CN107730045A - A kind of baseline load thermal inertia modification method based on discrete inertia force system - Google Patents

A kind of baseline load thermal inertia modification method based on discrete inertia force system Download PDF

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CN107730045A
CN107730045A CN201710984059.2A CN201710984059A CN107730045A CN 107730045 A CN107730045 A CN 107730045A CN 201710984059 A CN201710984059 A CN 201710984059A CN 107730045 A CN107730045 A CN 107730045A
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temperature
load
discrete
inertia force
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CN107730045B (en
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嵇文路
包宇庆
许洪华
马琎劼
沈培锋
王勇
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Nanjing Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Nanjing Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply

Abstract

The invention provides a kind of baseline load thermal inertia modification method based on discrete inertia force system, comprise the following steps：The history environment data on load periphery are collected, by selecting quantity method to excavate the influence relation of load and temperature；By continuous dynamic system discretization, the lagged relationship between temperature T ' and real air temperature T is corrected, and is finally calculated and considers temperature, the discrete inertia force system model of baseline load parameter；Inertia force system model key parameter is calculated using particle cluster algorithm；Acquisition amendment temperature T ' is modified to real air temperature T using inertia force system model, then replaces real air temperature T to obtain accurate baseline load prediction as the input quantity of load prediction amendment temperature T '.The baseline load thermal inertia modification method just can obtain revised temperature by establishing the baseline load thermal inertia correction model based on discrete inertia force system, according to the temperature that the parameter that optimization obtains is actual with history.

Description

A kind of baseline load thermal inertia modification method based on discrete inertia force system
Technical field
It is especially a kind of to be based on discrete inertia force system the present invention relates to a kind of baseline load thermal inertia modification method Baseline load thermal inertia modification method.
Background technology
In numerous factors for influenceing prediction load, temperature is a kind of important influence factor, because electric load is to temperature Spend it is extremely sensitive, temperature rise, load rise, vice versa.In summer, load is also acted on by thermal inertia.Thermal inertia has The reason for change that body is embodied in load lags behind the change of temperature, causes thermal inertia has following two aspects：
(1) because the insulation effect of building, indoor temperature lag behind the change of outdoor temperature；
(2) the electricity consumption behavior of people has certain hysteresis relative to environmental change, and when temperature raises suddenly, people are needed for height Wen Tian arrival is prepared.
Therefore, it is necessary to this thermal inertia of load is considered in load prediction, so as to strengthen precision of prediction.
The content of the invention
The technical problem to be solved in the present invention is that existing load temperature measurement accuracy is influenceed by thermal inertia, is led to not Accurate prediction load temperature.
In order to solve the above-mentioned technical problem, the invention provides a kind of baseline load based on discrete inertia force system Thermal inertia modification method, it is characterised in that comprise the following steps：
Step 1, the history environment data on load periphery are collected, by selecting quantity method to excavate the influence of load and temperature Relation；
Step 2, by continuous dynamic system discretization, the lagged relationship between temperature T ' and real air temperature T is corrected, this is stagnant Relation is described as with first order inertial loop afterwards：
In formula, T (s) and T ' (s) are input and output respectively, and h is time constant, recycle inverse Laplace transformation with from The method of dispersion show that the time domain iteration expression formula under discrete conditions is：
In formula, hsIt it is the sampling period of discrete system, T (n) and T ' (n) is first order inertial loop respectively in n-th of sampling The input and output in cycle, then makeObtain：
T ' (n)=kT ' (n-1)+(1-k) T (n)
T ' (0)=0 is made again, consideration temperature is shown by iterative calculation to above formula, the discrete inertia of baseline load parameter moves Mechanical system model is：
T ' (n)=kn-1(1-k)T(1)+kn-2(1-k)T(2)+…+k(1-k)T(n-1)+(1-k)T(n)
In formula, T (n) represents the actual highest temperature on the same day, and T (n-1) represents the actual highest temperature of the previous day, T (2) table The actual highest temperature of n-2 days before showing, the actual highest temperature of n-1 days before T (1) expressions；
Step 3, inertia force system model key parameter k is calculated using particle cluster algorithm, particle cluster algorithm Optimization aim is so that load and the temperature relative coefficient of amendment are maximum, i.e.,：
Min-COR(P,T′)
In formula, P is baseline load, and T ' is revised temperature, and function COR is coefficient correlation；
Step 4, acquisition amendment temperature T ' is modified to real air temperature T using inertia force system model, then will be repaiied Positive temperature T ' replaces input quantities of the real air temperature T as load prediction, obtains accurate baseline load prediction.
As the further limits scheme of the present invention, in step 3, concretely comprising the following steps for particle cluster algorithm is utilized：
Step 3.1, the position of each particle and speed in random initializtion population；
Step 3.2, the fitness of each particulate is evaluated, the position of current each particulate and adaptive value are stored in each particulate In individual extreme value, the position of adaptive value optimum individual and adaptive value in all individual extreme values are stored in globally optimal solution；
Step 3.3, update the speed of particle with more new formula and position, more new formula are：
vi,j(t+1)=wvi,j(t)+c1r1[pi,j-xi,j(t)]+c2r2[pg,j-xi,j(t)]
xi,j(t+1)=xi,j(t)+vi,j(t+1), j=1,2 ... n
In formula：The position and speed for setting i-th of particle in n dimensions search space are respectively Xi=(xi,1xi,2…xi,n) And Vi=(vi,1 vi,2…vi,n), individual extreme value Pi=(pi,1pi,2…pi,n), globally optimal solution Pg, w is inertia weight, c1With c2For positive Studying factors, r1And r2For the random number between 0 to 1；
Step 3.4, each particulate is made comparisons its adaptive value and its desired positions lived through, if preferably, will It is as current desired positions, and otherwise desired positions are constant；
Step 3.5, relatively more current all individual extreme values and the value of globally optimal solution, update globally optimal solution；
Step 3.6, if meeting stop condition, stop condition is default operational precision or iterations, and search stops, defeated Go out result, otherwise return to step 3.3 continues search for.
As the further limits scheme of the present invention, in step 2, n takes 3~5.
The beneficial effects of the present invention are：(1) can be accurately using the history environment data on the load periphery collected Lagged relationship of the load with temperature Change is described；(2) the baseline load correction model for considering thermal inertia is established, due to correcting gas Warm T ' considers thermal inertia, and it has higher linear degree with load P, and amendment temperature T ' is replaced into real air temperature as load The input quantity of prediction, baseline load precision of prediction can be significantly improved.
Brief description of the drawings
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is the temperature scatter diagram before the amendment of the present invention；
Fig. 3 is the revised temperature scatter diagram of the present invention.
Embodiment
As shown in figure 1, the baseline load thermal inertia modification method provided by the invention based on discrete inertia force system, Comprise the following steps：
Step 1, the history environment data on load periphery are collected, by selecting quantity method to excavate the influence of load and temperature Relation, wherein history environment data mainly collect a certain regional load, temperature, rainfall, humidity, wind-force, date type etc. Historical data, for the major influence factors of analysis load, it is determined that excavating load-temperature on the premise of other influence factors Relation；
Step 2, by continuous dynamic system discretization, the lagged relationship between temperature T ' and real air temperature T is corrected, this is stagnant Relation is described as with first order inertial loop afterwards：
In formula, T (s) and T ' (s) are input and output respectively, and h is time constant, recycle inverse Laplace transformation with from The method of dispersion show that the time domain iteration expression formula under discrete conditions is：
In formula, hsIt it is the sampling period of discrete system, T (n) and T ' (n) is first order inertial loop respectively in n-th of sampling The input and output in cycle, then makeObtain：
T ' (n)=kT ' (n-1)+(1-k) T (n)
T ' (0)=0 is made again, consideration temperature is shown by iterative calculation to above formula, the discrete inertia of baseline load parameter moves Mechanical system model is：
T ' (n)=kn-1(1-k)T(1)+kn-2(1-k)T(2)+…+k(1-k)T(n-1)+(1-k)T(n)
In formula, T (n) represents the actual highest temperature on the same day, and T (n-1) represents the actual highest temperature of the previous day, T (2) table The actual highest temperature of n-2 days before showing, the actual highest temperature of n-1 days before T (1) expressions；
Formula T ' (n)=kT ' (n-1)+(1-k) T (n) iterative calculation is concretely comprised the following steps：
T ' (n-1)=kT ' (n-2)+(1-k) T (n-1)
T ' (n-2)=kT ' (n-3)+(1-k) T (n-2)
……
T ' (3)=kT ' (2)+(1-k) T (3)
T ' (2)=kT ' (1)+(1-k) T (2)
T ' (1)=kT ' (0)+(1-k) T (1)
T ' (0)=0 in formula T ' (1)=kT ' (0)+(1-k) T (1) is made, then：
T ' (1)=(1-k) T (1)
Substitute into, that is, obtain from the bottom up：
T ' (2)=k (1-k) T (1)+(1-k) T (2)
T ' (3)=k2(1-k)T(1)+k(1-k)T(2)+(1-k)T(3)
It may finally obtain considering that temperature, the discrete inertia force system model of baseline load parameter are：
T ' (n)=kn-1(1-k)T(1)+kn-2(1-k)T(2)+…+k(1-k)T(n-1)+(1-k)T(n)；
Step 3, inertia force system model key parameter k is calculated using particle cluster algorithm, particle cluster algorithm Optimization aim is so that load and the temperature relative coefficient of amendment are maximum, i.e.,：
Min-COR(P,T′)
In formula, P is baseline load, and T ' is revised temperature, and function COR is coefficient correlation；
Step 4, acquisition amendment temperature T ' is modified to real air temperature T using inertia force system model, then will be repaiied Positive temperature T ' replaces input quantities of the real air temperature T as load prediction, obtains accurate baseline load prediction.
Wherein, in step 3, concretely comprising the following steps for particle cluster algorithm is utilized：
Step 3.1, the position of each particle and speed in random initializtion population；
Step 3.2, the fitness of each particulate is evaluated, the position of current each particulate and adaptive value are stored in each particulate In individual extreme value, the position of adaptive value optimum individual and adaptive value in all individual extreme values are stored in globally optimal solution；
Step 3.3, update the speed of particle with more new formula and position, more new formula are：
vi,j(t+1)=wvi,j(t)+c1r1[pi,j-xi,j(t)]+c2r2[pg,j-xi,j(t)]
xi,j(t+1)=xi,j(t)+vi,j(t+1), j=1,2 ... n
In formula：The position and speed for setting i-th of particle in n dimensions search space are respectively Xi=(xi,1xi,2…xi,n) And Vi=(vi,1 vi,2…vi,n), individual extreme value Pi=(pi,1pi,2…pi,n), globally optimal solution Pg, w is inertia weight, c1With c2For positive Studying factors, r1And r2For the random number between 0 to 1；
Step 3.4, each particulate is made comparisons its adaptive value and its desired positions lived through, if preferably, will It is as current desired positions, and otherwise desired positions are constant；
Step 3.5, relatively more current all individual extreme values and the value of globally optimal solution, update globally optimal solution；
Step 3.6, if meeting stop condition, stop condition is default operational precision or iterations, and search stops, defeated Go out result, otherwise return to step 3.3 continues search for.
Wherein, in a practical situation, the cumulative effect of temperature and Relationship between temperature within the next few days are closer, therefore step 2 In, n takes 3~5；Preferably 4, i.e. n=4, then：
T ' (4)=(1-k) T (4)+k (1-k) T (3)+k2(1-k)T(2)+k3(1-k)T(1)
Present embodiment is using above formula as the discrete inertia force system model for considering temperature, baseline load parameter, root Revised temperature is just can obtain according to the parameter that optimization the obtains temperature actual with history.
As shown in Figures 2 and 3, contrast two width figures can be seen that amendment temperature after Fig. 3 scatterplot distribution more concentrate, explanation Linear degree is more preferable, further illustrate establish the baseline load thermal inertia correction model based on discrete inertia force system must The property wanted.

Claims (3)

1. a kind of baseline load thermal inertia modification method based on discrete inertia force system, it is characterised in that including as follows Step：
Step 1, the history environment data on load periphery are collected, are closed by selecting quantity method to excavate the influence of load and temperature System；
Step 2, by continuous dynamic system discretization, the lagged relationship between temperature T ' and real air temperature T is corrected, the hysteresis is closed System is described as with first order inertial loop：
<mrow> <mi>G</mi> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msup> <mi>T</mi> <mo>&amp;prime;</mo> </msup> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mi>T</mi> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mi>h</mi> <mo>&amp;CenterDot;</mo> <mi>s</mi> <mo>+</mo> <mn>1</mn> </mrow> </mfrac> </mrow>
In formula, T (s) and T ' (s) are input and output respectively, and h is time constant, recycle inverse Laplace transformation and discretization Method show that the time domain iteration expression formula under discrete conditions is：
<mrow> <msup> <mi>T</mi> <mo>&amp;prime;</mo> </msup> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mi>h</mi> <mrow> <mi>h</mi> <mo>+</mo> <msub> <mi>h</mi> <mi>s</mi> </msub> </mrow> </mfrac> <msup> <mi>T</mi> <mo>&amp;prime;</mo> </msup> <mrow> <mo>(</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mi>h</mi> <mrow> <mi>h</mi> <mo>+</mo> <msub> <mi>h</mi> <mi>s</mi> </msub> </mrow> </mfrac> <mi>T</mi> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> </mrow>
In formula, hsIt it is the sampling period of discrete system, T (n) and T ' (n) is first order inertial loop respectively n-th of sampling period Input and output, then makeObtain：
T ' (n)=kT ' (n-1)+(1-k) T (n)
T ' (0)=0 is made again, draws the discrete inertia force for considering temperature, baseline load parameter by iterative calculation to above formula System model is：
T ' (n)=kn-1(1-k)T(1)+kn-2(1-k)T(2)+…+k(1-k)T(n-1)+(1-k)T(n)
In formula, before T (n) represents that the actual highest temperature on the same day, T (n-1) represent that the actual highest temperature of the previous day, T (2) represent The actual highest temperature of n-2 days, the actual highest temperature of n-1 days before T (1) expressions；
Step 3, inertia force system model key parameter k, the optimization of particle cluster algorithm is calculated using particle cluster algorithm Target is so that load and the temperature relative coefficient of amendment are maximum, i.e.,：
Min -COR(P,T′)
In formula, P is baseline load, and T ' is revised temperature, and function COR is coefficient correlation；
Step 4, acquisition amendment temperature T ' is modified to real air temperature T using inertia force system model, then gas will be corrected Warm T ' replaces input quantities of the real air temperature T as load prediction, obtains accurate baseline load prediction.
2. the baseline load thermal inertia modification method according to claim 1 based on discrete inertia force system, it is special Sign is, in step 3, utilizes concretely comprising the following steps for particle cluster algorithm：
Step 3.1, the position of each particle and speed in random initializtion population；
Step 3.2, the fitness of each particulate is evaluated, the position of current each particulate and adaptive value are stored in the individual of each particulate In extreme value, the position of adaptive value optimum individual and adaptive value in all individual extreme values are stored in globally optimal solution；
Step 3.3, update the speed of particle with more new formula and position, more new formula are：
vi,j(t+1)=wvi,j(t)+c1r1[pi,j-xi,j(t)]+c2r2[pg,j-xi,j(t)]
xi,j(t+1)=xi,j(t)+vi,j(t+1), j=1,2 ... n
In formula：The position and speed for setting i-th of particle in n dimensions search space are respectively Xi=(xi,1xi,2…xi,n) and Vi= (vi,1vi,2…vi,n), individual extreme value Pi=(pi,1pi,2…pi,n), globally optimal solution Pg, w is inertia weight, c1And c2For just Studying factors, r1And r2For the random number between 0 to 1；
Step 3.4, each particulate is made comparisons its adaptive value and its desired positions lived through, if preferably, made For current desired positions, otherwise desired positions are constant；
Step 3.5, relatively more current all individual extreme values and the value of globally optimal solution, update globally optimal solution；
Step 3.6, if meeting stop condition, stop condition is default operational precision or iterations, and search stops, output knot Fruit, otherwise return to step 3.3 continue search for.
3. the baseline load thermal inertia modification method according to claim 1 based on discrete inertia force system, it is special Sign is, in step 2, n takes 3~5.
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CN113112094A (en) * 2021-05-10 2021-07-13 瑞纳智能设备股份有限公司 Thermal load prediction method, system, device and storage medium for quantifying wind condition influence
CN117221008A (en) * 2023-11-07 2023-12-12 中孚信息股份有限公司 Multi-behavior baseline correction method, system, device and medium based on feedback mechanism

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CN106485339A (en) * 2015-08-31 2017-03-08 中车大连电力牵引研发中心有限公司 A kind of part throttle characteristics of power system determines method and system
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CN113112094A (en) * 2021-05-10 2021-07-13 瑞纳智能设备股份有限公司 Thermal load prediction method, system, device and storage medium for quantifying wind condition influence
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