CN118096225A - Method, device, terminal and storage medium for solving optimal pricing of comprehensive energy park - Google Patents
Method, device, terminal and storage medium for solving optimal pricing of comprehensive energy park Download PDFInfo
- Publication number
- CN118096225A CN118096225A CN202410495628.7A CN202410495628A CN118096225A CN 118096225 A CN118096225 A CN 118096225A CN 202410495628 A CN202410495628 A CN 202410495628A CN 118096225 A CN118096225 A CN 118096225A
- Authority
- CN
- China
- Prior art keywords
- park
- energy
- constraint
- station
- hydrogen
- 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
- 238000000034 method Methods 0.000 title claims abstract description 78
- 239000001257 hydrogen Substances 0.000 claims abstract description 157
- 229910052739 hydrogen Inorganic materials 0.000 claims abstract description 157
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims abstract description 153
- 238000005457 optimization Methods 0.000 claims abstract description 60
- 238000005984 hydrogenation reaction Methods 0.000 claims abstract description 30
- 230000006870 function Effects 0.000 claims description 62
- 238000004146 energy storage Methods 0.000 claims description 37
- 230000005611 electricity Effects 0.000 claims description 29
- 239000007789 gas Substances 0.000 claims description 21
- 238000003032 molecular docking Methods 0.000 claims description 19
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 18
- 239000000446 fuel Substances 0.000 claims description 16
- 230000008901 benefit Effects 0.000 claims description 15
- 238000007599 discharging Methods 0.000 claims description 10
- 239000003345 natural gas Substances 0.000 claims description 9
- 230000015556 catabolic process Effects 0.000 claims description 8
- 238000006731 degradation reaction Methods 0.000 claims description 8
- 238000010276 construction Methods 0.000 claims description 7
- 230000009977 dual effect Effects 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 abstract description 19
- 230000007246 mechanism Effects 0.000 abstract description 9
- 230000008859 change Effects 0.000 abstract description 4
- 238000006243 chemical reaction Methods 0.000 description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 8
- 238000005868 electrolysis reaction Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 238000009795 derivation Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000020169 heat generation Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 239000002028 Biomass Substances 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000002737 fuel gas Substances 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 229920003169 water-soluble polymer Polymers 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a comprehensive energy park optimal pricing solving method, a device, a terminal and a storage medium, wherein the method comprises the following steps: determining first uncertainty information of renewable energy output and user load in the comprehensive energy park, and constructing a first energy management model corresponding to the comprehensive energy park; determining second uncertainty information of output and load of renewable energy sources and a hydrogen storage tank in the hydrogen adding station, and constructing a second energy management model corresponding to the hydrogen adding station; and constructing an optimal pricing strategy model according to the first energy management model and the second energy management model to obtain an energy scheduling result of the comprehensive energy park and a pricing result of the comprehensive energy park for the hydrogen station. The reasonable pricing mechanism is realized by considering the cooperative optimization between the comprehensive energy park and the hydrogenation station and considering various uncertainties of renewable energy output fluctuation and load demand change faced by the comprehensive energy park and the hydrogenation station in the operation process.
Description
Technical Field
The invention relates to the technical field of power system automation, in particular to a comprehensive energy park optimal pricing solving method, a comprehensive energy park optimal pricing solving device, a terminal and a storage medium.
Background
The integrated energy park is used as an emerging energy management and supply mode, and can integrate various energy types, such as solar energy, wind energy, biomass energy and conventional energy, so as to realize optimal configuration and efficient utilization of energy. In the field of hydrogen energy, the construction and popularization of a hydrogen adding station are key links for realizing the commercialization of hydrogen fuel cell automobiles.
The integrated energy park and hydro-station, as different operational entities, assume important responsibilities for providing a stable and sustainable energy supply. Reasonable cost and benefit distribution mechanisms are critical to motivate participation of parties, improve operational efficiency, and promote collaboration and optimization of the overall energy system. In addition, integrated energy parks and hydrogen stations are faced with multiple uncertainties in renewable energy output fluctuations and load demand variations during operation.
In the prior art, when a pricing mechanism is established for the comprehensive energy park with the hydrogenation station, collaborative optimization between the comprehensive energy park and the hydrogenation station is not considered, and various uncertainties of renewable energy output fluctuation and load demand change faced by the comprehensive energy park and the hydrogenation station in the operation process are not considered, so that a reasonable pricing mechanism is not realized, and the problem of benefit distribution among multiple operators is caused.
Accordingly, the prior art has drawbacks and needs to be improved and developed.
Disclosure of Invention
The invention aims to solve the technical problems that aiming at the defects in the prior art, the invention provides the optimal pricing solving method, the optimal pricing solving device, the optimal pricing solving terminal and the optimal pricing solving storage medium for the comprehensive energy park, and aims to solve the problem that in the prior art, when a pricing mechanism is established for the comprehensive energy park containing a hydrogenation station, a reasonable pricing mechanism is not realized, so that the problem of benefit distribution among multiple operators is caused.
The technical scheme adopted for solving the technical problems is as follows:
a method for optimal pricing solving of an integrated energy park, the method comprising:
Constructing a first cost function of the comprehensive energy park, and determining first uncertainty information of renewable energy output and user load in the comprehensive energy park;
constructing a first energy management model corresponding to the comprehensive energy park according to preset constraint conditions in the park, the first cost function and the first uncertainty information;
Constructing a second cost function of the hydrogen adding station in the comprehensive energy park, and determining second uncertainty information of the output and load of renewable energy sources and the hydrogen storage tank in the hydrogen adding station;
constructing a second energy management model corresponding to the hydrogen adding station according to preset constraint conditions in the hydrogen adding station, the second cost function and the second uncertainty information;
and constructing an optimal pricing strategy model according to the first energy management model and the second energy management model, and solving by utilizing a column and constraint generation algorithm to obtain an energy scheduling result of the comprehensive energy park and a pricing result of the comprehensive energy park for the hydrogen station.
In one implementation, the first cost function includes: the electric energy transaction cost of the park and the main network, the operation cost of the park and the transaction income of the park and the hydrogenation station; the park operation cost comprises the natural gas use cost of the cogeneration unit and the gas boiler and the degradation cost of the energy storage equipment;
the electric energy transaction cost of the park and the main network is represented by the buying and selling electric quantity and the electric quantity price of the park and the main network; the park operation cost is represented by the gas price, the natural gas amount consumed by the cogeneration unit and the gas boiler, the degradation coefficient of the energy storage device, the charging power of the energy storage device and the discharging power of the energy storage device; the park and the docking station trade benefits are represented by the amount of electricity purchased from the docking station to the park, the amount of electricity sold from the docking station to the park, the electricity purchase price from the docking station and the electricity sale price from the docking station.
In one implementation, the first uncertainty information is represented by setting a positive and negative bias flag, a bias rate, and an uncertainty budget within a pre-constructed uncertainty set, the uncertainty set comprising: a first set of uncertainties for the wind generator and photovoltaic, and a second set of uncertainties for the electrical and thermal loads.
In one implementation, the on-campus constraints include: in-park operation constraints and in-park price constraints; the on-campus operation constraints include: trade constraint with main network electric energy, cogeneration unit and gas turbine boiler operation constraint, energy storage device operation constraint, park active power balance constraint and park thermal power balance constraint; the on-campus price constraint includes: price upper and lower limit constraints and average price constraints;
The first energy management model is a two-stage robust optimization model constrained by the first cost function as an objective function, and by the on-campus constraint conditions and the first uncertainty information.
In one implementation, the second cost function includes a hydrogen station buying cost and a selling benefit;
The second uncertainty information is represented by a fuzzy set based on Wasserstein distance and moment information; the fuzzy set includes: a first opportunity constraint for describing power balance of the photovoltaic device and a second opportunity constraint for simulating a hydrogen level variation range in the hydrogen storage tank, wherein the first opportunity constraint and the second opportunity constraint are both distributed robust opportunity constraints;
The intrabay constraints include: active power balance constraint in the hydrogen station, electrolyzer device constraint, fuel cell constraint, energy storage device operation constraint in the hydrogen station and hydrogen storage tank device operation constraint.
In one implementation manner, the constructing a second energy management model corresponding to the hydrogen adding station according to the preset constraint condition in the hydrogen adding station, the second cost function and the second uncertainty information includes:
Converting the first opportunity constraint and the second opportunity constraint into a deterministic constraint group;
and constructing a deterministic robust optimization model by taking the second cost function as an objective function and taking the intra-hydrogen adding station constraint condition and the deterministic constraint group as constraints to obtain a second energy management model corresponding to the hydrogen adding station.
In one implementation, an optimal pricing strategy model is constructed according to the first energy management model and the second energy management model, and a column and constraint generation algorithm is used for solving, so as to obtain an energy scheduling result of the integrated energy park and a pricing result of the integrated energy park for the hydrogen station, and the method comprises the following steps:
taking the first energy management model as an upper-layer optimization model, taking the second energy management model as a lower-layer optimization model, and constructing a double-layer model based on a master-slave game framework;
Converting a lower optimization model into constraint through KKT conditions, and eliminating bilinear terms in an objective function of an upper optimization model through strong dual theory so as to convert the double-layer model into a two-stage robust optimization model, thereby obtaining an optimal pricing strategy model;
Solving by using a column and constraint generation algorithm to obtain an energy scheduling result of the comprehensive energy park and a pricing result of the comprehensive energy park for the hydrogen station;
The upper layer of the double-layer model aims at minimizing the total cost of the comprehensive energy park, the lower layer of the double-layer model aims at maximizing the return of the hydrogen adding station, and the transaction electric quantity of the hydrogen adding station and the comprehensive energy park in the upper layer of the double-layer model is obtained by the lower layer of the optimization model.
The invention also provides a comprehensive energy park optimal pricing solving device, wherein the device comprises:
The first determining module is used for constructing a first cost function of the comprehensive energy park and determining first uncertainty information of renewable energy output and user load in the comprehensive energy park;
The first construction module is used for constructing a first energy management model corresponding to the comprehensive energy park according to preset constraint conditions in the park, the first cost function and the first uncertainty information;
the second determining module is used for constructing a second cost function of the hydrogen adding station in the comprehensive energy park and determining second uncertainty information of the output, load and hydrogen storage tank of renewable energy sources in the hydrogen adding station;
The second construction module is used for constructing a second energy management model corresponding to the hydrogenation station according to preset constraint conditions in the hydrogenation station, the second cost function and the second uncertainty information;
And the solving module is used for constructing an optimal pricing strategy model according to the first energy management model and the second energy management model, and solving by utilizing a column and constraint generation algorithm to obtain an energy scheduling result of the comprehensive energy park and a pricing result of the comprehensive energy park for the hydrogenation station.
The invention also provides a terminal, which comprises: the system comprises a memory, a processor and a comprehensive energy park optimal pricing solver stored on the memory and executable on the processor, wherein the comprehensive energy park optimal pricing solver when executed by the processor implements the steps of the comprehensive energy park optimal pricing solver method as described above.
The present invention also provides a computer readable storage medium storing a computer program executable to implement the steps of the integrated energy park optimal pricing solving method as described above.
The invention provides a comprehensive energy park optimal pricing solving method, which comprises the following steps: constructing a first cost function of the comprehensive energy park, and determining first uncertainty information of renewable energy output and user load in the comprehensive energy park; constructing a first energy management model corresponding to the comprehensive energy park according to preset constraint conditions in the park, the first cost function and the first uncertainty information; constructing a second cost function of the hydrogen adding station in the comprehensive energy park, and determining second uncertainty information of the output and load of renewable energy sources and the hydrogen storage tank in the hydrogen adding station; constructing a second energy management model corresponding to the hydrogen adding station according to preset constraint conditions in the hydrogen adding station, the second cost function and the second uncertainty information; and constructing an optimal pricing strategy model according to the first energy management model and the second energy management model, and solving by utilizing a column and constraint generation algorithm to obtain an energy scheduling result of the comprehensive energy park and a pricing result of the comprehensive energy park for the hydrogen station. According to the invention, by considering the cooperative optimization between the comprehensive energy park and the hydrogenation station and considering various uncertainties of renewable energy fluctuation and load demand change faced by the comprehensive energy park and the hydrogenation station in the operation process, a reasonable pricing mechanism is realized, and the benefit distribution problem among multiple main operators is solved.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the integrated energy park optimal pricing solving method of the present invention;
FIG. 2 is a structural framework and problem description diagram of an integrated energy system park including a hydrogen addition station;
FIG. 3 is a graph of robust dispatch plan uncertainty optimization results for photovoltaics in a campus;
FIG. 4 is a graph of robust dispatch plan uncertainty optimization results for wind power in a campus;
FIG. 5 is a graph of robust scheduling plan uncertainty optimization results for electrical loads on a campus;
FIG. 6 is a graph of robust scheduling plan uncertainty optimization results for thermal loads on a campus;
FIG. 7 is a graph of the results of park electric power schedule optimization;
FIG. 8 is a graph of the result of park thermal power schedule optimization;
FIG. 9 is a graph of electricity price for a docking station and power results for docking station and integrated energy park interactions;
FIG. 10 is a column and constraint generation algorithm convergence result diagram;
FIG. 11 is a functional block diagram of a preferred embodiment of the integrated energy park optimal pricing solving means of the present invention;
fig. 12 is a functional block diagram of a preferred embodiment of a terminal in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clear and clear, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In order to ensure robust operation of facilities in the integrated energy park under various uncertainty factors, reduce operating costs, fully utilize site load flexibility, and promote benefit sharing for all stakeholders, the integrated energy park operators need to establish a clear and fair pricing mechanism.
Aiming at the defects of the background technology, the invention provides an optimal pricing strategy for the comprehensive energy park with the hydrogen adding station, reduces the operation cost of the comprehensive energy park, improves the income of the hydrogen adding station, is beneficial to developing and utilizing the site load flexibility under various uncertain factors, and solves the problem of benefit distribution among multiple main operators.
According to the method, a comprehensive energy park cost model is built according to the electric energy transaction cost with a main network, the park operation cost and the income of the hydrogenation station transaction, uncertainty of output and load of renewable energy sources in the comprehensive energy park is described according to a robust optimization method, and then a two-stage robust optimization model of the comprehensive energy park is built; constructing a cost model of the hydrogen addition station according to the electricity buying cost and the electricity selling income which are traded with the comprehensive energy park, describing uncertainty of renewable energy output and load in the hydrogen addition station according to a data-driven distribution robust opportunity constraint method, and constructing a robust optimization model of the hydrogen addition station through mathematical conversion; and then, based on a master-slave game theory, constructing a double-layer optimal pricing strategy model, and comprehensively considering various uncertainties, wherein the upper model is a comprehensive energy park energy management model, and the lower model is a hydrogen station energy management model. The double-layer model is further converted into a two-stage robust optimization model through mathematical derivation, and the two-stage robust optimization model is solved by using a column and constraint generation algorithm. According to the optimal pricing strategy model, an energy scheduling result of the comprehensive energy park and a pricing result of the park for the hydrogen addition station can be obtained, the operation cost is reduced, the load flexibility of the hydrogen addition station is fully utilized, the strategy robustness of the park and the hydrogen addition station under various uncertainties is ensured, and the benefit sharing of the comprehensive energy park and the hydrogen addition station is promoted.
Referring to fig. 1, fig. 1 is a flowchart of a method for solving optimal pricing in an integrated energy park according to the present invention. As shown in fig. 1, the method for solving the optimal pricing of the integrated energy park according to the embodiment of the invention comprises the following steps:
And step S100, constructing a first cost function of the comprehensive energy park, and determining first uncertainty information of renewable energy output and user load in the comprehensive energy park.
The invention considers the uncertainty of renewable energy output and user load in the integrated energy park, and the structural framework and problem description of the integrated energy system park containing the hydrogen station are shown in figure 2.
In an embodiment of the present application, the first cost function includes: the electric energy transaction cost of the park and the main network, the operation cost of the park and the transaction income of the park and the hydrogenation station; the park operation cost comprises the natural gas use cost of the cogeneration unit and the gas boiler and the degradation cost of the energy storage equipment; the electric energy transaction cost of the park and the main network is represented by the buying and selling electric quantity and the electric quantity price of the park and the main network; the park operation cost is represented by the gas price, the natural gas amount consumed by the cogeneration unit and the gas boiler, the degradation coefficient of the energy storage device, the charging power of the energy storage device and the discharging power of the energy storage device; the park and the docking station trade benefits are represented by the amount of electricity purchased from the docking station to the park, the amount of electricity sold from the docking station to the park, the electricity purchase price from the docking station and the electricity sale price from the docking station.
Specifically, the cost function of an integrated energy parkThe method comprises the following steps:
；
；
；
；
Wherein, 、/>、/>The method is characterized by respectively trading the electric energy with a main network, the park operation cost and the income of trading with the hydrogenation station. The park operation cost comprises the natural gas use cost of the cogeneration unit and the gas boiler and the degradation cost of the energy storage equipment. /(I)To schedule cycles to/>For index,/>Is a time interval; /(I)And/>Buying and selling power for trading with the main network in the park, and their prices are respectively used/>And/>A representation; /(I)And/>Natural gas consumed by the cogeneration unit and the gas boiler; /(I)And/>Charging and discharging power for the energy storage device; /(I)For fuel gas price,/>Is the degradation coefficient of the energy storage device; /(I)And/>Respectively, buying electricity and selling electricity of the hydrogen station to the park,/>，/>The buying electricity price and the selling electricity price of the hydrogen station are respectively.
In the embodiment of the application, the first uncertainty information is represented by setting a positive and negative deviation sign, a deviation rate and an uncertainty budget in a pre-constructed uncertainty set, wherein the uncertainty set comprises: a first set of uncertainties for the wind generator and photovoltaic, and a second set of uncertainties for the electrical and thermal loads.
In particular, as shown in fig. 3, 4, 5 and 6, the result graphs are optimized for robust scheduling uncertainty for photovoltaic, wind power, electrical and thermal loads, respectively. The uncertainty of renewable energy output and user load in the integrated energy park can be expressed as an uncertainty set:
；
；
Wherein, Can be referred to as wind generators and photovoltaics,/>May be referred to as electricity and heat; /(I)Representing a first set of uncertainties corresponding to wind turbines and photovoltaics,/>Representing a second set of uncertainties corresponding to the electrical and thermal loads,/>Representing the active output of a wind power generator or photovoltaic,/>Representing electrical or thermal load,/>And/>Is the deviation ratio,/>And/>Representing positive and negative deviations,/>And/>Representing the uncertainty budget of renewable energy output and load. Within the defined set of uncertainties, the first constraint describes the relationship between the actual and predicted values, the second constraint ensures that positive and negative deviations do not occur simultaneously, and the third constraint is to adjust the uncertainty budget of model conservation.
As shown in fig. 1, the method for solving the optimal pricing of the integrated energy park according to the embodiment further includes:
and step 200, constructing a first energy management model corresponding to the comprehensive energy park according to preset intra-park constraint conditions, the first cost function and the first uncertainty information.
As shown in fig. 7 and 8, the scheduling optimization result graphs for the electric power and the thermal power of the campus, respectively. Specifically, the invention constructs the first energy management model corresponding to the comprehensive energy park by using a robust optimization method, wherein the first energy management model comprises a cost function, an operation constraint condition and a price constraint condition.
In an embodiment of the present application, the on-campus constraints include: in-park operation constraints and in-park price constraints; the on-campus operation constraints include: trade constraint with main network electric energy, cogeneration unit and gas turbine boiler operation constraint, energy storage device operation constraint, park active power balance constraint and park thermal power balance constraint; the on-campus price constraint includes: price upper and lower limit constraints and average price constraints.
Specifically, the comprehensive energy park operation constraints include:
And the constraint of electric energy transaction with the main network, namely, the constraint of upper and lower limits of electricity purchasing and selling in the comprehensive energy park:
，
，
In the method, in the process of the invention, Representation/>Electric power is purchased from main power grid in moment park,/>Representation/>Electric power is sold to a main power grid by a moment park,/>And/>Maximum power purchased and sold to the main power grid in the park respectively; /(I)The indicator variable for the trading of the park and the main power grid is 0-1 variable.
Cogeneration unit and gas turbine boiler operation constraints:
，
，
，
，
，
In the method, in the process of the invention, 、/>Respectively the cogeneration unit is at/>Power generation and heat generation at any time,/>Indicating that the cogeneration unit is in/>Power generation at moment,/>For the gas boiler at/>Heat power generated at any time,/>、/>Respectively, the electricity and heat conversion efficiency of the cogeneration unit,/>Is the heat conversion efficiency of the gas boiler,/>、/>Energy conversion coefficients of cogeneration unit and gas boiler respectively,/>、/>Respectively a cogeneration unit and a gas boilerThe natural gas consumption amount is changed at any moment,、/>The maximum power of the cogeneration unit and the maximum power of the gas boiler are respectively.
Energy storage device operation constraints in the integrated energy park:
，
，
，
，
，
，
In the method, in the process of the invention, ，/>Indicating the variable of charge and discharge of the energy storage device to be 0-1 variable; /(I)And/>Respectively express/>Charging and discharging power of moment energy storage device,/>、/>Respectively representing the maximum charging power and the maximum discharging power of the energy storage device,/>And/>Respectively the minimum and maximum capacities of the energy storage device,/>、/>Respectively charging and discharging efficiency of the energy storage device; /(I)For the energy storage deviceTime capacity; /(I)、/>The start and end times of the scheduling period, respectively.
Comprehensive energy park active power balance constraint:
；
In the method, in the process of the invention, Representation/>Electric power is purchased from main power grid in moment park,/>Representation/>Electric power is sold to a main power grid by a moment park,/>Generating electric power for the cogeneration unit; /(I)、/>The output power of the photovoltaic and wind driven generators is respectively; /(I)Representation/>Discharge power of time energy storage device,/>Representing sales of hydrogen station to park,/>For electric load,/>Representation/>Charging power of time energy storage device,/>Representing the amount of electricity purchased from the hydrogen station to the campus.
Comprehensive energy park thermal power balance constraint:
；
In the method, in the process of the invention, For the heat generation power of the gas boiler,/>For heat power generation of cogeneration unit,/>Is a thermal load.
The price constraint conditions in the comprehensive energy park comprise:
average price constraint on electricity prices set by the hydrogenation station prevents direct arbitrage of the comprehensive energy park:
；
In the method, in the process of the invention, Expressed at/>Trade electricity price of time hydrogenation station and park,/>Representing the buying power of the trading of the park and the main network.
Upper and lower limit constraints on electricity prices set by the hydro-station:
；
representing minimum value of electricity price of hydrogen station,/> Indicating the maximum value of the electricity price of the hydrogen station.
The first energy management model is a two-stage robust optimization model taking the first cost function as an objective function and taking the constraint conditions in the campus and the first uncertainty information as constraints, and specifically comprises the following steps:
；
In the method, in the process of the invention, Representing the first stage decision variables,/>Representing scene decision variables,/>Represents a second stage decision variable, Y represents a first stage decision variable set, X represents a second stage decision variable set,/>Representing a set of scene decision variables,，/>，/>，/>Respectively represent and describe a photovoltaic output uncertainty set, a fan output uncertainty set, an electric load uncertainty set and a thermodynamic load uncertainty set,/>，/>Respectively representing the active output of the photovoltaic and the fan.
As shown in fig. 1, the method for solving the optimal pricing of the integrated energy park according to the embodiment further includes:
And step S300, constructing a second cost function of the hydrogen adding station in the comprehensive energy park, and determining second uncertainty information of the renewable energy output, load and hydrogen storage tank in the hydrogen adding station.
The invention considers the uncertainty of the output, load and hydrogen storage tank of renewable energy sources in the hydrogen station through the distributed robust opportunity constraint based on data driving.
In an embodiment of the present application, the second cost function includes a hydrogen station buying cost and a selling benefit. As shown in fig. 9, fig. 9 is a graph of electricity price of the docking station and power results of docking station and integrated energy park interactions.
Specifically, the second cost function corresponding to the hydrogen addition station is:
。
The second uncertainty information is represented by a fuzzy set based on Wasserstein distance and moment information; the fuzzy set includes: a first opportunity constraint for describing power balance of the photovoltaic device and a second opportunity constraint for simulating a range of variation of hydrogen levels in the hydrogen storage tank, the first opportunity constraint and the second opportunity constraint being both distributed robust opportunity constraints.
Specifically, the uncertainty of the renewable energy output, load and hydrogen storage tank in the hydrogenation station is represented by fuzzy sets based on Wasserstein distance and moment information, and then represented by opportunity constraint:
first, there is uncertainty in the hydrogen fuel cell car load of the hydrogen station in daily operation, thus introducing random variables To simulate the uncertainty of hydrogen loading:
，
In the method, in the process of the invention, 、/>Representing the actual and predicted hydrogen fuel cell vehicle loads, respectively.
And fuzzy setCorresponding random variable/>As shown below, it is constructed based on moment information:
，
In the method, in the process of the invention, Is/>Covariance matrix,/>Representing probability measures for defining hydrogen fuel cell vehicle load distribution,/>Representing a reference probability measure,/>Representing real number set/>/>Sub-Cartesian product, where/>Representing the number of time steps,/>Representing a set of times,/>Expressed in probability measure/>The desired value below, T, represents the transpose.
Second, the hydrogen level in the hydrogen storage tankAlso depend on/>Expressed as/>. To ensure stable operation of the hydrogen station, the LOH of the hydrogen storage tanks should vary within reasonable limits. Considering the randomness of LOH, we use the opportunistic constraints to model the LOH variation range:
，
In the method, in the process of the invention, Is an ideal value of LOH,/>Is the confidence level.
Finally, the hydrogen adding station contains a photovoltaic device, and in order to cope with the random characteristic of the photovoltaic output, the power balance of the model is described by adopting opportunistic constraint:
，
In the method, in the process of the invention, Is the number of samples of the photovoltaic output,/>Representing risk of out-of-limit,/>Indicating the aggregate active power of the power plant,Representation/>Predicted values of photovoltaic output in the hydrogen station at the moment. This constraint ensures that the worst case probability distribution/>, is found in the Wasserstein sphereIn, the photovoltaic output will be at least/>The probability of (1) meeting or exceeding the aggregate active power/>。
The intrabay constraints include: active power balance constraint in the hydrogen station, electrolyzer device constraint, fuel cell constraint, energy storage device operation constraint in the hydrogen station and hydrogen storage tank device operation constraint.
Specifically, the hydrogen station operating constraints include:
Active power balance constraint within the hydrogen station:
，
In the method, in the process of the invention, Is the active power consumed by the water electrolysis device,/>Is the active power produced by the fuel cell.
Water electrolysis apparatus and fuel cell operation constraints within the hydrogen station:
，
，
，
In the method, in the process of the invention, And/>Representing the energy conversion efficiency of the water electrolysis device and the fuel cell respectively,/>AndRespectively representing the energy conversion coefficients of the water electrolysis device and the fuel cell; /(I)， />Respectively expressed in time/>In, the water electrolysis device generates hydrogen amount consumed by the fuel cell; /(I)Can refer to the collection of all devices (electrolyzers and fuel cells) in HESS,/>And/>Representing the upper and lower limits of active power of different devices, respectively.
Energy storage device operating constraints within the hydro-station:
；
In the middle of ，/>For the charging and discharging power of the energy storage device in the hydrogen station,/>，Maximum charging and discharging power for the energy storage device in the hydrogen station; /(I)For the energy storage device in the hydrogen adding stationCapacity of time of day,/>，/>Minimum and maximum capacities for the energy storage devices in the hydro-station; /(I)，The charging and discharging efficiency of the energy storage device in the hydrogen adding station is improved; /(I)，/>For scheduling the start and end times of the cycle. It should be noted that the non-linearisation of the first operating constraint of the energy storage device here may lead to an optimization problem that is difficult to solve, but from existing studies it can prove that this constraint is redundant in the optimization model herein, so that in actual operation it may be omitted.
The hydrogen storage tank constraints within the hydrogen station include:
hydrogen level balance constraint of hydrogen storage tank:
，
Represents the energy conversion efficiency of the hydrogen storage tank;
hydrogen storage tank hydrogen storage level limit constraints:
，
the hydrogen in the hydrogen storage tank at the end of the schedule is specified to be not less than the initial phase constraint:
，
And (3) ending the high-efficiency operation of the hydrogen storage tank:
，
wherein by setting the average value of the LOH of the hydrogen storage tank equal to its ideal value, efficient operation of the hydrogen storage tank has been achieved.
Wherein the hydrogen level in the hydrogen storage tank is determined byA representation; /(I)And/>Is the minimum and maximum value of LOH.
As shown in fig. 1, the method for solving the optimal pricing of the integrated energy park according to the embodiment further includes:
And step 400, constructing a second energy management model corresponding to the hydrogen adding station according to preset constraint conditions in the hydrogen adding station, the second cost function and the second uncertainty information.
The invention builds a second energy management model corresponding to the hydrogenation station, comprising a cost function and an operation constraint condition thereof, further converts the distribution robust opportunity constraint into a series of determination constraints, and builds the second energy management model corresponding to the hydrogenation station into a deterministic robust optimization model.
In the embodiment of the present application, the step S400 specifically includes:
Converting the first opportunity constraint and the second opportunity constraint into a deterministic constraint group;
and constructing a deterministic robust optimization model by taking the second cost function as an objective function and taking the intra-hydrogen adding station constraint condition and the deterministic constraint group as constraints to obtain a second energy management model corresponding to the hydrogen adding station.
The distribution robust opportunity constraint conversion of the embodiment of the application converts the distribution robust opportunity constraint into a definite constraint through a series of mathematical theories and propositions, thereby converting the distribution robust optimization model into a deterministic robust optimization model.
Specifically, the distributed robust opportunity constraints in the energy management model of the hydrogen plant may be converted into a series of deterministic constraints, which in turn are modeled as a deterministic robust optimization model:
first, the distribution robustness opportunity of photovoltaic output is constrained as:
，
May be rewritten as the following deterministic constraint set:
；
Wherein: ，/>，/>，/>，/> are all auxiliary variables; /(I) Wasserstein sphere radius representing uncertainty of photovoltaic output distribution, and the/>, of photovoltaic output in hydrogenation stationIndividual history samples are defined by/>Representation of/>Is that it is in the time period/>Is an active output of (a).
Second, define LOH for a hydrogen storage tank at nominal hydrogen load asThe description is as follows:
；
The hydrogen storage tank operation constraint is:
；
By making the following The method can obtain:
；
Then, based on the constraint
And constraint/>，
The method can be obtained by recursion:
；
next, the distributed robust opportunity constraint of hydrogen level randomness of the hydrogen storage tank is described:
，
Can be reconstructed into the following forms:
，
efficient operation constraint of hydrogen storage tank Can be reconstructed into the following forms:
，
In the above formula, the water content of the water-soluble polymer, Is Hadamard product,/>. Let/>Is a vector, i.e. >Wherein the former paragraph is composed of/>Personal/>Composed of, the latter section is composed of/>Zero composition, in,/>Representation/>Personal/>Continuous multiplication,/>Representation/>Personal/>The continuous taking of the products is carried out,Representation/>Personal/>Continuous multiplication,/>Representation/>Hydrogen amount generated by the water electrolysis device during water electrolysisRepresentation ofHydrogen amount consumed by fuel cell at the time,/>Representation/>Time predicted hydrogen fuel cell car load,/>Representation ofRandom variables that are used to simulate hydrogen fuel cell vehicle load uncertainty.
Finally, the distributed robust opportunity constraint of the converted hydrogen storage tank:
；
Can be further equivalently transformed by the following second order cone constraints:
；
In the method, in the process of the invention, And/>Is an auxiliary variable. Thus, the distributed robust optimization model of the hydrogen adding station can be equivalently converted into a deterministic model. The compact form is as follows:
；
In the method, in the process of the invention, 、/>、/>Is a constant matrix,/>、/>Is a constant vector. The dual variables are respectively represented by/>And/>And (3) representing.
As shown in fig. 1, the method for solving the optimal pricing of the integrated energy park according to the embodiment further includes:
And S500, constructing an optimal pricing strategy model according to the first energy management model and the second energy management model, and solving by utilizing a column and constraint generation algorithm to obtain an energy scheduling result of the comprehensive energy park and a pricing result of the comprehensive energy park for the hydrogenation station.
Specifically, the invention constructs the optimal pricing strategy model for the comprehensive energy park containing the hydrogen station, converts the model into a classical two-stage robust optimization model through strict mathematical derivation, and solves the model by using a column and constraint generation algorithm, wherein the convergence result of the column and constraint generation algorithm is shown in figure 10.
In the embodiment of the present application, the step S500 specifically includes:
taking the first energy management model as an upper-layer optimization model, taking the second energy management model as a lower-layer optimization model, and constructing a double-layer model based on a master-slave game framework;
Converting a lower optimization model into constraint through KKT conditions, and eliminating bilinear terms in an objective function of an upper optimization model through strong dual theory so as to convert the double-layer model into a two-stage robust optimization model, thereby obtaining an optimal pricing strategy model;
and solving by using a column and constraint generation algorithm to obtain an energy scheduling result of the comprehensive energy park and a pricing result of the comprehensive energy park for the hydrogen station.
The upper layer of the double-layer model aims at minimizing the total cost of the comprehensive energy park, the lower layer of the double-layer model aims at maximizing the return of the hydrogen adding station, and the transaction electric quantity of the hydrogen adding station and the comprehensive energy park in the upper layer of the double-layer model is obtained by the lower layer of the optimization model.
According to the embodiment of the application, the lower-layer optimization model is converted into a series of constraints through KKT (Karush-Kuhn-Tucker) conditions, bilinear terms in the upper-layer objective function are eliminated through strong dual theory, and finally the proposed double-layer model can be converted into a classical two-stage robust optimization model. The column and constraint generation algorithm is a precise algorithm for a traditional two-stage robust optimization model. Specifically, the algorithm decomposes the two-stage robust optimization model into a main problem and a sub-problem to be processed, and continuously solves the main problem and the reconstructed sub-problem in the iterative process to realize the quick solution of the optimization model.
The optimal pricing strategy model is:
；
The framework adopts a double-layer structure, specifically, the upper-layer problem is a two-stage robust optimization problem, and the lower-layer problem is a deterministic equivalent robust optimization problem converted from a distributed robust optimization problem.
The method converts the model equivalent into a classical two-stage robust optimization model through strict mathematical derivation, specifically, in view of the fact that the energy management model of the hydrogen addition station is a convex model, the model has strong dual properties, and the equivalent can be expressed as follows based on KKT conditions:
；
Specifically, in the KKT condition described above, the first line represents the original feasibility constraint; the second row describes stability constraints; the third row represents the complementary relaxation condition, which can be linearized using the large M method; the fourth behavior pairs a feasibility constraint.
Since there are bilinear terms in the objective function, based on strong dual theory, there can be equivalent conversions as follows:
；
Wherein, Representing a second cost function corresponding to the hydrogen addition station.
Thus, the optimal pricing model can be restated as a two-stage robust optimization model described below:
；
this problem can be written in the general form:
；
Wherein the method comprises the steps of For the first stage decision variables,/>Decision variables for uncertain scenes,/>As a second stage decision variable,，/>，/>，/>Respectively is constraint coefficient matrix,/>、/>、/>And/>Is a constant column vector.
When the column and constraint generation algorithm is utilized for carrying out efficient solving, the specific steps of the algorithm are as follows:
step 1: initializing, setting a lower bound LB as negative infinity, setting an upper bound UB as positive infinity, and iterating the times Set O is an empty set.
Step2: solving a main problem MP;
；
step 2.1: obtaining MP optimal solution ；/>
Step 2.2: updating lower bounds；
Step3: solving a sub-problem SP;
；
step 3.1: obtaining SP optimal solution ；
Step 3.2: updating upper bound；
Step4: judging whether a convergence condition is satisfied:；
step 4.1: if not, add a new variable The following constraints;
；
；
In the main question MP, ，/>Turning to step 2;
Step 4.2: if so, the algorithm ends.
In an embodiment, as shown in fig. 11, based on the above-mentioned method for solving the optimal pricing of the integrated energy park, the present invention further provides a device for solving the optimal pricing of the integrated energy park, including:
a first determining module 100, configured to construct a first cost function of the integrated energy farm, and determine first uncertainty information of renewable energy output and user load in the integrated energy farm;
The first construction module 200 is configured to construct a first energy management model corresponding to the comprehensive energy park according to a preset intra-park constraint condition, the first cost function and the first uncertainty information;
a second determining module 300, configured to construct a second cost function of the hydrogen adding station in the integrated energy park, and determine second uncertainty information of the renewable energy output, the load and the hydrogen storage tank in the hydrogen adding station;
a second construction module 400, configured to construct a second energy management model corresponding to the hydrogen adding station according to a preset constraint condition in the hydrogen adding station, the second cost function and the second uncertainty information;
And the solving module 500 is configured to construct an optimal pricing strategy model according to the first energy management model and the second energy management model, and solve the problems by using a column and constraint generating algorithm to obtain an energy scheduling result of the comprehensive energy park and a pricing result of the comprehensive energy park for the hydrogenation station.
Fig. 12 is a schematic structural diagram of a terminal according to an embodiment of the present application. The terminal may include:
memory 501, processor 502, and a computer program stored on memory 501 and executable on processor 502.
The processor 502, when executing the program, implements the comprehensive energy park optimal pricing solving method provided in the above embodiments.
Further, the terminal further includes:
A communication interface 503 for communication in the memory 501 and the processor 502.
Memory 501 for storing a computer program executable on processor 502.
The memory 501 may include high-speed RAM memory and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the integrated energy park optimal pricing solving method as above.
In summary, the method, the device, the terminal and the storage medium for solving the optimal pricing of the comprehensive energy park disclosed by the invention comprise the following steps: constructing a first cost function of the comprehensive energy park, and determining first uncertainty information of renewable energy output and user load in the comprehensive energy park; constructing a first energy management model corresponding to the comprehensive energy park according to preset constraint conditions in the park, the first cost function and the first uncertainty information; constructing a second cost function of the hydrogen adding station in the comprehensive energy park, and determining second uncertainty information of the output and load of renewable energy sources and the hydrogen storage tank in the hydrogen adding station; constructing a second energy management model corresponding to the hydrogen adding station according to preset constraint conditions in the hydrogen adding station, the second cost function and the second uncertainty information; and constructing an optimal pricing strategy model according to the first energy management model and the second energy management model, and solving by utilizing a column and constraint generation algorithm to obtain an energy scheduling result of the comprehensive energy park and a pricing result of the comprehensive energy park for the hydrogen station. According to the invention, by considering the cooperative optimization between the comprehensive energy park and the hydrogenation station and considering various uncertainties of renewable energy fluctuation and load demand change faced by the comprehensive energy park and the hydrogenation station in the operation process, a reasonable pricing mechanism is realized, and the benefit distribution problem among multiple main operators is solved.
It is to be understood that the invention is not limited in its application to the examples described above, but is capable of modification and variation in light of the above teachings by those skilled in the art, and that all such modifications and variations are intended to be included within the scope of the appended claims.
Claims (10)
1. An integrated energy park optimal pricing solving method, the method comprising:
Constructing a first cost function of the comprehensive energy park, and determining first uncertainty information of renewable energy output and user load in the comprehensive energy park;
constructing a first energy management model corresponding to the comprehensive energy park according to preset constraint conditions in the park, the first cost function and the first uncertainty information;
Constructing a second cost function of the hydrogen adding station in the comprehensive energy park, and determining second uncertainty information of the output and load of renewable energy sources and the hydrogen storage tank in the hydrogen adding station;
constructing a second energy management model corresponding to the hydrogen adding station according to preset constraint conditions in the hydrogen adding station, the second cost function and the second uncertainty information;
and constructing an optimal pricing strategy model according to the first energy management model and the second energy management model, and solving by utilizing a column and constraint generation algorithm to obtain an energy scheduling result of the comprehensive energy park and a pricing result of the comprehensive energy park for the hydrogen station.
2. The method of claim 1, wherein the first cost function comprises: the electric energy transaction cost of the park and the main network, the operation cost of the park and the transaction income of the park and the hydrogenation station; the park operation cost comprises the natural gas use cost of the cogeneration unit and the gas boiler and the degradation cost of the energy storage equipment;
the electric energy transaction cost of the park and the main network is represented by the buying and selling electric quantity and the electric quantity price of the park and the main network; the park operation cost is represented by the gas price, the natural gas amount consumed by the cogeneration unit and the gas boiler, the degradation coefficient of the energy storage device, the charging power of the energy storage device and the discharging power of the energy storage device; the park and the docking station trade benefits are represented by the amount of electricity purchased from the docking station to the park, the amount of electricity sold from the docking station to the park, the electricity purchase price from the docking station and the electricity sale price from the docking station.
3. The integrated energy park optimal pricing solving method of claim 1, wherein the first uncertainty information is represented by setting a positive and negative bias flag, bias rate, uncertainty budget within a pre-constructed set of uncertainties comprising: a first set of uncertainties for the wind generator and photovoltaic, and a second set of uncertainties for the electrical and thermal loads.
4. The integrated energy farm optimal pricing solving method of claim 1, wherein the on-farm constraints comprise: in-park operation constraints and in-park price constraints; the on-campus operation constraints include: trade constraint with main network electric energy, cogeneration unit and gas turbine boiler operation constraint, energy storage device operation constraint, park active power balance constraint and park thermal power balance constraint; the on-campus price constraint includes: price upper and lower limit constraints and average price constraints;
The first energy management model is a two-stage robust optimization model constrained by the first cost function as an objective function, and by the on-campus constraint conditions and the first uncertainty information.
5. The integrated energy park optimal pricing solving method of claim 1, wherein the second cost function comprises a hydrogen station buying cost and a selling benefit;
The second uncertainty information is represented by a fuzzy set based on Wasserstein distance and moment information; the fuzzy set includes: a first opportunity constraint for describing power balance of the photovoltaic device and a second opportunity constraint for simulating a hydrogen level variation range in the hydrogen storage tank, wherein the first opportunity constraint and the second opportunity constraint are both distributed robust opportunity constraints;
The intrabay constraints include: active power balance constraint in the hydrogen station, electrolyzer device constraint, fuel cell constraint, energy storage device operation constraint in the hydrogen station and hydrogen storage tank device operation constraint.
6. The method for optimal pricing and solving of an integrated energy park according to claim 5, wherein the constructing a second energy management model corresponding to the hydrogen addition station according to the preset constraints in the hydrogen addition station, the second cost function and the second uncertainty information comprises:
Converting the first opportunity constraint and the second opportunity constraint into a deterministic constraint group;
and constructing a deterministic robust optimization model by taking the second cost function as an objective function and taking the intra-hydrogen adding station constraint condition and the deterministic constraint group as constraints to obtain a second energy management model corresponding to the hydrogen adding station.
7. The integrated energy park optimal pricing solving method according to claim 1, wherein constructing an optimal pricing strategy model according to the first energy management model and the second energy management model, and solving by using a column and constraint generation algorithm to obtain an integrated energy park energy scheduling result and an integrated energy park pricing result for a hydrogenation station, comprises:
taking the first energy management model as an upper-layer optimization model, taking the second energy management model as a lower-layer optimization model, and constructing a double-layer model based on a master-slave game framework;
Converting a lower optimization model into constraint through KKT conditions, and eliminating bilinear terms in an objective function of an upper optimization model through strong dual theory so as to convert the double-layer model into a two-stage robust optimization model, thereby obtaining an optimal pricing strategy model;
Solving by using a column and constraint generation algorithm to obtain an energy scheduling result of the comprehensive energy park and a pricing result of the comprehensive energy park for the hydrogen station;
The upper layer of the double-layer model aims at minimizing the total cost of the comprehensive energy park, the lower layer of the double-layer model aims at maximizing the return of the hydrogen adding station, and the transaction electric quantity of the hydrogen adding station and the comprehensive energy park in the upper layer of the double-layer model is obtained by the lower layer of the optimization model.
8. An integrated energy park optimal pricing solving means, the means comprising:
The first determining module is used for constructing a first cost function of the comprehensive energy park and determining first uncertainty information of renewable energy output and user load in the comprehensive energy park;
The first construction module is used for constructing a first energy management model corresponding to the comprehensive energy park according to preset constraint conditions in the park, the first cost function and the first uncertainty information;
the second determining module is used for constructing a second cost function of the hydrogen adding station in the comprehensive energy park and determining second uncertainty information of the output, load and hydrogen storage tank of renewable energy sources in the hydrogen adding station;
The second construction module is used for constructing a second energy management model corresponding to the hydrogenation station according to preset constraint conditions in the hydrogenation station, the second cost function and the second uncertainty information;
And the solving module is used for constructing an optimal pricing strategy model according to the first energy management model and the second energy management model, and solving by utilizing a column and constraint generation algorithm to obtain an energy scheduling result of the comprehensive energy park and a pricing result of the comprehensive energy park for the hydrogenation station.
9. A terminal, comprising: the system comprises a memory, a processor and a comprehensive energy park optimal pricing solver stored on the memory and capable of running on the processor, wherein the comprehensive energy park optimal pricing solver realizes the steps of the comprehensive energy park optimal pricing solver method according to any one of claims 1-7 when the comprehensive energy park optimal pricing solver is executed by the processor.
10. A computer readable storage medium storing a computer program executable to implement the steps of the integrated energy park optimal pricing solving method of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410495628.7A CN118096225A (en) | 2024-04-24 | 2024-04-24 | Method, device, terminal and storage medium for solving optimal pricing of comprehensive energy park |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410495628.7A CN118096225A (en) | 2024-04-24 | 2024-04-24 | Method, device, terminal and storage medium for solving optimal pricing of comprehensive energy park |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118096225A true CN118096225A (en) | 2024-05-28 |
Family
ID=91142493
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410495628.7A Pending CN118096225A (en) | 2024-04-24 | 2024-04-24 | Method, device, terminal and storage medium for solving optimal pricing of comprehensive energy park |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118096225A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114696362A (en) * | 2022-04-22 | 2022-07-01 | 国网浙江省电力有限公司金华供电公司 | Power distribution network operation optimization method containing hydrogen production-storage-hydrogenation station |
| WO2023286500A1 (en) * | 2021-07-13 | 2023-01-19 | 矢崎総業株式会社 | Hydrogen station, control unit for hydrogen station, and program for hydrogen station |
| WO2023178715A1 (en) * | 2022-03-22 | 2023-09-28 | 东南大学 | Planning method for electricity-heat-hydrogen multi-energy flow energy supply device in net-zero energy consumption building |
| CN117649089A (en) * | 2023-12-14 | 2024-03-05 | 国家电网有限公司华北分部 | Robust low-carbon optimal scheduling method for integrated energy system integrating hydrogen energy |
-
2024
- 2024-04-24 CN CN202410495628.7A patent/CN118096225A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023286500A1 (en) * | 2021-07-13 | 2023-01-19 | 矢崎総業株式会社 | Hydrogen station, control unit for hydrogen station, and program for hydrogen station |
| WO2023178715A1 (en) * | 2022-03-22 | 2023-09-28 | 东南大学 | Planning method for electricity-heat-hydrogen multi-energy flow energy supply device in net-zero energy consumption building |
| CN114696362A (en) * | 2022-04-22 | 2022-07-01 | 国网浙江省电力有限公司金华供电公司 | Power distribution network operation optimization method containing hydrogen production-storage-hydrogenation station |
| CN117649089A (en) * | 2023-12-14 | 2024-03-05 | 国家电网有限公司华北分部 | Robust low-carbon optimal scheduling method for integrated energy system integrating hydrogen energy |
Non-Patent Citations (4)
| Title |
|---|
| "计及氢能多环节利用和混合需求响应的综合能源***多时间尺度低碳优化", 供用电, vol. 41, no. 2, 29 February 2024 (2024-02-29), pages 52 - 63 * |
| QI LI 等: "Stability-constrained Two-stage Robust Optimization for Integrated Hydrogen Hybrid Energy System", CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, vol. 7, no. 1, 31 January 2021 (2021-01-31), pages 162 - 170 * |
| YIBIN QIU 等: "Two‑stage distributionally robust optimization‑based coordinated scheduling of integrated energy system with electricity‑hydrogen hybrid energy storage", PROTECTION AND CONTROL OF MODERN POWER SYSTEMS, 11 July 2023 (2023-07-11), pages 1 - 14 * |
| 斯琴卓娅 等: "考虑风光出力不确定性的含电制氢装置的电-气耦合配网***经济运行分析", 技术经济, vol. 41, no. 11, 30 November 2022 (2022-11-30), pages 24 - 39 * |
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 | |
| Ramli et al. | Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm | |
| Chen et al. | A robust optimization framework for energy management of CCHP users with integrated demand response in electricity market | |
| Yang et al. | Risk-constrained stochastic scheduling for energy hub: Integrating renewables, demand response, and electric vehicles | |
| Bidgoli et al. | Multi-stage optimal scheduling of multi-microgrids using deep-learning artificial neural network and cooperative game approach | |
| Wang et al. | Operation strategy of a hybrid solar and biomass power plant in the electricity markets | |
| CN106372742A (en) | Power-to-gas multi-source energy storage type microgrid day-ahead optimal economic dispatching method | |
| Li et al. | Hierarchical optimal scheduling method for regional integrated energy systems considering electricity-hydrogen shared energy | |
| CN112821465B (en) | Industrial microgrid load optimization scheduling method and system containing cogeneration | |
| CN115271467A (en) | Virtual power plant scheduling optimization method considering electric carbon collaborative optimization and application | |
| Shang et al. | Optimal configuration of hybrid energy systems considering power to hydrogen and electricity-price prediction: A two-stage multi-objective bi-level framework | |
| CN115099725B (en) | Energy management and distribution method and system for comprehensive energy system | |
| Qin et al. | Robust optimal dispatching of integrated electricity and gas system considering refined power-to-gas model under the dual carbon target | |
| CN111967639A (en) | Power distribution network optimization method and system considering wind-solar power output and price demand response | |
| Basu | Heat and power generation augmentation planning of isolated microgrid | |
| CN114881296A (en) | Comprehensive energy system peak clipping and valley filling scheduling strategy based on paid distribution | |
| CN114493688A (en) | Electric-gas coupling virtual power plant scheduling method and device and electronic equipment | |
| Feng et al. | Risk management of energy communities with hydrogen production and storage technologies | |
| Yang et al. | A multi-objective dispatching model for a novel virtual power plant considering combined heat and power units, carbon recycling utilization, and flexible load response | |
| CN112633675A (en) | Energy scheduling method, device and equipment and computer readable storage medium | |
| Hu et al. | Distributed multi-energy trading for energy Internet: An aggregative game approach | |
| Gu et al. | Optimal economic dispatch for an industrial park with consideration of an elastic energy cloud model with integrated demand response uncertainty | |
| Zeynali et al. | Distributionally robust unit commitment in integrated multi-energy systems with coordinated electric vehicle fleets | |
| CN112865101B (en) | Linear transaction method considering uncertainty of output of renewable energy | |
| CN118096225A (en) | Method, device, terminal and storage medium for solving optimal pricing of comprehensive energy park |
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 |