CN106056256B - Interactive micro-grid scheduling method for balancing power supply and demand relationship - Google Patents
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Abstract
The invention discloses an interactive micro-grid scheduling method for balancing power supply and demand relations in the field of power system scheduling. The method comprises the following steps: fitting a day-ahead load prediction curve, a day-ahead wind power/photoelectric prediction curve and a power generation curve, and formulating a day-ahead predicted power price according to the fitting curve; correcting the day-ahead curve in time and in front; issuing a correction electricity price according to the time-front correction curve; collecting and monitoring state parameters; inputting the collected and monitored state parameters into a multi-target scheduling strategy, and performing multi-target nonlinear programming with minimum cost; making a manual decision; and (5) checking supply and demand balance and power flow distribution and repeating the next time interval scheduling control. The invention considers multi-dimensional peak shifting resources and multi-dimensional cost benefits, can solve the problem of single schedulable resource of power supply and demand balance, and simultaneously optimizes the economic benefits and the energy-saving and emission-reducing benefits.
Description
Technical Field
The invention relates to the field of power system scheduling, in particular to an interactive micro-grid scheduling method for balancing power supply and demand relations.
Background
The increasing increase of the power load, especially the peak load generated by the sharp increase of the power consumption in the peak period of summer, seriously destroys the relation of power supply and demand balance, even causes the large-area switching-off power limitation, and influences the power quality of part of users; on the other hand, the power grid is difficult to consume a large amount of currently accessed distributed new energy, which causes serious resource waste and economic loss. In addition, the problem of unbalanced supply and demand of the power system during peak load is generally solved by scheduling the peak shaving power plants, and the problems of insufficient capacity and the like of the peak shaving power plants are increasingly highlighted. The peak shaving plant can be divided into a hydraulic peak shaving plant and a thermal peak shaving plant, wherein the hydraulic peak shaving plant is taken as the main power of peak shaving due to the advantages of high response speed, high peak shaving efficiency, economy, environmental protection and the like, however, in the dry season, a thermal peak shaving unit is a main peak shaving resource, and the peak shaving means is single. Therefore, it is highly desirable to incorporate Distributed Generation (DG), customer-side Demand Response (DR), and Energy Storage (ES) resources for joint peak shaving based on conventional peak shaving resources.
However, unlike flexible scheduling of traditional power generation resources, distributed power generation, user-side demand response and energy storage resources all have certain rigidity, that is, supply and demand balance scheduling can only be realized through step change of the operating state of the distributed power generation, and therefore, a Virtual Power Plant (VPP) is required to be used as a scheduling control center to realize integration and integrated regulation of the resources.
The peak shaving resources are subjected to multi-dimensional modeling analysis, and particularly, the whole process of energy conservation and emission reduction and ES action in day-ahead scheduling is brought into the generation of the scheduling method, so that the optimization of the micro-grid operation scheduling can be realized. Under the condition of unbalanced supply and demand, the ES has the source-load dual characteristic, namely, when the power supply is larger than the demand, the DG is partially consumed by the ES, and the consumed power is sent to the power grid during the peak load period, so that the supply and demand shortage compensation caused by uncertainty and other factors can be realized. The application of the ES in the micro-grid scheduling reduces the wind and light abandonment quantity on one hand, reduces the peak load reduction on the other hand, and achieves the purposes of energy conservation, emission reduction and economic optimization on the basis of safety and reliability.
Disclosure of Invention
The invention aims to provide an interactive micro-grid scheduling method for balancing power supply and demand relations, which can solve the problem of single resource in power supply and demand balance scheduling and optimize economic benefits and energy conservation and emission reduction benefits. The method comprises the following specific steps:
Counting the historical data of the microgrid operation to form a day-ahead load prediction curveP L,t) Day-ahead wind power prediction curve (P W,t) And a photovoltaic power generation prediction curve before the day (P S,t) Fitting to the power generation curve and making the predicted price of electricity according to the curver；
Step 2, correcting the curve before the day
Dividing the day-ahead prediction curve into a plurality of time intervals, and carrying out comparison on the day-ahead prediction curve in the step 1 according to the load condition, the meteorological condition and the microgrid operation condition of the dayP X,t) A time-before correction ɛ, a correction curve fitted to the power generation curve to form a t period: (P X+ɛ,t) And accordingly correcting the predicted electricity price tor’Issue correction electricity pricer’；
Step 3, monitoring and collecting state quantity
Monitoring and acquiring state quantities, including actual operation parameters of the microgrid, peak regulation margins and capacities thereof, energy storage margins and capacities thereof, meteorological conditions and the like;
step 4, inputting a multi-target scheduling strategy to solve
Inputting a multi-target scheduling strategy, and based on the prediction data in the step (3) and the operation data acquired in the step (4), carrying out multi-target nonlinear programming solution by using cost minimization as a target function under the constraints of micro-grid operation, the reduced output of a virtual power plant, the climbing rate and the capacity of the virtual power plant, so as to form a scheduling scheme with minimized cost;
step 5, artificial decision
Submitting the scheme of the last step of virtual power plant aid decision to a dispatching center, and making a manual decision by a dispatcher according to the actual situation, if the scheme is optimal, performing the next step, and if not, returning to the step 3 to revise the electricity price again until the optimal scheme is obtained;
step 6, checking supply and demand balance and tide distribution
Checking whether the supply and demand balance and the tidal current distribution meet the standard requirements, and if not, adjusting through manual regulation and control behaviors until the standard requirements are met;
step 7, repeating the next time interval scheduling control
On the basis that the time period t meets the standard requirement, the scheduling control of the next time period is started from step 2.
The invention has the beneficial effects that compared with the existing microgrid scheduling method, the interactive microgrid scheduling method for balancing the power supply and demand relationship has the following main advantages and improvements:
by taking the virtual power plant as a dispatching control center, distributed power generation, user side demand response and energy storage resources are brought into the dispatching control center on the basis of traditional supply and demand balance resources to form supply and demand combined regulation, and prediction precision is improved by a prediction method of multi-time scale coordination before the day and before the time; in addition, the time-of-use electricity price is predicted by a linearization method, so that the difficulty of time-of-use electricity price prediction is reduced, and the method is simple and effective.
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Fig. 1 is an architecture diagram of supply and demand balanced scheduling.
FIG. 2 is an overall flow chart of the present invention.
FIG. 3 is a diagram illustrating the steps of the present invention.
Detailed Description
The invention relates to an interactive microgrid scheduling method for balancing power supply and demand relations, which comprises the following specific implementation steps of:
Counting the historical data of the microgrid operation to form a day-ahead load prediction curveP L,t) Day-ahead wind power prediction curve (P W,t) And a photovoltaic power generation prediction curve before the day (P S,t) Fitting to the power generation curve and making the predicted price of electricity according to the curver；
Definition ofδIs a correlation coefficient and is calculated by the following formula:
δ=(ΔP ,mƩt-1/Δr t-1)·( r B /P B)
wherein: deltaP ,mƩt-1Fitting the variation of the load, Delta, in the curve for the t-1 periodP ,mƩt-1=P ,mƩt-1-ΔP ,Bt-1；r BIs the retail price at the base charge in the fitted curve; deltar t-1Fitting the amount of change in electricity price, Delta, in the curve for the t-1 time periodr t-1=r t-1-Δr B；P BFor fitting the base line load in the curve
And (4) making a day-ahead predicted electricity price according to the fitted prediction curve, and calculating according to the following formula:
r= r B + r B · ΔP ,mƩt/(P B·δ)
wherein: deltaP ,mƩtFitting the variation of the load, Δ, in the curve for the period tP ,mƩt=P ,mƩt-ΔP ,BƩt
Step 2, correcting the curve before the day
Dividing the day-ahead prediction curve into a plurality of time intervals, and carrying out comparison on the day-ahead prediction curve in the step 1 according to the load condition, the meteorological condition and the microgrid operation condition of the dayP X,t) A time-before correction ɛ, a correction curve fitted to the power generation curve to form a t period: (P X+ɛ,t) And accordingly correcting the predicted electricity price tor’Issue correction electricity pricer’In which is based onP X+ɛ correcting electricity pricer’With reference to step 1
Step 3, monitoring and collecting state quantity
Monitoring and acquiring state quantities including actual operation parameters of the microgrid, peak regulation margin and capacity thereof, energy storage margin and capacity thereof, meteorological conditions and the like
Step 4, inputting a multi-target scheduling strategy to solve
Inputting a multi-target scheduling strategy, and based on the prediction data in the step 3 and the operation data acquired in the step 4, carrying out multi-target nonlinear programming solution by using cost minimization as an objective function under the constraints of micro-grid operation, virtual power plant reduced output, climbing rate and capacity thereof to form a scheduling scheme with minimized cost
The cost function of the thermal peak shaving unit in a certain time period t is calculated by the following formula:
f 1=Ʃ[C G,i,t+ C UG,i ·(1- U G,i,t-1)+λ i·exp(λ i P i t)] ·U G,i,t;(i=1···NG)
wherein:C G,i,tfor the operation cost of the thermal power generating unit i in the period t,C G,i,t=αP t,i 2+βP t,i+γ；C UG,ithe starting cost of the thermal power generating unit i in the period t is shown;U G,i,t-1the operating state of the thermal power generating unit i in a t-1 period (represented by two-dimensional discrete numerical values, wherein 0 is shutdown and 1 is running);U G,i,tthe operating state of the thermal power generating unit i in the period t (represented by two-dimensional discrete numerical values, wherein 0 is shutdown and 1 is operation);λ i·exp(λ i P i t) Pollution discharge cost of thermal power generating unit i for t period
In addition, load shedding compensation, wind curtailment compensation and light curtailment compensation are realized to a certain degree by the ES at a certain time t, namely:
f 2= P’ LS,t ·r LS + P’ WS,t ·r WS + P’ SS,t ·r SS
wherein:P’ LS,tin order to correct the amount of the cutting load,P’ LS,t = P LS,t - P CLS,t ，P LS,tin order to cut the amount of load,P CLS,tthe load shedding compensation quantity is determined by the ES energy storage condition when the supply is less than the demand;P’ WS,tin order to correct the air loss amount,P’ WS,t = P WS,t - P CWS,t ，P WS,tin order to discard the air volume,P CWS,tthe compensation amount of the abandoned wind is determined by the ES energy storage condition when the supply is larger than the demand;P’ SS,tin order to correct the amount of the light reject,P’ SS,t = P SS,t - P CSS,t ，P SS,tin order to reject the amount of light,P CSS,tin order to abandon the light compensation quantity, the ES energy storage condition determines when the supply is greater than the demand;r LS、r WS、r SSrespectively cutting load cost, abandoning wind cost and abandoning light cost
The user side implements demand response, and the cost function under a certain time period t is as follows:
f 3=ƩC I,n,t+ ƩC P,m,t;(n=1···NI, m=1···NP)
wherein:C I,n,tcalculating the incentive strategy of the user for the user n in the time period t according to the incentive demand response cost of the user n in the power market;C P,m,tcalculating the user incentive strategy for the user m in the time period t according to the price-based demand response cost under the power market
The final objective function is a multi-objective nonlinear programming:
min{ f 1, f 2, f 3}
the constraints are as follows:
1) and power balance constraint:
ƩP G,i,t+P W,t+P S,t+P ’ LS,t+ΔP ES,t = P L,t + P ’ WS,t +P ’ SS,t;(i=1···NG)
wherein: deltaP ES,t Is margin of energy storage
2) Wind power and photoelectric output constraint:
0≤P W,t ≤P Wmax,t
0≤P S,t ≤P Smax,t
3) and (3) constraint of compensation amount:
a. wind curtailment/light curtailment absorption compensation:
0≤P CWS,t+ P CSS,t ≤P ES
0≤P CWS,t ≤P ES
0≤P CSS,t ≤P ES
b. load shedding compensation:
0≤P CLS,t ≤P ES
wherein:P ESfor energy storage device capacity
4) And (3) generator constraint:
including upper and lower limits of output constraint, minimum start-stop time constraint, reserve capacity constraint and climbing constraint
All the collected data are transmitted to a dispatching center, and the solving process can be realized through the existing mature calculation program
Step 5, artificial decision
Submitting the scheme of the last step of virtual power plant aid decision to a dispatching center, and making a manual decision by a dispatcher according to the actual situation, if the scheme is optimal, performing the next step, and if not, returning to the step 3 to revise the electricity price again until the optimal scheme is obtained;
step 6, checking supply and demand balance and tide distribution
Checking whether the supply and demand balance and the tidal current distribution meet the standard requirements, and if not, adjusting through manual regulation and control behaviors until the standard requirements are met;
step 7, repeating the next time interval scheduling control
On the basis that the time period t meets the standard requirement, the scheduling control of the next time period is started from step 2.
The invention provides an interactive micro-grid scheduling method for balancing power supply and demand relations, which is characterized in that a micro-grid economic scheduling problem is modeled based on a virtual power plant and solved by a nonlinear programming method, so that on one hand, the coordinated interaction of multi-dimensional peak regulation resources is considered, and the peak regulation capacity and the economic benefit are improved; on the other hand, multi-dimensional economic benefits are considered, and energy conservation and emission reduction are realized. The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (1)
1. A microgrid scheduling method for balancing power supply and demand relations is characterized by comprising the following steps:
step 1, fitting a day-ahead prediction curve and making a day-ahead prediction electricity price
Counting the historical data of the microgrid operation to form a day-ahead load prediction curve (P)LT), day-ahead wind power prediction curve (P)WT) and a photovoltaic power generation prediction curve (P) before the dayST), fitting a power generation curve and establishing a predicted power price r according to the power generation curve;
δ is defined as a correlation coefficient and is calculated by the following formula:
wherein: delta Pm,Σt-1Fitting the variation of the load, Δ P, in the curve for the t-1 periodm,Σt-1＝Pm,Σt-1-PB,t-1；rBIs the retail price at the base charge in the fitted curve; Δ rt-1Variation of electricity price, Deltar, in a curve fitted for a period of t-1t-1＝rt-1-rB；PBIs the baseline load in the fitted curve;
and (3) making a day-ahead predicted electricity price r according to the fitted prediction curve, and calculating by the following formula:
wherein: delta Pm,ΣtFitting the variation of the load, Δ P, in the curve for the period tm,Σt＝Pm,Σt-PB,t；
Step 2, correcting the curve before the day
Dividing the day-ahead prediction curve into a plurality of time intervals, and carrying out comparison on the day-ahead prediction curve (P) in the step 1 according to the load condition of the day, the meteorological condition and the microgrid operation conditionXT) performing a time-front correction epsilon fitted to the power generation curve to form a correction curve (P) for a period tX+ epsilon, t), and accordingly correcting the predicted electricity price into r ', and issuing a corrected electricity price r';
step 3, monitoring and collecting state quantity
Monitoring and acquiring state quantities, including actual operation parameters of the microgrid, peak regulation margins and capacities thereof, energy storage margins and capacities thereof, meteorological conditions and the like;
step 4, inputting a multi-target scheduling strategy to solve
Inputting a multi-target scheduling strategy, and based on the prediction data in the step (1) and the operation data acquired in the step (3), carrying out multi-target nonlinear programming solution by taking cost minimization as a target function under the constraints of micro-grid operation, virtual power plant reduced output, climbing rate and capacity thereof to form a scheduling scheme with minimized cost; the scheduling scheme is realized by the following substeps:
wherein: cG,i,tFor the operation cost of the thermal power generating unit i in the period t,
CUG,ithe starting cost of the thermal power generating unit i in the period t is shown; u shapeG,i,t-1The operating state of the thermal power generating unit i in the t-1 period is represented by two-dimensional discrete numerical values, wherein 0 is shutdown, and 1 is operation; u shapeG,i,tFor the operation state of a thermal power generating unit i in a period tThe state is represented by two-dimensional discrete numerical values, wherein 0 is shutdown and 1 is running; lambda [ alpha ]iexp(λiPi t) The pollution discharge cost of the thermal power generating unit i at the time period t;
in addition, load shedding compensation, wind curtailment compensation and light curtailment compensation are realized to a certain degree by the ES at a certain time t, namely:
f2＝P'LS,t·rLS+P'WS,t·rWS+P'SS,t·rSS
wherein: p'LS,tTo correct the amount of cutting load, P'LS,t＝PLS,t-PCLS,t，PLS,tFor cutting load, PCLS,tThe load shedding compensation quantity is determined by the ES energy storage condition when the supply is less than the demand; p'WS,tTo correct the waste air volume, P'WS,t＝PWS,t-PCWS,t，PWS,tTo discard the air volume, PCWS,tThe compensation amount of the abandoned wind is determined by the ES energy storage condition when the supply is larger than the demand; p'SS,tTo correct the amount of waste light, P'SS,t＝PSS,t-PCSS,t，PSS,tTo reject the light quantity, PCSS,tIn order to abandon the light compensation quantity, the ES energy storage condition determines when the supply is greater than the demand; r isLS、rWS、rSSRespectively load cutting cost, wind abandoning cost and light abandoning cost;
the user side implements demand response, and the cost function under a certain time period t is as follows:
wherein: cI,n,tCalculating the incentive strategy of the user for the user n in the time period t according to the incentive demand response cost of the user n in the power market; cP,m,tCalculating the incentive strategy of the user m in the time period t according to the price-based demand response cost of the user m in the power market;
the final objective function is a multi-objective nonlinear programming:
min{f1,f2,f3}；
the constraints are as follows:
1) and power balance constraint:
wherein: delta PES,tIs the energy storage allowance;
2) wind power and photoelectric output constraint:
0≤PW,t≤PWmax,t
0≤PS,t≤PSmax,t
3) and (3) constraint of compensation amount:
a. wind curtailment/light curtailment absorption compensation:
0≤PCWS,t+PCSS,t≤PES
0≤PCWS,t≤PES
0≤PCSS,t≤PES
b. load shedding compensation:
0≤PCLS,t≤PES
wherein: pESIs the energy storage device capacity;
4) and (3) generator constraint:
the method comprises the following steps of output upper and lower limit constraints, minimum start-stop time constraints, standby capacity constraints and climbing constraints;
all the collected data are transmitted to a dispatching center, and the solving process can be realized through the existing mature calculation program;
step 5, artificial decision
Submitting the scheme of the last step of virtual power plant aid decision to a dispatching center, and making a manual decision by a dispatcher according to the actual situation, if the scheme is optimal, performing the next step, otherwise, returning to the step 1 to revise the electricity price again until the optimal scheme is obtained;
step 6, checking supply and demand balance and tide distribution
Checking whether the supply and demand balance and the tidal current distribution meet the standard requirements, and if not, adjusting through manual regulation and control behaviors until the standard requirements are met;
step 7, repeating the next time interval scheduling control
On the basis that the time period t meets the standard requirement, the scheduling control of the next time period is started from step 2.
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| CN108429256B (en) * | 2018-03-22 | 2020-03-10 | 国网河北省电力有限公司经济技术研究院 | Power system operation optimization method and terminal equipment |
| CN108764543B (en) * | 2018-05-17 | 2023-07-18 | 中国电力科学研究院有限公司 | Power scheduling method and system |
| CN110659788A (en) * | 2018-06-29 | 2020-01-07 | 中国电力科学研究院有限公司 | Supply and demand balance analysis method and system for user-side comprehensive energy system |
| CN109962498B (en) * | 2019-02-13 | 2021-02-09 | 北京北方智能科技股份有限公司 | Virtual power plant online power automatic adjusting device and adjusting method thereof |
| CN109784589B (en) * | 2019-03-19 | 2023-03-24 | 广东电网有限责任公司 | Power grid business expansion user access optimization method, device and equipment |
| CN110045626A (en) * | 2019-05-14 | 2019-07-23 | 华北电力大学 | The Intelligent housing strategy changed based on temperature |
| CN110336274B (en) * | 2019-07-01 | 2022-12-09 | 上海电力学院 | Virtual power plant operation method with additional virtual power plant regulator |
| CN110659830A (en) * | 2019-09-25 | 2020-01-07 | 国网天津市电力公司 | Multi-energy micro-grid planning method for comprehensive energy system |
| CN113627762B (en) * | 2021-07-30 | 2024-03-22 | 国网山西省电力公司电力科学研究院 | Virtual power plant peak shaving method based on excitation electricity price |
| CN115207917A (en) * | 2022-08-17 | 2022-10-18 | 国网山东省电力公司青岛供电公司 | Layered-distributed multi-target tracking method and system for virtual power plant |
| CN115936260B (en) * | 2023-01-13 | 2023-07-18 | 智光研究院(广州)有限公司 | Virtual power plant user energy storage scheduling method and device, electronic equipment and storage medium |
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| CN102184475B (en) * | 2011-05-11 | 2013-10-16 | 浙江大学 | Optimizing and dispatching method for microgrid economical operation on basis of multiple time scale coordination |
| CN102983573B (en) * | 2012-11-09 | 2014-10-15 | 天津大学 | Security constraint economic dispatch method based on security domains |
| CN103346562B (en) * | 2013-07-11 | 2015-06-17 | 江苏省电力设计院 | Multi-time scale microgrid energy control method considering demand response |
| CN103971181B (en) * | 2014-05-20 | 2017-09-19 | 河海大学 | A kind of economic load dispatching method a few days ago for virtual plant |
| CN104376385A (en) * | 2014-11-27 | 2015-02-25 | 重庆大学 | Microgrid power price optimizing method |
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| CN105631599A (en) * | 2015-12-30 | 2016-06-01 | 国网甘肃省电力公司电力科学研究院 | Multi-target operation scheduling method of virtual power plant |
| CN105634024A (en) * | 2016-02-23 | 2016-06-01 | 国网江苏省电力公司电力科学研究院 | Price demand response-based intraday economic scheduling model and linear solving method |
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