CN108683188A - Consider that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level optimization - Google Patents
Consider that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level optimization Download PDFInfo
- Publication number
- CN108683188A CN108683188A CN201810645899.0A CN201810645899A CN108683188A CN 108683188 A CN108683188 A CN 108683188A CN 201810645899 A CN201810645899 A CN 201810645899A CN 108683188 A CN108683188 A CN 108683188A
- Authority
- CN
- China
- Prior art keywords
- wind
- powered electricity
- electricity generation
- peak regulation
- peak
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Control Of Eletrric Generators (AREA)
- Wind Motors (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention relates to the multiple target wind-powered electricity generations of a kind of consideration environmental value and peak regulation abundant intensity to receive level optimization model, which is characterized in that includes the following steps：Step S1:Acquisition system information；Step S2:According to system information, establishes and consider that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level optimization model；Step S3:Level optimization model is received using multi-target quantum PSO Algorithm multiple target wind-powered electricity generation, obtains the Pareto forward position of totle drilling cost and peak regulation safety margin.Step S4:It is horizontal in the optimal receiving of the output and the wind-powered electricity generation of each period of each period to obtain thermoelectricity for the compromise solution that totle drilling cost and peak regulation safety margin Pareto forward position are acquired using ideal point method.The present invention optimizes the wind-powered electricity generation receiving level of each period on the basis of considering the safety issue of peak-load regulating caused by receiving wind-powered electricity generation while the environmental value that consideration wind-powered electricity generation is brought, be a kind of method based on economy and safety combined optimization.
Description
Technical field
The present invention relates to wind-powered electricity generations to receive field, and in particular to a kind of multiple target wind considering environmental value and peak regulation abundant intensity
Electricity receives level optimization.
Background technology
Wind-powered electricity generation is green, clean regenerative resource, since it is worth with higher development and utilization, in country《It can
Renewable sources of energy method》And under the excitation of related preferential policy, Wind Power Generation Industry has obtained developing quickly.But as a kind of intermittence
Extensive access with the power supply of fluctuation, wind-powered electricity generation also brings new problem to the management and running of electric system.Receive wind-powered electricity generation
Caused replacement conventional power unit power generation value and environmental value are the major reasons of wind-powered electricity generation extensive development.But receive wind-powered electricity generation mistake
It is more, and can be because the anti-tune peak character and randomness of wind-powered electricity generation cause the peak regulation of system difficult, it has to enable regulating units control
It contributes to ensure system dynamic equilibrium, system operation cost is caused to increase.If wind-powered electricity generation is received excessively, normal power supplies will not only follow
Load variations, it is also necessary to which balance new energy goes out fluctuation, when the regulating power beyond system, may result in system appearance
Accident.Therefore, the present invention is other than the economy that the environmental value that consideration wind-powered electricity generation is brought generates, while also contemplating receiving wind-powered electricity generation
The problem of peak-load regulating safety afterwards establishes Multiobjective programming models, and policymaker can obtain suitable compromise solution according to demand,
Effective reference is provided for final decision.Currently, not having the environmental value for combining wind-powered electricity generation to bring also and considering system tune simultaneously
The wind-powered electricity generation of peak safety receives level optimization.
Invention content
In view of this, the purpose of the present invention is to provide the multiple target wind-powered electricity generation receivings for considering environmental value and peak regulation abundant intensity
Level optimization, to obtain each period it is optimal wind-powered electricity generation receiving it is horizontal, rationally utilize wind-resources.
To achieve the above object, the present invention adopts the following technical scheme that：
Consider that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level optimization, which is characterized in that including following
Step：
Step S1:Acquisition system information；
Step S2:According to system information, establishes and consider that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level
Optimized model；
Step S3:Level optimization model is received using multi-target quantum PSO Algorithm multiple target wind-powered electricity generation, is obtained total
The Pareto forward position of cost and peak regulation safety margin；
Step S4:The compromise solution that totle drilling cost and peak regulation safety margin Pareto forward position are acquired using ideal point method, obtains fire
Electricity is horizontal in the optimal receiving of output and the wind-powered electricity generation of each period of each period.
Further, the system information include wind power prediction information, information on load, generating set cost of electricity-generating and
Disposal of pollutants information, day part load spinning reserve demand information.
Further, the step S2 is specially：
Step S21:With the minimum target 1 of the sum of the expense of cost of electricity-generating and the expense of disposal of pollutants, function representation is：
min f1=F+E
In formula：Wherein, min f1For target 1, F is cost of electricity-generating, and E is disposal of pollutants cost； ai, bi, ciFor generator
For i-th electrical power generators cost coefficient；Pi,tFor i-th generator t-th of period output；ρ is disposal of pollutants price；
αi, βi, γi, ξi,For the polluted gas emission factor of i-th conventional power unit；
Step S22:It is up to target 2 with the minimum value in the peak regulation safety margin of day part,
System t period peak modulation capacity abundant intensities are defined as the ratio of the peak-load regulating ability and peak regulation demand of t periods, specifically
Calculation formula is as follows：
B=min (Bt)
In formula, BtFor the peak-load regulating ability abundant intensity of t periods；PC,tFor t period peak-load regulating abilities；PN,tFor the t periods
Peak-load regulating demand；Peak-load regulating ability abundant intensity B is defined as the minimum value of each period peak regulation abundant intensity；
The peak-load regulating capacity calculation of wherein t periods is as follows：
In formula, Pi,minWhat is indicated is the minimum load of i-th generator.
The peak regulation demand of t periods is equal to the difference of the synthesis net load value and same day synthesis net load low ebb of the period,
Wherein net load refers to it being to subtract the new load curve that wind power output obtains by system loading, then the peak regulation demand of t periods
It is embodied as
PN,t=PLr,t+PLj,t-PLj,min
In formula, PLr,tFor the spinning reserve demand of the load of t periods；PLj,tFor the net load of t periods； PLj,minTo calculate
The low ebb value of net load in period.
According to the peak regulation safety margin of each period, target 2 is expressed as：
max f2=min (Bt)
Step S23:According to target 1 and target 2 and major constraints, the more mesh for considering environmental value and peak regulation abundant intensity are established
It marks wind-powered electricity generation and receives level optimization model.
Further, the major constraints include：
(1) fired power generating unit units limits
Pi,min≤Pi,t≤Pi,max
In formula：Pi,minAnd Pi,maxIt is the bound of i-th conventional power unit active power output, Pi,tIt is unit i in the t periods
Output size.
(2) supply and demand Real-time Balancing constrains
In formula：Pw,tIt is the wind-powered electricity generation receiving amount of t periods, PL,tIt is the load value of t periods.
(3) unit climbing units limits
Pi,t-Pi,t-1≤UR,iΔT
Pi,t-1-Pi,t≤DR,iΔT
In formula：UR,iAnd DR,iIt is scheduling time inter for the rising and decline climbing speed, Δ T of conventional power unit i.
(4) the positive rotation spare capacity constraint of system
In formula：wu% is that wind power output predicts the error service demand factor spare to positive rotation in period t, Lu% is negative for system
Lotus predicts the error service demand factor spare in period t positive rotation.
(5) the negative spinning reserve capacity constraint of system
In formula：wd% is that wind power output predicts service demand factor of the error in period t to negative spinning reserve, Ld% is negative for system
Lotus predicts that error bears the service demand factor of spinning reserve in period t.
(6) wind-powered electricity generation receives horizontal constraint
In formula：Prediction for wind-powered electricity generation in the t periods is contributed.
The present invention has the advantages that compared with prior art：
The present invention considers the peace of peak-load regulating caused by receiving wind-powered electricity generation while the environmental value that consideration wind-powered electricity generation is brought
The wind-powered electricity generation receiving level for optimizing each period on the basis of full sex chromosome mosaicism is a kind of based on economy and safety combined optimization
Method.The present invention can obtain optimal wind-powered electricity generation receiving level of each period, rationally utilize wind-resources.
Description of the drawings
Fig. 1 is flow chart of the present invention
Fig. 2 is the optimal receiving level of wind-powered electricity generation and wind-powered electricity generation prediction power curve figure in one embodiment of the invention
Fig. 3 is when considering peak-load regulating safety margin in one embodiment of the invention and not considering that peak-load regulating safety margin is each
The peak regulation nargin comparison diagram of section
Fig. 4 is electrical power predictive information and information on load figure in one embodiment of the invention
Specific implementation mode
The present invention will be further described with reference to the accompanying drawings and embodiments.
Embodiment 1：
Present embodiments provide a kind of multiple target wind-powered electricity generation receiving level optimization side of consideration environmental value and peak regulation abundant intensity
Method specifically includes following steps：
Step S1：Extraction system information；Extract wind power prediction information, information on load, generating set cost of electricity-generating and
Disposal of pollutants information, day part load spinning reserve demand information.Wherein network data uses the network of 10 machine systems, contains
There are one wind power plant, total installation of generating capacity 400MW；Generator 's parameter is shown in Table 1；Wind power prediction information and information on load are shown in
Fig. 4.
1 generator parameter of table
Step S2：With the minimum target of the sum of the expense of cost of electricity-generating and the expense of disposal of pollutants 1；With the peak regulation of day part
Minimum value in safety margin is up to target 2, establishes and considers that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives water
Flat Optimized model.
Step S3：The wind-powered electricity generation prediction output that the load level correspondence of day part is subtracted to day part obtains net load level,
And then obtain the peak regulation demand of each period.
Step S4：With the multi-target quantum PSO Algorithm model, the pa of totle drilling cost and peak regulation safety margin is obtained
Tired support forward position, as shown in Figure 3.
Step S5：The compromise solution of totle drilling cost and peak regulation safety margin Pareto forward position is acquired using ideal point method, and is obtained
Output and the wind-powered electricity generation of each period of the thermoelectricity in each period are received horizontal.The power generation and disposal of pollutants acquired using ideal point method
Totle drilling cost be 2.073 × 106$, peak-load regulating ability abundant intensity are 113.78%.
The foregoing is merely presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification should all belong to the covering scope of the present invention.
Claims (4)
1. a kind of considering that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level optimization model, which is characterized in that packet
Include following steps：
Step S1:Acquisition system information；
Step S2:According to system information, establishes and consider that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level optimization
Model；
Step S3:Level optimization model is received using multi-target quantum PSO Algorithm multiple target wind-powered electricity generation, obtains totle drilling cost
With the Pareto forward position of peak regulation safety margin；
Step S4:The compromise solution that totle drilling cost and peak regulation safety margin Pareto forward position are acquired using ideal point method, is obtained thermoelectricity and existed
The optimal receiving of the wind-powered electricity generation of the output of each period and each period is horizontal.
2. according to claim 1 consider that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level optimization mould
Type, it is characterised in that：The system information includes wind power prediction information, information on load, generating set cost of electricity-generating and dirt
Contaminate emission information, day part load spinning reserve demand information.
3. according to claim 1 consider that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level optimization mould
Type, it is characterised in that：The step S2 is specially：
Step S21:With the minimum target 1 of the sum of the expense of cost of electricity-generating and the expense of disposal of pollutants, function representation is：
minf1=F+E
In formula：Wherein, minf1For target 1, F is cost of electricity-generating, and E is disposal of pollutants cost；ai, bi, ciIt it is i-th for generator
Electrical power generators cost coefficient；Pi,tFor i-th generator t-th of period output；ρ is disposal of pollutants price；αi, βi,
γi, ξi,For the polluted gas emission factor of i-th conventional power unit；
Step S22:It is up to target 2 with the minimum value in the peak regulation safety margin of day part,
System t period peak modulation capacity abundant intensities are defined as the ratio of the peak-load regulating ability and peak regulation demand of t periods, specific to calculate
Formula is as follows：
B=min (Bt)
In formula, BtFor the peak-load regulating ability abundant intensity of t periods；PC,tFor t period peak-load regulating abilities；PN,tFor t period system tune
Peak demand；Peak-load regulating ability abundant intensity B is defined as the minimum value of each period peak regulation abundant intensity；
The peak-load regulating capacity calculation of wherein t periods is as follows：
In formula, Pi,minWhat is indicated is the minimum load of i-th generator.
The peak regulation demand of t periods is equal to the difference of the synthesis net load value and same day synthesis net load low ebb of the period, wherein
Net load refers to it being to subtract the new load curve that wind power output obtains by system loading, then the peak regulation demand of t periods is specific
It is expressed as
PN,t=PLr,t+PLj,t-PLj,min
In formula, PLr,tFor the spinning reserve demand of the load of t periods；PLj,tFor the net load of t periods；PLj,minFor in calculation interval
The low ebb value of net load.
According to the peak regulation safety margin of each period, target 2 is expressed as：
max f2=min (Bt)
Step S23:According to target 1 and target 2 and major constraints, the multiple target wind for considering environmental value and peak regulation abundant intensity is established
Electricity receives level optimization model.
4. according to claim 3 consider that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level optimization mould
Type, it is characterised in that：The major constraints include fired power generating unit units limits, the constraint of supply and demand Real-time Balancing, unit climbing output
Constraint, the positive rotation spare capacity constraint of system, the negative spinning reserve capacity constraint of system and wind-powered electricity generation receive horizontal constraint.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810645899.0A CN108683188A (en) | 2018-06-21 | 2018-06-21 | Consider that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810645899.0A CN108683188A (en) | 2018-06-21 | 2018-06-21 | Consider that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level optimization |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108683188A true CN108683188A (en) | 2018-10-19 |
Family
ID=63811955
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810645899.0A Pending CN108683188A (en) | 2018-06-21 | 2018-06-21 | Consider that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108683188A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110571863A (en) * | 2019-08-06 | 2019-12-13 | 国网山东省电力公司经济技术研究院 | Distributed power supply maximum acceptance capacity evaluation method considering flexibility of power distribution network |
| CN110880756A (en) * | 2019-11-19 | 2020-03-13 | 国网浙江省电力有限公司 | Method for judging peak regulation capacity adequacy of extra-high voltage receiving-end power grid based on peak regulation coefficient |
| CN111030161A (en) * | 2019-11-12 | 2020-04-17 | 国网安徽省电力有限公司 | Correlation analysis method for new energy consumption and power grid depth peak regulation margin |
| CN113489069A (en) * | 2021-07-28 | 2021-10-08 | 广东电网有限责任公司 | Peak regulation balance evaluation method and system for high-proportion renewable energy power system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102290829A (en) * | 2011-08-13 | 2011-12-21 | 东北电力大学 | Full-system large-scale wind power control method with low generating cost |
| CN103390904A (en) * | 2013-07-18 | 2013-11-13 | 国家电网公司 | Analysis method of renewable energy grid-connected power generation |
| CN104362673A (en) * | 2014-10-29 | 2015-02-18 | 国网甘肃省电力公司 | Wind power integration coordinated dispatching optimization method based on peak regulation margin |
| CN104600729A (en) * | 2014-08-19 | 2015-05-06 | 浙江工业大学 | V2G technology based participating economic dispatching optimizing control method for electric vehicle |
| CN106936128A (en) * | 2017-03-07 | 2017-07-07 | 广西电网有限责任公司电力科学研究院 | A kind of power network receives the appraisal procedure of renewable energy source capability in short term |
| CN107204632A (en) * | 2017-07-15 | 2017-09-26 | 东北电力大学 | A kind of flexible load Optimization Scheduling for lifting wind electricity digestion |
-
2018
- 2018-06-21 CN CN201810645899.0A patent/CN108683188A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102290829A (en) * | 2011-08-13 | 2011-12-21 | 东北电力大学 | Full-system large-scale wind power control method with low generating cost |
| CN103390904A (en) * | 2013-07-18 | 2013-11-13 | 国家电网公司 | Analysis method of renewable energy grid-connected power generation |
| CN104600729A (en) * | 2014-08-19 | 2015-05-06 | 浙江工业大学 | V2G technology based participating economic dispatching optimizing control method for electric vehicle |
| CN104362673A (en) * | 2014-10-29 | 2015-02-18 | 国网甘肃省电力公司 | Wind power integration coordinated dispatching optimization method based on peak regulation margin |
| CN106936128A (en) * | 2017-03-07 | 2017-07-07 | 广西电网有限责任公司电力科学研究院 | A kind of power network receives the appraisal procedure of renewable energy source capability in short term |
| CN107204632A (en) * | 2017-07-15 | 2017-09-26 | 东北电力大学 | A kind of flexible load Optimization Scheduling for lifting wind electricity digestion |
Non-Patent Citations (3)
| Title |
|---|
| 刘敦楠: "电网多时间尺度接纳可再生能源能力评估指标体系", 《电力建设》 * |
| 向红吉，戴朝华，明杰，等: "考虑低谷时刻负调峰能力及风电预测区间的多目标机组组合优化研究", 《电网技术》 * |
| 张春阳: "含风电场电力***的可用输电能力分层优化决策研究", 《陕西电力》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110571863A (en) * | 2019-08-06 | 2019-12-13 | 国网山东省电力公司经济技术研究院 | Distributed power supply maximum acceptance capacity evaluation method considering flexibility of power distribution network |
| CN110571863B (en) * | 2019-08-06 | 2020-12-11 | 国网山东省电力公司经济技术研究院 | Distributed power supply maximum acceptance capacity evaluation method considering flexibility of power distribution network |
| CN111030161A (en) * | 2019-11-12 | 2020-04-17 | 国网安徽省电力有限公司 | Correlation analysis method for new energy consumption and power grid depth peak regulation margin |
| CN111030161B (en) * | 2019-11-12 | 2023-07-21 | 国网安徽省电力有限公司 | New energy consumption and power grid depth peak regulation margin correlation analysis method |
| CN110880756A (en) * | 2019-11-19 | 2020-03-13 | 国网浙江省电力有限公司 | Method for judging peak regulation capacity adequacy of extra-high voltage receiving-end power grid based on peak regulation coefficient |
| CN113489069A (en) * | 2021-07-28 | 2021-10-08 | 广东电网有限责任公司 | Peak regulation balance evaluation method and system for high-proportion renewable energy power system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Cai et al. | Integration of hydrogen storage system and wind generation in power systems under demand response program: A novel p-robust stochastic programming | |
| WO2022100091A1 (en) | Centralized control method for scheduling of generalized source storage system | |
| WO2018059096A1 (en) | Combined decision method for power generation plans of multiple power sources, and storage medium | |
| CN105337294B (en) | Coordinate the energy storage configuration method that wind power plant participates in electric system primary frequency modulation | |
| CN108683188A (en) | Consider that the multiple target wind-powered electricity generation of environmental value and peak regulation abundant intensity receives level optimization | |
| CN108496288A (en) | Residential energy sources equipment and the operating method for operating residential energy sources equipment | |
| CN103151803A (en) | Method for optimizing wind power system-contained unit and backup configuration | |
| Kasseris et al. | Optimization of a wind-power fuel-cell hybrid system in an autonomous electrical network environment | |
| CN103956773B (en) | Backup configuration optimization method containing wind power system unit | |
| CN110783959B (en) | New forms of energy power generation system's steady state control system | |
| CN113792953A (en) | Virtual power plant optimal scheduling method and system | |
| CN110932261A (en) | Multi-energy system combined installation planning method based on global benefit maximization | |
| CN111327078A (en) | Household energy scheduling method, energy management module and household energy system | |
| CN111682531A (en) | PL-IMOCS-based wind, light, water and fire primary energy complementation short-term optimization scheduling method and device | |
| CN110165715B (en) | Method for connecting electric vehicle energy storage type charging station into virtual power plant | |
| CN114676991A (en) | Optimal scheduling method based on source-load double-side uncertain multi-energy complementary system | |
| Su et al. | Enhancing wind-solar hybrid hydrogen production through multi-state electrolyzer management and complementary energy optimization | |
| CN112307603A (en) | Hybrid energy storage capacity optimal configuration method and system considering large-scale wind power access | |
| CN111917137A (en) | Regulation and control method for multiple distributed energy sources in regional power grid | |
| JP2018133939A (en) | Power supply system and control method for power supply system | |
| CN116562554A (en) | Long-short-period energy storage joint planning method considering flexible supply-demand balance | |
| CN110571868A (en) | Optimal configuration method of microgrid | |
| CN114389262B (en) | Regional power grid dispatching method based on robust optimization in elastic environment | |
| CN116191493A (en) | Thermal power unit depth peak shaving and composite energy storage collaborative planning method and device | |
| CN107453377A (en) | Analysis method is matched based on the optimal solar energy thermal-power-generating heat accumulation of system cost benefit |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181019 |