CN105373842A - Micro-grid energy optimization and evaluation method based on full energy flow model - Google Patents
Micro-grid energy optimization and evaluation method based on full energy flow model Download PDFInfo
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Abstract
The invention relates to a micro-grid energy optimization and evaluation method based on a full energy flow model. The method includes following steps: (1) building the full energy flow model: including energy and energy flow analysis; energy conversion link analysis, energy distribution link analysis, energy storage link analysis, and energy utilization link and energy management link analysis; (2) building a full energy flow network model; (3) performing energy optimization management based on the full energy flow network model including energy static optimization management and energy dynamic optimization management; (4) establishing an index system of the full energy flow model; and (5) performing comprehensive evaluation of the full energy flow model. According to the method, processes of generation, conversion, transmission, storage, and utilization of different types of energies in a micro-grid are described via the manner of digital abstract representation, the index system is established, the micro-grid energy optimization method is proposed, unified quantification of different types of energies is realized via digital representation of energy flow, and comprehensive analysis and evaluation of energy efficiency utilization of overall and partial links are performed.
Description
Technical field
The present invention relates to a kind of optimization and evaluation method of micro-grid system, be specifically related to a kind of microgrid energy optimization and evaluation method based on all-round flow model.
Background technology
Going from bad to worse and non-renewable energy rare in recent years along with environment, obtains fast development with the renewable clean energy generation technology that wind energy, sun power are representative.Wherein, by small-size wind power-generating, photovoltaic generation etc. flexibly, access user distribution network dispersedly, local power reliability can be improved, reduce transmission losses, improve the utilization factor of primary energy and reduce toxic emission.Access the wind of distribution, light power generating system etc. by this way and be collectively referred to as distributed power generation.Micro-capacitance sensor refers to that it both can be incorporated into the power networks, also can from network operation by distributed power generation, energy storage, load autonomous intelligence system integrated together with protecting control device.
Dynamic perfromance in micro-capacitance sensor and energy management problem, there is no systematized optimum management and appraisal procedure at present.In existing management method, often therrmodynamic system and electric system separated, by different quantizing factor, difference control and management is carried out to them, thus have ignored relation that is interrelated between them and that intercouple, can not the feature of systematized description links energy flux, each link economic benefit of comprehensive assessment.
Summary of the invention
For the deficiencies in the prior art, the object of this invention is to provide a kind of microgrid energy optimization and evaluation method based on all-round flow model, this method propose a kind of concept of all-round flow model, and by the mode of digital abstract sign, the generation of dissimilar energy in micro-capacitance sensor is described, conversion, transmission, store and utilize process, establish systematized index system, and propose a kind of microgrid energy optimization method based on this, by the digital representation that can flow, realize the unified quantization of the dissimilar energy, can utilize overall and portion link efficiency and carry out comprehensive analysis and evaluation, and for the lower link of efficiency, be optimized improvement, thus realize multi-level oil ZOOM analysis, improve global energy Optimum utilization level.
The object of the invention is to adopt following technical proposals to realize:
The invention provides a kind of microgrid energy optimization and evaluation method based on all-round flow model, its improvements are, described method comprises the steps:
(1) all-round flow model is built: comprise energy and energy flow analysis, energy conversion link analysis, energy distribution link analysis, stored energy link analysis, Energy harvesting link and energy management link analysis;
(2) all-round flow network model is built;
(3) based on the energy-optimised management of all-round flow network model, the management of energy static optimization and energy dynamics optimum management is comprised;
(4) index system of all-round flow model is set up;
(5) to carrying out comprehensive eye exam by flow model.
Further, in described step (1), energy dvielement, device dvielement and info class element all can be comprised by flow model; Energy dvielement is that energy internet carries out the main body of conversion by device dvielement, and info class element is the abstractdesription of energy dvielement, device dvielement;
Described energy dvielement comprises the dissimilar primary energy of occurring in nature and secondary energy; Device dvielement comprises dissimilar energy and produces, changes, transmits, stores, utilizes device and control device; Info class element comprises the digital model of dissimilar energy and the digital model of each equipment.
Further, in described step (1), the grade of energy refer to unit energy there is the ratio of available energy, be the important indicator of its quality of mark; Setting amount of energy parameter is Q, and taste parameter is A, and different energy sources type Conversion of measurement unit parameter is K, then another expression-form of energy is as follows:
E＝KAQ1)；
Described energy conversion link analysis is undertaken by energy conversion device, and energy conversion device is the device all can changed form of energy or characteristic in flow model, comprises the conversion equipment of different-energy type and the conversion equipment of different-energy characteristic; The conversion equipment of different-energy type comprises generator and motor; The conversion equipment of different-energy characteristic comprises transformer and current transformer; Input energy can be divided into by flow process, recover energy and off-energy by stream;
Described energy distribution link analysis is undertaken by power distribution means, power distribution means to distribute or to turn the device of confession in flow model, power distribution means comprises the transformer that automatically can regulate feeder line capacity and the electric power electric transformer automatically regulated, according to networking energy demand, automatically carry out distributing, regulate and storing;
Described stored energy link analysis is undertaken by energy storing device, and energy accumulating device is the device that all can store energy in flow model, and energy accumulating device comprises accumulator and regenerative apparatus; Unnecessary energy is stored, the energy that release stores when Power supply is not enough when Power supply is rich;
Described Energy harvesting link analysis is undertaken by energy utilization device, and energy utilization device is the device that all can utilize energy in flow model or consume, and energy utilization device comprises electric light and heating installation;
Described energy management link analysis is undertaken by energy management apparatus, and the information flow of Characterization Energy carries out circulation between each device with mutual; Energy management apparatus is the system platform of energy stream information being carried out to analyzing and processing and global optimization management, comprises the energy management system of micro-capacitance sensor; Can the stream information flow rate information that comprises the quality information of energy, quantity information and can flow.
Further, in described step (2), describedly all can be coupled together by device dvielement by flow network, form the combination of energy transfer passage, for circulation and the utilization of energy, comprise global optimization layer, Distributed Autonomous layer and Access Layer on the spot;
Described global optimization layer refers to that the energy of multistage micro-capacitance sensor in regional extent is interconnected, is transmitted and turn confession by the form of electric energy, and transmission range is 1km-10km; Distributed autonomous layer refers to that the energy of single micro-capacitance sensor in some areas is interconnected, and transmitted by the form of electric energy or heat energy and turned confession, transmission range is 100m-1km; Access Layer is access and the utilization of single energy source device on the spot, and the various energy is to hot and cold, electric demand energy conversion;
Described all can flow network to flow formula as follows:
E
out＝E
in-E
los＝D
mnE
in2)；
Wherein: E
outalways export energy; E
inat total input energy; E
losit is the total input energy of loss; D
mnit is the sign coefficient of all-round flow model.
Further, in described step (3), the management of energy static optimization is for the low energy conversion link of efficiency and Energy harvesting link, carries out optimum technological transformation and device upgrade, improve static conversion efficiency or the utilization ratio of the energy, the management of energy static optimization comprises:
Based on all-round flow model, on the basis of each link energy conversion or utilization ratio statistical study, choose conversion efficiency or the low link of utilization ratio successively, calculate respective technological transformation and device upgrade expense, the cost C of technological transformation and device upgrade scheme
ijrepresent by following expression formula:
C
ij＝Σf(D
ij)3)；
Wherein: Di
jbe energy conversion in energy network or utilize device; Through comparative analysis, make the cost Ci of technological transformation and device upgrade scheme
jreach minimum, then selecting technology transformation and device upgrade scheme, realize the energy static optimization management of micro-capacitance sensor;
Described energy dynamics optimum management comprises mode decision, prediction plan, actual motion and Optimized Operation, and circulates successively;
Mode decision: undertaken dropping into by distributed power source, energy storage and load in microgrid or out of service time, it all can the information element class in flow model change thereupon, microgrid control system upgrades electrical network topological structure automatically according to information element change, determines the operational mode of micro-capacitance sensor;
Prediction plan: according to formation that is hot and cold, electric load, in conjunction with energy grade characteristic, extrapolated short-term forecasting value and the ultra-short term predicted value of energy production and utilization by neural network algorithm; According to predicting the outcome, in conjunction with productive target, formulate the production schedule of every day;
Actual motion: in the actual moving process of micro-capacitance sensor, can there are differences, and the conversion between the dissimilar energy also can there are differences between actual energizing quantity and demand energizing quantity, comprises the deficiency of actual heat supply or power supply or the deficiency of thermoelectricity conversion; The each service data of Real-time Collection, statistical demand difference value, determines to optimize and revise target;
Optimized Operation: on the basis of predicting plan and actual motion, in conjunction with micro-capacitance sensor stable operation constraint, energy conversion and utilize the conditions such as device constraint, make each objective function in micro-grid system minimum or very big, comprise that economic benefit is maximum, minimal energy loss, financial cost are minimum.And based on this, the scheduling energy supply of energy conversion apparatus and energy utilization device use energy.
Further, in described step (4), microgrid the evaluation object of flow model all can comprise energy class, device class, info class and system class; Evaluation index comprises: high efficiency, stability, security, reliability, economy, quality and accuracy.
Further, described high efficiency is reflection microgrid device, the transfer capability of the energy conversion of micro-grid system or utilization ratio (if in the identical or shorter time conversion or the energy that utilizes more, then this energy conversion device is described, micro-grid system has high efficiency).
Further, described stability reflects under the runtime environment, and micro-grid system bears the ability of maintenance stable operation after unexpected disturbance.
Further, described security reflects under regulation running environment, the ability that micro-grid system occurs accident adaptibility to response or opposing security incident.
Further, described reliability refers to that micro-grid system is under specific environment, time and condition, and namely failure-free operation ability continues the ability of energy supply by acceptable quality level and quantity required to user.
Further, microgrid energy economy is energy use cost, comprises gas cost; Microgrid device economy is device acquisition cost; Micro-grid system economy is under guarantee safe operation of electric network reliable prerequisite, the direct economic benefit produced or because of Loss reducing, reduce costs the indirect benefit brought.
Further, the quality of micro-grid system comprises electric energy quality, heat energy quality.Electric energy quality mainly pays close attention to power quality problem, can provide the electric power supply of high-quality for power consumer, comprises stable voltage and exports and harmonic pollution, be described with rate of qualified voltage, total harmonic distortion factor and three-phase imbalance index.
Further, the accuracy of micro-grid system refers to the accuracy of the communication information, and the information model comprising apparatus is consistent, and the information content of transmission is true and reliable, is described with model consistency and information accuracy rate index.
Further, in described step (5), based on all can the comprehensive assessment of flow model be by the assessment of microgrid overall economics best performance Complex multi-target decision-making, be decomposed into multiple single technical index or economic index, corresponding index flexible strategy are calculated, by the weighting determination overall economics best performance desired value of all indexs by qualitative index Fuzzy Quantifying;
To all can carrying out comprehensive eye exam and comprise the steps: by flow model
<1> sets up comprehensive eye exam model;
<2>, for the every evaluation index calculated, by qualitative index fuzzy quantization, is converted into the definite value of 0-9;
<3> carries out each subordinate index and determines the importance judgement of higher level's target and weighted value;
<4> carries out single assessment objective and determines the importance judgement of comprehensive assessment target and weighted value;
<5> determines overall economics best performance desired value.
Compared with the prior art, the beneficial effect that the present invention reaches is:
The theory proposing all-round flow model of novelty of the present invention, and propose a kind of microgrid energy optimum management and appraisal procedure based on this theory.The method is by the all-round streaming digital model of unified quantization, describe systematically to flow and producing, change, transmit, store and utilizing the energy response in process, contribute to optimizing two aspects from static state is energy-optimised with dynamic power, carry out the energy-optimised management of this energy network comprehensively, and carry out technical and economic evaluation.This theory is retrained by lump simultaneously, also can flexibly for energy-optimised management and the assessment of individual equipment.When practical application, according to demand, can links in the selection energy network of freedom and flexibility, the various situation of comprehensive consideration and the factor of influence under retraining, derive corresponding energy-optimised management method systematically, thus realize micro-capacitance sensor multi-level oil refinement energy management, improve global energy and utilize level, ensure that economic optimization runs.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the microgrid energy optimization and evaluation method based on all-round flow model provided by the invention.
Fig. 2 is all-round flow model figure provided by the invention;
Fig. 3 is all-round flow network illustraton of model provided by the invention;
Fig. 4 is energy dynamics Optimal management model figure provided by the invention;
Fig. 5 provided by the inventionly all can flow indicator evaluation system schematic diagram;
Fig. 6 is the all-round flow model figure of micro-capacitance sensor of specific embodiment provided by the invention;
Embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in further detail.
For the dynamic perfromance in current micro-capacitance sensor and energy management problem, there is no systematized optimum management and appraisal procedure at present.In existing management method, often therrmodynamic system and electric system separated, by different quantizing factor, difference control and management is carried out to them, thus have ignored relation that is interrelated between them and that intercouple, can not the feature of systematized description links energy flux, each link economic benefit of comprehensive assessment.
The theory proposing all-round flow model of novelty of the present invention, and provide a kind of microgrid energy optimization based on all-round flow model and appraisal procedure based on this theory.The process flow diagram of the method as shown in Figure 1, comprises the steps:
(1) all-round flow model is built: the all-round flow model of the present invention's proposition is formed primarily of three major types element: energy dvielement (Energy), device dvielement (Device), info class element (Information).Energy dvielement comprises dissimilar primary energy and the secondary energy of occurring in nature; Device dvielement mainly comprises the generation of dissimilar energy, conversion, transmission, storage, utilization and control device etc.; Info class element mainly comprises the digital model of dissimilar energy, the digital model etc. of each equipment.By the sign of this three dvielement, the all-round flow model in whole region can be set up.Concrete model structure is see Fig. 1.
This model is not only applicable to multilevel system, is also applicable to individual equipment, and according to different situations, the reasonable selection relative influence factor, can give full play to the guiding function of this model.Build and all can comprise energy and energy flow analysis, energy conversion link analysis, energy distribution link analysis, stored energy link analysis, Energy harvesting link and energy management link analysis by flow model;
1. energy and energy flow analysis:
Energy not only has the problem of quantity when conversion, also have the grade difference of energy.The grade of energy refer to unit energy there is the ratio of available energy, be the important indicator of its quality of mark.Setting amount of energy parameter is Q, and taste parameter is A, and different energy sources type Conversion of measurement unit parameter is K, then another expression-form of energy is as follows:
E＝KAQ1)；
2. energy conversion link analysis:
Energy conversion link is carried out mainly through energy conversion device, and energy flow is divided into input energy by flow process, recovers energy, off-energy and different energy product.Energy conversion device is the device all can changed form of energy/characteristic in flow model, mainly comprises the conversion equipment (as generator, motor etc.) of different-energy type, the conversion equipment (as transformer, current transformer etc.) of different-energy characteristic.As shown in Figure 2:
3. energy distribution link analysis:
Energy distribution link is carried out mainly through power distribution means, and energy flow is divided into input energy, storage power, off-energy and different output energy by flow process.Power distribution means to distribute/to turn the device (if automatically regulating transformer of feeder line capacity) of confession in flow model, can, according to each networking energy demand, automatically carry out distributing, regulate and storing.The electric power electric transformer that such as can automatically regulate.
4. energy storage link analysis:
Energy storage link is carried out mainly through energy accumulating device, and energy flow is divided into storage power by flow process, releases energy, off-energy.Energy accumulating device is the device (as accumulator, regenerative apparatus etc.) that all can store energy in flow model, can store unnecessary energy, the energy that release stores when Power supply is not enough when Power supply is rich.
5. Energy harvesting link analysis:
Energy harvesting link is carried out mainly through energy utilization device, and energy flow is divided into input energy by flow process, utilizes energy, off-energy.Energy utilization device is the device (electric light, heating installation etc.) that all can utilize energy/consume in flow model.
6. energy management link analysis:
Energy management link is carried out mainly through energy management apparatus, and the information flow of Characterization Energy carries out circulation between each device with mutual.Energy management apparatus is the system platform (energy management system as micro-capacitance sensor) of energy stream information being carried out to analyzing and processing and global optimization management.Can mainly comprise the information such as the quality of energy, quantity, the flow velocity that can flow by stream information.
(2) all-round flow network model is built:
All can flow network be by each device node contacts, an energy transfer passage combination of formation, thus realize circulation and the utilization of energy.As electric transmission network, thermodynamic transport net etc.Mainly properties of flow and its transmission channel all can determine according to dissimilar by flow network, be generally divided into global optimization layer, Distributed Autonomous layer and Access Layer on the spot.As shown in Figure 3:
All can D (m, n) be device D in flow model
mndigital representation model, comprise the attribute of this device multinomial, can according to practical application customization with expand, Ei
jpass through D
mnenergy, E
intotal input energy, E
outalways export energy.Described all can flow network to flow formula as follows:
E
out＝E
in-E
los＝D
mnE
in2)；
Wherein: E
outalways export energy; E
inat total input energy; E
losit is the total input energy of loss; D
mnall-round flow model device D
mnsign coefficient.
(3) based on the energy-optimised management of all-round flow network model
A) energy static optimization management:
The static optimization management of micro-capacitance sensor network, mainly for the lower energy conversion of efficiency with utilize link, carries out optimum technological transformation and device upgrade, improves static conversion efficiency or the utilization ratio of the energy.
Based on all-round flow model, on the basis of each link energy conversion or utilization ratio statistical study, choose conversion efficiency or the lower link of utilization ratio successively, calculate respective technological transformation and device upgrade expense, the cost C of technological transformation and device upgrade scheme
ijrepresent by following expression formula:
C＝Σf(D
ij)3)；
Wherein: Di
jbe energy conversion in energy network or utilize device; I is i-th in microgrid M hair energy subsystem, and j is the jth energy level transfer process in i-th hair energy subsystem.Through comparative analysis, make the cost C of technological transformation and device upgrade scheme
ij→ min, then selecting technology transformation and device upgrade scheme, realize the energy static optimization management of micro-capacitance sensor.
B) energy dynamics optimum management:
Adopt the thought of " global optimization, Distributed Autonomous ", predicted and ACTIVE CONTROL by all can flowing of Multiple Time Scales, realize the global optimum of micro-capacitance sensor network.Energy dynamics optimum management is mainly divided into mode decision, prediction plan, actual motion and Optimized Operation four partial content, and circulates successively, as shown in Figure 4:
Mode decision: undertaken dropping into by distributed power source, energy storage and load in microgrid or out of service time, it all can the information element class in flow model will change thereupon, microgrid control system will upgrade electrical network topological structure automatically according to information element change, determines the operational mode of micro-capacitance sensor.
Prediction plan: according to formation that is hot and cold, electric load, in conjunction with energy grade characteristic, extrapolate short-term forecasting value and the ultra-short term predicted value of energy production and utilization by neural network algorithm; According to predicting the outcome, in conjunction with productive target, formulate the production schedule of every day.
Actual motion: in the actual moving process of micro-capacitance sensor, can there are differences, and the conversion between the dissimilar energy also can there are differences between actual energizing quantity and demand energizing quantity, as the deficiency of actual heat supply or power supply, and the deficiency etc. of thermoelectricity conversion.Therefore each service data of Real-time Collection, statistical demand difference value, determines to optimize and revise target;
Optimized Operation: on the basis of predicting plan and actual motion, in conjunction with micro-capacitance sensor stable operation constraint, energy conversion and utilize the conditions such as device constraint, make each objective function in micro-grid system minimum (or very big), such as economic benefit is maximum, minimal energy loss, financial cost are minimum.And based on this, the scheduling energy supply of energy conversion apparatus and energy utilization device use energy.
(4) index system of all-round flow model is set up;
Microgrid the evaluation object of flow model all can have 4 classes, is energy class, device class, info class and system class respectively.Evaluation index has 7 kinds, and respectively: high efficiency, stability, security, reliability, economy, quality and accuracy, it all can flow indicator evaluation system and be described as follows:
1) high efficiency: high efficiency is reflection microgrid device, the energy conversion of micro-grid system or the transfer capability of utilization ratio.If in the identical or shorter time conversion or the energy that utilizes more, then this energy conversion device is described, micro-grid system has high efficiency.
2) stability: stability reflects under the runtime environment, micro-grid system bears the ability of maintenance stable operation after unexpected disturbance.
3) security: security reflects under regulation running environment, the ability that micro-grid system occurs accident adaptibility to response or opposing security incident.Comprise microgrid under accident conditions to subtract and to have a power failure ratio for the user that powers of microgrid under load proportion, accident conditions,
4) reliability: reliability refers to that micro-grid system is under specific environment, time and condition, namely failure-free operation ability continues the ability of energy supply by acceptable quality level and quantity required to user.
5) economy: microgrid energy economy is energy use cost, as gas cost.Microgrid device economy is device acquisition cost.Micro-grid system economy is under guarantee safe operation of electric network reliable prerequisite, the direct economic benefit produced or the indirect benefit brought because of Loss reducing, reduce costs etc.
6) quality: the quality of micro-grid system mainly comprises electric energy quality, heat energy quality.Electric energy quality mainly pays close attention to power quality problem, can provide the electric power supply of high-quality for power consumer, and as stable voltage exports, less harmonic pollution etc., is generally described with indexs such as rate of qualified voltage, total harmonic distortion factor, three-phase imbalances.
7) accuracy: the accuracy of micro-grid system mainly refers to the accuracy of the communication information.Information model as apparatus is consistent, and the information content of transmission is true and reliable, is generally described with indexs such as model consistency, information accuracys rate.
(5) to carrying out comprehensive eye exam by flow model:
Based on the comprehensive assessment of all-round flow model mainly by the assessment of this Complex multi-target decision-making of microgrid overall economics best performance, be decomposed into multiple single technical index or economic index, corresponding index flexible strategy are calculated, by the weighting determination overall economics best performance desired value of all indexs by qualitative index Fuzzy Quantifying.As shown in Figure 5:
To all can carrying out comprehensive eye exam and comprise the steps: by flow model
<1> sets up comprehensive eye exam model;
<2>, for the every evaluation index calculated, by qualitative index fuzzy quantization, is converted into the definite value of 0-9;
<3> carries out each subordinate index and determines the importance judgement of higher level's target and weighted value;
<4> carries out single assessment objective and determines the importance judgement of comprehensive assessment target and weighted value;
<5> determines overall economics best performance desired value.
Overall economics best performance desired value expression formula is as follows:
A＝ω
1B
1+ω
2B
2+ω
3B
3+ω
4B
44)
A represents overall economics best performance desired value, B
1represent energy class desired value, B
2indication device class desired value, B
3represent info class desired value, B
4represent system class desired value.ω
1, ω
2, ω
3, ω
4represent the index of correlation flexible strategy calculated by qualitative index Fuzzy Quantifying.
Embodiment
For making technical scheme of the present invention clearly, choosing containing honourable gas storage type micro-capacitance sensor is example, and composition graphs 6 and specific implementation process elaborate to the present invention.
First according to this project network topology structure, the relevant informations such as the miniature gas turbine selected, photovoltaic generation, wind-power electricity generation, energy storage device, cool and thermal power load are set up corresponding model.
(1) the all-round flow model of microgrid is built.Mainly comprise: the energy dvielement of photovoltaic generation subsystem, cold, heat and electricity triple supply subsystem, device dvielement and info class element.
Microgrid energy dvielement:
Sun power E
pv=A
pvp
pv, the chemical energy E of rock gas
f=A
fp
f, the electric energy E of electric load
p=A
pp
p, the heat energy E of refrigeration duty
c=A
cp
c, the heat energy E of thermal load
h=A
hp
h.
Microgrid device dvielement:
Photovoltaic power generation apparatus describes:
Gas combustion apparatus describes:
Blast Furnace Top Gas Recovery Turbine Unit (TRT) describes:
Refrigerating plant describes:
Heating plant describes:
The subsystem description of combustion gas trilogy supply:
Microgrid info class element:
In this example CIM expansion is carried out to photovoltaic generation subsystem, cold, heat and electricity triple supply subsystem, mainly set up photovoltaic CIM, micro-gas turbine group CIM, inverter CIM, AC-DC-AC converter CIM, cool and thermal power load CIM etc.
(2) the all-round flow model of micro-grid system is built.The selected micro-grid system of this example mainly comprises energy conversion link and Energy harvesting link.
Sun power E
pv=A
pvp
pv, the electric energy E that photovoltaic produces
p1=A
p1p
p1, the chemical energy E of rock gas
f=A
fp
f, the electric energy E that trilogy supply produces
p=A
pp
p, the heat energy E of refrigeration duty
c=A
cp
c, the heat energy E of thermal load
h=A
hp
h, the heat energy E that burning produces
1=A
1p
1, the heat energy E after generating
2=A
2p
2, the heat energy E after refrigeration
3=A
3p
3, the heat energy E after heat supply
4=A
4p
4, the chemical energy Δ E of combustion of natural gas loss
f=Δ A
fp
f, the heat energy Δ E of power generation loss
1=Δ A
1p
1, the heat energy Δ E of refrigeration losses
2=Δ A
2p
2, the heat energy Δ E of heat-loss
3=Δ A
3p
3.
(3) energy-optimised management.
Put aside energy static optimization in the present embodiment, only energy dynamics optimization is described.Mainly be divided into mode decision, prediction plan, actual motion and Optimized Operation four partial content, and circulate successively, as shown in Figure 3:
Mode decision: when photovoltaic generation subsystem because of failure cause out of service time, by the on off state change in the info class element of its active upload, original electrical network topology is reconstructed, the micro-capacitance sensor operational mode that must make new advances, namely only comprise cold, heat and electricity triple supply subsystem, whole micro-grid system re-starts dynamic optimization management.
Prediction plan: according to formation that is hot and cold, electric load, carry out short-term forecasting value and the ultra-short term predicted value of each type load;
PL
electricity=PL
1+ PL
2+ PL
3, 11)
PL in formula (11)
electricityrepresent electric load, PL
1represent important load, can not excise, PL
2represent controllable burden, can regulate, PL
3insignificant load, can excise.
HL
heat=HL
heating+ HL
hot water12)
HL in formula (12)
heatrepresent thermal load, HL
heatingrepresent the thermal load of heating, HL
hot waterrepresent the hot water that life uses.
CL
cold=CL
refrigeration13)
CL in formula (13)
coldrepresent refrigeration duty, disregard other refrigeration modes herein, only consider the CL of lithium bromide chiller absorption refrigeration
refrigerationload.
Form for cool and thermal power three type load, do short-term forecasting by neural network algorithm, using the history value in similar day situation, weather etc. as input vector, arranged by hidden layer, namely the output vector obtained is all kinds of workload demand predicted values of next day.In like manner, in conjunction with a upper moment value, the ultra-short term prediction of subsequent time also can be made.
Actual motion: in the actual moving process of micro-capacitance sensor, can there are differences between actual energizing quantity and demand energizing quantity, by the metric data of info class, gathers actual PL
electricity, HL
heat, CL
coldvalue, calculated difference value.
Optimized Operation: on the basis of predicting plan and actual motion, in conjunction with conditions such as micro-grid system general power Constraints of Equilibrium, the constraint of micro-gas turbine group output power, microgrid self-equilibrating degree constrain, make financial cost in micro-grid system minimum.And based on this, the scheduling energy supply of energy conversion apparatus and energy utilization device use energy.
(4) utilize evaluation index to carrying out comprehensive eye exam by flow model.
A) for the every evaluation index calculated, by qualitative index fuzzy quantization, the definite value of 0-9 is converted into:
| Single evaluation index | Symbol | Fuzzy quantization value |
| Energy quality index | C1 | 6 |
| Energy economy index | C2 | 7 |
| Device quality index | C3 | 6 |
| Device economic index | C4 | 5 |
| Information accuracy index | C5 | 6 |
| Security of system index | C6 | 8 |
| System stability index | C7 | 8 |
| Reliability Index | C8 | 8 |
| System high efficiency index | C9 | 7 |
B) overall economics performance index value:
C) overall economics best performance desired value:
| Comprehensive assessment target | Symbol | Value |
| Technical and economic performance is optimum | A | 6.92 |
D) overall economics performance index compares:
If the micro-grid system that another one is similar, its comprehensive assessment desired value is 5.42, is less than this micro-grid system comprehensive assessment desired value 6.92, then its combination property does not have native system combination property high.
Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit; with reference to above-described embodiment to invention has been detailed description; those of ordinary skill in the field can modify to the specific embodiment of the present invention or equivalent replacement; these do not depart from any amendment of spirit and scope of the invention or equivalent replacement, are all applying within the claims of the present invention awaited the reply.
Claims (14)
1., based on a microgrid energy optimization and evaluation method for all-round flow model, it is characterized in that, described method comprises the steps:
(1) all-round flow model is built: comprise energy and energy flow analysis, energy conversion link analysis, energy distribution link analysis, stored energy link analysis, Energy harvesting link and energy management link analysis;
(2) all-round flow network model is built;
(3) based on the energy-optimised management of all-round flow network model, the management of energy static optimization and energy dynamics optimum management is comprised;
(4) index system of all-round flow model is set up;
(5) to carrying out comprehensive eye exam by flow model.
2. microgrid energy optimization and evaluation method as claimed in claim 1, is characterized in that, in described step (1), all can comprise energy dvielement, device dvielement and info class element by flow model; Energy dvielement is that energy internet carries out the main body of conversion by device dvielement, and info class element is the abstractdesription of energy dvielement, device dvielement;
Described energy dvielement comprises the dissimilar primary energy of occurring in nature and secondary energy; Device dvielement comprises dissimilar energy and produces, changes, transmits, stores, utilizes device and control device; Info class element comprises the digital model of dissimilar energy and the digital model of each equipment.
3. microgrid energy optimization and evaluation method as claimed in claim 1, is characterized in that, in described step (1),
The grade of energy refer to unit energy there is the ratio of available energy, be the important indicator of its quality of mark; Setting amount of energy parameter is Q, and taste parameter is A, and different energy sources type Conversion of measurement unit parameter is K, then another expression-form of energy is as follows:
E＝KAQ1)；
Described energy conversion link analysis is undertaken by energy conversion device, and energy conversion device is the device all can changed form of energy or characteristic in flow model, comprises the conversion equipment of different-energy type and the conversion equipment of different-energy characteristic; The conversion equipment of different-energy type comprises generator and motor; The conversion equipment of different-energy characteristic comprises transformer and current transformer; Input energy can be divided into by flow process, recover energy and off-energy by stream; Described energy distribution link analysis is undertaken by power distribution means, power distribution means to distribute or to turn the device of confession in flow model, power distribution means comprises the transformer that automatically can regulate feeder line capacity and the electric power electric transformer automatically regulated, according to networking energy demand, automatically carry out distributing, regulate and storing;
Described stored energy link analysis is undertaken by energy storing device, and energy accumulating device is the device that all can store energy in flow model, and energy accumulating device comprises accumulator and regenerative apparatus; Unnecessary energy is stored, the energy that release stores when Power supply is not enough when Power supply is rich;
Described Energy harvesting link analysis is undertaken by energy utilization device, and energy utilization device is the device that all can utilize energy in flow model or consume, and energy utilization device comprises electric light and heating installation;
Described energy management link analysis is undertaken by energy management apparatus, and the information flow of Characterization Energy carries out circulation between each device with mutual; Energy management apparatus is the system platform of energy stream information being carried out to analyzing and processing and global optimization management, comprises the energy management system of micro-capacitance sensor; Can the stream information flow rate information that comprises the quality information of energy, quantity information and can flow.
4. microgrid energy optimization and evaluation method as claimed in claim 1, it is characterized in that, in described step (2), describedly all can be coupled together by device dvielement by flow network, form the combination of energy transfer passage, for circulation and the utilization of energy, comprise global optimization layer, Distributed Autonomous layer and Access Layer on the spot;
Described global optimization layer refers to that the energy of multistage micro-capacitance sensor in regional extent is interconnected, is transmitted and turn confession by the form of electric energy, and transmission range is 1km-10km; Distributed autonomous layer refers to that the energy of single micro-capacitance sensor in some areas is interconnected, and transmitted by the form of electric energy or heat energy and turned confession, transmission range is 100m-1km; Access Layer is access and the utilization of single energy source device on the spot, and the various energy is to hot and cold, electric demand energy conversion;
Described all can flow network to flow formula as follows:
E
out＝E
in-E
los＝D
mnE
in2)；
Wherein: E
outalways export energy; E
inat total input energy; E
losit is the total input energy of loss; D
mnit is the sign coefficient of all-round flow model.
5. microgrid energy optimization and evaluation method as claimed in claim 1, it is characterized in that, in described step (3), the management of energy static optimization is for the low energy conversion link of efficiency and Energy harvesting link, carry out optimum technological transformation and device upgrade, improve static conversion efficiency or the utilization ratio of the energy, the management of energy static optimization comprises:
Based on all-round flow model, on the basis of each link energy conversion or utilization ratio statistical study, choose conversion efficiency or the low link of utilization ratio successively, calculate respective technological transformation and device upgrade expense, the cost C of technological transformation and device upgrade scheme
ijrepresent by following expression formula:
C
ij＝Σf(D
ij)3)；
Wherein: Di
jbe energy conversion in energy network or utilize device; Through comparative analysis, make the cost Ci of technological transformation and device upgrade scheme
jreach minimum, then selecting technology transformation and device upgrade scheme, realize the energy static optimization management of micro-capacitance sensor;
Described energy dynamics optimum management comprises mode decision, prediction plan, actual motion and Optimized Operation, and circulates successively;
Mode decision: undertaken dropping into by distributed power source, energy storage and load in microgrid or out of service time, it all can the information element class in flow model change thereupon, microgrid control system upgrades electrical network topological structure automatically according to information element change, determines the operational mode of micro-capacitance sensor;
Prediction plan: according to formation that is hot and cold, electric load, in conjunction with energy grade characteristic, extrapolated short-term forecasting value and the ultra-short term predicted value of energy production and utilization by neural network algorithm; According to predicting the outcome, in conjunction with productive target, formulate the production schedule of every day;
Actual motion: in the actual moving process of micro-capacitance sensor, can there are differences, and the conversion between the dissimilar energy also can there are differences between actual energizing quantity and demand energizing quantity, comprises the deficiency of actual heat supply or power supply or the deficiency of thermoelectricity conversion; The each service data of Real-time Collection, statistical demand difference value, determines to optimize and revise target;
Optimized Operation: on the basis of predicting plan and actual motion, in conjunction with micro-capacitance sensor stable operation constraint, energy conversion and utilize the conditions such as device constraint, make each objective function in micro-grid system minimum or very big, comprise that economic benefit is maximum, minimal energy loss, financial cost are minimum.And based on this, the scheduling energy supply of energy conversion apparatus and energy utilization device use energy.
6. microgrid energy optimization and evaluation method as claimed in claim 1, is characterized in that, in described step (4), microgrid the evaluation object of flow model all can comprise energy class, device class, info class and system class; Evaluation index comprises: high efficiency, stability, security, reliability, economy, quality and accuracy.
7. microgrid energy optimization and evaluation method as claimed in claim 6, is characterized in that, described high efficiency is reflection microgrid device, the energy conversion of micro-grid system or the transfer capability of utilization ratio.
8. microgrid energy optimization and evaluation method as claimed in claim 6, it is characterized in that, described stability reflects under the runtime environment, and micro-grid system bears the ability of maintenance stable operation after unexpected disturbance.
9. microgrid energy optimization and evaluation method as claimed in claim 6, is characterized in that, under described security is reflected in regulation running environment, and the ability that micro-grid system occurs accident adaptibility to response or opposing security incident.
10. microgrid energy optimization and evaluation method as claimed in claim 6, it is characterized in that, described reliability refers to that micro-grid system is under specific environment, time and condition, and namely failure-free operation ability continues the ability of energy supply by acceptable quality level and quantity required to user.
11. microgrid energy optimization and evaluation methods as claimed in claim 6, it is characterized in that, microgrid energy economy is energy use cost, comprises gas cost; Microgrid device economy is device acquisition cost; Micro-grid system economy is under guarantee safe operation of electric network reliable prerequisite, the direct economic benefit produced or because of Loss reducing, reduce costs the indirect benefit brought.
12. microgrid energy optimization and evaluation methods as claimed in claim 6, is characterized in that, the quality of micro-grid system comprises electric energy quality, heat energy quality.Electric energy quality mainly pays close attention to power quality problem, can provide the electric power supply of high-quality for power consumer, comprises stable voltage and exports and harmonic pollution, be described with rate of qualified voltage, total harmonic distortion factor and three-phase imbalance index.
13. microgrid energy optimization and evaluation methods as claimed in claim 6, it is characterized in that, the accuracy of micro-grid system refers to the accuracy of the communication information, the information model comprising apparatus is consistent, the information content of transmission is true and reliable, is described with model consistency and information accuracy rate index.
14. microgrid energy optimization and evaluation methods as claimed in claim 1, it is characterized in that, in described step (5), based on all can the comprehensive assessment of flow model be by the assessment of microgrid overall economics best performance Complex multi-target decision-making, be decomposed into multiple single technical index or economic index, corresponding index flexible strategy are calculated, by the weighting determination overall economics best performance desired value of all indexs by qualitative index Fuzzy Quantifying;
To all can carrying out comprehensive eye exam and comprise the steps: by flow model
<1> sets up comprehensive eye exam model;
<2>, for the every evaluation index calculated, by qualitative index fuzzy quantization, is converted into the definite value of 0-9;
<3> carries out each subordinate index and determines the importance judgement of higher level's target and weighted value;
<4> carries out single assessment objective and determines the importance judgement of comprehensive assessment target and weighted value;
<5> determines overall economics best performance desired value.
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