CN113780781A - Capacity expansion equipment selection method and device of comprehensive energy system and terminal - Google Patents
Capacity expansion equipment selection method and device of comprehensive energy system and terminal Download PDFInfo
- Publication number
- CN113780781A CN113780781A CN202111014896.5A CN202111014896A CN113780781A CN 113780781 A CN113780781 A CN 113780781A CN 202111014896 A CN202111014896 A CN 202111014896A CN 113780781 A CN113780781 A CN 113780781A
- Authority
- CN
- China
- Prior art keywords
- energy system
- capacity expansion
- equipment
- capacity
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000010187 selection method Methods 0.000 title claims description 8
- 238000000034 method Methods 0.000 claims abstract description 35
- 238000012216 screening Methods 0.000 claims abstract description 8
- 230000006870 function Effects 0.000 claims description 27
- 238000004590 computer program Methods 0.000 claims description 22
- 238000012545 processing Methods 0.000 claims description 22
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 16
- 230000009466 transformation Effects 0.000 claims description 16
- 238000010276 construction Methods 0.000 claims description 11
- 239000003345 natural gas Substances 0.000 claims description 8
- 238000012423 maintenance Methods 0.000 claims description 6
- 230000004044 response Effects 0.000 claims description 5
- 238000010248 power generation Methods 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 7
- 239000011159 matrix material Substances 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 5
- 230000008878 coupling Effects 0.000 description 5
- 238000010168 coupling process Methods 0.000 description 5
- 238000005859 coupling reaction Methods 0.000 description 5
- 239000013598 vector Substances 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 230000005611 electricity Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 239000002245 particle Substances 0.000 description 4
- 238000009434 installation Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000006735 deficit Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 239000002737 fuel gas Substances 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 238000005293 physical law Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000009420 retrofitting Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention is suitable for the technical field of comprehensive energy systems, and provides a method, a device and a terminal for selecting expansion equipment of a comprehensive energy system, wherein the method comprises the following steps: acquiring scene characteristic factor data of an area where a target comprehensive energy system is located; based on scene characteristic factor data, screening similar areas from a preset instance library, and extracting equipment type combinations of the comprehensive energy system corresponding to the similar areas; determining the type of the capacity expansion equipment according to the equipment type combination; and determining the capacity of the capacity expansion equipment which enables the total cost of the target comprehensive energy system after capacity expansion to be the lowest in the whole life cycle, and obtaining the capacity expansion equipment selection result of the target comprehensive energy system. The invention can select proper capacity expansion equipment aiming at the comprehensive energy systems in different areas, reduce the overall cost of the comprehensive energy system and ensure the stable operation of the comprehensive energy system.
Description
Technical Field
The invention belongs to the technical field of comprehensive energy systems, and particularly relates to a method, a device and a terminal for selecting expansion equipment of a comprehensive energy system.
Background
The comprehensive energy system is a novel integrated energy system which integrates multiple energy sources such as coal, petroleum, natural gas, electric energy, heat energy and the like in a certain area by utilizing advanced physical information technology and innovative management modes, meets diversified energy utilization requirements and can promote sustainable development of the energy sources.
The inventor of the application finds that for the comprehensive energy system, along with the increase of user load in the system, the electricity purchasing cost of the system is higher and higher, capacity expansion transformation is carried out on the comprehensive energy system, and the addition of renewable energy equipment is a feasible solution. However, whether the expanded integrated energy system can reduce the cost and stably operate on the whole depends on the type selection and the capacity planning of the expansion equipment. Therefore, the selection of the proper type and capacity of the expansion equipment is very important for the expansion and transformation of the comprehensive energy system.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, and a terminal for selecting expansion devices of an integrated energy system, so as to select suitable expansion devices, reduce the overall cost of the integrated energy system, and ensure stable operation of the integrated energy system.
A first aspect of an embodiment of the present invention provides a method for selecting expansion devices of an integrated energy system, including:
acquiring scene characteristic factor data of an area where a target comprehensive energy system is located; the scene characteristic factor data comprises regional resource endowment data, load data and construction data;
based on scene characteristic factor data, screening similar areas from a preset instance library, and extracting equipment type combinations of the comprehensive energy system corresponding to the similar areas;
determining the type of the capacity expansion equipment according to the equipment type combination;
and determining the capacity of the capacity expansion equipment which enables the total cost of the target comprehensive energy system after capacity expansion to be the lowest in the whole life cycle, and obtaining the capacity expansion equipment selection result of the target comprehensive energy system.
A second aspect of the embodiments of the present invention provides a capacity expansion device selection apparatus for an integrated energy system, including:
the acquisition module is used for acquiring scene characteristic factor data of an area where the target comprehensive energy system is located; the scene characteristic factor data comprises regional resource endowment data, load data and construction data;
the first processing module is used for screening similar areas from a preset instance library based on scene characteristic factor data and extracting equipment type combinations of the comprehensive energy system corresponding to the similar areas;
the second processing module is used for determining the type of the capacity expansion equipment according to the equipment type combination;
and the third processing module is used for determining the capacity of the capacity expansion equipment which enables the total cost of the expanded target comprehensive energy system to be the lowest in the whole life cycle, and obtaining the capacity expansion equipment selection result of the target comprehensive energy system.
A third aspect of the embodiments of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and operable on the processor, where the processor implements the steps of the method for selecting a capacity expansion device of an integrated energy system when executing the computer program.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the method for selecting a capacity expansion device of an integrated energy system are implemented.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
according to the method, the scene characteristic factor data of the area where the target comprehensive energy system is located is obtained, the similar area is screened from the preset instance library based on the scene characteristic factor data, the equipment type combination of the comprehensive energy system corresponding to the similar area is extracted, the type of the capacity expansion equipment is determined according to the equipment type combination, the type of the capacity expansion equipment suitable for the area where the target comprehensive energy system is located can be selected, and the stable operation of the system is guaranteed. Further, the capacity of the capacity expansion equipment which enables the total cost of the target integrated energy system after capacity expansion to be the lowest in the whole life cycle is determined, so that the overall cost of the target integrated energy system can be reduced. The invention can select proper capacity expansion equipment aiming at the comprehensive energy systems in different areas, reduce the overall cost of the comprehensive energy system and ensure the stable operation of the comprehensive energy system.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic energy flow diagram of an exemplary integrated energy system provided by an embodiment of the present invention;
fig. 2 is a flowchart of a capacity expansion device selection method of an integrated energy system according to an embodiment of the present invention;
fig. 3 is a detailed flowchart of a method for selecting expansion devices of an integrated energy system according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an expansion device selection apparatus of an integrated energy system according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
The comprehensive energy system is a novel integrated energy system which can be used for comprehensively coordinating links such as energy supply, conversion, transmission, storage and consumption aiming at energy consumption requirements of different types of users according to various types of energy sources which can be obtained in an area, integrally planning and constructing energy supply equipment, a transmission pipe network and the like in the area, effectively improving the energy utilization efficiency and promoting the sustainable development of energy sources when meeting the diversified energy consumption requirements in the system. The integrated energy system has the following characteristics:
(1) the comprehensive energy system can organically coordinate various energy supply and utilization systems;
(2) the comprehensive energy system can use different energy subsystems and realize optimal energy distribution and scheduling by utilizing the interaction function of the energy subsystems;
(3) the comprehensive energy system can realize the optimal utilization of various energy sources;
(4) the comprehensive energy system is beneficial to reducing carbon emission and realizing clean energy transformation.
By studying the coupling characteristics of the integrated energy system (including energy, economy, space-time, stability, etc.), the coupling characteristics of key devices in the integrated energy system, and the transmission characteristics of the integrated energy system (including power system, thermal system, natural gas system), a power flow structure diagram of the integrated energy system can be established, and a typical power flow structure diagram is shown in fig. 2 as an example. The method is based on an energy flow structure diagram of the comprehensive energy system, a comprehensive energy system capacity expansion equipment selection model is constructed according to the thought of 'inner layer constant volume and outer layer selection', the outer layer selection utilizes an example clustering reasoning technology to find an equipment type combination matched with a planning scene from an example library, and the inner layer constant volume calculates the optimal capacity of capacity expansion equipment.
Based on the above conception, an embodiment of the present invention provides a method for selecting expansion devices of an integrated energy system, and as shown in fig. 2, the method may include the following steps:
step S201, acquiring scene characteristic factor data of an area where a target comprehensive energy system is located; the scene characteristic factor data comprises regional resource endowment data, load data and construction data.
In the embodiment of the present invention, the integrated energy system is generally divided into regions according to the campus, the resource endowment data may include data such as average wind speed and average illumination intensity of the campus, the load data may include data such as average electrical load, average thermal load and average cold load of the campus, and the construction data may include data such as population and floor area of the campus.
And S202, screening similar areas from a preset example library based on scene characteristic factor data, and extracting equipment type combinations of the comprehensive energy system corresponding to the similar areas.
Step S203, determining the type of the capacity expansion equipment according to the equipment type combination.
The energy equipment of the comprehensive energy system is various in types, can comprise photovoltaic, energy storage battery, CCHP, heat outlet pipe and the like, the type of the energy equipment is selected according to local conditions, and corresponding energy equipment is selected according to the actual available energy condition of the park where the comprehensive energy system is located.
In the embodiment of the invention, a plurality of garden examples are stored in the preset example library, and the garden examples comprise scene characteristic factor data and equipment type combinations corresponding to the garden. Through the similarity of the scene characteristic factor data, a park similar to the park where the target comprehensive energy system is located can be selected, and then the equipment type combination of the comprehensive energy system corresponding to the similar park is extracted as a reference. And selecting a proper capacity expansion equipment type from the equipment type combination of the comprehensive energy system corresponding to the similar park, so that the stable operation of the system can be ensured. For example, for a sunny park, a photovoltaic device may be selected as the capacity expansion device.
And step S204, determining the capacity of the capacity expansion equipment which enables the total cost of the expanded target comprehensive energy system to be the lowest in the whole life cycle, and obtaining the capacity expansion equipment selection result of the target comprehensive energy system.
In the embodiment of the invention, the capacity of the capacity expansion equipment which enables the total cost of the target integrated energy system after capacity expansion to be the lowest in the whole life cycle is calculated, so that the overall cost of the integrated energy system can be reduced.
According to the method, the scene characteristic factor data of the area where the target comprehensive energy system is located is obtained, the similar area is screened from the preset instance library based on the scene characteristic factor data, the equipment type combination of the comprehensive energy system corresponding to the similar area is extracted, the type of the capacity expansion equipment is determined according to the equipment type combination, the type of the capacity expansion equipment suitable for the area where the target comprehensive energy system is located can be selected, and the stable operation of the system is guaranteed. Further, the capacity of the capacity expansion equipment which enables the total cost of the target integrated energy system after capacity expansion to be the lowest in the whole life cycle is determined, so that the overall cost of the target integrated energy system can be reduced. The invention can select proper capacity expansion equipment aiming at the comprehensive energy systems in different areas, reduce the overall cost of the comprehensive energy system and ensure the stable operation of the comprehensive energy system.
Optionally, as a possible implementation manner, the preset instance library includes scene feature factor data and device type combinations corresponding to multiple instance areas; based on the scene characteristic factor data, similar areas are screened from a preset instance library, which can be detailed as follows:
calculating the similarity between the scene characteristic factor data and the scene characteristic factor data corresponding to each instance region in a preset instance library based on a preset instance clustering reasoning model;
and selecting the example area with the corresponding similarity smaller than a preset threshold value as the similar area.
In embodiments of the present invention, the example clustering inference model may be implemented based on PNN neural network technology. The PNN neural network clustering technology is an artificial neural network algorithm with simple structure and concise training, and is a practical example reasoning method in complex relationships. The essence of the method is a parallel classification algorithm based on Bayesian minimum risk criterion, and the method has strong nonlinear classification function. The model of the PNN probabilistic neural network structure consists of an input layer, a mode layer, a summation layer and an output layer. The PNN probabilistic neural network takes a characteristic factor standardization matrix and a device type coding matrix in an integrated energy system instance library as input and outputs a device type coding set in a planning scene.
The PNN probabilistic neural network example clustering reasoning process is as follows:
(1) an input layer is determined. And (3) transferring the feature vectors of the example areas to the PNN network, namely a scene feature factor standardization matrix and a device type coding matrix in the n groups of example areas.
(2) The hidden layer is computed. Taking the feature vector of the region where the target integrated energy system is located as a sample, calculating the matching relation between the sample and the feature vector of each example region, and outputting the layer as follows:
in the formula: wiThe connection weight of the input layer and the hidden layer; δ is a smoothing factor.
(3) The summation layer accumulates. And accumulating the probability that the sample belongs to a certain class by the summation layer, and calculating to obtain the probability density function estimation of the failure mode, wherein the estimation formula is as follows:
in the formula: waiThe ith training vector for the failure mode, m the number of failure mode samples, and δ the smoothing parameter.
(4) An output layer is determined. The output decision layer of the network consists of a threshold discriminator and is used for calculating a probability density function of each mode occurrence so as to realize classification output:
wherein E ═ E1,E2,…,EL]TAre combinations of the types of equipment to be employed.
Optionally, as a possible implementation, the number of similar areas is at least one, and correspondingly, the number of device type combinations is at least one; determining the type of the capacity expansion device according to the device type combination, which may be detailed as follows:
determining the device type different from the existing device type in each device type combination as a type to be selected; wherein the existing equipment type is the equipment type in the target integrated energy system;
counting the occurrence frequency of each to-be-selected type in all equipment type combinations;
selecting the first N types to be selected with the highest frequency of occurrence as the types of the capacity expansion equipment; and N is a preset value and is less than or equal to S, and S is the total number of the types to be selected.
In the embodiment of the invention, the types of energy equipment which are not available in the comprehensive energy system of the park and are applied more in the similar parks are selected as the types of capacity expansion equipment, so that the stability of the system is further improved.
Optionally, as a possible implementation manner, determining the capacity of the capacity expansion device that minimizes the total cost of the expanded target integrated energy system in the full life cycle may be detailed as follows:
establishing a target function by taking the capacity of the capacity expansion equipment as a variable and the minimum total cost of the target comprehensive energy system after capacity expansion in the whole life cycle as a target;
establishing a constraint condition of an objective function;
and solving the objective function according to the constraint condition to obtain the capacity of the capacity expansion equipment.
In the embodiment of the invention, a particle swarm algorithm can be adopted to solve the objective function.
Optionally, as a possible implementation, the objective function is:
F＝min[fin(x)+fop(p)+fmc(p)-fbt(p)]
wherein F is the total cost of the target integrated energy system in the whole life cycle, x is the capacity of the capacity expansion equipment, and Fin(x) For expanding the transformation cost, fop(p) operating cost of the target integrated energy system over life cycle, fmc(p) maintenance cost of the target integrated energy system over life cycle, fbtAnd (p) is a power generation subsidy of the comprehensive energy system.
In the embodiment of the present invention, the expansion transformation cost may be composed of the purchase cost, installation cost, land cost, and other costs of the expansion device, that is:
where y is the design life of the system, r is the discount rate, ciCost per purchase, x, for capacity expansion equipmentiTo expand the number of devices, jiCost of land use for capacity expansion equipment, tiThe installation cost of the capacity expansion equipment is el, and the el is other cost spent in the capacity expansion reconstruction construction stage.
The operating cost of the integrated energy system in the full life cycle can be composed of the fuel consumption cost and the electric energy purchase cost in the full life cycle, namely:
in the formula: piAnd ηiAre respectively consumedOperating output and power consumption proportionality coefficient of electrical apparatus, GiAnd kappaiRespectively the output of the natural gas consumption equipment and the proportion coefficient of the consumed fuel gas.
The maintenance cost inside the integrated energy system can be calculated by the following formula:
in the formula: f. ofmc(p) maintenance costs for all equipment in the integrated energy system over the life cycle, wiThe maintenance cost of a single device.
Optionally, as one possible implementation, the constraints include investment cost constraints, usable area constraints, grid supply constraints, plant power constraints, natural gas network capacity constraints, reliability constraints, and demand response constraints.
In the embodiment of the present invention, the constraint condition may specifically be as follows:
and (3) investment cost constraint:
Tmax≥fin(x)
in the formula: t ismaxThe maximum investment capacity for the capacity expansion and reconstruction construction of the comprehensive energy system.
Area constraints can be used:
in the formula, miLand area occupied for expansion of equipment installation, AZmaxThe land area which can be used for expansion construction is provided. In addition, each capacity expansion device may also consider the constraint of exclusive area, that is, consider the geographical location where the capacity expansion device is installed, for example: when the solar photovoltaic panel is installed on the roof of a building, the maximum value of the effective illumination area of the roof of the building is required to be used as a constraint.
And power supply restraint of a power grid:
in the formula, DmaxFor maximum power supply capacity of the grid, Pmax iIs the maximum power consumption, U, of the ith equipmentmax iIs the maximum power generation power, L, of the ith equipmentq maxThe maximum power load designed for the interior of the park of the comprehensive energy system is provided, and S is a safe power utilization coefficient.
Device power constraints:
in the formula: qi minAnd Qmax iRespectively the minimum power and the maximum power of the cooling/heating of the ith equipment,
and
the climbing rates of the reduced output and the increased output of the ith equipment are respectively.
Natural gas network capacity constraints (which are to be met with the corresponding physical laws between gas pressure and power flow):
in the formula: PQmax,l、PQmin,JRespectively representing the upper and lower limits of the flow of the natural gas pipeline l, cll.yFor safe fluctuation coefficient of pipe transmission flow, Vmax,s、Vmin,sRespectively representing the upper and lower limits of the supplied air quantity.
Reliability constraints (the electrical energy deficit has to meet the upper limit):
ΔLb s≤ΔLmax
in the formula,. DELTA.LmaxThe upper limit of the electric energy shortage is.
And (3) constraint of demand response:
Pt min≤Pt≤Pt max
in the formula: ptFor the system load at time t, Pt max、Pt minRespectively, the upper limit and the lower limit of the demand response load of the system at the time t.
Optionally, as a possible implementation manner, after obtaining a result of selecting the capacity expansion device of the target integrated energy system, the method further includes:
determining the equipment type combination of the target integrated energy system after capacity expansion according to the type of the capacity expansion equipment and the equipment type in the target integrated energy system;
and combining the scene characteristic factor data of the area where the target integrated energy system is located and the expanded equipment type of the target integrated energy system as a group of example data, and storing the example data into a preset example library.
In the embodiment of the invention, the scene characteristic factor data and the equipment type of the region where the target integrated energy system is located after capacity expansion are combined and stored into the preset instance library, so that the instance library is further enriched, and more instance data are provided for subsequent capacity expansion of other integrated energy systems.
On the basis of the above steps, the present invention further provides a more specific capacity expansion device selection process, which is shown in fig. 3.
Illustratively, the feasibility of the capacity expansion device selection method for the integrated energy system provided by the embodiment of the invention is verified through simulation calculation.
And (3) carrying out simulation planning by using the scene characteristic factor data of 8760 hours all the year in a certain park, wherein the existing equipment in the park is a direct-fired machine, a steam boiler, a vacuum boiler, a screw machine and a centrifuge.
The data in the example base and the scene characteristic factor data of the planned area are shown in table 1 and table 2, respectively.
TABLE 1 example library scene characteristic factor data
TABLE 2 pseudo-planning park scene characteristic factor data
Real number coding is carried out on scene characteristic factor data in the instance base to obtain a standardized scene characteristic factor matrix fm：
Binary coding is carried out on the equipment type combination in the instance library to obtain an equipment type combination binary matrix T:
PNN probabilistic neural network with fmThe constructed 7-dimensional vector is an input layer, and the relevant parameters are shown in table 3.
TABLE 3 PNN neural network parameters
| Implicit layer function | Function of output layer | Number of training sessions | Accuracy of measurementTarget | Learning rate |
| Tansig | Purelin | 1000 | 0.02 | 0.1 |
Example clustering inference results are shown in table 4.
Table 4 example clustering inference results
The PNN neural network outputs the device type combination of the example area 1 and the example area 2 similar to the current park scene, for the result, according to the steps, the device type different from the existing device type in each device type combination is determined as the type to be selected, the occurrence frequency of each type to be selected in all the device type combinations is counted, and the first N types to be selected with the highest occurrence frequency are selected as the type of the capacity expansion device. Or manually selecting one or more types of equipment which are not available in the current park from various equipment type combinations as the types of the capacity expansion equipment.
In this example, for convenience, the combination of the device types corresponding to the example area 1 is directly used as a capacity expansion transformation reference scheme, and the combination of the device types corresponding to the example area 2 is used as a capacity expansion transformation reference scheme (without a direct combustion engine and a centrifugal machine), and the capacities of the newly added devices are respectively calculated.
The parameters of the particle swarm algorithm are shown in table 5.
TABLE 5 particle swarm algorithm parameter settings
Inputting basic data required by planning, including planning load, single equipment cost, energy conversion efficiency and the like, when the particle swarm optimization is iterated to about 30-40 generations, the schemes of the example 1 and the example 2 obtain the optimal solution, and the optimal result is shown in table 6.
TABLE 6 capacity optimization results
Accordingly, the part costs of the integrated energy system before the capacity expansion modification, after the capacity expansion modification according to example 1, and after the capacity expansion modification according to example 2 in the entire life cycle are shown in table 7.
TABLE 7 fractional cost over life cycle
| Scene | Cost of retrofitting | Running cost | Cost of electricity purchase | Cost of gas purchase | Total cost of |
| At present | 0 | 2561.57 | 10200166.03 | 2337087.39 | 17350535.24 |
| Example 1 | 8388000 | 2713.24 | 8303971.64 | 2277720.35 | 16070359.48 |
| Example 1 | 2462000 | 1135.68 | 8932124.525 | 3295810.645 | 13939741.58 |
Compared with the capacity expansion transformation, the capacity expansion transformation is carried out according to the schemes of the example 1 and the example 2 before capacity expansion transformation, the electricity purchasing cost is reduced, the independence of the power distribution network is improved, and the dependence degree on a large power grid is reduced. And the annual total cost before the capacity expansion transformation is 1735.05 ten thousand yuan, the annual total cost after the capacity expansion transformation according to the example 1 is 1607.04 ten thousand yuan, and the annual total cost after the capacity expansion transformation according to the example 2 is 1393.97 ten thousand yuan, which saves 128.01 ten thousand yuan and 341.08 ten thousand yuan respectively compared with the annual total cost before the capacity expansion transformation, thereby achieving the purposes of reducing the electricity purchasing cost and reducing the overall cost of the comprehensive energy system.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
An embodiment of the present invention further provides a device for selecting expansion equipment of an integrated energy system, and referring to fig. 4, the device 40 includes:
the obtaining module 41 is configured to obtain scene characteristic factor data of an area where the target integrated energy system is located; the scene characteristic factor data comprises regional resource endowment data, load data and construction data.
And the first processing module 42 is configured to screen similar areas from a preset instance library based on the scene characteristic factor data, and extract a device type combination of the integrated energy system corresponding to the similar areas.
And a second processing module 43, configured to determine the type of the capacity expansion device according to the device type combination.
And the third processing module 44 is configured to determine the capacity of the capacity expansion device that minimizes the total cost of the target integrated energy system after capacity expansion in the full life cycle, and obtain a capacity expansion device selection result of the target integrated energy system.
Optionally, as a possible implementation manner, the preset instance library includes scene feature factor data and device type combinations corresponding to multiple instance areas; the first processing module 42 is configured to:
calculating the similarity between the scene characteristic factor data and the scene characteristic factor data corresponding to each instance region in a preset instance library based on a preset instance clustering reasoning model;
and selecting the example area with the corresponding similarity smaller than a preset threshold value as the similar area.
Optionally, as a possible implementation, the number of similar areas is at least one, and correspondingly, the number of device type combinations is at least one; the second processing module 43 is configured to:
determining the device type different from the existing device type in each device type combination as a type to be selected; wherein the existing equipment type is the equipment type in the target integrated energy system;
counting the occurrence frequency of each to-be-selected type in all equipment type combinations;
selecting the first N types to be selected with the highest frequency of occurrence as the types of the capacity expansion equipment; and N is a preset value and is less than or equal to S, and S is the total number of the types to be selected.
Optionally, as a possible implementation, the third processing module 44 is configured to:
establishing a target function by taking the capacity of the capacity expansion equipment as a variable and the minimum total cost of the target comprehensive energy system after capacity expansion in the whole life cycle as a target;
establishing a constraint condition of an objective function;
and solving the objective function according to the constraint condition to obtain the capacity of the capacity expansion equipment.
Optionally, as a possible implementation, the objective function is:
F＝min[fin(x)+fop(p)+fmc(p)-fbt(p)]
wherein F is the total cost of the target integrated energy system in the whole life cycle, x is the capacity of the capacity expansion equipment, and Fin(x) For expanding the transformation cost, fop(p) cost of operation of the target integrated energy system over the life cycle, fmc(p) maintenance cost of target integrated energy system in full life cycle, fbtAnd (p) is a power generation subsidy of the comprehensive energy system.
Optionally, as one possible implementation, the constraints include investment cost constraints, usable area constraints, grid supply constraints, plant power constraints, natural gas network capacity constraints, reliability constraints, and demand response constraints.
Optionally, as a possible implementation manner, after obtaining the capacity expansion device selection result of the target integrated energy system, the third processing module 44 is further configured to:
determining the equipment type combination of the target integrated energy system after capacity expansion according to the type of the capacity expansion equipment and the equipment type in the target integrated energy system;
and combining the scene characteristic factor data of the area where the target integrated energy system is located and the expanded equipment type of the target integrated energy system as a group of example data, and storing the example data into a preset example library.
Fig. 5 is a schematic diagram of a terminal according to an embodiment of the present invention. As shown in fig. 5, the terminal 50 of this embodiment includes: a processor 51, a memory 52 and a computer program 53 stored in the memory 52 and executable on the processor 51. The processor 51 executes the computer program 53 to implement the steps of the above-mentioned capacity expansion device selection method embodiments of the integrated energy system, such as the steps S201 to S204 shown in fig. 1. Alternatively, the processor 51 implements the functions of the modules in the above-described device embodiments, such as the functions of the modules 41 to 44 shown in fig. 4, when executing the computer program 53.
Illustratively, the computer program 53 may be divided into one or more modules/units, which are stored in the memory 52 and executed by the processor 51 to carry out the invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 53 in the terminal 50. For example, the computer program 53 may be divided into the acquisition module 41, the first processing module 42, the second processing module 43, and the third processing module 44 (modules in the virtual device), and the specific functions of each module are as follows:
the obtaining module 41 is configured to obtain scene characteristic factor data of an area where the target integrated energy system is located; the scene feature factor data includes resource endowment data, load data and construction data of the region.
And the first processing module 42 is configured to screen similar areas from a preset instance library based on the scene characteristic factor data, and extract a device type combination of the integrated energy system corresponding to the similar areas.
And a second processing module 43, configured to determine the type of the capacity expansion device according to the device type combination.
And the third processing module 44 is configured to determine the capacity of the capacity expansion device that minimizes the total cost of the target integrated energy system after capacity expansion in the full life cycle, and obtain a capacity expansion device selection result of the target integrated energy system.
The terminal 50 may be a computing device such as a desktop computer, a notebook, a palm top computer, and a cloud server. The terminal may include, but is not limited to, a processor 51, a memory 52. Those skilled in the art will appreciate that fig. 5 is merely an example of a terminal 50 and does not constitute a limitation of terminal 50 and may include more or less components than those shown, or combine certain components, or different components, e.g., terminal 50 may also include input-output devices, network access devices, buses, etc.
The Processor 51 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 52 may be an internal storage unit of the terminal 50, such as a hard disk or a memory of the terminal 50. The memory 52 may also be an external storage device of the terminal 50, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal 50. Further, the memory 52 may also include both internal and external memory units of the terminal 50. The memory 52 is used for storing computer programs and other programs and data required by the terminal 50. The memory 52 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other ways. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the embodiments of the method. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A capacity expansion equipment selection method of an integrated energy system is characterized by comprising the following steps:
acquiring scene characteristic factor data of an area where a target comprehensive energy system is located; the scene characteristic factor data comprises resource endowment data, load data and construction data of the region;
based on the scene characteristic factor data, screening similar areas from a preset instance library, and extracting equipment type combinations of the comprehensive energy system corresponding to the similar areas;
determining the type of the capacity expansion equipment according to the equipment type combination;
and determining the capacity of the capacity expansion equipment which enables the total cost of the target comprehensive energy system after capacity expansion to be the lowest in the whole life cycle, and obtaining the capacity expansion equipment selection result of the target comprehensive energy system.
2. The capacity expansion device selection method of the integrated energy system according to claim 1, wherein the preset instance library includes scene characteristic factor data and device type combinations corresponding to a plurality of instance areas; based on the scene characteristic factor data, screening similar areas from a preset example library, wherein the screening comprises the following steps:
calculating the similarity between the scene characteristic factor data and the scene characteristic factor data corresponding to each instance region in the preset instance library based on a preset instance clustering inference model;
and selecting the example area with the corresponding similarity smaller than a preset threshold value as the similar area.
3. The method according to claim 1, wherein the number of the similar areas is at least one, and accordingly, the number of the device type combinations is at least one; determining the type of the capacity expansion device according to the device type combination, including:
determining the device type different from the existing device type in each device type combination as a type to be selected; wherein the existing equipment type is an equipment type in the target integrated energy system;
counting the occurrence frequency of each to-be-selected type in all equipment type combinations;
selecting the first N types to be selected with the highest frequency of occurrence as the types of the capacity expansion equipment; and N is a preset value and is less than or equal to S, and S is the total number of the types to be selected.
4. The method of selecting capacity expansion devices for an integrated energy system of claim 1, wherein determining the capacity of the capacity expansion device that minimizes the total cost of the target integrated energy system after capacity expansion over the life cycle comprises:
establishing a target function by taking the capacity of the capacity expansion equipment as a variable and taking the minimum total cost of the target comprehensive energy system after capacity expansion in a full life cycle as a target;
establishing a constraint condition of the objective function;
and solving the objective function according to the constraint condition to obtain the capacity of the capacity expansion equipment.
5. The capacity expansion device selection method of the integrated energy system according to claim 4, wherein the objective function is:
F＝min[fin(x)+fop(p)+fmc(p)-fbt(p)]
wherein F is the total cost of the target integrated energy system in the whole life cycle, x is the capacity of the capacity expansion equipment, and Fin(x) For expanding the transformation cost, fop(p) operating cost of the target integrated energy system over life cycle, fmc(p) maintenance cost of the target integrated energy system over life cycle, fbtAnd (p) is a power generation subsidy of the comprehensive energy system.
6. The method of capacity expansion device selection for an integrated energy system of claim 4, wherein the constraints include investment cost constraints, usable area constraints, grid power constraints, plant power constraints, natural gas network capacity constraints, reliability constraints, and demand response constraints.
7. The method for selecting capacity expansion devices of an integrated energy system according to any one of claims 1 to 6, further comprising, after obtaining a result of selecting the capacity expansion devices of the target integrated energy system:
determining the equipment type combination of the target integrated energy system after capacity expansion according to the type of the capacity expansion equipment and the equipment type in the target integrated energy system;
and combining the scene characteristic factor data of the area where the target integrated energy system is located and the expanded equipment type of the target integrated energy system as a group of example data, and storing the example data into the preset example library.
8. The utility model provides a device is selected to comprehensive energy system's dilatation equipment which characterized in that includes:
the acquisition module is used for acquiring scene characteristic factor data of an area where the target comprehensive energy system is located; the scene characteristic factor data comprises resource endowment data, load data and construction data of the region;
the first processing module is used for screening similar areas from a preset instance library based on the scene characteristic factor data and extracting equipment type combinations of the comprehensive energy system corresponding to the similar areas;
the second processing module is used for determining the type of the capacity expansion equipment according to the equipment type combination;
and the third processing module is used for determining the capacity of the capacity expansion equipment which enables the total cost of the target integrated energy system after capacity expansion to be the lowest in the whole life cycle, and obtaining a capacity expansion equipment selection result of the target integrated energy system.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111014896.5A CN113780781B (en) | 2021-08-31 | Capacity expansion equipment selection method and device of comprehensive energy system and terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111014896.5A CN113780781B (en) | 2021-08-31 | Capacity expansion equipment selection method and device of comprehensive energy system and terminal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113780781A true CN113780781A (en) | 2021-12-10 |
| CN113780781B CN113780781B (en) | 2024-07-09 |
Family
ID=
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3301623A1 (en) * | 2016-10-03 | 2018-04-04 | Bartucci S.p.A. | Method for increasing energy efficiency of a plant, and related system |
| CN109510224A (en) * | 2018-11-16 | 2019-03-22 | 上海交通大学 | Photovoltaic energy storage and the united capacity configuration of distributed energy and running optimizatin method |
| CN110570026A (en) * | 2019-08-21 | 2019-12-13 | 国网江苏省电力有限公司经济技术研究院 | construction planning method for power grid system, computer device and storage medium |
| CN110941978A (en) * | 2019-05-23 | 2020-03-31 | 罗普特科技集团股份有限公司 | Face clustering method and device for unidentified personnel and storage medium |
| CN112365021A (en) * | 2020-09-21 | 2021-02-12 | 华北电力大学 | Regional comprehensive energy system planning optimization method based on hybrid energy storage |
| CN112598195A (en) * | 2020-12-31 | 2021-04-02 | 国网河北省电力有限公司经济技术研究院 | Building type comprehensive energy system operation optimization method and device and terminal equipment |
| CN112884270A (en) * | 2020-12-31 | 2021-06-01 | 国网山东省电力公司聊城供电公司 | Multi-scene power distribution network planning method and system considering uncertainty factors |
| CN113239529A (en) * | 2021-04-29 | 2021-08-10 | 国网江苏省电力有限公司苏州供电分公司 | Multi-objective optimization method of comprehensive energy system |
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3301623A1 (en) * | 2016-10-03 | 2018-04-04 | Bartucci S.p.A. | Method for increasing energy efficiency of a plant, and related system |
| CN109510224A (en) * | 2018-11-16 | 2019-03-22 | 上海交通大学 | Photovoltaic energy storage and the united capacity configuration of distributed energy and running optimizatin method |
| CN110941978A (en) * | 2019-05-23 | 2020-03-31 | 罗普特科技集团股份有限公司 | Face clustering method and device for unidentified personnel and storage medium |
| CN110570026A (en) * | 2019-08-21 | 2019-12-13 | 国网江苏省电力有限公司经济技术研究院 | construction planning method for power grid system, computer device and storage medium |
| CN112365021A (en) * | 2020-09-21 | 2021-02-12 | 华北电力大学 | Regional comprehensive energy system planning optimization method based on hybrid energy storage |
| CN112598195A (en) * | 2020-12-31 | 2021-04-02 | 国网河北省电力有限公司经济技术研究院 | Building type comprehensive energy system operation optimization method and device and terminal equipment |
| CN112884270A (en) * | 2020-12-31 | 2021-06-01 | 国网山东省电力公司聊城供电公司 | Multi-scene power distribution network planning method and system considering uncertainty factors |
| CN113239529A (en) * | 2021-04-29 | 2021-08-10 | 国网江苏省电力有限公司苏州供电分公司 | Multi-objective optimization method of comprehensive energy system |
Non-Patent Citations (4)
| Title |
|---|
| YANRONG WU 等: "The optimal configuration of energy storage capacity of the regional integrated energy system considering the integrated demand response", 《 2021 IEEE 4TH INTERNATIONAL ELECTRICAL AND ENERGY CONFERENCE (CIEEC)》, 17 August 2021 (2021-08-17), pages 1 - 5 * |
| 姚志力 等: "计及风光不确定性的综合能源系统两层级协同优化配置方法", 《电网技术》, vol. 44, no. 12, 21 July 2020 (2020-07-21), pages 4521 - 4529 * |
| 徐楠等: "：基于设备初选的综合能源系统扩容规划研究", 《电网与清洁能源》, vol. 38, no. 12, 25 December 2022 (2022-12-25), pages 124 - 30 * |
| 王永利 等: "基于定额投资条件下的综合能源系统规划效益研究", 《电力需求侧管理》, vol. 22, no. 5, 30 September 2020 (2020-09-30), pages 38 - 44 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Joint planning of distributed generations and energy storage in active distribution networks: A Bi-Level programming approach | |
| CN109325608B (en) | Distributed power supply optimal configuration method considering energy storage and considering photovoltaic randomness | |
| Maleki et al. | Optimal sizing of autonomous hybrid photovoltaic/wind/battery power system with LPSP technology by using evolutionary algorithms | |
| Jalili et al. | Stochastic optimal operation of a microgrid based on energy hub including a solar-powered compressed air energy storage system and an ice storage conditioner | |
| Guo et al. | A novel typical day selection method for the robust planning of stand-alone wind-photovoltaic-diesel-battery microgrid | |
| CN108565863A (en) | A kind of regional complex energy resource system multiple target tide optimization method considering randomness | |
| CN112952807B (en) | Multi-objective optimization scheduling method considering wind power uncertainty and demand response | |
| Kou et al. | Many-objective optimization for coordinated operation of integrated electricity and gas network | |
| CN111027807A (en) | Distributed power generation site selection and volume fixing method based on power flow linearization | |
| Noorollahi et al. | A scenario-based approach for optimal operation of energy hub under different schemes and structures | |
| Li et al. | Energy hub-based optimal planning for integrated energy systems considering part-load characteristics and synergistic effect of equipment | |
| Liu et al. | Two-layer multiple scenario optimization framework for integrated energy system based on optimal energy contribution ratio strategy | |
| Zhu et al. | Stochastic economic dispatching strategy of the active distribution network based on comprehensive typical scenario set | |
| CN116961052A (en) | Method and system for locating and sizing distributed energy storage system | |
| CN115439000A (en) | Power distribution network block division method considering wind-solar-load power uncertainty and correlation | |
| CN112418732B (en) | Planning method and system for comprehensive energy system with multi-energy hub | |
| CN113708418A (en) | Micro-grid optimization scheduling method | |
| CN116862068A (en) | Transformer substation planning distribution robust optimization method and system considering excitation type response uncertainty | |
| Su et al. | Optimal placement and capacity sizing of energy storage systems via NSGA-II in active distribution network | |
| CN110909779A (en) | Energy supply mode classification method of regional comprehensive energy supply system | |
| CN113780781B (en) | Capacity expansion equipment selection method and device of comprehensive energy system and terminal | |
| CN111126675A (en) | Multi-energy complementary microgrid system optimization method | |
| CN113780781A (en) | Capacity expansion equipment selection method and device of comprehensive energy system and terminal | |
| CN115776138A (en) | Micro-grid capacity planning method and device considering multi-dimensional uncertainty and energy management strategy | |
| CN114781703A (en) | Hierarchical multi-objective optimization method, terminal equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |