CN117455076A - Multi-time scale optimization method and system for comprehensive energy system - Google Patents
Multi-time scale optimization method and system for comprehensive energy system Download PDFInfo
- Publication number
- CN117455076A CN117455076A CN202311618098.2A CN202311618098A CN117455076A CN 117455076 A CN117455076 A CN 117455076A CN 202311618098 A CN202311618098 A CN 202311618098A CN 117455076 A CN117455076 A CN 117455076A
- Authority
- CN
- China
- Prior art keywords
- capacity
- battery
- kwh
- heat
- cost
- 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.)
- Pending
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 70
- 238000000034 method Methods 0.000 title claims abstract description 66
- 230000008569 process Effects 0.000 claims abstract description 24
- 230000003993 interaction Effects 0.000 claims abstract description 12
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 11
- 230000002068 genetic effect Effects 0.000 claims abstract description 10
- 238000003860 storage Methods 0.000 claims description 56
- 238000005338 heat storage Methods 0.000 claims description 40
- 230000006870 function Effects 0.000 claims description 21
- 230000005611 electricity Effects 0.000 claims description 20
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 19
- 229910052799 carbon Inorganic materials 0.000 claims description 19
- 238000002485 combustion reaction Methods 0.000 claims description 12
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 12
- 230000007613 environmental effect Effects 0.000 claims description 11
- 238000010521 absorption reaction Methods 0.000 claims description 10
- 230000008859 change Effects 0.000 claims description 10
- 239000007789 gas Substances 0.000 claims description 10
- 238000007599 discharging Methods 0.000 claims description 9
- 238000010248 power generation Methods 0.000 claims description 9
- 238000012423 maintenance Methods 0.000 claims description 8
- 239000000446 fuel Substances 0.000 claims description 7
- 239000003345 natural gas Substances 0.000 claims description 6
- 239000003507 refrigerant Substances 0.000 claims description 6
- 230000032683 aging Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000001816 cooling Methods 0.000 claims description 3
- 238000011084 recovery Methods 0.000 claims description 3
- 238000005057 refrigeration Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims 1
- 238000005265 energy consumption Methods 0.000 abstract description 4
- 238000004590 computer program Methods 0.000 description 17
- 239000003795 chemical substances by application Substances 0.000 description 14
- 238000010586 diagram Methods 0.000 description 8
- 230000004044 response Effects 0.000 description 8
- 230000009471 action Effects 0.000 description 7
- 238000012545 processing Methods 0.000 description 6
- 238000004146 energy storage Methods 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 4
- 230000002787 reinforcement Effects 0.000 description 4
- 230000007704 transition Effects 0.000 description 4
- 230000006399 behavior Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 101150067055 minC gene Proteins 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000012549 training Methods 0.000 description 1
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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/092—Reinforcement learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/06—Power analysis or power optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/12—Timing analysis or timing optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Economics (AREA)
- Biophysics (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Human Resources & Organizations (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Marketing (AREA)
- Data Mining & Analysis (AREA)
- General Business, Economics & Management (AREA)
- Computational Linguistics (AREA)
- Tourism & Hospitality (AREA)
- Bioinformatics & Computational Biology (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- Evolutionary Biology (AREA)
- Quality & Reliability (AREA)
- Geometry (AREA)
- Computer Hardware Design (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
Abstract
The invention relates to the field of comprehensive energy scheduling optimization, in particular to a method, a system, equipment and a medium for optimizing a plurality of time scales of a comprehensive energy system, which comprise the following steps: constructing a system multi-objective robust optimal configuration model based on renewable energy output and a user load prediction result; solving a multi-target robust optimization configuration model of the system by utilizing a multi-target genetic algorithm, and determining the optimal robust structure configuration of the system; the regulation and control process of the comprehensive energy system is divided into three time phases, and the three time phases are optimized according to the optimal system robust structure configuration. The most unfavorable condition of the source load is considered, the robust optimization configuration is carried out on the comprehensive energy system, the obtained equipment capacity result is relatively large, but the park load can be met as much as possible, the external large power grid and the large heat supply network are not relied on as much as possible, the interaction between the external world and the park comprehensive energy system is reduced, the independence of the system park-level energy system is improved, and the near-zero energy consumption degree of the park is improved.
Description
Technical Field
The invention belongs to the field of comprehensive energy scheduling optimization, and particularly relates to a multi-time scale optimization method and system of a comprehensive energy system.
Background
Most of the energy supply systems of the park constructed at present only consider meeting the cold-hot electric load of the park, but do not consider the independence of the park and achieve the purpose of near zero energy consumption. The constructed energy supply system can meet the load of a park, has the capability of being independent of a large power grid and a large heat supply network to a certain extent, reduces the energy input from the outside, and is a trend of the development of the current comprehensive energy system.
The park-level comprehensive energy system fully utilizes renewable energy sources to supply energy. However, renewable energy sources have the characteristics of intermittence, dispersivity, volatility and the like, and park user loads are influenced by factors such as environment, personnel behaviors, price policies and the like, so that energy supply and demand on two sides of the source load have uncertainty. In addition, the operation characteristics of all the devices of the comprehensive energy system are greatly different, including a 'quick response' device related to electric power and a 'slow response' device related to cold and hot, so that the operation characteristics of the 'quick response' device and the 'slow response' device need to be fully coordinated, and the real-time energy demand and the instantaneous load change of a user are met.
Defects and deficiencies of the prior art: (1) In the optimization process of the comprehensive energy system, only multiple target demands and source load supply and demand balance are considered, and a large power grid is used as a backup energy source for optimization instead of robust optimization, so that the interaction amount of the power grid between the power grid and the park system is large, and the independence of the park system is reduced. However, the large power grid does not want to consume excessive electric energy in the park, which affects the electric energy quality of the park, and the park system does not want to purchase excessive electric energy from the large power grid, so that transition dependence on the large power grid is caused; (2) The regulation and control mode of the existing comprehensive energy system is generally electric fixed heating or heat fixed electricity or an operation mode obtained based on multi-objective optimization, the condition of mismatching of source charge energy caused by multiple uncertainty factors such as source charge energy randomness, fluctuation, intermittence and the like is not considered, namely the prior literature only considers the day-ahead optimal scheduling of the system, and the source charge condition at the actual operation time of the system is not considered, namely the operation mode of the system in the day is not considered; (3) The prior art has few consideration on the mode of converting electricity into heat and converting electricity into cold for the multi-element energy storage equipment. Even if a plurality of energy storage modes of electricity storage, heat storage and cold storage are considered in some projects, a cooperative operation model does not exist among different energy storage modes, but only a surplus electric energy is stored by an electricity storage device, electricity is released when the electric load of a user cannot be met, a surplus heat energy is stored by a heat storage device, heat is released when the heat load of the user cannot be met, a surplus cold energy is stored by a cold storage device, the cold load of the user cannot be met, and cooperative operation optimization does not exist among the energy storage devices.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a robust optimal configuration method, a system, equipment and a medium for a comprehensive energy system, so as to solve the problem that a functional system cannot achieve zero energy consumption.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the present invention provides a multi-time scale optimization method for an integrated energy system, comprising:
constructing a system multi-objective robust optimal configuration model based on renewable energy output and a user load prediction result;
solving a multi-target robust optimization configuration model of the system by utilizing a multi-target genetic algorithm, and determining the optimal robust structure configuration of the system;
the regulation and control process of the comprehensive energy system is divided into three time phases, and the three time phases are optimized according to the optimal system robust structure configuration.
Further, constructing a system multi-objective robust optimization configuration model, wherein the system multi-objective robust optimization configuration model comprises an objective function and constraint conditions;
the objective function includes: the carbon emission is minimum, the annual total cost is minimum, the primary energy utilization rate is highest, and the independence of the comprehensive energy system is highest;
the constraint conditions include: the energy supply and demand balance and the equipment capacity are smaller than or equal to the equipment maximum capacity constraint.
Further, the energy supply and demand balance includes: using an electric balance constraint, a heat balance constraint and a cold balance constraint;
using an electrical balance constraint: e (E) ICE (t)+E grid (t)+P pv (t)+P Battery，out (t)+P WT (t)≥E build (t)+E ASHP (t)+E HP (t); wherein t is time-by-time data; e (E) ICE Is the power generation capacity of the combustion engine, kWh; e (E) grid kWh for electricity purchase from municipal power grid; p (P) pv Is photovoltaic power generation amount kwh; p (P) Battery，out The electric energy is the discharge capacity of the storage battery, kWh; p (P) WT The discharging amount of wind power generation is kWh; e (E) build kWh is the park electrical load; e (E) ASHP kWh is the power consumption of the electric refrigerator; e (E) HP The power consumption of the ground source heat pump is kWh;
heat balance constraint: q (Q) ex (t)+Q GB (t)+Q solar (t)+P HTS，out (t)≥Q build (t); wherein Q is ex The heat release amount of the heat exchanger is kWh; q (Q) GB The heat is released for the boiler, kWh; q (Q) solar Heat is collected for a photo-thermal system, and kWh; p (P) HTS，out The heat is released for the heat storage system, and kWh; q (Q) build kWh for park heat load;
cold balance constraint: c (C) ASHP (t)+C HP (t)+C AHP (t)≥C build (t), wherein C ASHP The refrigerating capacity of the electric refrigerator is kWh; c (C) HP The refrigeration capacity of the ground source heat pump is kWh; c (C) AHP kWh, which is the refrigerating capacity of the absorption type refrigerant; c (C) build Is the cold load of the park, kWh.
Further, the device capacity being equal to or less than the device maximum capacity constraint comprises: g Battery，in ≤G Battery，in，max ，G Battery，out ≤G Battery，out，max ，G HST，in ≤G HST，in，max ，G HTS，out ≤G HTS，out，max ，G pv ≤G pv，max ，G solar ≤G solar，max ，G ICE ≤G ICE，max ，G ASHP ≤G ASHP，max ，G GB ≤G GB，max ，G WT ≤G WT，max ，G HP ≤G HP，max ，G AHP ≤G AHP，max Wherein G represents the capacity of the apparatus, kW; g Battery，tn The charging capacity of the storage battery is kW; g Battery，out The discharge capacity of the storage battery is kW; g HST，in The heat storage capacity of the heat storage system is kW; g HTS，out The heat release capacity of the heat storage system is kW; g pv Is the capacity of the photovoltaic system, kW; g solar The capacity of the photo-thermal system, kW;G ICE is the capacity of the internal combustion engine, kW; g ASHP The capacity of the electric refrigerator, kW; g GB Is the capacity of the gas boiler, kW; g WT The capacity of the wind driven generator is kW; g HP The capacity of the ground source heat pump, kW; g AHP Is the capacity of the absorption refrigerant, kW.
Further, the carbon emission is at least minC yx ＝min(C yx，hear +C yx，cold +C yx，ele ) Wherein C yx Is total carbon yield, tCO of full life cycle in the operation process of the park system 2 ；C yx，hear Full life cycle carbon yield, tCO, in the process of supplying heat to users for park systems 2 ；C yx，cold Full life cycle carbon yield, tCO, in the process of cooling a user for a park system 2 ；C yx，ele Full life cycle carbon production, tCO, in the process of powering users for park systems 2 。
Further, the annual total cost is a minimum of min (C cost )＝min(∑ i A i ×c inv，i ×G eq，i ×n i )+∑ i (A i ×c ope，i ×n i )，Wherein i is a device class; c (C) cost Is the annual total cost of the system; a is that i Is the cost recovery coefficient; c inv Initial investment cost per unit capacity of the plant, yuan/kW; g eq For equipment capacity, kW; n is the number of the devices; r represents interest rate; y is i Indicating the life cycle of the device, year; c ope The maintenance cost coefficient for the equipment operation comprises the system maintenance cost and the fuel consumption cost.
Further, the primary energy utilization rate is as high as:wherein P is user The consumption of cold, heat and electricity of the user throughout the year is kWh; p (P) con kWh, the total energy consumed by the system.
Further, the independence of the comprehensive energy system is as high as:wherein P is grid，buy The sum of electric energy purchased and sold by the system from an external large power grid and a large heat supply network is represented, and kWh is represented; p (P) grid，sell Representing the sum of heat energy purchased and sold by the system from an external large power grid and a large heat supply network, and kWh; p (P) load Refers to the total amount of cold and hot electric load of a user and kWh.
Further, the three time phases include: a long time scale stage, a medium time scale stage, and a short time scale stage;
constraints of the long-time scale phase and the medium-time scale phase include: the electricity balance constraint, the heat balance constraint, the cold balance constraint and the equipment output are smaller than or equal to the equipment maximum capacity constraint;
constraints of the short time scale phase include: the power consumption balance constraint and the equipment output are smaller than or equal to the equipment maximum capacity constraint;
the device output is less than or equal to a device capacity constraint: p (P) Battery，tn ≤G Battery，in ，P Battery，out ≤G Battery，out ，P HST，in ≤G HST，in ，P HTS，out ≤G HTS，out ，P ICE ≤G ICE ，P ASHP ≤G ASHP ，P GB ≤G GB ，P HP ≤G HP ，P AHP ≤G AHP Wherein P represents the output of the device, kW; g represents the capacity of the plant, kW; p (P) Battery，in Indicating the charging output of the storage battery; g Battery，in The charging capacity of the storage battery is kW; p (P) Battery，out Discharging force for the storage battery; g Battery，out The discharge capacity of the storage battery is kW; p (P) HST，in The heat storage output of the heat storage system is kW; g HST，in The heat storage capacity of the heat storage system is kW; p (P) HTS，out Exothermic output of the heat storage system is kW; g HTS，out The heat release capacity of the heat storage system is kW; p (P) ICE For internal combustion enginesOutput, kW; g ICE Is the capacity of the internal combustion engine, kW; p (P) ASHP The output of the electric refrigerator is kW; g ASHP Kw is the capacity of the electric refrigerator; p (P) GB The output of the gas boiler is Kw; g GB Is the capacity of the gas boiler, kW; p (P) HP The output of the ground-edge heat pump is kW; g HP The capacity of the ground source heat pump, kW;
the output of the electric energy equipment is smaller than or equal to the capacity constraint of the equipment: p (P) Battery,in ≤G Battery,in ,P Battery,out ≤G Battery,out, ，P ASHP ≤G ASHP ,P HP ≤G HP Wherein P represents the output of the device, kW; g represents the capacity of the plant, kW; p (P) Battery,in Indicating the charging output of the storage battery; g Battery,in The charging capacity of the storage battery is kW; p (P) Battery,out Discharging force for the storage battery; g Battery,out The discharge capacity of the storage battery is kW; p (P) ASHP The output of the electric refrigerator is kW; g ASHP Kw is the capacity of the electric refrigerator; p (P) HP The output of the ground-edge heat pump is kW; g HP The capacity of the ground source heat pump, kW;
the long time scale stage scheduling optimization targets areWherein C is D h is the running cost of the system; t is a scheduling period, 24 hours; />The method is a CCHP micro-grid system and power grid interaction cost, element; />Is the cost of natural gas; />The aging cost of the storage battery is the element; />For the operation of the systemMaintenance cost and metadata; />Is the environmental cost of the system;
the time scale stage scheduling optimization targets are as follows Wherein M is the time span, 4h; />The adjustment quantity cost of the power grid interaction is the element; />The cost of the adjustment amount of the fuel is as follows; />Punishment cost is paid for the change of the charge and discharge power of the storage battery; />Punishment cost is paid for the change of heat storage and release power of the heat storage/cold tank;
the short time scale stage scheduling optimization targets are Wherein (1)>The power adjustment amount is for purchasing power in real time, kW; />The power adjustment amount is real-time electric power selling, and kW; />The power adjustment amount of the boiler is real-time, kW; />The power adjustment amount is real-time electric power selling, and kW; />The adjustment amount of the air purchasing power is kW in real time;real-time power adjustment quantity for the absorption refrigerator, kW; />Real-time power adjustment quantity for the electric refrigerator, kW;and (5) adjusting the real-time power of the ground source heat pump by kW.
In a second aspect, the present invention provides a multi-time scale optimization system for an integrated energy system, comprising:
and (3) constructing a model module: the system multi-objective robust optimization configuration model is used for constructing the system multi-objective robust optimization configuration model based on renewable energy output and a user load prediction result;
and a calculation solving module: the method comprises the steps of solving a system multi-target robust optimization configuration model by utilizing a multi-target genetic algorithm, and determining optimal system robust structure configuration;
and (3) a configuration optimizing module: the method is used for dividing the comprehensive energy system into three time phases, and optimizing the three time phases according to the optimal system robust structure configuration.
The invention has at least the following beneficial effects:
1. the invention provides a multi-time scale optimization method of a comprehensive energy system, which comprises the following steps: constructing a system multi-objective robust optimal configuration model based on renewable energy output and a user load prediction result; solving a multi-target robust optimization configuration model of the system by utilizing a multi-target genetic algorithm, and determining the optimal robust structure configuration of the system; the regulation and control process of the comprehensive energy system is divided into three time phases, and the three time phases are optimized according to the optimal system robust structure configuration. The most unfavorable condition of the source load is considered, robust optimization configuration is carried out on the comprehensive energy system, the obtained equipment capacity result is relatively large, but the park load can be met as much as possible, the external large power grid and the large heat supply network are not relied on as much as possible, the interaction between the external world and the park comprehensive energy system is reduced, the independence of the system park-level energy system is improved, and the near-zero energy consumption degree of the park is improved;
2. the invention provides a multi-time scale optimization method of a comprehensive energy system, which comprises the following three time phases: a long time scale stage, a medium time scale stage, and a short time scale stage; constraints of the long-time scale phase and the medium-time scale phase include: the electricity balance constraint, the heat balance constraint, the cold balance constraint and the equipment output are smaller than or equal to the equipment maximum capacity constraint; constraints of the short time scale phase include: the power utilization balance constraint and the power output of the electric energy equipment are smaller than or equal to the equipment maximum capacity constraint; the long time scale stage scheduling optimization targets areWherein C is D h is the running cost of the system; t is a scheduling period, 24 hours; />The method is a CCHP micro-grid system and power grid interaction cost, element; />Is the cost of natural gas; />The aging cost of the storage battery is the element; />The cost of maintenance for the operation of the system,a meta-element; />Is the environmental cost of the system; the time scale stage scheduling optimization target in the middle is +.> Wherein M is the time span, 4h; />The adjustment quantity cost of the power grid interaction is the element; />The cost of the adjustment amount of the fuel is as follows; />Punishment cost is paid for the change of the charge and discharge power of the storage battery; />Punishment cost is paid for the change of heat storage and release power of the heat storage/cold tank; the short time scale stage scheduling optimization targets are
Wherein (1)>The power adjustment amount is for purchasing power in real time, kW; />The power adjustment amount is real-time electric power selling, and kW; />The power adjustment amount of the boiler is real-time, kW; />The power adjustment amount is real-time electric power selling, and kW; />The adjustment amount of the air purchasing power is kW in real time; />Real-time power adjustment quantity for the absorption refrigerator, kW; />Real-time power adjustment quantity for the electric refrigerator, kW; />And (5) adjusting the real-time power of the ground source heat pump by kW. By using a time interval method, a scheduling time interval is not set manually, the difference of the operation characteristics of each device and the energy supply quality of a system are comprehensively considered, the optimal scheduling time interval of each stage is determined, a long-time scale-middle-time scale-short time scale optimal scheduling scheme is provided, the operation characteristics of 'quick response' equipment related to electric energy and 'slow response' equipment related to cold and hot are comprehensively considered, and the characteristics of randomness, fluctuation, intermittence and the like of source and charge on two sides are comprehensively considered, so that a multi-time scale optimal operation method of a park-level comprehensive energy system is provided.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a multi-time scale optimization operation method of an energy supply system of the invention;
FIG. 2 is a flow chart of the system optimization control of the present invention;
fig. 3 is a block diagram of an electronic device according to the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings in connection with embodiments. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The following detailed description is exemplary and is intended to provide further details of the invention. Unless defined otherwise, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the invention.
Example 1
The invention provides a robust optimal configuration method of a comprehensive energy system, which comprises the following steps:
constructing a system multi-objective robust optimal configuration model based on renewable energy output and a user load prediction result;
solving a multi-target robust optimization configuration model of the system by utilizing a multi-target genetic algorithm, and determining the optimal robust structure configuration of the system;
the regulation and control process of the comprehensive energy system is divided into three time phases, and the three time phases are optimized according to the optimal system robust structure configuration.
The method comprises the following specific steps of constructing a typical architecture of a park-level comprehensive energy system, and developing multi-objective robust optimal configuration of the system:
(1) Based on the renewable energy output and the user load prediction result, the renewable energy output and the user load prediction result are converted into deterministic results.
The changes of environmental factors such as temperature, humidity, wind power and the like make the renewable energy quantity difficult to accurately predict, and meanwhile, the factors such as manpower, market and the like can cause the change of load demands. In connection with the concept of robust optimization, the source charge energy can be described as a form of a robust set of uncertainties.
The uncertainty is described as follows:wherein P is RE Is the true value of the output of renewable energy sources, < >>Is the nominal value of renewable energy output, L is the true load value, L 0 Is the nominal load value, deltaP RE The maximum fluctuation amount of the renewable energy output is given, and Δl is the maximum fluctuation amount of the load. Considering that the worst case of renewable energy output and load demand does not occur during each time period, uncertainty factors can be further described as: />Wherein T is RE T represents renewable energy output or user load time period aggregate, Γ respectively RE And Γ is a robust measure of renewable energy output and user load, and can take integers and non-integers, and in a limited time period, the uncertainty factor of the robust measure Γ deviates from the maximum number of nominal values, namely that the renewable energy output or load demand cannot simultaneously generate worst random fluctuation in all time periods. If Γ takes 0, the output of renewable energy and the load demand do not randomly fluctuate, and the corresponding true value is equal to the nominal value, namely the deterministic condition; if Γ RE Taking 7, the output of the renewable energy source is from 0 to T RE The worst randomness condition occurs in any 7 time periods of (1), and the worst randomness condition occurs in any 7 time periods of 0 to T in the cold-hot electric load requirement; if Γ RE And Γ takes T RE And T, indicating that the worst randomness condition of the renewable energy output and the load demand occurs in each period, and the optimal solution has the highest conservation degree. The true description of the random scene is realized by controlling the value of the robust measure, and the cost and conservation paid by the robust optimization are adjusted. Thus, robust measures of renewable energy output and load demand are important regulators of the level of conservation of economic operation of integrated energy systems.
(2) Constructing system multi-target robust optimal configuration model
Taking economy, environmental protection, energy efficiency, independence and the like as multiple targets, and constructing a multi-target robust optimal configuration model of the system aiming at the worst application scene of the factors such as renewable energy output, user cold and hot electric load and the like in the whole life cycle of the system. The optimization variable is the capacity of each device of the park-level comprehensive energy system. The constructed comprehensive energy system needs to meet the following constraints:
(1) energy supply and demand balance equation
1) Using an electrical balance constraint: e (E) ICE (t)+E grid (t)+P pv (t)+P Battery，out (t)+P WT (t)≥E build (t)+E ASHP (t)+E HP (t); wherein t is time-by-time data; e (E) ICE Is the power generation capacity of the combustion engine, kWh; e (E) grid kWh for electricity purchase from municipal power grid; p (P) pv Is photovoltaic power generation amount kwh; p (P) Battery，out The electric energy is the discharge capacity of the storage battery, kWh; p (P) WT The discharging amount of wind power generation is kWh; e (E) build kWh is the park electrical load; e (E) ASHP kWh is the power consumption of the electric refrigerator; e (E) HP The power consumption of the ground source heat pump is kWh;
2) Heat balance constraint: q (Q) ex (t)+Q GB (t)+Q solar (t)+P HTs，out (t)≥Q build (t); wherein Q is ex The heat release amount of the heat exchanger is kWh; q (Q) GB The heat is released for the boiler, kWh; q (Q) solar Heat is collected for a photo-thermal system, and kWh; p (P) HTS，out The heat is released for the heat storage system, and kWh; q (Q) build kWh for park heat load;
3) Cold balance constraint: c (C) ASHP (t)+C HP (t)+C AHP (t)≥C build (t), wherein C ASHP The refrigerating capacity of the electric refrigerator is kWh; c (C) HP The refrigeration capacity of the ground source heat pump is kWh; c (C) AHP kWh, which is the refrigerating capacity of the absorption type refrigerant; c (C) build Is the cold load of the park, kWh.
(2) The device capacity is less than or equal to the device maximum capacity constraint: g Battery，in ≤G Battery，in，max ，G Battery，out ≤G Battery，out，max ，G HST，in ≤G HST，in，max ，G HTS，out ≤G HTS，out，max ，G pv ≤G pv，max ，G solar ≤G solar，max ，G ICE ≤G ICE，max ，G ASHP ≤G ASHP，max ，G GB ≤G GB，max ，G WT ≤G wT，max ，G HP ≤G HP，max ，G AHP ≤G AHP，max Wherein G represents the capacity of the apparatus, kW; g Battery，in The charging capacity of the storage battery is kW; g Battery，out The discharge capacity of the storage battery is kW; g HST，in The heat storage capacity of the heat storage system is kW; g HTS，out The heat release capacity of the heat storage system is kW; g pv Is the capacity of the photovoltaic system, kW; g solar The capacity of the photo-thermal system, kW; g ICE Is the capacity of the internal combustion engine, kW; g ASHP The capacity of the electric refrigerator, kW; g GB Is the capacity of the gas boiler, kW; g WT The capacity of the wind driven generator is kW; g HP The capacity of the ground source heat pump, kW; g AHP Is the capacity of the absorption refrigerant, kW.
(3) And solving the system model by utilizing a multi-target genetic algorithm, and determining the optimal system robust structural configuration. The objective function includes: the objective function comprises the following steps of minimizing the carbon emission and curing the total cost in the whole life cycle of the energy utilization process of the park cost Meta) is minimum, the primary energy utilization rate (R) of the system is highest, and the independence of the park-level comprehensive energy system is highest. The calculation method of each objective function is as follows, and each objective weight is determined by using a TOPSIS method.
(1) The minimum carbon emission generated in the energy utilization process of the park in the whole life cycle (comprising electricity purchasing and carbon emission generated by natural gas consumption)
mainC yx ＝min(C yx，hear +C yx，cold +C yx，ele ) Wherein C yx Is total carbon yield, tCO of full life cycle in the operation process of the park system 2 ；C yx，hear Full life cycle carbon yield, tCO, in the process of supplying heat to users for park systems 2 ；C yx，cold Full life cycle carbon yield, tCO, in the process of cooling a user for a park system 2 ；C yx，ele Full life cycle carbon production, tCO, in the process of powering users for park systems 2 。
(2) Total annual cost (C) cost Meta) minimum (including system initial investment cost and operation maintenance cost)
min(C cost )＝min(∑ i A i ×c inv，i ×G eq，i ×n i )+∑ i (A i ×c ope，i ×n i )，Wherein i is a device class; c (C) cost Is the annual total cost of the system; a is that i Is the cost recovery coefficient; c inv Initial investment cost per unit capacity of the plant, yuan/kW; g eq For equipment capacity, kW; n is the number of the devices; r represents interest rate; y is i Indicating the life cycle of the device, year; c ope The maintenance cost coefficient for the equipment operation comprises the system maintenance cost and the fuel consumption cost.
(3) The primary energy utilization rate (R) of the system is highest
Wherein P is user The consumption of cold, heat and electricity of the user throughout the year is kWh; p (P) con kWh, the total energy consumed by the system.
(4) Park-level comprehensive energy system with highest independence
Wherein P is grid，buy The sum of electric energy purchased and sold by the system from an external large power grid and a large heat supply network is represented, and kWh is represented;P grid，sell representing the sum of heat energy purchased and sold by the system from an external large power grid and a large heat supply network, and kWh; p (P) load Refers to the total amount of cold and hot electric load of a user and kWh.
"mesotime scale" and "short time scale" scheduling time intervals (Δt m ) The determining method comprises the following steps: (1) Δt (delta t) m ≥Δt 2 Taking deltat of each device m Minimum value as system optimum Δt m The scheduling times of the equipment are reduced; (2) the environmental conditions are considered according to the worst situation, and delta t is calculated to ensure the energy supply quality m The size is not too large; (3) considering the thermal inertia of the building body Δt m A suitable increase; (4) different scheduling time intervals are set due to the load fluctuation frequency of the user in different time periods. Thus, the time interval formula is:
wherein Δt is zl Representing a scheduling time interval upper bound (accounting for energy quality); Δt (delta t) dx Indicating the scheduled time interval increment (accounting for thermal inertia); j represents different devices; f (f) load Representing the load fluctuation frequency; f (f) ref Representing the ripple frequency reference.
The regulation and control process of the comprehensive energy system is divided into three stages of a long time scale, a medium time scale and a short time scale, and the influence of uncertainty on the operation of the system is gradually reduced through gradual coordination. Wherein each device participates in the optimization operation of two stages of a long time scale and a medium time scale, and the short time scale only participates in a quick response device related to electricity, and the whole thinking is divided into the following steps: long time scale, medium time scale and segment time scale;
(one) "Long time Scale" stage
Constraints for the long time scale phase include: and the electricity balance constraint, the heat balance constraint, the cold balance constraint and the equipment output force are smaller than or equal to the equipment maximum capacity constraint. Performed once a day (resolution 1 h). Based on renewable energy and use before dayAnd (3) predicting data of the household cold and heat electric load, and determining a long-time scale operation scheme to obtain the start-stop state, the output plan and the like of each device in the next day. The optimized variable at this stage is the time-by-time output power and start-stop state of various devices. The objective function is the lowest daily operating cost. The daily operational cost mainly comprises power grid interaction cost, natural gas purchasing cost, battery aging cost, equipment operational cost and environmental cost, and the daily scheduling optimization targets are as follows:wherein C is D j is the running cost of the system and element; t is a scheduling period, 24 hours; />The method is a CCHP micro-grid system and power grid interaction cost, element; />Is the cost of natural gas; />The aging cost of the storage battery is the element; />Maintaining cost for the operation of the system; />Is the environmental cost of the system.
(two) "mesotime scale" phase
Constraints for the mesoscale phase include: and the electricity balance constraint, the heat balance constraint, the cold balance constraint and the equipment output force are smaller than or equal to the equipment maximum capacity constraint. Every Δt m ， 1 One time (time span 4 h). And adjusting the output plan of the equipment in the long time scale stage based on the short-term source load prediction data, and determining a medium time scale operation scheme. The optimization variable at this stage is the power adjustment variable quantity of the equipment in the day, and the objective function is the power purchase cost in the rolling time domainAnd the energy storage output change punishment cost is the lowest.Wherein M is the time span, 4h;the adjustment quantity cost of the power grid interaction is the element; />The cost of the adjustment amount of the fuel is as follows; />Punishment cost is paid for the change of the charge and discharge power of the storage battery; />Punishment costs are incurred for heat storage/release power variation of the heat storage/cold storage tank.
(III) "short time scale" stage
Constraints for the short time scale phase include: and the power utilization balance constraint and the power output of the electric energy equipment are smaller than or equal to the equipment maximum capacity constraint. Every Δt m，2 Is performed once. And determining the output of the quick response device based on the ultra-short-term source load prediction data and a 'middle time scale' stage plan of the energy supply system. The phase optimization variable is the device real-time power adjustment, and the objective function is to minimize the controllable device total adjustment for the next 1 period (e.g., 15 min) at the beginning of each real-time period (e.g., each 5 min). Wherein (1)>The power adjustment amount is for purchasing power in real time, kW; />The power adjustment amount is real-time electric power selling, and kW; />The power adjustment amount of the boiler is real-time, kW; />The power adjustment amount is real-time electric power selling, and kW; />The adjustment amount of the air purchasing power is kW in real time; />Real-time power adjustment quantity for the absorption refrigerator, kW; />Real-time power adjustment quantity for the electric refrigerator, kW; />And (5) adjusting the real-time power of the ground source heat pump by kW.
The "long time scale" scheduling plan is formulated once per day at 24:00, while at the same time every Δt m，1 Scrolling makes a "medium time scale" scheduling plan once per Δt m，2 Scrolling makes a "short time scale" scheduling plan once. The time periods corresponding to the "medium time scale" and "short time scale" scheduling plans are continually shifted forward over time.
The device output is less than or equal to the device capacity constraint: p (P) Battery，in ≤G Battery，in ，P Battery，out ≤G Battery，out ，P HST，in ≤G HST，in ，P HTS，out ≤G HTS，out ，P ICE ≤G ICE ，P ASHP ≤G ASHP ，P GB ≤G GB ，P HP ≤G HP ，P AHP ≤G AHP Wherein P representsThe output of the equipment, kW; g represents the capacity of the plant, kW; p (P) Battery，in Indicating the charging output of the storage battery; g Battery，in The charging capacity of the storage battery is kW; p (P) Battery，out Discharging force for the storage battery; g Battery，out The discharge capacity of the storage battery is kW; p (P) HST，in The heat storage output of the heat storage system is kW; g HST，in The heat storage capacity of the heat storage system is kW; p (P) HTS，out Exothermic output of the heat storage system is kW; g HTS，out The heat release capacity of the heat storage system is kW; p (P) ICE The output of the internal combustion engine is kW; g ICE Is the capacity of the internal combustion engine, kW; p (P) ASHP The output of the electric refrigerator is kW; g ASHP Kw is the capacity of the electric refrigerator; p (P) GB The output of the gas boiler is Kw; g GB Is the capacity of the gas boiler, kW; p (P) HP The output of the ground-edge heat pump is kW; g Hp The capacity of the ground source heat pump, kW.
The output of the electric energy equipment is smaller than or equal to the capacity constraint of the equipment: p (P) Battery，in ≤G Battery，in ，P Battery，out ≤G Battery，out ，P ASHP ≤G ASHP ，P HP ≤G HP Wherein P represents the output of the device, kW; g represents the capacity of the plant, kW; p (P) Battery，in Indicating the charging output of the storage battery; g Battery，in The charging capacity of the storage battery is kW; p (P) Battery，out Discharging force for the storage battery; g Battery，out The discharge capacity of the storage battery is kW; p (P) ASHP The output of the electric refrigerator is kW; g ASHP Kw is the capacity of the electric refrigerator; p (P) HP The output of the ground-edge heat pump is kW; g HP The capacity of the ground source heat pump, kW.
Aiming at constructing a comprehensive energy system, performing multi-time scale optimization control on the comprehensive energy system based on deep reinforcement learning;
1. and constructing a management model of the upper, middle and lower three layers of the comprehensive energy system, wherein the management model comprises intelligent agents, environment space, action space and rewarding functions of each layer. The action space of each layer of model is the output of each energy supply device, and the environment space is the cold, heat, electricity, gas load and renewable energy output condition of the system in each period.
2. Based on the deep reinforcement learning algorithm, interactive trial and error learning is carried out with the system environment, and upper, middle and lower management models are trained, so that the real-time rolling management requirements of the park comprehensive energy system can be met in an offline mode.
(1) Agent action selection and state transition
For time slot t, the agent inputs the current environmental state in the primary policy network, which outputs the executed actions based on its deterministic behavior policies. After the action selection is completed, the agent reduces the exploration factor by using the attenuation coefficient, and the occurrence of the overfitting condition is prevented.
(2) Gradient agent knowledge storage
The agent performs the selected action in the environment, and the environment transitions to a new state and returns a corresponding reward to the agent. The agent integrates this state transition process into a piece of "knowledge" and stores it in the experience playback pool. When the number of experience playback pools "knowledge" stores exceeds its capacity, the agent randomly draws several pieces of "knowledge" at each iteration to help train the neural network.
(3) Intelligent network training
In combination with the extracted "knowledge", the agent first transmits the actions output by the primary policy network and the current environmental state to the primary network to calculate its corresponding value and loss function. Next, the agent calculates the gradient of its loss function with respect to the network parameters and uses the reinforcement learning optimizer to reverse the transfer of gradient information and update the network. The agent then obtains a policy gradient for the policy by calculating a gradient of the discount jackpot function with respect to the primary policy network. Meanwhile, the agent combines the randomly extracted 'knowledge' to calculate the unbiased estimation value of the strategy gradient, and the reinforcement learning optimizer is utilized to reversely transfer strategy gradient information and update the network. And finally, the agent updates the target network and the target strategy network in a soft update mode, namely, the soft update coefficient is utilized to control the proportion of the original network parameter as a new network parameter when each iteration update is performed.
Finally, based on optimizationThe obtained park-level comprehensive energy system structure and multi-time scale optimization operation method calculate the economical efficiency (annual cost) and environmental protection (CO) of the system 2 Emission), energy efficiency (primary energy utilization rate), independence, comparing the energy efficiency with a separate production system, comparing the energy efficiency with a system with only a day-ahead scheduling plan, and analyzing to obtain the advantages of the park-level comprehensive energy system in a robust optimization and multi-time-scale optimization operation mode.
Example 2
The invention provides a multi-time scale optimization system of a comprehensive energy system, which comprises the following components:
and (3) constructing a model module: the system multi-objective robust optimization configuration model is used for constructing the system multi-objective robust optimization configuration model based on renewable energy output and a user load prediction result;
and a calculation solving module: the method comprises the steps of solving a system multi-target robust optimization configuration model by utilizing a multi-target genetic algorithm, and determining optimal system robust structure configuration;
and (3) a configuration optimizing module: the method is used for dividing the comprehensive energy system into three time phases, and optimizing the three time phases according to the optimal system robust structure configuration.
Example 3
Referring to fig. 3, the present invention further provides an electronic device 100 for a multi-time scale optimization method of an integrated energy system; the electronic device 100 comprises a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
The memory 101 may be used to store the computer program 103, and the processor 102 implements the integrated energy system multi-time scale optimization method steps described in embodiment 1 by running or executing the computer program stored in the memory 101 and invoking the data stored in the memory 101. The memory 101 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data) created according to the use of the electronic device 100, and the like. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), at least one disk storage device, a Flash memory device, or other non-volatile solid state storage device.
The at least one processor 102 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The processor 102 may be a microprocessor or the processor 102 may be any conventional processor or the like, the processor 102 being a control center of the electronic device 100, the various interfaces and lines being utilized to connect various portions of the overall electronic device 100.
The memory 101 in the electronic device 100 stores a plurality of instructions to implement an integrated energy system multi-time scale optimization method, the processor 102 is operable to execute the plurality of instructions to implement:
constructing a system multi-objective robust optimal configuration model based on renewable energy output and a user load prediction result;
solving a multi-target robust optimization configuration model of the system by utilizing a multi-target genetic algorithm, and determining the optimal robust structure configuration of the system;
and dividing the regulation and control process of the comprehensive energy system into three time phases, and optimizing according to the optimal system robust structure configuration.
Example 4
The modules/units integrated in the electronic device 100 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, and a Read-Only Memory (ROM).
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. The multi-time scale optimization method of the comprehensive energy system is characterized by comprising the following steps of:
constructing a system multi-objective robust optimal configuration model based on renewable energy output and a user load prediction result;
solving a multi-target robust optimization configuration model of the system by utilizing a multi-target genetic algorithm, and determining the optimal robust structure configuration of the system;
the regulation and control process of the comprehensive energy system is divided into three time phases, and the three time phases are optimized according to the optimal system robust structure configuration.
2. The multi-time scale optimization method of the comprehensive energy system according to claim 1, wherein the construction of the multi-objective robust optimization configuration model of the system comprises objective functions and constraint conditions;
the objective function includes: the carbon emission is minimum, the annual total cost is minimum, the primary energy utilization rate is highest, and the independence of the comprehensive energy system is highest;
the constraint conditions include: the energy supply and demand balance and the equipment capacity are smaller than or equal to the equipment maximum capacity constraint.
3. The integrated energy system multi-time scale optimization method of claim 2, wherein the energy supply and demand balance comprises: using an electric balance constraint, a heat balance constraint and a cold balance constraint;
using an electrical balance constraint: e (E) ICE (t)+E grid (t)+P pv (t)+P Battery，out (t)+P WT (t)≥E build (t)+E ASHP (t)+E HP (t); wherein t is time-by-time data; e (E) ICE Is the power generation capacity of the combustion engine, kWh; e (E) grid kWh for electricity purchase from municipal power grid; p (P) pv Is photovoltaic power generation amount kwh; p (P) Battery，out The electric energy is the discharge capacity of the storage battery, kWh; p (P) WT The discharging amount of wind power generation is kWh; e (E) build kWh is the park electrical load; e (E) ASHP kWh is the power consumption of the electric refrigerator; e (E) HP The power consumption of the ground source heat pump is kWh;
heat balance constraint: q (Q) ex (t)+Q GB (t)+Q solar (t)+P HTS，out (t)≥Q build (t); wherein Q is ex The heat release amount of the heat exchanger is kWh; q (Q) GB The heat is released for the boiler, kWh; q (Q) solar Heat is collected for a photo-thermal system, and kWh; p (P) HTS，out The heat is released for the heat storage system, and kWh; q (Q) build kWh for park heat load;
cold balance constraint: c (C) ASHP (t)+C HP (t)+C AHP (t)≥C build (t), wherein C ASHP The refrigerating capacity of the electric refrigerator is kWh; c (C) HP The refrigeration capacity of the ground source heat pump is kWh; c (C) AHP kWh, which is the refrigerating capacity of the absorption type refrigerant; c (C) build Is the cold load of the park, kWh.
4. The integrated energy system multi-time scale optimization method of claim 2, wherein the device capacity is equal to or less than a device maximum capacity constraint comprises: g Battery，in ≤G Battery，in，max ，G Battery，out ≤G Battery，out，max ，G HST，in ≤G HST，in，max ，G HTS，out ≤G HTS，out，max ，G pv ≤G pv，max ，G solar ≤G solar，max ，G ICE ≤G ICE，max ，G ASHP ≤G ASHP，max ，G GB ≤G GB，max ，G WT ≤G WT，max ，G HP ≤G HP，max ，G AHP ≤G AHP，max Wherein G represents the capacity of the apparatus, kW; g Battery，in The charging capacity of the storage battery is kW; g Battery，out The discharge capacity of the storage battery is kW; g HST，in The heat storage capacity of the heat storage system is kW; g HTS，out The heat release capacity of the heat storage system is kW; g pv Is the capacity of the photovoltaic system, kW; g solar The capacity of the photo-thermal system, kW; g ICE Is the capacity of the internal combustion engine, kW; g ASHP The capacity of the electric refrigerator, kW; g GB Is the capacity of the gas boiler, kW; g WT The capacity of the wind driven generator is kW; g HP The capacity of the ground source heat pump, kW; g AHP Is the capacity of the absorption refrigerant, kW.
5. The integrated energy system multi-time scale optimization method according to claim 2, wherein the carbon emission is minimized as mainC yx ＝min(C yx，hear +C yx，cold +C yx，ele ) Wherein C yx Is total carbon yield, tCO of full life cycle in the operation process of the park system 2 ；C vx，hear Full life cycle carbon yield, tCO, in the process of supplying heat to users for park systems 2 ；C yx，cold Full life cycle carbon yield, tCO, in the process of cooling a user for a park system 2 ；C yx，ele For park system asFull life cycle carbon yield, tCO in user power supply process 2 。
6. The integrated energy system multi-time scale optimization method according to claim 2, wherein the annual total cost is min (C cost )＝min(∑ i A i ×c inv，i ×G eq，i ×n i )+∑ i (A i ×c ope，i ×n i )，Wherein i is a device class; c (C) cost Is the annual total cost of the system; a is that i Is the cost recovery coefficient; c inv Initial investment cost per unit capacity of the plant, yuan/kW; g eq For equipment capacity, kW; n is the number of the devices; r represents interest rate; y is i Indicating the life cycle of the device, year; c ope The maintenance cost coefficient for the equipment operation comprises the system maintenance cost and the fuel consumption cost.
7. The integrated energy system multi-time scale optimization method according to claim 2, wherein the primary energy utilization rate is at most:wherein P is user The consumption of cold, heat and electricity of the user throughout the year is kWh; p (P) con kWh, the total energy consumed by the system.
8. The integrated energy system multi-time scale optimization method of claim 2, wherein the integrated energy system has a maximum independence of:wherein P is grid，buy The sum of electric energy purchased and sold by the system from an external large power grid and a large heat supply network is represented, and kWh is represented; p (P) grid，sell Indicating that the system is large in power grid and large in heat from outsideThe sum of the heat energy purchased and sold by the network, kWh; p (P) load Refers to the total amount of cold and hot electric load of a user and kWh.
9. The integrated energy system multi-time scale optimization method of claim 3, wherein the three time phases comprise: a long time scale stage, a medium time scale stage, and a short time scale stage;
constraints of the long-time scale phase and the medium-time scale phase include: the electricity balance constraint, the heat balance constraint, the cold balance constraint and the equipment output are smaller than or equal to the equipment maximum capacity constraint;
constraints of the short time scale phase include: the power utilization balance constraint and the power output of the electric energy equipment are smaller than or equal to the equipment maximum capacity constraint;
the device output is less than or equal to a device capacity constraint: p (P) Battery，in ≤G Battery，in ，P Battery，out ≤G Battery，out， ，P HST，in ≤G HST，in ，P HTS，out ≤G HTS，out ，P ICE ≤G ICE ，P ASHP ≤G ASHP ，P GB ≤G GB ，P HP ≤G HP ，P AHP ≤G AHP Wherein P represents the output of the device, kW; g represents the capacity of the plant, kW; p (P) Battery，in Indicating the charging output of the storage battery; g Battery，in The charging capacity of the storage battery is kW; p (P) Battery，out Discharging force for the storage battery; g Battery，out The discharge capacity of the storage battery is kW; p (P) HST，in The heat storage output of the heat storage system is kW; g HST，in The heat storage capacity of the heat storage system is kW; p (P) HTS，out Exothermic output of the heat storage system is kW; g HTS，out The heat release capacity of the heat storage system is kW; p (P) ICE The output of the internal combustion engine is kW; g ICE Is the capacity of the internal combustion engine, kW; p (P) ASHP The output of the electric refrigerator is kW; g ASHP Kw is the capacity of the electric refrigerator; p (P) GB The output of the gas boiler is Kw; g GB Is the capacity of the gas boiler, kW; p (P) HP The output of the ground-edge heat pump is kW; g HP The capacity of the ground source heat pump, kW;
the output of the electric energy equipment is smaller than or equal to the capacity constraint of the equipment: p (P) Battery，in ≤G Battery，in ，P Battery，out ≤G Battery，out ，P ASHP ≤G ASHP ，P HP ≤G HP Wherein P represents the output of the device, kW; g represents the capacity of the plant, kW; p (P) Battery，in Indicating the charging output of the storage battery; g Battery，in The charging capacity of the storage battery is kW; p (P) Battery，out Discharging force for the storage battery; g Battery，out The discharge capacity of the storage battery is kW; p (P) ASHP The output of the electric refrigerator is kW; g ASHP Kw is the capacity of the electric refrigerator; p (P) HP The output of the ground-edge heat pump is kW; g HP The capacity of the ground source heat pump, kW;
the long time scale stage scheduling optimization targets areWherein C is Dh The system running cost is the element; t is a scheduling period, 24 hours; />The method is a CCHP micro-grid system and power grid interaction cost, element; />Is the cost of natural gas; />The aging cost of the storage battery is the element; />Maintaining cost for the operation of the system; />Is the environmental cost of the system;
the time scale stage scheduling optimization targets are as follows Wherein M is the time span, 4h; />The adjustment quantity cost of the power grid interaction is the element; />The cost of the adjustment amount of the fuel is as follows; />Punishment cost is paid for the change of the charge and discharge power of the storage battery; />Punishment cost is paid for the change of heat storage and release power of the heat storage/cold tank;
the short time scale stage scheduling optimization targets are Wherein (1)>The power adjustment amount is for purchasing power in real time, kW; />The power adjustment amount is real-time electric power selling, and kW; />The power adjustment amount of the boiler is real-time, kW; />The power adjustment amount is real-time electric power selling, and kW; />The adjustment amount of the air purchasing power is kW in real time;real-time power adjustment quantity for the absorption refrigerator, kW; />Real-time power adjustment quantity for the electric refrigerator, kW;and (5) adjusting the real-time power of the ground source heat pump by kW.
10. The multi-time scale optimization system of the comprehensive energy system is characterized by comprising the following components:
and (3) constructing a model module: the system multi-objective robust optimization configuration model is used for constructing the system multi-objective robust optimization configuration model based on renewable energy output and a user load prediction result;
and a calculation solving module: the method comprises the steps of solving a system multi-target robust optimization configuration model by utilizing a multi-target genetic algorithm, and determining optimal system robust structure configuration;
and (3) a configuration optimizing module: the method is used for dividing the comprehensive energy system into three time phases, and optimizing the three time phases according to the optimal system robust structure configuration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311618098.2A CN117455076A (en) | 2023-11-29 | 2023-11-29 | Multi-time scale optimization method and system for comprehensive energy system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311618098.2A CN117455076A (en) | 2023-11-29 | 2023-11-29 | Multi-time scale optimization method and system for comprehensive energy system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117455076A true CN117455076A (en) | 2024-01-26 |
Family
ID=89583742
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311618098.2A Pending CN117455076A (en) | 2023-11-29 | 2023-11-29 | Multi-time scale optimization method and system for comprehensive energy system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117455076A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118378866A (en) * | 2024-06-25 | 2024-07-23 | 国网山东省电力公司滨州供电公司 | Construction method and system of intelligent agent and optimization method and system of energy storage system |
-
2023
- 2023-11-29 CN CN202311618098.2A patent/CN117455076A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118378866A (en) * | 2024-06-25 | 2024-07-23 | 国网山东省电力公司滨州供电公司 | Construction method and system of intelligent agent and optimization method and system of energy storage system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Baniasadi et al. | Optimal sizing design and operation of electrical and thermal energy storage systems in smart buildings | |
| CN109727158B (en) | Electric heating comprehensive energy system scheduling method based on improved weak robust optimization | |
| CN114004476B (en) | Multi-time scale optimal scheduling method for comprehensive energy system | |
| CN107800157B (en) | Double-layer optimized scheduling method for virtual power plant containing polymer temperature control load and new energy | |
| McLarty et al. | Micro-grid energy dispatch optimization and predictive control algorithms; A UC Irvine case study | |
| WO2016113925A1 (en) | Electrical power management device | |
| CN110322056A (en) | It is a kind of meter and central air conditioner system the random ADAPTIVE ROBUST Optimization Scheduling of virtual plant | |
| WO2015037307A1 (en) | Power storage control device, management system, power storage control method, power storage control program, and memory medium | |
| CN111539584B (en) | User-level comprehensive energy system planning method, system and equipment | |
| CN117455076A (en) | Multi-time scale optimization method and system for comprehensive energy system | |
| CN112308411B (en) | Comprehensive energy station random planning method and system based on dynamic carbon trading model | |
| CN111126675A (en) | Multi-energy complementary microgrid system optimization method | |
| CN112564102A (en) | Multi-microgrid load optimization scheduling method and system | |
| CN114926254A (en) | Bidding method for energy storage power station participating in frequency modulation auxiliary service market | |
| CN115659096A (en) | Micro-grid multi-time scale energy scheduling method and device considering source load uncertainty | |
| CN105930919A (en) | Two-stage stochastic planning-based virtual power plant risk avoidance optimization operation method | |
| CN115099509A (en) | Source-network load-storage multi-target cooperative regulation and control method and system | |
| Song et al. | Distributed scheduling of HVACs based on transactive energy and ADMM | |
| JP2016134933A (en) | Energy management system, computer program, and calculation method for operation plan | |
| CN112365108B (en) | Multi-objective optimization collaborative operation method for park comprehensive energy system | |
| Qiu et al. | Optimal scheduling for microgrids considering long-term and short-term energy storage | |
| Amadeh et al. | Building cluster demand flexibility: An innovative characterization framework and applications at the planning and operational levels | |
| CN116780627A (en) | Micro-grid regulation and control method in building park | |
| Chen et al. | Robust optimization based multi-level coordinated scheduling strategy for energy hub in spot market | |
| CN110943487B (en) | Energy optimization method and device for park energy system |
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 |