CN111864758B - Energy storage system operation scheduling method considering load demand difference - Google Patents

Energy storage system operation scheduling method considering load demand difference Download PDF

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CN111864758B
CN111864758B CN202010745415.7A CN202010745415A CN111864758B CN 111864758 B CN111864758 B CN 111864758B CN 202010745415 A CN202010745415 A CN 202010745415A CN 111864758 B CN111864758 B CN 111864758B
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load
energy storage
time
formula
storage system
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CN111864758A (en
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杨贺钧
郝振宇
马英浩
张大波
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Hefei University of Technology
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    • 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
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • 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
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • 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
    • 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

Abstract

The invention discloses an energy storage system operation scheduling method considering load demand difference, which comprises the following steps: 1, load prediction is carried out based on historical load data, and loads at all times of the next day are obtained; 2, establishing a user response model based on the electricity price elastic matrix, and calculating the load at each moment after the time-of-use electricity price; 3, dividing charging and discharging time periods, and constructing a demand index of the load to the charging and discharging of the energy storage system at each moment to represent the differentiated demand of the changed load curve to the energy storage charging and discharging electric quantity; and 4, establishing an energy storage system charge-discharge model and related evaluation indexes to depict the influence of different energy storage operation scheduling strategies participating in demand side management on the smoothness of the daily load curve. The invention can improve the utilization rate of the energy storage system when the energy storage system performs peak clipping and valley filling, thereby effectively smoothing daily load curves under the limited installation capacity of the energy storage system, reducing peak-valley difference and improving peak clipping and valley filling level.

Description

Energy storage system operation scheduling method considering load demand difference
Technical Field
The invention relates to the field of demand response participation of an energy storage system through peak clipping and valley filling, in particular to an energy storage system operation scheduling method considering load demand difference.
Background
With the rapid increase of the economic level, the modernization degree is steadily improved, the demand of a user on the power load is sharply increased, and the peak-to-valley difference of the power system is gradually increased. Meanwhile, with the continuous reform of energy structures and the continuous development of energy storage technologies, energy storage grid connection becomes the current development trend. The energy storage system participates in the response of the demand side, so that the operation reliability and the electric energy quality of the power system are improved, the load curve can be smoother and the peak-valley difference can be reduced through the charging in the valley period and the discharging in the peak period, and the safe, economical and efficient operation of a power grid can be more effectively realized. However, although the energy storage system has the advantages of fast response speed, high controllability and the like, the installation capacity is limited by the higher cost at the present stage, and the condition of large-scale use is not met.
In the current research on energy storage operation scheduling strategies, the energy storage operation scheduling strategies can be basically divided into constant power charge and discharge strategies and variable power charge and discharge strategies. Although the constant power charging and discharging strategy is easy to control, due to the limitation of energy storage capacity, when the energy storage system performs peak clipping and valley filling and operates at the maximum charging and discharging power all the time, the energy storage electric quantity is quickly exhausted or fully filled at the initial stage of the peak-valley period, effective peak clipping and valley filling are not performed, and for the research of the variable power charging and discharging strategy, the requirement degree indexes of the energy storage electric quantity are not established aiming at different load levels, the difference requirements of the variable load curve on the energy storage electric quantity are not considered, and the phenomena that the utilization rate of the electric quantity of the energy storage system is low and the charging and discharging power distribution is unreasonable at each moment can still be caused. Therefore, improving the utilization rate and the economy of energy storage facilities under the limited installation capacity is still the focus of the urgent development of the current research.
Disclosure of Invention
The invention aims to avoid the defects of the prior art and provides the energy storage system operation scheduling method considering the load demand difference so as to effectively smooth daily load curves under the limited installation capacity of the energy storage system, reduce peak-valley difference and improve the peak clipping and valley filling level, thereby improving the reliability and the power quality of the system and more effectively realizing the safe, economical and efficient operation of a power grid.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention relates to an energy storage system operation scheduling method considering load demand difference, which is characterized by comprising the following steps of;
step one, load prediction is carried out based on historical load data, and loads at all times of the next day are obtained:
step 1.1, obtaining an observed value of a local power load in a period close to H;
step 1.2, solving the load change coefficient of the daily load curve of the l day of the h week:
step 1.2.1, calculating the average daily load power on the l day of the h week by using the formula (1)
Figure GDA0003165301170000011
Figure GDA0003165301170000021
In the formula (1), LghRepresents the load at the g hour of the h week; t is the number of hours in a day;
step 1.2.2, using the load change factor Z in the h-th week g-th hour of formula (2)gh：
Figure GDA0003165301170000022
Step 1.3, smoothly predicting the load change coefficient of the l day of the H +1 week by using the primary index, smoothly predicting the average load power of the l day of the H +1 week by using the linear index, and multiplying the two to obtain a daily load curve of the l day of the H +1 week;
step two, establishing a demand response model based on the electricity price elastic matrix, and calculating the hour load after the time-of-use electricity price:
step 2.1, obtaining the electrovalence elastic modulus rho of the m time period by using the formula (3) and the formula (4) respectivelymmAnd cross elastic coefficient rho of m-period load change with respect to n-period electricity pricemn：
Figure GDA0003165301170000023
Figure GDA0003165301170000024
In formulae (3) and (4), Lm0Load m time period before time-of-use electricity price; Δ LmThe load variation in m time period after the time-of-use electricity price; p is a radical ofm0The electricity price m time period before the time of use of electricity price; Δ pmThe electricity price variation quantity of m time period after the time-of-use electricity price is obtained; m is not equal to n, pn0The time-of-use electricity price is n time periods before the time-of-use electricity price; Δ pnThe electricity price variation quantity is n time intervals after the time-of-use electricity price; k is the number of time segments;
step 2.2, constructing an electricity price elastic matrix E by using the formula (5):
Figure GDA0003165301170000025
step 2.3, after the time-of-use electricity price is implemented, establishing a demand response model by using a formula (6):
Figure GDA0003165301170000026
in the formula (6), l (k) is a load required for a period k after the time-of-use electricity price is applied;
step three, dividing charging and discharging time periods, and constructing demand indexes of the load to the charging and discharging of the energy storage system at all times to represent the differentiated demand of the changed load curve to the energy storage charging and discharging electric quantity:
step 3.1, determining a load critical value L at the moment of energy storage charging by using the formula (7) and the formula (8)chmAnd load threshold value L at the time of dischargedchm：
Figure GDA0003165301170000031
Figure GDA0003165301170000032
In the formula (7) -formula (8), LmaxThe daily maximum load; l isminThe daily minimum load;
step 3.2, constructing the charge and discharge demand index of the load at each moment on the energy storage system:
step 3.2.1, obtaining the loads corresponding to the N times needing to be charged in one day and sequencing the loads in an ascending order to obtain a sequenced charging load set LcrAs shown in formula (9):
Lcr＝{L(tc1)，L(tc2)，...，L(tcN)} (9)
in formula (9), L (t)cN) The load value corresponding to the Nth time needing charging after ascending sorting is obtained;
let Tc＝{tc1,tc2,...,tcNDenotes the charging load set L after ascending sortingcrAt each charging load stationCorresponding to a set of time points, tcNThe Nth time point needing charging after sequencing is obtained;
step 3.2.2, obtaining the loads corresponding to the M discharging-needed moments in one day and sequencing the loads in a descending order to obtain a sequenced discharging load set LdrAs shown in equation (10):
Ldr＝{L(td1)，L(td2)，...，L(tdM)} (10)
in the formula (10), L (t)dM) The load value corresponding to the Mth time needing discharging after descending sorting is obtained;
let Td＝{td1,td2,...,tdMThe discharge load set L after descending order sortingdrSet of time points corresponding to each discharge load, tdMThe M time point needing discharging after sequencing is obtained;
step 3.2.3, calculate t using equation (11)ciConstantly stored energy charging power demand wc(tci)：
Figure GDA0003165301170000033
In the formula (11), LcrminMinimum system load at the moment of charging;
step 3.2.4, calculate t using equation (12)djInstantaneous energy storage discharge power demand wd(tdj)：
Figure GDA0003165301170000034
In the formula (12), LdrmaxThe maximum system load at the moment of discharge;
establishing an energy storage system charge-discharge model and related evaluation indexes to depict the influence of different energy storage operation scheduling strategies participating in demand side management on daily load curve smoothness:
step 4.1, calculating the charging and discharging power of the energy storage system at each moment in a day:
step 4.1.1, defining an energy storage charge and discharge power constraint factor delta epsilon (0, 1) to limit charge and discharge power;
step 4.1.2, calculating t by respectively using the formula (13) and the formula (14)ciTime of day potential charging power P'tc(tci) And tdjTime of day potential discharge power P'td(tdj)：
P′tc(tci)＝-δ×wc(tci)×Pr×η, i＝1,2,...,N (13)
Figure GDA0003165301170000041
In the formula (13) -formula (14), η is the charge-discharge efficiency of the energy storage system; prRated charge and discharge power for the energy storage system;
the constraint is constructed using equation (15):
Figure GDA0003165301170000042
in the formula (15), LaverageIs the daily load average;
step 4.2, calculating the actual charging power at each moment in a day:
step 4.2.1, calculating the energy storage system at t according to the formula (16)ciReal-time electrical quantity of time ES (t)ci)：
Figure GDA0003165301170000043
In formula (16), T is a time interval; ES (ES)maxThe maximum capacity of the energy storage system;
step 4.2.2, calculating the energy storage system at t by using the formula (17)ciActual charging power P at a timetc(tci)：
Figure GDA0003165301170000044
Step 4.3, calculating the actual discharge power at each moment in a day:
step 4.3.1, calculating the energy storage system at t according to the formula (18)djReal-time electrical quantity of time ES (t)dj)：
Figure GDA0003165301170000045
Step 4.3.2, calculating the energy storage system t by using the formula (19)djActual discharge power P at a timetd(tdj)：
Figure GDA0003165301170000046
Step 4.4, determining the actual energy storage charging and discharging power P at the time t in one day by using the formula (20)t(t)：
Figure GDA0003165301170000047
Step 4.5, according to the actual charging and discharging power P in one daytAnd (t) determining the hourly output condition of the energy storage system on the prediction day, and taking the hourly output condition as an energy storage operation scheduling scheme considering the load difference requirements to perform peak clipping and valley filling.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, load prediction is carried out by utilizing an exponential smoothing method according to the existing historical load data, the next 24-hour load data is obtained, and the energy storage system is scheduled day by day, so that the output of the energy storage system at each moment can be more reasonably arranged, and the result that the peak clipping and valley filling effects are poor due to the fact that the energy storage system consumes electric quantity too early is avoided;
2. the invention provides the charge and discharge power constraint factor of the energy storage system, effectively prevents the inversion of peaks and valleys after the energy storage system is charged and discharged, and improves the operation safety of a power grid;
3. the invention defines the difference index of different load levels at each time interval to the charging and discharging requirements of the energy storage system in a demand side response strategy aiming at peak clipping and valley filling, and provides the energy storage charging and discharging power demand degree to determine the real-time charging and discharging power of the energy storage system, namely, the energy storage system is charged and operated at the maximum power when the lowest point of a load curve is met preferentially in the valley period, and the lower the load level is, the more the energy storage charging electric quantity is required; in a peak time period, the energy storage system operates in a maximum power discharge mode when the highest point of the load curve is preferentially met, and the higher the load level is, the more the demand is on the discharge capacity of the energy storage system, so that the load curve is effectively smoothed, and the peak clipping and valley filling effects of the energy storage system participating in demand side management are more remarkable.
Drawings
Fig. 1 is a schematic diagram of a scheduling operation process of an energy storage system in consideration of load demand differences according to the present invention.
Detailed Description
In this embodiment, as shown in fig. 1, an energy storage system operation scheduling method considering load demand differences includes first defining an energy storage power demand degree to represent a degree of differentiation of a changing load curve from energy storage charging and discharging demands; secondly, an energy storage system operation scheduling strategy considering load demand difference is provided, namely the demand of the energy storage system for energy storage discharge gradually increases from peak load to average load, and the demand of the energy storage system for energy storage charge gradually increases from valley load to average load; thirdly, providing an energy storage operation strategy evaluation index to depict the influence of different energy storage operation strategies participating in demand side management on the smoothness of the daily load curve; the method comprises the following steps:
step one, load prediction is carried out based on historical load data, and loads at all times of the next day are obtained:
step 1.1, obtaining an observed value of a local power load in a period close to H;
step 1.2, solving the load change coefficient of the daily load curve of the l day of the h week:
step 1.2.1, calculating the average daily load power on the l day of the h week by using the formula (1)
Figure GDA0003165301170000051
Figure GDA0003165301170000052
In the formula (1), LghRepresents the load at the g hour of the h week; t is the number of hours in a day;
step 1.2.2, using the load change factor Z in the h-th week g-th hour of formula (2)gh：
Figure GDA0003165301170000061
Step 1.3, smoothly predicting the load change coefficient of the l day of the H +1 week by using the primary index, smoothly predicting the average load power of the l day of the H +1 week by using the linear index, and multiplying the two to obtain a daily load curve of the l day of the H +1 week;
step two, establishing a demand response model based on the electricity price elastic matrix, and calculating the hour load after the time-of-use electricity price:
step 2.1, obtaining the electrovalence elastic modulus rho of the m time period by using the formula (3) and the formula (4) respectivelymmAnd cross elastic coefficient rho of m-period load change with respect to n-period electricity pricemn：
Figure GDA0003165301170000062
Figure GDA0003165301170000063
In formulae (3) and (4), Lm0Load m time period before time-of-use electricity price; Δ LmThe load variation in m time period after the time-of-use electricity price; p is a radical ofm0The electricity price m time period before the time of use of electricity price; Δ pmThe electricity price variation quantity of m time period after the time-of-use electricity price is obtained; m is not equal to n, pn0The time-of-use electricity price is n time periods before the time-of-use electricity price; Δ pnThe electricity price variation quantity is n time intervals after the time-of-use electricity price; k is the number of time segments;
step 2.2, constructing an electricity price elastic matrix E by using the formula (5), wherein the main diagonal elements are self-elastic and the value of the main diagonal elements is negative; off-diagonal elements are cross-elasticities, which have positive values:
Figure GDA0003165301170000064
step 2.3, after the time-of-use electricity price is implemented, establishing a demand response model by using a formula (6):
Figure GDA0003165301170000065
in the formula (6), l (k) is a load required for a period k after the time-of-use electricity price is applied;
step three, dividing charging and discharging time periods, and constructing demand indexes of the load to the charging and discharging of the energy storage system at all times to represent the differentiated demand of the changed load curve to the energy storage charging and discharging electric quantity:
step 3.1, determining a load critical value L at the moment of energy storage charging by using the formula (7) and the formula (8)chmAnd load threshold value L at the time of dischargedchm：
Figure GDA0003165301170000066
Figure GDA0003165301170000071
In the formula (7) -formula (8), LmaxThe daily maximum load; l isminThe daily minimum load; i.e. when the load power is not greater than LchmBelongs to the energy storage charging period, and when the load power is not less than LdchmThe time of (a) belongs to the energy storage discharge period;
step 3.2, constructing the charge and discharge demand index of the load at each moment on the energy storage system:
step 3.2.1, acquiring the loads corresponding to the N times needing to be charged in one day and sequencing the loads in an ascending order to obtain a rowSet of charging loads L after sequencecrAs shown in formula (9):
Lcr＝{L(tc1)，L(tc2)，...，L(tcN)} (9)
in formula (9), L (t)cN) The load value corresponding to the Nth time needing charging after ascending sorting is obtained;
let Tc＝{tc1,tc2,...,tcNDenotes the charging load set L after ascending sortingcrTime point set, t, corresponding to each charging loadcNThe Nth time point needing charging after sequencing is obtained;
step 3.2.2, obtaining the loads corresponding to the M discharging-needed moments in one day and sequencing the loads in a descending order to obtain a sequenced discharging load set LdrAs shown in equation (10):
Ldr＝{L(td1)，L(td2)，...，L(tdM)} (10)
in the formula (10), L (t)dM) The load value corresponding to the Mth time needing discharging after descending sorting is obtained;
let Td＝{td1,td2,...,tdMThe discharge load set L after descending order sortingdrSet of time points corresponding to each discharge load, tdMThe M time point needing discharging after sequencing is obtained;
step 3.2.3, calculate t using equation (11)ciConstantly stored energy charging power demand wc(tci)：
Figure GDA0003165301170000072
In the formula (11), LcrminMinimum system load at the moment of charging;
step 3.2.4, calculate t using equation (12)djInstantaneous energy storage discharge power demand wd(tdj)：
Figure GDA0003165301170000073
In the formula (12), LdrmaxThe maximum system load at the moment of discharge;
establishing an energy storage system charge-discharge model and related evaluation indexes to depict the influence of different energy storage operation scheduling strategies participating in demand side management on daily load curve smoothness:
step 4.1, calculating the charging and discharging power of the energy storage system at each moment in a day:
step 4.1.1, defining an energy storage charge and discharge power constraint factor delta epsilon (0, 1) to limit charge and discharge power, and avoiding negative effects on load peak clipping and valley filling of a power grid caused by excessive charge and discharge;
step 4.1.2, calculating t by respectively using the formula (13) and the formula (14)ciTime of day potential charging power P'tc(tci) And tdjTime of day potential discharge power P'td(tdj):
P′tc(tci)＝-δ×wc(tci)×Pr×η, i＝1,2,...,N (13)
Figure GDA0003165301170000081
In the formula (13) -formula (14), η is the charge-discharge efficiency of the energy storage system; prRated charge and discharge power for the energy storage system; p'tc(tci) And P'td(tdj) Potential charge and discharge power corresponding to each time point respectively, namely the corresponding charge and discharge power of each time point when the constraint of the total energy storage capacity is not considered;
the constraint is constructed using equation (15):
Figure GDA0003165301170000082
in the formula (15), LaverageIs the daily load average;
step 4.2, calculating the actual charging power at each moment in a day:
step 4.2.1, calculating the energy storage system at t according to the formula (16)ciReal-time electrical quantity of time ES (t)ci)：
Figure GDA0003165301170000083
In the formula (16), T is a time interval and is taken as 1 h; ES (ES)maxThe maximum capacity of the energy storage system;
step 4.2.2, calculating the energy storage system at t by using the formula (17)ciActual charging power P at a timetc(tci)：
Figure GDA0003165301170000084
Step 4.3, calculating the actual discharge power at each moment in a day:
step 4.3.1, calculating the energy storage system at t according to the formula (18)djReal-time electrical quantity of time ES (t)dj)：
Figure GDA0003165301170000085
Step 4.3.2, calculating the energy storage system t by using the formula (19)djActual discharge power P at a timetd(tdj)：
Figure GDA0003165301170000086
Step 4.4, determining the actual energy storage charging and discharging power P at the time t in one day by using the formula (20)t(t)：
Figure GDA0003165301170000091
Step 4.5, according to the actual charging and discharging power P in one dayt(t) determining hourly output of the energy storage system on the forecast day to obtainAnd performing more effective peak clipping and valley filling on the energy storage operation scheduling scheme after considering the load difference requirements.

Claims (1)

1. An energy storage system operation scheduling method considering load demand difference is characterized by comprising the following steps of;
step one, load prediction is carried out based on historical load data, and loads at all times of the next day are obtained:
step 1.1, obtaining an observed value of a local power load in a period close to H;
step 1.2, solving the load change coefficient of the daily load curve of the l day of the h week:
step 1.2.1, calculating the average daily load power on the l day of the h week by using the formula (1)
Figure FDA0003180214100000011
Figure FDA0003180214100000012
In the formula (1), LghRepresents the load at the g hour of the h week; t is the number of hours in a day;
step 1.2.2 obtaining the load change coefficient Z of the g hour of the h week by using the formula (2)gh：
Figure FDA0003180214100000013
Step 1.3, smoothly predicting the load change coefficient of the l day of the H +1 week by using the primary index, smoothly predicting the average load power of the l day of the H +1 week by using the linear index, and multiplying the two to obtain a daily load curve of the l day of the H +1 week;
step two, establishing a demand response model based on the electricity price elastic matrix, and calculating the hour load after the time-of-use electricity price:
step 2.1, obtaining the electrovalence elastic modulus rho of the m time period by using the formula (3) and the formula (4) respectivelymmAnd mTime-interval load change cross elastic coefficient rho relative to n-interval electricity pricemn：
Figure FDA0003180214100000014
Figure FDA0003180214100000015
In formulae (3) and (4), Lm0Load m time period before time-of-use electricity price; Δ LmThe load variation in m time period after the time-of-use electricity price; p is a radical ofm0The electricity price m time period before the time of use of electricity price; Δ pmThe electricity price variation quantity of m time period after the time-of-use electricity price is obtained; m is not equal to n, pn0The time-of-use electricity price is n time periods before the time-of-use electricity price; Δ pnThe electricity price variation quantity is n time intervals after the time-of-use electricity price; k is the number of time segments;
step 2.2, constructing an electricity price elastic matrix E by using the formula (5):
Figure FDA0003180214100000016
step 2.3, after the time-of-use electricity price is implemented, establishing a demand response model by using a formula (6):
Figure FDA0003180214100000021
in the formula (6), l (k) is a load required for a period k after the time-of-use electricity price is applied;
step three, dividing charging and discharging time periods, and constructing demand indexes of the load to the charging and discharging of the energy storage system at all times to represent the differentiated demand of the changed load curve to the energy storage charging and discharging electric quantity:
step 3.1, determining a load critical value L at the moment of energy storage charging by using the formula (7) and the formula (8)chmAnd load threshold value L at the time of dischargedchm：
Figure FDA0003180214100000022
Figure FDA0003180214100000023
In the formula (7) -formula (8), LmaxThe daily maximum load; l isminThe daily minimum load;
step 3.2, constructing the charge and discharge demand index of the load at each moment on the energy storage system:
step 3.2.1, obtaining the loads corresponding to the N times needing to be charged in one day and sequencing the loads in an ascending order to obtain a sequenced charging load set LcrAs shown in formula (9):
Lcr＝{L(tc1)，L(tc2)，…，L(tcN)} (9)
in formula (9), L (t)cN) The load value corresponding to the Nth time needing charging after ascending sorting is obtained;
let Tc＝{tc1,tc2,…,tcNDenotes the charging load set L after ascending sortingcrTime point set, t, corresponding to each charging loadcNThe Nth time point needing charging after sequencing is obtained;
step 3.2.2, obtaining the loads corresponding to the M discharging-needed moments in one day and sequencing the loads in a descending order to obtain a sequenced discharging load set LdrAs shown in equation (10):
Ldr＝{L(td1)，L(td2)，…，L(tdM)} (10)
in the formula (10), L (t)dM) The load value corresponding to the Mth time needing discharging after descending sorting is obtained;
let Td＝{td1,td2,…,tdMThe discharge load set L after descending order sortingdrSet of time points corresponding to each discharge load, tdMThe M time point needing discharging after sequencing is obtained;
step 3.2.3, calculate t using equation (11)ciConstantly stored energy charging power demand wc(tci)：
Figure FDA0003180214100000024
In the formula (11), LcrminMinimum system load at the moment of charging;
step 3.2.4, calculate t using equation (12)djInstantaneous energy storage discharge power demand wd(tdj)：
Figure FDA0003180214100000031
In the formula (12), LdrmaxThe maximum system load at the moment of discharge;
establishing an energy storage system charge-discharge model and related evaluation indexes to depict the influence of different energy storage operation scheduling strategies participating in demand side management on daily load curve smoothness:
step 4.1, calculating the charging and discharging power of the energy storage system at each moment in a day:
step 4.1.1, defining an energy storage charge and discharge power constraint factor delta epsilon (0, 1) to limit charge and discharge power;
step 4.1.2, calculating t by respectively using the formula (13) and the formula (14)ciTime of day potential charging power P'tc(tci) And tdjTime of day potential discharge power P'td(tdj)：
P′tc(tci)＝-δ×wc(tci)×Pr×η,i＝1,2,…,N (13)
Figure FDA0003180214100000032
In the formula (13) -formula (14), η is the charge-discharge efficiency of the energy storage system; prRated charging and discharging power for energy storage system；
The constraint is constructed using equation (15):
Figure FDA0003180214100000033
in the formula (15), LaverageIs the daily load average;
step 4.2, calculating the actual charging power at each moment in a day:
step 4.2.1, calculating the energy storage system at t according to the formula (16)ciReal-time electrical quantity of time ES (t)ci)：
Figure FDA0003180214100000034
In formula (16), T is a time interval; ES (ES)maxThe maximum capacity of the energy storage system;
step 4.2.2, calculating the energy storage system at t by using the formula (17)ciActual charging power P at a timetc(tci)：
Figure FDA0003180214100000035
Step 4.3, calculating the actual discharge power at each moment in a day:
step 4.3.1, calculating the energy storage system at t according to the formula (18)djReal-time electrical quantity of time ES (t)dj)：
Figure FDA0003180214100000036
Step 4.3.2, calculating the energy storage system t by using the formula (19)djActual discharge power P at a timetd(tdj)：
Figure FDA0003180214100000041
Step 4.4, determining the actual energy storage charging and discharging power P at the time t in one day by using the formula (20)t(t)：
Figure FDA0003180214100000042
Step 4.5, storing actual charging and discharging power P according to energy in one daytAnd (t) determining the hourly output condition of the energy storage system on the prediction day, and taking the hourly output condition as an energy storage operation scheduling scheme considering the load difference requirements to perform peak clipping and valley filling.
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