CN113779792A - Affine-based comprehensive energy system optimal configuration method - Google Patents
Affine-based comprehensive energy system optimal configuration method Download PDFInfo
- Publication number
- CN113779792A CN113779792A CN202111053009.5A CN202111053009A CN113779792A CN 113779792 A CN113779792 A CN 113779792A CN 202111053009 A CN202111053009 A CN 202111053009A CN 113779792 A CN113779792 A CN 113779792A
- Authority
- CN
- China
- Prior art keywords
- power
- energy
- affine
- cost
- uncertainty
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- PXFBZOLANLWPMH-UHFFFAOYSA-N 16-Epiaffinine Natural products C1C(C2=CC=CC=C2N2)=C2C(=O)CC2C(=CC)CN(C)C1C2CO PXFBZOLANLWPMH-UHFFFAOYSA-N 0.000 title claims abstract description 100
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000005457 optimization Methods 0.000 claims abstract description 101
- 238000004146 energy storage Methods 0.000 claims description 72
- 230000003993 interaction Effects 0.000 claims description 34
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 32
- 239000007789 gas Substances 0.000 claims description 25
- 230000005611 electricity Effects 0.000 claims description 21
- 238000011144 upstream manufacturing Methods 0.000 claims description 20
- 239000000446 fuel Substances 0.000 claims description 17
- 239000003345 natural gas Substances 0.000 claims description 16
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 14
- 229910052799 carbon Inorganic materials 0.000 claims description 14
- 238000006243 chemical reaction Methods 0.000 claims description 12
- 238000012423 maintenance Methods 0.000 claims description 12
- 238000007599 discharging Methods 0.000 claims description 11
- 238000010168 coupling process Methods 0.000 claims description 8
- 238000005859 coupling reaction Methods 0.000 claims description 8
- 239000011159 matrix material Substances 0.000 claims description 8
- 238000003860 storage Methods 0.000 claims description 7
- 230000008878 coupling Effects 0.000 claims description 6
- 238000005338 heat storage Methods 0.000 claims description 6
- 239000000126 substance Substances 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 230000014509 gene expression Effects 0.000 claims description 4
- 238000009434 installation Methods 0.000 claims description 4
- 239000013598 vector Substances 0.000 claims description 4
- 238000009826 distribution Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 230000002068 genetic effect Effects 0.000 claims description 3
- 239000002131 composite material Substances 0.000 claims description 2
- 239000013256 coordination polymer Substances 0.000 claims description 2
- 238000000354 decomposition reaction Methods 0.000 claims description 2
- 230000020169 heat generation Effects 0.000 claims description 2
- 238000011084 recovery Methods 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 claims 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000013076 uncertainty analysis Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Educational Administration (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to an affine-based optimal configuration method for an integrated energy system. The method comprises the steps of establishing an integrated energy system optimization configuration model considering uncertainty by adopting affine variables, decomposing the integrated energy system optimization configuration model considering uncertainty into two sub-problems of minimum central value of a target affine function and minimum affine function variation under the influence of uncertainty factors, wherein the former is a deterministic optimization model only considering source load prediction power, and the latter considers source load prediction error, and the configuration result is influenced by the uncertainty factors to the minimum through alternating iteration of a max model and a min model.
Description
Technical Field
The invention relates to an affine-based optimal configuration method for an integrated energy system.
Background
The wind-light-cold-heat-electricity combined energy supply system can reasonably utilize renewable energy sources, improve the energy utilization efficiency through energy gradient utilization and reduce pollutant emission. However, uncertainty in the randomness, intermittency, volatility, and load of the renewable energy sources can affect the economic operation of the integrated energy system. When the comprehensive energy system is planned, if uncertainty of wind, light and power output and load is not considered, a series of problems of over-ideal system capacity configuration, low equipment utilization rate and the like can be caused. Therefore, cooperative planning of campus micro-energy networks with multiple uncertainties taken into account has become a research hotspot for domestic and foreign scholars. In the planning problem of the park micro-energy network, the main method for processing the uncertainty problem of the comprehensive energy system comprises the following steps: probability method, robust optimization, interval optimization and the like. The probability method needs to obtain an accurate probability density function of the uncertain variable, the fitted model has a difference with a real model, and the interval optimization and the robust optimization only need to obtain a fluctuation interval of the uncertain variable, so that the method has a good engineering application value in the absence of statistical information. Compared with interval optimization, the robust optimization method focuses on considering extreme conditions, requires that constraint conditions are met in the worst case, results of the robust optimization method are conservative, and the considered operation condition is single. In interval optimization, the interval range obtained by interval arithmetic operation is usually much larger than the actual range, and the conservatism is increased; there are also partial studies that primarily consider single uncertainties within the system, such as renewable energy uncertainties, while ignoring multiple uncertainty factors. Based on the above, the invention provides an affine-based comprehensive energy system optimization configuration method.
Disclosure of Invention
Compared with the conventional comprehensive energy optimization configuration method, the fitted affine model is more real than a subjective probability model, and the conservative property of a solution result is reduced compared with robust optimization and interval optimization.
In order to achieve the purpose, the technical scheme of the invention is as follows: the affine-based optimization configuration method of the comprehensive energy system is characterized in that affine variables are adopted to establish an affine optimization model of the comprehensive energy system, the affine optimization model of the comprehensive energy system is decomposed into two sub-problems of minimum central value of a target affine function and minimum affine function variation under the influence of uncertainty factors, the former is a deterministic optimization model only considering source load prediction power, the latter is an uncertainty optimization model only considering source load prediction errors, and configuration results are influenced by the uncertainty factors to the minimum through alternating iteration of a max model and a min model.
Compared with the prior art, the invention has the following beneficial effects: the invention provides a comprehensive energy system configuration method, in particular to an affine-based comprehensive energy system collaborative planning method. According to the configuration scheme method, multiple uncertain factors such as multiple wind power, photovoltaic and loads are considered, the operation requirement of the park can be met under extreme conditions, the influence of the uncertain factors on the total annual comprehensive cost is reduced, and the economy and the equipment utilization rate of the planning scheme are improved. Compared with the existing comprehensive energy optimization configuration method, the fitted affine model is more real than the subjective probability model, and the conservative property of the solution result is reduced compared with the robust optimization and the interval optimization.
Drawings
FIG. 1 is a view showing a basic structure of a park.
FIG. 2 is a flow chart of the present invention.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
The invention relates to an affine-based optimal configuration method for an integrated energy system, which is characterized in that an affine variable is adopted to establish an affine optimization model of the integrated energy system, the affine optimization model of the integrated energy system is decomposed into two sub-problems of minimum central value of a target affine function and minimum affine function variation under the influence of uncertainty factors, the former is a deterministic optimization model only considering source load prediction power, the latter is an uncertainty optimization model only considering source load prediction errors, and configuration results are influenced by the uncertainty factors to the minimum through alternating iteration of a max model and a min model.
The following is a specific implementation process of the present invention.
1. Comprehensive energy system
The energy hub EH (energy hub) is used as a coupling point of the comprehensive energy system and a conversion station of each energy carrier, and organically integrates each energy link with a support system such as information and the like. A typical EH basic framework is shown in fig. 1, wherein the micro-electrical system comprises a photovoltaic power generation device, an electrical energy storage device, an electrical boiler, a cogeneration device, the micro-thermal system comprises a cogeneration device, a gas boiler, a thermal energy storage device, and the micro-gas system comprises a cogeneration device, a gas boiler, and the like. The EH purchases electric energy and natural gas from an upstream power grid company and a gas company, and reasonably distributes the electric energy and the natural gas to each energy conversion and storage device, so that the energy of each micro system is balanced, and the electric, heat and gas load requirements of a user side are met.
For modeling the EH, considering the energy storage device, the coupling relationship between the input and the output of the EH in fig. 1 is:
L＝CP\*MERGEFORMAT(1)
in the formula: l ise、LhAnd LgRespectively the electricity, heat and gas loads of users in the garden;
respectively the electricity intake quantity and the air intake quantity of the whole park. Wherein
Comprises photovoltaic set generating power PPVAnd buying power P to upstream power gridbuy,e；ηebHeat generation efficiency for EB (electric Boiler); etagbHeat generation efficiency for GB (gas Boiler);
the electricity and Heat generation efficiency of CHP (combined Heat and Power), respectively; hgasThe natural gas heat value is 9.78 kW.h, alpha is the distribution coefficient of electric energy, beta and gamma are the heat energy distribution coefficients, and the values are all [0,1 ]]And beta + gamma is less than or equal to 1; s, S,
The variation of the energy storage coupling matrix and the energy storage capacity in the device is obtained; se、ShCoupling elements of electricity storage ES (electric storage) and heat storage HS (heat storage) respectively; the amount of change in the energy storage capacity of the energy storage device over the time period Δ t may be determined by
And E (t) is obtained by calculation, and is the energy storage capacity of the energy storage device in the time period t.
The variation of the energy storage capacity in the electricity storage device and the heat storage device is respectively.
2. Affine-based comprehensive energy system uncertainty analysis
2.1, affine arithmetic
In affine mathematics, an uncertainty x can be represented in affine form as a linear combination of noise elements.
In the formula: x is the number of0As an affine center value; epsiloniIs a noise element and represents an uncertain factor with the value range of [ -1,1]；xiRepresenting the noise element epsilon as a noise element coefficientiThe degree of influence on the affine number. Multiplication, division, reciprocal, and square operations produce new noise bins.
2.2 Integrated energy System uncertainty affine modeling
In the operation process of the comprehensive energy system, due to the influences of weather, physics and other factors, the loads of the wind power, photovoltaic output power, electricity, heat and natural gas systems have uncertainty, the uncertainty variables are described by affine, and the established affine model of the wind power, photovoltaic and loads is as follows:
in the formula:
is an affine value of the wind power active power,
representing affine centre value, epsilonWAffine noise element, P, for active power of a fant WIs the affine noise element coefficient corresponding to the noise element coefficient.
The affine value is the affine value of the wind power active power;
representing affine centre value, epsilonPVAffine noise element, P, for active power of a fant PVIs the affine noise element coefficient corresponding to the noise element coefficient.
Affine value of load power;
representing affine centre value, epsilonLAffine noise element, P, for active power of a fant LIs the affine noise element coefficient corresponding to the noise element coefficient.
Because the power of the energy storage device and the interaction power of the park and the upstream energy network can be changed rapidly and flexibly, the power of the energy storage device can be changed to adapt to the change of the source load power, so that the power balance of the park is maintained.
And (4) considering that the source load power has prediction error in the actual operation of the park. Therefore, the output of the energy storage device and the interaction power between the campus and the upstream energy network will fluctuate. Equations (9) and (10) are affine expressions of the energy storage device power and the interaction power between the park and the energy network, which are established according to the source charge power prediction error.
In the formula: the subscript l is 1,2, and 3, which indicates that the uncertainty factors causing the power change are wind power, photovoltaic, and load, respectively.
Respectively representing affine forms of charging and discharging energy power of j-type energy storage equipment at the time t;
simulation of representing stored energy powerA shot center value;
respectively representing the energy storage power variation caused by wind power, photovoltaic and load power prediction errors;
power for purchasing/selling electricity in the park at the time t respectively;
an affine form of corresponding power of the gas purchasing quantity at the time t;
as an affine center value;
respectively representing the variation of the purchased power caused by prediction errors of wind power, photovoltaic power and load power;
respectively representing the electricity selling power variation caused by wind power, photovoltaic and load uncertainty;
respectively representing the power variation quantity corresponding to the purchased gas quantity caused by wind power, photovoltaic and load uncertainty.
3. Optimized configuration model accounting for uncertainty
3.1 two sub-problems for optimal configuration
The general form of the affine optimization problem is
In the formula: decision variables
Affine vector, C cost coefficient matrix, and C (C, x) objective function; the matrix a in the constraint condition is a conventional coefficient matrix and parameters
And
known as affine vectors.
In an objective function of the optimal configuration of the comprehensive energy system, noise elements including wind power, photovoltaic and load in decision variables are considered, and the annual total integrated cost of the optimal configuration can be written as follows:
in the formula: c0(x0) A campus year composite cost for deterministic optimization; c1(x1)εW+C2(x2)εPV+C3(x3)εLAnd the total annual comprehensive cost variation of the park caused by uncertainty of wind, light and load is represented as C'.
In order to reduce the influence of uncertainty factors, the investment cost of the comprehensive energy system plan is defined to be optimal as follows: central value C of total annual cost0(x0) Minimum, and the uncertainty results in the smallest annual total combined cost variation C'.
Accordingly, the comprehensive energy system optimization configuration model considering uncertainty can be split into two sub-problems, and the total annual comprehensive cost central value and the total annual comprehensive cost variable quantity C' caused by uncertainty are respectively minimized.
The sub-problem 1 is deterministic optimization configuration, and wind power, photovoltaic power and load power are predicted values in the optimization process. And the energy storage power, the park and the upstream energy network interaction power obtained by deterministic optimization are used as affine central values of the power corresponding to the subproblem 2.
And the sub-problem 2 is uncertainty optimization configuration, and the total annual comprehensive cost variation caused by uncertainty factors is minimized. And according to the source load prediction error, adjusting the power of the energy storage equipment and the interaction power of the park and the upstream energy network to ensure the power balance of the park when the source load power fluctuates. The constraint conditions comprise the energy storage power and the affine form of the interaction power of the park and the upstream energy network, and are shown in the formula-in. The affine center value is to be determined by deterministic optimization. And overlapping the optimization results of the two sub-problems to obtain an optimization configuration scheme of the comprehensive energy system.
The deterministic optimization model and the uncertain optimization model are both double-layer, and the details are as follows.
3.2 double-layer optimization model
The upper layer of the deterministic optimization model takes the minimum annual integrated cost as an objective function, and the decision variable is the installation capacity of various devices. The upper layer of the uncertainty optimization model minimizes the annual total comprehensive cost variation caused by source load prediction errors, and the decision variable is the energy storage equipment capacity variation caused by uncertainty. The objective function is as follows:
min C0＝CInv,0+COP,0 \*MERGEFORMAT(13)
min C′＝CInv,l+C′OP(l＝1,2,3) \*MERGEFORMAT(14)
in the formula: the subscript l is 1,2, and 3, which indicates that the uncertainty factors are wind power, photovoltaic, and load, respectively, as follows.
The formula is an upper-layer objective function of the deterministic optimization model. Wherein, C0The annual comprehensive cost; cInv,0Equipment annual investment cost; cOP,0And returning the operation cost of the park to the upper layer from the lower layer optimization model.
The formula is an upper-layer objective function of the uncertainty optimization model. Wherein, CInv,lAn annual investment cost variation of the energy storage device capacity variation caused by uncertainty; c'OPCaused by uncertaintyThe park operation cost variable quantity is returned to the upper layer from the lower layer optimization model; cInv,0、CInv,lThe calculation formula is similar, and specifically as follows:
in the formula:
the installation cost of the apparatus γ;
for the recovery rate of the equivalent annual fund of the equipment, the expression is as follows:
in the formula: r is the discount rate, yγThe service life of the equipment.
The lower layers of the two sub-problems are both optimized scheduling models. The lower layer of the deterministic optimization model minimizes the operation cost of the park, and the decision variable is the power value of the equipment at each moment. And the lower layer of the uncertainty optimization model minimizes the park operation cost variation caused by source load prediction errors, and the decision variables are the energy storage equipment power variation and the park and energy network interaction power variation caused by uncertainty. The objective function is as follows:
in the formula: the subscript s indicates the different typical days, as follows. The formula is the underlying objective function of the deterministic optimization model. COP,0Representing the operating cost of the campus. D is the total days of the year; msTotal typical number of days; pi(s) is the typical daily s ratio; cOM,s,0Cost for equipment maintenance; cTrade,s,0Is the energy interaction cost; cFuel,s,0Cost for fuel purchase; cCO2,s,0Which is a carbon emission cost.
The formula is the lower layer objective function of the uncertainty optimization model. C'OP、COM,s,l、CTrade,s,l、CFuel,s,l、CCO2,s,lRespectively representing the park operation cost variation, the equipment maintenance cost variation, the energy interaction cost variation, the fuel purchase cost variation and the carbon emission cost variation caused by the source load prediction error. The cost calculation formula is as follows:
(1) cost of equipment maintenance
The deterministic optimized equipment maintenance cost is calculated. Wherein the content of the first and second substances,
the input power of the ith type energy conversion equipment is t time period;
and the charging and discharging energy power of the jth type energy storage equipment in the t period is respectively.
The energy conversion and the operation and maintenance cost of the energy storage equipment are reduced.
The equation calculates the amount of equipment maintenance cost variation due to uncertainty factors. Wherein the content of the first and second substances,
storage of j-type energy respectively caused by uncertainty of source load in t periodAnd the charging and discharging power variation of the equipment. 1,2 and 3 represent uncertainty factors causing power change of the energy storage device, namely wind power, photovoltaic and load respectively, and the following description refers to the uncertainty factors.
(2) Cost of energy interaction
And (4) respectively calculating the energy interaction cost of the deterministic optimization model and the energy interaction cost of the uncertain optimization model. Wherein the content of the first and second substances,
and
the power purchasing/selling power of the park energy Internet and the power grid at the time t;
and
the purchase/sale price of the time t;
the power purchasing/selling power variation of the park and the power grid caused by the uncertainty of the source load in the t period is respectively. 1,2 and 3 represent uncertainty factors causing power purchasing/selling change, namely wind power, photovoltaic and load.
(3) Cost of fuel purchase
And (4) respectively calculating the fuel purchase cost of the deterministic optimization model and the fuel purchase cost of the uncertain optimization model. Wherein
The gas purchasing power of the park network at the time t; c. CFuelIs the natural gas price;
the method is characterized in that the change of the gas purchasing power of the park network caused by uncertainty of source load in the t period is realized. 1,2 and 3 represent that the uncertain factors causing the change of the gas purchase amount are wind power, photovoltaic and load respectively.
(4) Cost of carbon emissions
And (4) respectively calculating the carbon emission cost of the deterministic optimization model and the carbon emission cost of the non-deterministic optimization model. Wherein, aeAnd agCarbon emission coefficient of electric energy and natural gas, respectively, ccIs the carbon tax price.
And determining constraint conditions of the optimization model, wherein the constraint conditions comprise energy power balance constraint, energy equipment operation constraint, energy interaction power constraint and the like. The wind power, the photovoltaic power and the load power only consider predicted values (affine central values), and the output of each device is a conventional number.
The constraint condition of the uncertainty optimization model is shown as the formula-in. The power of the energy storage device and the interaction power of the park and the upstream energy network are both in affine forms, and the affine center value is obtained by deterministic optimization.
(1) Energy power balance constraint
In the formula:
the input electricity and the natural gas power of the ith type of energy conversion equipment in the t time period are respectively;
outputting electric power and thermal power by the ith type energy conversion equipment in the time period t respectively;
affine forms of power corresponding to the purchase/sale/electric power and the gas purchase quantity of the park at the time period t are shown as the formula;
and
the affine forms of discharging, charging, heat releasing and heat storing power of the jth type energy storage equipment in the t period are similar in formula;
and
the affine forms of the electric load, the heat load and the natural gas load in the period t are respectively shown, and the formulas are similar as shown in the formula.
(2) Energy storage device operational constraints
In the formula:
the affine forms of the charging and discharging power of the j-type energy storage equipment at the time t are shown as the formula.
The maximum charge and discharge energy of the j-type energy storage equipment. Sj,0And Sj,TOptimizing the energy of the kth j-type energy equipment in the initial period and the ending period respectively;
and
the upper limit and the lower limit of the j-th type energy storage equipment respectively; u. ofjk,tIs a variable for representing the charging and discharging states of the jth energy equipment in the t period. In the charging state, uj,t1, in the relaxed state uj,t＝0。
(3) Energy interaction power constraint
In the formula:
and
the maximum gas purchasing power of the park energy Internet and the maximum electricity purchasing and selling power of an upstream network are respectively;
state variable for purchasing power for park energy Internet
Rest states
3.3 optimal configuration model solution
In a Genetic Algorithm (GA), decision variables of an upper-layer planning model are few, so that the upper layer adopts the GA to optimize the type and the number of devices; the lower-layer operation model is a multi-constraint linear model and is solved by a cplex solver. And the upper-layer capacity optimization result acts on the lower-layer objective function, the lower-layer operation optimization result is fed back to the upper layer, and the global optimal solution is finally obtained through the upper-layer and lower-layer optimization iteration. And taking the solved output of the energy storage equipment and the interaction power of the park and the energy network as affine central values of the corresponding power of the uncertainty optimization model.
In the solving process, the upper layer adopts a GA algorithm to initialize the capacity of the energy storage equipment and transmits the capacity to the lower layer, and the lower layer is an optimized scheduling model considering the source load prediction error. From the equation, when the noise cell is adjusted to maximize the operation cost variation, that is, the maximum uncertainty effect that can be generated under the current noise cell coefficient is represented, the worst possibility corresponds to the situation that the operation cost variation of the park is the largest due to the prediction error. Under the worst condition, the influence of uncertainty factors on the operation cost can be minimized by adjusting the noise element coefficient, namely adjusting the power of the energy storage equipment and the interaction power of the park and the energy network. The uncertainty-optimized lower layer can be translated into the running cost minimization problem (i.e., adjusting the noise bin coefficient, min problem) under the worst case (i.e., adjusting the noise bin, max problem). Two problems after decomposition of the lower model are as follows:
(1) max problem
The objective function is shown as:
solving the noise element epsilon causing the largest change of the park operation cost by the max problem modelW,εPV,εL. The constraint conditions comprise power balance constraint, energy storage equipment operation constraint and energy trading constraint, and are expressed as formula-shown in the specification. Wherein the energy storage device functionsRate of change
Interaction value of park and upstream energy network
The min problem is solved.
(2) min problem
The objective function is shown as:
correcting power of energy storage equipment by min problem
And the interaction value P of the park and the upstream energy networkl buy,e、Pl buy,g、Pl buy,gThe constraint conditions comprise power balance constraint, energy storage equipment operation constraint and energy interaction constraint, and are expressed as formula-shown in the specification. Wherein, wind power, photovoltaic and load noise element
Solved by the max problem.
The max problem and min problem models are solved by a cplex solver. And alternately iterating the max problem model and the min problem model until the total annual integrated cost of the two models is equal, and then iterating and converging. And feeding back the lower-layer operation optimization result to the upper layer, and finally obtaining a global optimal solution through upper-layer iteration and lower-layer iteration. A flow chart for optimizing the configuration model is shown in fig. 2.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (7)
1. The affine-based optimization configuration method of the comprehensive energy system is characterized in that affine variables are adopted to establish an affine optimization model of the comprehensive energy system, the affine optimization model of the comprehensive energy system is decomposed into two sub-problems of minimum central value of a target affine function and minimum affine function variation under the influence of uncertainty factors, the former is a deterministic optimization model only considering source load prediction power, the latter is an uncertainty optimization model only considering source load prediction errors, and configuration results are influenced by the uncertainty factors to the minimum through alternating iteration of a max model and a min model.
2. The affine-based optimal configuration method for the integrated energy system according to claim 1, wherein the integrated energy system uses an energy hub EH as a coupling point and a conversion station of each energy carrier, and the EH purchases electric energy and natural gas from an upstream power grid company and a upstream natural gas company and distributes the electric energy and the natural gas to each energy conversion and energy storage device, so that the energy of each micro system is balanced, and the electricity, heat and gas load requirements of a user side are met; for the EH modeling, considering the energy storage device, the coupling relation of the input and the output of the EH is as follows:
L＝CP \*MERGEFORMAT(1)
in the formula: l ise、LhAnd LgRespectively the electricity, heat and gas loads of users in the garden;
respectively the electricity intake quantity and the air intake quantity of the whole park, wherein
Comprises photovoltaic set generating power PPVAnd buying power P to upstream power gridbuy,e；ηebHeat production efficiency for EB; etagbHeat generation efficiency of GB;
electricity and heat generation efficiency of CHP respectively; hgasThe natural gas heat value, alpha, beta and gamma are the distribution coefficients of electric energy, the value ranges are [0,1 ]]And beta + gamma is less than or equal to 1; s, S,
The variation of the energy storage coupling matrix and the energy storage capacity in the device is obtained; se、ShCoupling elements of electricity storage ES and heat storage HS are respectively; the variation of the energy storage capacity of the energy storage device in the time interval delta t
Calculating to obtain E (t) which is the energy storage capacity of the energy storage device in the time period t;
the variation of the energy storage capacity in the electricity storage device and the heat storage device is respectively.
3. The affine-based optimal configuration method for the integrated energy system according to claim 1, wherein the uncertainty-considered optimal configuration model of the integrated energy system is established as follows:
an uncertainty x can be represented affine-wise as a linear combination of noise elements;
in the formula: x is the number of0As an affine center value; epsiloniFor noise elements, representing oneUncertainty factor with value range of [ -1,1]；xiRepresenting the noise element epsilon as a noise element coefficientiDegree of influence on affine number;
in the operation process of the comprehensive energy system, the established affine models of wind power, photovoltaic and load are as follows:
in the formula:
is an affine value of the wind power active power,
representing affine centre value, epsilonWAffine noise element, P, for active power of a fant WIs the affine noise element coefficient corresponding to the noise element coefficient;
the affine value is the affine value of the wind power active power;
representing affine centre value, epsilonPVAffine noise element, P, for active power of a fant PVIs the affine noise element coefficient corresponding to the noise element coefficient;
affine value of load power;
representing affine centre value, epsilonLAffine noise element, P, for active power of a fant LIs the affine noise element coefficient corresponding to the noise element coefficient;
because the power of the energy storage equipment and the interaction power of the park and the upstream energy network can be changed rapidly and flexibly, the power of the energy storage equipment can be changed to adapt to the change of the source load power, so that the power balance of the park is maintained; considering that the source load power has a prediction error in the actual operation of the park; therefore, the output of the energy storage equipment and the interaction power of the park and the upstream energy network can fluctuate; equations (9) and (10) are affine expressions of the energy storage device power and the park and energy network interaction power established according to the source charge power prediction error:
in the formula: the subscript l is 1,2,3 to indicate uncertainty factors causing power change, wind power, photovoltaic, load, respectively;
respectively representing affine forms of charging and discharging energy power of j-type energy storage equipment at the time t;
an affine center value representing the stored energy power;
respectively representing the energy storage power variation caused by wind power, photovoltaic and load power prediction errors;
power for purchasing/selling electricity in the park at the time t respectively;
an affine form of corresponding power of the gas purchasing quantity at the time t;
as an affine center value;
respectively representing the variation of the purchased power caused by prediction errors of wind power, photovoltaic power and load power;
respectively representing the electricity selling power variation caused by wind power, photovoltaic and load uncertainty;
respectively representing the power variation quantity corresponding to the purchased gas quantity caused by wind power, photovoltaic and load uncertainty.
4. The affine-based optimal configuration method for the integrated energy system according to claim 3, wherein the model for optimal configuration of the integrated energy system considering uncertainty is decomposed into two sub-problems of minimum central value of a target affine function and minimum affine function variation under the influence of uncertainty factors, the former is a deterministic optimization model considering only source load prediction power, the latter is an uncertainty optimization model, and the concrete implementation manner considering source load prediction errors is as follows:
the general form of the affine optimization problem is
In the formula: decision variables
Affine vector, C cost coefficient matrix, and C (C, x) objective function; the matrix a in the constraint condition is a conventional coefficient matrix and parameters
And
is a known affine vector;
in the objective function of the comprehensive energy system optimization configuration model considering uncertainty, noise elements including wind power, photovoltaic and load are considered in decision variables, and the total annual comprehensive cost of the optimization configuration model can be written as:
in the formula: c0(x0) A campus year composite cost for deterministic optimization; c1(x1)εW+C2(x2)εPV+C3(x3)εLRepresenting the annual total comprehensive cost variation of the park caused by uncertainty of wind, light and load, and recording as C';
in order to reduce the influence of uncertainty factors, the investment cost of the comprehensive energy system plan is defined to be optimal as follows: central value of total annual costC0(x0) The minimum, and the annual total integrated cost variation C' caused by uncertainty is minimum; therefore, the comprehensive energy system optimization configuration model considering uncertainty is divided into two sub-problems, and the central value of the total annual comprehensive cost and the variable quantity C' of the total annual comprehensive cost caused by uncertainty are respectively minimized; the former is a deterministic optimization model only considering source load prediction power, and the latter is an uncertain optimization model which considers source load prediction error;
the upper layer of the deterministic optimization model takes the minimum annual comprehensive cost as an objective function, and the decision variable is the installation capacity of various devices; the upper layer of the uncertainty optimization model minimizes the annual total comprehensive cost variation caused by source load prediction errors, and the decision variable is the energy storage equipment capacity variation caused by uncertainty; the objective function is as follows:
min C0＝CInv,0+COP,0 \*MERGEFORMAT(13)
min C′＝CInv,l+C′OP(l＝1,2,3) \*MERGEFORMAT(14)
in the formula: subscript l is 1,2,3 to indicate that uncertainty factors are wind power, photovoltaic and load respectively;
the formula is an upper-layer objective function of the deterministic optimization model; wherein, C0The annual comprehensive cost; cInv,0Equipment annual investment cost; cOP,0Returning the operation cost of the park to the upper layer from the lower layer optimization model;
the formula is an upper layer objective function of the uncertainty optimization model; wherein, CInv,lAn annual investment cost variation of the energy storage device capacity variation caused by uncertainty; c'OPReturning the park operation cost variable quantity caused by uncertainty to the upper layer from the lower layer optimization model; cInv,0、CInv,lThe calculation formula is similar, and specifically as follows:
in the formula:
the installation cost of the apparatus γ;
for the recovery rate of the equivalent annual fund of the equipment, the expression is as follows:
the lower layers of the two sub-problems are both optimized scheduling models; determining the operation cost of a lower-layer minimized park of the optimization model, wherein a decision variable is a power value of equipment at each moment; the method comprises the following steps that the lower layer of an uncertainty optimization model minimizes the park operation cost variation caused by source load prediction errors, and decision variables are the energy storage equipment power variation and the park and energy network interaction power variation caused by uncertainty; the objective function is as follows:
in the formula: subscript s represents different typical days; the formula is a lower-layer objective function of the deterministic optimization model; cOP,0Represents the operating cost of the campus; d is the total days of the year; msTotal typical number of days; pi(s) is the typical daily s ratio; cOM,s,0Cost for equipment maintenance; cTrade,s,0Is the energy interaction cost; cFuel,s,0Cost for fuel purchase; cCO2,s,0Is the carbon emission cost;
the formula is a lower layer objective function of the uncertainty optimization model; c'OP、COM,s,l、CTrade,s,l、CFuel,s,l、CCO2,s,lRespectively representing the park operation cost variation, the equipment maintenance cost variation, the energy interaction cost variation, the fuel purchase cost variation and the carbon emission cost variation caused by the source load prediction error.
5. The affine-based integrated energy system optimal configuration method according to claim 4, wherein the calculation formulas of the equipment maintenance cost, the energy interaction cost, the fuel purchase cost and the carbon emission cost are as follows:
(1) cost of equipment maintenance
Calculating the equipment maintenance cost of determinacy optimization; wherein the content of the first and second substances,
the input power of the ith type energy conversion equipment is t time period;
the charging and discharging energy power of the jth type energy storage equipment in the t time period respectively;
the operating and maintaining costs of energy conversion and energy storage equipment;
calculating the equipment maintenance cost variable quantity caused by uncertainty factors; wherein the content of the first and second substances,
the variable quantities of charging and discharging power of the j-type energy storage equipment caused by uncertainty of source load in the t period are respectively; 1,2,3 represents the work induced in the energy storage deviceThe uncertainty factors of the rate change are wind power, photovoltaic and load respectively;
(2) cost of energy interaction
Respectively calculating the energy interaction cost of a deterministic optimization model and an uncertainty optimization model; wherein the content of the first and second substances,
and
the power purchasing/selling power of the park energy Internet and the power grid at the time t;
and
the purchase/sale price of the time t;
the power purchasing/selling power variation of the park and the power grid caused by the uncertainty of the source load in the t period is respectively; 1,2 and 3 represent that the uncertain factors causing the power change of electricity purchase/sale are wind power, photovoltaic and load respectively;
(3) cost of fuel purchase
Respectively calculating the fuel purchase cost of the deterministic optimization model and the fuel purchase cost of the non-deterministic optimization model; wherein
The gas purchasing power of the park network at the time t; c. CFuelIs the natural gas price;
the method comprises the following steps of (1) obtaining a park network gas purchasing power variable quantity caused by source load uncertainty in a t period; 1,2 and 3 represent that the uncertain factors causing the change of the gas purchase amount are wind power, photovoltaic and load respectively;
(4) cost of carbon emissions
Respectively calculating the carbon emission cost of a deterministic optimization model and a non-deterministic optimization model; wherein, aeAnd agCarbon emission coefficient of electric energy and natural gas, respectively, ccIs the carbon tax price.
6. The affine-based integrated energy system optimization configuration method according to claim 5, wherein the constraint conditions of the deterministic optimization model include energy power balance constraint, energy device operation constraint, energy interaction power constraint, and the like; the wind power, the photovoltaic power and the load power only consider predicted values, namely affine central values, and the output of each device is a conventional number;
the constraint condition of the uncertainty optimization model is shown as the formula-; the power of the energy storage equipment and the interaction power of the park and the upstream energy network are in affine forms, and affine central values are obtained by deterministic optimization;
(1) energy power balance constraint
In the formula:
the input electricity and the natural gas power of the ith type of energy conversion equipment in the t time period are respectively;
outputting electric power and thermal power by the ith type energy conversion equipment in the time period t respectively;
affine forms of power corresponding to the purchase sale/electric power and the gas purchase quantity of the park at the time period t are respectively formed;
and
the affine forms of discharging, charging, heat releasing and heat storing power of the jth type energy storage equipment in the t period are respectively formed;
and
respectively affine forms of electric, thermal and natural gas loads in a time period t;
(2) energy storage device operational constraints
In the formula:
respectively affine forms of charging and discharging power of the j-type energy storage equipment at the time t;
the maximum charge-discharge energy power of the j-type energy storage equipment is obtained; sj,0And Sj,TOptimizing the energy of the kth j-type energy equipment in the initial period and the ending period respectively;
and
the upper limit and the lower limit of the j-th type energy storage equipment respectively; u. ofjk,tThe method comprises the following steps of (1) representing a variable of a j-th energy device charge-discharge state in a t period; in the charging state, uj,t1, in the relaxed state uj,t＝0；
(3) Energy interaction power constraint
In the formula:
and
the maximum gas purchasing power of the park energy Internet and the maximum electricity purchasing and selling power of an upstream network are respectively;
state variable for purchasing power for park energy Internet
Rest states
7. The affine-based optimal configuration method for the integrated energy system according to claim 6, wherein the concrete implementation manner that the configuration result is minimally affected by uncertainty factors through alternating iteration of the max model and the min model is as follows:
in the process of solving the optimized configuration model, initializing the capacity of the energy storage equipment by adopting a genetic algorithm GA (genetic algorithm) at the upper layer, and transmitting the capacity to the lower layer, wherein the lower layer is an optimized scheduling model considering source load prediction errors; as can be seen from the equation, when the noise cell is adjusted to maximize the operation cost change, that is, the maximum uncertainty effect that can be generated under the current noise cell coefficient is represented, corresponding to the worst possibility, which represents the situation where the operation cost change of the park is the greatest due to the prediction error; under the worst condition, the influence of uncertainty factors on the operation cost can be minimized by adjusting the noise element coefficient, namely adjusting the power of the energy storage equipment and the interaction power of the park and the energy network; therefore, the lower layer of uncertainty optimization can be converted into the problem of minimizing the running cost under the worst condition, namely the problem of adjusting the noise element and max; the minimum problem of the operation cost is the problem of adjusting the noise element coefficient and min; two problems after the decomposition of the lower model:
1) max problem
The objective function is shown as:
max problem model solution guideNoise element epsilon with largest change of operating cost in gardenW,εPV,εL(ii) a The constraint conditions comprise power balance constraint, energy storage equipment operation constraint and energy transaction constraint; wherein the energy storage device power
Interaction value of park and upstream energy network
Solving by the min problem;
2) min problem
The objective function is shown as:
correcting power of energy storage equipment by min problem
And the interaction value P of the park and the upstream energy networkl buy,e、Pl buy,g、Pl buy ,gThe constraint conditions comprise power balance constraint, energy storage equipment operation constraint and energy interaction constraint; wherein, wind power, photovoltaic and load noise element
Solving by a max problem;
solving the max problem model and the min problem model by adopting a cplex solver; alternately iterating the max problem model and the min problem model until the total annual integrated cost of the two models is equal, and then iterating and converging; and feeding back the lower-layer operation optimization result to the upper layer, and finally obtaining a global optimal solution through upper-layer iteration and lower-layer iteration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111053009.5A CN113779792A (en) | 2021-09-08 | 2021-09-08 | Affine-based comprehensive energy system optimal configuration method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111053009.5A CN113779792A (en) | 2021-09-08 | 2021-09-08 | Affine-based comprehensive energy system optimal configuration method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113779792A true CN113779792A (en) | 2021-12-10 |
Family
ID=78842020
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111053009.5A Pending CN113779792A (en) | 2021-09-08 | 2021-09-08 | Affine-based comprehensive energy system optimal configuration method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113779792A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114417603A (en) * | 2022-01-18 | 2022-04-29 | 福州大学 | Electric heating integrated energy system affine energy flow calculation method considering heat supply network transmission and distribution capacity |
| CN114662756A (en) * | 2022-03-23 | 2022-06-24 | 福州大学 | Thermoelectric micro-energy network double-layer affine optimization scheduling method considering source-load uncertainty |
| CN115600761A (en) * | 2022-11-09 | 2023-01-13 | 山东大学（Cn） | Self-scheduling method and system of integrated energy production unit considering uncertainty factor |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106940869A (en) * | 2017-03-10 | 2017-07-11 | 国网山东省电力公司经济技术研究院 | Consider the reference power network robust optimizing evaluation method of negative rules |
| CN109190271A (en) * | 2018-09-13 | 2019-01-11 | 东北大学 | A kind of electric heating integrated energy system economic optimization dispatching method considering transmission loss |
| CN111668877A (en) * | 2020-04-28 | 2020-09-15 | 中国电力科学研究院有限公司 | Active power distribution network distribution robust optimization scheduling method and system |
| CN112381262A (en) * | 2020-09-17 | 2021-02-19 | 南京信息工程大学滨江学院 | Micro-grid planning method considering load voltage characteristics and new energy strong uncertainty |
| CN112561732A (en) * | 2020-12-14 | 2021-03-26 | 南方电网科学研究院有限责任公司 | Method and device for optimizing active power scheduling of power system and readable storage medium |
| CN112736926A (en) * | 2020-12-25 | 2021-04-30 | 华北电力大学 | Interval affine power flow dynamic optimization method for distributed new energy access power distribution network |
| CN113036819A (en) * | 2021-03-10 | 2021-06-25 | 浙江大学 | Electric heating comprehensive energy system robust scheduling method considering source-load bilateral uncertainty |
| CN113239574A (en) * | 2021-06-09 | 2021-08-10 | 福州大学 | Power system multistage robust unit combination decision method considering source load double uncertainty |
-
2021
- 2021-09-08 CN CN202111053009.5A patent/CN113779792A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106940869A (en) * | 2017-03-10 | 2017-07-11 | 国网山东省电力公司经济技术研究院 | Consider the reference power network robust optimizing evaluation method of negative rules |
| CN109190271A (en) * | 2018-09-13 | 2019-01-11 | 东北大学 | A kind of electric heating integrated energy system economic optimization dispatching method considering transmission loss |
| CN111668877A (en) * | 2020-04-28 | 2020-09-15 | 中国电力科学研究院有限公司 | Active power distribution network distribution robust optimization scheduling method and system |
| CN112381262A (en) * | 2020-09-17 | 2021-02-19 | 南京信息工程大学滨江学院 | Micro-grid planning method considering load voltage characteristics and new energy strong uncertainty |
| CN112561732A (en) * | 2020-12-14 | 2021-03-26 | 南方电网科学研究院有限责任公司 | Method and device for optimizing active power scheduling of power system and readable storage medium |
| CN112736926A (en) * | 2020-12-25 | 2021-04-30 | 华北电力大学 | Interval affine power flow dynamic optimization method for distributed new energy access power distribution network |
| CN113036819A (en) * | 2021-03-10 | 2021-06-25 | 浙江大学 | Electric heating comprehensive energy system robust scheduling method considering source-load bilateral uncertainty |
| CN113239574A (en) * | 2021-06-09 | 2021-08-10 | 福州大学 | Power system multistage robust unit combination decision method considering source load double uncertainty |
Non-Patent Citations (2)
| Title |
|---|
| 包伟涔;韦钢;刘佳;李渊;龚博杰;何晨可;: "基于区间模型的多能互补系统优化运行", 电力系统自动化, no. 11 * |
| 李利利;丁恰;涂孟夫;谢丽荣;: "机组组合问题的仿射可调整鲁棒优化模型与算法", 电力工程技术, no. 03 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114417603A (en) * | 2022-01-18 | 2022-04-29 | 福州大学 | Electric heating integrated energy system affine energy flow calculation method considering heat supply network transmission and distribution capacity |
| CN114662756A (en) * | 2022-03-23 | 2022-06-24 | 福州大学 | Thermoelectric micro-energy network double-layer affine optimization scheduling method considering source-load uncertainty |
| CN115600761A (en) * | 2022-11-09 | 2023-01-13 | 山东大学（Cn） | Self-scheduling method and system of integrated energy production unit considering uncertainty factor |
| CN115600761B (en) * | 2022-11-09 | 2023-05-02 | 山东大学 | Comprehensive energy production unit self-dispatching method and system considering uncertainty factors |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ju et al. | A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response | |
| Siqin et al. | A two-stage distributionally robust optimization model for P2G-CCHP microgrid considering uncertainty and carbon emission | |
| Li et al. | Coordinated scheduling for improving uncertain wind power adsorption in electric vehicles—Wind integrated power systems by multiobjective optimization approach | |
| CN113779792A (en) | Affine-based comprehensive energy system optimal configuration method | |
| CN110659830A (en) | Multi-energy micro-grid planning method for comprehensive energy system | |
| Jin et al. | Game theoretical analysis on capacity configuration for microgrid based on multi-agent system | |
| CN112701687B (en) | Robust optimization operation method of gas-electricity distribution network system considering price type combined demand response | |
| Xu et al. | Look-ahead risk-constrained scheduling for an energy hub integrated with renewable energy | |
| CN115018230A (en) | Low-carbon robust economic optimization operation method of comprehensive energy system considering emission reduction cost | |
| CN113592133A (en) | Energy hub optimal configuration method and system | |
| CN116050585A (en) | Nash negotiation-based hydrogen-containing gas comprehensive energy multi-microgrid optimal scheduling method | |
| CN112418488A (en) | Comprehensive energy system scheduling method and device based on two-stage energy optimization | |
| CN115860413A (en) | Grid-connected combined cooling heating and power micro-grid system economic scheduling method based on load demand response and double-layer adjustable robust optimization | |
| Lv et al. | Optimal capacity configuration model of power-to-gas equipment in wind-solar sustainable energy systems based on a novel spatiotemporal clustering algorithm: A pathway towards sustainable development | |
| Han et al. | Multi-stage distributionally robust optimization for hybrid energy storage in regional integrated energy system considering robustness and nonanticipativity | |
| Deng et al. | Optimal sizing of residential battery energy storage systems for long-term operational planning | |
| Lv et al. | A robust optimization model for capacity configuration of PV/battery/hydrogen system considering multiple uncertainties | |
| Yuan et al. | Carbon dioxide recycling in hydrogen-based energy systems using power-to-gas facility and stochastic multi-objective optimization | |
| Deckmyn et al. | Multi-objective optimization for environomic scheduling in microgrids | |
| Yang et al. | Optimal microgrid programming based on an energy storage system, price-based demand response, and distributed renewable energy resources | |
| Yang et al. | An optimized scheduling strategy combining robust optimization and rolling optimization to solve the uncertainty of RES-CCHP MG | |
| Armioun et al. | Optimal scheduling of CCHP-based resilient energy distribution system considering active microgrids' multi-carrier energy transactions | |
| Weishang et al. | Study on optimal model of micro-energy network operation configuration considering flexible load characteristics | |
| Xie et al. | Low-carbon economic operation of IES based on life cycle method and hydrogen energy utilization | |
| Wang et al. | Day-ahead scheduling of integrated energy systems considering carbon emissions |
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 |