CN110555595A - biogas-wind-light all-renewable energy system based on energy hub and method thereof - Google Patents
biogas-wind-light all-renewable energy system based on energy hub and method thereof Download PDFInfo
- Publication number
- CN110555595A CN110555595A CN201910734841.8A CN201910734841A CN110555595A CN 110555595 A CN110555595 A CN 110555595A CN 201910734841 A CN201910734841 A CN 201910734841A CN 110555595 A CN110555595 A CN 110555595A
- Authority
- CN
- China
- Prior art keywords
- energy
- wind
- output
- model
- hub
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims abstract description 120
- 230000008878 coupling Effects 0.000 claims abstract description 20
- 238000010168 coupling process Methods 0.000 claims abstract description 20
- 238000005859 coupling reaction Methods 0.000 claims abstract description 20
- 238000005457 optimization Methods 0.000 claims description 54
- 238000006243 chemical reaction Methods 0.000 claims description 41
- 238000003860 storage Methods 0.000 claims description 34
- 230000005611 electricity Effects 0.000 claims description 21
- 238000009826 distribution Methods 0.000 claims description 20
- 239000007789 gas Substances 0.000 claims description 19
- 238000005338 heat storage Methods 0.000 claims description 16
- 238000012546 transfer Methods 0.000 claims description 12
- 238000007599 discharging Methods 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 8
- 239000013256 coordination polymer Substances 0.000 claims description 6
- 238000010248 power generation Methods 0.000 claims description 6
- 238000010438 heat treatment Methods 0.000 claims description 4
- 238000004519 manufacturing process Methods 0.000 claims description 4
- 239000000126 substance Substances 0.000 claims description 4
- 239000002918 waste heat Substances 0.000 claims description 4
- 101100001674 Emericella variicolor andI gene Proteins 0.000 claims 1
- 239000002028 Biomass Substances 0.000 abstract description 7
- 230000000694 effects Effects 0.000 abstract description 3
- 238000004321 preservation Methods 0.000 abstract description 3
- 238000004146 energy storage Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- RZGZMLICFFEUIQ-UHFFFAOYSA-N 5-[(1-phenylcyclohexyl)amino]pentanoic acid Chemical compound C=1C=CC=CC=1C1(NCCCCC(=O)O)CCCCC1 RZGZMLICFFEUIQ-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005381 potential energy Methods 0.000 description 1
- 230000033772 system development Effects 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- 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)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
the invention discloses a biogas-wind-light fully-renewable energy system based on an energy hub and a method thereof. The invention can realize the promotion effect on the consumption of renewable energy sources, promote the consumption of biomass energy, realize the heat preservation and heat supply of the methane tank in winter and ensure the stable supply of methane; the thermoelectric coupling degree is weakened, the consumption of wind and light renewable energy sources is effectively promoted, and the comprehensive scheduling cost of the system is reduced.
Description
Technical Field
The invention belongs to the technical field of optimization scheduling of an integrated energy system, and particularly relates to a biogas-wind-light full-renewable energy system based on an energy hub and a method thereof.
Background
Currently, uncertainty in renewable energy output presents a significant challenge to renewable energy system development. Therefore, the method has important practical significance in comprehensively considering uncertain factors, constructing the renewable energy system and researching the optimal scheduling model of the renewable energy system. At the present stage, a plurality of scholars research a comprehensive energy system containing renewable energy sources based on an energy hub concept, but an energy hub coupling matrix is not constructed aiming at specific problems, so that the validity and rationality of the energy operation optimization problem of energy hub analysis cannot be fully explained, and in addition, the system usually contains a conventional unit and does not further research on a fully renewable energy system; in recent years, a great deal of research has been conducted by domestic and foreign scholars on a solution method of a system collaborative optimization problem considering uncertainty of renewable energy output. Besides the traditional random optimization and robust optimization, some scholars try to combine the two methods to process uncertain quantity by using a distributed robust optimization method, and the solution process is too complex although the method effectively balances the economy and the conservation of the system.
disclosure of Invention
The present invention is directed to solve or improve the above-mentioned problems by providing a biogas-wind-light fully renewable energy system based on an energy hub and a method thereof.
In order to achieve the purpose, the invention adopts the technical scheme that:
A marsh-wind-light full renewable energy system based on energy hub and method thereof, which comprises:
the methane-wind energy-solar energy hub model is used for feeding back part of heat energy and electric energy generated in the energy hub to the methane tank, increasing the reaction temperature and increasing the methane yield; the biogas can be directly supplied to a gas load and can also be converted into electric energy, and meanwhile, the waste heat of the generator in the CHP and the heat generated by the biogas furnace and the electric boiler can be directly supplied to a heat load;
The renewable energy system optimization scheduling model is used for controlling the running state of the energy hub equipment and the energy production, conversion, storage and consumption processes with minimized cost and realizing the optimal coordination and utilization among multiple energies;
The uncertainty-considering day-ahead-real-time two-stage distributed robust optimization scheduling model is used for optimizing CHP startup and shutdown cost and other cost under a wind-light basic prediction scene in a day-ahead scheduling stage, and the real-time scheduling stage adjusts the output of each device in a hub according to the actual output of wind-light on the basis that the output of day-ahead scheduling is known.
Preferably, the coupling relationship between the output of the methane tank and the temperature in the methane-wind energy-solar energy hub model is as follows:
Ebio＝a|TZ-TO|+b
Wherein E isbiois the output per unit time of the methane tank, TZAnd TORespectively the actual reaction temperature and the optimum reaction temperature, the time T is calculatedOTaking the temperature of 35 ℃, and taking a and b as coefficients obtained by data fitting;
The heat transfer model of the methane tank considering the electric heat feedback is as follows:
QR＝ηBSef+Shf
Wherein Q isREnergy, eta, injected into the biogas digester for the energy hubBFor the conversion efficiency of the electric boiler, SefAnd ShfRespectively representing residual electricity and residual heat feedback, R, in an energy hubin、Rout、RwInternal and external convective heat transfer resistances and pool wall conductive heat transfer resistances, T, respectivelyin、Tout、TwThe temperature of the inner part, the outer part and the wall of the methane tank respectively, and the T can be known from the reaction principle of the methane tankin＝TZ；CZ、CWThe heat capacities of the interior and the wall of the methane tank are respectively, and t represents unit time;
The energy pivot coupling model is as follows:
wherein L ise、Lh、LgRespectively the electric load, the gas load and the heat load of the energy hub, namely the output quantity of the energy hub model; qW、QPVT、Ebiothe power generation power of the fan, the total power output of the PVT system and the biogas output in unit time are respectively the input quantity of the energy hub model; pe、Ph、VbioRespectively the net output electricity and the heat power of the electricity storage device and the heat storage device and the net output quantity of the gas storage device in unit time;andThe power generation and heating efficiency of the PVT system;Andis the gas-to-electricity and gas-to-heat efficiency of the CHP; etaBAnd ηFThe energy conversion efficiency of the electric boiler and the energy conversion efficiency of the methane furnace are respectively; q. q.sbioThe heat value of the biogas is; v isB、νCHP、νFThe dispatching factors of the electric boiler, the CHP and the methane furnace are respectively;
The conversion relationship between the scheduling factor and the efficiency is as follows:
νB＝PB/ηB
νF＝PF/ηF
wherein, PB、PFheat output, P, of electric boiler and methane furnace respectivelyCHPAn electrical output that is CHP;
The deformed energy pivot coupling model is as follows:
Preferably, the renewable energy system optimization scheduling model under the deterministic condition is represented in a matrix form:
s.t.Ax≤b
Cy≤Dξ
Gx+Hy≤g
Jx+Ky＝h
Wherein x represents a 0-1 variable of the equipment operation state in the energy hub, y represents a continuous variable of the output of each equipment in the hub, and xi represents a continuous variable of the output of the fan and the PVT system;
Representing a model objective function with an optimization objective of comprehensive scheduling cost minimization, wherein aTx represents the cost only related to the variable 0-1, and the cost is not influenced by the output of the fan and the PVT, particularly the starting/stopping cost of the CHP unit; bTy+cTXi represents the cost related to continuous variables, including energy conversion, charge-discharge loss cost, wind abandoning cost and light abandoning penalty cost;
ax is less than or equal to b represents 0-1 variable inequality constraint, including charge and discharge state constraint of the electricity storage and heat storage device;
cy is less than or equal to D xi represents continuous variable inequality constraint, including fan, PVT system, electric boiler, biogas furnace output constraint and biogas digester state constraint;
G is not less than Gx + Hy, and Jx + Ky h represents the coupling relation between the variables in the two stages, wherein the g is not less than Gx + Hy and comprises CHP equipment constraint and gas storage device constraint, and the Jx + Ky h is energy balance constraint.
Preferably, the renewable energy system optimization scheduling model objective function is:
min(CCHP+CT+CL+CP)
Wherein, CCHPFor the start-up/shut-down costs of the CHP units, CTFor energy conversion loss cost, CLfor energy charge and discharge loss cost, CPPenalizing cost for wind abandoning and light abandoning; t is an operation period, and T is an operation period,λloss、λwAnd λpthe unit cost of CHP starting, stopping, energy loss and wind and light abandoning is respectively; u. oftIs a variable from 0 to 1, and represents that the CHP is in an opening or closing state at the time t; Δ t represents the length of each period;AndAndthe charging and discharging power of the battery and the charging and discharging power of the heat storage device are respectively in a time t;andAndAndthe charging and discharging efficiencies of the electricity storage equipment, the gas storage equipment and the heat storage equipment are respectively obtained;andthe predicted and actual output of the fan is obtained;andis the predicted and actual total contribution of the PVT system.
preferably, based on the matrix description of the deterministic renewable energy system optimization scheduling model, when the uncertainty of the wind power and photovoltaic output is described by using a data-driven distributed robust optimization method, the objective function of the day-ahead-real-time two-stage optimization scheduling model considering the uncertainty is as follows:
wherein the content of the first and second substances,Representing the output adjustment cost in the k scene of the real-time scheduling stage; p is a radical ofkRepresenting the value of the probability distribution in the k scene, { p }kAnd omega is a feasible domain of scene probability values.
preferably, a probability distribution set which is based on the initial probability distribution value of each discrete scene and is subjected to constraint fluctuation by adopting a 1-norm and an infinity-norm set is constructed, namely, omega is omega1∩Ω∞The results are as follows:
wherein, theta1、θ∞the probability fluctuation allowable deviation limit under the 1-norm and infinity-norm constraints is shown.
preferably, a CCG algorithm is adopted to solve the day-ahead-real-time two-stage optimization scheduling model, and the model is decomposed into a main problem and a sub-problem to be repeatedly solved in an iterative manner;
Solving the optimal solution meeting the constraint condition under the known severe probability distribution, realizing the day-ahead scheduling of the energy hub, and providing a lower bound value for the two-stage robust model:
wherein m represents the number of iterations;
The sub-problem is that under the condition that the main problem gives a first-stage variable x, the worst probability distribution under real-time operation is searched and returned to the main problem for next iteration, and the sub-problem provides an upper bound value for the two-stage robust model:
because the probability scene set omega is not associated with the constraint set Y of the second-stage variable Y in each scene, the inner-layer min optimization problems in each scene are independent of each other and can be processed by adopting a parallel solving method, and the subproblems can be rewritten as follows:
and repeating iteration of the main problem and the rewritten subproblems until the difference between the upper bound value and the lower bound value reaches the required precision, stopping iteration, and returning to the optimal solution.
A biogas-wind-light full renewable energy method based on an energy hub comprises the following steps:
s1, constructing a methane-wind energy-solar energy hub model;
S2, constructing a renewable energy system optimization scheduling model according to the aim of minimizing the comprehensive scheduling cost;
S3, considering the output uncertainty of the wind-solar renewable energy, and constructing a data-driven day-ahead-real-time two-stage distributed robust optimization scheduling model;
and S4, dividing the model into a main problem and a sub problem by adopting a CCG algorithm, repeatedly carrying out iterative solution, stopping iteration when the difference between the upper bound value and the lower bound value of the model reaches the required precision, and returning to the optimal solution.
The biogas-wind-light all-renewable energy system based on the energy hub and the method thereof have the following beneficial effects:
the invention constructs a full renewable energy system based on a biogas-wind energy-solar energy hub, carries out detailed modeling on an energy hub containing an energy storage element, processes the energy hub into a linearized matrix more suitable for system optimization scheduling, further considers the uncertainty of wind and light renewable energy output, and constructs a day-ahead-real-time two-stage distributed robust optimization scheduling model based on data driving.
the invention can realize the promotion effect on the consumption of renewable energy sources, promote the consumption of biomass energy, realize the heat preservation and heat supply of the methane tank in winter and ensure the stable supply of methane; the thermoelectric coupling degree is weakened, the consumption of wind and light renewable energy sources is effectively promoted, and the comprehensive scheduling cost of the system is reduced.
drawings
fig. 1 is a schematic structural diagram of a biogas-wind-light energy hub of a biogas-wind-light all-renewable energy system based on an energy hub and a method thereof.
FIG. 2 is a diagram of a heat transfer network of a biogas pool considering electric heat feedback in an energy hub of a biogas-wind-light fully renewable energy system and a method thereof based on the energy hub.
Detailed Description
the following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
according to an embodiment of the present application, referring to fig. 1, the present solution of the energy hub-based biogas-wind-light fully renewable energy system and the method thereof includes:
Construction of methane-wind energy-solar energy pivot model
the model feeds back part of heat energy and electric energy generated in the energy hub to the methane tank so as to improve the reaction temperature and increase the methane yield; the biogas can be directly supplied to a gas load, and can also be converted into electric energy by a biogas generator in the biomass cogeneration unit, and meanwhile, the waste heat of the generator in the CHP and the heat generated by a biogas furnace and an electric boiler can be directly supplied to the heat load; in addition, the electric boiler, the methane furnace and the electricity and heat storage device can break through the traditional operation mode of 'fixing electricity by heat' of CHP, and realize thermoelectric decoupling.
the device can realize the coupling connection and the optimized scheduling of 3 energy sources jointly, provides certain flexibility and synergy for regional multi-energy supply, and promotes regional renewable energy consumption, energy conservation and emission reduction.
the constructed biogas-wind energy-solar energy hub inputs energy sources of wind energy, solar energy and biomass energy, and the renewable energy sources can be converted and consumed through a wind driven generator, a PVT system and a biogas reaction tank to generate usable secondary energy sources such as electric energy, heat energy, biogas and the like; still contained multiple energy conversion equipment (CHP, electric boiler, marsh gas stove) in the hub, the internal energy conversion principle of hub: the biogas obtained from the biomass can be directly supplied to a gas load, or can be converted into electric energy by using a CHP gas engine, and in addition, the waste heat of the CHP engine and the heat generated by a biogas furnace and an electric boiler can be directly supplied to a heat load or used for feedback heating of a biogas digester; an energy storage device is additionally arranged in the energy hub, and can provide large-capacity storage for electric power, heat and methane.
the coupling relation between the output of the methane tank and the temperature in the energy hub is as follows:
Ebio＝a|TZ-TO|+b
wherein E isbioThe yield of the biogas digester in unit time; t isZAnd TORespectively the actual reaction temperature and the optimum reaction temperature, the time T is calculatedOtaking the mixture to 35 ℃; and a and b are coefficients obtained by data fitting.
The heat transfer model of the methane tank considering the electric heat feedback is as follows:
QR＝ηBSef+Shf
wherein Q isRenergy injected into the methane tank for the energy hub; etaBIs the conversion efficiency of the electric boiler; sefAnd Shfrespectively representing residual electricity and residual heat feedback in the energy hub; rin，Rout，RwThe thermal resistance of internal and external convection heat transfer and the thermal resistance of pool wall conduction heat transfer are respectively; t isin，Tout，Twthe temperature of the inner part, the outer part and the wall of the methane tank respectively, and the T can be known from the reaction principle of the methane tankin＝TZ；CZ，CWrespectively the heat capacity of the inner part and the wall of the methane tank; t represents a unit time.
the energy pivot coupling model is as follows:
Wherein L ise、Lh、Lgthe electric, gas and heat loads of the energy hub are the output quantity of the energy hub model; qW、QPVT、EbioRespectively the power generated by the fan and PThe VT system outputs total power and methane output in unit time, namely the input quantity of the energy hub model; pe、Ph、Vbiorespectively the net output electricity and the heat power of the electricity storage device and the heat storage device and the net output quantity of the gas storage device per unit time (the net output quantity is the output quantity-the input quantity);andThe power generation and heating efficiency of the PVT system;AndIs the gas-to-electricity and gas-to-heat efficiency of the CHP; etaBAnd ηFthe energy conversion efficiency of the electric boiler and the energy conversion efficiency of the methane furnace are respectively; q. q.sbiois the heat value (1 m) of the marsh gas3Energy that can be released when the biogas is completely combusted); v isB、νCHP、νFThe electric boiler, the CHP and the methane furnace are respectively used as scheduling factors.
the conversion relation between the scheduling factor and the efficiency in the model is as follows:
νB＝PB/ηB
νF＝PF/ηF
Wherein, PB、PFHeat output, P, of electric boiler and methane furnace respectivelyCHPis the electrical output of the CHP.
the deformed energy pivot coupling model is as follows:
referring to fig. 2, the network modeling is performed on the heat conduction relationship between the inside and the outside of the methane tank considering the electric heat feedback, and the actual reaction temperature of the methane tank can be solved by using a node thermodynamic equilibrium equation, knowing that the output of the methane per unit time is determined by the temperature. The heat transfer and storage are respectively expressed by means of thermal resistance and thermal capacity, the temperature inside the methane tank, the temperature of the methane wall and the temperature of the outside can be expressed by nodes with potential energy, and finally, a methane tank node thermodynamic equilibrium equation is derived according to a network structure.
according to one embodiment of the application, a renewable energy system optimization scheduling model is constructed.
The renewable energy system scheduling optimization aims to control the running state of the energy hub equipment and the energy production, conversion, storage and consumption processes with minimized cost and realize the optimal coordination and utilization among multiple energies.
the renewable energy system optimization scheduling model aims to control the running state of the energy hub equipment and the energy production, conversion, storage and consumption processes at the minimized cost and realize the optimal coordination utilization among multiple energies.
Firstly, a renewable energy system optimization scheduling model under a deterministic condition is represented in a matrix form:
s.t.Ax≤b
Cy≤Dξ
Gx+Hy≤g
Jx+Ky＝h
Wherein x represents a 0-1 variable of the operating state of the equipment in the energy hub; y represents the continuous variable of the output of each device in the hub (excluding the fan and the PVT system); ξ is a continuous variable representing the fan to PVT system output.
Representing a model objective function with an optimization objective of comprehensive scheduling cost minimization, wherein aTx represents the cost associated with the variable 0-1 only, which is not affected by the fan and PVT output, particularly the CHP unit start/stopThen, the process is carried out; bTy+cTξ represents the cost related to continuous variables, and specifically comprises energy conversion, charge-discharge loss cost, wind abandoning cost and light abandoning penalty cost.
and Ax is less than or equal to b represents a variable inequality constraint of 0-1, in particular to a charge-discharge energy state constraint of the electricity storage and heat storage device.
and D xi which is less than or equal to Cy represents continuous variable inequality constraint, and the continuous variable inequality constraint specifically comprises a fan, a PVT system, an electric boiler, a biogas furnace output constraint and a biogas digester state constraint.
G which is less than or equal to Gx + Hy and Jx + Ky which is h represent the coupling relation between two-stage variables, wherein g which is less than or equal to Gx + Hy specifically refers to CHP equipment constraint and gas storage device constraint, and Jx + Ky which is h is energy balance constraint. The objective function and the constraint are specifically described below.
The renewable energy system optimization scheduling model objective function is as follows:
min(CCHP+CT+CL+CP)
Wherein, CCHPfor the start-up/shut-down costs of the CHP units, CTFor energy conversion loss cost, CLFor energy charge and discharge loss cost, CPpenalizing cost for wind abandoning and light abandoning; t is an operation period, and T is an operation period,λloss、λwAnd λprespectively starting and stopping CHP, energy loss,Unit cost of wind and light abandoning; u. oftis a variable from 0 to 1, and represents that the CHP is in an opening or closing state at the time t; Δ t represents the length of each period;andAndThe charging and discharging power of the battery and the charging and discharging power of the heat storage device are respectively in a time t;andAndAndThe charging and discharging efficiencies of the electricity storage equipment, the gas storage equipment and the heat storage equipment are respectively obtained;Andthe predicted and actual output of the fan is obtained;Andis the predicted and actual total contribution of the PVT system.
the energy balance constraint of the renewable energy system optimization scheduling model is as follows:
wherein L ise,t、Lh,t、Lg,tthe electric load, the heat load and the air load are respectively in the t period; [ C 'S' D ]]and [ Q 'P' H]TThe detailed expression of the method is shown in an energy pivot coupling model after deformation.
The constraint of output equipment of the renewable energy system optimization scheduling model is as follows:
the output of the wind turbine generator is as follows:
PVT system total output:
Reaction temperature limit of the methane tank: t isZ,min≤TZ,t≤TZ,max
Note: the coupling model of the output and the temperature of the methane tank, the heat transfer model and the reaction temperature limit jointly form the state constraint of the methane tank.
the coupling element constraint of the renewable energy system optimization scheduling model is as follows:
CHP equipment:
|PCHP,t-PCHP,t-Δt|≤rCHP
Wherein the content of the first and second substances,Andrespectively representing the lower limit and the upper limit of the CHP power generation output;AndRespectively representing the lower and upper limits of the CHP heat output; r isCHPIs the ramp rate of CHP.
The output of the electric boiler is as follows: p is more than or equal to 0B,t≤PB,max
in the formula, PB,maxIs the upper limit of the output of the electric boiler.
The output of the methane furnace: p is more than or equal to 0F,t≤PF,max
in the formula: pF,maxis the upper limit of the output of the methane furnace.
constraint of energy storage devices of the renewable energy system optimization scheduling model:
An electricity storage device:
Ee,min≤Ee,t≤Ee,max
Wherein E ise,tthe battery power storage amount at the time t; ee,minAnd Ee,maxRespectively, a lower limit and an upper limit of the capacity of the electric storage device;The variables are 0-1 and respectively represent the charge and discharge states of the device at the time t;AndAndRespectively minimum and maximum charge and discharge power.
the gas storage device:
Rbio,t＝Rbio,t-Δt-Vbio,t
Rbio,min≤Rbio,t≤Rbio,max
kt·Vbio,min≤Vbio,t≤kt·Vbio,max
Wherein k istis a variable of 0 to 1 and indicates that the methane tank is in an input or output state at the moment t; rbio,tthe biogas storage capacity is t time period; rbio,minAnd Rbio,maxrespectively the lower limit and the upper limit of the capacity of the gas storage device; vbio,tis the net output of the methane tank in the period of t, when Vbio,t>when 0 is shown as the biogas output in the period, when V isbio,t<And 0 represents input. Vbio,minAnd Vbio,maxrespectively the lower limit and the upper limit of the input and the output of the marsh gas.
a heat storage device:
Eh,min≤Eh,t≤Eh,max
Wherein E ish,tthe battery power storage amount at the time t; eh,minAnd Eh,maxRespectively, a lower limit and an upper limit of the capacity of the electric storage device;the variables are 0-1 and respectively represent the charge and discharge states of the device at the time t;AndAndRespectively minimum and maximum charge and discharge power.
According to one embodiment of the application, a day-ahead-real-time two-phase distributed robust optimization scheduling model is constructed, wherein uncertainty is taken into account.
Because the wind speed and the illumination intensity can be influenced by factors such as weather and seasons, uncertainty exists between the output of the fan and the PVT system.
by combining the above considerations, a day-ahead-real-time two-stage distributed robust optimization scheduling model of the renewable energy system is constructed, and the CHP startup and shutdown cost and other costs under the scene of wind-light basic prediction are optimized in the day-ahead scheduling stage; and in the real-time scheduling stage, the output of each device in the hub is adjusted according to the actual wind and light output on the basis that the output of the day-ahead scheduling is known.
in a day-ahead-real-time two-stage distributed robust optimization scheduling model considering uncertainty, optimizing CHP starting and stopping cost and other costs under a scene of wind-light basic prediction in a day-ahead scheduling stage; and in the real-time scheduling stage, the output of each device in the hub is adjusted according to the actual wind and light output on the basis that the output of the day-ahead scheduling is known.
Based on the matrix description of the deterministic renewable energy system optimization scheduling model, when the uncertainty of wind power and photovoltaic output is described by adopting a distributed robust optimization method based on data driving, the objective function of the day-ahead-real-time two-stage optimization scheduling model can be expressed in the following form:
wherein the content of the first and second substances,Representing the output adjustment cost in the k scene of the real-time scheduling stage; p is a radical ofkRepresenting the value of the probability distribution in the k scene, { p }kand omega is a feasible domain of scene probability values.
theoretically, the probability distribution can be valued in any range, however, in order to make the scene probability distribution more appropriate to the actual operation data and fluctuate in a reasonable range, a probability distribution set which uses the initial probability distribution value of each discrete scene as a reference and adopts a 1-norm and an infinity-norm set to constrain fluctuation, namely, Ω and Ω are constructed1∩Ω∞The results are as follows:
In the formula, theta1、θ∞The probability fluctuation allowable deviation limit under the 1-norm and infinity-norm constraints is shown.
And further adopting a CCG algorithm to solve the day-ahead-real-time two-stage optimization scheduling model, and decomposing the model into a Main Problem (MP) and a Sub Problem (SP) for repeated iterative solution. Solving the optimal solution meeting the constraint condition under the known severe probability distribution, realizing the day-ahead scheduling of the energy hub, and providing a lower bound value for the two-stage robust model:
in the formula, m represents the number of iterations.
The sub-problem is to find the worst probability distribution under real-time operation given the first-stage variable x by the main problem and return to the main problem for the next iteration, and the sub-problem provides an upper bound for equation (26):
because the probability scene set omega is not associated with the constraint set Y of the second-stage variable Y in each scene, the inner-layer min optimization problems in each scene are independent of each other and can be processed by adopting a parallel solving method, and the subproblems can be rewritten as follows:
And repeating iteration of the main problem and the rewritten subproblems until the difference between the upper bound value and the lower bound value reaches the required precision, stopping iteration, and returning to the optimal solution.
according to one embodiment of the application, a marsh-wind-light full renewable energy method based on an energy hub comprises the following steps:
step S1, constructing a methane-wind energy-solar energy hub model;
S2, constructing a renewable energy system optimization scheduling model according to the aim of minimizing the comprehensive scheduling cost;
The deterministic model of the step S1 and the step S2 can effectively ensure that the temperature of the biogas digester is within the reaction range, improve the biogas yield, ensure the energy supply under extreme weather conditions and promote the absorption and utilization of biomass energy in the energy hub; in addition, the energy hub is additionally provided with an electric boiler, a methane furnace and an electricity storage and heat storage device, so that the consumption of wind and light renewable energy sources can be effectively promoted, and the comprehensive scheduling cost of the system is reduced.
s3, considering the uncertainty of wind-solar renewable energy output, and constructing a data-driven day-ahead-real-time two-stage distributed robust optimization scheduling model;
Deciding the starting and stopping cost of the unit and other costs under the scene of wind and light basic prediction in the day-ahead scheduling stage; in the real-time scheduling stage, on the basis that the day-ahead scheduling output is known, the uncertain scene probability distribution set under the constraint of the 1-norm and the infinity-norm is comprehensively considered, so that a real-time cost decision result under the worst probability distribution is obtained.
And step S4, dividing the model into a main problem and a sub problem by adopting a CCG algorithm, repeatedly carrying out iterative solution, stopping iteration when the difference between the upper bound value and the lower bound value of the model reaches the required precision, and returning to the optimal solution.
The CCG algorithm can quickly and effectively solve the distributed robust model provided by the method; in addition, the confidence degree α1And alpha∞The increase of the total scheduling cost of the optimized target system increases the uncertain probability distribution range and the uncertainty.
The biogas-wind-light full renewable energy system day-ahead-real-time two-stage optimization scheduling model based on the energy hub can realize the promotion effect on the consumption of renewable energy sources, such as:
The method for supplying heat to the methane tank by adopting the residual heat and the residual electricity can promote the consumption of biomass energy, particularly realize heat preservation and heat supply of the methane tank in winter, and ensure stable supply of methane;
And secondly, the electric boiler, the methane furnace and the electricity storage and heat storage devices are arranged, so that the thermoelectric coupling degree can be weakened, the absorption of wind and light renewable energy sources is effectively promoted, and the comprehensive scheduling cost of the system is reduced.
in addition, the distributed robust model can be solved quickly and effectively by the CCG algorithm, the distributed robust algorithm can process uncertainty problems and can appropriately consider the economic and robust requirements of the target, and the superiority of the distributed robust optimization algorithm is verified.
while the embodiments of the invention have been described in detail in connection with the accompanying drawings, it is not intended to limit the scope of the invention. Various modifications and changes may be made by those skilled in the art without inventive step within the scope of the appended claims.
Claims (8)
1. a biogas-wind-light all-renewable energy system based on an energy hub is characterized by comprising:
the methane-wind energy-solar energy hub model is used for feeding back part of heat energy and electric energy generated in the energy hub to the methane tank, increasing the reaction temperature and increasing the methane yield; the biogas can be directly supplied to a gas load and can also be converted into electric energy, and meanwhile, the waste heat of the generator in the CHP and the heat generated by the biogas furnace and the electric boiler can be directly supplied to a heat load;
The renewable energy system optimization scheduling model is used for controlling the running state of the energy hub equipment and the energy production, conversion, storage and consumption processes with minimized cost and realizing the optimal coordination and utilization among multiple energies;
the uncertainty-considering day-ahead-real-time two-stage distributed robust optimization scheduling model is used for optimizing CHP startup and shutdown cost and other cost under a wind-light basic prediction scene in a day-ahead scheduling stage, and the real-time scheduling stage adjusts the output of each device in a hub according to the actual output of wind-light on the basis that the output of day-ahead scheduling is known.
2. The energy hub-based biogas-wind-light fully-renewable energy system according to claim 1, wherein the coupling relationship between the output and the temperature of the biogas digester in the biogas-wind-solar hub model is as follows:
Ebio＝a|TZ-TO|+b
Wherein E isbioIs the output per unit time of the methane tank, TZAnd TOrespectively the actual reaction temperature and the optimum reaction temperature, the time T is calculatedOtaking the temperature of 35 ℃, and taking a and b as coefficients obtained by data fitting;
The heat transfer model of the methane tank considering the electric heat feedback is as follows:
QR＝ηBSef+Shf
wherein Q isRenergy, eta, injected into the biogas digester for the energy hubBfor the conversion efficiency of the electric boiler, Sefand ShfRespectively representing residual electricity and residual heat feedback, R, in an energy hubin、Rout、Rwinternal and external convective heat transfer resistances and pool wall conductive heat transfer resistances, T, respectivelyin、Tout、TwThe temperature of the inner part, the outer part and the wall of the methane tank respectively, and the T can be known from the reaction principle of the methane tankin＝TZ；CZ、CWThe heat capacities of the interior and the wall of the methane tank are respectively, and t represents unit time;
The energy pivot coupling model is as follows:
wherein L ise、Lh、LgRespectively the electric load, the gas load and the heat load of the energy hub, namely the output quantity of the energy hub model; qW、QPVT、Ebiothe power generation power of the fan, the total power output of the PVT system and the biogas output in unit time are respectively the input quantity of the energy hub model; pe、Ph、VbioRespectively the net output electricity and the heat power of the electricity storage device and the heat storage device and the net output quantity of the gas storage device in unit time;AndThe power generation and heating efficiency of the PVT system;andIs the gas-to-electricity and gas-to-heat efficiency of the CHP; etaBAnd ηFThe energy conversion efficiency of the electric boiler and the energy conversion efficiency of the methane furnace are respectively; q. q.sbioThe heat value of the biogas is; v isB、νCHP、νFThe dispatching factors of the electric boiler, the CHP and the methane furnace are respectively;
the conversion relation between the scheduling factor and the efficiency is as follows:
νB＝PB/ηB
νF＝PF/ηF
Wherein, PB、PFHeat output, P, of electric boiler and methane furnace respectivelyCHPan electrical output that is CHP;
the deformed energy pivot coupling model is as follows:
3. the energy hub-based biogas-wind-light fully renewable energy system according to claim 1, characterized in that the renewable energy system optimization scheduling model under deterministic conditions is represented in the form of a matrix:
s.t.Ax≤b
Cy≤Dξ
Gx+Hy≤g
Jx+Ky＝h
wherein x represents a 0-1 variable of the equipment operation state in the energy hub, y represents a continuous variable of the output of each equipment in the hub, and xi represents a continuous variable of the output of the fan and the PVT system;
representing a model objective function with an optimization objective of comprehensive scheduling cost minimization, wherein aTx represents the cost only related to the variable 0-1, and the cost is not influenced by the output of the fan and the PVT, particularly the starting/stopping cost of the CHP unit; bTy+cTxi represents the cost related to continuous variables, including energy conversion, charge-discharge loss cost, wind abandoning cost and light abandoning penalty cost;
Ax is less than or equal to b represents 0-1 variable inequality constraint, including charge and discharge state constraint of the electricity storage and heat storage device;
cy is less than or equal to D xi represents continuous variable inequality constraint, including fan, PVT system, electric boiler, biogas furnace output constraint and biogas digester state constraint;
G is not less than Gx + Hy, and Jx + Ky h represents the coupling relation between the variables in the two stages, wherein the g is not less than Gx + Hy and comprises CHP equipment constraint and gas storage device constraint, and the Jx + Ky h is energy balance constraint.
4. the energy hub-based biogas-wind-light fully renewable energy system according to claim 3, wherein the renewable energy system optimization scheduling model objective function is:
min(CCHP+CT+CL+CP)
Wherein, CCHPis a CHP machineStartup/shutdown costs of the group, CTFor energy conversion loss cost, CLFor energy charge and discharge loss cost, CPpenalizing cost for wind abandoning and light abandoning; t is an operation period, and T is an operation period,λloss、λwand λpthe unit cost of CHP starting, stopping, energy loss and wind and light abandoning is respectively; u. oftis a variable from 0 to 1, and represents that the CHP is in an opening or closing state at the time t; Δ t represents the length of each period;andAndThe charging and discharging power of the battery and the charging and discharging power of the heat storage device are respectively in a time t;andandAndthe charging and discharging efficiencies of the electricity storage equipment, the gas storage equipment and the heat storage equipment are respectively obtained;andThe predicted and actual output of the fan is obtained;AndIs the predicted and actual total contribution of the PVT system.
5. the energy hub-based biogas-wind-light fully renewable energy system according to claim 1, characterized in that based on the matrix description of the deterministic renewable energy system optimization scheduling model, when describing the uncertainty of the wind and photovoltaic output using the data-driven based distributed robust optimization method, the objective function of the day-ahead-real time two-stage optimization scheduling model taking into account the uncertainty is:
Wherein the content of the first and second substances,representing the output adjustment cost in the k scene of the real-time scheduling stage; p is a radical ofkrepresenting the value of the probability distribution in the k scene, { p }kand omega is a feasible domain of scene probability values.
6. The energy hub-based biogas-wind-light fully-renewable energy system according to claim 5, wherein a probability distribution set is constructed that constrains fluctuation with 1-norm and infinity-norm set, i.e., Ω - Ω, based on initial probability distribution values of each discrete scene1∩Ω∞The results are as follows:
Wherein, theta1、θ∞representing probability fluctuation allowance under 1-norm and infinity-norm constraintsAllowable deviation limit value.
7. The energy hub-based biogas-wind-light fully renewable energy system according to claim 1, characterized in that a CCG algorithm is used to solve a day-ahead-real-time two-stage optimization scheduling model, and the model is decomposed into a main problem and a sub-problem to be repeatedly solved in an iterative manner;
solving the optimal solution meeting the constraint condition under the known severe probability distribution, realizing the day-ahead scheduling of the energy hub, and providing a lower bound value for the two-stage robust model:
wherein m represents the number of iterations;
the sub-problem is that under the condition that the main problem gives a first-stage variable x, the worst probability distribution under real-time operation is searched and returned to the main problem for next iteration, and the sub-problem provides an upper bound value for the two-stage robust model:
Because the probability scene set omega is not associated with the constraint set Y of the second-stage variable Y in each scene, the inner-layer min optimization problems in each scene are independent of each other and can be processed by adopting a parallel solving method, and the subproblems can be rewritten as follows:
and repeating iteration of the main problem and the rewritten subproblems until the difference between the upper bound value and the lower bound value reaches the required precision, stopping iteration, and returning to the optimal solution.
8. a renewable energy method for an energy hub based biogas-wind-light total renewable energy system according to any one of claims 1 to 7, comprising:
S1, constructing a methane-wind energy-solar energy hub model;
S2, constructing a renewable energy system optimization scheduling model according to the aim of minimizing the comprehensive scheduling cost;
s3, considering the output uncertainty of the wind-solar renewable energy, and constructing a data-driven day-ahead-real-time two-stage distributed robust optimization scheduling model;
And S4, dividing the model into a main problem and a sub problem by adopting a CCG algorithm, repeatedly carrying out iterative solution, stopping iteration when the difference between the upper bound value and the lower bound value of the model reaches the required precision, and returning to the optimal solution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910734841.8A CN110555595B (en) | 2019-08-09 | 2019-08-09 | Biogas-wind-light all-renewable energy system based on energy hub and method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910734841.8A CN110555595B (en) | 2019-08-09 | 2019-08-09 | Biogas-wind-light all-renewable energy system based on energy hub and method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110555595A true CN110555595A (en) | 2019-12-10 |
| CN110555595B CN110555595B (en) | 2023-04-11 |
Family
ID=68737385
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910734841.8A Active CN110555595B (en) | 2019-08-09 | 2019-08-09 | Biogas-wind-light all-renewable energy system based on energy hub and method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110555595B (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111242806A (en) * | 2020-02-19 | 2020-06-05 | 武汉理工大学 | Planning method of electric-thermal-hydrogen multi-energy system considering uncertainty |
| CN111539572A (en) * | 2020-04-26 | 2020-08-14 | 湖南大云新能电力技术有限公司 | Optimization planning method for optical-methane micro-energy network |
| CN111969602A (en) * | 2020-08-14 | 2020-11-20 | 山东大学 | Day-ahead random optimization scheduling method and device for comprehensive energy system |
| CN112257899A (en) * | 2020-09-22 | 2021-01-22 | 国网河北省电力有限公司营销服务中心 | CCHP system optimal scheduling method and terminal equipment |
| CN112418732A (en) * | 2020-12-14 | 2021-02-26 | 国电南瑞科技股份有限公司 | Multi-energy hub-containing comprehensive energy system planning method and system based on maximum flow and minimum cut theorem |
| CN112531785A (en) * | 2020-12-02 | 2021-03-19 | 四川大学 | Photo-thermal-heat-storage active power distribution network main and auxiliary combined scheduling method under multiple time scales |
| CN113256045A (en) * | 2020-08-04 | 2021-08-13 | 四川大学 | Park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty |
| CN113537559A (en) * | 2021-06-03 | 2021-10-22 | 浙江华云电力工程设计咨询有限公司 | Rural comprehensive energy planning resource optimization system and method |
| CN113806952A (en) * | 2021-09-24 | 2021-12-17 | 沈阳工程学院 | Source-load-storage-considered cooling, heating and power comprehensive energy system and optimized operation method thereof |
| CN116644866A (en) * | 2023-07-27 | 2023-08-25 | 国网江西省电力有限公司经济技术研究院 | Comprehensive energy system robust optimization method and system considering wind-light uncertainty |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012151545A2 (en) * | 2011-05-04 | 2012-11-08 | Ztek Corporation | Zero emission power plant with co2 waste utilization |
| CN104410092A (en) * | 2014-12-08 | 2015-03-11 | 国网新疆电力公司经济技术研究院 | Energy coordinated optimization method for multi-element complementary new energy power generating system |
| CN107967537A (en) * | 2017-11-27 | 2018-04-27 | 湖南大学 | The energy management method and device of a kind of micro- energy net in scene natural pond |
| CN108832665A (en) * | 2018-07-04 | 2018-11-16 | 四川大学 | A kind of probabilistic electric heating integrated system Robust distributed coordination optimization scheduling model of consideration wind-powered electricity generation |
-
2019
- 2019-08-09 CN CN201910734841.8A patent/CN110555595B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012151545A2 (en) * | 2011-05-04 | 2012-11-08 | Ztek Corporation | Zero emission power plant with co2 waste utilization |
| CN104410092A (en) * | 2014-12-08 | 2015-03-11 | 国网新疆电力公司经济技术研究院 | Energy coordinated optimization method for multi-element complementary new energy power generating system |
| CN107967537A (en) * | 2017-11-27 | 2018-04-27 | 湖南大学 | The energy management method and device of a kind of micro- energy net in scene natural pond |
| CN108832665A (en) * | 2018-07-04 | 2018-11-16 | 四川大学 | A kind of probabilistic electric heating integrated system Robust distributed coordination optimization scheduling model of consideration wind-powered electricity generation |
Non-Patent Citations (1)
| Title |
|---|
| BIN ZHOU 等: "Optimal Scheduling of Biogas–Solar–Wind Renewable Portfolio for Multicarrier Energy Supplie", 《IEEE》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111242806A (en) * | 2020-02-19 | 2020-06-05 | 武汉理工大学 | Planning method of electric-thermal-hydrogen multi-energy system considering uncertainty |
| CN111539572A (en) * | 2020-04-26 | 2020-08-14 | 湖南大云新能电力技术有限公司 | Optimization planning method for optical-methane micro-energy network |
| CN111539572B (en) * | 2020-04-26 | 2023-07-18 | 湖南大云新能电力技术有限公司 | Optimal planning method for optical biogas micro energy network |
| CN113256045A (en) * | 2020-08-04 | 2021-08-13 | 四川大学 | Park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty |
| CN113256045B (en) * | 2020-08-04 | 2023-04-07 | 四川大学 | Park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty |
| CN111969602A (en) * | 2020-08-14 | 2020-11-20 | 山东大学 | Day-ahead random optimization scheduling method and device for comprehensive energy system |
| CN112257899A (en) * | 2020-09-22 | 2021-01-22 | 国网河北省电力有限公司营销服务中心 | CCHP system optimal scheduling method and terminal equipment |
| CN112531785A (en) * | 2020-12-02 | 2021-03-19 | 四川大学 | Photo-thermal-heat-storage active power distribution network main and auxiliary combined scheduling method under multiple time scales |
| CN112531785B (en) * | 2020-12-02 | 2023-04-18 | 四川大学 | Photo-thermal-heat-storage active power distribution network main and auxiliary combined scheduling method under multiple time scales |
| CN112418732B (en) * | 2020-12-14 | 2022-09-13 | 国电南瑞科技股份有限公司 | Planning method and system for comprehensive energy system with multi-energy hub |
| CN112418732A (en) * | 2020-12-14 | 2021-02-26 | 国电南瑞科技股份有限公司 | Multi-energy hub-containing comprehensive energy system planning method and system based on maximum flow and minimum cut theorem |
| CN113537559A (en) * | 2021-06-03 | 2021-10-22 | 浙江华云电力工程设计咨询有限公司 | Rural comprehensive energy planning resource optimization system and method |
| CN113806952A (en) * | 2021-09-24 | 2021-12-17 | 沈阳工程学院 | Source-load-storage-considered cooling, heating and power comprehensive energy system and optimized operation method thereof |
| CN113806952B (en) * | 2021-09-24 | 2024-02-27 | 沈阳工程学院 | Cold-hot electricity comprehensive energy system considering source-charge-storage and optimal operation method thereof |
| CN116644866A (en) * | 2023-07-27 | 2023-08-25 | 国网江西省电力有限公司经济技术研究院 | Comprehensive energy system robust optimization method and system considering wind-light uncertainty |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110555595B (en) | 2023-04-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110555595B (en) | Biogas-wind-light all-renewable energy system based on energy hub and method thereof | |
| CN109327042B (en) | Multi-energy joint optimization scheduling method for micro-grid | |
| Maleki et al. | Harmony search optimization for optimum sizing of hybrid solar schemes based on battery storage unit | |
| CN111144668B (en) | Method for establishing comprehensive energy system random optimization model considering scene simulation | |
| CN109978625B (en) | Comprehensive energy system multi-target operation optimization method considering electric and thermal gas network | |
| CN112736939B (en) | Optimized capacity configuration method for hydrogen production and storage device of hydrogen-doped natural gas comprehensive energy system | |
| CN112464477A (en) | Multi-energy coupling comprehensive energy operation simulation method considering demand response | |
| CN109449925B (en) | Self-adaptive dynamic planning method for multi-objective joint optimization scheduling | |
| CN111242388B (en) | Micro-grid optimization scheduling method considering combined supply of cold, heat and power | |
| CN111539572B (en) | Optimal planning method for optical biogas micro energy network | |
| CN113159407B (en) | Multi-energy storage module capacity optimal configuration method based on regional comprehensive energy system | |
| Nguyen et al. | Optimization in energy and power management for renewable-diesel microgrids using dynamic programming algorithm | |
| KR20120103777A (en) | New renewable energy hybrid power generation system | |
| CN113779783A (en) | Multi-uncertainty-considered planning and operation joint optimization method for regional comprehensive energy system | |
| CN114784797A (en) | Thermoelectric optimization day-ahead scheduling method for residential comprehensive energy system considering multiple uncertainties | |
| Zhang et al. | Modeling and optimization of integrated energy system for renewable power penetration considering carbon and pollutant reduction systems | |
| CN113313305A (en) | Non-cooperative game-based comprehensive energy system optimization scheduling method | |
| Wang et al. | Simulation of a solar‐biogas hybrid energy system for heating, fuel supply, and power generation | |
| CN116646985A (en) | Rural residential light-biogas energy storage comprehensive energy system two-stage robust optimization scheduling model considering carbon emission | |
| CN110070216B (en) | Economic operation optimization method for industrial park comprehensive energy system | |
| CN111555362A (en) | Optimal regulation and control method and device for full-renewable energy source thermoelectric storage coupling system | |
| CN113624052B (en) | Combined cooling heating and power system and waste heat recovery method thereof | |
| CN115906456A (en) | Hydrogen-containing energy IES scheduling optimization model considering response uncertainty of demand side | |
| CN113410850B (en) | Light-heat wind-power combined frequency modulation model and frequency modulation strategy based on MPC | |
| CN114943376A (en) | User side comprehensive energy optimal utilization interval planning method and 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |