CN107392791B - Distributed photovoltaic and gas-electricity hybrid capacity planning method and system for multi-energy complementary system - Google Patents
Distributed photovoltaic and gas-electricity hybrid capacity planning method and system for multi-energy complementary system Download PDFInfo
- Publication number
- CN107392791B CN107392791B CN201710546330.4A CN201710546330A CN107392791B CN 107392791 B CN107392791 B CN 107392791B CN 201710546330 A CN201710546330 A CN 201710546330A CN 107392791 B CN107392791 B CN 107392791B
- Authority
- CN
- China
- Prior art keywords
- period
- capacity
- unit
- scene
- illumination intensity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000000295 complement effect Effects 0.000 title claims abstract description 100
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000005286 illumination Methods 0.000 claims abstract description 74
- 239000001257 hydrogen Substances 0.000 claims description 56
- 229910052739 hydrogen Inorganic materials 0.000 claims description 56
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims description 46
- 238000010248 power generation Methods 0.000 claims description 44
- 238000009826 distribution Methods 0.000 claims description 32
- 239000007789 gas Substances 0.000 claims description 26
- 239000000446 fuel Substances 0.000 claims description 20
- 238000004364 calculation method Methods 0.000 claims description 18
- 238000006243 chemical reaction Methods 0.000 claims description 17
- 230000005611 electricity Effects 0.000 claims description 13
- 150000001875 compounds Chemical class 0.000 claims description 11
- 238000012423 maintenance Methods 0.000 claims description 11
- 238000003860 storage Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 9
- 239000013256 coordination polymer Substances 0.000 claims description 5
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 32
- 238000004146 energy storage Methods 0.000 description 10
- 239000003345 natural gas Substances 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 238000009434 installation Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 230000001580 bacterial effect Effects 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000002431 foraging effect Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 238000002347 injection Methods 0.000 description 2
- 239000007924 injection Substances 0.000 description 2
- 238000011144 upstream manufacturing Methods 0.000 description 2
- 241001387976 Pera Species 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- -1 hydrogen Chemical class 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011946 reduction process Methods 0.000 description 1
- 230000001932 seasonal effect Effects 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a distributed photovoltaic and gas-electricity hybrid capacity planning method and system of a multi-energy complementary system. The distributed photovoltaic and gas-electricity hybrid capacity planning method of the multi-energy complementary system comprises the following steps: constructing an original scene data set according to the illumination intensity data in each unit statistical period in the total statistical period and the load curve data of the multi-energy complementary system in each unit statistical period; the original scene data set comprises a plurality of illumination intensity data and a plurality of load curve data in each unit statistical period; performing scene reduction on the original scene data set to obtain two illumination intensity data and two load curve data of each unit statistical period, and respectively inputting the two illumination intensity data and the two load curve data into a multi-energy complementary system capacity planning model; and calculating a capacity planning parameter when the capacity planning model of the multi-energy complementary system obtains the minimum value, and planning the distributed photovoltaic and gas-electricity mixed capacity according to the capacity planning parameter.
Description
Technical Field
The invention relates to the technical field of energy scheduling, in particular to a distributed photovoltaic and gas-electricity hybrid capacity planning method and system of a multi-energy complementary system.
Background
Comprehensive utilization of multi-energy complementary distributed energy is an important way for efficient utilization of clean energy and renewable energy. Among the forms of multiple complementary systems, a regional multiple complementary distributed energy power distribution system containing distributed photovoltaic power generation and 'gas-electricity hybrid' is a typical form, and the 'gas-electricity hybrid' refers to the cooperative operation of a distribution network and a natural gas network through energy conversion and interconnection between the distribution network and the natural gas network. The capacity reasonable planning is carried out on the regional multifunctional complementary system containing the mixed distributed photovoltaic and gas-electricity, and the absorption capacity of the distributed photovoltaic can be improved on the basis of considering the system economy. The regional multi-energy complementary power distribution system containing distributed photovoltaic power generation and gas-electricity hybrid is located at the tail end of energy consumption and mainly comprises a distributed photovoltaic power generation system, a gas-electricity hybrid device (converting electricity into hydrogen or methane), a hydrogen energy storage system, a distribution network, a gas network, a control system and the like.
The planning scheme of the distributed photovoltaic and gas-electricity mixed capacity in the traditional multi-energy complementary system is generally analyzed under the condition of a deterministic typical day or a deterministic load peak, the expected calculation of the operation cost based on probabilistic analysis is lacked, the obtained optimization result can only adapt to certain typical day scenes, and the unit statistics periodic difference, the day difference and the medium-term and long-term characteristics of the distributed photovoltaic power generation and the load cannot be reflected; and related capacity planning is respectively carried out only for distributed photovoltaic power generation or only for a gas-electric hybrid system; the actual operation strategy is often not considered in the planning stage, so that the planning result is easily disconnected from the actual operation, and the accuracy of the distributed photovoltaic and gas-electricity mixed capacity planning is easily influenced.
Disclosure of Invention
Based on this, it is necessary to provide a method and a system for planning the distributed photovoltaic and gas-electricity hybrid capacity of the multi-energy complementary system, aiming at the technical problem that the traditional scheme easily affects the accuracy of planning the distributed photovoltaic and gas-electricity hybrid capacity of the multi-energy complementary system.
A distributed photovoltaic and gas-electricity hybrid capacity planning method for a multi-energy complementary system comprises the following steps:
constructing an original scene data set according to the illumination intensity data in each unit statistical period in the total statistical period and the load curve data of the multi-energy complementary system in each unit statistical period; the original scene data set comprises a plurality of illumination intensity data and a plurality of load curve data in each unit statistical period;
performing scene reduction on the original scene data set to obtain two illumination intensity data and two load curve data of each unit statistical period, and respectively inputting the two illumination intensity data and the two load curve data into a preset multi-energy complementary system capacity planning model;
and calculating a capacity planning parameter when the capacity planning model of the multi-energy complementary system obtains the minimum value, and planning the distributed photovoltaic and gas-electricity mixed capacity of the multi-energy complementary system according to the capacity planning parameter.
A distributed photovoltaic and combined gas and electricity capacity planning system for a multi-energy complementary system, comprising:
the building module is used for building an original scene data set according to the illumination intensity data in each unit statistical period in the total statistical period and the load curve data of the multi-energy complementary system in each unit statistical period; the original scene data set comprises a plurality of illumination intensity data and a plurality of load curve data in each unit statistical period;
the reduction module is used for carrying out scene reduction on the original scene data set to obtain two illumination intensity data and two load curve data of each unit statistical period, and the two illumination intensity data and the two load curve data are respectively input into a preset multi-energy complementary system capacity planning model;
and the planning module is used for calculating a capacity planning parameter when the capacity planning model of the multi-energy complementary system obtains the minimum value, and planning the distributed photovoltaic and gas-electricity mixed capacity of the multi-energy complementary system according to the capacity planning parameter.
The method and the system for planning the distributed photovoltaic and gas-electricity mixed capacity of the multi-energy complementary system can construct an original scene data set according to the illumination intensity data in each unit statistical period in a total statistical period and the load curve data of the multi-energy complementary system in each unit statistical period so as to obtain two illumination intensity data and two load curve data of each unit statistical period from the original scene data set, respectively input the two illumination intensity data and the two load curve data into a preset multi-energy complementary system capacity planning model, and then calculate the capacity planning parameter when the multi-energy complementary system capacity planning model obtains the minimum value, so as to plan the distributed photovoltaic and gas-electricity mixed capacity of the multi-energy complementary system according to the capacity planning parameter, wherein the planning process of the distributed photovoltaic and gas-electricity mixed capacity of the multi-energy complementary system is based on the statistical illumination intensity data and the load curve data, corresponding planning results are combined with actual operation data, and accuracy of the distributed photovoltaic and gas-electricity hybrid capacity planning in the multi-energy complementary system is effectively improved.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the distributed photovoltaic and gas-electric hybrid capacity planning method of a multi-energy complementary system as described above.
The computer program stored on the computer-readable storage medium can be executed by the processor to implement the method for planning the distributed photovoltaic and gas-electricity hybrid capacity of the multi-energy complementary system, so that the accuracy of planning the distributed photovoltaic and gas-electricity hybrid capacity of the multi-energy complementary system can be improved.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing the distributed photovoltaic and combined gas and electric capacity planning method of a multi-energy complementary system as described above.
In the computer device, when the processor executes the program, the distributed photovoltaic and gas-electricity hybrid capacity planning method of the multi-energy complementary system can be realized, and the accuracy of corresponding capacity planning is effectively improved.
Drawings
Fig. 1 is a flow chart of a distributed photovoltaic and gas-electricity hybrid capacity planning method of a multi-energy complementary system according to an embodiment;
FIG. 2 is a schematic diagram of an embodiment of a multi-energy complementation system architecture;
FIG. 3 is a schematic structural diagram of a distributed photovoltaic and gas-electricity hybrid capacity planning system of the multi-energy complementary system according to an embodiment;
FIG. 4 is a diagram illustrating an exemplary computer device configuration.
Detailed Description
The following describes in detail specific embodiments of the distributed photovoltaic and gas-electric hybrid capacity planning method and system of the multi-energy complementary system according to the present invention with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flowchart illustrating a distributed photovoltaic and gas-electricity hybrid capacity planning method of a multi-energy complementary system according to an embodiment, including the following steps:
s10, constructing an original scene data set according to the illumination intensity data in each unit statistical period in the total statistical period and the load curve data of the multi-energy complementary system in each unit statistical period; the original scene data set comprises a plurality of illumination intensity data and a plurality of load curve data in each unit statistical period;
the multi-energy complementary system can be referred to as fig. 2, can be located at the end of energy consumption, and mainly comprises a distributed photovoltaic power generation system, a gas-electricity hybrid device (converting electricity into hydrogen or methane), a hydrogen energy storage system, a distribution network, a gas network, a control system and the like. The distributed photovoltaic power generation realizes 'self-generation and self-utilization and internet surfing of surplus' at a user side. When the surplus of distributed photovoltaic power generation is large, the distribution network cannot be consumed: on one hand, the electric methane conversion device is started to convert redundant distributed photovoltaic power generation into methane and inject the methane into a natural gas network; on the other hand, the electricity-to-hydrogen device can also be started to convert the redundant distributed photovoltaic power generation into hydrogen energy to be stored in the hydrogen energy storage device. When the power supply capacity of a superior power grid is insufficient or the distributed photovoltaic power generation amount is small, the hydrogen fuel cell converts hydrogen energy in the hydrogen energy storage into electric energy to be sent to the power grid. The gas-electricity hybrid has the main effect of solving the problem of consumption of the distributed photovoltaic high-permeability power distribution network after the distributed photovoltaic high-permeability power distribution network is accessed into a regional multifunctional complementary power distribution system. .
The total statistical period may be a longer statistical time of a year and the like, and the total statistical period may include a plurality of unit statistical periods with equal or approximately equal time lengths, such as four quarters of a year and the like. The illumination intensity data in each unit statistical period comprises illumination intensity data corresponding to each statistical time unit in the corresponding unit statistical period, and the load curve data in each unit statistical period comprises load curve data corresponding to each statistical time unit in the corresponding unit statistical period. The statistical time unit can be a time period smaller than the unit statistical period, such as one day or half day; the unit counting period is composed of respective counting time units therebetween. For example, if the total statistical period is one year and the unit statistical period is four quarters of the year, the statistical time unit may be one day, in this case, the illumination intensity data in a certain unit statistical period may include illumination intensity data for each day in the corresponding quarter, the load curve data in a certain unit statistical period may include load curve data (daily curve data) for each day in the corresponding quarter, and a set of statistical data includes illumination intensity data and load curve data corresponding to the same day.
S20, performing scene reduction on the original scene data set to obtain two illumination intensity data and two load curve data of each unit statistical period, and respectively inputting the two illumination intensity data and the two load curve data into a preset multi-energy complementary system capacity planning model;
the above steps can reduce the scene of the illumination intensity data and the load curve data in each unit statistical period, so that the illumination intensity data and the load curve data in each unit statistical period are reduced to 2 respectively.
Specifically, in the scene reduction process, it can be considered that the distributed photovoltaic power generation and the load have periodicities such as daily regularity and seasonal regularity. In the planning stage, the medium-long term characteristics and the space-time complementary effect of the distributed photovoltaic power generation and the load are more concerned, and the illumination intensity data and the load curve data corresponding to statistical time units such as a typical daily curve and the like are extracted from the illumination intensity historical curve and the load historical curve according to unit statistical cycles such as different seasons and the like and are used as input data of the capacity planning model of the multi-energy complementary system.
And S30, calculating a capacity planning parameter when the capacity planning model of the multi-energy complementary system obtains the minimum value, and planning the distributed photovoltaic and gas-electricity mixed capacity of the multi-energy complementary system according to the capacity planning parameter.
The minimum value obtained by the capacity planning model of the multi-energy complementary system is the minimum value obtained by the capacity planning model of the multi-energy complementary system when the corresponding multi-energy complementary system and various devices meet the corresponding operation constraint conditions. The capacity planning model of the multi-energy complementary system can take power distribution network operation constraint, gas network operation constraint, distributed photovoltaic output constraint, power-to-methane device energy conversion constraint, power-to-hydrogen energy storage-hydrogen fuel cell combined system energy conversion constraint and the like as constraint conditions. The electricity-to-hydrogen device, the hydrogen energy storage device and the hydrogen fuel cell jointly form a combined system. When surplus of distributed photovoltaic power generation in a distribution network is large, electric energy is converted into hydrogen energy by electricity-to-hydrogen gas and stored in a hydrogen energy storage device; when the distributed photovoltaic power generation is small, hydrogen in the hydrogen energy storage device is conveyed to the hydrogen fuel cell for power generation, and the stored hydrogen energy is transmitted to the distribution network in the form of electric energy. The three devices run jointly to perform corresponding analysis and modeling, and the capacity planning model of the multi-energy complementary system is obtained. The capacity planning model of the multi-energy complementary system obtains the minimum value, which shows that various cost values consumed by the multi-energy complementary system are the lowest, and the corresponding capacity planning parameter is the optimal planning parameter of the multi-energy complementary system at the moment, so that the distributed photovoltaic and gas-electricity mixed capacity of the multi-energy complementary system is planned, and the corresponding planning effect can be improved on the basis of ensuring the planning accuracy.
In an embodiment, the capacity planning model of the multi-energy complementary system can be solved by using a bacterial foraging algorithm, and the bacterial foraging algorithm has the advantages of strong global search and fine search capability, easiness in jumping out of local minimum values and the like in the process of solving a continuous optimization problem containing low-dimensional variables1,θ2,…,θS]TThe capacity of various devices can be formed into a space vector, namely the installed capacities of distributed photovoltaic power generation, an electricity-to-hydrogen device, an electricity-to-methane device, a hydrogen energy storage device and a hydrogen fuel cell, which are decision variables in the multi-energy complementary system capacity planning model.
The method for planning the distributed photovoltaic and gas-electricity mixed capacity of the multi-energy complementary system can construct an original scene data set according to the illumination intensity data in each unit statistical period in a total statistical period and the load curve data of the multi-energy complementary system in each unit statistical period so as to obtain two illumination intensity data and two load curve data of each unit statistical period from the original scene data set, respectively input the two illumination intensity data and the two load curve data into a preset multi-energy complementary system capacity planning model, and then calculate the capacity planning parameter when the multi-energy complementary system capacity planning model obtains the minimum value, so as to plan the distributed photovoltaic and gas-electricity mixed capacity of the multi-energy complementary system according to the capacity planning parameter, wherein the planning process of the distributed photovoltaic and gas-electricity mixed capacity of the multi-energy complementary system is based on the statistical illumination intensity data and the load curve data, corresponding planning results are combined with actual operation data, and accuracy of the distributed photovoltaic and gas-electricity hybrid capacity planning in the multi-energy complementary system is effectively improved.
In one embodiment, the total statistical period is one year; each unit statistical period in the total statistical period is four quarters in one year respectively; the illumination intensity data in the unit counting period is illumination intensity data of each day in a corresponding quarter, and the load curve data in the unit counting period is load curve data of each day in the corresponding quarter.
The embodiment can read the illumination intensity data and the load curve data of a certain place (an area corresponding to the multi-energy complementary system) all year round; and classifying daily curve data of the load curve and the illumination intensity data according to seasons to construct an original scene data set.
The embodiment considers that the distributed photovoltaic power generation and the load have daily regularity and seasonality. In the planning stage, the medium-long term characteristics and the space-time complementary effect of the distributed photovoltaic power generation and the load are more concerned, the idea that typical daily curves are extracted from the illumination intensity historical curve and the load historical curve according to different seasons and used as input data of a planning model is put forward, and a multi-scene mathematical optimization model is constructed on the basis of the typical daily curves.
In one embodiment, the aforementioned multi-energy complementary system capacity planning model may be:
in the formula, I is a set of distributed photovoltaic power generation equipment, an electric gas conversion device and a hydrogen fuel cell; cCP,iEqual annual equipment investment cost corresponding to the ith equipment capacity; cM,iAnnual maintenance costs for the ith equipment;the constant coefficient can be used for further equating the equal-year-value equipment investment cost and the year maintenance cost to the cost of each day of equal statistical time units, and the constant coefficient can be 1/365 equivalent; j represents a set of unit statistical periods, such as a set of spring, summer, autumn and winter seasons and the like; pijThe probability of the statistical period of the j first unit can be equal to 0.25; omegaPV,jCounting the illumination intensity scene of the period for the jth unit; sPV,jCounting a set of illumination intensity scenes of a period for the jth cell; omegaL,jCounting the load demand scenario of the period for the jth unit; sL,jA set of load demand scenarios for a jth cell statistics period;counting the probability of the illumination intensity scene of the period for the jth unit;counting the probability of the load scene of the period for the jth unit; t is the set of time periods per statistical unit of time, e.g. perA set of time periods of day, each of which may be m hours, m being the time values 1, 2, 3, …, 24, T representing the T-th time period in T;and counting the system operation cost parameters of the period and the omega scene for the jth unit.
If the total statistical period is one year, the unit statistical period is four quarters of one year, the statistical time unit can be one day, daily curve data of the load curve data and the illumination intensity meteorological data are classified according to seasons, and an original scene data set is constructed. And substituting the obtained scene reduction results (a group of statistical data) of the load curves in spring, autumn, summer and winter and the daily curve data of the illumination intensity meteorological data and the contained parameters of the distributed photovoltaic power generation and gas-electricity hybrid multi-energy complementary system into the multi-energy complementary system capacity planning model, and solving the mathematical optimization model by adopting a bacterial foraging algorithm to obtain the planning result of the distributed photovoltaic power generation and gas-electricity hybrid capacity.
in the formula, N represents a distribution network load node set,the node electricity consumption cost parameters of the jth unit statistical period, the omega scene, the tth time period and the nth node are represented,the network loss equivalent cost parameters of the jth unit statistical period, the omega scene and the tth time period are represented,representing the comprehensive operation cost parameters of the power-to-methane device of the jth unit statistical period, the omega scene and the tth time period,an operation cost parameter of the hydrogen conversion device representing the jth unit statistical period and the omega scene,representing the distributed photovoltaic power generation light abandoning cost parameters of the jth unit statistical period, the omega scene and the tth time period,representing the operation income parameters of the hydrogen fuel cell of the jth unit statistical cycle, the omega scene and the tth time period,and representing the operation income parameters of the distributed photovoltaic power generation in the jth unit statistical period, the omega scene and the tth time period.
The system operation cost parameter is related to illumination intensity and load requirements, has certain randomness, and is influenced by a system operation mode and a control strategy. The embodiment uses the above system operation cost parameterDetermined by the calculation formula ofHas higher accuracy.
As an example, the above-mentioned i-th equipment capacity corresponds to the equal annual equipment investment cost CCP,iThe calculation formula of (2) is as follows:
wherein r is the mark rate, which can be equal to 6.7%; βiPrice per capacity for the ith device; pCapacity,iCapacity of the ith device; r isiThe cost parameters of the design, installation, debugging and matching secondary equipment investment of the ith equipment account for the proportion of the cost parameters of the equipment, and the value is 12 percent; y isiFor the financial cycle of the ith equipment, 20 years worth can be taken.
As an example, the annual maintenance cost C of the above-mentioned i-th equipmentM,iThe calculation formula of (c) may be:
CM,i=kM,iPCapacity,i,
in the formula, kM,iAn annual maintenance cost factor for unit capacity of the ith equipment; pCapacity,iThe capacity of the ith device.
Annual maintenance cost C of the above-mentioned i-th equipmentM,iThe calculation formula can consider the factors of annual maintenance cost parameters, equipment types, capacity scales, the number of operation and maintenance personnel, the times of periodical overhaul and inspection per year, the fault rate of spare parts, the replacement price parameters and the like of the equipment, effectively simplifies the equipment on the basis of ensuring the accuracy, and can improve the corresponding calculation efficiency.
As an embodiment, the electricity cost parameter of the node of the nth node in the distribution network in the jth time period in the jth unit statistical period and the ω th sceneThe calculation formula of (a) is as follows:
in the formula (I), the compound is shown in the specification,representing the load size of the nth node in the distribution network in the t time period under the corresponding scene of the jth unit statistical period;under the corresponding scene of the jth unit statistical period, the distributed power generation amount of the nth node in the distribution network in the tth time period can include distributed photovoltaic power generation, fuel cells and the like, N is a distribution network load node set, N ∈ N and lambdaloadFor the power price parameter of distribution network, assuming that the load is an industrial and commercial user, and temporarily not considering the time-of-use power price and the step power price, lambdaloadCan take the value of lambdaload1 yuan per kWh (yuan per kWh).
The jth unit statistics period, the ω th scene, and the network loss equivalent cost parameter of the distribution network in the t time periodThe calculation formula of (a) is as follows:
in the formula (I), the compound is shown in the specification,representing the jth unit statistical period, the omega scene and the network loss of the distribution network in the t time period;
the jth unit statistics period, the omega scene and the comprehensive operation cost parameter of the device for converting electricity into methane in the tth time periodThe calculation formula of (a) is as follows:
in the formula, λgasFor the price of natural gas, 2.5 yuan/m 3 can be taken;the power consumed by the methane gas production device in the tth time slot under the corresponding scene of the jth unit statistical period ηele-P2GFor converting electricity into methaneThe energy conversion efficiency of the device can reach 55%;for low calorific value of methane, 9.7kWh/m can be taken3。
The operation cost parameters of the electricity-to-hydrogen device in the jth unit statistical period, the ω th scene and the t time periodThe calculation formula of (a) is as follows:
in the formula (I), the compound is shown in the specification,and under the corresponding scene of the jth unit statistical cycle, the electric quantity consumed by the hydrogen production device is converted into the electric quantity consumed by the hydrogen production device in the tth time period.
The operation cost parameter of the hydrogen fuel cell in the jth unit statistical period, the omega scene and the tth time periodThe calculation formula of (a) is as follows:
in the formula (I), the compound is shown in the specification,the power generation power of the hydrogen fuel cell is calculated for the jth unit statistical cycle under the corresponding scene in the tth time period; lambda [ alpha ]gridFor the power price on the internet, the power price of the coal-fired thermal power post can be taken as reference and 0.4 yuan/kWh can be taken,and the distributed power generation amount in the distribution network in the t time period under the corresponding scene of the j unit statistical period is shown.
The jth unit statistics period, the omega scene and the t time period distributed photovoltaic power generation operation cost parametersThe calculation formula of (a) is as follows:
in the formula (I), the compound is shown in the specification,counting the distributed photovoltaic power generation amount in the t time period for the jth unit under the corresponding scene of the cycle; lambda [ alpha ]subsidyFor the subsidy of distributed photovoltaic power generation, the utility model can take 0.42 yuan/kWh.
The jth unit statistics period, the omega scene and the distributed photovoltaic power generation light abandon cost parameter in the t time periodThe calculation formula of (a) is as follows:
in the formula (I), the compound is shown in the specification,counting the light curtailment quantity of the distributed photovoltaic in the t time period under the corresponding scene of the j unit counting period; lambda [ alpha ]PVlossTo discard the light penalty unit price, 1 yuan/kWh can be taken.
As an embodiment, taking the total statistical period as one year, the unit statistical period as four quarters of the year, and the statistical time unit as one day, each constraint condition of the capacity planning model of the multi-energy complementary system is further defined by combining the actual operating environment of the multi-energy complementary system:
(1) and (3) distribution network operation constraint:
1) the active balance constraint, to any illumination intensity and load scene under any season, join in marriage the net and satisfy active balance constraint (power generation is the power consumption), have promptly:
in the formula, P2G, P2H, PV and FC are respectively a set of an electricity-to-methane device, an electricity-to-hydrogen device, distributed photovoltaic power generation and a hydrogen fuel cell;and the network distribution power of the main network pair is distributed.
2) Node voltage constraint, for any illumination intensity and load scene in any season, the operating voltage level of each node i in the distribution network should be limited within a limit range, namely:
in the formula of Ui,minAnd Ui,maxRespectively represent the minimum allowable voltage value and the maximum allowable voltage value of the node i, and can respectively take 0.93p.u. and 1.07p.u.
3) For any illumination intensity and load scene in any season, the current of each branch I in the distribution network is limited within the allowed maximum current value, namely:
in the formula (I), the compound is shown in the specification,the current value passing through the branch l; i isl,maxRepresenting the maximum allowable ampacity for that branch.
(2) And (3) operation constraint of a gas network:
1) the flow of the gas network pipeline is restricted, and for any illumination intensity and load scene in any season, the flow of the gas network is restricted by the maximum flow of the pipeline:
in the formula (I), the compound is shown in the specification,the flow rate of the pipe gl; qgl,maxThe upper gl flow limit of the channel is mainly determined by the sectional area of the pipeline.
2) Gas network node flow balance constraint
For any lighting intensity and load scenario in any season, each node gn in the gas grid has a flow balance condition similar to the grid kirchhoff current law, i.e. the node inlet flow is equal to the outlet flow:
in the formula (I), the compound is shown in the specification,the natural gas flow injected into the gas network by the node gn is mainly injected by the electric methane conversion device; u and d represent the upstream injection node and the downstream egress node of the node gn, respectively; ingnRepresents a set of upstream injection nodes associated with node gn; outgnRepresents a set of downstream egress nodes associated with node gn;is the natural gas load flow of the gn node.
(3) Distributed photovoltaic output constraint:
for any illumination intensity and load scene in any season, the actual output of the distributed photovoltaic systemIntensity of light IrcRated power P of distributed photovoltaic power generation installation' standard rated conditionCapacity_PVConstraint, and can not exceed the photovoltaic maximum corresponding to the current illumination intensityLarge power generation capacity
In the formula: ircIrradiance at the current operating point, β power temperature coefficient, TcThe battery surface temperature, which is the operating point, is taken here approximately as the ambient temperature; t isSTCIs a standard rated condition temperature, 25 ℃.
(4) Energy conversion constraint of the electric methane conversion device:
gas production rate of electricity-to-methane deviceAnd power consumptionThe relationship is as follows:
power consumption of electric methane-converting apparatusIs loaded by itCapacity_P2GAnd (3) constraint:
(5) energy conversion constraint of the combined system of electricity-to-hydrogen energy storage and hydrogen fuel cell:
hydrogen equivalent electric power output by electric hydrogen conversion deviceAnd power consumptionSatisfies the following equality constraints:
in the formula, ηele-P2H80% of the energy conversion efficiency of the device for converting electricity into hydrogen is obtained.
total energy of hydrogen in hydrogen energy storage device in next time periodNot only the energy of the hydrogen injected by the device for converting electricity into hydrogen in the time periodIn connection with this, the hydrogen energy consumed by the hydrogen fuel cell during this period of time is also concernedThe following steps are involved:
where Δ t represents a unit time period, which may be 1 hour;
in the formula (I), the compound is shown in the specification,representing the residual hydrogen amount of the hydrogen storage tank in the current time period;respectively is the upper limit and the lower limit of the residual gas storage amount of the gas storage tank; wherein the upper limitEqual to the installed capacity of the gas storage tank
Hydrogen fuel cell power generationWith the consumption of hydrogenThe following equality constraints are satisfied:
in the formula, ηFCThe comprehensive conversion efficiency of the hydrogen fuel cell can be 55%.
referring to fig. 3, fig. 3 is a schematic structural diagram of a distributed photovoltaic and gas-electricity hybrid capacity planning system of a multi-energy complementary system according to an embodiment, including:
the building module 10 is configured to build an original scene data set according to the illumination intensity data in each unit statistical period in the total statistical period and the load curve data of the multi-energy complementary system in each unit statistical period; the original scene data set comprises a plurality of illumination intensity data and a plurality of load curve data in each unit statistical period;
a reduction module 20, configured to perform scene reduction on the original scene data set to obtain two illumination intensity data and two load curve data of each unit statistical period, and input the two illumination intensity data and the two load curve data into a preset multi-energy complementary system capacity planning model respectively;
and the planning module 30 is configured to calculate a capacity planning parameter when the capacity planning model of the multi-energy complementary system obtains a minimum value, and plan the distributed photovoltaic and gas-electricity hybrid capacity of the multi-energy complementary system according to the capacity planning parameter.
In one embodiment, the total statistical period is one year; each unit statistical period in the total statistical period is four quarters in one year respectively; the illumination intensity data in the unit counting period is illumination intensity data of each day in a corresponding quarter, and the load curve data in the unit counting period is load curve data of each day in the corresponding quarter.
The technical characteristics and the beneficial effects described in the embodiment of the distributed photovoltaic and gas-electricity hybrid capacity planning method for the multi-energy complementary system are applicable to the embodiment of the distributed photovoltaic and gas-electricity hybrid capacity planning system for the multi-energy complementary system, and therefore the description is made.
Based on the examples described above, there is also provided in one embodiment a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the distributed photovoltaic and combined gas and electric capacity planning method of a multi-energy complementary system as described above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and executed by at least one processor of a computer system according to the embodiments of the present invention, to implement the processes of the embodiments including the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Based on the above example, referring to fig. 4, the present invention further provides a computer device 60, which includes a memory 61, a processor 62 and a computer program stored on the memory 62 and executable on the processor 61, wherein the processor 61 executes the program to implement the distributed photovoltaic and gas-electricity hybrid capacity planning method of any one of the above embodiments.
The computer device 60 may include an intelligent processing device such as a computer. It will be appreciated by those skilled in the art that the memory 61 stores computer programs, and that the processor 62 may be configured to execute other executable instructions stored by the memory 61 corresponding to the description of the distributed photovoltaic and combined gas and electric capacity planning method embodiment of the multi-energy complementary system described above.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A distributed photovoltaic and gas-electricity hybrid capacity planning method for a multi-energy complementary system is characterized by comprising the following steps:
constructing an original scene data set according to the illumination intensity data in each unit statistical period in the total statistical period and the load curve data of the multi-energy complementary system in each unit statistical period; the original scene data set comprises a plurality of illumination intensity data and a plurality of load curve data in each unit statistical period;
performing scene reduction on the original scene data set to obtain two illumination intensity data and two load curve data of each unit statistical period, and respectively inputting the two illumination intensity data and the two load curve data into a preset multi-energy complementary system capacity planning model;
calculating a capacity planning parameter when the capacity planning model of the multi-energy complementary system obtains a minimum value, and planning the distributed photovoltaic and gas-electricity mixed capacity of the multi-energy complementary system according to the capacity planning parameter; the capacity planning model of the multi-energy complementary system is as follows:
in the formula, I is a set of distributed photovoltaic power generation equipment, an electric gas conversion device and a hydrogen fuel cell; cCP,iEqual annual equipment investment cost corresponding to the ith equipment capacity; cM,iAnnual maintenance costs for the ith equipment;is a constant coefficient; j represents a set of unit statistical periods; pijCounting the probability of the period for the jth cell; omegaPV,jCounting the illumination intensity scene of the period for the jth unit; sPV,jCounting a set of illumination intensity scenes of a period for the jth cell; omegaL,jCounting the load demand scenario of the period for the jth unit; sL,jA set of load demand scenarios for a jth cell statistics period;counting the probability of the illumination intensity scene of the period for the jth unit;counting the probability of the load scene of the period for the jth unit; t is a time period set of each statistical time unit;and counting system operation cost parameters of a cycle, a omega scene and a t time period for the jth unit.
2. The method for planning mixed photovoltaic and gas-electric capacity of a distributed system according to claim 1, wherein the total statistical period is one year; each unit statistical period in the total statistical period is four quarters in one year respectively; the illumination intensity data in the unit counting period is illumination intensity data of each day in a corresponding quarter, and the load curve data in the unit counting period is load curve data of each day in the corresponding quarter.
3. The method of claim 1, wherein the system operating cost parameter is a distributed photovoltaic and combined gas and electricity capacity planning method for a multi-energy complementary systemThe calculation formula of (2) is as follows:
in the formula, N represents a distribution network load node set,the node electricity consumption cost parameters of the jth unit statistical period, the omega scene, the tth time period and the nth node are represented,the network loss equivalent cost parameters of the jth unit statistical period, the omega scene and the tth time period are represented,representing the comprehensive operation cost parameters of the power-to-methane device of the jth unit statistical period, the omega scene and the tth time period,the operation cost parameters of the electric-to-hydrogen device representing the jth unit statistical period, the omega scene and the tth time period,representing the distributed photovoltaic power generation light abandoning cost parameters of the jth unit statistical period, the omega scene and the tth time period,representing the operation income parameters of the hydrogen fuel cell of the jth unit statistical cycle, the omega scene and the tth time period,and representing the operation income parameters of the distributed photovoltaic power generation in the jth unit statistical period, the omega scene and the tth time period.
4. The distributed photovoltaic and gas-electricity hybrid capacity planning method for the multi-energy complementary system according to claim 3, wherein the jth unit statistical period, the ω -th scenario, and the node electricity utilization cost of the nth node in the distribution network at the t-th time periodThe calculation formula of the parameters is as follows:
in the formula (I), the compound is shown in the specification,representing the load size of the nth node in the distribution network in the t time period under the corresponding scene of the jth unit statistical period;under the corresponding scene of the jth unit counting period, the distributed power generation amount of the nth node in the distribution network in the tth time period includes distributed photovoltaic power generation and fuel cells, N is a distribution network load node set, N ∈ N and lambdaloadFor the power price parameter of distribution network, assuming that the load is an industrial and commercial user, and temporarily not considering the time-of-use power price and the step power price, lambdaloadIs taken asload1-membered/kWh.
5. The method for planning the capacity of a distributed photovoltaic and gas-electric hybrid system according to claim 1, wherein the i-th equipment capacity corresponds to an equal-annual-value equipment investment cost CCP,iThe calculation formula of (2) is as follows:
wherein r is the discount rate βiPrice per capacity for the ith device; pCapacity,iCapacity of the ith device; r isiThe cost parameters of designing, installing, debugging and matching secondary equipment investment of the ith equipment account for the proportion of the cost parameters of the equipment; y isiThe financial cycle for the ith device.
6.The method for planning the capacity of a distributed photovoltaic and gas-electric hybrid system according to claim 1, wherein the annual maintenance cost C of the ith plantM,iThe calculation formula of (2) is as follows:
CM,i=kM,iPCapacity,i,
in the formula, kM,iAn annual maintenance cost factor for unit capacity of the ith equipment; pCapacity,iThe capacity of the ith device.
7. A distributed photovoltaic and gas-electricity hybrid capacity planning system for a multi-energy complementary system, comprising:
the building module is used for building an original scene data set according to the illumination intensity data in each unit statistical period in the total statistical period and the load curve data of the multi-energy complementary system in each unit statistical period; the original scene data set comprises a plurality of illumination intensity data and a plurality of load curve data in each unit statistical period;
the reduction module is used for carrying out scene reduction on the original scene data set to obtain two illumination intensity data and two load curve data of each unit statistical period, and the two illumination intensity data and the two load curve data are respectively input into a preset multi-energy complementary system capacity planning model;
the planning module is used for calculating a capacity planning parameter when the capacity planning model of the multi-energy complementary system obtains the minimum value, and planning the distributed photovoltaic and gas-electricity mixed capacity of the multi-energy complementary system according to the capacity planning parameter; the capacity planning model of the multi-energy complementary system is as follows:
in the formula, I is a set of distributed photovoltaic power generation equipment, an electric gas conversion device and a hydrogen fuel cell; cCP,iEqual annual equipment investment cost corresponding to the ith equipment capacity; cM,iAnnual maintenance costs for the ith equipment;is a constant coefficient; j represents a set of unit statistical periods; pijCounting the probability of the period for the jth cell; omegaPV,jCounting the illumination intensity scene of the period for the jth unit; sPV,jCounting a set of illumination intensity scenes of a period for the jth cell; omegaL,jCounting the load demand scenario of the period for the jth unit; sL,jA set of load demand scenarios for a jth cell statistics period;counting the probability of the illumination intensity scene of the period for the jth unit;counting the probability of the load scene of the period for the jth unit; t is a time period set of each statistical time unit;and counting system operation cost parameters of a cycle, a omega scene and a t time period for the jth unit.
8. The distributed photovoltaic and combined gas and electricity capacity planning system according to claim 7, wherein the total statistical period is one year; each unit statistical period in the total statistical period is four quarters in one year respectively; the illumination intensity data in the unit counting period is illumination intensity data of each day in a corresponding quarter, and the load curve data in the unit counting period is load curve data of each day in the corresponding quarter.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method for distributed photovoltaic and combined gas and electric capacity planning for a multi-energy complementary system according to any one of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, implements the distributed photovoltaic and combined gas and electric capacity planning method of a multi-energy complementary system according to any one of claims 1 to 7.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710546330.4A CN107392791B (en) | 2017-07-06 | 2017-07-06 | Distributed photovoltaic and gas-electricity hybrid capacity planning method and system for multi-energy complementary system |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710546330.4A CN107392791B (en) | 2017-07-06 | 2017-07-06 | Distributed photovoltaic and gas-electricity hybrid capacity planning method and system for multi-energy complementary system |
Publications (2)
Publication Number | Publication Date |
---|---|
CN107392791A CN107392791A (en) | 2017-11-24 |
CN107392791B true CN107392791B (en) | 2020-06-30 |
Family
ID=60335404
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201710546330.4A Active CN107392791B (en) | 2017-07-06 | 2017-07-06 | Distributed photovoltaic and gas-electricity hybrid capacity planning method and system for multi-energy complementary system |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN107392791B (en) |
Families Citing this family (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108023348B (en) * | 2017-12-13 | 2020-10-23 | 国网安徽省电力有限公司 | Method for determining optimal peak shaving installed scale of wind power based on outgoing channel |
CN108599146B (en) * | 2018-04-09 | 2021-09-21 | 华南理工大学 | Method for configuring capacity of household photovoltaic and battery energy storage system by considering stepped electricity price |
CN112994116B (en) * | 2021-02-03 | 2022-11-22 | 国网能源研究院有限公司 | Coal-fired and biomass power generation capacity planning method for rural areas |
CN117689087B (en) * | 2024-02-04 | 2024-05-10 | 山东大学 | Solar event-oriented photovoltaic field station output prediction method and system |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103490410A (en) * | 2013-08-30 | 2014-01-01 | 江苏省电力设计院 | Micro-grid planning and capacity allocation method based on multi-objective optimization |
KR20150058690A (en) * | 2013-11-20 | 2015-05-29 | 한국전기연구원 | Apparatus for determining optimal capacity of distributed energy resources in stand-alone micro-grid system and method thereof |
CN105139085A (en) * | 2015-08-13 | 2015-12-09 | 浙江工业大学 | Micro-grid and micro-source capacity optimization address distribution method based on islanding |
CN106451529A (en) * | 2016-08-09 | 2017-02-22 | 国网浙江省电力公司湖州供电公司 | Method for planning capacities of distributed power supplies and capacitors |
CN106600022A (en) * | 2015-10-20 | 2017-04-26 | 上海交通大学 | Wind-light-gas-seawater pumped storage isolated power system capacity optimal configuration method based on multi-objective optimization |
-
2017
- 2017-07-06 CN CN201710546330.4A patent/CN107392791B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103490410A (en) * | 2013-08-30 | 2014-01-01 | 江苏省电力设计院 | Micro-grid planning and capacity allocation method based on multi-objective optimization |
KR20150058690A (en) * | 2013-11-20 | 2015-05-29 | 한국전기연구원 | Apparatus for determining optimal capacity of distributed energy resources in stand-alone micro-grid system and method thereof |
CN105139085A (en) * | 2015-08-13 | 2015-12-09 | 浙江工业大学 | Micro-grid and micro-source capacity optimization address distribution method based on islanding |
CN106600022A (en) * | 2015-10-20 | 2017-04-26 | 上海交通大学 | Wind-light-gas-seawater pumped storage isolated power system capacity optimal configuration method based on multi-objective optimization |
CN106451529A (en) * | 2016-08-09 | 2017-02-22 | 国网浙江省电力公司湖州供电公司 | Method for planning capacities of distributed power supplies and capacitors |
Also Published As
Publication number | Publication date |
---|---|
CN107392791A (en) | 2017-11-24 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109510224B (en) | Capacity allocation and operation optimization method combining photovoltaic energy storage and distributed energy | |
CN108009693B (en) | Grid-connected micro-grid double-layer optimization method based on two-stage demand response | |
CN106849190B (en) | A kind of microgrid real-time scheduling method of providing multiple forms of energy to complement each other based on Rollout algorithm | |
Abolhosseini et al. | A review of renewable energy supply and energy efficiency technologies | |
CN109523060A (en) | Ratio optimization method of the high proportion renewable energy under transmission and distribution network collaboration access | |
CN108764519B (en) | Optimal configuration method for capacity of park energy Internet energy equipment | |
CN107392791B (en) | Distributed photovoltaic and gas-electricity hybrid capacity planning method and system for multi-energy complementary system | |
CN111681130B (en) | Comprehensive energy system optimal scheduling method considering conditional risk value | |
CN102930351B (en) | A kind of synthesis energy saving Optimized Operation daily planning generation method | |
CN103577891B (en) | A kind of micro-network optimization chemical combination of many isolated islands containing distributed power source makes operation method | |
Wu et al. | Optimal generation scheduling of a microgrid | |
CN112583017A (en) | Hybrid micro-grid energy distribution method and system considering energy storage operation constraint | |
CN114330827B (en) | Distributed robust self-scheduling optimization method for multi-energy flow virtual power plant and application thereof | |
CN106529737A (en) | Planning and distribution method for peak load regulation power source on supply side of power distribution network | |
Mohammadi et al. | Optimal operation management of microgrids using the point estimate method and firefly algorithm while considering uncertainty | |
Tan et al. | Feasibility study on the construction of multi-energy complementary systems in rural areas—Eastern, central, and western parts of China are taken as examples | |
CN111668878A (en) | Optimal configuration method and system for renewable micro-energy network | |
Song et al. | A fuzzy‐based multi‐objective robust optimization model for a regional hybrid energy system considering uncertainty | |
CN114301081B (en) | Micro-grid optimization method considering storage battery energy storage life loss and demand response | |
Stadler | Model-based sizing of building energy systems with renewable sources | |
Xiao et al. | A multi‐energy complementary coordinated dispatch method for integrated system of wind‐photovoltaic‐hydro‐thermal‐energy storage | |
CN115204562A (en) | Interconnected micro energy network distributed collaborative optimization scheduling method and system considering multi-energy sharing | |
CN116914732A (en) | Deep reinforcement learning-based low-carbon scheduling method and system for cogeneration system | |
CN115936336A (en) | Virtual power plant capacity configuration and regulation operation optimization method | |
Wei et al. | Optimal generation planning in a micro-grid for supplying electrical and thermal loads in order to reduce pollutant emissions |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |