CN107730032B - Universal energy network electricity-cooling-heating triple co-generation optimized scheduling system and method - Google Patents

Universal energy network electricity-cooling-heating triple co-generation optimized scheduling system and method Download PDF

Info

Publication number
CN107730032B
CN107730032B CN201710874681.8A CN201710874681A CN107730032B CN 107730032 B CN107730032 B CN 107730032B CN 201710874681 A CN201710874681 A CN 201710874681A CN 107730032 B CN107730032 B CN 107730032B
Authority
CN
China
Prior art keywords
energy
entropy
electricity
operation cost
internal combustion
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
CN201710874681.8A
Other languages
Chinese (zh)
Other versions
CN107730032A (en
Inventor
许裕栗
周欢
周静
甘中学
刘正
杜磊
周涛
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.)
Enn Energy Power Technology Shanghai Co ltd
ENN Science and Technology Development Co Ltd
Original Assignee
Enn Energy Power Technology Shanghai Co ltd
ENN Science and Technology Development 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 Enn Energy Power Technology Shanghai Co ltd, ENN Science and Technology Development Co Ltd filed Critical Enn Energy Power Technology Shanghai Co ltd
Priority to CN201710874681.8A priority Critical patent/CN107730032B/en
Publication of CN107730032A publication Critical patent/CN107730032A/en
Application granted granted Critical
Publication of CN107730032B publication Critical patent/CN107730032B/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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Operations Research (AREA)
  • Development Economics (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Health & Medical Sciences (AREA)
  • Educational Administration (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Feedback Control In General (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to the field of energy Internet, in particular to a universal energy network electricity-cooling-heating triple co-generation optimization scheduling system and method. The input module is used for obtaining demand prediction data of electricity and heat in a pre-divided four-ring section of electricity and heat triple supply of the universal energy network, fixed parameters of each device, current state parameters of each device and a preset optimization step length, the optimization scheduling module is used for respectively establishing an optimization scheduling model of operation cost and entropy increase to obtain an optimization scheduling result, and the output module is used for outputting the optimization scheduling result to each device to control the operation of each device.

Description

Universal energy network electricity-cooling-heating triple co-generation optimized scheduling system and method
Technical Field
The invention relates to the field of energy Internet, in particular to a universal energy network electricity-cooling-heating triple-generation optimized scheduling system and method.
Background
The rapid development of renewable energy sources and the establishment of an energy internet connecting an energy network and an information network are both the development trend of the world and the inevitable choice for establishing a modern energy system, solving the national energy safety and environmental safety of China and realizing sustainable development. At present, the economic status of China is still in extensive development, the energy utilization efficiency is low and is only 36.8 percent, which is lower than the average level (50 percent) of the world.
The energy internet can be understood as a novel power network node formed by a large number of distributed energy acquisition devices, distributed energy storage devices and various loads and interconnected by comprehensively utilizing an advanced power electronic technology, an information technology and an intelligent management technology so as to realize energy peer-to-peer exchange and sharing network with bidirectional energy flow. The energy internet is formed by connecting a plurality of energy local area networks, and the energy local area networks are formed by energy routers, power generation equipment, energy storage equipment and alternating current/direct current loads, can be connected to the power grid for working, and can also be disconnected from the power grid for independent operation.
The energy internet has a distributed characteristic, and as a new generation of energy supply mode, a distributed energy system comprises a plurality of energy devices, such as photovoltaic cells, wind driven generators, internal combustion engines, gas turbines, gas boilers, absorption type waste heat units and the like, and can simultaneously meet the requirements of various energy load types such as electricity, heat and cold. Thus, there may be different energy supply modes and scheduling strategies for the same energy demand.
In the prior art, the optimization scheduling for the distributed energy system is mainly from the economic perspective, the capacity of equipment is optimized, the optimization is single, in practice, different energies have different qualities, and the energy utilization rate is very important when the system is actually operated, and in the prior art, the optimization problem of the energy utilization rate is not considered.
Disclosure of Invention
The embodiment of the invention provides a system and a method for optimizing and scheduling universal energy grid power supply, cold supply and hot supply, and aims to solve the problems that in the prior art, the optimization of an energy system is single, and the energy utilization rate in actual operation is not considered.
The embodiment of the invention provides the following specific technical scheme:
an optimal scheduling system for electricity, cold and heat triple supply of a universal energy network comprises:
the input module is used for respectively acquiring demand prediction data of electric heating and cooling in a pre-divided four-ring section of the universal energy network electric heating and cooling triple supply, fixed parameters of each device, current state parameters of each device and a preset optimization step length;
the optimization scheduling module is used for respectively establishing a calculation model of the operation cost and the entropy increase of each device in the four pre-divided links according to the data acquired by the input module and the determined energy grades in different energy forms, respectively establishing an optimization scheduling model of the operation cost and the entropy increase of the universal energy network electricity-cooling-heating triple supply by taking the operation cost and the entropy increase as optimization targets based on the calculation model of the operation cost and the entropy increase of each device, and respectively performing optimization scheduling based on the optimization scheduling model of the operation cost and the entropy increase to obtain an optimization scheduling result;
and the output module is used for respectively outputting the optimized scheduling result obtained by the optimized scheduling module to each device in the pre-divided four links of the universal energy network electricity, cooling and heating triple co-generation so as to enable each device to adjust and operate based on the optimized scheduling result.
Preferably, the pre-divided four sections for electricity, cold and heat triple supply of the universal energy network are respectively a production section, a storage and transportation section, a recovery section and a user section;
the production link at least comprises a photovoltaic cell, an internal combustion engine, a gas boiler and a compression type water chilling unit, the storage and transportation link at least comprises a storage battery, the recovery link at least comprises a waste heat boiler and an absorption type waste heat unit, and the user link at least comprises electric equipment, heat equipment and cold equipment;
the input module is specifically configured to: acquiring demand forecast data of electric heating and cooling, which at least comprises the following steps: acquiring the electricity generation forecast of a photovoltaic cell 24 hours a day, the electricity/heat demand forecast of a user 24 hours a day in winter and the electricity/cold demand forecast of the user 24 hours a day in summer;
obtaining fixed parameters of each device, at least comprising: acquiring rated power and minimum start-stop time of an internal combustion engine, rated power of a gas boiler, rated power of a compression type water chilling unit and unit electricity selling price of a power grid;
acquiring the maximum capacity limit and the minimum capacity limit of the storage battery;
acquiring rated power of a waste heat boiler and rated power of an absorption type waste heat unit;
acquiring current state parameters of each device, wherein the current state parameters at least comprise: acquiring the current power generation power of the internal combustion engine, the current operating power of the gas boiler, the current refrigeration power of the compression type water chilling unit and the current stored electric quantity value of the storage battery;
and acquiring a preset optimization step length.
Preferably, according to the data acquired by the input module and the determined energy grades of different energy forms, a calculation model of the operation cost and entropy increase of each device in the four pre-divided links is respectively established, and the optimization scheduling module is specifically configured to:
determining an entropy-increasing function of the combustion gas fuel based on the determined entropy-increasing calculation mode according to the determined available heat of the combustion gas fuel of the internal combustion engine and the energy grade of the combustion gas fuel;
determining an entropy increasing function of the combustion gas fuel based on a determined entropy increasing calculation mode according to the heat quantity taken away by the waste heat flue gas and the energy grade of the flue gas;
determining an entropy increasing function of the cylinder sleeve hot water based on the determined entropy increasing calculation mode according to the heat quantity taken away by the cylinder sleeve hot water and the energy grade of the hot water;
determining an operating cost function of the internal combustion engine based on the unit price of the gaseous fuel and the flow rate of the gaseous fuel used;
and establishing a power generation constraint condition of the internal combustion engine according to the maximum value and the minimum value of the rated power of the internal combustion engine, and establishing a minimum start-stop time constraint condition of the internal combustion engine according to the minimum start-stop time of the internal combustion engine.
Preferably, according to the data acquired by the input module and the determined energy grades of different energy forms, a calculation model of the operation cost and entropy increase of each device in the four pre-divided links is respectively established, and the optimization scheduling module is specifically configured to:
determining an entropy increasing function of the gas boiler based on the determined entropy increasing calculation mode according to the heat supply quantity of the gas boiler and the energy grade of the gas fuel;
determining an operation cost function of the gas boiler according to the unit price of the gas fuel and the flow rate of the used gas fuel;
and establishing an operation constraint condition of the gas boiler according to the maximum value and the minimum value of the rated power of the gas boiler.
Preferably, according to the data acquired by the input module and the determined energy grades of different energy forms, a calculation model of the operation cost and entropy increase of each device in the four pre-divided links is respectively established, and the optimization scheduling module is specifically configured to:
determining an operation cost function of the compression type water chilling unit according to the unit electricity selling price of the power grid and the refrigeration power consumption of the compression type water chilling unit;
and establishing an operation constraint condition of the compression type water chilling unit according to the maximum value and the minimum value of the rated power of the compression type water chilling unit.
Preferably, according to the data acquired by the input module and the determined energy grades of different energy forms, a calculation model of the operation cost and entropy increase of each device in the four pre-divided links is respectively established, and the optimization scheduling module is specifically configured to:
determining the operation cost generated by purchasing or selling electricity to the large power grid according to the unit electricity selling price of the power grid and the electricity purchasing or selling electricity to the large power grid;
when electricity is purchased to a large power grid, an entropy increasing function generated by thermal power generation is determined based on a determined entropy increasing calculation mode according to a heat value generated by burning coal fuel and the energy grade of the coal fuel.
Preferably, the optimization scheduling module is further configured to:
establishing an electric quantity constraint condition of the storage battery according to the maximum capacity limit and the minimum capacity limit of the storage battery;
and establishing a constraint condition of electric cold and heat supply and demand balance at least according to the generated energy of the internal combustion engine, the power consumption and refrigeration amount of the compression type water chilling unit, the generated energy of the photovoltaic cell, the heat supply amount of the gas boiler, the electric energy for purchasing or selling electricity to a large power grid, the electric energy for charging or discharging the storage battery, the predicted electric, cold and heat demands of a user, the refrigerating/heat supply amount by using the generated waste heat of the internal combustion engine and the cold/heat accumulation amount.
Preferably, based on the calculation model of the operation cost and the entropy increase of each device, with the minimum operation cost and the minimum entropy increase as an optimization objective, the optimal scheduling model of the operation cost and the entropy increase of the electricity-cooling-heating triple supply of the smart energy grid is respectively established, and the optimal scheduling module is specifically configured to:
establishing an operation cost objective function by taking the minimum operation cost as an optimization target according to the operation cost function of the internal combustion engine, the operation cost function of the gas-fired boiler, the operation cost of the compression type water chilling unit and the operation cost of purchasing/selling electricity to a large power grid;
establishing an entropy increase objective function by taking the minimum entropy increase as an optimization objective according to the entropy increase of the internal combustion engine, the entropy increase of the gas boiler and the entropy increase caused by thermal power generation when power is purchased to a large power grid;
and taking the power generation constraint condition of the internal combustion engine, the minimum start-stop time constraint condition of the internal combustion engine, the operation constraint condition of the gas boiler, the operation constraint condition of the compression type water chilling unit, the electric quantity constraint condition of the storage battery and the constraint condition of the electricity-cooling-heat supply-demand balance as the constraint conditions of the operation cost objective function and the entropy increasing objective function.
An optimal scheduling method for electricity, cooling and heating triple co-generation of a universal energy network comprises the following steps:
respectively acquiring demand prediction data of electric heating and cooling in a pre-divided four-ring section for electricity heating and cooling triple supply of the universal energy network, fixed parameters of each device, current state parameters of each device and a preset optimization step length;
respectively establishing a calculation model of the operation cost and entropy increase of each device in the four pre-divided links according to the data acquired by the input module and the determined energy grades of different energy forms;
on the basis of the calculation models of the operation cost and the entropy increase of each device, respectively establishing an optimization scheduling model of the operation cost and the entropy increase of the electricity-cooling-heating triple co-generation of the smart energy network by taking the operation cost and the entropy increase as optimization targets, and respectively carrying out optimization scheduling on the basis of the optimization scheduling models of the operation cost and the entropy increase to obtain an optimization scheduling result;
and respectively outputting the optimized scheduling result obtained by the optimized scheduling module to each device in the pre-divided four links of the universal energy network electricity, cooling and heating triple supply so as to enable each device to adjust and operate based on the optimized scheduling result.
Preferably, the pre-divided four sections for electricity, cold and heat triple supply of the universal energy network are respectively a production section, a storage and transportation section, a recovery section and a user section;
the production link at least comprises a photovoltaic cell, an internal combustion engine, a gas boiler and a compression type water chilling unit, the storage and transportation link at least comprises a storage battery, the recovery link at least comprises a waste heat boiler and an absorption type waste heat unit, and the user link at least comprises electric equipment, heat equipment and cold equipment;
the method includes the steps of obtaining demand prediction data of electric heating and cooling water, fixed parameters of each device and current state parameters of each device in pre-divided four-ring sections of electricity heating and cooling triple supply of the universal energy grid respectively, and specifically includes the following steps:
acquiring demand forecast data of electric heating and cooling, which at least comprises the following steps: acquiring the electricity generation forecast of a photovoltaic cell 24 hours a day, the electricity/heat demand forecast of a user 24 hours a day in winter and the electricity/cold demand forecast of the user 24 hours a day in summer;
obtaining fixed parameters of each device, at least comprising: acquiring rated power and minimum start-stop time of an internal combustion engine, rated power of a gas boiler, rated power of a compression type water chilling unit and unit electricity selling price of a power grid;
acquiring the maximum capacity limit and the minimum capacity limit of the storage battery;
acquiring rated power of a waste heat boiler and rated power of an absorption type waste heat unit;
acquiring current state parameters of each device, wherein the current state parameters at least comprise: and acquiring the current power generation power of the internal combustion engine, the current operating power of the gas boiler, the current refrigeration power of the compression type water chilling unit and the current stored electric quantity value of the storage battery.
Preferably, the method includes respectively establishing a calculation model of the operation cost and entropy increase of each device in the four pre-divided links according to the data acquired by the input module and the determined energy grades of different energy forms, and specifically includes:
determining an entropy-increasing function of the combustion gas fuel based on the determined entropy-increasing calculation mode according to the determined available heat of the combustion gas fuel of the internal combustion engine and the energy grade of the combustion gas fuel;
determining an entropy increasing function of the combustion gas fuel based on a determined entropy increasing calculation mode according to the heat quantity taken away by the waste heat flue gas and the energy grade of the flue gas;
determining an entropy increasing function of the cylinder sleeve hot water based on the determined entropy increasing calculation mode according to the heat quantity taken away by the cylinder sleeve hot water and the energy grade of the hot water;
determining an operating cost function of the internal combustion engine based on the unit price of the gaseous fuel and the flow rate of the gaseous fuel used;
and establishing a power generation constraint condition of the internal combustion engine according to the maximum value and the minimum value of the rated power of the internal combustion engine, and establishing a minimum start-stop time constraint condition of the internal combustion engine according to the minimum start-stop time of the internal combustion engine.
Preferably, the method includes respectively establishing a calculation model of the operation cost and entropy increase of each device in the four pre-divided links according to the data acquired by the input module and the determined energy grades of different energy forms, and specifically includes:
determining an entropy increasing function of the gas boiler based on the determined entropy increasing calculation mode according to the heat supply quantity of the gas boiler and the energy grade of the gas fuel;
determining an operation cost function of the gas boiler according to the unit price of the gas fuel and the flow rate of the used gas fuel;
and establishing an operation constraint condition of the gas boiler according to the maximum value and the minimum value of the rated power of the gas boiler.
Preferably, the method includes respectively establishing a calculation model of the operation cost and entropy increase of each device in the four pre-divided links according to the data acquired by the input module and the determined energy grades of different energy forms, and specifically includes:
determining an operation cost function of the compression type water chilling unit according to the unit electricity selling price of the power grid and the refrigeration power consumption of the compression type water chilling unit;
and establishing an operation constraint condition of the compression type water chilling unit according to the maximum value and the minimum value of the rated power of the compression type water chilling unit.
Preferably, the method includes respectively establishing a calculation model of the operation cost and entropy increase of each device in the four pre-divided links according to the data acquired by the input module and the determined energy grades of different energy forms, and specifically includes:
determining the operation cost generated by purchasing or selling electricity to the large power grid according to the unit electricity selling price of the power grid and the electricity purchasing or selling electricity to the large power grid;
when electricity is purchased to a large power grid, an entropy increasing function generated by thermal power generation is determined based on a determined entropy increasing calculation mode according to a heat value generated by burning coal fuel and the energy grade of the coal fuel.
Preferably, further comprising:
establishing an electric quantity constraint condition of the storage battery according to the maximum capacity limit and the minimum capacity limit of the storage battery;
and establishing a constraint condition of electric cold and heat supply and demand balance at least according to the generated energy of the internal combustion engine, the power consumption and refrigeration amount of the compression type water chilling unit, the generated energy of the photovoltaic cell, the heat supply amount of the gas boiler, the electric energy for purchasing or selling electricity to a large power grid, the electric energy for charging or discharging the storage battery, the predicted electric, cold and heat demands of a user, the refrigerating/heat supply amount by using the generated waste heat of the internal combustion engine and the cold/heat accumulation amount.
Preferably, based on the calculation model of the operation cost and the entropy increase of each device, with the minimum operation cost and the minimum entropy increase as an optimization objective, the optimal scheduling model of the operation cost and the entropy increase of the electricity-cooling-heating triple supply of the smart energy grid is respectively established, which specifically includes:
establishing an operation cost objective function by taking the minimum operation cost as an optimization target according to the operation cost function of the internal combustion engine, the operation cost function of the gas-fired boiler, the operation cost of the compression type water chilling unit and the operation cost of purchasing/selling electricity to a large power grid;
establishing an entropy increase objective function by taking the minimum entropy increase as an optimization objective according to the entropy increase of the internal combustion engine, the entropy increase of the gas boiler and the entropy increase caused by thermal power generation when power is purchased to a large power grid;
and taking the power generation constraint condition of the internal combustion engine, the minimum start-stop time constraint condition of the internal combustion engine, the operation constraint condition of the gas boiler, the operation constraint condition of the compression type water chilling unit, the electric quantity constraint condition of the storage battery and the constraint condition of the electricity-cooling-heat supply-demand balance as the constraint conditions of the operation cost objective function and the entropy increasing objective function.
The invention has the following beneficial effects: the cold and hot trigeminy of smart energy network electricity supplies optimization dispatch system includes: the input module is used for respectively acquiring demand prediction data of electric heating and cooling in a pre-divided four-ring section of the universal energy network electric heating and cooling triple supply, fixed parameters of each device, current state parameters of each device and a preset optimization step length; the optimization scheduling module is used for respectively establishing a calculation model of the operation cost and the entropy increase of each device in the four pre-divided links according to the data acquired by the input module and the determined energy grades in different energy forms, respectively establishing an optimization scheduling model of the operation cost and the entropy increase of the universal energy network electricity-cooling-heating triple supply by taking the operation cost and the entropy increase as optimization targets based on the calculation model of the operation cost and the entropy increase of each device, and respectively performing optimization scheduling based on the optimization scheduling model of the operation cost and the entropy increase to obtain an optimization scheduling result; and the output module is used for respectively outputting the optimized scheduling results obtained by the optimized scheduling module to each device in the pre-divided four links of the combined supply of power, cooling and heating of the smart energy network so as to enable each device to adjust and operate based on the optimized scheduling results, thus, the combined supply of power, cooling and heating of the smart energy network is divided into a four-link structure, each device in the four-link structure is fully combined, energy grades in different energy forms are introduced, different energies of power, cooling and heating in the combined supply of the smart energy network are fully utilized, the energy utilization rate is improved, meanwhile, an optimized scheduling model taking the minimum running cost and entropy increase as an optimization target is considered, different optimized scheduling models can be selected under different requirements, and the optimization flexibility is improved.
Drawings
FIG. 1 is a basic frame diagram of the electric cooling and heating triple supply of the universal energy grid in the embodiment of the invention;
FIG. 2 is a schematic diagram of a four-ring structure for electric cooling and heating triple supply of the universal energy network in the embodiment of the invention;
FIG. 3 is a diagram illustrating an optimized scheduling system for electricity, cooling and heating triple co-generation in the smart power grid according to an embodiment of the present invention;
fig. 4 is an optimized scheduling method for electricity, cooling and heating triple co-generation of the smart energy grid in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Aiming at the universal energy network electricity-cooling-heating triple generation, all links are fully combined, energy grade is introduced, energy in the system is fully utilized, each device in the four pre-divided links of the universal energy network electricity-cooling-heating triple generation is modeled, an optimized scheduling model of the universal energy network electricity-cooling-heating triple generation based on operation cost and entropy increase is built respectively, an optimized scheduling model based on the operation cost and the entropy increase is built respectively, optimized scheduling is carried out, and optimized scheduling results are obtained, so that the purpose of optimizing and controlling each device by using the minimum operation cost and the minimum entropy increase is achieved.
The present invention will be described in detail with reference to specific examples, but it is to be understood that the present invention is not limited to the examples.
The embodiment of the invention mainly aims at the electricity, cold and heat triple supply of the universal energy network, and carries out optimized scheduling on the electricity, cold and heat triple supply so as to control the operation of each device. Referring to fig. 1, a basic frame diagram of the universal energy grid for electric cooling and heating triple generation is shown.
The universal energy network electricity cold and heat triple supply is a system which integrates the electricity generation, refrigeration and heating processes and connects an electric network, a heat network and a cold network. In addition, in the embodiment of the invention, the electricity cold and heat triple generation of the universal energy grid is divided into the four-ring structure according to the transmission and conversion process of the energy, and the flow directions of three energy forms of electricity, cold and heat in the four-ring structure are determined.
In the embodiment of the invention, the optimal scheduling system for electricity, cooling and heating triple supply of the universal energy network is respectively communicated with the four pre-divided nodes to realize the optimal scheduling of the nodes.
The pre-divided four sections for the electricity, cold and heat triple supply of the universal energy network are respectively a production section, a storage and transportation section, a recovery section and a user section.
The production link at least comprises a photovoltaic cell, an internal combustion engine, a gas boiler and a compression type water chilling unit, the storage and transportation link at least comprises a storage battery, the recovery link at least comprises a waste heat boiler and an absorption type waste heat unit, the user link at least comprises electric equipment, heat equipment and cold equipment, and further the storage and transportation link can also comprise water heat storage and water cold storage.
Fig. 2 is a schematic diagram of a four-ring structure for electric cooling and heating triple supply of the smart grid according to the embodiment of the invention. Therefore, the connection between the four sections of the universal energy grid for electricity, cold and heat supply and the flow direction of three energy forms of electricity, cold and heat in the four sections of the structure can be known.
The electric energy generated in the production link is supplied to the electric energy demand of a user, the waste heat discharged after the equipment generates electricity in the generation link is used for supplying heat and cooling to the user through the waste heat recycling equipment in the recycling link, the cascade utilization of energy is realized, and the primary energy utilization rate of the whole system is improved.
Referring to fig. 3, in the embodiment of the present invention, the optimal scheduling system for electricity, cooling and heating triple supply of the smart grid includes:
and the input module is used for respectively acquiring demand prediction data of electric heating and cooling in the pre-divided four-ring section of the universal energy network electric heating and cooling triple supply, fixed parameters of each device, current state parameters of each device and preset optimization step length.
And the optimization scheduling module is used for respectively establishing a calculation model of the operation cost and the entropy increase of each device in the four pre-divided links according to the data acquired by the input module and the determined energy grades in different energy forms, respectively establishing an optimization scheduling model of the operation cost and the entropy increase of the universal energy network electricity-cooling-heating triple supply by taking the operation cost and the entropy increase as optimization targets based on the calculation model of the operation cost and the entropy increase of each device, and respectively performing optimization scheduling based on the optimization scheduling model of the operation cost and the entropy increase to obtain an optimization scheduling result.
And the output module is used for respectively outputting the optimized scheduling result obtained by the optimized scheduling module to each device in the pre-divided four links of the universal energy network electricity, cooling and heating triple co-generation so as to enable each device to adjust and operate based on the optimized scheduling result.
The input module, the optimized scheduling module and the output module are respectively described in detail below:
i, an input module.
The input module is respectively connected with the pre-divided four rings of the electricity, cold and heat triple supply of the universal energy network, and is specifically used for:
1) acquiring demand forecast data of electric heating and cooling, which at least comprises the following steps: acquiring the electricity generation forecast of a photovoltaic cell 24 hours a day, the electricity/heat demand forecast of a user 24 hours a day in winter and the electricity/cold demand forecast of the user 24 hours a day in summer;
2) obtaining fixed parameters of each device, at least comprising: acquiring rated power and minimum start-stop time of an internal combustion engine, rated power of a gas boiler, rated power of a compression type water chilling unit and unit electricity selling price of a power grid;
acquiring the maximum capacity limit and the minimum capacity limit of the storage battery;
acquiring rated power of a waste heat boiler and rated power of an absorption type waste heat unit;
3) acquiring current state parameters of each device, wherein the current state parameters at least comprise: the current generating power of the internal combustion engine, the current operating power of the gas boiler, the current refrigerating power of the compression type water chilling unit and the current stored electric quantity value of the storage battery.
4) And acquiring a preset optimization step length.
That is to say, in the embodiment of the present invention, according to actual requirements, the input module may respectively obtain relevant data of the devices required in the four links, and of course, in the embodiment of the present invention, the data obtained by the input module is not limited to the data parameters, and may also obtain other data according to actual requirements, so as to establish a model for the optimal scheduling module and provide data for optimal scheduling.
And II, optimizing a scheduling module.
And the optimization scheduling module is connected with the input module, establishes a model according to the data acquired by the input module, and performs optimization scheduling to acquire an optimization scheduling result. Specifically, the method comprises the following steps:
firstly, according to data acquired by an input module and the determined energy grades of different energy forms, a calculation model of the operation cost and entropy increase of each device in the four pre-divided links is respectively established.
In the embodiment of the invention, the energy grade is introduced into the calculation of entropy increase, wherein the energy grade represents the quality of different energy forms, and the energy quality is reflected in the process of transferring and converting different energies. The different species of different energy "qualities" are distinguished, on the basis of the degree of conversion of the different energy, into fully convertible energy, partially convertible energy and non-convertible energy.
For the convenience of the following understanding, in the embodiments of the present invention, the entropy is defined and given first
Figure BDA0001417856720000123
And the calculation of the energy grade in different energy forms.
In practice, a system is generally regarded as a steady-flow system in engineering, and the flows of the inlet and outlet of the system are in energy exchange with the environment
Figure BDA0001417856720000124
The values may be defined as follows: when the system is reversibly changed from an arbitrary state to an equilibrium state, the part of energy in the system which can be maximally converted into 'high-grade' energy is called
Figure BDA0001417856720000125
Analyzing stable flow system, determining the flow kinetic energy and potential energy in case of neglecting flow
Figure BDA0001417856720000126
Definition of (1):
E＝Eph+Ech
wherein E isphIs physics of
Figure BDA0001417856720000127
EchIs made of chemical
Figure BDA0001417856720000128
For simplified calculations, for current-stabilizing systems, and physics
Figure BDA0001417856720000129
Compared with chemical
Figure BDA00014178567200001210
Negligible and therefore obtainable from the first law of thermodynamics, of current-stabilizing systems
Figure BDA00014178567200001211
Comprises the following steps: e ≈ Eph＝H-H0-T0(S-S0)。
Wherein H is the enthalpy of the steady flow working medium, S is the entropy of the steady flow working medium, H0For constant flow of working fluid in the ambient state of enthalpy, S0For entropy of steady-flow working medium in ambient state, T0Is the standard ambient temperature.
According to
Figure BDA00014178567200001212
Before and after the thermodynamic process
Figure BDA00014178567200001213
The variation can be expressed as: Δ E ═ Δ H-T0ΔS
Therefore, the calculation formulas of the energy grade λ and the entropy increase Δ S can be determined respectively:
Figure BDA0001417856720000121
Figure BDA0001417856720000122
according to a calculation formula for determining the energy grade lambda, the energy grades of different energy forms can be respectively calculated, which are respectively as follows: hot water lambdahElectric lambdaeLambda of natural gasgAnd flue gas lambdar。
Based on the analysis and calculation, respectively establishing a calculation model of the operation cost and the entropy increase of each device in the four pre-divided links, specifically respectively determining a target function and constraint conditions of each device based on the operation cost and the entropy increase, and respectively:
1) an internal combustion engine.
a. Entropy increase of internal combustion engines can be divided into three parts: entropy increase of combustion gas fuel, entropy increase of waste heat smoke and entropy increase of cylinder sleeve hot water. The waste heat flue gas and the cylinder sleeve hot water are power generation waste heat forms of internal combustion gas, although the internal combustion engine can have various power generation waste heat forms, the heat of the waste heat flue gas and the cylinder sleeve hot water is larger and is a main part which can be utilized, and therefore in the embodiment of the invention, the entropy increase of the power generation waste heat forms of the two parts is only calculated.
(1) The entropy of burning the gaseous fuel increases.
Determining an entropy-increasing function of the combustion gas fuel based on the determined entropy-increasing calculation mode according to the determined available heat of the combustion gas fuel and the energy grade of the gas fuel, and specifically comprising the following steps:
flow rate M of gaseous fuel in k periodg,k(Nm3H) and the heat Q generated by combustion in the combustion chamberg',kCan be approximated as a linear relationship: qg',k＝Mg,k·LHV·ηf。
The available heat can be expressed as: qg,k＝Qg',k·ηg＝Mg,k·LHV·ηf·ηg
The entropy increase of the combustion gas fuel can be expressed as:
Figure BDA0001417856720000131
wherein LHV is the lower fuel calorific value, etafFor combustion efficiency, ηpFor efficiency of power generation, λgIs the energy grade, P, of the gas fuelg,kElectrical output power for the k period.
Electric power generation efficiency η of internal combustion enginepThe generating efficiency of the generating set under different load rates is different along with the change of the electrical load alpha of the generating set.
(2) Entropy of the waste heat smoke increases.
And determining an entropy increasing function of the combustion gas fuel based on the determined entropy increasing calculation mode according to the heat quantity taken away by the waste heat flue gas and the energy grade of the flue gas.
The heat taken away by the waste heat flue gas can be expressed as: qh,g,k＝Qg',k·ηh,g
The residual heat smoke entropy increase can be expressed as:
Figure BDA0001417856720000141
wherein eta ish,gIs the heat coefficient taken away by the waste heat flue gas.
(3) The entropy of the cylinder liner hot water increases.
And determining an entropy increasing function of the cylinder sleeve hot water based on the determined entropy increasing calculation mode according to the heat taken away by the cylinder sleeve hot water and the energy grade of the hot water.
The amount of heat removed by the cylinder liner hot water can be expressed as: qh,w,k＝Qg',k·ηh,w
The entropy increase of the liner hot water can be expressed as:
Figure BDA0001417856720000142
wherein eta ish,wIs the coefficient of heat taken away by the cylinder liner water.
b. The operating cost of the internal combustion engine, i.e., the power generation cost, is mainly reflected in the cost of the gaseous fuel, and the operating cost function of the internal combustion engine is determined based on the unit price of the gaseous fuel and the flow rate of the gaseous fuel used, for example, the power generation cost of the internal combustion engine may be represented as CgMg,k。
Wherein, CgRepresenting the unit price of a gaseous fuel, e.g. natural gas, Mg,kRepresenting the flow rate of the gaseous fuel during the period k,
Figure BDA0001417856720000143
the cost of power generation by the internal combustion engine can be expressed as:
Figure BDA0001417856720000144
c. constraints of the internal combustion engine. The power generation of the internal combustion engine needs to be subjected to constraint conditions at least including the constraints of the upper and lower limits of the generated energy, the constraint of start-stop time and the like.
(1) In order to ensure the stable and safe operation of the internal combustion engine, the power generation amount of the internal combustion engine must be constrained by upper and lower limits of the power generation amount, and a power generation constraint condition of the internal combustion engine is established according to the maximum value and the minimum value of the rated power of the internal combustion engine, wherein the power generation constraint condition can be expressed as: p is a radical ofg,min·νg,t≤pg,t≤pg,max·νg,t t＝k+1,k+2,...,k+T
Wherein the content of the first and second substances,
Figure BDA0001417856720000151
pg,kthe running power of the internal combustion engine and the generating efficiency eta of the internal combustion engine in the system state quantity acquired at the last momentpAs the unit electrical load rate α changes, the electrical output power during the k period can be expressed as: pg,k＝Qg,k·ηp。
νg,tIs a variable from 0 to 1, when vg,tWhen the value is 1, the internal combustion engine set is in an operating state, and when v isg,tWhen 0, it means that it is in the off state.
(2) Establishing a minimum start-stop time constraint condition of the internal combustion engine according to the minimum start-stop time of the internal combustion engine, which specifically comprises the following steps: in order to describe the minimum start-stop time constraint of the unit, a start-stop variable v of the internal combustion engine is introducedg,kThe variable is a variable from 0 to 1 when vg,kWhen the value is 1, the internal combustion engine set is in an operating state, and when v isg,kWhen 0, it means that it is in the off state.
When the unit is initially in a starting state, the variable is utilized to describe the constraint condition of the starting and stopping time of the internal combustion engine as a linear expression of mixed integers:
Figure BDA0001417856720000152
Figure BDA0001417856720000153
Figure BDA0001417856720000154
where NT is the total duration, TonIs the shortest starting time length, UT is the time length that the unit must be in the starting state when the unit is started initially, and UT is max {0, min [ NT (T)on-Xon)vg,0]}。
When the unit is initially in a shutdown state, the internal combustion engine start-stop time constraint condition can be described as follows:
Figure BDA0001417856720000155
Figure BDA0001417856720000156
Figure BDA0001417856720000157
wherein, ToffIs the shortest shutdown duration, DT is the duration that the unit must be in shutdown state when initially starting, and DT ═ max {0, min [ NT, (T)off-Xoff)(1-vg,0)]}。
2) A gas boiler.
a. The relationship between the heat supply amount of the gas boiler and the fuel consumption is as follows: qb,k＝Mb,k·LHV·ηb
Wherein M isb,kFor a period of k natural gas flow, Qb,kThe heat supply of the boiler is nbThe operation efficiency of the gas boiler.
Determining an entropy-increasing function of the gas boiler based on the determined entropy-increasing calculation mode according to the heat supply amount of the gas boiler and the energy grade of the gas fuel, wherein the entropy increase of the gas boiler can be represented as:
Figure BDA0001417856720000161
b. determining an operation cost function of the gas boiler according to the unit price of the gas fuel and the flow rate of the used gas fuel, wherein the operation cost of the gas boiler can be expressed as:
Figure BDA0001417856720000162
c. operating constraints of the gas boiler.
In order to safely operate the gas boiler during heating/cooling, the operation power of the gas boiler must be limited by upper and lower limits, and the operation constraint condition of the gas boiler is established according to the maximum value and the minimum value of the rated power of the gas boiler, so the operation constraint condition can be expressed as: qb,min·νb,t≤Qb,t≤Qb,max·νb,t t＝k+1,k+2,...,k+T
Wherein the content of the first and second substances,
Figure BDA0001417856720000163
Qb,kand the operation power of the gas boiler in the system state quantity acquired at the last moment is obtained. V isb,tIs a variable from 0 to 1, when vb,tWhen the value is 1, the gas boiler is in the running state, and v isb,tWhen 0, it means that it is in the off state.
3) Compression type cooling water set.
a. According to the unit electricity selling price of the power grid and the refrigeration power consumption of the compression type water chilling unit, determining an operation cost function of the compression type water chilling unit, which specifically comprises the following steps:
the relationship between the power consumption and the refrigerating capacity of the compression type water chilling unit during refrigeration can be expressed as follows:
Figure BDA0001417856720000164
wherein Q iscc,kIs the refrigerating capacity of the unit in the period k, Pc,kIs the power consumption, COP, of the unit in the k time periodcIs the refrigeration coefficient of the unit.
Refrigeration cost of compression type water chilling unitCan be expressed as:
Figure BDA0001417856720000171
wherein, CeThe price of the unit electric quantity of the power grid is.
b. In order to ensure that the compression type water chilling unit can safely operate during heating/cooling, the operation power of the compression type water chilling unit must be restricted by upper and lower limits, and operation restriction conditions of the compression type water chilling unit are established according to the maximum value and the minimum value of rated power of the compression type water chilling unit, and the restriction conditions can be expressed as:
Qcc,min·νc,t≤Qcc,t≤Qcc,max·νc,t t＝k+1,k+2,...,k+T
wherein the content of the first and second substances,
Figure BDA0001417856720000172
Qcc,kthe running power of the electric compression type unit in the system state quantity acquired at the last moment is obtained. V isc,tIs a variable from 0 to 1, when vc,tWhen the motor-driven compression unit is in an operating state, v is equal to 1c,tWhen 0, it means that it is in the off state.
4) And (4) a storage battery.
In order to prevent the overcharge or the overdischarge of the battery, the charge of the battery needs to be maintained at a maximum charge capacity and a minimum charge capacity, and according to the capacity limit of the battery, including the maximum capacity limit and the minimum capacity limit of the battery, the charge constraint condition of the battery can be described as follows:
SOCmin≤SOCt≤SOCmax t＝k+1,k+2,...,k+T
wherein the content of the first and second substances,
Figure BDA0001417856720000173
pb,tis the amount of charge (discharge) of the battery during the k period when
Figure BDA0001417856720000174
When, indicates that the battery is charged, when pb,tIf < 0, it indicates that the battery is discharged. p is a radical ofb,kIs composed ofQcc,kThe battery capacity in the system state quantity collected at the last moment.
5) Large power grid
a. According to the unit electricity selling price of the power grid and the electricity purchasing or selling quantity of electricity to the large power grid, determining the operation cost generated by electricity purchasing or selling to the large power grid, wherein the operation cost can be expressed as: fm,k＝Cepm,k
Wherein p ism,kThe electricity quantity of purchasing or selling electricity to the large power grid in the period k, when pm,kWhen the power is more than 0, the power is purchased to a large power grid, and when p is greater than 0m,kAnd when the power is less than 0, the power is sold to a large power grid.
b. In the embodiment of the invention, when electricity is purchased from a large power grid, entropy increase caused by the electricity needs to be considered. In fact, the mainstream power generation mode in China still is thermal power generation at present. Coal is used as a fossil fuel, and in a power generation system, chemical energy thereof is generally converted into physical energy by direct combustion, and thermal power conversion is performed by a rankine cycle. High-temperature flue gas is generated by coal combustion, and the energy level value is greatly reduced.
Determining an entropy increase function generated by thermal power generation based on a determined entropy increase calculation mode according to a heat value generated by burning coal fuel and the energy grade of the coal fuel, and specifically comprising the following steps:
the power generation amount and coal consumption amount of coal combustion power generation can be expressed as: m isk＝μ·pm,k
The heat generated thereby can be expressed as: qc,k＝mk·qc
Wherein q iscThe calorific value of the coal is equal to 29307 kJ/kg.
The entropy increase due to the thermal power generation can be expressed as:
Figure BDA0001417856720000181
6) and determining the electric cooling and heating supply and demand balance constraint condition.
The power supply/heat supply/refrigeration of the production link of the electricity-cold-heat triple supply of the universal energy grid needs to meet the requirements of users, the utilization of energy in the electricity-cold-heat triple supply of the universal energy grid is fully considered, and the constraint condition of the balance of electricity-cold-heat supply and demand is established at least according to the generated energy of an internal combustion engine, the power consumption and the refrigerating capacity of a compression type water chilling unit, the generated energy of a photovoltaic cell, the heat supply capacity of a gas boiler, the electric quantity of electricity purchased or sold to a large power grid, the electric quantity of charging or discharging of a storage battery, the electricity, cold and heat demand forecast quantity of the users, the refrigeration/heat supply quantity by using the power generation waste heat of the internal combustion engine and the cold/heat quantity of the cold storage/heat. The electric cooling and heating supply and demand balance constraint condition can be expressed as:
Pg,t+Ppv,t+Pm,t-Pb,t＝PL,t(+Pc,t) t＝k+1,k+2,...,k+T
QC,t+Qcc,k-Qs,t＝QCL,t t＝k+1,k+2,...,k+T
QH,t+Qb,k-Qs,t＝QHL,t t＝k+1,k+2,...,k+T
wherein, Pc,tFormula is the power consumption of the electric compression unit in the t period in summer, Pg,tIs the power generation of the internal combustion engine in the period of t, Ppv,tIs the photovoltaic power generation amount in the period t, Pm,tThe electricity quantity is purchased (sold) to a large power grid in the period of t, Pb,tIs the amount of charge (discharge) of the accumulator during the period t, PL,tIs the power demand of the user during the period t. QC/H,tThe quantity of refrigeration/heat supply by using the power generation waste heat of the internal combustion engine in the period of t, Qcc,tIs the refrigerating capacity of a compression type water chiller unit in the time period of t, Qb,tIs a gas boilertTime interval heat supply, Qs,tIs the cold (heat) storage amount in the period t, QCL,tAnd QHL,tRespectively, the cold and hot demands during the period t.
And then, based on the models of the devices based on the operation cost and the entropy increase, respectively establishing an optimal scheduling model of the operation cost and the entropy increase of the electricity-cooling-heating triple supply of the universal energy network with the minimum operation cost and the minimum entropy increase as optimization targets.
The method specifically comprises the following two optimal scheduling models:
a. and optimizing a scheduling model based on the operation cost.
And establishing an operation cost objective function by taking the minimum operation cost as an optimization objective according to the operation cost of the internal combustion engine, the operation cost of the gas-fired boiler, the operation cost of the compression type water chilling unit and the operation cost of purchasing/selling electricity to the large power grid.
In the period k, when the optimization step size is considered as T, the operation cost objective function is as follows:
Figure BDA0001417856720000191
further, in the embodiment of the present invention, the lowest energy efficiency utilization rate is selected, and the power generation efficiency of the internal combustion engine is written into the objective function:
ηp＝(8.935+33.157·α-27.081·α2+17.989·α3)≥λ％
where λ is the given lowest energy efficiency utilization in the target conditions.
The constraint conditions of the operation cost objective function at least comprise a power generation constraint condition of the internal combustion engine, a minimum start-stop time constraint condition of the internal combustion engine, an operation constraint condition of the gas boiler, an operation constraint condition of the compression type water chilling unit, an electric quantity constraint condition of the storage battery and a constraint condition of electric cold and heat supply and demand balance.
b. An entropy increase based optimized scheduling model.
And establishing an entropy increase objective function by taking the minimum entropy increase as an optimization objective according to the entropy increase of the internal combustion engine, the entropy increase of the gas boiler and the entropy increase of the thermal power generation when purchasing electricity to a large power grid.
In period k, considering the optimization step as T, the objective function is:
Figure BDA0001417856720000192
the constraint conditions of the entropy increase objective function at least comprise a power generation constraint condition of the internal combustion engine, a minimum start-stop time constraint condition of the internal combustion engine, an operation constraint condition of a gas boiler, an operation constraint condition of a compression type water chilling unit, an electric quantity constraint condition of a storage battery and a constraint condition of electric-cold heat supply and demand balance.
And finally, performing optimized scheduling respectively based on the operation cost and the optimized scheduling model of entropy increase to obtain an optimized scheduling result.
According to the two different optimization scheduling models, different optimization scheduling models can be selected according to actual requirements, optimization scheduling can be carried out with minimum operation cost or with minimum entropy increase as an optimization target,
the method specifically comprises the following steps:
a. and (5) running an optimized scheduling model of the cost.
Based on the operation cost optimization scheduling model, an optimization step length is set to be 5, for example, and a lambda value is set, so that the minimum operation cost under different lambda values can be solved, rolling optimization is performed by the optimization step length, and an optimization scheduling result is obtained.
The lambda value can be set according to the objective function, the relation between the operation cost and the power generation efficiency is obtained, two factors of economic benefit and energy efficiency utilization are integrated, and a better working point is selected. Then, at the working point, namely after the lambda value is selected, respectively calculating an optimization problem of 24-hour combined supply of summer cold power and winter heat power.
b. An entropy increase based optimized scheduling model.
And setting an optimization step length to be 5 for example based on the entropy-increasing optimization scheduling model, and performing rolling optimization to obtain an optimization scheduling result.
Specifically, the winter electricity/heat optimized scheduling and summer electricity/cold optimized scheduling problems with the economic operation as the optimization target, and the winter electricity/heat optimized scheduling and summer electricity/cold optimized scheduling problems with the entropy increase minimum as the optimization target can be solved respectively. For example, the current time k is taken as 1, and the current time k is substituted into the established optimization model to calculate the value of the optimization variable. Then, the current time k is updated to be k +1, and the step is repeated until the optimal scheduling problem of 24 hours a day is solved.
Therefore, in the embodiment of the invention, when an optimized scheduling model is established, all equipment in four links of universal energy grid electricity-cooling-heating triple supply are fully combined, power generation and a gas turbine set are fully combined, peak shaving equipment such as a gas boiler and a compression type water chilling unit is used, distributed renewable clean energy and waste heat generated in the power generation process of an internal combustion engine are fully utilized, and the energy utilization rate is improved.
Meanwhile, the optimization scheduling is carried out by taking the minimum economic operation cost and entropy increase as optimization targets, different optimization scheduling models can be selected under different requirements, rolling optimization is carried out, corresponding optimization scheduling results are obtained, and the optimization results are richer.
And thirdly, an output module.
The output module is connected with the optimized scheduling module, is connected with a generation link, a storage and transportation link and a user link of the universal energy network electricity, cold and heat triple supply, and is specifically used for:
and respectively outputting the optimized scheduling result obtained by the optimized scheduling module to each device in the pre-divided four links of the universal energy network electricity, cooling and heating triple supply so as to enable each device to adjust and operate based on the optimized scheduling result.
For example, the output module may output the optimized scheduling result to a control unit of each device, such as an internal combustion engine, a gas boiler, a compression-type water chiller, a storage battery, and the like, respectively, and the control unit controls the operation of the corresponding device.
Referring to fig. 4, in the embodiment of the present invention, an optimal scheduling method for electricity, cooling and heating triple co-generation in an universal energy grid includes:
step 400: and respectively acquiring demand prediction data of electric heating and cooling water, fixed parameters of each device, current state parameters of each device and preset optimization step length in the pre-divided four-ring section for electricity heating and cooling combined supply of the universal energy network.
In the embodiment of the invention, the pre-divided four sections for electricity, cold and heat triple supply of the universal energy network are respectively a production section, a storage and transportation section, a recovery section and a user section.
The production link at least comprises a photovoltaic cell, an internal combustion engine, a gas boiler and a compression type water chilling unit, the storage and transportation link at least comprises a storage battery, the recovery link at least comprises a waste heat boiler and an absorption type waste heat unit, and the user link at least comprises electric equipment, heat equipment and cold equipment.
Wherein, the demand forecast data of electricity cold heat at least includes: the method comprises the steps of photovoltaic cell power generation pre-measurement 24 hours a day, user electricity/heat demand pre-measurement 24 hours a day in winter and user electricity/cold demand pre-measurement 24 hours a day in summer.
The fixed parameters of each device at least comprise: rated power and minimum start-stop time of the internal combustion engine, rated power of the gas boiler, rated power of the compression type water chilling unit and unit electricity selling price of the power grid. Acquiring the maximum capacity limit and the minimum capacity limit of the storage battery; and acquiring the rated power of the waste heat boiler and the rated power of the absorption type waste heat unit.
The current state parameters of each device at least comprise: the current generating power of the internal combustion engine, the current operating power of the gas boiler, the current refrigerating power of the compression type water chilling unit and the current stored electric quantity value of the storage battery.
Step 410: and respectively establishing a calculation model of the operation cost and entropy increase of each device in the four pre-divided links according to the data acquired by the input module and the determined energy grades of different energy forms.
When step 410 is executed, the following cases can be classified:
in the first case: to an internal combustion engine. The entropy increase of the internal combustion engine can be divided into three parts, namely: entropy increase of combustion gas fuel, entropy increase of waste heat smoke and entropy increase of cylinder sleeve hot water.
1) And determining an entropy-increasing function of the combustion gas fuel based on the determined entropy-increasing calculation mode according to the determined available heat of the combustion gas fuel of the internal combustion engine and the energy grade of the combustion gas fuel.
2) And determining an entropy increasing function of the combustion gas fuel based on the determined entropy increasing calculation mode according to the heat quantity taken away by the waste heat flue gas and the energy grade of the flue gas.
3) And determining an entropy increasing function of the cylinder sleeve hot water based on the determined entropy increasing calculation mode according to the heat taken away by the cylinder sleeve hot water and the energy grade of the hot water.
4) An operating cost function of the internal combustion engine is determined based on the unit price of the gaseous fuel and the flow rate of the gaseous fuel used.
5) And establishing a power generation constraint condition of the internal combustion engine according to the maximum value and the minimum value of the rated power of the internal combustion engine, and establishing a minimum start-stop time constraint condition of the internal combustion engine according to the minimum start-stop time of the internal combustion engine.
In the second case: the utility model relates to a gas boiler.
1) And determining an entropy increasing function of the gas boiler based on the determined entropy increasing calculation mode according to the heat supply quantity of the gas boiler and the energy grade of the gas fuel.
2) The operating cost function of the gas boiler is determined according to the unit price of the gas fuel and the flow rate of the used gas fuel.
3) And establishing an operation constraint condition of the gas boiler according to the maximum value and the minimum value of the rated power of the gas boiler.
In the third case: aiming at a compression type water chilling unit.
1) And determining the running cost function of the compression type water chilling unit according to the unit electricity selling price of the power grid and the refrigeration power consumption of the compression type water chilling unit.
2) And establishing an operation constraint condition of the compression type water chilling unit according to the maximum value and the minimum value of the rated power of the compression type water chilling unit.
In a fourth case: the electricity is purchased or sold for a large power grid.
1) And determining the operation cost generated by purchasing or selling electricity to the large power grid according to the unit electricity selling price of the power grid and the electricity purchasing or selling electricity to the large power grid.
2) When electricity is purchased to a large power grid, an entropy increasing function generated by thermal power generation is determined based on a determined entropy increasing calculation mode according to a heat value generated by burning coal fuel and the energy grade of the coal fuel.
Further, the following constraints are also established:
1) to a battery.
And establishing an electric quantity constraint condition of the storage battery according to the maximum capacity limit and the minimum capacity limit of the storage battery.
2) Aiming at cold and heat supply and demand balance of the electric cold and heat triple power supply of the universal energy network.
And establishing a constraint condition of electric cold and heat supply and demand balance at least according to the generated energy of the internal combustion engine, the power consumption and refrigeration amount of the compression type water chilling unit, the generated energy of the photovoltaic cell, the heat supply amount of the gas boiler, the electric energy for purchasing or selling electricity to a large power grid, the electric energy for charging or discharging the storage battery, the predicted electric, cold and heat demands of a user, the refrigerating/heat supply amount by using the generated waste heat of the internal combustion engine and the cold/heat accumulation amount.
Of course, the method is not limited to establishing the calculation model of the operating cost and the entropy increase of the above several devices, and other more devices may be selected or arbitrarily selected from the above several devices according to actual needs, which is not limited in the embodiment of the present invention.
Step 420: and respectively establishing an optimal scheduling model of the operation cost and the entropy increase of the universal energy network electricity-cooling-heating triple co-generation and carrying out optimal scheduling on the basis of the calculation model of the operation cost and the entropy increase of each device and taking the operation cost and the entropy increase as optimization targets, and respectively carrying out optimal scheduling on the basis of the optimal scheduling model of the operation cost and the entropy increase to obtain an optimal scheduling result.
When step 420 is executed, the method specifically includes:
1) and establishing an operation cost objective function by taking the minimum operation cost as an optimization target according to the operation cost function of the internal combustion engine, the operation cost function of the gas-fired boiler, the operation cost of the compression type water chilling unit and the operation cost of purchasing/selling electricity to the large power grid.
2) And establishing an entropy increase objective function by taking the minimum entropy increase as an optimization objective according to the entropy increase of the internal combustion engine, the entropy increase of the gas boiler and the entropy increase caused by thermal power generation when power is purchased to a large power grid.
3) And taking the power generation constraint condition of the internal combustion engine, the minimum start-stop time constraint condition of the internal combustion engine, the operation constraint condition of the gas boiler, the operation constraint condition of the compression type water chilling unit, the electric quantity constraint condition of the storage battery and the constraint condition of the electricity-cooling-heat supply-demand balance as the constraint conditions of the operation cost objective function and the entropy increasing objective function.
Step 430: and respectively outputting the optimized scheduling result obtained by the optimized scheduling module to each device in the pre-divided four links of the universal energy network electricity, cooling and heating triple supply so as to enable each device to adjust and operate based on the optimized scheduling result.
It should be noted that, the specific execution method is the same as the execution method in the foregoing universal energy grid power supply, cooling and heating triple supply optimization scheduling system, and details are not repeated here.
In summary, in the embodiment of the present invention, the optimal scheduling system for electricity, cooling and heating triple supply of the smart energy grid includes: the input module is used for respectively acquiring demand prediction data of electric heating and cooling in a pre-divided four-ring section of the universal energy network electric heating and cooling triple supply, fixed parameters of each device, current state parameters of each device and a preset optimization step length; the optimization scheduling module is used for respectively establishing a calculation model of the operation cost and the entropy increase of each device in the four pre-divided links according to the data acquired by the input module and the determined energy grades in different energy forms, respectively establishing an optimization scheduling model of the operation cost and the entropy increase of the universal energy network electricity-cooling-heating triple supply by taking the operation cost and the entropy increase as optimization targets based on the calculation model of the operation cost and the entropy increase of each device, and respectively performing optimization scheduling based on the optimization scheduling model of the operation cost and the entropy increase to obtain an optimization scheduling result; and the output module is used for respectively outputting the optimized scheduling results obtained by the optimized scheduling module to each device in the pre-divided four links of the combined supply of power, cooling and heating of the smart energy network so as to enable each device to adjust and operate based on the optimized scheduling results, thus, the combined supply of power, cooling and heating of the smart energy network is divided into a four-link structure, each device in the four-link structure is fully combined, energy grades in different energy forms are introduced, different energies of power, cooling and heating in the combined supply of the smart energy network are fully utilized, the energy utilization rate is improved, meanwhile, an optimized scheduling model taking the minimum running cost and entropy increase as an optimization target is considered, different optimized scheduling models can be selected under different requirements, and the optimization flexibility is improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the embodiments of the present invention without departing from the spirit or scope of the embodiments of the invention. Thus, if such modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to encompass such modifications and variations.

Claims (16)

1. The utility model provides an optimal scheduling system that cold and hot trigeminy of smart ability net supplied, its characterized in that includes:
the input module is used for respectively acquiring demand prediction data of electric heating and cooling in a pre-divided four-ring section of the universal energy network electric heating and cooling triple supply, fixed parameters of each device, current state parameters of each device and a preset optimization step length; the system comprises a universal energy network, a power supply system, a heat supply system and a heat supply system, wherein the universal energy network is divided into four sections, namely a production section, a storage and transportation section, a recovery section and a user section;
the optimization scheduling module is used for respectively establishing a calculation model of the operation cost and the entropy increase of each device in the four pre-divided links according to the data acquired by the input module and the determined energy grades in different energy forms, respectively establishing an optimization scheduling model of the operation cost and the entropy increase of the universal energy network electricity-cooling-heating triple supply by taking the operation cost and the entropy increase as optimization targets based on the calculation model of the operation cost and the entropy increase of each device, and respectively performing optimization scheduling based on the optimization scheduling model of the operation cost and the entropy increase to obtain an optimization scheduling result;
the output module is used for respectively outputting the optimized scheduling result obtained by the optimized scheduling module to each device in the pre-divided four links of the universal energy network electricity, cooling and heating triple co-generation so as to enable each device to perform adjustment operation based on the optimized scheduling result;
the input module is specifically configured to obtain the fixed parameters of each device, and at least includes: acquiring rated power and minimum start-stop time of an internal combustion engine, rated power of a gas boiler, rated power of a compression type water chilling unit and unit electricity selling price of a power grid; acquiring the maximum capacity limit and the minimum capacity limit of the storage battery; acquiring rated power of a waste heat boiler and rated power of an absorption type waste heat unit;
the obtaining of the current state parameters of each device at least includes: and acquiring the current power generation power of the internal combustion engine, the current operating power of the gas boiler, the current refrigeration power of the compression type water chilling unit and the current stored electric quantity value of the storage battery.
2. The system of claim 1,
the production link at least comprises a photovoltaic cell, an internal combustion engine, a gas boiler and a compression type water chilling unit, the storage and transportation link at least comprises a storage battery, the recovery link at least comprises a waste heat boiler and an absorption type waste heat unit, and the user link at least comprises electric equipment, heat equipment and cold equipment;
the input module is specifically configured to: acquiring demand forecast data of electric heating and cooling, which at least comprises the following steps: acquiring the electricity generation forecast of a photovoltaic cell 24 hours a day, the electricity/heat demand forecast of a user 24 hours a day in winter and the electricity/cold demand forecast of the user 24 hours a day in summer;
and acquiring a preset optimization step length.
3. The system according to claim 2, wherein a calculation model of the operating cost and entropy increase of each device in the four pre-divided links is respectively established according to the data acquired by the input module and the determined energy grades of different energy forms, and the optimization scheduling module is specifically configured to:
determining an entropy-increasing function of the combustion gas fuel based on the determined entropy-increasing calculation mode according to the determined available heat of the combustion gas fuel of the internal combustion engine and the energy grade of the combustion gas fuel;
determining an entropy increasing function of the combustion gas fuel based on a determined entropy increasing calculation mode according to the heat quantity taken away by the waste heat flue gas and the energy grade of the flue gas;
determining an entropy increasing function of the cylinder sleeve hot water based on the determined entropy increasing calculation mode according to the heat quantity taken away by the cylinder sleeve hot water and the energy grade of the hot water;
determining an operating cost function of the internal combustion engine based on the unit price of the gaseous fuel and the flow rate of the gaseous fuel used;
and establishing a power generation constraint condition of the internal combustion engine according to the maximum value and the minimum value of the rated power of the internal combustion engine, and establishing a minimum start-stop time constraint condition of the internal combustion engine according to the minimum start-stop time of the internal combustion engine.
4. The system according to claim 2, wherein a calculation model of the operating cost and entropy increase of each device in the four pre-divided links is respectively established according to the data acquired by the input module and the determined energy grades of different energy forms, and the optimization scheduling module is specifically configured to:
determining an entropy increasing function of the gas boiler based on the determined entropy increasing calculation mode according to the heat supply quantity of the gas boiler and the energy grade of the gas fuel;
determining an operation cost function of the gas boiler according to the unit price of the gas fuel and the flow rate of the used gas fuel;
and establishing an operation constraint condition of the gas boiler according to the maximum value and the minimum value of the rated power of the gas boiler.
5. The system according to claim 2, wherein a calculation model of the operating cost and entropy increase of each device in the four pre-divided links is respectively established according to the data acquired by the input module and the determined energy grades of different energy forms, and the optimization scheduling module is specifically configured to:
determining an operation cost function of the compression type water chilling unit according to the unit electricity selling price of the power grid and the refrigeration power consumption of the compression type water chilling unit;
and establishing an operation constraint condition of the compression type water chilling unit according to the maximum value and the minimum value of the rated power of the compression type water chilling unit.
6. The system according to claim 2, wherein a calculation model of the operating cost and entropy increase of each device in the four pre-divided links is respectively established according to the data acquired by the input module and the determined energy grades of different energy forms, and the optimization scheduling module is specifically configured to:
determining the operation cost generated by purchasing or selling electricity to the large power grid according to the unit electricity selling price of the power grid and the electricity purchasing or selling electricity to the large power grid;
when electricity is purchased to a large power grid, an entropy increasing function generated by thermal power generation is determined based on a determined entropy increasing calculation mode according to a heat value generated by burning coal fuel and the energy grade of the coal fuel.
7. The system of claim 2, wherein the optimized scheduling module is further to:
establishing an electric quantity constraint condition of the storage battery according to the maximum capacity limit and the minimum capacity limit of the storage battery;
and establishing a constraint condition of electric cold and heat supply and demand balance at least according to the generated energy of the internal combustion engine, the power consumption and refrigeration amount of the compression type water chilling unit, the generated energy of the photovoltaic cell, the heat supply amount of the gas boiler, the electric energy for purchasing or selling electricity to a large power grid, the electric energy for charging or discharging the storage battery, the predicted electric, cold and heat demands of a user, the refrigerating/heat supply amount by using the generated waste heat of the internal combustion engine and the cold/heat accumulation amount.
8. The system according to any one of claims 1 to 7, wherein an optimized scheduling model of the operation cost and entropy increase of the electricity-cooling-heating triple co-generation of the smart energy grid is established based on the calculation model of the operation cost and entropy increase of each device with the minimum operation cost and entropy increase as optimization targets, and the optimized scheduling module is specifically configured to:
establishing an operation cost objective function by taking the minimum operation cost as an optimization target according to the operation cost function of the internal combustion engine, the operation cost function of the gas-fired boiler, the operation cost of the compression type water chilling unit and the operation cost of purchasing/selling electricity to the large power grid;
establishing an entropy increase objective function by taking the minimum entropy increase as an optimization objective according to the entropy increase of the internal combustion engine, the entropy increase of the gas boiler and the entropy increase caused by thermal power generation when power is purchased to a large power grid;
and taking the power generation constraint condition of the internal combustion engine, the minimum start-stop time constraint condition of the internal combustion engine, the operation constraint condition of the gas boiler, the operation constraint condition of the compression type water chilling unit, the electric quantity constraint condition of a storage battery and the constraint condition of electric heating and cooling heat supply and demand balance as the constraint conditions of the operation cost objective function and the entropy increase objective function.
9. An optimal scheduling method for electricity, cooling and heating triple co-generation of a universal energy network is characterized by comprising the following steps:
respectively acquiring demand prediction data of electric heating and cooling in a pre-divided four-ring section for electricity heating and cooling triple supply of the universal energy network, fixed parameters of each device, current state parameters of each device and a preset optimization step length; the system comprises a universal energy network, a power supply system, a heat supply system and a heat supply system, wherein the universal energy network is divided into four sections, namely a production section, a storage and transportation section, a recovery section and a user section;
respectively establishing a calculation model of the operation cost and entropy increase of each device in the four pre-divided links according to the acquired data and the determined energy grades of different energy forms;
on the basis of the calculation models of the operation cost and the entropy increase of each device, respectively establishing an optimization scheduling model of the operation cost and the entropy increase of the electricity-cooling-heating triple co-generation of the smart energy network by taking the operation cost and the entropy increase as optimization targets, and respectively carrying out optimization scheduling on the basis of the optimization scheduling models of the operation cost and the entropy increase to obtain an optimization scheduling result;
respectively outputting the obtained optimized scheduling result to each device in the pre-divided four links of the universal energy network electricity, cold and heat triple supply so as to enable each device to adjust and operate based on the optimized scheduling result;
wherein, obtaining the fixed parameters of each device at least comprises: acquiring rated power and minimum start-stop time of an internal combustion engine, rated power of a gas boiler, rated power of a compression type water chilling unit and unit electricity selling price of a power grid; acquiring the maximum capacity limit and the minimum capacity limit of the storage battery; acquiring rated power of a waste heat boiler and rated power of an absorption type waste heat unit;
acquiring current state parameters of each device, wherein the current state parameters at least comprise: and acquiring the current power generation power of the internal combustion engine, the current operating power of the gas boiler, the current refrigeration power of the compression type water chilling unit and the current stored electric quantity value of the storage battery.
10. The method of claim 9,
the production link at least comprises a photovoltaic cell, an internal combustion engine, a gas boiler and a compression type water chilling unit, the storage and transportation link at least comprises a storage battery, the recovery link at least comprises a waste heat boiler and an absorption type waste heat unit, and the user link at least comprises electric equipment, heat equipment and cold equipment;
the method includes the steps of obtaining demand prediction data of electric heating and cooling water, fixed parameters of each device and current state parameters of each device in pre-divided four-ring sections of electricity heating and cooling triple supply of the universal energy grid respectively, and specifically includes the following steps:
acquiring demand forecast data of electric heating and cooling, which at least comprises the following steps: and acquiring the electricity generation forecast of the photovoltaic cell 24 hours a day, the electricity/heat demand forecast of a user 24 hours a day in winter and the electricity/cold demand forecast of the user 24 hours a day in summer.
11. The method according to claim 10, wherein the step of respectively establishing a calculation model of the operation cost and entropy increase of each device in the four pre-divided links according to the acquired data and the determined energy grades in different energy forms comprises:
determining an entropy-increasing function of the combustion gas fuel based on the determined entropy-increasing calculation mode according to the determined available heat of the combustion gas fuel of the internal combustion engine and the energy grade of the combustion gas fuel;
determining an entropy increasing function of the combustion gas fuel based on a determined entropy increasing calculation mode according to the heat quantity taken away by the waste heat flue gas and the energy grade of the flue gas;
determining an entropy increasing function of the cylinder sleeve hot water based on the determined entropy increasing calculation mode according to the heat quantity taken away by the cylinder sleeve hot water and the energy grade of the hot water;
determining an operating cost function of the internal combustion engine based on the unit price of the gaseous fuel and the flow rate of the gaseous fuel used;
and establishing a power generation constraint condition of the internal combustion engine according to the maximum value and the minimum value of the rated power of the internal combustion engine, and establishing a minimum start-stop time constraint condition of the internal combustion engine according to the minimum start-stop time of the internal combustion engine.
12. The method according to claim 10, wherein the step of respectively establishing a calculation model of the operation cost and entropy increase of each device in the four pre-divided links according to the acquired data and the determined energy grades in different energy forms comprises:
determining an entropy increasing function of the gas boiler based on the determined entropy increasing calculation mode according to the heat supply quantity of the gas boiler and the energy grade of the gas fuel;
determining an operation cost function of the gas boiler according to the unit price of the gas fuel and the flow rate of the used gas fuel;
and establishing an operation constraint condition of the gas boiler according to the maximum value and the minimum value of the rated power of the gas boiler.
13. The method according to claim 10, wherein the step of respectively establishing a calculation model of the operation cost and entropy increase of each device in the four pre-divided links according to the acquired data and the determined energy grades in different energy forms comprises:
determining an operation cost function of the compression type water chilling unit according to the unit electricity selling price of the power grid and the refrigeration power consumption of the compression type water chilling unit;
and establishing an operation constraint condition of the compression type water chilling unit according to the maximum value and the minimum value of the rated power of the compression type water chilling unit.
14. The method according to claim 10, wherein the step of respectively establishing a calculation model of the operation cost and entropy increase of each device in the four pre-divided links according to the acquired data and the determined energy grades in different energy forms comprises:
determining the operation cost generated by purchasing or selling electricity to the large power grid according to the unit electricity selling price of the power grid and the electricity purchasing or selling electricity to the large power grid;
when electricity is purchased to a large power grid, an entropy increasing function generated by thermal power generation is determined based on a determined entropy increasing calculation mode according to a heat value generated by burning coal fuel and the energy grade of the coal fuel.
15. The method of claim 10, further comprising:
establishing an electric quantity constraint condition of the storage battery according to the maximum capacity limit and the minimum capacity limit of the storage battery;
and establishing a constraint condition of electric cold and heat supply and demand balance at least according to the generated energy of the internal combustion engine, the power consumption and refrigeration amount of the compression type water chilling unit, the generated energy of the photovoltaic cell, the heat supply amount of the gas boiler, the electric energy for purchasing or selling electricity to a large power grid, the electric energy for charging or discharging the storage battery, the predicted electric, cold and heat demands of a user, the refrigerating/heat supply amount by using the generated waste heat of the internal combustion engine and the cold/heat accumulation amount.
16. The method according to any one of claims 9 to 15, wherein the establishing of the optimal scheduling model of the operation cost and entropy increase of the electricity-cooling-heating triple co-generation of the smart energy grid based on the calculation model of the operation cost and entropy increase of each device with the operation cost and entropy increase minimum as the optimization target respectively comprises:
establishing an operation cost objective function by taking the minimum operation cost as an optimization target according to the operation cost function of the internal combustion engine, the operation cost function of the gas-fired boiler, the operation cost of the compression type water chilling unit and the operation cost of purchasing/selling electricity to the large power grid;
establishing an entropy increase objective function by taking the minimum entropy increase as an optimization objective according to the entropy increase of the internal combustion engine, the entropy increase of the gas boiler and the entropy increase caused by thermal power generation when power is purchased to a large power grid;
and taking the power generation constraint condition of the internal combustion engine, the minimum start-stop time constraint condition of the internal combustion engine, the operation constraint condition of the gas boiler, the operation constraint condition of the compression type water chilling unit, the electric quantity constraint condition of a storage battery and the constraint condition of electric heating and cooling heat supply and demand balance as the constraint conditions of the operation cost objective function and the entropy increase objective function.
CN201710874681.8A 2017-09-25 2017-09-25 Universal energy network electricity-cooling-heating triple co-generation optimized scheduling system and method Active CN107730032B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710874681.8A CN107730032B (en) 2017-09-25 2017-09-25 Universal energy network electricity-cooling-heating triple co-generation optimized scheduling system and method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710874681.8A CN107730032B (en) 2017-09-25 2017-09-25 Universal energy network electricity-cooling-heating triple co-generation optimized scheduling system and method

Publications (2)

Publication Number Publication Date
CN107730032A CN107730032A (en) 2018-02-23
CN107730032B true CN107730032B (en) 2021-10-26

Family

ID=61207935

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710874681.8A Active CN107730032B (en) 2017-09-25 2017-09-25 Universal energy network electricity-cooling-heating triple co-generation optimized scheduling system and method

Country Status (1)

Country Link
CN (1) CN107730032B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110619405B (en) * 2018-06-19 2022-12-02 新智数字科技有限公司 Method and device for optimizing mixed integer piecewise nonlinearity of universal energy station
CN109539480B (en) * 2018-10-26 2021-05-07 国电南瑞科技股份有限公司 Cold and hot load green energy-saving optimized dispatching system for distributed energy station
CN109919474B (en) * 2019-02-28 2021-08-27 新奥数能科技有限公司 Comprehensive energy quantitative acquisition and purchase optimization method and system based on Internet platform
CN116260165B (en) * 2023-03-09 2023-10-20 苏州深蓝万维能源科技有限公司 Multi-micro-grid energy storage system scheduling method, device, equipment and medium

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101739610A (en) * 2009-11-30 2010-06-16 华南理工大学 Paper mill cogeneration energy system optimization system and working method thereof
CN102236343A (en) * 2010-04-30 2011-11-09 新奥科技发展有限公司 Smart energy network system and control method thereof
CN102236833A (en) * 2010-04-30 2011-11-09 新奥科技发展有限公司 Universal energy network for realizing energy optimal utilization and method for providing energy transaction and service
CN102236349A (en) * 2010-04-30 2011-11-09 新奥科技发展有限公司 System energy efficiency controller, energy efficiency grain device and intelligent energy service system for energy utilization
US20130282184A1 (en) * 2008-06-06 2013-10-24 Saudi Arabian Oil Company Systems, Program Code, Computer Readable Media For Planning and Retrofit of Energy Efficient Eco-Industrial Parks Through Inter-Time-Inter-Systems Energy Integration
CN103439941A (en) * 2013-08-23 2013-12-11 贵州电网公司电网规划研究中心 Optimizing operation method of combined cooling heating and power system of gas engine
CN103942610A (en) * 2014-04-04 2014-07-23 同济大学 Reconfigurable manufacturing system polymorphic configuration optimization method based on tasks
CN104808638A (en) * 2015-04-29 2015-07-29 中山大学 Heat-work conversion and dispatching optimization decision-making assisting system and heat-work conversion and dispatching optimization decision-making assisting method for complementary DMG (distributed multi-generation) system
US20170083873A1 (en) * 2015-08-20 2017-03-23 Service Power Technologies PLC Infeasible schedules in a quantum annealing optimization process
CN107025519A (en) * 2017-03-30 2017-08-08 厦门大学 Area distribution formula cool and thermal power polygenerations systeme MIXED INTEGER nonlinear model optimization method

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130282184A1 (en) * 2008-06-06 2013-10-24 Saudi Arabian Oil Company Systems, Program Code, Computer Readable Media For Planning and Retrofit of Energy Efficient Eco-Industrial Parks Through Inter-Time-Inter-Systems Energy Integration
CN101739610A (en) * 2009-11-30 2010-06-16 华南理工大学 Paper mill cogeneration energy system optimization system and working method thereof
CN102236343A (en) * 2010-04-30 2011-11-09 新奥科技发展有限公司 Smart energy network system and control method thereof
CN102236833A (en) * 2010-04-30 2011-11-09 新奥科技发展有限公司 Universal energy network for realizing energy optimal utilization and method for providing energy transaction and service
CN102236349A (en) * 2010-04-30 2011-11-09 新奥科技发展有限公司 System energy efficiency controller, energy efficiency grain device and intelligent energy service system for energy utilization
CN103439941A (en) * 2013-08-23 2013-12-11 贵州电网公司电网规划研究中心 Optimizing operation method of combined cooling heating and power system of gas engine
CN103942610A (en) * 2014-04-04 2014-07-23 同济大学 Reconfigurable manufacturing system polymorphic configuration optimization method based on tasks
CN104808638A (en) * 2015-04-29 2015-07-29 中山大学 Heat-work conversion and dispatching optimization decision-making assisting system and heat-work conversion and dispatching optimization decision-making assisting method for complementary DMG (distributed multi-generation) system
US20170083873A1 (en) * 2015-08-20 2017-03-23 Service Power Technologies PLC Infeasible schedules in a quantum annealing optimization process
CN107025519A (en) * 2017-03-30 2017-08-08 厦门大学 Area distribution formula cool and thermal power polygenerations systeme MIXED INTEGER nonlinear model optimization method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
"Hybrid modeling and optimization for Energy Management System of MicroGrid";Zheng Chaoyue;《Proceedings of the 35th Chinese Control Conference》;20160729;10013-10018 *
"分布式冷热电联供系统优化运行方法";代宪亚;《电力科学与技术学报》;20170331;第32卷(第1期);55-64 *

Also Published As

Publication number Publication date
CN107730032A (en) 2018-02-23

Similar Documents

Publication Publication Date Title
CN107832979B (en) Factory comprehensive energy system economic optimization scheduling method considering energy cascade utilization
CN109659927B (en) Energy storage capacity configuration method of comprehensive energy microgrid considering energy storage participation degree
CN107730032B (en) Universal energy network electricity-cooling-heating triple co-generation optimized scheduling system and method
CN113112087A (en) Comprehensive energy system operation cost optimization method considering electric heating load demand response
CN107808216B (en) Method for constructing comprehensive minimum optimization model for wind curtailment and electrical thermal load reduction of electricity-gas-heat interconnection system
CN109523065B (en) Micro energy network optimization scheduling method based on improved quantum particle swarm algorithm
CN112464477A (en) Multi-energy coupling comprehensive energy operation simulation method considering demand response
CN109634119B (en) Energy internet optimization control method based on rolling optimization in day
CN107358345B (en) Distributed combined cooling heating and power system optimization operation method considering demand side management
CN110188492B (en) Combined cooling heating and power micro-grid optimized scheduling method considering heat supply network characteristics
CN103745268A (en) Distributed power supply-containing microgrid multi-target optimization scheduling method
CN110941799B (en) Energy hub stochastic programming method considering comprehensive uncertainty factors of system
CN110391655B (en) Multi-energy-coupling micro-energy-network economic optimization scheduling method and device
CN109325621B (en) Park energy internet two-stage optimal scheduling control method
CN110361969B (en) Optimized operation method of cooling, heating and power comprehensive energy system
CN110365062B (en) Markov model-based multi-energy system coordination control method
CN109543889A (en) A kind of regional complex energy resource system cooperates with optimizing operation method a few days ago
CN111724045B (en) Comprehensive energy system energy efficiency evaluation and improvement method based on data driving
CN112329988A (en) Demand side response calculation method based on park adjustability analysis
CN111324849A (en) Electric heating combined system optimal scheduling method considering heat supply network characteristics
CN109583623A (en) Region multipotency source supply system in a few days rolls collaboration optimizing operation method and device
CN113435095A (en) Method and system for optimizing scheduling of comprehensive energy system
CN112785065A (en) Comprehensive energy system planning method and system based on hybrid artificial fish swarm algorithm
CN105244870A (en) Method for rapidly calculating wind curtailment rate of power grid wind power plant and generating capacity of unit
CN117350419A (en) Park comprehensive energy system optimization operation method considering flexible load and carbon flow

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
CB02 Change of applicant information

Address after: 201306 101 new office building, A, 7 new industrial park, Shanghai, Pudong New Area, 600 new Yuan South Road, Pudong New Area.

Applicant after: New Austrian energy power technology (Shanghai) Co., Ltd.

Applicant after: ENN SCIENCE & TECHNOLOGY DEVELOPMENT Co.,Ltd.

Address before: 201406 228 yongyang Road, Yang Wang Industrial Park, South Bridge Town, Fengxian District, Shanghai

Applicant before: Shanghai wisdom energy equipment Co., Ltd.

Applicant before: ENN SCIENCE & TECHNOLOGY DEVELOPMENT Co.,Ltd.

CB02 Change of applicant information
GR01 Patent grant
GR01 Patent grant