CN112836882B - Regional comprehensive energy system operation optimization method considering equipment load rate change - Google Patents
Regional comprehensive energy system operation optimization method considering equipment load rate change Download PDFInfo
- Publication number
- CN112836882B CN112836882B CN202110172976.7A CN202110172976A CN112836882B CN 112836882 B CN112836882 B CN 112836882B CN 202110172976 A CN202110172976 A CN 202110172976A CN 112836882 B CN112836882 B CN 112836882B
- Authority
- CN
- China
- Prior art keywords
- energy
- equipment
- ries
- load rate
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 97
- 230000008859 change Effects 0.000 title claims abstract description 57
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000006243 chemical reaction Methods 0.000 claims abstract description 37
- 238000013178 mathematical model Methods 0.000 claims abstract description 16
- 238000005096 rolling process Methods 0.000 claims abstract description 6
- 238000004146 energy storage Methods 0.000 claims description 50
- 230000005611 electricity Effects 0.000 claims description 28
- 238000007599 discharging Methods 0.000 claims description 23
- 230000008569 process Effects 0.000 claims description 12
- 238000010276 construction Methods 0.000 claims description 6
- 230000008878 coupling Effects 0.000 claims description 5
- 238000010168 coupling process Methods 0.000 claims description 5
- 238000005859 coupling reaction Methods 0.000 claims description 5
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 3
- 239000007789 gas Substances 0.000 description 24
- 238000011160 research Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 7
- 230000008901 benefit Effects 0.000 description 4
- 238000013439 planning Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000000295 complement effect Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000002068 genetic effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000010485 coping Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 101100289061 Drosophila melanogaster lili gene Proteins 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000003889 chemical engineering Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000005057 refrigeration Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 239000002699 waste material Substances 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
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—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)
- Strategic Management (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Finance (AREA)
- Entrepreneurship & Innovation (AREA)
- Accounting & Taxation (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Water Supply & Treatment (AREA)
- Primary Health Care (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an operation optimization method of an area comprehensive energy system considering equipment load rate change. The invention adopts the technical scheme that: constructing a fitting equation model of the efficiency of the energy conversion equipment along with the change of the load rate; constructing an RIES model considering the change of the efficiency of the energy conversion equipment along with the load rate; constructing a two-stage regional comprehensive energy system operation optimization mathematical model considering the change of the equipment load rate, solving a day-ahead optimization problem by taking the minimized operation cost as an objective function, and obtaining a regional comprehensive energy system operation optimization scheme considering the change of the equipment load rate; considering load fluctuation, combining a rolling optimization method in a daily stage, taking RIES running and punishment cost minimum as an optimization target, adjusting a daily front scheme, and solving an optimization scheduling scheme in a small time scale. The invention reduces the cost prediction error of the operation scheme and provides a more reasonable and economic operation scheme for RIES.
Description
Technical Field
The invention belongs to the technical field of operation optimization of regional comprehensive energy systems, and particularly relates to a two-stage regional comprehensive energy system operation optimization method considering equipment load rate change.
Background
The advent of regional integrated energy systems (Regional Integrated Energy System, RIES) has provided opportunities to address the contradiction between greatly increased energy demands and increasingly stringent environmental protection. The traditional mode of independent planning, independent design and independent operation among various energy supply systems in the energy industry is broken through, and the complementary characteristics among different energy sources such as electricity, gas, heat and the like are utilized by coordinating, matching and optimizing the different energy supply systems on the whole in the planning, design, construction and operation stages, so that the comprehensive utilization of various energy forms is realized. The construction of RIES and the development of related technologies are beneficial to realizing the complementary advantages among different energy sources, improving the comprehensive utilization efficiency of energy sources, promoting the large-scale development and utilization of renewable energy sources, and finally realizing the safe, economic and sustainable supply of energy sources. Therefore, the development of related researches is significant.
RIES equipment is various, and operation mechanism is complicated, in order to give full play to the economy and flexibility of RIES, need to formulate reasonable and effective operation scheme in order to coordinate the operation of multiple equipment, realize the complementary optimization of multiple energy sources. To simplify computation and complexity, current research modeling of RIES devices tends to ignore the effects of their load rate changes, considering device efficiency as a constant. However, in general, the load rate and the environment fluctuation in the operation process can cause the efficiency of the energy conversion device to be in a variable working condition state, and simplifying the efficiency into a constant can cause a larger error in the model analysis result. Deviations between the RIES plant model and the actual system would make ICES operation economics inconsistent with expectations, ultimately affecting the rationality of the operating scenario. Therefore, considering the device load rate variation has a great significance for the RIES operation optimization.
The coupling relation between the energy structure and the equipment in RIES is complex, and the modeling modes are various. The Energy Hub (EH) model provided by Geidl M and Andersson two scholars of Zurich Federal administration college provides a general model for RIES, describes the Energy transmission, conversion and storage relationship of the RIES through a coupling matrix, can realize the collaborative optimization of various forms of Energy sources, and is widely applied to ICES optimal configuration and scheduling research. Document [1] proposes a RIES operation optimization model containing energy storage devices; the documents [2-4] further consider the influence of the demand side response and renewable energy uncertainty in the operation optimization on the basis of the model; there are documents to build a dynamic economic dispatch model with minimum running cost as an optimization target and to solve the model.
In the above studies, the influence of load factor variation on the optimization of the RIES operation has not been considered. In practice, the efficiency of energy conversion devices typically fluctuates significantly with load factor, i.e., there is a significant difference in the efficiency of the device at different load factors. Neglecting the load rate variation of each device affects the accuracy of the device model to a certain extent, thereby affecting the rationality and economy of the RIES scheduling scheme.
In the current characteristic research on the change of the efficiency of RIES equipment along with the load rate, the literature [5] analyzes the change of the efficiency of a cogeneration (combined heat and power, CHP) unit under different loads, and proves that the economical efficiency of the CHP unit under different loads is greatly different and the necessity of considering the change of the load rate is considered; document [6] summarizes the form of a fitting equation of the efficiency of a part of the commonly used energy conversion devices as a function of the load rate, but does not give its specific application in the optimization of the RIES operation; the literature [7-8] researches the variable working condition characteristics of the heat pump, and experimental researches are carried out by constructing a test platform, so that the influence of the parameter changes such as load rate, air temperature and the like on the efficiency of the heat pump is analyzed; literature [9-11] researches the variable working condition characteristics of the boiler, establishes corresponding calculation models aiming at different types of boilers, and is used for solving the modeling problem of the variable working condition characteristics of the boiler; documents [12-13] summarize the form of the variable operating condition characteristic fitting equation for some of the commonly used energy conversion devices.
Currently, the research on the variable working condition characteristics of the energy conversion equipment is limited to single energy conversion equipment, a unified modeling framework suitable for the variable working condition characteristics of various energy conversion equipment is lacking, and an application method of the equipment variable working condition characteristic model under the RIES modeling framework is not proposed.
The documents mentioned above are as follows:
[1]Geidl M，Koeppel G，Favreperrod P，et al.Energy hubs for the future[J].IEEE Power&Energy Magazine，2007，5(1)：24-30。
[2]Heidari A，Mortazavi S S，Bansal R C.Stochastic effects of ice storage on improvement of an energy hub optimal operation including demand response and renewable energies[J].Applied Energy，2020，261(1)：114393。
[3]Pazouki S，Haghifam M R R，Moser A.Uncertainty modeling in optimal operation of energy hub in presence of wind，storage and demand response[J].International Journal of Electrical Power&Energy Systems，2014，61(8)：335-345。
[4]Zhang X，Shahidehpour M，Alabdulwahab A，et al.Hourly electricity demand response in the stochastic day-ahead scheduling of coordinated electricity and natural gas network[J].IEEE Transactions on Power Systems，2016，31(1)：592-601。
[5] feng Zhibing, jin Gongguang gas turbine combined heat and power generation system and energy storage variable working condition characteristic [ J ]. Chinese motor engineering report, 2006, 26 (4): 25-30.
[6] Cheng Haozhong, hu Xiao, wang Li, et al, regional comprehensive energy system planning study overview [ J ]. Power system automation, 2019,43 (07): 8-19.
[7] Wu Ji, ma Yimin, cao Wensheng. Variable working condition performance of air-cooled heat pump system driven by marsh gas machine [ J ]. Chemical engineering report, 2020,71 (08): 3789-3796.
[8] Ding Caifeng, cao Yu Water source heat pump variable working condition experimental study [ J ]. Refrigeration technology, 2017,40 (02): 42-45.
[9] Cai, zhong application of the boiler variable working condition thermodynamic calculation analysis system [ J ]. Inner Mongolia electric power technology, 2011,29 (5): 72-75.
[10] Cai Ruixian, hu Ziqin, working condition variable calculation of waste heat boiler [ J ]. Engineering thermophysical journal, 1990 (01): 17-20.
[11]Liu F,Sui J,Liu T,et al.Energy and exergy analysis in typical days of a steam generation system with gas boiler hybrid solar-assisted absorption heat transformer[J].Applied Thermal Engineering,2017,115:715-725。
[12] Cheng Haozhong, hu Xiao, wang Li, et al, overview of regional integrated energy system planning research [ J ]. Electric power system automation, 2019,43 (07): 8-19.
[13] Li Xinxuan, zhang Zhongping, liu Lili. Micro-grid system plant operating characteristics and control study overview [ J ]. Energy conservation 2020, 39 (05): 173-176.
Disclosure of Invention
The invention aims to provide a two-stage regional comprehensive energy system operation optimization method considering equipment load rate change, so as to improve equipment modeling accuracy and system operation economy, and a scheduling scheme is more reasonable.
Therefore, the invention adopts the following technical scheme: the regional comprehensive energy system operation optimization method considering the equipment load rate change comprises the following steps:
constructing a fitting equation model of the efficiency of the energy conversion equipment along with the change of the load rate;
constructing an RIES model considering the change of the efficiency of the energy conversion equipment along with the load rate;
constructing a two-stage regional comprehensive energy system operation optimization mathematical model considering the equipment load rate change, wherein the two-stage regional comprehensive energy system operation optimization mathematical model consists of a RIES day-ahead operation optimization model and a RIES day-in operation optimization model, and solving the day-ahead optimization problem by taking the minimum operation cost as an objective function to obtain a regional comprehensive energy system operation optimization scheme considering the equipment load rate change; considering load fluctuation, combining a rolling optimization method in a daily stage, taking RIES running and punishment cost minimum as an optimization target, adjusting a daily front scheme, and solving an optimization scheduling scheme in a small time scale.
Further, the fitting equation model construction process of the efficiency of the energy conversion device along with the change of the load rate is as follows:
to uniformly represent the relation between the efficiency and the load rate of each device, the following polynomials are constructed:
wherein eta is the equipment efficiency; n is the fitting order; k (k) n Fitting coefficients; n is the equipment load rate;
the equipment load factor is obtained by the following formula:
wherein P is out The energy is output for the equipment, and the unit is kW;the capacity of the plant is expressed in kW.
Further, the construction process of the RIES model considering the efficiency of the energy conversion device as a function of the load factor is as follows:
the relation between the input energy and the output energy of the energy conversion device in RIES is expressed by the following formula:
P out ＝ηP in (3)
wherein P is in Inputting energy into the equipment, wherein the unit is kW; p (P) out The energy is output for the equipment, and the unit is kW; η is the efficiency of the device;
considering the relation between the efficiency and the load rate of each device, the method is expressed as follows:
wherein n is a fitting order; k (k) n Fitting coefficients; n is the equipment load rate;
the energy conversion device output power constraint is expressed as:
in the method, in the process of the invention,outputting a maximum value of energy for the device;
the energy storage device model is built as follows:
assuming that the charge and discharge energy power of the energy storage device is constant in the Δt time period, the energy relationship stored by the device before and after the charge and discharge energy is:
W ES,t+1 ＝W ES,t (1-σ ES )+E ES,t Δt (6)
in which W is ES,t 、W ES,t+1 Respectively charging and discharging energy storage energy of the equipment before and after energy storage, wherein the unit is kWh; sigma (sigma) ES The self-discharging rate of the energy storage equipment is set; e (E) ES,t Taking the actual charging and discharging energy power of the energy storage device after the charging and discharging energy efficiency loss into consideration at the moment t, so as to charge energy to the device positively, wherein the unit is kW; Δt is the running optimization step length, and the unit is h;
the limitation of the charge and discharge energy power of the energy storage device is described as follows:
wherein eta is ES,C 、η ES,D Respectively charging and discharging energy efficiency of the energy storage equipment;the upper limit of the charging and discharging energy power of the equipment is respectively shown in kW;
the energy storage constraints of the energy storage device are as follows:
in the method, in the process of the invention,the unit is kWh for the maximum energy storage of the equipment;
the RIES model is as follows:
wherein alpha is an energy form; l (L) α Load level in energy form α; p (P) out,α,i Output in alpha energy form for device i; s is(s) α The energy efficiency is charged and discharged for the energy form alpha; e (E) α The energy source form alpha is the actual charge-discharge energy vector so as to charge energy to the charge-energy equipment to be positive,
wherein eta is α,C 、η α,D The charging and discharging efficiency is respectively.
Further, in the mathematical model of the RIES day-ahead running optimization model,
the RIES operation optimization objective is to minimize the operation cost on the premise of meeting the load demand of the user, and the objective function is shown as formula (12):
minf c ＝C elec +C gas (12)
wherein C is elec The unit is primary for electricity purchasing cost; c (C) gas The unit is the unit of gas purchasing cost;
wherein, c elec,t The electricity purchase price is the unit of t time period, and the unit is Yuan/(kWh); p (P) elec,t The power is purchased for the t period, and the unit is kW; c gas The unit of the gas purchase price is yuan/m 3 ；P gas,t The unit of the air purchasing quantity is m in t time period 3 。
Further, in the mathematical model of the RIES day-ahead running optimization model, the constraint conditions include equipment model constraints:
1) Energy conversion device constraints
The energy conversion device constraints include formulas (4) - (5);
2) Energy storage device constraints
The energy storage device constraints include formulas (6) - (9).
In addition, considering the periodicity and time period coupling characteristics of the energy stored by the energy storage device, it is assumed that the energy stored is consistent at the beginning and end of the scheduling period, that is:
W ES,start ＝W ES,end (15)
wherein: w (W) ES,start 、W ES,end The energy storage energy at the beginning and the end of the scheduling period of the equipment is respectively expressed in kWh.
Still further, in the mathematical model of the rias day-ahead running optimization model, the constraint conditions further include a system running constraint:
1) System energy balance constraint
The energy balance constraint of the system is shown as a formula (10);
2) Tie line power constraint
The tie-line power constraint is expressed as:
wherein:the upper limit of the power of the tie line is represented in kW; p (P) elec,t The link power is expressed in kW.
Further, in the mathematical model of the RIES intra-day operation optimization model,
objective function: in order to realize cooperative scheduling of two stages in the day and before, and ensure the global performance of a short time scale day scheduling scheme, the equipment output is corrected on the basis of the day scheduling scheme, the punishment cost is brought into an objective function, the objective function of the day operation optimization scheduling model is the minimum RIES operation cost and punishment cost in each control time domain, and the objective function is shown in a formula (17):
f c ＝C elec +C gas +C p (17)
wherein C is p The penalty cost is a penalty term which is introduced in an objective function by considering the deviation degree of the output power of each device and the scheduling plan before the day, and the unit is a unit.
Still further, the method further comprises the steps of,
wherein, c p,i The unit is a penalty factor; ΔP out,i,t The unit of deviation of the output power of each device from the day-ahead schedule is kWh.
Further, in the mathematical model of the RIES day-ahead running optimization model, the constraint condition is consistent with that of the RIES day-ahead running optimization model.
Aiming at the model, the invention solves the problem by using a genetic algorithm.
The invention has the following beneficial effects: the traditional optimized operation method cannot describe the change process of the equipment efficiency along with the load rate. The invention models the relation between the equipment efficiency and the load rate, so that the relation of the variable efficiency of the conversion equipment along with the change of the load rate can be reflected in the operation optimization process; based on the two-stage RIES operation optimization method considering the equipment load rate change, the efficiency change characteristic of the energy conversion equipment is considered, and the accuracy of the equipment model is improved; the method provided by the invention is more in line with the actual running condition of the equipment, reduces the cost prediction error of the running scheme, and provides a more reasonable and economic running scheme for RIES.
Drawings
FIG. 1 is a graph of the thermoelectric load of a RIES algorithm in an embodiment of the invention;
FIG. 2 is a schematic diagram of the time-of-use electricity price of the RIES example system in an embodiment of the invention;
FIG. 3 is a schematic diagram of an optimization scheme of the operation of the RIES system according to the present invention (FIG. 3a is a schematic diagram of a power scheduling scheme in consideration of a change in load rate of a device, and FIG. 3b is a schematic diagram of a power scheduling scheme in consideration of no change in load rate of a device);
fig. 4 is a schematic diagram of a daily thermal power operation optimization scheme of the rias computing system in the application example of the present invention (fig. 4a is a schematic diagram of a thermal power scheduling scheme under consideration of a device load rate change, and fig. 4b is a schematic diagram of a thermal power scheduling scheme under consideration of no device load rate change).
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Examples
The embodiment provides an area comprehensive energy system optimizing operation method considering equipment load rate change.
Firstly, constructing a fitting equation model of the efficiency of energy conversion equipment along with the change of the load rate; subsequently, a mathematical model of RIES is constructed; and finally, constructing a two-stage RIES operation optimization model considering the variable working condition characteristics of the equipment, solving an optimization problem by adopting a genetic algorithm, and providing an operation optimization scheme considering the load rate change of the equipment for the ICES. The method provided by the invention can provide an operation optimization scheme for RIES, the operation optimization result reasonably reflects the efficiency change caused by the equipment load rate change, the accuracy of an equipment model is improved, and a more reasonable and economic operation scheme is provided for RIES.
Step one, a fitting equation model of the change of the efficiency of the energy conversion equipment along with the load rate is established:
to uniformly represent the relation between the efficiency and the load rate of each device, the following polynomials are constructed:
wherein: η is the efficiency of the device; n is the fitting order; k (k) n Fitting coefficients; n is the device load factor.
The equipment load factor can be obtained by the following formula:
wherein: p (P) out Energy is output for the equipment, kW;representing the capacity of the plant, kW.
Step two, establishing a RIES model considering the change of the efficiency of the energy conversion equipment along with the load rate:
in general, the input-output relationship of the energy conversion device in the RIES can be expressed by the following formula:
P out ＝ηP in (3)
wherein: p (P) in The energy is input to the plant, kW.
Considering the relationship between the efficiency and the load factor of each device, it can be expressed as:
the energy conversion device output power constraint can be expressed as:
the energy storage device model is built as follows:
the charging and discharging energy mechanisms and the energy conversion relations of different energy storage devices are similar, and the invention is described by adopting a general energy storage device model. Assuming that the charge and discharge energy power of the energy storage device is constant in the Δt time period, the energy relationship stored by the device before and after the charge and discharge energy is:
W ES,t+1 ＝W ES,t (1-σ ES )+E ES,t Δt (6)
wherein: w (W) ES,t 、W ES,t+1 Respectively charging and discharging energy storage energy before and after energy storage and kWh of the equipment; sigma (sigma) ES The self-discharging rate of the energy storage equipment is set; e (E) ES,t Taking the actual charging and discharging energy power of the energy storage device after the charging and discharging energy efficiency loss into consideration at the moment t to charge energy to the device to be positive, and kW; Δt is the running optimization step length, h.
The limitation of the charge and discharge energy power of the energy storage device can be described as follows:
wherein: η (eta) ES,C 、η ES,D Respectively charging and discharging energy efficiency of the energy storage equipment;and respectively charging and discharging energy power upper limit and kW of the equipment.
The energy storage constraints of the energy storage device are as follows:
wherein:kWh is the maximum energy storage of the device.
The RIES model available so far is as follows:
wherein: alpha is in the form of energy, such as electricity, heat and the like; l (L) α Load level in energy form α; p (P) out,α,i Output in alpha energy form for device i; s is(s) α The energy efficiency is charged and discharged for the energy form alpha; e (E) α The energy source form alpha is the actual charge-discharge energy vector so as to charge energy to the energy charging equipment positively.
Wherein: η (eta) α,C 、η α,D Respectively the charge and discharge energy efficiency.
Step three, establishing an RIES day-ahead running optimization model
1. Objective function
The RIES operation optimization objective is to minimize the operation cost on the premise of meeting the user load demand.
The objective function is shown in equation (15):
minf c ＝C elec +C gas (12)
wherein: c (C) elec The cost of electricity purchase is the element; c (C) gas For purchasing gas, the cost is first.
Wherein: c elec,t The electricity purchase price is t time period units, and the unit/(kWh); p (P) elec,t Purchasing electric power for a period t, kW;c gas is the unit gas purchase price, yuan/m 3 ；P gas,t For the air purchase amount in t period, m 3 。
2. Constraint conditions
The RIES operation optimization model constraints that take into account the device load rate variation are as follows.
2.1 device model constraints
1) Energy conversion device constraints
The energy conversion device constraints include formulas (4) - (5).
2) Energy storage device constraints
The energy storage device constraints include formulas (6) - (9).
In addition, considering the periodicity and time period coupling characteristics of the energy stored by the energy storage device, it is assumed that the energy stored is consistent at the beginning and end of the scheduling period, that is:
W ES,start ＝W ES,end (15)
wherein: w (W) ES,start 、W ES,end Respectively representing the energy storage energy at the beginning and the end of a device scheduling period, and kWh.
2.2 System operation constraints
1) System energy balance constraint
The system energy balance constraint is shown in formula (10).
2) Tie line power constraint
The tie-line power constraint may be expressed as:
wherein:representing the upper limit of tie line power, kW.
The present invention solves the above-described optimization problem using genetic algorithms.
Step four, establishing an RIES intra-day operation optimization model
1. Objective function
In order to realize cooperative scheduling of the two stages before and during the day, the global performance of a short time scale intra-day scheduling scheme is ensured to a certain extent, and the equipment output is corrected on the basis of the pre-day scheduling scheme. And the objective function of the daily optimization scheduling model is that RCES running cost and punishment cost in each control time domain are minimum. The objective function is shown in equation (17):
f c ＝C elec +C gas +C p (17)
wherein C is p The penalty cost is a penalty term which is introduced in an objective function by considering the deviation degree of the output power of each device and the scheduling plan before the day, and the unit is a unit.
Wherein, c p,i A penalty factor, a primitive; ΔP out,i,t kWh is the degree of deviation of the output power of each device from the day-ahead schedule.
2. Constraint conditions
And the constraint condition of the RIES intra-day operation optimization model considering the change characteristic of the equipment load rate is consistent with the constraint condition of the pre-day operation optimization model.
Application example
The application example adopts a certain park in the south of China, and the structure of the application example is shown in figure 2. The system comprises 1600kW of a CHP unit, 2050kW of a gas boiler and 200kWh of a storage battery. The relevant parameters of the device are shown in table 1.
TABLE 1 Main Equipment parameters
Taking a typical day as an example, the load curve is shown in fig. 1. Time-of-use electricity price referring to fig. 2, tie line powerThe upper limit is 3MW, and the gas price is 4.16 yuan/m 3 . The running period is optimized for 24h, the unit time delta t=1h before the day, the unit time delta t=0.5h in the day, and the prediction time domain t=4h.
In order to verify the effectiveness of the method, the following two scenes are constructed for comparison:
scene I: RIES operates according to a scheduling scheme obtained by a two-stage operation optimization method taking into account the change of the load rate of the equipment.
Scene II: RIES operates according to a scheduling scheme obtained by an operation optimization method that does not take into account changes in the load rate of the device.
The scheduling scheme obtained in the scene II constructed by the invention is only a prediction scheme in an ideal state because the actual efficiency change of the equipment is not considered, and the corresponding obtained cost is the prediction cost. However, in actual operation, since the operating efficiency of the equipment is difficult to reach the rated efficiency, the scheduling scheme has larger error and is difficult to execute, and certain correction is required to be carried out on the output of the equipment. The strategy adopted by the invention is as follows: the electric load is purchased by the power grid, and the heat load is complemented by other equipment. And compares the corrected scheduling scheme cost as the actual cost.
The results of the RIES running cost comparison for the two scenarios of taking into account the device load rate change versus not taking into account the device load rate change are shown in Table 2.
Table 2 running costs in two scenarios
The total cost of RIES predicted operation in the day of the day-ahead scheduling stage scene I is 52009.56 yuan, and the total cost of RIES predicted operation in the day of scene II is 50587.84 yuan.
Compared with the two scenes, the operation cost is predicted in advance day by day, the gas purchase cost of the scene I is reduced by 37.7 percent relative to the scene II, the electricity purchase cost is increased by 58.8 percent, and the dependence on a power grid is higher in the visible scene I. In the intra-day scheduling stage, the scene I carries out fine adjustment on a day-ahead scheduling scheme through rolling optimization, and the load requirement is met under the condition that the running cost is improved by 2.5% relative to the day-ahead predicted value, but the running cost is improved by 5.9% relative to the day-ahead predicted value in the scene II. And the actual running cost is 1255.86 yuan higher than that of the scene I.
In order to analyze the running optimization of RIES in more detail, the invention compares and discusses the electric and thermal running optimization schemes under two scenes.
The power operation optimization scheme in both scenarios is shown in fig. 3. As can be seen from the figure, the electric load operation optimization schemes in the two scenarios are greatly different, in scenario I, electricity is mainly purchased from the power grid to satisfy most of the electric load, but in scenario II CHP is used as the main output device to satisfy most of the electric load.
In scenario I, when considering the change in the equipment load rate, in the time period (0-6 hours, 23 hours) when the electricity price is low, since the electricity price is low when purchasing electricity from the grid, the economic benefit of using CHP to supply the electrical load is not as high as when purchasing electricity from the grid, so most of the electrical load is supplied by the grid; and during this period the battery is charged for coping with the electric load at peak electricity price times (10 hours, 19-20 hours). At peak electricity prices, the electricity price increases to increase the electricity purchasing cost from the power grid and reduce the economy, so that the CHP unit is started to supply partial electric loads, but the CHP unit is limited by the heat load, and cannot supply all the electric loads, and still needs to purchase electricity from the power grid.
In the scene II, when the change of the equipment load rate is not considered, the charge and discharge states of the storage battery in different time periods are mainly influenced by electricity prices, so that the charge and discharge rules are basically similar to the scene I. Because the device load rate change is not considered in the scene II, the device efficiency is constant, the CHP is high in total energy utilization rate and good in economy due to cogeneration, and is always used as main output equipment in each period, and the CHP operates in a heat electricity fixing mode to supply partial electric loads, and the residual electric power requirement is met by purchasing electricity from a power grid.
The thermal power operation optimization scheme in both scenarios is shown in fig. 4. It can be seen from the figure that the heat load operation optimization schemes in the two scenes are also quite different, most of the time in the scene I takes the gas boiler as main energy supply equipment, but the CHP bears most of the heat load in the scene II, and the gas boiler only participates in energy supply in the energy consumption peak.
Considering the change of the load rate of the device in the scene I, when the electricity price is fixed, the scheduling scheme is related to the efficiency characteristic of the device, and also related to the load level at the current moment. When the heat load is smaller, the CHP and the HP are both operated at a low load rate, and the efficiency of the CHP and the HP is lower than that of the CHP when the CHP is operated at a full load, but the economical efficiency of RIES operation can be improved to a certain extent due to the better economical efficiency of CHP cogeneration, so the CHP is used as heating equipment (4); when the load is gradually increased, the load level gradually approaches the rated capacity of the gas boiler, and the energy supply of the gas boiler is only used at the moment, so that the load rate is ensured to be at a higher level, and the efficiency is also at a higher level; however, when the heat load is greater than the capacity of the gas-fired boiler, the gas-fired boiler is insufficient to meet the full heat load, at which time the CHP can be operated at full load to ensure that the CHP is at a high load rate, with the remaining heat load being met by the gas-fired boiler (12 hours). But when in peak electricity prices (10 hours, 19-20 hours), it is more economical to use CHP to supply heat and electricity loads simultaneously due to the higher cost of purchasing electricity from the grid.
Whereas in case II, the device efficiency is considered constant regardless of the device load rate variation. When the device is on a definite time, the CHP is used as main heating equipment due to better heat and power cogeneration economy, and the gas boiler is used for coping with heat load peaks. In practice, however, at some point in time, the actual operating efficiency is lower than the nominal efficiency due to the lower CHP load rate, and the use of CHP to power would instead increase the running cost of the RIES, so that there may be some irrational in the operating scheme without regard to the change in the plant load rate, and the operating scheme predicts the running cost to be a large difference from the actual running cost.
It can be seen that when the change of the equipment load rate is not considered, the influence of the equipment load rate on the equipment efficiency is not considered, and the equipment is always operated in a low load rate state, so that the actual operation efficiency of the equipment is lower, and the RIES economic operation is not facilitated; the actual running efficiency of the equipment changes greatly along with the load rate, so that the running scheme of the system is not in accordance with the actual running condition, and the cost prediction has great deviation, so that the economic dispatching and analysis of the RIES are inaccurate, and the running economy of the RIES is affected; when the change of the load rate of the equipment is considered, under a certain energy price, the load level and the equipment capacity influence the operation efficiency of the equipment, and further influence the operation scheme of RIES, so that the equipment with similar capacity is selected as the output equipment according to the load level, and the equipment is more economical.
Due to the existence of the load prediction error, the method is executed according to a day-ahead optimal scheduling scheme, on one hand, the output of the equipment in a part of time period is difficult to completely meet the daily operation requirement, and on the other hand, the energy supply of the equipment in the part of time period is larger than the load requirement, so that the energy waste is caused, and the RIES operation economy is affected. In the scene I, the small time scale optimized scheduling scheme is generated and issued and executed by rolling optimization on the basis of the day-ahead optimized scheduling scheme, and the RIES equipment output is adjusted, so that the RIES running economy can be improved by fine adjustment of the day-ahead optimized scheduling scheme under the condition that the RIES load demand is met.
In summary, the invention provides a two-stage park comprehensive energy system operation optimization method considering the change of the equipment load rate, and the conclusion is as follows:
1) When the traditional RIES operation optimization method does not consider the change of the equipment load rate, the change process of the equipment efficiency along with the load rate cannot be described. The equipment efficiency does not accord with the actual running condition, and the running optimization scheme is unreasonable.
2) The RIES operation optimization method considering the equipment load rate change considers the efficiency variation of the energy conversion equipment along with the load rate, and improves the accuracy of the equipment model.
3) Compared with the operation scheme obtained by the traditional RIES, the method provided by the invention is more in line with the actual operation condition of the equipment, reduces the cost prediction error of the scheduling scheme, and provides a more reasonable and economic operation scheme for the RIES.
4) Due to the existence of load prediction errors, the day-ahead optimal scheduling scheme is difficult to completely meet the day-ahead operating requirements. The one-time full-time optimization method has the advantages that the scheduling is rough, the fluctuation of the load cannot be responded in time, the load supply is easy to deviate from the actual load, and the scheduling scheme has poor applicability; the two-stage optimization method comprising rolling optimization is fine in scheduling, and on the basis of a day-ahead optimization scheduling plan, the output of equipment is optimally adjusted according to the change of load, so that the system load requirement can be met, and the influence of load prediction errors on the running economy of RIES is reduced.
The examples described above merely represent embodiments of the present invention and are not to be construed as limiting the scope of the invention, nor are they intended to be limiting in any way. It should be noted that variations and modifications can be made by those skilled in the art without departing from the spirit of the invention, which falls within the scope of the invention.
Claims (5)
1. The regional comprehensive energy system operation optimization method considering the equipment load rate change is characterized by comprising the following steps:
constructing a fitting equation model of the efficiency of the energy conversion equipment along with the change of the load rate;
constructing an RIES model considering the change of the efficiency of the energy conversion equipment along with the load rate;
constructing a two-stage regional comprehensive energy system operation optimization mathematical model considering the equipment load rate change, wherein the two-stage regional comprehensive energy system operation optimization mathematical model consists of a RIES day-ahead operation optimization model and a RIES day-in operation optimization model, and solving the day-ahead optimization problem by taking the minimum operation cost as an objective function to obtain a regional comprehensive energy system operation optimization scheme considering the equipment load rate change; considering load fluctuation, combining a rolling optimization method in a daily stage, taking RIES running and punishment cost minimum as an optimization target, adjusting a daily front scheme, and solving an optimization scheduling scheme in a small time scale;
the fitting equation model construction process of the efficiency of the energy conversion equipment along with the change of the load rate is as follows:
to uniformly represent the relation between the efficiency and the load rate of each device, the following polynomials are constructed:
wherein eta is the equipment efficiency; n is the fitting order；k n Fitting coefficients; n is the equipment load rate;
the equipment load factor is obtained by the following formula:
wherein P is out The energy is output for the equipment, and the unit is kW;representing the capacity of the plant in kW;
the construction process of the RIES model considering the change of the efficiency of the energy conversion equipment along with the load rate is as follows:
the relation between the input energy and the output energy of the energy conversion device in RIES is expressed by the following formula:
P out ＝ηP in (3)
wherein P is in Inputting energy into the equipment, wherein the unit is kW; p (P) out The energy is output for the equipment, and the unit is kW; η is the efficiency of the device;
considering the relation between the efficiency and the load rate of each device, the method is expressed as follows:
wherein n is a fitting order; k (k) n Fitting coefficients; n is the equipment load rate;
the energy conversion device output power constraint is expressed as:
in the method, in the process of the invention,outputting a maximum value of energy for the device;
the energy storage device model is built as follows: when the energy storage device is constant in charging and discharging energy power in the delta t time period, the energy relation stored by the device before and after charging and discharging energy is as follows:
W ES,t+1 ＝W ES,t (1-σ ES )+E ES,t Δt (6)
in which W is ES,t 、W ES,t+1 Respectively charging and discharging energy storage energy of the equipment before and after energy storage, wherein the unit is kWh; sigma (sigma) ES The self-discharging rate of the energy storage equipment is set; e (E) ES,t Taking the actual charging and discharging energy power of the energy storage device after the charging and discharging energy efficiency loss into consideration at the moment t, so as to charge energy to the device positively, wherein the unit is kW; Δt is the running optimization step length, and the unit is h;
the limitation of the charge and discharge energy power of the energy storage device is described as follows:
wherein eta is ES,C 、η ES,D Respectively charging and discharging energy efficiency of the energy storage equipment;the upper limit of the charging and discharging energy power of the equipment is respectively shown in kW;
the energy storage constraints of the energy storage device are as follows:
in the method, in the process of the invention,the unit is kWh for the maximum energy storage of the equipment;
the RIES model is as follows:
wherein alpha is an energy form; l (L) α Load level in energy form α; p (P) out,α,i Output in alpha energy form for device i; s is(s) α The energy efficiency is charged and discharged for the energy form alpha; e (E) α The energy source form alpha is the actual charge-discharge energy vector so as to charge energy to the charge-energy equipment to be positive,
wherein eta is α,C 、η α,D The charging and discharging efficiency is respectively;
in the mathematical model of the RIES day-ahead running optimization model, the RIES running optimization objective is to minimize running cost on the premise of meeting the load demand of users, and the objective function is shown in a formula (12):
minf c ＝C elec +C gas (12)
wherein C is elec The unit is primary for electricity purchasing cost; c (C) gas The unit is the unit of gas purchasing cost;
wherein, c elec,t The electricity purchase price is the unit of t time period, and the unit is Yuan/(kWh); p (P) elec,t The power is purchased for the t period, and the unit is kW; c gas The unit of the gas purchase price is yuan/m 3 ；P gas,t For the air purchasing amount in t time period, unitIs m 3 ；
In the mathematical model of the RIES daily operation optimization model, the objective function is as follows: in order to realize cooperative scheduling of two stages in the day and before, and ensure the global performance of a short time scale day scheduling scheme, the equipment output is corrected on the basis of the day scheduling scheme, the punishment cost is brought into an objective function, the objective function of the day operation optimization scheduling model is the minimum RIES operation cost and punishment cost in each control time domain, and the objective function is shown in a formula (17):
f c ＝C elec +C gas +C p (15)
wherein C is p The penalty cost is a penalty term which is introduced in an objective function by considering the deviation degree of the output power of each device and the day-ahead scheduling plan, and the unit is a unit;
wherein, c p,i The unit is a penalty factor; ΔP out,i,t The unit of deviation of the output power of each device from the day-ahead schedule is kWh.
2. The regional comprehensive energy system operation optimization method considering equipment load rate variation according to claim 1, wherein the constraint conditions in the mathematical model of the rias day-ahead operation optimization model include equipment model constraints:
1) Energy conversion device constraints
The energy conversion device constraints include formulas (4) - (5);
2) Energy storage device constraints
The energy storage device constraints include formulas (6) - (9).
3. The method for optimizing operation of a regional integrated energy system according to claim 2, wherein the energy storage device is set to keep consistent at the beginning and end of a scheduling period, taking into consideration the periodicity and the period coupling characteristic of the energy storage energy, namely:
W ES,start ＝W ES,end (17)
wherein: w (W) ES,start 、W ES,end The energy storage energy at the beginning and the end of the scheduling period of the equipment is respectively expressed in kWh.
4. The regional comprehensive energy system operation optimization method considering the equipment load rate variation according to claim 2, wherein the constraint conditions in the mathematical model of the rias day-ahead operation optimization model further comprise system operation constraints:
1) System energy balance constraint
The energy balance constraint of the system is shown as a formula (10);
2) Tie line power constraint
The tie-line power constraint is expressed as:
wherein:the upper limit of the power of the tie line is represented in kW; p (P) elec,t The link power is expressed in kW.
5. The regional comprehensive energy system operation optimization method considering equipment load rate variation according to claim 1, wherein constraint conditions in the mathematical model of the RIES day-ahead operation optimization model are consistent with constraint conditions of the RIES day-ahead operation optimization model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110172976.7A CN112836882B (en) | 2021-02-08 | 2021-02-08 | Regional comprehensive energy system operation optimization method considering equipment load rate change |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110172976.7A CN112836882B (en) | 2021-02-08 | 2021-02-08 | Regional comprehensive energy system operation optimization method considering equipment load rate change |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112836882A CN112836882A (en) | 2021-05-25 |
| CN112836882B true CN112836882B (en) | 2024-03-01 |
Family
ID=75932826
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110172976.7A Active CN112836882B (en) | 2021-02-08 | 2021-02-08 | Regional comprehensive energy system operation optimization method considering equipment load rate change |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112836882B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113793052A (en) * | 2021-09-23 | 2021-12-14 | 长沙理工大学 | Robust optimization scheduling method for regional comprehensive energy system |
| CN114362256B (en) * | 2022-03-16 | 2022-06-17 | 深圳市泽天数控机床有限公司 | Hybrid renewable energy power plant optimization method based on battery energy storage system |
| CN115392052B (en) * | 2022-09-22 | 2023-07-14 | 华北电力大学（保定） | Comprehensive energy system interval optimization method considering variable working condition operation characteristics of equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010237745A (en) * | 2009-03-30 | 2010-10-21 | Tokyo Gas Co Ltd | Method and device for optimizing energy system, and program |
| CN109740827A (en) * | 2019-02-14 | 2019-05-10 | 华北电力大学 | A kind of regional complex energy system planning optimization method based on dual-layer optimization |
| CN110163411A (en) * | 2019-04-11 | 2019-08-23 | 华北电力大学 | A kind of regional complex energy system operation optimization method |
| CN111769561A (en) * | 2020-09-02 | 2020-10-13 | 国网江西省电力有限公司电力科学研究院 | RIES operation optimization method and device |
| CN111859683A (en) * | 2020-07-24 | 2020-10-30 | 天津大学 | Optimal configuration method of park comprehensive energy system based on dynamic energy concentrator |
-
2021
- 2021-02-08 CN CN202110172976.7A patent/CN112836882B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010237745A (en) * | 2009-03-30 | 2010-10-21 | Tokyo Gas Co Ltd | Method and device for optimizing energy system, and program |
| CN109740827A (en) * | 2019-02-14 | 2019-05-10 | 华北电力大学 | A kind of regional complex energy system planning optimization method based on dual-layer optimization |
| CN110163411A (en) * | 2019-04-11 | 2019-08-23 | 华北电力大学 | A kind of regional complex energy system operation optimization method |
| CN111859683A (en) * | 2020-07-24 | 2020-10-30 | 天津大学 | Optimal configuration method of park comprehensive energy system based on dynamic energy concentrator |
| CN111769561A (en) * | 2020-09-02 | 2020-10-13 | 国网江西省电力有限公司电力科学研究院 | RIES operation optimization method and device |
Non-Patent Citations (1)
| Title |
|---|
| 区域综合能源系统的综合评估指标与方法;陈柏森;廖清芬;刘涤尘;王文怡;王志义;陈思远;;电力系统自动化(第04期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112836882A (en) | 2021-05-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112836882B (en) | Regional comprehensive energy system operation optimization method considering equipment load rate change | |
| CN106058942B (en) | Energy hub optimization model considering wind power uncertainty and comprising electricity-to-gas conversion and CCHP | |
| CN111950807B (en) | Comprehensive energy system optimization operation method considering uncertainty and demand response | |
| CN110889600A (en) | Regional comprehensive energy system optimization scheduling method considering flexible thermal load | |
| CN112464477A (en) | Multi-energy coupling comprehensive energy operation simulation method considering demand response | |
| CN110361969B (en) | Optimized operation method of cooling, heating and power comprehensive energy system | |
| CN113256045A (en) | Park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty | |
| CN112446546B (en) | Comprehensive energy system two-stage optimal configuration method considering energy reliability | |
| CN115186902A (en) | Regulating and controlling method, device, terminal and storage medium of greenhouse comprehensive energy system | |
| CN115099007B (en) | Comprehensive energy system optimized operation method based on comprehensive cost-energy consumption curve | |
| Shen et al. | Optimal dispatch of regional integrated energy system based on a generalized energy storage model | |
| CN109376406B (en) | Energy supply system superstructure model, modeling method, computer device and storage medium | |
| CN117081143A (en) | Method for promoting coordination and optimization operation of park comprehensive energy system for distributed photovoltaic on-site digestion | |
| CN111125611A (en) | Multi-scene-oriented cold-heat-electricity micro-energy network group two-stage optimization scheduling method | |
| CN116316888A (en) | Optimized scheduling method, system and device for hydrogen electric coupling system | |
| CN111769561A (en) | RIES operation optimization method and device | |
| CN114330835A (en) | Optimal configuration method of electricity/heat hybrid energy storage system in comprehensive energy microgrid | |
| Wang et al. | Multi-time scale optimization study of integrated energy system considering dynamic energy hub and dual demand response | |
| Wang et al. | Low-carbon Optimal Dispatch of Integrated Energy System Considering Concentrating Solar Plant | |
| Guo et al. | Study on Optimal Model of Micro-Energy Network Operation Configuration Considering Flexible Load Characteristics | |
| Luo et al. | Optimal Scheduling of Integrated Energy System Considering Integrated Demand Response | |
| Zhao et al. | Optimal Capacity Allocation of Integrated Energy System Considering Multiple Uncertainties | |
| Lv et al. | Economic analysis of trigeneration systems considering participations of energy storage | |
| CN116436099B (en) | Micro-grid robust optimal scheduling method and system | |
| Xiaoting et al. | Optimal scheduling of CCHP microgrid considering real-time demand |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |