CN110176765B - Energy storage peak regulation day-ahead optimization scheduling method driven by peak-valley electricity price - Google Patents

Energy storage peak regulation day-ahead optimization scheduling method driven by peak-valley electricity price Download PDF

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CN110176765B
CN110176765B CN201910459626.1A CN201910459626A CN110176765B CN 110176765 B CN110176765 B CN 110176765B CN 201910459626 A CN201910459626 A CN 201910459626A CN 110176765 B CN110176765 B CN 110176765B
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power
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CN110176765A (en
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李军徽
张嘉辉
穆钢
葛长兴
燕博
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Northeast Electric Power University
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Northeast Dianli University
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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
    • G06Q10/06—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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • 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]
    • 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
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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

Abstract

The invention relates to a peak-valley electricity price driven energy storage peak regulation day-ahead optimization scheduling method, which is characterized by comprising the following steps: the method comprises the steps of determining priority of a charging and discharging interval, optimizing a scheduling process before the peak shaving day of energy storage, meeting constraint conditions required by an energy storage system in the operation process, constraining charging and discharging states of the energy storage system, optimizing a power optimization method based on a particle swarm algorithm, evaluating indexes of a scheduling scheme and the like, and the peak shaving and valley filling effect and the economical efficiency of the energy storage system are improved by fully utilizing the energy storage system. Has the advantages of scientific and reasonable method, strong applicability, good effect and the like.

Description

Energy storage peak regulation day-ahead optimization scheduling method driven by peak-valley electricity price
Technical Field
The invention relates to the technical field of energy storage peak shaving, in particular to a peak-valley electricity price driven energy storage peak shaving day-ahead optimization scheduling method.
Background
At present, the conventional energy storage peak regulation scheduling method is generally a constant power method or a power difference method. However, although the constant power method is simple in calculation process, when the actual load deviates from the predicted load, the constant power method cannot be well handled, and the peak clipping and valley filling effects are poor. Although the power difference method can better deal with the problem of actual load fluctuation through improvement, the effect of an energy storage system cannot be fully exerted when the power difference method is adopted due to different load characteristics in different periods, so that the peak load shifting effect of energy storage and the economic efficiency of energy storage are reduced. In addition, a series of algorithms are introduced to make an optimal scheduling method. However, in most scheduling methods, the aim of minimizing load fluctuation, maximizing wind power admission or prolonging the service life of a battery is mostly taken, and the influence of the economy of an energy storage system on the operation is ignored.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provide a scientific and reasonable energy storage peak-load shifting day-ahead optimization scheduling method driven by peak-valley electrovalence with strong applicability and good effect, and improve the peak-load shifting effect and the economical efficiency of an energy storage system by fully utilizing the energy storage system.
The purpose of the invention is realized by the following technical scheme: an energy storage peak regulation day-ahead optimization scheduling method driven by peak-valley electricity prices is characterized by comprising the following steps:
1) priority establishment of charge/discharge section
The power rate state time sequence matrix of each time interval of the power grid has an initial point which is the initial moment of a valley power rate time interval:
Figure GDA0003419457680000011
wherein SpriceIs a time sequence matrix of the electricity price state of each time interval of the power grid, Sp,1～Sp,TThe electricity price state quantity of each time interval is represented by a valley electricity price time interval of-1, a flat electricity price time interval of 0 and a peak electricity price time interval of 1; t is the transition time of the first valley value and the peak value time interval or the transition time of the first flat value and the peak value time interval; order SpcAnd SpdThe corresponding intervals in the energy storage device are charge and discharge intervals of the energy storage respectively; according to SpriceDetermining a charge-discharge priority interval;
at SpcThe method comprises the following steps: sp,iThe time interval of-1 is a primary charging interval, Sp,iThe time interval of 0 is a secondary charging interval i epsilon [1, t ∈]；
At SpdThe method comprises the following steps: sp,jThe period 1 is a first-stage discharge interval, Sp,jThe time period of 0 is two stagesDischarge interval j ∈ [ T +1, T >]；
2) Energy storage peak regulation day-ahead optimization scheduling process
Establishing an energy storage peak regulation optimal scheduling scheme by combining the priority of the charging and discharging interval in the step 1), wherein the process comprises the following steps:
a) for a given predicted load curve, find its trough Pload,minLet the energy storage system have the maximum charging power PCmaxThe initial value is the rated power P, in PCmax+Pload,minTaking a straight line L as the value, and alternating a load curve at two points t1、t2The charging capacity of the energy storage system in the two points is as follows:
Figure GDA0003419457680000021
wherein ETCharging the energy storage system at two points, ESThe remaining capacity of the previous day; pload,tThe load power at each moment; pload,minIs the minimum value of the load power; pCmaxIs the maximum valley filling power; etaCCharging efficiency for the energy storage system;
b) with ETAnd the energy storage rated capacity E is taken as a judgment condition, if ET>E, the charging capacity can not meet the rated capacity constraint, and P isCmaxIterating downwards with Δ P as a step size until the constraint is satisfied:
0<E-ET<ε (3)
wherein ε is a constant close to 0;
if ETE is less than or equal to E, and the energy storage system has surplus chargeable electric quantity EY＝E-ETAt the removal of t1～t2Then, S is first calculatedpcInner remaining number of primary charging time period T1The average value method is used to distribute the charging quantity P of each time intervalC＝EY/T1×ηCCalculating whether surplus electric quantity still exists under the constraint of the energy storage power P, if the surplus electric quantity still exists, charging in a secondary charging time interval, and calculating SpcInternal remaining number of secondary charging periods T2Second stage ofThe surplus electric quantity of the stored energy in the charging time period is EY’＝EY–P×T1×ηCCalculating surplus electric quantity under the condition of meeting the energy storage power constraint, and outputting the electric quantity state E after the energy storage system is chargedBT；
c) Electric quantity state E after charging through energy storage systemBTFinding the peak value P according to the given predicted load curveload,maxLet the energy storage system have the maximum discharge power PDmaxThe initial value is 0 and iterates upward with Δ P as the step size, Pload,max-(PDmax×ηD) Taking a straight line L' as the value, and alternating a load curve at N two points t1’、t2' the discharge capacity of the energy storage system in the two points is as follows:
Figure GDA0003419457680000022
wherein EPDischarging the energy storage system at two points, Pload,tThe load power at each moment; pload,maxIs the maximum load power; pDmaxIs the maximum peak clipping power; etaDDischarging efficiency for the energy storage system;
d) with EPAnd EBTAs a determination condition, if PDmaxBefore reaching the rated power P, the constraint is satisfied:
0<EBT-EP<ε (5)
the iteration is ended; if the constraint condition can not be met, the energy storage system has residual dischargeable electric quantity ES＝EBT-EPAfter removing N t1’～t2' thereafter, S is first calculatedpdInner remaining number of first stage discharge time period T1' distributing discharge electric quantity P of each time interval by using average value methodD＝ES/T1', calculating whether residual electric quantity still exists under the constraint of energy storage power P, if the residual electric quantity still exists, charging in a secondary discharge time interval, and calculating SpdNumber of internal residual secondary discharge periods T2', the energy storage residual capacity in the secondary discharge period isES’＝ES–P×T1' calculating the residual electric quantity under the condition of meeting the energy storage power constraint and outputting the electric quantity state E after the energy storage system dischargesBSTo maximize the utilization thereof;
3) constraint conditions to be met in the running process of the energy storage system
a) Energy storage system power constraints
Figure GDA0003419457680000031
Wherein P isC,tFor charging power of the energy storage system at time t, PD,tThe discharge power of the energy storage system at the moment t; pCmaxThe maximum charging power of the energy storage system is obtained; pDmaxThe maximum discharge power of the energy storage system;
b) energy storage system state of charge constraint
Figure GDA0003419457680000032
ESOC,min≤ESOC,t≤ESOC,max (8)
ESOC,start＝ESOC,end (9)
In the formula ESOC,tThe state of charge of the energy storage system at the moment t; e is the rated capacity of the energy storage system; etaCCharging efficiency of the energy storage system; eSOC,minIs the lower limit of the charge of the energy storage system, ESOC,maxThe upper limit value of the charge of the energy storage system is set; eSOC,startThe state of charge of the energy storage system at the initial moment; eSOC,endThe state of charge of the energy storage system at the end moment;
4) energy storage system charge-discharge state constraint
BC,t×BD,t＝0，(BC,t、BD,t)∈[0,1] (10)
Wherein B isC,tIs the charging operating state of the energy storage system at time t, BD,tThe discharge running state of the energy storage system at the time t is 1, and the stop is 0;
5) power optimization method based on particle swarm optimization
Removing the basic charge-discharge time t of the energy storage system1～t2And N t1’～t2Besides, the charge and discharge power of the rest periods is selected according to the principle of reducing load fluctuation as much as possible; in the method, average distribution is carried out only according to surplus or residual electric quantity and corresponding time intervals, although the calculation speed is high, the optimal effect cannot be achieved, so that the charging and discharging power of the energy storage system at each time interval is optimized by adopting an improved particle swarm optimization algorithm;
the Particle Swarm Optimization (PSO) is a swarm intelligence algorithm with the advantages of high search speed and high search efficiency, however, the basic PSO also has the defect of easily generating a local optimal solution. Therefore, the improved PSO is adopted to improve the velocity inertia weight and the learning factor as follows, and the corrected position velocity formula is:
Figure GDA0003419457680000033
wherein v isid,tFor the d-dimensional velocity, x, of the ith particle in the t-th iterationid,tThe position of the ith particle in the d dimension in the t iteration is taken as the position of the ith particle; p is a radical ofid,tThe individual optimal value of the ith particle in the d dimension; p is a radical ofgd,tA global optimum value of the population in the d-th dimension is obtained; c. C1And c2Is a learning factor; r is1And r2Are random numbers between (0, 1); ω is the inertial weight, and the specific solving process is as follows:
a) establishing an objective function, namely a fitness function, wherein the fitness function is as follows:
Figure GDA0003419457680000041
wherein P isload,tThe load power at each moment; ptCharging and discharging power for the energy storage system at each moment; paload,verThe average value of the load power after peak clipping and valley filling is obtained; t is the total number of sampling points in one day;
in the actual calculation process, the charging power of the energy storage system needs to be optimized in a valley charging interval, so that T is the total sampling point number in the charging interval; in the peak discharge interval, the discharge power of the energy storage system is optimized, so that T is the total number of sampling points in the discharge interval;
b) determining the initial position and velocity of the particle:
the dimension of each particle is determined according to the actually required charging and discharging time period of each day due to different daily load characteristics, and the total value of all the dimensions of each particle is equal to the required charging and discharging electric quantity;
c) calculating particle fitness value
Taking the value of each dimension in the particles as an input quantity, and taking the load standard deviation of the corresponding interval as a calculation formula to calculate the fitness value of the particles;
d) carry out iteration
Performing iteration according to the determined iteration times and the population number, updating the individual optimal value and the population optimal value until the iteration is finished, and outputting an optimal solution;
6) scheduling scheme evaluation index
a) Utilization of energy storage system
The charging electric quantity of the energy storage system at the load valley time and the discharging electric quantity of the energy storage system at the load peak time are used as judgment bases of the index, and the formula is as follows:
Figure GDA0003419457680000042
wherein P isC,tCharging power of the energy storage system at each moment; pD,tThe discharge power of the system at each moment is enabled; etaCCharging efficiency for the energy storage system; e is the rated capacity of the energy storage system;
b) interest rate of energy storage system
The arbitrage index of the energy storage system consists of the electricity purchasing cost of the energy storage system in the charging period and the electricity selling income of the energy storage system in the discharging period, the result is determined according to the specific charging and discharging electric quantity and the electricity price in each period, and the calculation formula is as follows:
Figure GDA0003419457680000043
wherein EC1Charging electric quantity of the energy storage system in a primary charging time period; eC2Charging electric quantity of the energy storage system in a secondary charging period; eD1The discharge electric quantity of the energy storage system in a first-stage discharge time period is obtained; eD2The discharge electric quantity of the energy storage system in the secondary discharge time period is obtained; hpAt peak electricity rate, FpFlat value of electricity price, LpThe valley value electricity price; etaCEfficiency of charging the energy storage system, ηDDischarging efficiency for the energy storage system; e is the rated capacity of the energy storage system;
c) rate of improvement of peak-to-valley difference
The index of the improvement rate of the peak-valley difference is the ratio of the reduced peak-valley difference to the original load peak-valley difference after the energy storage system cuts peaks and fills valleys, and is the comparison between load extreme values, the function of the energy storage system is evaluated from a local angle, and the calculation formula is as follows:
Figure GDA0003419457680000051
wherein P isCminThe maximum charging power of the energy storage system during peak clipping and valley filling; pDmaxThe maximum discharge power of the energy storage system during peak clipping and valley filling; pload,minIs the minimum value of the load curve, Pload,maxIs the maximum value of the load curve;
d) standard deviation of load
After the peak clipping and valley filling of the energy storage system, a standard deviation is introduced, the effect of the energy storage system is evaluated from the global angle, and the calculation formula is as follows:
Figure GDA0003419457680000052
wherein P isload,tThe load power at each moment; ptCharging and discharging power for the energy storage system at each moment; paload,verThe average value of the load power after peak clipping and valley filling is obtained; t is the total number of sampling points in one day;
e) rate of load fluctuation
And calculating the load fluctuation rate in the valley period by the formula:
Figure GDA0003419457680000053
wherein P isload,tThe load power at each moment; paload,tThe load power at each moment after peak clipping and valley filling; t is t1As the start of the valley fill period, t2The end time of the valley filling period.
The invention provides a peak-valley electricity price driven energy storage peak regulation day-ahead optimization scheduling method, which determines the priority of an energy storage charging and discharging interval through a peak-valley electricity price time period so as to ensure the economical efficiency of a system; the charging and discharging priority interval is used as a constraint condition, the highest utilization rate of an energy storage system is used as a target on the basis of a power difference method, and the charging and discharging electric quantity in each level interval is determined, so that the defects that the energy storage utilization rate is low and the peak clipping and valley filling effects are not obvious when the power difference method is continuously operated are overcome; respectively distributing the electric quantity in each interval by an average value method and an improved particle swarm optimization algorithm to obtain the energy storage charge-discharge power in each time interval; and establishing an evaluation index of the scheduling scheme, and verifying the effectiveness of the method by taking the predicted load data of the power grid as an example condition. By fully utilizing the energy storage system, the peak clipping and valley filling effects and the economical efficiency of the energy storage system are improved. Has the advantages of scientific and reasonable method, strong applicability, good effect and the like.
Drawings
FIG. 1 is a graph of typical day predicted load data;
FIG. 2 is a graph comparing load curves;
FIG. 3 is a comparison of energy storage system state of charge curves;
FIG. 4 is a graph comparing load curves;
FIG. 5 is a graph comparing charge and discharge power of an energy storage system;
FIG. 6 is a comparison of energy storage system state of charge curves.
Detailed Description
The following describes a peak-to-valley electricity price driven energy storage peak-shaving day-ahead optimization scheduling method according to the present invention with reference to the accompanying drawings and embodiments.
In the peak-valley electricity price driven energy storage peak regulation day-ahead optimization scheduling method, the lithium ion energy storage battery is adopted for simulation calculation, and the specific parameters are shown as the following table:
TABLE 1 parameter Table of lithium ion battery
Figure GDA0003419457680000061
The embodiment of the invention calculates the predicted load data of a provincial power grid, wherein the sampling interval is 1h, the sampling point T is 24 every day, and the total days D is 365.
The peak-to-valley electricity rate periods of this province are shown in the following table:
TABLE 2 Peak-valley electricity price parameter table of a certain province
Figure GDA0003419457680000062
The specific calculation process comprises the following three steps:
1) firstly, the priority of an energy storage charging and discharging interval is established, and the calculation steps are as follows:
the power rate state time sequence matrix of each time interval of the power grid is as follows, and the starting point is the initial moment of the valley power rate time interval:
Figure GDA0003419457680000063
wherein Sp,1～Sp,TThe electricity price state quantity of each time interval is that the valley electricity price time interval is-1, equalThe value electricity price time interval is 0, and the peak electricity price time interval is 1; t is the transition time of the first valley value and the peak value time interval or the transition time of the first flat value and the peak value time interval; order SpcAnd SpdThe corresponding intervals in the energy storage device are charging and discharging intervals of the energy storage device respectively.
According to SpriceAnd determining a charge and discharge priority interval.
At SpcThe method comprises the following steps: sp,iThe time interval of-1 is a primary charging interval, Sp,iThe time interval of 0 is a secondary charging interval i epsilon [1, t ∈]。
At SpdThe method comprises the following steps: sp,jThe period 1 is a first-stage discharge interval, Sp,jThe time interval of 0 is a secondary discharge interval j ∈ [ T +1, T ∈ T]。
Through calculation, the electricity price state time sequence matrix can be obtained through the peak-valley electricity price policy of the province: sprice＝[Spc|Spd]＝[-1 -1 -1 -1 -1 -1 -1 -1 0|1 1 1 0 0 0 0 0 0 0 1 1 1 1 1]The corresponding time period is 23: 00-22: 00. Through the power rate state time sequence matrix, the charging interval can be determined to be 23: 00-8: 00, wherein the primary charging time interval is 23: 00-7: 00, and the secondary charging time interval is 7: 00-8: 00. The discharge interval is 8: 00-23: 00, wherein the first-stage discharge time interval is 8: 00-11: 00 and 18: 00-23: 00, and the second-stage discharge time interval is 11: 00-18: 00.
2) The average value of year-round predicted load data of the province is taken as typical day data to carry out simulation calculation, and the load data is shown in figure 1. And establishing an energy storage peak regulation optimal scheduling scheme by combining the priority of the charging and discharging interval, wherein the process comprises the following steps:
a) for a given predicted load curve, find its trough Pload,minLet the energy storage system have the maximum charging power PCmaxThe initial value is the rated power P, in PCmax+Pload,minTaking a straight line L as the value, and alternating a load curve at two points t1、t2The charging capacity of the energy storage system in the two points is as follows:
Figure GDA0003419457680000071
wherein ETCharging the energy storage system at two points, ESThe remaining capacity of the previous day; pload,tThe load power at each moment; pload,minIs the minimum value of the load power; pCmaxIs the maximum valley filling power; etaCCharging efficiency for the energy storage system;
b) with ETAnd the energy storage rated capacity E is taken as a judgment condition, if ET>E, the charging capacity can not meet the rated capacity constraint, and P isCmaxIterating downwards with Δ P as a step size until the constraint is satisfied:
0<E-ET<ε (3)
wherein ε is a constant close to 0;
if ETE is less than or equal to E, and the energy storage system has surplus chargeable electric quantity EY＝E-ETAt the removal of t1～t2Then, S is first calculatedpcInner remaining number of primary charging time period T1The average value method is used to distribute the charging quantity P of each time intervalC＝EY/T1×ηCCalculating whether surplus electric quantity still exists under the constraint of the energy storage power P, if the surplus electric quantity still exists, charging in a secondary charging time interval, and calculating SpcInternal remaining number of secondary charging periods T2And the surplus energy storage electric quantity in the second-stage charging period is EY’＝EY–P×T1×ηCCalculating surplus electric quantity under the condition of meeting the energy storage power constraint, and outputting the electric quantity state E after the energy storage system is chargedBT；
c) Electric quantity state E after charging through energy storage systemBTFinding the peak value P according to the given predicted load curveload,maxLet the energy storage system have the maximum discharge power PDmaxThe initial value is 0 and iterates upward with Δ P as the step size, Pload,max-(PDmax×ηD) Taking a straight line L' as the value, and alternating a load curve at N two points t1’、t2' the discharge capacity of the energy storage system in the two points is as follows:
Figure GDA0003419457680000072
wherein EPDischarging the energy storage system at two points, Pload,tThe load power at each moment; pload,maxIs the maximum load power; pDmaxIs the maximum peak clipping power; etaDDischarging efficiency for the energy storage system;
d) with EPAnd EBTAs a determination condition, if PDmaxBefore reaching the rated power P, the constraint is satisfied:
0<EBT-EP<ε (5)
the iteration is ended; if the constraint condition can not be met, the energy storage system has residual dischargeable electric quantity ES＝EBT-EPAfter removing N t1’～t2' thereafter, S is first calculatedpdInner remaining number of first stage discharge time period T1' distributing discharge electric quantity P of each time interval by using average value methodD＝ES/T1', calculating whether residual electric quantity still exists under the constraint of energy storage power P, if the residual electric quantity still exists, charging in a secondary discharge time interval, and calculating SpdNumber of internal residual secondary discharge periods T2', the residual energy in the secondary discharge period is ES’＝ES–P×T1' calculating the residual electric quantity under the condition of meeting the energy storage power constraint and outputting the electric quantity state E after the energy storage system dischargesBSTo maximize the utilization thereof;
3) constraint conditions to be met in the running process of the energy storage system
a) Energy storage system power constraints
Figure GDA0003419457680000081
Wherein P isC,tFor charging power of the energy storage system at time t, PD,tThe discharge power of the energy storage system at the moment t; pCThe maximum charging power of the energy storage system is obtained; pDThe maximum discharge power of the energy storage system;
b) energy storage system state of charge constraint
Figure GDA0003419457680000082
ESOC,min≤ESOC,t≤ESOC,max (8)
ESOC,start＝ESOC,end (9)
In the formula ESOC,tThe state of charge of the energy storage system at the moment t; e is the rated capacity of the energy storage system; etaCThe charge-discharge efficiency of the energy storage system; eSOC,minFor the upper limit of the charge of the energy storage system, ESOC,maxThe charge lower limit value of the energy storage system is set; eSOC,startThe state of charge of the energy storage system at the initial moment; eSOC,endThe state of charge of the energy storage system at the end moment;
4) energy storage system charge-discharge state constraint
BC,t×BD,t＝0，(BC,t、BD,t)∈[0,1] (10)
Wherein B isC,tIs the charging operating state of the energy storage system at time t, BD,tThe discharge running state of the energy storage system at the time t is 1, and the stop is 0;
5) power optimization method based on particle swarm optimization
Removing the basic charge-discharge time t of the energy storage system1～t2And N t1’～t2In addition, the charge and discharge power in each of the other periods should be selected on the basis of minimizing the load fluctuation. In the method, the average distribution is carried out only according to surplus or residual electric quantity and corresponding time intervals, although the calculation speed is high, the optimal effect cannot be achieved, and therefore the charging and discharging power of the energy storage system in each time interval is optimized by adopting an improved particle swarm optimization algorithm.
The Particle Swarm Optimization (PSO) is a swarm intelligence algorithm and has the advantages of high searching speed and high searching efficiency. However, the basic PSO also has a disadvantage of easily producing a locally optimal solution. Therefore, the improved PSO is adopted to improve the velocity inertia weight and the learning factor as follows, and the corrected position velocity formula is:
Figure GDA0003419457680000091
wherein v isid,tFor the d-dimensional velocity, x, of the ith particle in the t-th iterationid,tThe position of the ith particle in the d dimension in the t iteration is taken as the position of the ith particle; p is a radical ofid,tThe individual optimal value of the ith particle in the d dimension; p is a radical ofgd,tA global optimum value of the population in the d-th dimension is obtained; c. C1And c2Is a learning factor; r is1And r2Are random numbers between (0, 1); ω is the inertial weight, and the specific solving process is as follows:
a) establishing an objective function, namely a fitness function, wherein the fitness function is as follows:
Figure GDA0003419457680000092
wherein P isload,tThe load power at each moment; ptCharging and discharging power for the energy storage system at each moment; paload,verThe average value of the load power after peak clipping and valley filling is obtained; t is the total number of sampling points in one day;
in the actual calculation process, the charging power of the energy storage system needs to be optimized in the valley charging interval, so that T is the total number of sampling points in the charging interval; and in the peak discharge interval, the discharge power of the energy storage system is optimized, so T is the total number of sampling points in the discharge interval.
b) Determining the initial position and velocity of the particle:
the dimension of each particle is determined according to the actually required charging and discharging time period of each day due to different daily load characteristics, and the total value of all the dimensions of each particle is equal to the required charging and discharging electric quantity;
c) calculating particle fitness value
Taking the value of each dimension in the particles as an input quantity, and taking the load standard deviation of the corresponding interval as a calculation formula to calculate the fitness value of the particles;
d) carry out iteration
Performing iteration according to the determined iteration times and the population number, updating the individual optimal value and the population optimal value until the iteration is finished, and outputting an optimal solution;
the results of simulation calculation of the typical daily load are shown in fig. 2 and 3.
As can be seen from FIG. 2, when the typical day data is used for analysis, the effect of different scheduling schemes on load peak clipping and valley filling is obvious, and the improvement amount of peak-valley difference reaches 800 MW. As can be seen from fig. 3, the energy storage system has a state of charge trend of 0.1 → 0.9 → 0.1 when the optimized scheduling scheme herein is employed, and a state of charge trend of 0.1 → 0.9 → 0.148 when the power difference method is employed.
It can be seen that the residual electric quantity exists in the energy storage system due to the fact that the characteristics of the load in the peak period are different from those in the valley period, so that the initial charge state of the energy storage system in the next scheduling day is increased, the peak clipping and valley filling effects are affected, and the optimization scheme just overcomes the defect.
6) Scheduling scheme evaluation index
In order to evaluate the peak regulation scheduling scheme, the corresponding evaluation indexes are established as follows:
a) utilization of energy storage system
The charging electric quantity of the energy storage system at the load valley time and the discharging electric quantity of the energy storage system at the load peak time are used as judgment bases of the index, and the formula is as follows:
Figure GDA0003419457680000101
wherein P isC,tCharging power of the energy storage system at each moment; pD,tThe discharge power of the system at each moment is enabled; etaCCharging efficiency for the energy storage system; e is the rated capacity of the energy storage system;
b) interest rate of energy storage system
The arbitrage index of the energy storage system consists of the electricity purchasing cost of the energy storage system in the charging period and the electricity selling income of the energy storage system in the discharging period, the result is determined according to the specific charging and discharging electric quantity and the electricity price in each period, and the calculation formula is as follows:
Figure GDA0003419457680000102
wherein EC1Charging electric quantity of the energy storage system in a primary charging time period; eC2Charging electric quantity of the energy storage system in a secondary charging period; eD1The discharge electric quantity of the energy storage system in a first-stage discharge time period is obtained; eD2The discharge electric quantity of the energy storage system in the secondary discharge time period is obtained; hpAt peak electricity rate, FpFlat value of electricity price, LpThe valley value electricity price; etaCEfficiency of charging the energy storage system, ηDDischarging efficiency for the energy storage system; e is the rated capacity of the energy storage system;
c) rate of improvement of peak-to-valley difference
The index of the improvement rate of the peak-valley difference is the ratio of the reduced peak-valley difference to the original load peak-valley difference after the energy storage system cuts peaks and fills valleys, and is the comparison between load extreme values, the function of the energy storage system is evaluated from a local angle, and the calculation formula is as follows:
Figure GDA0003419457680000103
wherein P isCminThe maximum charging power of the energy storage system during peak clipping and valley filling; pDmaxThe maximum discharge power of the energy storage system during peak clipping and valley filling; pload,minIs the minimum value of the load curve, Pload,maxIs the maximum value of the load curve;
d) standard deviation of load
After the peak clipping and valley filling of the energy storage system, a standard deviation is introduced, the effect of the energy storage system is evaluated from the global angle, and the calculation formula is as follows:
Figure GDA0003419457680000111
wherein P isload,tThe load power at each moment; ptCharging and discharging power for the energy storage system at each moment; paload,verThe average value of the load power after peak clipping and valley filling is obtained; t is the total number of sampling points in one day;
e) rate of load fluctuation
And calculating the load fluctuation rate in the valley period by the formula:
Figure GDA0003419457680000112
wherein P isload,tThe load power at each moment; paload,tThe load power at each moment after peak clipping and valley filling; t is t1As the start of the valley fill period, t2The end time of the valley filling period.
The calculation results are as follows, and the following table is a statistical table of evaluation indexes of three scheduling schemes:
table 3 statistical table of scheduling scheme evaluation indexes
Figure GDA0003419457680000113
As can be seen from the above table, the three methods are not very different in terms of load peak clipping and valley filling effects. In the aspect of the utilization rate of the energy storage system, the utilization rate of the power difference method is 96.5% because the power difference method cannot completely release electric quantity, the reduction of the utilization rate also causes the reduction of the interest rate, but the total difference value is not much different from the optimal scheduling method.
Based on the result analysis of the province annual prediction data, the calculation steps are as follows:
firstly, according to the calculation methods 1) and 2), simulation calculation is carried out by using year-round predicted load data of the province, and analysis is carried out. Comparative plots for the 11 th to 13 th scheduling days are given below for analysis.
As can be seen from FIGS. 4 and 5, the predicted load curves of the 11-13 scheduling days are different from the data of the typical day, and in the three scheduling days, the peak clipping and valley filling effects of the two optimized scheduling schemes are obviously better than those of the power difference method. In the load valley period, the maximum valley filling power of the power difference method is 212MW, 285MW and 281MW respectively, while the maximum valley filling power of the two optimized scheduling methods reaches 400MW, and the average maximum valley filling power of the power difference method is only 64.8% of that of the optimized scheduling method. And in the peak load period, the maximum peak clipping power of the three scheduling schemes is 400 MW.
The results were analyzed in conjunction with fig. 6. When the power difference method is adopted, although the maximum peak clipping power in the peak load period is 400MW, the load trend in the peak load period is steep, and the demand on the discharge capacity of the energy storage system is reduced, so that the initial electric quantity of the energy storage system in the next scheduling day is increased, as can be seen from fig. 6, the initial SOC of the energy storage system in the three scheduling days is 0.63, 0.52 and 0.62, and the increase of the initial electric quantity inevitably reduces the peak clipping and valley filling effect.
It can be seen that, due to the difference of the load characteristics every day, when the power difference method is adopted, a large amount of residual electric quantity exists in the energy storage system, and therefore the peak clipping and valley filling effects of the next scheduling day are influenced.
The scheduling scheme of the invention can fully charge and discharge the energy storage system, thereby improving the peak clipping and valley filling effects. As can also be seen from fig. 5, when the optimal scheduling scheme is adopted, the energy storage system allocates the surplus chargeable or dischargeable electric quantity in a suitable interval.
The following table is a statistical table of the average values of the indexes in 365 scheduling days:
table 4 scheduling scheme evaluation index statistical table
Figure GDA0003419457680000121
As can be seen from the above table, when the power difference scheduling scheme is adopted, the energy storage system cannot be fully utilized, so that the arbitrage rate is reduced, but a certain effect is still achieved on load peak clipping and valley filling. After the optimized scheduling scheme is adopted, the utilization rate of the energy storage system is improved to 80% from 37.2%, and the improvement amplitude is 215.05%. The improvement of the utilization rate of the energy storage system enables the arbitrage rate to reach 84.7 percent, the promotion amplitude to be 199.76 percent, and the peak clipping and valley filling effects are improved to a certain extent, particularly the load standard deviation is reduced by 48.3 MW.
Therefore, the scheme improves the utilization rate of the energy storage system through a reasonable charging and discharging interval, and can improve the peak clipping and valley filling effects while ensuring the economical efficiency of the energy storage system.
The computing conditions, illustrations and the like in the embodiments of the present invention are only used for further description of the present invention, are not exhaustive, and do not limit the scope of the claims, and those skilled in the art can conceive other substantially equivalent alternatives without inventive step in light of the teachings of the embodiments of the present invention, which are within the scope of the present invention.

Claims (1)

1. An energy storage peak regulation day-ahead optimization scheduling method driven by peak-valley electricity prices is characterized by comprising the following steps:
1) priority establishment of charge/discharge section
The power rate state time sequence matrix of each time interval of the power grid has an initial point which is the initial moment of a valley power rate time interval:
Figure FDA0003419457670000011
wherein SpriceIs a time sequence matrix of the electricity price state of each time interval of the power grid, Sp,1～Sp,TThe electricity price state quantity of each time interval is represented by a valley electricity price time interval of-1, a flat electricity price time interval of 0 and a peak electricity price time interval of 1; t is the transition time of the first valley value and the peak value time interval or the transition time of the first flat value and the peak value time interval; order SpcAnd SpdThe corresponding intervals in the energy storage device are charge and discharge intervals of the energy storage respectively; according to SpriceDetermining a charge-discharge priority interval;
at SpcThe method comprises the following steps: sp,iThe time interval of-1 is a primary charging interval, Sp,iPeriod of 0For a secondary charging interval i e [1, t]；
At SpdThe method comprises the following steps: sp,jThe period 1 is a first-stage discharge interval, Sp,jThe time interval of 0 is a secondary discharge interval j ∈ [ T +1, T ∈ T]；
2) Energy storage peak regulation day-ahead optimization scheduling process
Establishing an energy storage peak regulation optimal scheduling scheme by combining the priority of the charging and discharging interval in the step 1), wherein the process comprises the following steps:
a) for a given predicted load curve, find its trough Pload,minLet the energy storage system have the maximum charging power PCmaxThe initial value is the rated power P, in PCmax+Pload,minTaking a straight line L as the value, and alternating a load curve at two points t1、t2The charging capacity of the energy storage system in the two points is as follows:
Figure FDA0003419457670000012
wherein ETCharging the energy storage system at two points, ESThe remaining capacity of the previous day; pload,tThe load power at each moment; pload,minIs the minimum value of the load power; pCmaxIs the maximum valley filling power; etaCCharging efficiency for the energy storage system;
b) with ETAnd the energy storage rated capacity E is taken as a judgment condition, if ET>E, the charging capacity can not meet the rated capacity constraint, and P isCmaxIterating downwards with Δ P as a step size until the constraint is satisfied:
0<E-ET<ε (3)
wherein ε is a constant close to 0;
if ETE is less than or equal to E, and the energy storage system has surplus chargeable electric quantity EY＝E-ETAt the removal of t1～t2Then, S is first calculatedpcInner remaining number of primary charging time period T1The average value method is used to distribute the charging quantity P of each time intervalC＝EY/T1×ηCCalculated under the constraint of stored energy power PIf surplus electric quantity still exists, charging in the secondary charging time interval, and calculating SpcInternal remaining number of secondary charging periods T2And the surplus energy storage electric quantity in the second-stage charging period is EY’＝EY–P×T1×ηCCalculating surplus electric quantity under the condition of meeting the energy storage power constraint, and outputting the electric quantity state E after the energy storage system is chargedBT；
c) Electric quantity state E after charging through energy storage systemBTFinding the peak value P according to the given predicted load curveload,maxLet the energy storage system have the maximum discharge power PDmaxThe initial value is 0 and iterates upward with Δ P as the step size, Pload,max-(PDmax×ηD) Taking a straight line L' as the value, and alternating a load curve at N two points t1’、t2' the discharge capacity of the energy storage system in the two points is as follows:
Figure FDA0003419457670000021
wherein EPDischarging the energy storage system at two points, Pload,tThe load power at each moment; pload,maxIs the maximum load power; pDmaxIs the maximum peak clipping power; etaDDischarging efficiency for the energy storage system;
d) with EPAnd EBTAs a determination condition, if PDmaxBefore reaching the rated power P, the constraint is satisfied:
0<EBT-EP<ε (5)
the iteration is ended; if the constraint condition can not be met, the energy storage system has residual dischargeable electric quantity ES＝EBT-EPAfter removing N t1’～t2' thereafter, S is first calculatedpdInner remaining number of first stage discharge time period T1' distributing discharge electric quantity P of each time interval by using average value methodD＝ES/T1' calculating if there is any remaining charge under the energy storage power constraint P, e.g. stillIf residual electric quantity exists, charging is carried out in the secondary discharge time period, and S is calculatedpdNumber of internal residual secondary discharge periods T2', the residual energy in the secondary discharge period is ES’＝ES–P×T1' calculating the residual electric quantity under the condition of meeting the energy storage power constraint and outputting the electric quantity state E after the energy storage system dischargesBSTo maximize the utilization thereof;
3) constraint conditions to be met in the running process of the energy storage system
a) Energy storage system power constraints
Figure FDA0003419457670000022
Wherein P isC,tFor charging power of the energy storage system at time t, PD,tThe discharge power of the energy storage system at the moment t; pCmaxThe maximum charging power of the energy storage system is obtained; pDmaxThe maximum discharge power of the energy storage system;
b) energy storage system state of charge constraint
Figure FDA0003419457670000023
ESOC,min≤ESOC,t≤ESOC,max (8)
ESOC,start＝ESOC,end (9)
In the formula ESOC,tThe state of charge of the energy storage system at the moment t; e is the rated capacity of the energy storage system; etaCCharging efficiency of the energy storage system; eSOC,minIs the lower limit of the charge of the energy storage system, ESOC,maxThe upper limit value of the charge of the energy storage system is set; eSOC,startThe state of charge of the energy storage system at the initial moment; eSOC,endThe state of charge of the energy storage system at the end moment;
4) energy storage system charge-discharge state constraint
BC,t×BD,t＝0，(BC,t、BD,t)∈[0,1] (10)
Wherein B isC,tIs the charging operating state of the energy storage system at time t, BD,tThe discharge running state of the energy storage system at the time t is 1, and the stop is 0;
5) power optimization method based on particle swarm optimization
Removing the basic charge-discharge time t of the energy storage system1～t2And N t1’～t2Besides, the charge and discharge power of the rest periods is selected according to the principle of reducing load fluctuation as much as possible; in the method, average distribution is carried out only according to surplus or residual electric quantity and corresponding time intervals, although the calculation speed is high, the optimal effect cannot be achieved, so that the charging and discharging power of the energy storage system at each time interval is optimized by adopting an improved particle swarm optimization algorithm;
the Particle Swarm Optimization (PSO) is a swarm intelligence algorithm with the advantages of high search speed and high search efficiency, however, the basic PSO also has the defect of easily generating a local optimal solution, so the improved PSO is adopted to improve the speed inertia weight and the learning factor as follows, and the position and speed formula after the correction is as follows:
Figure FDA0003419457670000031
wherein v isid,tFor the d-dimensional velocity, x, of the ith particle in the t-th iterationid,tThe position of the ith particle in the d dimension in the t iteration is taken as the position of the ith particle; p is a radical ofid,tThe individual optimal value of the ith particle in the d dimension; p is a radical ofgd,tA global optimum value of the population in the d-th dimension is obtained; c. C1And c2Is a learning factor; r is1And r2Are random numbers between (0, 1); ω is the inertial weight, and the specific solving process is as follows:
a) establishing an objective function, namely a fitness function, wherein the fitness function is as follows:
Figure FDA0003419457670000032
wherein P isload,tThe load power at each moment; ptCharging and discharging power for the energy storage system at each moment; paload,verThe average value of the load power after peak clipping and valley filling is obtained; t is the total number of sampling points in one day;
in the actual calculation process, the charging power of the energy storage system needs to be optimized in a valley charging interval, so that T is the total sampling point number in the charging interval; in the peak discharge interval, the discharge power of the energy storage system is optimized, so that T is the total number of sampling points in the discharge interval;
b) determining the initial position and velocity of the particle:
the dimension of each particle is determined according to the actually required charging and discharging time period of each day due to different daily load characteristics, and the total value of all the dimensions of each particle is equal to the required charging and discharging electric quantity;
c) calculating particle fitness value
Taking the value of each dimension in the particles as an input quantity, and taking the load standard deviation of the corresponding interval as a calculation formula to calculate the fitness value of the particles;
d) carry out iteration
Performing iteration according to the determined iteration times and the population number, updating the individual optimal value and the population optimal value until the iteration is finished, and outputting an optimal solution;
6) scheduling scheme evaluation index
a) Utilization of energy storage system
The charging electric quantity of the energy storage system at the load valley time and the discharging electric quantity of the energy storage system at the load peak time are used as judgment bases of the index, and the formula is as follows:
Figure FDA0003419457670000041
wherein P isC,tCharging power of the energy storage system at each moment; pD,tThe discharge power of the system at each moment is enabled; etaCFor storing energySystem charging efficiency; e is the rated capacity of the energy storage system;
b) interest rate of energy storage system
The arbitrage index of the energy storage system consists of the electricity purchasing cost of the energy storage system in the charging period and the electricity selling income of the energy storage system in the discharging period, the result is determined according to the specific charging and discharging electric quantity and the electricity price in each period, and the calculation formula is as follows:
Figure FDA0003419457670000042
wherein EC1Charging electric quantity of the energy storage system in a primary charging time period; eC2Charging electric quantity of the energy storage system in a secondary charging period; eD1The discharge electric quantity of the energy storage system in a first-stage discharge time period is obtained; eD2The discharge electric quantity of the energy storage system in the secondary discharge time period is obtained; hpAt peak electricity rate, FpFlat value of electricity price, LpThe valley value electricity price; etaCEfficiency of charging the energy storage system, ηDDischarging efficiency for the energy storage system; e is the rated capacity of the energy storage system;
c) rate of improvement of peak-to-valley difference
The index of the improvement rate of the peak-valley difference is the ratio of the reduced peak-valley difference to the original load peak-valley difference after the energy storage system cuts peaks and fills valleys, and is the comparison between load extreme values, the function of the energy storage system is evaluated from a local angle, and the calculation formula is as follows:
Figure FDA0003419457670000043
wherein P isCminThe maximum charging power of the energy storage system during peak clipping and valley filling; pDmaxThe maximum discharge power of the energy storage system during peak clipping and valley filling; pload,minIs the minimum value of the load curve, Pload,maxIs the maximum value of the load curve;
d) standard deviation of load
After the peak clipping and valley filling of the energy storage system, a standard deviation is introduced, the effect of the energy storage system is evaluated from the global angle, and the calculation formula is as follows:
Figure FDA0003419457670000044
wherein P isload,tThe load power at each moment; ptCharging and discharging power for the energy storage system at each moment; paload,verThe average value of the load power after peak clipping and valley filling is obtained; t is the total number of sampling points in one day;
e) rate of load fluctuation
And calculating the load fluctuation rate in the valley period by the formula:
Figure FDA0003419457670000051
wherein P isload,tThe load power at each moment; paload,tThe load power at each moment after peak clipping and valley filling; t is t1As the start of the valley fill period, t2The end time of the valley filling period.
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