CN110752602A - Method for evaluating new energy consumption capability of system through load response and energy storage - Google Patents
Method for evaluating new energy consumption capability of system through load response and energy storage Download PDFInfo
- Publication number
- CN110752602A CN110752602A CN201911241407.2A CN201911241407A CN110752602A CN 110752602 A CN110752602 A CN 110752602A CN 201911241407 A CN201911241407 A CN 201911241407A CN 110752602 A CN110752602 A CN 110752602A
- Authority
- CN
- China
- Prior art keywords
- load
- new energy
- energy storage
- price
- output
- 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
- 238000004146 energy storage Methods 0.000 title claims abstract description 54
- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000005265 energy consumption Methods 0.000 title abstract description 9
- 230000005611 electricity Effects 0.000 claims abstract description 18
- 238000012886 linear function Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 abstract description 13
- 238000004364 calculation method Methods 0.000 abstract description 2
- 238000010248 power generation Methods 0.000 description 8
- 238000011161 development Methods 0.000 description 3
- 230000005684 electric field Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000007599 discharging Methods 0.000 description 2
- 238000011835 investigation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
-
- 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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- 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
-
- 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
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/82—Energy audits or management systems therefor
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Power Engineering (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Water Supply & Treatment (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to the technical field of operation and optimization of a power system, and discloses a method for evaluating the capacity of a system for absorbing new energy through load response and energy storage, which is used for acquiring related parameters of new energy, thermal power, load and energy storage; determining a load demand price elastic function; determining clear electricity price constraint; determining the load amount of an access system when the load is elastic; determining a load response cost and an energy storage cost; determining an optimization function of the output of each unit to obtain the output of each unit in a scheduling period; and calculating the new energy power abandon rate of each time interval in the scheduling cycle. Compared with the prior art, the load elastic model based on the market supply-demand ratio is established, the calculation of the load response and the energy storage on the new energy consumption capacity of the system is realized, the load response and the energy storage cost are optimized through the weight coefficient, the utilization rate of new energy of the system can be effectively improved, the influence of the new energy volatility on the power grid safety is stabilized, and the resource optimization configuration level of the system is further improved.
Description
Technical Field
The invention relates to the technical field of operation and optimization of power systems, in particular to a method for evaluating new energy consumption capability of a system through load response and energy storage.
Background
With the rapid development of modern socioeconomic power demand is increasing. However, due to the continuous exhaustion of the conventional fossil energy, further development of the power industry is limited, and thus the new energy industry receives a wide attention. Due to the characteristics of no energy consumption, no pollution, no emission and the like, new energy power generation gradually becomes a relatively mature power generation mode with larger development scale. However, the output of the new energy is influenced by natural factors, and the new energy has the characteristics of obvious fluctuation, intermittence, randomness, inverse peak regulation and the like, and the safe and reliable operation of the power system is greatly challenged by large-scale new energy grid connection. The power structure of China is mainly based on thermal power, and the problem of unsmooth consumption of large-scale new energy grid connection causes a serious new energy electricity abandonment phenomenon, so that standby resources on a power generation side are lacked, and the peak regulation capacity of a system is seriously insufficient. Therefore, the method has very important practical significance for ensuring safe and reliable operation of the system, reducing negative influence generated by the new energy accessed into the power grid and improving the new energy consumption capability of the system.
Therefore, the influence of load response on the new energy consumption capability of the system in the smart grid environment needs to be analyzed, and then the new energy containing system can be effectively scheduled and managed, so that the new energy consumption capability of the system is improved.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides a method for evaluating the capacity of a system for absorbing new energy through load response and energy storage, which is characterized by establishing a load elasticity model based on market supply-demand ratio, determining the load response cost and the energy storage cost by introducing a weight coefficient, and then providing a method for calculating the capacity of the system for absorbing new energy, thereby obtaining a method for calculating the capacity of the system for absorbing new energy.
The technical scheme is as follows: the invention provides a method for evaluating the capacity of a system for absorbing new energy by load response and energy storage, which comprises the following specific steps:
step 1: setting and collecting new energy, thermal power generating units, load and energy storage related parameter data;
step 2: considering the self-elasticity coefficient of the load demand, and determining a load demand price elastic function based on load demand price response under the condition of not considering the cross elasticity coefficient;
and step 3: obtaining clear price constraint by determining a linear function relation between the clear price and the required supply ratio of the system;
and 4, step 4: determining the load quantity of the access system when the load is elastic according to the load demand price elastic function in the step 2 and the clear electricity price constraint in the step 3;
and 5: introducing a weight coefficient, and determining load response cost and energy storage cost;
step 6: determining an optimized objective function of the output of each unit, and obtaining the output of each unit in a scheduling period according to the optimized objective function;
and 7: and under the condition of obtaining the actual output of the new energy, calculating the new energy power abandon rate of each time period in the scheduling period by the system.
Further, the load demand price elastic function model in step 2 is:
wherein d istAs a function of the load demand elasticity, dR,tFor portions requiring rigid loads, dF,t,maxIs the maximum value of the elastic load, aF,t、bF,tFor the parameter corresponding to the load demand elasticity curve, ptAnd clearing the price for the market.
Further, the constraint of the price of the clear electricity in the step 3 is as follows:
pt＝a rt+b，
wherein p istThe clearing price is given to the market, a and b are related coefficients of the clearing price, rtFor market demand, stFor market supply, dtIs a load demand elasticity function.
Further, the load model of the access system when the load in step 4 is elastic is as follows:
wherein d istAs a function of the load demand elasticity, dR,tFor portions requiring rigid loads, dF,t,maxIs the maximum value of the elastic load, ptClearing price for market, pta、ptbRespectively providing upper and lower limits of the clear electricity price for normal markets, I is the number of the thermal power generating units, I is the number of the thermal power generating units, and P isi,maxIs the upper limit of the output of the thermal power generating unit, wy,tPredicted value of new energy output, Δ wf,tFor the prediction of the deviation of the new energy output, aF,t、bF,tA parameter corresponding to the elastic curve of the load demand, and aF,t＜0，bF,tAnd if the sum is more than 0, a and b are clearing price correlation coefficients.
Further, the load response cost and energy storage cost model in step 5 is as follows:
wherein the content of the first and second substances,CD.jt、CESS.jtthe standby cost and the energy storage cost respectively determined for the new energy source unit j, α is a weight coefficient, the value range is between 0 and 1, and delta wf,tPredicting deviation for new energy output, fESSFor the price of stored energy, fDThe load response price.
Further, the optimization objective function of the output of the unit in the step 6 is as follows:
wherein T is a scheduling time interval, T is a total time interval in a scheduling cycle, I is the number of thermal power generating units, I is the number of thermal power generating units, J is the number of new energy source units, J is the number of new energy source units, and C (P)i,t) For the generating cost of thermal power generating units, C (P)j,t) Cost of electricity generation for a new energy unit, CD.jt、CESS.jtThe standby cost and the energy storage cost are respectively determined for the new energy source unit j.
Further, the new energy power abandonment model of each time period in the step 7 is as follows:
wt'＝wy,t+Δwf,t-Pj,t
wherein, wt' As a new energy power-off model, wy,tPredicted value of new energy output, Δ wf,tPredicting a deviation, P, for the new energy outputj,tThe power is output by the new energy unit.
Has the advantages that:
the invention develops research aiming at the influence of load response and energy storage on the capacity of a system for absorbing new energy under the environment of an intelligent power grid, establishes a load elasticity model based on a demand-supply ratio according to the relevant theory of economics, determines the load response cost and the energy storage cost by introducing a weight coefficient, and optimizes the load response energy storage cost and the energy storage cost through the weight coefficient, thereby effectively helping the system to reduce the power abandonment rate of the new energy, improving the utilization rate of new energy resources, stabilizing the influence of the fluctuation of the new energy on the safety of the power grid, increasing the social welfare of the system and further improving the resource optimization configuration level of the system.
Drawings
FIG. 1 is a schematic view of a load demand spring curve.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
The invention relates to the technical field of operation and optimization of power systems, and discloses a method for evaluating new energy consumption capability of a system through load response and energy storage. Referring to fig. 1, as shown, the normal clear electricity price is located in the area of the dashed box in the figure, and it can be found that as the electricity price continuously increases, the load demand has a lower limit, and the load will not change with the electricity price and is a rigid load demand part. And as the electricity price is continuously reduced, the load demand has an upper limit limited by rated power, and the part between the upper limit and the lower limit of the load demand is the demand elastic load part.
Therefore, according to the above description in the drawings, the method for evaluating the capacity of the system for absorbing new energy through load response and energy storage disclosed by the invention is implemented by the following steps:
step 1: and setting and collecting related parameters of new energy, a thermal power generating unit, a load and energy storage.
Step 1.1, setting and collecting related parameters of new energy:
and (3) new energy related parameters: w is ay,tPredicted value of new energy output, Δ wf,tThe predicted deviation of the new energy output is calculated, J is the number of the new energy unit, J is the number of the new energy unit, Pj,tForce for new energy unit, C (P)j,t) Cost of power generation for a new energy unit, wt' model of electric quantity abandoned for new energy, CD.jt、CESS.jtLoad response cost and energy storage cost, f, determined for the new energy electric field j, respectivelyESSFor the price of stored energy, fDThe load response price.
Step 1.2, setting and collecting related parameters of the thermal power generating unit:
the thermal power generating unit related parameters are as follows: pi,minIs the lower limit of output, P, of the thermal power generating uniti,maxThe output upper limit of the thermal power generating unit is represented by I, I and Pi,tAs heat powerMachine output, C (P)i,t) The power generation cost of the thermal power generating unit is reduced.
Step 1.3, setting and collecting load related parameters:
load-related parameters: dtAs a function of the load demand elasticity, dR,tFor portions requiring rigid loads, dF,tIs a portion of elastic load, aF,t、bF,tA parameter corresponding to the elastic curve of the load demand, and aF,t＜0，bF,t＞0，pta、ptbRespectively issuing upper and lower limits of the price of the clear electricity for the normal market, dF,t,maxIs the maximum value of the elastic load, ptClearing price for market rtFor market demand, stFor market supply, a and b are the relevant coefficient of clearing price.
Step 1.4, setting and collecting energy storage related parameters:
energy storage related data: u. ofc、ufIn a charge-discharge state for energy storage, uc＝1、uf1 represents the stored energy in a charging and discharging state, uc＝0、uf0 indicates that the stored energy is in an idle state, PESSC.tCharging power for storing energy for a period of t, PESSF.tIs the discharge power of the energy storage system during the period t, PESSCRated charging power, P, for energy storage systemsESSFRated discharge power for energy storage systems, EESSE.tFor the energy stored by the energy storage system at the end of the period t, EESSTo the initial capacity of the energy storage system, EESSE.minFor minimum energy storage capacity, P, of the energy storage systemESSC.tmin、PESSF.tminRespectively the minimum charging and discharging power of the energy storage system;
step 2: and (3) according to the setting of the load related parameters in the step (1), considering the self-elasticity coefficient of the load demand, and determining a load demand price elasticity function based on the load demand price response under the condition of not considering the cross elasticity coefficient.
Load in the market dtIt consists of two parts, here a linear price elasticity function, which can be expressed as:
dt＝dR,t+dF,t1≤t≤T
dF,t＝aF,tpt+bF,t1≤t≤T
the load demand price elasticity function is:
and step 3: and (3) determining clear electricity price constraint by determining a linear function relation between the clear price and the required supply ratio of the system according to the setting of the load related parameters in the step (1).
And 4, step 4: and (3) determining a load model of the access system when the load is elastic according to the setting of the load related parameters in the step (1) and the determination of the load demand price elastic function and the clear electricity price constraint in the step (2) and the step (3).
The load demand has elasticity, influences the demand-to-supply ratio in market to a certain extent, and along with new forms of energy are incorporated into the power networks, market supply changes, promptly:
expression of the required elastic load:
(1) when 0 < pt＜ptaIn time, the elastic load:
dF,t＝dF,t,max
(2) when p ista≤pt≤ptbIn time, the load demand elasticity is in the linear region, and we can obtain:
(3) when p ist＞ptbTime, demand load dt＝dR,tThe electric load reaches the lower limit, and the load only has rigid load but no elastic load.
To sum up, the elastic load of the access system when the load is elastic is:
and 5: load response costs and energy storage costs are determined.
Step 6: and determining an optimization function of the output of each unit, and obtaining the output of each unit in the scheduling period according to the optimization function.
The optimized objective function of the unit output is as follows:
constraint conditions are as follows:
Pi,min≤Pi,t≤Pi,max
0≤Pj,t≤wy,t+Δwf,t
ucPESSC.tmin≤PESSC.t≤ucPESSC
ufPESSF.tmin≤PESSF.t≤ufPESSF
EESSE.min≤EESSE.t≤EESS
and 7: and under the condition of obtaining the actual output of the new energy unit, calculating the electric quantity of the new energy abandoned at each time interval in the scheduling cycle by the system.
wt'＝wy,t+Δwf,t-Pj,t
According to the invention, 10 machine systems and 1 new energy electric field and energy storage station are selected for simulation analysis, and a series of investigation and research show that under a power generation mode with dominant thermal power generation, the installed capacity of new energy does not exceed 15% and the load fluctuation range of the system is larger than 15%. That is, the system can completely consume new energy through load response and energy storage optimization. Of course, the capacity of the new energy source machine assembly machine reaches 30%, and the load response and energy storage optimization cannot be completely adjusted, so that the capacity redistribution of the thermal power generating unit needs to be considered. If the spare capacity can not be adjusted, only the power limitation by pulling out the brake is adopted, the power generation cost is greatly increased, the microcosmic arrangement of the start, stop and output of the unit is involved, and the invention is not researched.
The invention develops research aiming at the influence of load response and energy storage on the capacity of a system for absorbing new energy under the environment of an intelligent power grid, establishes a load elasticity model based on a demand-supply ratio according to the relevant theory of economics, and adopts calculation data of a 10-machine system, a new energy electric field and an energy storage station to calculate and verify the scientificity of the model, and load response and energy storage optimization can effectively help the system to reduce the electricity abandonment of new energy, improve the utilization rate of the new energy, stabilize the influence of new energy volatility on the safety of the power grid, increase the social benefit of the system and further improve the resource optimization configuration level of the system.
The above embodiments are merely illustrative of the technical concepts and features of the present invention, and the purpose of the embodiments is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.
Claims (7)
1. A method for evaluating the capacity of a system for absorbing new energy through load response and energy storage is characterized by comprising the following specific steps:
step 1: setting and collecting new energy, thermal power generating units, load and energy storage related parameter data;
step 2: considering the self-elasticity coefficient of the load demand, and determining a load demand price elastic function based on load demand price response under the condition of not considering the cross elasticity coefficient;
and step 3: obtaining clear price constraint by determining a linear function relation between the clear price and the required supply ratio of the system;
and 4, step 4: determining the load quantity of the access system when the load is elastic according to the load demand price elastic function in the step 2 and the clear electricity price constraint in the step 3;
and 5: introducing a weight coefficient, and determining load response cost and energy storage cost;
step 6: determining an optimized objective function of the output of each unit, and obtaining the output of each unit in a scheduling period according to the optimized objective function;
and 7: and under the condition of obtaining the actual output of the new energy, calculating the new energy power abandon rate of each time period in the scheduling period by the system.
2. The method for evaluating the capacity of a system for absorbing new energy through load response and energy storage according to claim 1, wherein the load demand price elastic function model in the step 2 is as follows:
wherein d istAs a function of the load demand elasticity, dR,tFor portions requiring rigid loads, dF,t,maxIs the maximum value of the elastic load, aF,t、bF,tFor the parameter corresponding to the load demand elasticity curve, ptAnd clearing the price for the market.
3. The method for evaluating the capacity of a system for absorbing new energy through load response and energy storage according to claim 1, wherein the constraint on the price of electricity at the power off in the step 3 is as follows:
pt＝a rt+b，
wherein p istThe clearing price is given to the market, a and b are related coefficients of the clearing price, rtFor market demand, stFor market supply, dtIs a load demand elasticity function.
4. The method for evaluating the capacity of a system for absorbing new energy through load response and energy storage according to claim 1, wherein the load model of the access system when the load is elastic in step 4 is:
wherein d istAs a function of the load demand elasticity, dR,tFor portions requiring rigid loads, dF,t,maxIs the maximum value of the elastic load, ptClearing price for market, pta、ptbRespectively providing upper and lower limits of the clear electricity price for normal markets, I is the number of the thermal power generating units, I is the number of the thermal power generating units, and P isi,maxIs the upper limit of the output of the thermal power generating unit, wy,tPredicted value of new energy output, Δ wf,tFor the prediction of the deviation of the new energy output, aF,t、bF,tA parameter corresponding to the elastic curve of the load demand, and aF,t＜0，bF,tAnd if the sum is more than 0, a and b are clearing price correlation coefficients.
5. The method for evaluating the capacity of a system for absorbing new energy through load response and energy storage according to claim 1, wherein the load response cost and energy storage cost model in the step 5 is as follows:
wherein, CD.jt、CESS.jtThe standby cost and the energy storage cost respectively determined for the new energy source unit j, α is a weight coefficient, the value range is between 0 and 1, and delta wf,tPredicting deviation for new energy output, fESSFor the price of stored energy, fDThe load response price.
6. The method for evaluating the capacity of a system for absorbing new energy through load response and energy storage according to claim 1, wherein the optimal objective function of the unit output in the step 6 is as follows:
wherein T is a scheduling time interval, T is a total time interval in a scheduling cycle, I is the number of thermal power generating units, I is the number of thermal power generating units, J is the number of new energy source units, J is the number of new energy source units, and C (P)i,t) For the generating cost of thermal power generating units, C (P)j,t) Cost of electricity generation for a new energy unit, CD.jt、CESS.jtThe standby cost and the energy storage cost are respectively determined for the new energy source unit j.
7. The method for evaluating the capacity of a system for absorbing new energy through load response and energy storage according to claim 1, wherein the new energy power curtailment model of each time period in the step 7 is as follows:
wt'＝wy,t+Δwf,t-Pj,t
wherein, wt' As a new energy power-off model, wy,tPredicted value of new energy output, Δ wf,tPredicting a deviation, P, for the new energy outputj,tThe power is output by the new energy unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911241407.2A CN110752602A (en) | 2019-12-06 | 2019-12-06 | Method for evaluating new energy consumption capability of system through load response and energy storage |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911241407.2A CN110752602A (en) | 2019-12-06 | 2019-12-06 | Method for evaluating new energy consumption capability of system through load response and energy storage |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110752602A true CN110752602A (en) | 2020-02-04 |
Family
ID=69285708
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911241407.2A Pending CN110752602A (en) | 2019-12-06 | 2019-12-06 | Method for evaluating new energy consumption capability of system through load response and energy storage |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110752602A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111581809A (en) * | 2020-04-30 | 2020-08-25 | 华北电力大学 | Comprehensive energy scheduling method for multi-energy coupling elasticity |
| CN113537562A (en) * | 2021-06-09 | 2021-10-22 | 西安交通大学 | New energy station energy storage configuration calculation method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104300585A (en) * | 2014-10-13 | 2015-01-21 | 江苏大学 | Economical dispatching optimization method based on large-scale wind power integration |
| CN107886197A (en) * | 2017-11-13 | 2018-04-06 | 裕成电器有限公司 | A kind of optimization method of load responding to the renewable power capability of system consumption |
-
2019
- 2019-12-06 CN CN201911241407.2A patent/CN110752602A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104300585A (en) * | 2014-10-13 | 2015-01-21 | 江苏大学 | Economical dispatching optimization method based on large-scale wind power integration |
| CN107886197A (en) * | 2017-11-13 | 2018-04-06 | 裕成电器有限公司 | A kind of optimization method of load responding to the renewable power capability of system consumption |
Non-Patent Citations (1)
| Title |
|---|
| 沈风，等: ""智能电网环境下负荷响应对系统消纳风电能力影响的研究"", 《电测与仪表》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111581809A (en) * | 2020-04-30 | 2020-08-25 | 华北电力大学 | Comprehensive energy scheduling method for multi-energy coupling elasticity |
| CN111581809B (en) * | 2020-04-30 | 2024-02-06 | 华北电力大学 | Comprehensive energy scheduling method of multi-energy coupling elasticity |
| CN113537562A (en) * | 2021-06-09 | 2021-10-22 | 西安交通大学 | New energy station energy storage configuration calculation method and system |
| CN113537562B (en) * | 2021-06-09 | 2023-07-25 | 西安交通大学 | New energy station energy storage configuration calculation method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103151803B (en) | Method for optimizing wind power system-contained unit and backup configuration | |
| CN108487994B (en) | A kind of micro- energy net composite energy storage system | |
| CN110829408B (en) | Multi-domain scheduling method considering energy storage power system based on power generation cost constraint | |
| CN106532764A (en) | Electric vehicle charging load regulation and control method for locally consuming photovoltaic power generation | |
| CN109474026B (en) | Multi-source coordination system optimal scheduling method based on large-scale solid-state heat storage | |
| CN110768302A (en) | Evaluation and regulation method for renewable energy consumption capability of regional power grid | |
| CN110752602A (en) | Method for evaluating new energy consumption capability of system through load response and energy storage | |
| CN115640982A (en) | Pumped storage priority regulation-based day-ahead optimal scheduling method for multi-energy complementary system | |
| CN115860413A (en) | Grid-connected combined cooling heating and power micro-grid system economic scheduling method based on load demand response and double-layer adjustable robust optimization | |
| Huangfu et al. | An optimal energy management strategy with subsection bi-objective optimization dynamic programming for photovoltaic/battery/hydrogen hybrid energy system | |
| CN114154790A (en) | Industrial park light storage capacity configuration method based on demand management and flexible load | |
| CN113689023B (en) | Wind-abandoning energy-dissipating management method for wind/storage/hydrogen grid-connected power generation system | |
| CN115940284B (en) | Operation control strategy of new energy hydrogen production system considering time-of-use electricity price | |
| CN112436510B (en) | Wind-light-fire ultrahigh voltage direct current outgoing dispatching method and system | |
| CN107886197A (en) | A kind of optimization method of load responding to the renewable power capability of system consumption | |
| Ma et al. | Two-stage optimal dispatching based on wind-photovoltaic-pumped storage-thermal power combined power generation system | |
| CN108933454B (en) | Output lower limit coefficient optimization method for peak shaving unit containing wind power grid-connected power system | |
| Zhang et al. | Research on Optimal Scheduling of Power System with High-penetration Renewable Energy Considering Flexibility of Power Supply Side and Load Side | |
| CN109782602A (en) | A kind of intelligent dynamically optimized method of phase-change heat accumulation system operation | |
| CN112766680B (en) | Controllable thermal load scheduling method for virtual power plant | |
| CN112232693B (en) | Ultra-short-term operation scheduling method and device for power system | |
| CN114186850A (en) | Virtual power plant operation control method based on peak regulation effect evaluation index | |
| CN117096957B (en) | Multi-source collaborative optimization method and system for power distribution network | |
| Sun et al. | Adaptive Energy Management Strategy of Fuel Cell Electric Vehicle | |
| Huimin | Flexible Adjustment Resource Allocation Method for High Proportion of New Energy Areas based on K-means Clustering |
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: 20200204 |