternative complementary electricity rates, where the daily electricity rate calculation formula specifically includes:
Figure BDA0002667459780000101
Figure BDA0002667459780000102
wherein the time step is set to 1 minute, and the behavior of the device k in the period t is defined as the switching probability by (ON _ pro)tk) To represent; the logic working state of the device k in the t period is StatiFrom (ON _ pro)tk) Generating a random function of (1); random () is a random function; pr (total reflection)tThe electricity price of the standby complementary electricity price in the time period t; k is the power consumption of the device K in the period t; 60 indicates 60 minutes for 1 hour and 1440 indicates 1440 minutes for 1 day.
In the above-described embodiment, it can be seen that the daily electricity rate at the alternative complementary electricity rate is about ON _ protkThe behavior of the function.
Specifically, the first constraint function, the second constraint function, and the third constraint function are further derived by combining a worker wage calculation formula under the alternative complementary electricity price, where the worker wage calculation formula specifically is:
Figure BDA0002667459780000103
Figure BDA0002667459780000104
in the above scheme, it can be known that the wages of workers at the standby complementary electricity prices are about ON _ potkFunction behavior, wherein the workers required by a certain equipment in different periods of the day have a deep relationship with the operation of the equipment, equipment behavior ON _ protkAnd number of workers pnumtkStatistics can be summarized by survey data; if payroll remains unchanged for all time periods within 1 day and the total "on" time of all devices within 1 day, then it may be deleted from the objective function (salt-salt)0) Part (c) of (a).
Specifically, the mathematical model established by the power grid dispatching simplification optimization module 112 includes:
an objective function:
Figure BDA0002667459780000111
the constraint function is:
Figure BDA0002667459780000112
Figure BDA0002667459780000113
Figure BDA0002667459780000114
wherein, Ci，tThe power generation cost at the moment t of the ith generator, Pi，tIs the power generation amount at the moment t of the ith generator, Dj，tFor the optimum time-sequential load at the jth load time t, PLmaxThe maximum tidal current power limit of the grid line, SF is the transfer factorSubmatrix, KP being a generator correlation matrix, PtIs a unit matrix at the time t, KD is a load incidence matrix, DtFor the optimal time-series load group matrix at time t, Pmin，PmaxAnd the generator set generating capacity lower limit matrix and the generator set generating capacity upper limit matrix are obtained, NG is the number of the generator sets, and ND is the optimal time sequence conforming group number.
In the above scheme, formula
Figure BDA0002667459780000117
For power balance constraints, formulas
Figure BDA0002667459780000118
For power flow constraint, formula
Figure BDA0002667459780000119
Generating constraint for the unit; the optimal power grid cost under the alternative complementary type electricity price can be obtained by solving through a branch-and-bound method, a Lagrange relaxation method and the like.
Specifically, the expression of the index constraint function output by the index constraint module 12 is as follows:
Figure BDA0002667459780000115
Figure BDA0002667459780000116
among them, Cost-oldCost for traditional peak-to-valley electricity prices; cost-newFor the cost at the alternative complementary electricity prices, α is the minimum percentage reduction in cost at the complementary electricity prices, ON _ protkRepresenting the probability of switching of device k during time t, ON _ protk，BBSFor setting the switching probability of the device k in the time period t according to the alternative complementary electricity price, ON _ protk，0The switching probability of the device k in the time period t is set according to the traditional peak-valley electricity price.
In the scheme, the function restricts whether the high-component flexible load user group 4 is willing to participate in a scheduling mechanism under the complementary new power price of the power grid.
Specifically, the mathematical model established by the power grid dispatching module 3 is as follows:
the objective function is:
Figure BDA0002667459780000121
the constraint function is:
Figure BDA0002667459780000122
Figure BDA0002667459780000123
Figure BDA0002667459780000124
Figure BDA0002667459780000125
Figure BDA0002667459780000126
Figure BDA0002667459780000127
Figure BDA0002667459780000128
wherein: NG is the number of units, NT is the number of hours, Fi(Pit) Generating cost at time t, P, of a cost function for the ith unititIs the generated power of the ith unit at the moment t, IitFor start-stop state of ith unit at time t, SUitAnd SDitFor the start-stop cost of the ith unit for t hours, Dnew_tFor the total load of the high-component flexible load user group at time t, Dold_tIs the total load of other user groups at time t, LosstIs the net rack loss at time t, URiFor the power rise slope, UP, between any two times of the ith moduleiFor the power rise slope, DR, at the initial time of the ith unitiFor the power down slope, DP, between any two times of the ith groupiFor the power down slope, P, at the initial moment of the ith unittThe generated power at the time t, KP is the generator set incidence matrix of the power plant, KDnewLoad incidence matrix, KD, for a high-component flexible load user groupoldThe load correlation matrix of other user groups, B is the node susceptance matrix,
Figure BDA0002667459780000131
is the voltage phase angle, xb is the impedance matrix, KL is the line correlation matrix, KLtFor network flow at time t, PLmin，PLmaxThe minimum value and the maximum value of the network power flow.
In the scheme, the objective function realizes the minimization of the operation cost; the constraint function comprises generator set constraint and transmission network complete constraint; wherein, the formula
Figure BDA0002667459780000132
For system power balance constraints, formula
Figure BDA0002667459780000133
For actual generator capacity constraints, formula
Figure BDA0002667459780000134
For generator set power ramp constraints, formula
Figure BDA0002667459780000135
For generator set power droop constraints, formula
Figure BDA0002667459780000136
For node power balance constraints, formulas
Figure BDA0002667459780000137
For tidal current equation constraints, formulas
Figure BDA0002667459780000138
Is a power flow constraint; the mathematical model can be solved by, for example, a branch-and-bound method, a Lagrange relaxation method, and the like, so as to obtain the power grid cost of the optimal demander response.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. A demand response complementary electricity price system facing high-component flexible loads is characterized by comprising an optimal demander response complementary new electricity price formulation system (1), an identification judgment module (2), a power grid scheduling module (3), a high-component flexible load user group (4) and other user groups (5); wherein:
the optimal demander response complementary new electricity price making system (1) is connected with the identification judging module (2);
the optimal demander response complementary new electricity price making system (1) is connected with the power grid dispatching module (3);
the optimal demander response complementary new electricity price making system (1) is connected with a high-component flexible load user group (4);
the optimal demander response complementary new electricity price making system (1) is connected with other user groups (5);
the identification judgment module (2) is connected with the high-component flexible load user group (4);
the power grid dispatching module (3) is connected with the high-component flexible load user group (4);
the power grid dispatching module (3) is connected with other user groups (5).
2. The high-composition flexible load-oriented demand response complementary electricity price system according to claim 1, wherein the optimal demand response complementary new electricity price formulating system (1) comprises an objective function module (11), an index constraint module (12) and an optimization algorithm module (13); wherein:
the input end of the objective function module (11) is connected with the input end of the high-component flexible load user group (4);
the output end of the objective function module (11) is connected with the input end of the optimization algorithm module (13);
the output end of the index constraint module (12) is connected with the input end of the optimization algorithm module (13).
3. The high-component flexible load-oriented demand response complementary electricity price system according to claim 2, wherein the objective function module (11) comprises an optimal user load calculation module (111), a power grid dispatching simplification optimization module (112); wherein:
the input end of the optimal user load calculation module (111) is connected with the output end of the high-component flexible load user group (4);
the output end of the optimal user load calculation module (111) is connected with the input end of the power grid dispatching simplification optimization module (112).
4. A demand response complementary electricity pricing system for high ingredient flexible loads according to claim 3, characterized in that the mathematical model established by the optimal customer load calculating module (111) comprises:
an objective function: min: cost-new＝(Bill-Bill0)+(Salary-Salary0) ①
Wherein, (Bill-Bill)0) For typical daily bill changes of electricity charges, (Salary-Salary0) Bill is the daily electricity charge at alternative complementary rates of electricity for typical daily payroll changes, Bill0Salary is the wage of workers at alternative complementary electricity rates, Salary for the daily electricity rate at the traditional peak-to-valley electricity rates0The method is the wage of workers under the traditional peak-valley electricity price.
5. The demand response complementary electricity pricing system for high-ingredient flexible loads according to claim 3, characterized in that the mathematical model established by the optimal customer load calculating module (111) further comprises:
the first constraint function is:
Figure FDA0002667459770000021
the second constraint function is:
Stat，unite＝random(ON_prot，unite) ③；
Stat，k1＝Stat，k2＝…＝Stat，m＝…＝Stat，unite ④；
Figure FDA0002667459770000022
wherein Stat，uniteA logical operating state as a device operating in parallel;
the third constraint function is:
ON_prot，k，t，old＝ON_prt，k，new ⑥；
Figure FDA0002667459770000023
wherein, ON _ prot，k，oldAnd ON_prot，k，newThe switching probability of the device k at the conventional peak-to-valley electricity rate in the t period and the switching probability of the device k at the alternative complementary electricity rate in the t period are expressed.
6. The demand response complementary electricity rate system for high-component flexible loads according to claim 5, wherein the first constraint function, the second constraint function and the third constraint function are derived by combining a daily electricity rate calculation formula under the alternative complementary electricity rate, wherein the daily electricity rate calculation formula is specifically as follows:
Figure FDA0002667459770000031
Figure FDA0002667459770000032
wherein the time step is set to 1 minute, and the behavior of the device k in the period t is defined as the switching probability by (ON _ pro)tk) To represent; the logic working state of the device k in the t period is StatiFrom (ON _ pro)tk) Generating a random function of (1); random () is a random function; pr (total reflection)tThe electricity price of the standby complementary electricity price in the time period t; k is the power consumption of the device K in the period t; 60 indicates 60 minutes for 1 hour and 1440 indicates 1440 minutes for 1 day.
7. The demand response complementary electricity price system for the high-component flexible load according to claim 5, wherein the first constraint function, the second constraint function and the third constraint function are further derived by combining a worker wage calculation formula under the alternative complementary electricity price, wherein the worker wage calculation formula is specifically as follows:
Figure FDA0002667459770000033
Figure FDA0002667459770000034
8. the complementary demand response electricity price system for high-component flexible loads according to claim 3, wherein the mathematical model established by the grid dispatching simplification optimization module (112) comprises:
an objective function:
Figure FDA0002667459770000035
the constraint function is:
Figure FDA0002667459770000036
Figure FDA0002667459770000041
Figure FDA0002667459770000042
wherein, Ci，tThe power generation cost at the moment t of the ith generator, Pi，tIs the power generation amount at the moment t of the ith generator, Dj，tFor the optimum time-sequential load at the jth load time t, PLmaxFor maximum tidal power limit of grid line, SF is transfer factor matrix, KP is generator correlation matrix, PtIs a unit matrix at the time t, KD is a load incidence matrix, DtFor the optimal time-series load group matrix at time t, Pmin，PmaxAnd the generator set generating capacity lower limit matrix and the generator set generating capacity upper limit matrix are obtained, NG is the number of the generator sets, and ND is the optimal time sequence conforming group number.
9. A demand response complementary electricity price system for high ingredient flexible loads according to claim 2, characterized in that the expression of the target constraint function output by the target constraint module (12) is as follows:
Figure FDA0002667459770000043
Figure FDA0002667459770000044
among them, Cost-oldCost for traditional peak-to-valley electricity prices; cost-newAlpha is the minimum percentage reduction of the cost at the complementary electricity price,
Figure FDA0002667459770000045
representing the probability of switching of device k during time t,
Figure FDA0002667459770000046
for setting the switching probability of the device k in the time period t according to the alternative complementary electricity price, ON _ protk，0The switching probability of the device k in the time period t is set according to the traditional peak-valley electricity price.
10. A high-component flexible load oriented demand response complementary electricity pricing system according to claim 1, characterized in that the grid dispatching module (3) builds a mathematical model as follows:
the objective function is:
Figure FDA0002667459770000047
the constraint function is:
Figure FDA0002667459770000051
Figure FDA0002667459770000052
Figure FDA0002667459770000053
Figure FDA0002667459770000054
Figure FDA0002667459770000055
Figure FDA0002667459770000056
Figure FDA0002667459770000057
wherein: NG is the number of units, NT is the number of hours, Fi(Pit) Generating cost at time t, P, of a cost function for the ith unititIs the generated power of the ith unit at the moment t, IitFor start-stop state of ith unit at time t, SUitAnd SDitFor the start-stop cost of the ith unit for t hours, Dnew_tFor the total load of the high-component flexible load user group at time t, Dold_tIs the total load of other user groups at time t, LosstIs the net rack loss at time t, URiFor the power rise slope, UP, between any two times of the ith moduleiFor the power rise slope, DR, at the initial time of the ith unitiFor power reduction between any two times of the ith unitSlope, DPiFor the power down slope, P, at the initial moment of the ith unittThe generated power at the time t, KP is the generator set incidence matrix of the power plant, KDnewLoad incidence matrix, KD, for a high-component flexible load user groupoldIs a load incidence matrix of other user groups, B is a node susceptance matrix, theta is a voltage phase angle, xb is an impedance matrix, KL is a line incidence matrixtFor network flow at time t, PLmin，PLmaxThe minimum value and the maximum value of the network power flow.
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