CN111126675A - Multi-energy complementary microgrid system optimization method - Google Patents

Multi-energy complementary microgrid system optimization method Download PDF

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CN111126675A
CN111126675A CN201911234674.7A CN201911234674A CN111126675A CN 111126675 A CN111126675 A CN 111126675A CN 201911234674 A CN201911234674 A CN 201911234674A CN 111126675 A CN111126675 A CN 111126675A
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power
microgrid system
investment
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energy complementary
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李重杭
苏宁
王哲
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Shenzhen Power Supply Bureau Co Ltd
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    • 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
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Abstract

The application relates to a multi-energy complementary microgrid system optimization method. According to the multi-energy complementary microgrid system optimization method, the parameters of the multi-energy complementary microgrid system are obtained, and an objective function taking the lowest electricity purchase cost and gas purchase cost as optimization targets is established. And optimizing the objective function by taking system operation power balance, power grid operation, distributed power supply operation, supervision limitation and renewable energy capacity limitation as constraint conditions to obtain optimized fuel consumption parameters and electricity purchasing parameters. And optimizing the investment recovery period of the multi-energy complementary micro-grid system according to the optimized fuel consumption parameters and the optimized electricity purchasing parameters. The method comprehensively integrates the traditional genetic optimization algorithm and the modern intelligent mixed integer linear programming optimization algorithm, and develops the efficient and practical combined optimization algorithm so as to improve the utilization efficiency and the power generation efficiency of equipment and ensure the economic benefit and the social benefit of power enterprises.

Description

Multi-energy complementary microgrid system optimization method
Technical Field
The application relates to the field of power supply, in particular to a multi-energy complementary microgrid system optimization method.
Background
The core idea of the energy internet is the interaction of various energy sources. In the process of the Shenzhen city power grid developing to the international advanced level, the Shenzhen city power grid has the characteristics of high power supply reliability, high power quality requirement, high load density and high load peak-valley difference. Meanwhile, under the condition of insufficient resource investment, especially serious shortage of land resources, in order to meet the requirements of internationalization and modernization development of Shenzhen city, efficient utilization methods of various energy sources need to be researched.
Microgrid systems may have a variety of operational objectives, the most common and practical ones among which to meet load demands, reduce operating costs, gain economic benefits, and reduce carbon emissions. Because real-time data processed by an energy management system in the operation of the microgrid has uncertainty, the uncertainty increases along with the increase of time, and the prediction precision is reduced, the economic operation optimization is a very complex system optimization problem.
Disclosure of Invention
Based on the method, the traditional genetic optimization algorithm and the modern intelligent mixed integer linear programming optimization algorithm are integrated comprehensively, and the efficient and practical combined optimization algorithm is developed so as to improve the utilization efficiency and the power generation efficiency of equipment and ensure the economic benefit and the social benefit of power enterprises.
A multi-energy complementary microgrid system optimization method comprises the following steps:
s10, acquiring the parameters of the multi-energy complementary microgrid system, and establishing an objective function taking the lowest electricity and gas purchasing costs as an optimization objective;
s20, optimizing the objective function by taking system operation power balance, power grid operation, distributed power supply operation, supervision limitation and capital limitation as constraint conditions to obtain optimized fuel consumption parameters and electricity purchasing parameters;
and S30, optimizing the investment recovery period of the multi-energy complementary microgrid system according to the optimized fuel consumption parameters and the optimized electricity purchasing parameters.
In one embodiment, the objective function is
Figure BDA0002304572230000021
Wherein, CDIs a typical daily fee, Pgrid,hFor purchasing power from the large grid in the h-th hour, Ce,hThe h hour electricity rate, Pgas,hAmount of natural gas to be purchased in the h-th hour system, CgasIs the natural gas price.
In one embodiment, the system operates with a power balance constraint of
Figure BDA0002304572230000022
Figure BDA0002304572230000023
Figure BDA0002304572230000024
The power grid operation constraint is Pgrid,h≤Pgrid,max
The distributed power supply operating constraint is
Figure BDA0002304572230000025
Figure BDA0002304572230000026
The capital limit is constrained to
Figure BDA0002304572230000027
Wherein L isEo,hFor pure electrical loads, LC,hIn order to electrically cool the load,
Figure BDA0002304572230000028
is the sum of the electrical output power of the distributed power supply,
Figure BDA0002304572230000031
for non-renewable distributed power supply electrical output power, PPV,hIn order to output the power for the photovoltaic system,
Figure BDA0002304572230000032
for storing energy and outputting power, LTs,hFor heating load, LTw,hFor hot water load, QT,AC,hAbsorbed heat power, Q, required for refrigeration in absorption refrigeration systemsGB,hThe heat power for supplying heat for the gas boiler burning natural gas,
Figure BDA0002304572230000033
thermal power, Q, provided by waste heat recovery of a cogeneration systemTS0,hThermal power, P, output for thermal energy storagegrid,maxIn order to reach the upper limit of the capacity of the power grid,
Figure BDA0002304572230000034
respectively represent the upper limit and the lower limit of the electric power output of the ith type distributed power supply,
Figure BDA0002304572230000035
respectively representing the upper limit and the lower limit of the thermal power output of the ith type distributed energy, wherein delta K is the added investment after the microgrid system is accessed, IDER,iInitial investment costs for class i distributed facilities, CbatFor replacement of stored energy costs, IairInvestment for electric air-conditioning, CairFor replacement of electric air-conditioners, IfuelInvest for spare gas turbine.
In one embodiment, when the distributed equipment is discrete equipment, the initial investment cost of the ith type of distributed equipment is expressed as
IDER,i＝IIvp,i*PN,i
Wherein, IIvp,iRefers to the variable investment cost per unit power, P, of the class i equipmentN,iIs the rated power of device i.
In one embodiment, when the distributed equipment is continuous equipment, the initial investment cost of the ith type of distributed equipment is expressed as
IDER,i＝IIf,i+IIvp,i*EN,i
Wherein, IIf,iMeans a fixed initial investment cost for equipment i, EN,iRefers to the rated capacity of device i.
In one embodiment, the step S30 of optimizing the investment recovery period of the multi-energy complementary microgrid system according to the optimized fuel consumption parameter and the optimized electricity purchasing parameter includes:
acquiring annual electricity purchasing cost and annual gas purchasing cost according to the optimized fuel consumption parameter and the optimized electricity purchasing parameter, and further acquiring the operation cost of the multi-energy complementary micro-grid system;
and providing the operation cost before the access of the multi-energy complementary microgrid system, and obtaining the income after the access of the multi-energy complementary microgrid system according to the operation cost of the multi-energy complementary microgrid system so as to obtain the investment recovery period of the multi-energy complementary microgrid system.
In one embodiment, when the return on investment from the beginning year of the investment is:
Figure BDA0002304572230000041
wherein R is the income after the multi-energy complementary microgrid system is accessed, K0For the total investment of the project, T0In the construction period.
In one embodiment, when the return on investment period from the year on production is:
Figure BDA0002304572230000042
and R is the income of the multi-energy complementary microgrid system after the microgrid system is accessed.
A computer device comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor implements the steps of the method for optimizing a multi-energy complementary microgrid system according to any one of the preceding embodiments when executing the computer program.
A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for optimizing a multi-energy complementary microgrid system according to any one of the preceding embodiments.
According to the multi-energy complementary microgrid system optimization method, the parameters of the multi-energy complementary microgrid system are obtained, and an objective function taking the lowest electricity purchase cost and gas purchase cost as optimization targets is established. And optimizing the objective function by taking system operation power balance, power grid operation, distributed power supply operation, supervision limitation and renewable energy capacity limitation as constraint conditions to obtain optimized fuel consumption parameters and electricity purchasing parameters. And optimizing the investment recovery period of the multi-energy complementary micro-grid system according to the optimized fuel consumption parameters and the optimized electricity purchasing parameters. The method comprehensively integrates the traditional genetic optimization algorithm and the modern intelligent mixed integer linear programming optimization algorithm, and develops the efficient and practical combined optimization algorithm so as to improve the utilization efficiency and the power generation efficiency of equipment and ensure the economic benefit and the social benefit of power enterprises.
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Fig. 1 is a flowchart of a method for optimizing a multi-energy complementary microgrid system according to an embodiment of the present application;
fig. 2 is a flowchart of a system optimization solution according to an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the present application are described in detail below with reference to the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of embodiments in many different forms than those described herein and those skilled in the art will be able to make similar modifications without departing from the spirit of the application and it is therefore not intended to be limited to the embodiments disclosed below.
It will be understood that when an element is referred to as being "disposed on" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1, the present application provides a method for optimizing a multi-energy complementary microgrid system. The method for optimizing the multi-energy complementary microgrid system comprises the following steps:
and S10, acquiring the parameters of the multi-energy complementary microgrid system, and establishing an objective function taking the lowest electricity and gas purchasing costs as an optimization objective. In step S10, the parameters of the multi-energy complementary microgrid system may include device characteristic parameters, device economic parameters, load prediction parameters, peripheral device capacity parameters, photovoltaic power generation prediction parameters, price prediction parameters, and the like.
And S20, optimizing the objective function by taking system operation power balance, power grid operation, distributed power supply operation, supervision limitation and capital limitation as constraint conditions to obtain optimized fuel consumption parameters and electricity purchasing parameters. In step S20, the system operation power balance includes electric power balance and cold power balance. The regulatory limits include minimum energy utilization, maximum gas emission limits, generator annual operating hours limits, and the like. The constraints may also include renewable energy capacity limits. The upper limit and the lower limit of the renewable energy capacity limit are determined according to the actual construction environment of the microgrid construction site, and the contents of the part are embodied in the optimization calculation in the form of optimization variables.
And S30, optimizing the investment recovery period of the multi-energy complementary microgrid system according to the optimized fuel consumption parameters and the optimized electricity purchasing parameters. In step S30, the payback period may include a static payback period.
In this embodiment, the method for optimizing the multi-energy complementary microgrid system obtains parameters of the multi-energy complementary microgrid system, and establishes an objective function with the lowest electricity and gas purchase costs as an optimization target. And optimizing the objective function by taking system operation power balance, power grid operation, distributed power supply operation, supervision limitation and renewable energy capacity limitation as constraint conditions to obtain optimized fuel consumption parameters and electricity purchasing parameters. And optimizing the investment recovery period of the multi-energy complementary micro-grid system according to the optimized fuel consumption parameters and the optimized electricity purchasing parameters. The method comprehensively integrates the traditional genetic optimization algorithm and the modern intelligent mixed integer linear programming optimization algorithm, and develops the efficient and practical combined optimization algorithm so as to improve the utilization efficiency and the power generation efficiency of equipment and ensure the economic benefit and the social benefit of power enterprises.
Referring to fig. 2, the multi-energy complementary energy supply microgrid system uses a mixed integer nonlinear programming, and adopts an economically optimal single-target dynamic scheduling to realize optimal scheduling, wherein the scheduling takes the lowest electricity purchase cost and gas purchase cost of the system in 24 hours per typical day as an optimal target. In one embodiment, the objective function is
Figure BDA0002304572230000061
Wherein, CDIs a typical daily fee, Pgrid,hFor purchasing power from the large grid in the h-th hour, Ce,hThe h hour electricity rate, Pgas,hAmount of natural gas to be purchased in the h-th hour system, CgasIs the natural gas price.
In one embodiment, the electrical power balance constraint is
Figure BDA0002304572230000071
Figure BDA0002304572230000072
In the formula (2), the left side of the equal sign is the electric power demand of the microgrid in the h period. The right side of the equal sign is the electric power source.
The cold power balance constraint is
Figure BDA0002304572230000073
In the formula (3), the left side of the equal sign is the thermal power requirement of the microgrid at the h time period. The right side of the equal sign is a heat power source in the h time period of the microgrid. The thermal power requirement in the h period can be smaller than the thermal power provided by the microgrid, at the moment, the waste heat recovered by the CHP system cannot be completely utilized, the waste heat is wasted, and the thermal power requirement cannot be larger than the upper limit of the thermal power provided by the microgrid.
When the microgrid system runs, the microgrid is specified not to transmit power to the power grid, and the capacity of the power grid is considered to have an upper limit, namely, the power grid operation constraint is Pgrid,h≤Pgrid,maxFormula (4)
The distributed power supply operating constraint is
Figure BDA0002304572230000074
Figure BDA0002304572230000075
For the generator, the upper power limit is rated power, and the lower power limit is a certain proportion of the rated power, such as 0.5 times of the rated power; for electricity/cold storage energy, the upper power limit represents the maximum discharge/cold power, and the lower power limit represents the maximum charge/cold storage power.
The capital limit is constrained to
Figure BDA0002304572230000081
Wherein L isEo,hFor pure electrical loads, LC,hIn order to electrically cool the load,
Figure BDA0002304572230000082
is the sum of the electrical output power of the distributed power supply,
Figure BDA0002304572230000083
for non-renewable distributed power supply electrical output power, PPV,hIn order to output the power for the photovoltaic system,
Figure BDA0002304572230000084
for storing energy and outputting power, LTs,hFor heating load, LTw,hFor hot water load, QT,AC,hAbsorbed heat power, Q, required for refrigeration in absorption refrigeration systemsGB,hThe heat power for supplying heat for the gas boiler burning natural gas,
Figure BDA0002304572230000085
thermal power, Q, provided by waste heat recovery of a cogeneration systemTS0,hThermal power, P, output for thermal energy storagegrid,maxIn order to reach the upper limit of the capacity of the power grid,
Figure BDA0002304572230000086
respectively represent the upper limit and the lower limit (kW) of the electric power output of the ith type distributed power supply,
Figure BDA0002304572230000087
Figure BDA0002304572230000088
respectively representing the upper limit and the lower limit (kW) of the thermal power output of the ith type distributed energy, wherein delta K is the added investment after the microgrid system is accessed, IDER,iInitial investment costs for class i distributed facilities, CbatFor replacement of stored energy costs, IairInvestment for electric air-conditioning, CairFor replacement of electric air-conditioners, IfuelInvest for spare gas turbine.
The calculation formula of the initial investment cost and the operation and maintenance cost of the distributed equipment can have different expression forms according to different equipment, and the equipment is divided into discrete equipment (such as a combustion engine) and continuous equipment (such as storage batteries, PV and the like) according to different equipment. In one of which is selectableIn the embodiment of (1), when the distributed equipment is discrete equipment, the initial investment cost of the ith type of distributed equipment is represented as IDER,i＝IIvp,i*PN,iFormula (7)
Wherein, IIvp,iRefers to the variable investment cost per unit power, P, of the class i equipmentN,iThe initial investment cost of the discrete equipment is mainly determined by the rated power of the equipment, wherein the capacity of the equipment is not continuous, and the equipment is selected by taking the number of the equipment, such as the equipment of a combustion engine.
In one alternative embodiment, the continuous type equipment refers to equipment with continuous equipment capacity, and the initial investment cost of the equipment is related to the equipment capacity and the fixed cost of the equipment investment when the equipment is selected by taking the equipment capacity as a selection unit, such as a storage battery, a photovoltaic device and the like. When the distributed equipment is continuous equipment, the initial investment cost of the ith type of distributed equipment is represented as IDER,i＝IIf,i+IIvp,i*EN,iFormula (8)
Wherein, IIf,iMeans a fixed initial investment cost for equipment i, EN,iRefers to the rated capacity of device i.
In one embodiment, the step S30 of optimizing the investment recovery period of the multi-energy complementary microgrid system according to the optimized fuel consumption parameter and the optimized electricity purchasing parameter includes:
and S310, acquiring annual electricity purchasing cost and annual gas purchasing cost according to the optimized fuel consumption parameters and the optimized electricity purchasing parameters, and further acquiring the operation cost of the multi-energy complementary micro-grid system. The operation cost of the multi-energy complementary micro-grid system is
Figure BDA0002304572230000091
Wherein, CEFor annual electricity purchase charge, CFFor annual gas purchase cost, COM，iThe annual operating and maintenance cost of the i-th equipment is referred to.
The annual electricity purchasing cost is calculated and considered comprehensively, and the monthly demand cost and the electricity purchasing fixed cost of the electricity fee are considered besides the real-time electricity price cost. The electricity purchase cost calculation formula is as follows:
CE＝12*Cfc+Cdc+Cgridformula (10)
Wherein, CfcFixed charge for electricity charge (Yuan/Yuan), CdcAnnual demand charge (Yuan /), CgridWhich means the annual electricity charge.
The monthly demand cost of electricity purchase is mainly determined by the monthly maximum power of electricity purchase, and the calculation formula is as follows:
Figure BDA0002304572230000092
wherein, Cdc，mMeans the mth month demand charge (Yuan/kW-month), max Pgrid，mAnd in the mth month, the electricity is purchased at the maximum per hour.
The annual real-time electricity price cost is related to the real-time electricity price and the electricity consumption per hour, and the calculation formula is as follows:
Figure BDA0002304572230000093
wherein, Cε,hThe h hour electricity price (yuan/kWh), Pgrid，m，d，hMeans that the electricity quantity (kW) is purchased at the h hour on the typical day d of the m month, Nm，dRefers to the number of days on typical day d of month m.
The annual gas purchase cost consists of annual gas consumption cost and fixed gas purchase cost, and the calculation formula is as follows:
CF＝12*Cfcf+Cgasformula (13)
Wherein, CfcfFixed cost of fuel (Yuan/Yuan), CgasNatural gas price (yuan).
The fuel consumption cost is determined by the gas consumption and the gas price, and the electricity and electricity purchasing cost is calculated according to the following formula:
Figure BDA0002304572230000101
wherein, Cgas，mIs the fuel price (yuan/kW), Pgas，m，d，hRefers to fuel consumption (kW).
The operation and maintenance cost of the discrete type equipment consists of fixed operation and maintenance cost and variable operation and maintenance cost, and the calculation formula is as follows:
COM,i＝cOMf,i*PN,i+cOMv,i*Ea,iformula (15)
Wherein, COMf，iMeans the unit rated power fixed operation maintenance cost (yuan/kW), C of the equipment iOMv，iVariable operating maintenance cost per unit power (dollar/kWh), E for a device ia，iRefers to the annual output (kWh) of device i.
The operation and maintenance cost of the continuous type equipment is only related to the fixed operation and maintenance cost per rated capacity (power), and the calculation formula is as follows:
COM,i＝cOMf,i*EN,iformula (16)
Wherein, COMf，iRefers to the fixed operation and maintenance cost (yuan/kWh/year or yuan/kW/year) of the ith type of equipment per rated capacity (power).
And S320, providing the operation cost before the access of the multi-energy complementary microgrid system, and obtaining the income after the access of the multi-energy complementary microgrid system according to the operation cost of the multi-energy complementary microgrid system so as to obtain the investment recovery period of the multi-energy complementary microgrid system.
In step S320, the pre-access operation cost of the multi-energy complementary microgrid system is
Csys＝Cele+CcoFormula (17)
Wherein, CeleCost for consumption of pure electric load, CcoThe cost of power consumption for the refrigeration load.
The yield after the multi-energy complementary microgrid system is accessed is
R＝Csys-CDERFormula (17)
In one alternative embodiment, when the return on investment from the beginning year of the investment is:
Figure BDA0002304572230000111
wherein R is the income after the multi-energy complementary microgrid system is accessed, K0For the total investment of the project, T0In the construction period.
In one of the alternative embodiments, when the return on investment period from the year on production is:
Figure BDA0002304572230000112
and R is the income of the multi-energy complementary microgrid system after the microgrid system is accessed.
When the analysis of the static investment recovery period is carried out aiming at the investment increased after the system is accessed, the calculation formula is as follows:
Figure BDA0002304572230000113
wherein R istNet profit (i.e. net cash flow) for the t year after project input operation, K0For a full investment of a project, j is the year in which the investment is fully recovered, and t refers to time (year).
In the application, double-layer control is adopted, and the NSGA-II genetic algorithm is adopted at the periphery to carry out system design optimization so as to optimize the capacity. And the inner layer adopts mixed integer linear programming to carry out scheduling optimization on the output of each device by taking the economic optimization as a target, and returns an optimization result (increment investment recovery period) to the periphery as a system adaptability value.
It should be noted that the dynamic optimization scheduling part takes the output of seven devices such as each combustion engine and the like and the power grid in a twenty-four hour system as optimization variables, and adds the limits of the electric energy storage and ice storage air conditioner SOC and the flag bits of the combustion engine on/off, etc., to total 288 variables to be optimized. Solving each optimized variable to obtain five initial solutions of x, y, z, s and w, determining the solving direction of the initial solutions to obtain a corrected solution, and finally comparing the size of the dual gap with the size of an initial given value (randomly calculated by a genetic algorithm), and outputting an optimal solution if the dual gap is less than or equal to the initial given value; if the dual gap is greater than the initial given value, the correction solution is returned. And if the iteration times exceed N, outputting a solution. The start-stop flag bit of the combustion engine is taken as an integral part. Please refer to fig. 2.
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 application, and the description thereof is more specific and detailed, but not construed as limiting the claims. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A multi-energy complementary microgrid system optimization method is characterized by comprising the following steps:
s10, acquiring the parameters of the multi-energy complementary microgrid system, and establishing an objective function taking the lowest electricity and gas purchasing costs as an optimization objective;
s20, optimizing the objective function by taking system operation power balance, power grid operation, distributed power supply operation, supervision limitation and capital limitation as constraint conditions to obtain optimized fuel consumption parameters and electricity purchasing parameters;
and S30, optimizing the investment recovery period of the multi-energy complementary microgrid system according to the optimized fuel consumption parameters and the optimized electricity purchasing parameters.
2. The method of claim 1, wherein the objective function is
Figure FDA0002304572220000011
Wherein, CDIs a typical daily fee, Pgrid,hFor purchasing power from the large grid in the h-th hour, Ce,hThe h hour electricity rate, Pgas,hAmount of natural gas to be purchased in the h-th hour system, CgasIs the natural gas price.
3. The method of claim 2, wherein the system operating power balance constraint is
Figure FDA0002304572220000012
Figure FDA0002304572220000013
Figure FDA0002304572220000014
The power grid operation constraint is
Pgrid,h≤Pgrid,max
The distributed power supply operating constraint is
Pi low≤Pi,h≤Pi up
Figure FDA0002304572220000021
The capital limit is constrained to
Figure FDA0002304572220000022
Wherein L isEo,hFor pure electrical loads, LC,hIn order to electrically cool the load,
Figure FDA0002304572220000023
is the sum of the electrical output power of the distributed power supply,
Figure FDA0002304572220000024
for non-renewable distributed power supply electrical output power, PPV,hIn order to output the power for the photovoltaic system,
Figure FDA0002304572220000025
for storing energy and outputting power, LTs,hFor heating load, LTw,hFor hot water load, QT,AC,hAbsorbed heat power, Q, required for refrigeration in absorption refrigeration systemsGB,hThe heat power for supplying heat for the gas boiler burning natural gas,
Figure FDA0002304572220000026
thermal power, Q, provided by waste heat recovery of a cogeneration systemTS0,hThermal power, P, output for thermal energy storagegrid,maxTo the upper limit of the grid capacity, Pi up、Pi lowRespectively represent the upper limit and the lower limit of the electric power output of the ith type distributed power supply,
Figure FDA0002304572220000027
respectively representing the upper limit and the lower limit of the thermal power output of the ith type distributed energy, wherein delta K is the added investment after the microgrid system is accessed, IDER,iInitial investment costs for class i distributed facilities, CbatFor replacement of stored energy costs, IairInvestment for electric air-conditioning, CairFor replacement of electric air-conditioners, IfuelInvest for spare gas turbine.
4. The method as claimed in claim 3, wherein when the distributed devices are discrete devices, the initial investment cost of the ith type of distributed device is expressed as
IDER,i＝IIvp,i*PN,i
Wherein, IIvp,iRefers to the variable investment cost per unit power, P, of the class i equipmentN,iIs the rated power of device i.
5. The method as claimed in claim 3, wherein when the distributed devices are continuous devices, the initial investment cost of the ith distributed device is expressed as
IDER,i＝IIf,i+IIvp,i*EN,i
Wherein, IIf,iMeans a fixed initial investment cost for equipment i, EN,iRefers to the rated capacity of device i.
6. The method for optimizing the multipotential complementary microgrid system according to claim 1, wherein the step of optimizing the investment recovery period of the multipotential complementary microgrid system according to the optimized fuel consumption parameter and the optimized electricity purchasing parameter at S30 comprises:
acquiring annual electricity purchasing cost and annual gas purchasing cost according to the optimized fuel consumption parameter and the optimized electricity purchasing parameter, and further acquiring the operation cost of the multi-energy complementary micro-grid system;
and providing the operation cost before the access of the multi-energy complementary microgrid system, and obtaining the income after the access of the multi-energy complementary microgrid system according to the operation cost of the multi-energy complementary microgrid system so as to obtain the investment recovery period of the multi-energy complementary microgrid system.
7. The method for optimizing the multipotential complementation microgrid system according to claim 6, wherein when the investment recovery period from the beginning of the investment is:
Figure FDA0002304572220000031
wherein R is the income after the multi-energy complementary microgrid system is accessed, K0For the total investment of the project, T0In the construction period.
8. The method for optimizing the multipotential complementation microgrid system according to claim 6, wherein when the investment recovery period from the production year is:
Figure FDA0002304572220000032
and R is the income of the multi-energy complementary microgrid system after the microgrid system is accessed.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the steps of the method for optimizing a multipotent complementary microgrid system according to any one of claims 1 to 8 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps of the method for optimizing a multipotent complementary microgrid system according to any one of claims 1 to 8.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112329260A (en) * 2020-11-24 2021-02-05 国网黑龙江省电力有限公司电力科学研究院 Multi-energy complementary micro-grid multi-element multi-target optimization configuration and optimization operation method
CN114928105A (en) * 2022-06-01 2022-08-19 中国长江三峡集团有限公司 Flexible power grid configuration method and device, electronic equipment and storage medium

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130024014A1 (en) * 2011-07-20 2013-01-24 Nec Laboratories America, Inc. Optimal energy management of a rural microgrid system using multi-objective optimization
CN104112169A (en) * 2014-06-19 2014-10-22 广东电网公司电力科学研究院 Energy supply system optimization configuration method targeting shortest pay back period
CN109217373A (en) * 2018-11-03 2019-01-15 国网上海市电力公司 Store up lotus coordination optimizing method in the source of business garden energy mix system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130024014A1 (en) * 2011-07-20 2013-01-24 Nec Laboratories America, Inc. Optimal energy management of a rural microgrid system using multi-objective optimization
CN104112169A (en) * 2014-06-19 2014-10-22 广东电网公司电力科学研究院 Energy supply system optimization configuration method targeting shortest pay back period
CN109217373A (en) * 2018-11-03 2019-01-15 国网上海市电力公司 Store up lotus coordination optimizing method in the source of business garden energy mix system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
刘文建: "微网能量管理与容量优化设计方法研究", 《中国优秀博硕士学位论文全文数据库（硕士）》 *
汪伟 等: "冷热电蓄能联供微网系统优化配置的影响因素", 《电力系统及其自动化学报》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112329260A (en) * 2020-11-24 2021-02-05 国网黑龙江省电力有限公司电力科学研究院 Multi-energy complementary micro-grid multi-element multi-target optimization configuration and optimization operation method
CN114928105A (en) * 2022-06-01 2022-08-19 中国长江三峡集团有限公司 Flexible power grid configuration method and device, electronic equipment and storage medium
CN114928105B (en) * 2022-06-01 2023-07-25 中国长江三峡集团有限公司 Flexible power grid configuration method and device, electronic equipment and storage medium

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