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 PDF

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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
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capacity
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scene
illumination intensity
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马溪原
雷金勇
郭晓斌
李鹏
周长城
练依情
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CSG Electric Power Research Institute
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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

Distributed photovoltaic and gas-electricity hybrid capacity planning method and system for multi-energy complementary system
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:
Figure BDA0001343209170000071
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;
Figure BDA0001343209170000072
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;
Figure BDA0001343209170000073
counting the probability of the illumination intensity scene of the period for the jth unit;
Figure BDA0001343209170000074
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;
Figure BDA0001343209170000075
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.
As one embodiment, the system operation cost parameter
Figure BDA0001343209170000076
The calculation formula of (c) may be:
Figure BDA0001343209170000077
in the formula, N represents a distribution network load node set,
Figure BDA0001343209170000081
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,
Figure BDA0001343209170000082
the network loss equivalent cost parameters of the jth unit statistical period, the omega scene and the tth time period are represented,
Figure BDA0001343209170000083
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,
Figure BDA0001343209170000084
an operation cost parameter of the hydrogen conversion device representing the jth unit statistical period and the omega scene,
Figure BDA0001343209170000085
representing the distributed photovoltaic power generation light abandoning cost parameters of the jth unit statistical period, the omega scene and the tth time period,
Figure BDA0001343209170000086
representing the operation income parameters of the hydrogen fuel cell of the jth unit statistical cycle, the omega scene and the tth time period,
Figure BDA0001343209170000087
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 parameter
Figure BDA0001343209170000088
Determined by the calculation formula of
Figure BDA0001343209170000089
Has 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:
Figure BDA00013432091700000810
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 scene
Figure BDA0001343209170000091
The calculation formula of (a) is as follows:
Figure BDA0001343209170000092
in the formula (I), the compound is shown in the specification,
Figure BDA0001343209170000093
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;
Figure BDA0001343209170000094
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 period
Figure BDA0001343209170000095
The calculation formula of (a) is as follows:
Figure BDA0001343209170000096
in the formula (I), the compound is shown in the specification,
Figure BDA0001343209170000097
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 period
Figure BDA0001343209170000098
The calculation formula of (a) is as follows:
Figure BDA0001343209170000099
in the formula, λgasFor the price of natural gas, 2.5 yuan/m 3 can be taken;
Figure BDA00013432091700000910
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%;
Figure BDA00013432091700000911
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 period
Figure BDA0001343209170000101
The calculation formula of (a) is as follows:
Figure BDA0001343209170000102
in the formula (I), the compound is shown in the specification,
Figure BDA0001343209170000103
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 period
Figure BDA0001343209170000104
The calculation formula of (a) is as follows:
Figure BDA0001343209170000105
in the formula (I), the compound is shown in the specification,
Figure BDA0001343209170000106
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,
Figure BDA0001343209170000107
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 parameters
Figure BDA0001343209170000108
The calculation formula of (a) is as follows:
Figure BDA0001343209170000109
in the formula (I), the compound is shown in the specification,
Figure BDA00013432091700001010
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 period
Figure BDA00013432091700001011
The calculation formula of (a) is as follows:
Figure BDA00013432091700001012
in the formula (I), the compound is shown in the specification,
Figure BDA00013432091700001013
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:
Figure BDA0001343209170000111
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;
Figure BDA0001343209170000112
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:
Figure BDA0001343209170000113
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:
Figure BDA0001343209170000114
in the formula (I), the compound is shown in the specification,
Figure BDA0001343209170000115
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:
Figure BDA0001343209170000121
in the formula (I), the compound is shown in the specification,
Figure BDA0001343209170000122
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:
Figure BDA0001343209170000123
in the formula (I), the compound is shown in the specification,
Figure BDA0001343209170000124
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;
Figure BDA0001343209170000125
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 system
Figure BDA0001343209170000126
Intensity 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
Figure BDA0001343209170000127
Figure BDA0001343209170000128
Figure BDA0001343209170000129
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 device
Figure BDA00013432091700001210
And power consumption
Figure BDA00013432091700001211
The relationship is as follows:
Figure BDA0001343209170000131
power consumption of electric methane-converting apparatus
Figure BDA0001343209170000132
Is loaded by itCapacity_P2GAnd (3) constraint:
Figure BDA0001343209170000133
(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 device
Figure BDA0001343209170000134
And power consumption
Figure BDA0001343209170000135
Satisfies the following equality constraints:
Figure BDA0001343209170000136
in the formula, ηele-P2H80% of the energy conversion efficiency of the device for converting electricity into hydrogen is obtained.
Power consumption of hydrogen conversion device
Figure BDA0001343209170000137
Is restricted by the installation thereof:
Figure BDA0001343209170000138
total energy of hydrogen in hydrogen energy storage device in next time period
Figure BDA0001343209170000139
Not only the energy of the hydrogen injected by the device for converting electricity into hydrogen in the time period
Figure BDA00013432091700001310
In connection with this, the hydrogen energy consumed by the hydrogen fuel cell during this period of time is also concerned
Figure BDA00013432091700001311
The following steps are involved:
Figure BDA00013432091700001312
where Δ t represents a unit time period, which may be 1 hour;
Figure BDA00013432091700001313
in the formula (I), the compound is shown in the specification,
Figure BDA00013432091700001314
representing the residual hydrogen amount of the hydrogen storage tank in the current time period;
Figure BDA00013432091700001315
respectively is the upper limit and the lower limit of the residual gas storage amount of the gas storage tank; wherein the upper limit
Figure BDA00013432091700001316
Equal to the installed capacity of the gas storage tank
Figure BDA00013432091700001317
Figure BDA0001343209170000141
Hydrogen fuel cell power generation
Figure BDA0001343209170000142
With the consumption of hydrogen
Figure BDA0001343209170000143
The following equality constraints are satisfied:
Figure BDA0001343209170000144
in the formula, ηFCThe comprehensive conversion efficiency of the hydrogen fuel cell can be 55%.
Hydrogen fuel cell power generation
Figure BDA0001343209170000145
Also subject to its installation constraints, namely:
Figure BDA0001343209170000146
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:
Figure FDA0002480107440000011
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;
Figure FDA0002480107440000012
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;
Figure FDA0002480107440000013
counting the probability of the illumination intensity scene of the period for the jth unit;
Figure FDA0002480107440000014
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;
Figure FDA0002480107440000015
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 system
Figure FDA0002480107440000021
The calculation formula of (2) is as follows:
Figure FDA0002480107440000022
in the formula, N represents a distribution network load node set,
Figure FDA0002480107440000023
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,
Figure FDA0002480107440000024
the network loss equivalent cost parameters of the jth unit statistical period, the omega scene and the tth time period are represented,
Figure FDA0002480107440000025
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,
Figure FDA0002480107440000026
the operation cost parameters of the electric-to-hydrogen device representing the jth unit statistical period, the omega scene and the tth time period,
Figure FDA0002480107440000027
representing the distributed photovoltaic power generation light abandoning cost parameters of the jth unit statistical period, the omega scene and the tth time period,
Figure FDA0002480107440000028
representing the operation income parameters of the hydrogen fuel cell of the jth unit statistical cycle, the omega scene and the tth time period,
Figure FDA0002480107440000029
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 period
Figure FDA00024801074400000210
The calculation formula of the parameters is as follows:
Figure FDA00024801074400000211
in the formula (I), the compound is shown in the specification,
Figure FDA00024801074400000212
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;
Figure FDA00024801074400000213
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:
Figure FDA0002480107440000031
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:
Figure FDA0002480107440000041
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;
Figure FDA0002480107440000042
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;
Figure FDA0002480107440000043
counting the probability of the illumination intensity scene of the period for the jth unit;
Figure FDA0002480107440000044
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;
Figure FDA0002480107440000045
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.
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