CN112465263A - Comprehensive energy operation optimization method suitable for multiple scenes - Google Patents
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Abstract
The invention discloses a comprehensive energy operation optimization method suitable for multiple scenes, which comprises the following steps: the method comprises the four steps of establishing an energy flow structure diagram of the comprehensive energy system, establishing a single-target operation optimization model, establishing an operation optimization model of the comprehensive energy system and performing operation optimization on the comprehensive energy system through the operation optimization model of the comprehensive energy system. The method comprises the steps of establishing an energy flow structure diagram of the comprehensive energy system by analyzing typical characteristics of the comprehensive energy system, establishing a single-target operation optimization model by taking the lowest annual total operation cost as a target, establishing the comprehensive energy operation optimization model by taking power supply of a power grid, operation of energy supply equipment, power balance, reliability and the like as constraints, and finally solving the comprehensive energy operation optimization model by using a genetic algorithm to obtain an optimal scheme, thereby providing a feasible method for comprehensive energy operation optimization research of a multi-element main body.
Description
Technical Field
The invention relates to the technical field of comprehensive energy system operation optimization, in particular to a comprehensive energy operation optimization method suitable for multiple scenes.
Background
At present, with the continuous deepening of the reform of the electric power market in China and the continuous development of the social and economic technology, the coupling relation between different energy supply systems (such as cold, heat, electricity, gas and other energy supply systems) is tighter, and a comprehensive energy system with a multi-element main body becomes a development trend. A large amount of research is carried out by many scholars at home and abroad for pursuing a clean and efficient energy system, improving the consumption rate of new energy and reducing the operation cost.
However, most of research is based on operation optimization of an electric-gas combined network coupling relation, with rapid development of renewable energy sources and wide application of various energy conversion devices, coupling relations among different energy supply systems are tighter, and an operation optimization method of a comprehensive energy system aiming at a multi-element main body does not achieve an ideal research result.
Therefore, how to provide a comprehensive energy operation optimization method capable of improving the consumption rate of new energy and reducing the operation cost is a problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a multi-scenario-adaptive comprehensive energy operation optimization method, which constructs an operation optimization model of a comprehensive energy system by analyzing the characteristics of the comprehensive energy system and aiming at the lowest annual total operation cost on the premise of establishing an energy flow structure diagram of the comprehensive energy system, thereby achieving the purpose of optimizing the operation of the comprehensive energy system with multiple main bodies.
In order to achieve the purpose, the invention adopts the following technical scheme:
a comprehensive energy operation optimization method adapting to multiple scenes comprises the following steps:
establishing an energy flow structure chart of the comprehensive energy system based on the characteristics of the comprehensive energy system;
establishing a single-target operation optimization model based on an operation optimization target of a diversified comprehensive energy system;
establishing an integrated energy system operation optimization model according to the energy flow structure diagram of the integrated energy system and the single-target operation optimization model;
and optimizing the operation of the comprehensive energy system through the comprehensive energy system operation optimization model.
The comprehensive energy system has the following basic characteristics:
the comprehensive energy system can realize organic coordination among different energy supply and utilization systems;
secondly, the comprehensive energy system can realize the optimal scheduling of energy by utilizing the interaction capacity among different energy supply and utilization systems;
the comprehensive energy system can realize the optimized utilization of various energy sources;
and fourthly, the comprehensive energy system can effectively reduce the carbon emission.
Based on the characteristics, the energy flow structure diagram of the comprehensive energy system is established according to the following steps:
firstly, researching the coupling characteristics of a comprehensive energy system, including the aspects of energy, economy, space-time, stability and the like; secondly, researching the coupling characteristics of key equipment of the comprehensive energy system; and finally, researching the transmission characteristics of the comprehensive energy system, including a power system, a thermodynamic system and a natural gas system.
Further, the operation optimization goal of the comprehensive energy system in the invention includes reducing the total juvenile operation cost as much as possible on the premise of meeting the user load demand, and based on the goal, establishing a single-goal operation optimization model, wherein the objective function expression of the single-goal operation optimization model is as follows:
Call＝Cinv+Cop+Com
wherein, CallFor full life cycle cost, CinvCost for facility construction, CopFor facility operating costs, ComAnd equipment maintenance costs.
Further, the calculation formula of the facility construction cost is as follows:
wherein n is the number of devices, nyIs the life cycle, in units of years, MiUnit price for construction of i-th equipment, CiFor ith device capacity, Rcon,tConverting the cost of the t year to the current year coefficient; rdeThe depreciation rate.
Further, the calculation formula of the facility operation cost is as follows:
wherein, Cint,jThe purchase cost of the jth energy in a year, Wbuy,j(t)、Wsell,j(t) the purchase and sales of the jth energy during the period t, Pbuy,j(t)、Psell,j(t) the purchase price and the sale price of the jth energy source in the t period respectively.
Further, the calculation formula of the equipment maintenance cost is as follows:
wherein, Com,jFor the operating maintenance costs of the j-th plant, Cpom,j、Ceom,jThe unit power and unit capacity operation and maintenance cost of j devices are respectively; pjThe output power of j devices; wch,j(t) is the amount of charge of the jth device during the t period; etach,jThe charging efficiency of the jth device.
Further, the process of establishing the comprehensive energy system operation optimization model specifically includes:
establishing constraint conditions according to the energy flow structure diagram of the comprehensive energy system;
and taking the lowest annual operation cost as a planning target, and constraining the objective function of the single-target operation optimization model through the constraint conditions to obtain the comprehensive energy system operation optimization model.
Further, the constraints include power balance constraints, plant power limit constraints, cold grid supply constraints, supply plant operation constraints, and reliability constraints.
Further, the process of optimizing the operation of the integrated energy system through the integrated energy system operation optimization model specifically includes:
initializing data, coding each device of the comprehensive energy system to obtain a population, and determining the initial population size and the maximum iteration times;
randomly generating an initialization population with the scale of N;
constructing a fitness function according to the comprehensive energy system operation optimization model, and calculating the individual fitness of the initialization population;
selecting, crossing and mutating the initialization population to generate a progeny population of the initialization population;
calculating economic and environmental target values of the filial generation population to obtain individual fitness of the filial generation population;
and selecting the optimal offspring from the mixed population of the parent population and the offspring population, iterating until the maximum iteration times is reached, and outputting the optimal scheme.
Further, the fitness function is expressed as:
fit＝min(F)
F＝min(Cin+Cop+Cmc+fce(p))
wherein fit is a fitness function, F is an objective function of the individual, CinFor the initial investment cost of the system investment, CopThe annual operating cost of the system in the life cycle, namely the expenses spent on purchasing natural gas, purchasing electricity from a power grid and the like by the system; cmcAnnual maintenance costs for the system; f. ofce(p) the annual carbon emission cost of the system.
The objective function is a target value of system economy and environment, and can directly reflect the quality of chromosomes, so the objective function is directly selected as a standard for evaluating the fitness. That is, the present invention directly treats the objective function as a fitness function.
The evaluation rule of the fitness in the invention is as follows: the smaller the objective function value of an individual is, the higher the fitness function is; conversely, a larger value of the objective function of an individual indicates a lower fitness.
Further, the process of selecting, crossing, and mutating the initialized population to generate the offspring population of the initialized population specifically includes:
selecting an individual: randomly selecting two individuals on the premise that the individuals can not be selected repeatedly;
selecting a cross mode: randomly distributing individual crossing modes from two crossing modes of row crossing and column crossing;
and (3) cross operation: interchanging columns of the two individuals behind the crossing position, and combining to generate two new individuals;
mutation operation: and carrying out mutation operation on the offspring chromosomes according to a preset mutation probability.
According to the technical scheme, compared with the prior art, the comprehensive energy operation optimization method suitable for multiple scenes is provided, the method is characterized in that an energy flow structure diagram of a comprehensive energy system is established by analyzing typical characteristics of the comprehensive energy system, a single-target operation optimization model is established by taking the total annual operation cost as the lowest goal, a comprehensive energy operation optimization model is established by taking power supply of a power grid, energy supply equipment operation, power balance, reliability and the like as constraints, finally, a genetic algorithm is used for solving the comprehensive energy operation optimization model to obtain an optimal scheme, and a feasible method is provided for comprehensive energy operation optimization research of a multi-element main body.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic diagram of an implementation flow of a multi-scenario-adaptive comprehensive energy operation optimization method provided by the present invention;
FIG. 2 is an energy flow diagram of an integrated energy system in a typical scenario according to an embodiment of the present invention;
FIG. 3 is an energy flow diagram of a comprehensive energy system when photovoltaic, power grid, CCHP, energy storage battery, and electric refrigeration equipment are selected in the embodiment of the present invention;
FIG. 4 is a schematic diagram of a flow chart of a single-target genetic algorithm implemented in an embodiment of the present invention;
FIG. 5 is a 24 hour electrical load curve for an energy center in an embodiment of the present invention;
FIG. 6 is a 24 hour thermal load curve for an energy center in an embodiment of the present invention;
FIG. 7 is a 24 hour cooling load curve for an energy center in an embodiment of the present invention;
FIG. 8 is a 24-hour wind speed curve at the center of the energy source in an embodiment of the present invention;
FIG. 9 is a 24-hour illumination curve of an energy center according to an embodiment of the present invention;
FIG. 10 is a comprehensive energy system energy flow diagram of energy center scenario A in an embodiment of the present invention;
FIG. 11 is a statistical chart of hourly charging and discharging data of the energy storage device in an embodiment of the invention;
fig. 12 is a statistical chart of power supply data of the combustion engine in each hour in the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to the attached drawing 1, the embodiment of the invention discloses a comprehensive energy operation optimization method suitable for multiple scenes, which comprises the following steps:
s1: and establishing an energy flow structure chart of the comprehensive energy system based on the characteristics of the comprehensive energy system.
The integrated energy system has some basic features: the comprehensive energy system can realize organic coordination among different energy supply and utilization systems; secondly, the comprehensive energy system can realize the optimal scheduling of energy by utilizing the interaction capacity among different energy supply and utilization systems; the comprehensive energy system can realize the optimized utilization of various energy sources; and fourthly, the comprehensive energy system can effectively reduce the carbon emission.
Comprehensive energy system is according to the all kinds of energy that can obtain in the region in this embodiment, to different grade type user's energy demand, carry out overall planning and coordination in links such as energy supply, conversion, transmission, storage, consumption, carry out integration planning and construction to energy supply equipment in the region, transmission pipe network etc. when satisfying the pluralism energy demand of using in the system, promote energy utilization efficiency effectively, promote the energy system of the novel integration of energy sustainable development, its typical scene energy flow graph is as shown in fig. 2.
Different devices can be selected under different specific scenes to generate different energy flow diagrams, for example, when photovoltaic devices, power grids, CCHP devices, energy storage batteries and electric refrigeration devices are selected, the energy flow diagrams are shown in fig. 3, and the transmission processes of electric energy flows, thermal energy flows and cold energy flows which are respectively transmitted to industrial users, commercial users and civil users are analyzed.
S2: and establishing a single-target operation optimization model based on the operation optimization target of the diversified comprehensive energy system, wherein the planning target of the comprehensive energy system is the lowest annual total operation cost on the premise of meeting the requirements.
The total life operating cost is the total cost of the whole scheme life cycle, including the facility construction cost, the facility operating cost and the equipment maintenance cost.
Thus, the objective function of the modeling is as follows:
Call＝Cinv+Cop+Com
wherein, CallFor full life cycle cost, CinvCost for facility construction, CopFor facility operating costs, ComAnd equipment maintenance costs.
The facility construction cost is calculated by the following formula:
wherein n is the number of devices, nyIs the life cycle, in units of years, MiUnit price for construction of i-th equipment, CiFor ith device capacity, Rcon,tConverting the cost of the t year to the current year coefficient; rdeThe depreciation rate.
Rcon,t、RdeThe calculation formulas of (A) and (B) are respectively as follows:
wherein, IRateThe annual interest rate.
The calculation formula of the facility operation cost is as follows:
wherein, Cint,jThe purchase cost of the jth energy in a year, Wbuy,j(t)、Wsell,j(t) the purchase and sales of the jth energy during the period t, Pbuy,j(t)、Psell,j(t) the purchase price and the sale price of the jth energy source in the t period respectively.
The calculation formula of the equipment maintenance cost is as follows:
wherein, Com,jFor the operating maintenance costs of the j-th plant, Cpom,j、Ceom,jThe unit power and unit capacity operation and maintenance cost of j devices are respectively; pjThe output power of j devices; wch,j(t) is the amount of charge of the jth device during the t period; etach,jThe charging efficiency of the jth device.
S3: and establishing an operation optimization model of the comprehensive energy system according to the energy flow structure diagram of the comprehensive energy system and the single-target operation optimization model.
The process of establishing the comprehensive energy system operation optimization model specifically comprises the following steps:
s301: establishing constraint conditions according to an energy flow structure diagram of the comprehensive energy system, wherein the constraint conditions specifically comprise:
1) power balance constraint
The power balance constraint covers the balance of electricity, heat and cold power, and the related formula is as follows:
PESS+PBUY＝PLP+PECR+PICE
wherein, PESSThe energy storage actual power is the transmitted power when the value is positive, and the absorbed power when the value is negative; pICELoad cold power; pBUYElectrical power that is the amount of electricity purchased from the grid; pECRLoad thermal power; pLPIs the load electric power.
2) Device power limit constraints
The output power of each device is limited, so the power P of the devicejThere are upper and lower limits, namely:
Pj.min≤Pj≤Pj.max
wherein, Pj.minLower limit of output power of jth device, Pj.maxThe upper limit of the output power of the jth device.
3) Cold net energy supply restraint
Wherein, CmaxFor maximum heating capacity of the cooling network, Pcmax iFor the cooling power consumption of the ith plant, Ucmax iCooling power for the i-th equipment, Lc maxThe cooling load is designed for the interior of the park of the comprehensive energy system.
4) Energy supply equipment operating constraints
Wherein: qmin iAnd Qmax iRespectively the minimum power and the maximum power of the cooling/heating of the ith equipment; qdown iAnd Δ Qup iThe ramp rates of the reduced output and the increased output of the ith equipment are respectively.
5) Reliability constraints
The insufficient amount of electric energy, heat energy and cold energy needs to meet the upper limit constraint, and the formula is as follows:
ΔLe s≤ΔEmax
ΔLq s≤ΔQmax
ΔLc s≤ΔCmax
wherein: delta Emax、ΔQmax、ΔCmaxThe upper limits of the insufficient electric energy, the insufficient heat energy and the insufficient cold energy are respectively.
S302: and taking the lowest annual operation cost as a planning target, and constraining the objective function of the single-target operation optimization model through constraint conditions to obtain the comprehensive energy system operation optimization model.
S4: and (4) performing operation optimization on the comprehensive energy system by using a single-target genetic algorithm through the comprehensive energy system operation optimization model.
Referring to fig. 4, the operation optimization process of the single-target genetic algorithm is as follows:
the first step is as follows: initializing data, coding each device of the comprehensive energy system to obtain a population, and determining the initial population size and the maximum iteration times;
the second step is that: randomly generating an initialization population S with the scale of N;
the third step: calculating individual fitness of the population;
the objective function is a target value of system economy and environment, and can directly reflect the quality of chromosomes, so the objective function is directly selected as a standard for evaluating the fitness. That is to say directly regarding the objective function as a fitness function. The expressions of the individual target function F and the individual fitness function fit are respectively as follows:
F＝min(Cin+Cop+Cmc+fce(p))
fit＝min(F)
the evaluation rule for the fitness of the method is as follows: the smaller the objective function value of an individual is, the higher the fitness function is; conversely, a larger value of the objective function of an individual indicates a lower fitness.
The fourth step: selecting, crossing and mutating the initialization population S and generating a child population Q of the initialization population S; the method specifically comprises the following steps:
step 1: individual selection, namely randomly selecting two individuals on the premise that the individuals can not be selected repeatedly;
step 2: selecting a crossing mode, randomly distributing an individual crossing mode, wherein the operation has two crossing modes of row crossing and column crossing;
and 4, step 4: performing cross operation, namely interchanging columns of the two individuals behind the cross position, and combining to generate two new individuals;
and 5: mutation operation, namely performing mutation operation on the offspring chromosomes according to the mutation probability PmCarrying out the following mutation operation, PmThe value is 0.001-0.1.
The fifth step: calculating economic and environmental target values of the offspring population Q to obtain individual fitness of the offspring population Q;
and a sixth step: and selecting the optimal offspring from the mixed population of the parent population and the offspring population, and iterating until the maximum iteration times is reached, and outputting the optimal scheme.
The following describes the implementation process of the above method with a specific example:
in the embodiment, the simulation is performed by using a certain energy center in Ningxia, and the effectiveness of the comprehensive energy operation optimization model established in the embodiment is verified.
The operation optimization is subjected to simulation on the basis of the annual electric load data, and the electric demand is shown in FIG. 5; because the heat demand data are not given in the provided data, the measurement and calculation are carried out on the 24-hour heat load condition of the energy center according to the typical load characteristics in a simulation mode, and a heat demand curve is shown in fig. 6; based on the data of the total cold load estimation in the construction period and according to the typical characteristic of the cold load, a cold load demand curve of the energy center for one year is simulated, and a cold demand curve is obtained and is shown in fig. 7, and 24-hour wind speed and illumination information of the energy center are respectively shown in fig. 8 and fig. 9.
According to the analysis of resources and terrains in the park, the upper limit of the installed photovoltaic capacity is set to 1200 kW. Due to the lack of alternative equipment data, the measurement and calculation are carried out by taking kilowatt as a unit, and the charge-discharge power and the capacity ratio of the energy storage and heat storage equipment are set as 1: 4, and setting the range of the available energy storage capacity to be 10-95%.
According to the user requirements, a multi-energy complementary intelligent energy system of photovoltaic, a fan, an energy storage battery, a gas turbine, a gas boiler, lithium bromide and electric refrigeration is built in the scene, and the scene of the measurement and calculation adopts an electric energy grid-connected non-internet-surfing mode. Wherein, the equipment construction scheme in the scene A is shown in the following table 1:
table 1 scene a equipment construction table
| Scene | Power supply equipment | Heating equipment | Cooling equipment |
| Scene A | Large power grid, photovoltaic, fan, energy storage battery and gas turbine | Gas turbine and gas boiler | Lithium bromide, electric refrigeration |
Accordingly, the integrated energy system energy flow for energy center scenario a is shown in fig. 10.
In this measurement, the annual cost of each equipment is calculated by adopting the full-period cost depreciation, and the specific parameters are as follows:
TABLE 2 Equipment cost Specifications
In this embodiment, the single power purchase cost and the single power annual operation and maintenance cost of each device are both simulation data, and can provide local actual condition data for measurement and calculation.
Planning the energy price: the electricity prices and gas prices in the planned section are shown in table 3 below, based on the data provided:
TABLE 3 time of use price
Because the price of the natural gas in the planned area of the center without providing energy is not provided, the price of the natural gas is calculated in a fixed price mode, and the price is 2.4 yuan/Nm3。
Because the planning side does not provide complete equipment parameter data and other equipment models and parameters which are not provided, the parameters of the equipment commonly used by the team are adopted in the measurement and calculation, and the main performance parameters are set as shown in the following table 4:
TABLE 4 in-System device Performance parameters
The output results were analyzed as follows:
the energy center is the existing equipment in the garden under this scene: photovoltaic, fan, energy storage battery, gas turbine, big electric wire netting, gas turbine, gas boiler, lithium bromide, electric refrigeration. Under the conditions that the installed capacity of a combustion engine is 2698kW, the installed capacity of a boiler is 3102kW, the installed capacity of lithium bromide is 300kW, the installed capacity of an electric refrigerator is 50kW and the like, the charging and discharging conditions of the energy storage device are that the energy storage device is discharged from 9 am to 3 pm, the electricity utilization peak is at the moment, the energy storage device is charged from 9 pm to 8 am at night, the electricity utilization valley is at the moment, and the energy storage battery is charged at the moment.
The output result can refer to fig. 11 and fig. 12, and it can be seen from the output result that the energy storage battery is discharged during the peak period of power utilization and is charged in the early morning or late night, so that the most economical and economic effect is achieved in the charging and discharging state. The original operation cost is about 5000 yuan, the comprehensive energy operation optimization model reaches about 3000 yuan, and therefore the operation cost of the park comprehensive energy system is reduced compared with the original operation cost through the optimization at the stage.
The measurement uses the current stage equipment parameters and the load data in the park, and the park is optimized on the premise that the load of the user is met by hundreds. However, since no consideration is given to the campus, the model number of the individual energy supply devices and the price are unknown. The data of the efficiency, the investment cost and the operation cost of all the equipment in the measurement and calculation are from the result consulted by the manufacturer.
The calculation considers the economic factors of the photovoltaic generator set and the electricity storage, heat storage and cold storage equipment, realizes the maximum investment and operation cost saving on the basis of meeting the load demand based on a plurality of users, and can meet the requirements of load fluctuation and future development in the park.
The power supply of the original operation scheme of the park is from a power grid. Considering that the photovoltaic power generation and the energy storage battery can cut peaks and fill valleys, responding to local time-of-use electricity price to the maximum extent, reducing electricity purchasing cost, and installing distributed photovoltaic and energy storage batteries in an electric system in an operation optimization scheme; meanwhile, a heat storage tank and an ice storage tank are respectively introduced into a cold and hot system, so that the pressure of a power grid is relieved.
Due to the fact that partial data are lost, the measurement and calculation use general parameters of equipment at the present stage and load simulation data in a park, and certain errors exist in results. Therefore, with the deep investigation, the measurement and calculation can continue to add corresponding constraint conditions, so that the constraint conditions are more in fit with the actual construction of the park.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A comprehensive energy operation optimization method adapting to multiple scenes is characterized by comprising the following steps:
establishing an energy flow structure chart of the comprehensive energy system based on the characteristics of the comprehensive energy system;
establishing a single-target operation optimization model based on an operation optimization target of a diversified comprehensive energy system;
establishing an integrated energy system operation optimization model according to the energy flow structure diagram of the integrated energy system and the single-target operation optimization model;
and optimizing the operation of the comprehensive energy system through the comprehensive energy system operation optimization model.
2. The comprehensive energy operation optimization method adapting to multiple scenes according to claim 1, wherein an objective function expression of the single-objective operation optimization model is as follows:
Call＝Cinv+Cop+Com
wherein, CallFor full life cycle cost, CinvCost for facility construction, CopFor facility operating costs, ComAnd equipment maintenance costs.
3. The comprehensive energy operation optimization method adapting to multiple scenes according to claim 2, wherein the calculation formula of the facility construction cost is as follows:
wherein n is the number of devices, nyIs the life cycle, in units of years, MiUnit price for construction of i-th equipment, CiFor ith device capacity, Rcon,tConverting the cost of the t year to the current year coefficient; rdeThe depreciation rate.
4. The comprehensive energy operation optimization method adapting to multiple scenes according to claim 2, wherein the calculation formula of the facility operation cost is as follows:
wherein, Cint,jFor the purchase of the jth energy in a year, nyIs the life cycle, in units of years, Wbuy,j(t)、Wsell,j(t) the purchase and sales of the jth energy during the period t, Pbuy,j(t)、Psell,j(t) the purchase price and the sale price of the jth energy source in the t period respectively.
5. The comprehensive energy operation optimization method suitable for multiple scenes according to claim 2, wherein the calculation formula of the equipment maintenance cost is as follows:
wherein, Com,jThe operating maintenance cost for the jth device; cpom,j、Ceom,jThe unit power and unit capacity operation and maintenance cost of j devices are respectively; pjThe output power of j devices; wch,j(t) is the amount of charge of the jth device during the t period; n isyIs the life cycle in units of years, etach,jThe charging efficiency of the jth device.
6. The multi-scenario-adaptive comprehensive energy operation optimization method according to claim 1, wherein the process of establishing the comprehensive energy system operation optimization model specifically comprises:
establishing constraint conditions according to the energy flow structure diagram of the comprehensive energy system;
and taking the lowest annual operation cost as a planning target, and constraining the objective function of the single-target operation optimization model through the constraint conditions to obtain the comprehensive energy system operation optimization model.
7. The method of claim 6, wherein the constraints include power balance constraints, equipment power limit constraints, cold grid energy supply constraints, energy supply equipment operation constraints, and reliability constraints.
8. The multi-scenario-adaptive comprehensive energy operation optimization method according to claim 1, wherein the process of performing operation optimization on the comprehensive energy system through the comprehensive energy system operation optimization model specifically comprises:
initializing data, coding each device of the comprehensive energy system to obtain a population, and determining the initial population size and the maximum iteration times;
randomly generating an initialization population with the scale of N;
constructing a fitness function according to the comprehensive energy system operation optimization model, and calculating the individual fitness of the initialization population;
selecting, crossing and mutating the initialization population to generate a progeny population of the initialization population;
calculating economic and environmental target values of the filial generation population to obtain individual fitness of the filial generation population;
and selecting the optimal offspring from the mixed population of the parent population and the offspring population, iterating until the maximum iteration times is reached, and outputting the optimal scheme.
9. The method of claim 8, wherein the fitness function is expressed as:
fit＝min(F)
F＝min(Cin+Cop+Cmc+fce(p))
wherein fit is a fitness function, F is an objective function of the individual, CinFor the initial investment cost of the system investment, CopThe annual operating cost of the system in the life cycle; cmcAnnual maintenance costs for the system; f. ofce(p) the annual carbon emission cost of the system.
10. The method according to claim 8, wherein the process of selecting, crossing, and mutating the initialized population to generate the offspring population of the initialized population comprises:
selecting an individual: randomly selecting two individuals on the premise that the individuals can not be selected repeatedly;
selecting a cross mode: randomly distributing individual crossing modes from two crossing modes of row crossing and column crossing;
and (3) cross operation: interchanging columns of the two individuals behind the crossing position, and combining to generate two new individuals;
mutation operation: and carrying out mutation operation on the offspring chromosomes according to a preset mutation probability.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113159567A (en) * | 2021-04-19 | 2021-07-23 | 北京交通大学 | Industrial park off-grid scheduling method considering power failure time uncertainty |
| CN113793052A (en) * | 2021-09-23 | 2021-12-14 | 长沙理工大学 | Robust optimization scheduling method for regional comprehensive energy system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109784569A (en) * | 2019-01-23 | 2019-05-21 | 华北电力大学 | A kind of regional complex energy resource system optimal control method |
| CN110110897A (en) * | 2019-04-11 | 2019-08-09 | 华北电力大学 | A kind of integrated energy system optimization method considering different storage energy operation strategies |
| CN110110904A (en) * | 2019-04-17 | 2019-08-09 | 华北电力大学 | Consider the integrated energy system optimization method of economy, independence and carbon emission |
| CN111523213A (en) * | 2020-04-15 | 2020-08-11 | 南京清然能源科技有限公司 | Artificial intelligence-based optimized energy supply method for electricity core type comprehensive energy system |
| CN111899120A (en) * | 2020-06-19 | 2020-11-06 | 国网浙江省电力有限公司经济技术研究院 | Method for establishing comprehensive energy planning and operation combined optimization model |
-
2020
- 2020-12-11 CN CN202011461613.7A patent/CN112465263A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109784569A (en) * | 2019-01-23 | 2019-05-21 | 华北电力大学 | A kind of regional complex energy resource system optimal control method |
| CN110110897A (en) * | 2019-04-11 | 2019-08-09 | 华北电力大学 | A kind of integrated energy system optimization method considering different storage energy operation strategies |
| CN110110904A (en) * | 2019-04-17 | 2019-08-09 | 华北电力大学 | Consider the integrated energy system optimization method of economy, independence and carbon emission |
| CN111523213A (en) * | 2020-04-15 | 2020-08-11 | 南京清然能源科技有限公司 | Artificial intelligence-based optimized energy supply method for electricity core type comprehensive energy system |
| CN111899120A (en) * | 2020-06-19 | 2020-11-06 | 国网浙江省电力有限公司经济技术研究院 | Method for establishing comprehensive energy planning and operation combined optimization model |
Non-Patent Citations (1)
| Title |
|---|
| 孙强 等: "含电-热-冷-气负荷的园区综合能源系统经济优化调度研究" * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113159567A (en) * | 2021-04-19 | 2021-07-23 | 北京交通大学 | Industrial park off-grid scheduling method considering power failure time uncertainty |
| CN113793052A (en) * | 2021-09-23 | 2021-12-14 | 长沙理工大学 | Robust optimization scheduling method for regional comprehensive energy system |
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