CN114421523B - Multi-scene step-by-step optimized power generation regulation and control system and method based on source load uncertainty - Google Patents
Multi-scene step-by-step optimized power generation regulation and control system and method based on source load uncertainty Download PDFInfo
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Abstract
Multi-scene step-by-step optimization power generation regulation and control system and method based on source load uncertainty, comprising the following steps: receiving daily planned power generation capacity which is optimized by a discrete adjustment model and is aimed at the next moment; the daily planned power generation amount is adjusted through a continuous adjustment model, so that the adjusted daily planned power generation amount is obtained and is recorded as target power generation amount; the system comprises a high-energy load, a target power generation device, a dispatching center and a power generation control device, wherein the high-energy load comprises a discrete high-energy load and a continuous high-energy load, and the target power generation device comprises a wind power generation device and a thermal power generation device. The method and the device can optimize the planned generated energy, so that the optimized target generated energy is used for generating control on the target power generation equipment, and flexible power generation is facilitated.
Description
Technical Field
The invention relates to the field of power generation regulation and control, in particular to a multi-scene step-by-step optimization power generation regulation and control method based on source load uncertainty.
Background
The dispatching center of the power system usually automatically makes a power generation plan according to a certain period, including making a day-ahead power generation plan usually with a day as a period and making an in-day power generation plan with a moment as a period. The day-ahead power generation schedule is generally used for scheduling the power generation data of the next day, and the day-in schedule is generally used for further more accurate scheduling of the power generation data of the next moment.
The power generation source of the power system generally comprises wind power and thermal power, and the actual power generation amount of the wind power is greatly uncertain under the influence of specific climate conditions and the like.
The high energy load of an electrical power system generally includes a discrete high energy load and a continuous high energy load:
the discrete high-load energy load changes the switching state after a period of switching, changes the switching state again after a period of time, has relatively high switching frequency, and has relatively stable load power when in the switching state, and the load power of the continuous high-load energy load is in a square waveform which is regularly switched between fixed power and 0 load power in a plurality of switching periods from the aspect of long-term running;
the continuous high-load energy load can be kept in a switching or switching state for a long time, the switching frequency is relatively low, and when in a switching state, the load power can fluctuate (such as +/-5% - +/-10%) near a certain fixed value, and the continuous high-load energy load has certain uncertainty, and in a single switching period, the load power of the continuous high-load energy load is distributed at scattered points on two sides of a certain horizontal straight line.
Because the source load of the power system has uncertainty, the actual supply and demand change is difficult to be predicted by a pre-established power generation plan, and the pre-established power generation data can not be timely adjusted and accurately regulated under the premise of absorbing the wind power as much as possible and reducing the total running cost of the power generation system.
Disclosure of Invention
The invention aims to solve the technical problem of providing a multi-scene step-by-step optimizing power generation regulation and control method based on source load uncertainty, which well makes up the defects of the prior art, can timely regulate pre-established thermal power generation data and wind power generation data, and accurately regulates and controls power generation.
The invention is realized by the following technical scheme:
the multi-scene step-by-step optimizing power generation regulation and control system based on source load uncertainty is characterized by comprising high-energy load, target power generation equipment, a dispatching center and power generation control equipment, wherein:
the high-energy load comprises a continuous high-energy load and a discrete high-energy load, wherein the continuous high-energy load and the discrete high-energy load can respectively provide daily load prediction of each set time interval in a period for a scheduling center according to each daily scheduling period, and the daily load prediction value of the discrete high-energy load can be adjusted; for each intra-day scheduling period, the continuous high-load energy load can provide intra-day load prediction for each set time interval in the period for the scheduling center, and the intra-day load prediction value of the continuous high-load energy load can be adjusted;
a target power generation device comprising at least one wind power plant provided with a plurality of wind power generation devices, and at least one thermal power plant provided with a plurality of thermal power generation devices; aiming at each day-ahead dispatching cycle and each day-in dispatching cycle, the wind power plant can respectively make day-ahead wind power output power prediction and day-in wind power output power prediction of each set time interval in the cycle;
the dispatching center comprises an information processing part, a day-ahead dispatching part and a day-in dispatching part, and the dispatching center performs power generation regulation and control according to the following modes:
A. for each set time interval within each day-ahead scheduling period:
the information processing part receives the daily load prediction of the high-energy load and the daily wind power output power prediction of the wind power plant, and makes a daily planned power generation amount of the wind power plant and the thermal power plant; according to the daily planned power generation amount and the daily wind power output power prediction of the wind power plant, acquiring daily wind power waste air quantity, keeping the daily load prediction of continuous high-load energy load fixed, transferring the daily wind power waste air quantity to discrete high-load energy load to guide the discrete high-load energy load to adjust the daily load prediction value, and carrying out daily scheduling on the daily load prediction adjustable allowance of the discrete high-load energy load;
a day-ahead scheduling part for predicting an adjustable allowance according to the day-ahead load of the discrete high-load energy load, establishing a discrete adjustment model, optimizing model parameters through deep learning, maximizing the daily power consumption of the day-ahead scheduling, adjusting the day-ahead scheduled power generation amount by using the optimized discrete adjustment model to serve as the daily planned power generation amount, and scheduling the corresponding discrete high-load energy load day-ahead and post-day scheduled power to serve as a discrete high-load energy load daily internal load predicted value;
B. for each set time interval within each intra-day scheduling period:
the information processing part obtains the daily wind power waste air quantity according to the received daily wind power output power prediction of the wind power plant and the daily planned power generation quantity of the wind power plant, keeps the daily load prediction value of the discrete high-load energy load fixed, transfers the daily wind power waste air quantity to the continuous high-load energy load to guide the continuous high-load energy load to adjust the daily load prediction value, and carries out daily scheduling on the daily load prediction adjustable allowance of the continuous high-load energy load;
an intra-day scheduling part for predicting an adjustable allowance according to the intra-day load of the continuous high-energy load, establishing a continuous adjustment model, optimizing model parameters through deep learning, minimizing the total running cost of the power generation system after adjustment, and adjusting the intra-day planned power generation amount by utilizing the optimized continuous adjustment model to obtain the adjusted intra-day planned power generation amount as a target power generation amount;
and the power generation control equipment receives the target power generation amount and controls the target power generation equipment to generate power according to the target power generation amount.
Further, the day-ahead scheduling period is 24 hours, and the set time interval of the day-ahead scheduling period is 1 hour.
Further, the daily scheduling period is 1 hour, and the set time interval of the daily scheduling period is 15 minutes.
A multi-scene step-by-step optimization power generation regulation and control method based on source load uncertainty comprises the following steps:
s1, acquiring daily load prediction of continuous high-load energy load and discrete high-load energy load;
s2, acquiring a daily wind power output power prediction of a wind power plant;
s3, making daily planned power generation of the wind power plant and the thermal power plant according to daily load prediction in S1 and daily wind power output power prediction in S2;
s4, predicting and acquiring the wind power waste amount of the day-ahead wind power according to the planned daily power generation amount of the wind power plant in the S3 and the wind power output power of the day-ahead wind power in the S2;
s5, according to the daily wind power waste air quantity in the S4, the daily load prediction of the continuous high-load energy load in the S1 is kept unchanged, and the daily scheduling is carried out on the daily load prediction adjustable allowance of the discrete high-load energy load;
s6, predicting adjustable quantity according to the daily load of the discrete high-energy load in S5, adjusting daily planned power generation in S3 to maximize the daily power consumption of the daily scheduled wind power, and obtaining corresponding daily scheduled power of the discrete high-energy load;
s7, transmitting the day-ahead planned power generation amount adjusted in the S6 to a thermal power plant and a wind power plant through a day-ahead scheduling plan to serve as the day-ahead planned power generation amount;
s8, acquiring daily wind power output power prediction of the wind power plant;
s9, acquiring the daily wind power waste air quantity according to the daily wind power output power prediction in the S8 and the daily planned power generation amount of the wind power plant in the S7;
s10, according to the daily wind power waste air quantity in S9, keeping a daily load predicted value of the discrete high-load energy load fixed to be the daily power used after the daily scheduling of the discrete high-load energy load in S6, and performing daily scheduling on the daily load predicted adjustable allowance of the continuous high-load energy load;
s11, according to the daily load prediction adjustable allowance of the continuous high-energy load in S10, adjusting the daily planned power generation amount in S7, and recording as a target power generation amount, so that the total operation cost of the adjusted power generation system is minimized;
and S12, transmitting the target power generation amount to a thermal power plant and a wind power plant through an intra-day scheduling plan to generate power according to the intra-day scheduling plan.
Further, in the step S6, a discrete adjustment model is established to adjust the planned daily power generation amount, and an objective function of the discrete adjustment model is as follows:
wherein E is W For regulating day-ahead wind power consumption, maxE W Maximum wind power consumption for day-ahead scheduling, T a For the day-ahead scheduling period, N W For the number of wind power plants in a wind power plant,the wind power generation equipment k outputs power according to a plan of a wind power plant day-ahead scheduling plan at a moment t after the discrete high-energy load adjustable quantity participates in day-ahead adjustment, and delta t is a time interval of day-ahead scheduling;in the objective function, ++>For solving parameters, other parameters are set parameters;
the constraint conditions of the objective function include:
a, the discrete high-energy load adjustable quantity participates in the day-ahead adjustment and the increment of wind power output power is equal to the increment of the discrete high-energy load electric power:
wherein,the adjustable quantity for discrete high-energy load does not participate in the planned output power of wind power generation equipment k at the moment t according to the day-ahead scheduling of a wind power plant before the day-ahead scheduling, N HL For the number of discrete high-energy loads involved in day-ahead scheduling +.>For the ith discrete high energy load adjustable quantity, participating in planned power at time t after day-ahead scheduling,/for the time of day-ahead scheduling>The power prediction at the moment t before the day-ahead scheduling is not participated in for the i-th discrete type high-energy load adjustable quantity;
B，value range constraint:
wherein P is HLi,max 、P HLi,min Respectively represent discrete high-load energy load adjustable quantity to participate in day-ahead schedulingUpper and lower limits of the back throw-in capacity, B i,t The switching state of the ith discrete high-energy load at the time t is that 0 represents switching and 1 represents switching;
C. switching times of discrete high-energy load in day-ahead scheduling period, continuous time after each switchingConstraint:
wherein B is i,t-1 The switching state of the i-1 discrete high-energy load at the time t is that 0 represents switching and 1 represents switching; m is M HL,max For the maximum number of times the discrete high energy load is allowed to switch in the day-ahead scheduling period,for the continuous input time of the ith discrete type high energy load in the period t,/for the period t>For the continuous interruption time of the i-th discrete type high energy load in the period t,minimum input time for the ith discrete high energy load, < >>Minimum interruption time for the ith discrete high energy load, < >>And predicting the wind power output power of the wind power plant before the day of the period t.
Further, the day-ahead scheduling period T a The time interval Δt scheduled before day is 1 hour for 24 hours. And acquiring a day-ahead planned power generation amount on a day before the current date, wherein the day-ahead planned power generation amount is a power generation planned amount for each hour on the current date.
Further, in S11, a continuous adjustment model is established to adjust the planned daily power generation amount, and an objective function of the continuous adjustment model is as follows:
wherein F is the total running cost, and minF is the minimum total running cost; t (T) i Scheduling a period for a day; n (N) G The number of the conventional thermal power generation equipment; u (u) j,t The method is characterized in that the method is in a start-stop state of the thermal power generation equipment j in a period t, wherein 0 represents stop and 1 represents start; p (P) j,t For the output power of the thermal power plant j in the period t, f j (P j,t )＝a j +b j P j,t +c j (P j,t ) 2 ，a j 、b j 、c j Is an operation cost parameter of the thermal power generation equipment j; s is S j For the starting-up cost of the thermal power plant j, u j,t-1 The method is characterized in that the method is in a start-stop state of the thermal power generation equipment j in a period t-1, wherein 0 represents stop and 1 represents start; c w Punishment costs are incurred for the system unit of the abandoned wind,predicted power of wind power at t moment before day scheduling for continuous high-energy load participation +.>Wind power output power value delta T of time T after continuous high-energy load adjustable quantity participates in intra-day scheduling i For time intervals of intra-day scheduling periods, N HC C, for the number of continuous high-energy load participating in daily scheduling h Adjusting the cost, delta P, for the unit of the h group continuous high energy load HLc The power variation amount is the continuous high-energy load;
in the objective function, P j,t 、u j,t 、ΔP HLc For solving parameters, other parameters are set parameters;
the constraint conditions of the objective function include:
A. system source load power balance:
wherein N is W For the number of wind power plants in a wind power plant,after participating in the daily scheduling for the continuous high energy load, the wind power generation equipment k is powered according to the daily scheduling plan of the wind power plant at the time t, and is in a state of +.>The adjustable quantity of the discrete high-energy load participates in the planned output power of the wind power generation equipment k at the moment t according to the wind power plant day-ahead scheduling plan after day-ahead adjustment, N G U is the number of thermal power generation equipment in the thermal power plant j,t The method is characterized in that the method is a starting and stopping state of thermal power generation equipment j at a moment t, wherein 0 represents stopping and 1 represents starting; p (P) j,t,inter For the output power of thermal power plant j at time t of day schedule, P j,t,ahead For the planned output power of thermal power plant j at time t of day schedule, N HC To participate in the number of consecutive high energy loads scheduled in the day,for continuous high-energy load i, taking part in the power value at t moment after the intra-day scheduling,/for the power value at t moment>The method comprises the steps that a continuous high-energy load i participates in power prediction at a time t before intra-day scheduling;
B、P j,t the value range is as follows:
u j,t P j,min ≤P j,t ≤u j,t P j,max
wherein P is j,min For minimum output power of thermal power plant j, P j,max Maximum output power for thermal power plant j;
C，u j,t the value range is as follows:
wherein u is j,t-1 In the start-stop state of thermal power plant j at t-1 time, u j,t Is in a start-stop state of the thermal power generation equipment j at the time t,for the start-up duration of the thermal power plant j at time t,/->For the shutdown duration of thermal power plant j at time t,/-)>For the minimum operating time of thermal power plant j, +.>Minimum downtime for thermal power plant j;
d, constraint conditions of climbing speed of thermal power generation equipment:
u j,t P j,t -u j,t-1 P j,t-1 ≤P j,up
u j,t-1 P j,t-1 -u j,t P j,t ≤P j,down
wherein P is j,t-1 For the output power of thermal power plant j at time t-1, P j,up For limiting the ascending climbing speed of the generator set j, P j,down Is the downward hill climbing speed limit for genset j.
Still further, the constraint condition of the continuous adjustment model objective function further includes:
wherein ρ is 1 To correspond to the rotational redundancy factor of the load demand,the power predictive value of the time period t before the intra-day scheduling is participated in for the continuous high-energy load i, u w To correspond to the rotation reserve coefficient of wind power fluctuation, P HLci,min Lower limit of capacity which can be input for continuous high-energy load participation in intra-day scheduling, P HLci,max The upper limit of capacity which can be put into for continuous high-energy load participation in intra-day scheduling is +.>Power, M, for continuous high energy load participation in intra-day scheduling s Number of supply and demand scenarios for generating electricityPi(s) is the probability of occurrence of the s-th scene;
in the above-mentioned constraint condition(s),u j,t ,π(s),/>to solve for the parameters, P HLci,max ,P HLci,min ,P j,max ,ρ 1 ,u w To set parameters.
Still further, the intra-day scheduling period T i For 1 hour, time interval DeltaT of intra-day scheduling period i 15 minutes.
A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements a multi-scenario step-by-step optimization power generation regulation method based on source load uncertainty as described above.
The invention has the beneficial effects that:
on the basis of daily planned generated energy, a daily load predicted value of continuous high-load energy load is fixed, daily scheduling is carried out on daily load predicted adjustable allowance of discrete high-load energy load based on a discrete adjustment model according to daily wind power waste amount, so that daily power consumption of daily scheduled generated energy is maximum; on the basis of daily planned generated energy, a daily load prediction value of a discrete high-load energy load is fixed, daily scheduling is carried out on a daily load prediction adjustable allowance of the continuous high-load energy load based on a continuous adjustment model according to daily wind power waste air quantity, so that the total running cost of a power generation system is minimum, and a target generated energy is obtained; through multiple adjustment of the power generation plan, the high-energy load power consumption with load uncertainty and the wind power generation capacity with output uncertainty are matched as much as possible, the power generation regulation and control is accurately carried out, and meanwhile, the environment protection and the economic efficiency are considered.
Drawings
FIG. 1 is a system architecture and flow diagram of a day-ahead scheduling stage
FIG. 2 is a system architecture and flow diagram of an intra-day scheduling phase
FIG. 3 is a block diagram of a multi-scenario step-wise optimized power generation regulation system based on source-load uncertainty
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The multi-scenario step-by-step optimizing power generation regulation and control system based on source load uncertainty as shown in figures 1-3 comprises high-energy load, target power generation equipment, a dispatching center and power generation control equipment.
The high-energy load comprises a continuous high-energy load and a discrete high-energy load, wherein the continuous high-energy load and the discrete high-energy load can respectively provide daily load prediction of each set time interval in a period for a scheduling center according to each daily scheduling period, and the daily load prediction value of the discrete high-energy load can be adjusted; for each intra-day scheduling period, the continuous high-load energy load can provide intra-day load prediction for each set time interval in the period to the scheduling center, and the intra-day load prediction value of the continuous high-load energy load can be adjusted.
A target power generation device comprising at least one wind power plant provided with a plurality of wind power generation devices, and at least one thermal power plant provided with a plurality of thermal power generation devices; aiming at each day-ahead dispatching cycle and each day-in dispatching cycle, the wind power plant can respectively make day-ahead wind power output power prediction and day-in wind power output power prediction of each set time interval in the cycle; in practice, the target power plant may also include a photovoltaic power plant, a tidal power plant, or the like, having an uncertainty in the output.
The dispatching center comprises an information processing part, a day-ahead dispatching part and a day-in dispatching part, and the dispatching center performs power generation regulation and control according to the following modes:
A. for each set time interval within each day-ahead scheduling period:
the information processing part receives the daily load prediction of the high-energy load and the daily wind power output power prediction of the wind power plant, and makes a daily planned power generation amount of the wind power plant and the thermal power plant; according to the daily planned power generation amount and the daily wind power output power prediction of the wind power plant, acquiring daily wind power waste air quantity, keeping the daily load prediction of continuous high-load energy load fixed, transferring the daily wind power waste air quantity to discrete high-load energy load to guide the discrete high-load energy load to adjust the daily load prediction value, and carrying out daily scheduling on the daily load prediction adjustable allowance of the discrete high-load energy load;
a day-ahead scheduling part for predicting an adjustable allowance according to the day-ahead load of the discrete high-load energy load, establishing a discrete adjustment model, optimizing model parameters through deep learning, maximizing the daily power consumption of the day-ahead scheduling, adjusting the day-ahead scheduled power generation amount by using the optimized discrete adjustment model to serve as the daily planned power generation amount, and scheduling the corresponding discrete high-load energy load day-ahead and post-day scheduled power to serve as a discrete high-load energy load daily internal load predicted value;
B. for each set time interval within each intra-day scheduling period:
the information processing part obtains the daily wind power waste air quantity according to the received daily wind power output power prediction of the wind power plant and the daily planned power generation quantity of the wind power plant, keeps the daily load prediction value of the discrete high-load energy load fixed, transfers the daily wind power waste air quantity to the continuous high-load energy load to guide the continuous high-load energy load to adjust the daily load prediction value, and carries out daily scheduling on the daily load prediction adjustable allowance of the continuous high-load energy load;
an intra-day scheduling part for predicting an adjustable allowance according to the intra-day load of the continuous high-energy load, establishing a continuous adjustment model, optimizing model parameters through deep learning, minimizing the total running cost of the power generation system after adjustment, and adjusting the intra-day planned power generation amount by utilizing the optimized continuous adjustment model to obtain the adjusted intra-day planned power generation amount as a target power generation amount;
and the power generation control equipment receives the target power generation amount and controls the target power generation equipment to generate power according to the target power generation amount. The power generation control apparatus is generally provided in a power plant. After the target power generation amount is obtained, the power generation control device sends a command for controlling the target power generation device to generate power according to the target power generation amount to the target power generation device, and the target power generation device executes a power generation plan after receiving the command.
Generally, the day-ahead scheduling period is 24 hours, and the set time interval of the day-ahead scheduling period is 1 hour; the intra-day scheduling period is 1 hour, and the set time interval of the intra-day scheduling period is 15 minutes.
The regulation and control system provided by the embodiment receives the daily planned power generation amount which is optimized by the discrete regulation model and aims at the next moment; the daily planned power generation amount is adjusted through the continuous adjustment model, so that the adjusted daily planned power generation amount is obtained, the daily planned power generation amount can be adjusted timely, namely, the preset power generation amount data can be adjusted timely.
The multi-scenario step-by-step optimization power generation regulation and control method based on source load uncertainty as shown in fig. 1 and 2 comprises a power generation regulation and control method in a day-ahead scheduling stage as shown in fig. 1 and a power generation regulation and control method in a day-ahead scheduling stage as shown in fig. 2. In the present embodiment, the execution subject of the power generation regulation method is typically a computer or other automatic control device.
The day-ahead scheduling stage includes the following steps:
s1, acquiring daily load prediction of continuous high-load energy load and discrete high-load energy load;
s2, acquiring a daily wind power output power prediction of a wind power plant;
s3, making daily planned power generation of the wind power plant and the thermal power plant according to daily load prediction in S1 and daily wind power output power prediction in S2;
s4, predicting and acquiring the wind power waste amount of the day-ahead wind power according to the planned daily power generation amount of the wind power plant in the S3 and the wind power output power of the day-ahead wind power in the S2;
s5, according to the daily wind power waste air quantity in the S4, the daily load prediction of the continuous high-load energy load in the S1 is kept unchanged, and the daily scheduling is carried out on the daily load prediction adjustable allowance of the discrete high-load energy load;
s6, predicting an adjustable allowance according to the daily pre-load of the discrete high-load energy load in S5, adjusting the daily pre-planned power generation amount in S3 to maximize the daily pre-scheduled daily consumed wind power amount, and obtaining the corresponding daily pre-scheduled post-scheduled power of the discrete high-load energy load;
and S7, transmitting the day-ahead planned power generation amount adjusted in the S6 to a thermal power plant and a wind power plant through a day-ahead scheduling plan as the day-ahead planned power generation amount.
In S3, the planned power generation amount before the current date may be the planned power generation amount planned in the previous date of the current date. The day-ahead schedule power generation amount is a power generation schedule amount for each hour on the current date, specifically, the day-ahead schedule period T a For 24 hours, the time interval Δt scheduled before the day is 1 hour; the planned daily power generation amount may include planned power generation amounts for 24 hours, respectively. That is, the dispatch center may receive 24 pieces of data of the daily planned power generation amount, 1 piece of data being the daily planned power generation amount for 1 hour.
In S6, the discrete adjustment model is input as the planned daily power generation amount, and output as the planned daily power generation amount for the next time. In S11, the input of the continuous adjustment model is the planned daily power generation amount, and the output is the adjusted planned daily power generation amount, that is, the target power generation amount. In practice, the obtained target power generation amount can generally meet the current power supply and demand relationship.
The adjustment objective of S6 is to achieve as much wind power generation as possible. Because wind energy belongs to renewable energy sources, the realization mode can save non-renewable energy sources, and is helpful for protecting ecological environment:
s6, a discrete adjustment model is established to adjust the planned daily power generation amount, and an objective function of the discrete adjustment model is as follows:
wherein E is W For regulating day-ahead wind power consumption, maxE W Maximum wind power consumption for day-ahead scheduling, T a For the day-ahead scheduling period, N W For the number of wind power plants in a wind power plant,the wind power generation equipment k outputs power according to a plan of a wind power plant day-ahead scheduling plan at a moment t after the discrete high-energy load adjustable quantity participates in day-ahead adjustment, and delta t is a time interval of day-ahead scheduling; in the objective function, ++>For solving parameters, other parameters are set parameters;
the constraint conditions of the objective function include:
a, the discrete high-energy load adjustable quantity participates in the day-ahead adjustment and the increment of wind power output power is equal to the increment of the discrete high-energy load electric power:
wherein,the adjustable quantity for discrete high-energy load does not participate in the planned output power of wind power generation equipment k at the moment t according to the day-ahead scheduling of a wind power plant before the day-ahead scheduling, N HL For the number of discrete high-energy loads involved in day-ahead scheduling +.>For the ith discrete high energy load adjustable quantity, participating in planned power at time t after day-ahead scheduling,/for the time of day-ahead scheduling>The power prediction at the moment t before the day-ahead scheduling is not participated in for the i-th discrete type high-energy load adjustable quantity;
B，value range constraint:
wherein P is HLi,max 、P HLi,min Respectively representing the upper limit and the lower limit of the input capacity after the discrete high-load energy load adjustable quantity participates in the day-ahead scheduling, B i,t The switching state of the ith discrete high-energy load at the time t is that 0 represents switching and 1 represents switching;
C. switching times of discrete high-energy load in day-ahead scheduling period, continuous time after each switchingConstraint:
wherein B is i,t-1 The switching state of the i-1 discrete high-energy load at the time t is that 0 represents switching and 1 represents switching; m is M HL,max For the maximum number of times the discrete high energy load is allowed to switch in the day-ahead scheduling period,for the continuous input time of the ith discrete type high energy load in the period t,/for the period t>For the ith discrete high energy load in period tThe time of the continuous interruption is that,minimum input time for the ith discrete high energy load, < >>Minimum interruption time for the ith discrete high energy load, < >>And predicting the wind power output power of the wind power plant before the day of the period t.
It should be noted that each of the above constraints is generally a condition for describing a power generation supply-demand scenario. The execution main body adjusts the daily planned power generation amount by adopting the objective function constraint condition of the discrete adjustment model, so that the daily planned power generation amount obtained after adjustment can be more capable of meeting the current power supply and demand relationship. Therefore, the finally obtained target power generation amount can meet the current power supply and demand relation.
The intra-day scheduling phase includes the steps of:
s8, acquiring daily wind power output power prediction of the wind power plant;
s9, acquiring the daily wind power waste air quantity according to the daily wind power output power prediction in the S8 and the daily planned power generation amount of the wind power plant in the S7;
s10, according to the daily wind power waste air quantity in S9, keeping a daily load predicted value of the discrete high-load energy load fixed to be the daily power used after the daily scheduling of the discrete high-load energy load in S6, and performing daily scheduling on the daily load predicted adjustable allowance of the continuous high-load energy load;
s11, according to the daily load prediction adjustable allowance of the continuous high-energy load in S10, adjusting the daily planned power generation amount in S7, and recording as a target power generation amount, so that the total operation cost of the adjusted power generation system is minimized;
and S12, transmitting the target power generation amount to a thermal power plant and a wind power plant through an intra-day scheduling plan to generate power according to the intra-day scheduling plan.
The adjustment aim of S11 is to comprehensively consider various factors, so that the total operation cost is reduced as much as possible, and the economic efficiency of power generation is improved:
in S11, a continuous regulation model is established to regulate the planned daily power generation amount, and the objective function of the continuous regulation model is as follows:
wherein F is the total running cost, and minF is the minimum total running cost; t (T) i Scheduling a period for a day; n (N) G The number of the conventional thermal power generation equipment; u (u) j,t The method is characterized in that the method is in a start-stop state of the thermal power generation equipment j in a period t, wherein 0 represents stop and 1 represents start; p (P) j,t For the output power of the thermal power plant j in the period t, f j (P j,t )＝a j +b j P j,t +c j (P j,t ) 2 ，a j 、b j 、c j Is an operation cost parameter of the thermal power generation equipment j; s is S j For the starting-up cost of the thermal power plant j, u j,t-1 The method is characterized in that the method is in a start-stop state of the thermal power generation equipment j in a period t-1, wherein 0 represents stop and 1 represents start; c w Punishment costs are incurred for the system unit of the abandoned wind,predicted power of wind power at t moment before day scheduling for continuous high-energy load participation +.>Wind power output power value delta T of time T after continuous high-energy load adjustable quantity participates in intra-day scheduling i For time intervals of intra-day scheduling periods, N HC C, for the number of continuous high-energy load participating in daily scheduling h Adjusting the cost, delta P, for the unit of the h group continuous high energy load HLc The power variation amount is the continuous high-energy load;
in the objective function, P j,t 、u j,t 、ΔP HLc In order to solve for the parameters of the solution,the other parameters are set parameters;
the constraint conditions of the objective function include:
A. system source load power balance:
wherein N is W For the number of wind power plants in a wind power plant,after participating in the daily scheduling for the continuous high energy load, the wind power generation equipment k is powered according to the daily scheduling plan of the wind power plant at the time t, and is in a state of +.>The adjustable quantity of the discrete high-energy load participates in the planned output power of the wind power generation equipment k at the moment t according to the wind power plant day-ahead scheduling plan after day-ahead adjustment, N G U is the number of thermal power generation equipment in the thermal power plant j,t The method is characterized in that the method is a starting and stopping state of thermal power generation equipment j at a moment t, wherein 0 represents stopping and 1 represents starting; p (P) j,t,inter For the output power of thermal power plant j at time t of day schedule, P j,t,ahead For the planned output power of thermal power plant j at time t of day schedule, N HC To participate in the number of consecutive high energy loads scheduled in the day,for continuous high-energy load i, taking part in the power value at t moment after the intra-day scheduling,/for the power value at t moment>The method comprises the steps that a continuous high-energy load i participates in power prediction at a time t before intra-day scheduling;
B、P j,t the value range is as follows:
u j,t P j,min ≤P j,t ≤u j,t P j,max
wherein P is j,min For minimum output power of thermal power plant j, P j,max Maximum output power for thermal power plant j;
C，u j,t the value range is as follows:
wherein u is j,t-1 In the start-stop state of thermal power plant j at t-1 time, u j,t Is in a start-stop state of the thermal power generation equipment j at the time t,for the start-up duration of the thermal power plant j at time t,/->For the shutdown duration of thermal power plant j at time t,/-)>For the minimum operating time of thermal power plant j, +.>Minimum downtime for thermal power plant j;
d, constraint conditions of climbing speed of thermal power generation equipment:
u j,t P j,t -u j,t-1 P j,t-1 ≤P j,up
u j,t-1 P j,t-1 -u j,t P j,t ≤P j,down
wherein P is j,t-1 For the output power of thermal power plant j at time t-1, P j,up For limiting the ascending climbing speed of the generator set j, P j,down For the direction of the generator set jAnd (5) limiting the climbing speed.
Still further, the constraint condition of the continuous adjustment model objective function further includes:
/>
wherein ρ is 1 To correspond to the rotational redundancy factor of the load demand,the power predictive value of the time period t before the intra-day scheduling is participated in for the continuous high-energy load i, u w To correspond to the rotation reserve coefficient of wind power fluctuation, P HLci,min Lower limit of capacity which can be input for continuous high-energy load participation in intra-day scheduling, P HLci,max The upper limit of capacity which can be put into for continuous high-energy load participation in intra-day scheduling is +.>Power, M, for continuous high energy load participation in intra-day scheduling s Pi(s) is the occurrence probability of the s-th scene for generating the number of supply and demand scenes;
in the above-mentioned constraint condition(s),u j,t ,π(s),/>to solve for the parameters, P HLci,max ,P HLci,min ,P j,max ,ρ 1 ,u w To set parameters.
Note that each of the above constraints is generally a condition for describing a power generation supply-demand scenario and adjustment accuracy. The execution main body adjusts the daily planned power generation amount by adopting the constraint condition of the continuous adjustment model objective function, and can accurately adjust the daily planned power generation amount, so that the daily planned power generation amount obtained after adjustment can meet the adjustment precision under the condition of meeting the current power supply and demand relationship. That is, the obtained target power generation amount contributes to achieving accurate power generation.
Claims (10)
1. The multi-scene step-by-step optimizing power generation regulation and control system based on source load uncertainty is characterized by comprising high-energy load, target power generation equipment, a dispatching center and power generation control equipment, wherein:
the high-energy load comprises a continuous high-energy load and a discrete high-energy load, wherein the continuous high-energy load and the discrete high-energy load can respectively provide daily load prediction of each set time interval in a period for a scheduling center according to each daily scheduling period, and the daily load prediction value of the discrete high-energy load can be adjusted; for each intra-day scheduling period, the continuous high-load energy load can provide intra-day load prediction for each set time interval in the period for the scheduling center, and the intra-day load prediction value of the continuous high-load energy load can be adjusted;
a target power generation device comprising at least one wind power plant provided with a plurality of wind power generation devices, and at least one thermal power plant provided with a plurality of thermal power generation devices; aiming at each day-ahead dispatching cycle and each day-in dispatching cycle, the wind power plant can respectively make day-ahead wind power output power prediction and day-in wind power output power prediction of each set time interval in the cycle;
the dispatching center comprises an information processing part, a day-ahead dispatching part and a day-in dispatching part, and the dispatching center performs power generation regulation and control according to the following modes:
A. for each set time interval within each day-ahead scheduling period:
the information processing part receives the daily load prediction of the high-energy load and the daily wind power output power prediction of the wind power plant, and makes a daily planned power generation amount of the wind power plant and the thermal power plant; according to the daily planned power generation amount and the daily wind power output power prediction of the wind power plant, acquiring daily wind power waste air quantity, keeping the daily load prediction of continuous high-load energy load fixed, transferring the daily wind power waste air quantity to discrete high-load energy load to guide the discrete high-load energy load to adjust the daily load prediction value, and carrying out daily scheduling on the daily load prediction adjustable allowance of the discrete high-load energy load;
a day-ahead scheduling part for predicting an adjustable allowance according to the day-ahead load of the discrete high-load energy load, establishing a discrete adjustment model, optimizing model parameters through deep learning, maximizing the daily power consumption of the day-ahead scheduling, adjusting the day-ahead scheduled power generation amount by using the optimized discrete adjustment model to serve as the daily planned power generation amount, and scheduling the corresponding discrete high-load energy load day-ahead and post-day scheduled power to serve as a discrete high-load energy load daily internal load predicted value;
B. for each set time interval within each intra-day scheduling period:
the information processing part obtains the daily wind power waste air quantity according to the received daily wind power output power prediction of the wind power plant and the daily planned power generation quantity of the wind power plant, keeps the daily load prediction value of the discrete high-load energy load fixed, transfers the daily wind power waste air quantity to the continuous high-load energy load to guide the continuous high-load energy load to adjust the daily load prediction value, and carries out daily scheduling on the daily load prediction adjustable allowance of the continuous high-load energy load;
an intra-day scheduling part for predicting an adjustable allowance according to the intra-day load of the continuous high-energy load, establishing a continuous adjustment model, optimizing model parameters through deep learning, minimizing the total running cost of the power generation system after adjustment, and adjusting the intra-day planned power generation amount by utilizing the optimized continuous adjustment model to obtain the adjusted intra-day planned power generation amount as a target power generation amount;
and the power generation control equipment receives the target power generation amount and controls the target power generation equipment to generate power according to the target power generation amount.
2. The multi-scenario step-by-step optimal power generation regulation system based on source load uncertainty of claim 1, wherein the day-ahead scheduling period is 24 hours, and the set time interval of the day-ahead scheduling period is 1 hour.
3. The multi-scenario step-by-step optimal power generation regulation system based on source load uncertainty of claim 1, wherein the intra-day scheduling period is 1 hour, and the set time interval of the intra-day scheduling period is 15 minutes.
4. A multi-scene step-by-step optimization power generation regulation and control method based on source load uncertainty comprises the following steps:
s1, acquiring daily load prediction of continuous high-load energy load and discrete high-load energy load;
s2, acquiring a daily wind power output power prediction of a wind power plant;
s3, making daily planned power generation of the wind power plant and the thermal power plant according to daily load prediction in S1 and daily wind power output power prediction in S2;
s4, predicting and acquiring the wind power waste amount of the day-ahead wind power according to the planned daily power generation amount of the wind power plant in the S3 and the wind power output power of the day-ahead wind power in the S2;
s5, according to the daily wind power waste air quantity in the S4, the daily load prediction of the continuous high-load energy load in the S1 is kept unchanged, and the daily scheduling is carried out on the daily load prediction adjustable allowance of the discrete high-load energy load;
s6, predicting an adjustable allowance according to the daily pre-load of the discrete high-load energy load in S5, adjusting the daily pre-planned power generation amount in S3 to maximize the daily pre-scheduled daily consumed wind power amount, and obtaining the corresponding daily pre-scheduled post-scheduled power of the discrete high-load energy load;
s7, transmitting the day-ahead planned power generation amount adjusted in the S6 to a thermal power plant and a wind power plant through a day-ahead scheduling plan to serve as the day-ahead planned power generation amount;
s8, acquiring daily wind power output power prediction of the wind power plant;
s9, acquiring the daily wind power waste air quantity according to the daily wind power output power prediction in the S8 and the daily planned power generation amount of the wind power plant in the S7;
s10, according to the daily wind power waste air quantity in S9, keeping a daily load predicted value of the discrete high-load energy load fixed to be the daily power used after the daily scheduling of the discrete high-load energy load in S6, and performing daily scheduling on the daily load predicted adjustable allowance of the continuous high-load energy load;
s11, according to the daily load prediction adjustable allowance of the continuous high-energy load in S10, adjusting the daily planned power generation amount in S7, and recording as a target power generation amount, so that the total operation cost of the adjusted power generation system is minimized;
and S12, transmitting the target power generation amount to a thermal power plant and a wind power plant through an intra-day scheduling plan to generate power according to the intra-day scheduling plan.
5. The multi-scenario step-by-step optimization power generation regulation and control method based on source load uncertainty of claim 4, wherein the method is characterized by comprising the following steps of: in the step S6, a discrete adjustment model is established to adjust the planned daily power generation amount, and the objective function of the discrete adjustment model is as follows:
wherein E is W For adjusting day-ahead wind power consumption, max E W Maximum wind power consumption for day-ahead scheduling, T a For the day-ahead scheduling period, N W For the number of wind power plants in a wind power plant,the wind power generation equipment k outputs power according to a plan of a wind power plant day-ahead scheduling plan at a moment t after the discrete high-energy load adjustable quantity participates in day-ahead adjustment, and delta t is a time interval of day-ahead scheduling; in the objective function, ++>For solving parameters, other parameters are set parameters;
the constraint conditions of the objective function include:
a, the discrete high-energy load adjustable quantity participates in the day-ahead adjustment and the increment of wind power output power is equal to the increment of the discrete high-energy load electric power:
wherein,the adjustable quantity for discrete high-energy load does not participate in the planned output power of wind power generation equipment k at the moment t according to the day-ahead scheduling of a wind power plant before the day-ahead scheduling, N HL To participate in the number of discrete high energy loads scheduled in the day before,for the ith discrete high energy load adjustable quantity, participating in planned power at time t after day-ahead scheduling,/for the time of day-ahead scheduling>The power prediction at the moment t before the day-ahead scheduling is not participated in for the i-th discrete type high-energy load adjustable quantity;
B、value range constraint:
wherein P is HLi,max 、P HLi,min Respectively representing the upper limit and the lower limit of the input capacity after the discrete high-load energy load adjustable quantity participates in the day-ahead scheduling, B i,t The switching state of the ith discrete high-energy load at the time t is that 0 represents switching and 1 represents switching;
C. switching times of discrete high-energy load in day-ahead scheduling period, continuous time after each switchingConstraint:
wherein B is i,t-1 The switching state of the i-1 discrete high-energy load at the time t is that 0 represents switching and 1 represents switching; m is M HL,max For the maximum number of times the discrete high energy load is allowed to switch in the day-ahead scheduling period,for the continuous input time of the ith discrete type high energy load in the period t,/for the period t>For the continuous interruption time of the ith discrete type high energy load in period t,/for the period t>Minimum input time for the ith discrete high energy load, < >>Minimum interruption time for the ith discrete high energy load, < >>And predicting the wind power output power of the wind power plant before the day of the period t.
6. The multi-scenario step-by-step optimization power generation regulation and control method based on source load uncertainty of claim 5, wherein the method is characterized by comprising the following steps of: the day-ahead scheduling period T a The time interval Δt scheduled before day is 1 hour for 24 hours.
7. The multi-scenario step-by-step optimization power generation regulation and control method based on source load uncertainty of claim 4, wherein the method is characterized by comprising the following steps of: in the step S11, a continuous adjustment model is established to adjust the planned daily power generation amount, and an objective function of the continuous adjustment model is as follows:
wherein F is the total running cost, and minF is the minimum total running cost; t (T) i Scheduling a period for a day; n (N) G The number of the conventional thermal power generation equipment; u (u) j,t The method is characterized in that the method is in a start-stop state of the thermal power generation equipment j in a period t, wherein 0 represents stop and 1 represents start; p (P) j,t For the output power of the thermal power plant j in the period t, f j (P j,t )＝a j +b j P j,t +c j (P j,t ) 2 ，a j 、b j 、c j Is an operation cost parameter of the thermal power generation equipment j; s is S j For the starting-up cost of the thermal power plant j, u j,t-1 The method is characterized in that the method is in a start-stop state of the thermal power generation equipment j in a period t-1, wherein 0 represents stop and 1 represents start; c w Punishment costs are incurred for the system unit of the abandoned wind,predicted power of wind power at t moment before day scheduling for continuous high-energy load participation +.>Wind power output power value delta T of time T after continuous high-energy load adjustable quantity participates in intra-day scheduling i For time intervals of intra-day scheduling periods, N HC C, for the number of continuous high-energy load participating in daily scheduling h Adjusting the cost, delta P, for the unit of the h group continuous high energy load HLc The power variation amount is the continuous high-energy load;
in the objective function, P j,t 、u j,t 、ΔP HLc For solving parameters, other parameters are set parameters;
the constraint conditions of the objective function include:
A. system source load power balance:
wherein N is W For the number of wind power plants in a wind power plant,after participating in the daily scheduling for the continuous high energy load, the wind power generation equipment k is powered according to the daily scheduling plan of the wind power plant at the time t, and is in a state of +.>The adjustable quantity of the discrete high-energy load participates in the planned output power of the wind power generation equipment k at the moment t according to the wind power plant day-ahead scheduling plan after day-ahead adjustment, N G U is the number of thermal power generation equipment in the thermal power plant j,t The method is characterized in that the method is a starting and stopping state of thermal power generation equipment j at a moment t, wherein 0 represents stopping and 1 represents starting; p (P) j,t,inter For the output power of thermal power plant j at time t of day schedule, P j,t,ahead Is thermal power generationDevice j schedules the planned output power at time t, N, in the day HC To participate in the number of consecutive high energy loads scheduled in the day,for continuous high-energy load i, taking part in the power value at t moment after the intra-day scheduling,/for the power value at t moment>The method comprises the steps that a continuous high-energy load i participates in power prediction at a time t before intra-day scheduling;
B、P j,t value range u j,t P j,min ≤P j,t ≤u j,t P j,max
Wherein P is j,min For minimum output power of thermal power plant j, P j,max Maximum output power for thermal power plant j;
C、u j,t the value range is as follows:
wherein u is j,t-1 In the start-stop state of thermal power plant j at t-1 time, u j,t Is in a start-stop state of the thermal power generation equipment j at the time t,for the start-up duration of the thermal power plant j at time t,/->For the shutdown duration of thermal power plant j at time t,/-)>For the minimum operating time of thermal power plant j, +.>Minimum downtime for thermal power plant j;
D. constraint conditions of climbing speed of thermal power generation equipment:
u j,t P j,t -u j,t-1 P j,t-1 ≤P j,up
u j,t-1 P j,t-1 -u j,t P j,t ≤P j,down
wherein P is j,t-1 For the output power of thermal power plant j at time t-1, P j,up For limiting the ascending climbing speed of the generator set j, P j,down Is the downward hill climbing speed limit for genset j.
8. The multi-scenario step-by-step optimization power generation regulation and control method based on source load uncertainty of claim 7, wherein the method is characterized by comprising the following steps of: the constraint condition of the continuous adjustment model objective function further comprises:
wherein ρ is 1 To correspond to the rotational redundancy factor of the load demand,participation of continuous high energy load i in pre-day scheduling period tPower predictors of u w To correspond to the rotation reserve coefficient of wind power fluctuation, P HLci,min Lower limit of capacity which can be input for continuous high-energy load participation in intra-day scheduling, P HLci,max The upper limit of capacity which can be put into for continuous high-energy load participation in intra-day scheduling is +.>Power, M, for continuous high energy load participation in intra-day scheduling s Pi(s) is the occurrence probability of the s-th scene for generating the number of supply and demand scenes;
in the above-mentioned constraint condition(s),u j,t ,π(s),/>to solve for the parameters, P HLci,max ,P HLci,min ,P j,max ,ρ 1 ,u w To set parameters.
9. The multi-scenario step-by-step optimization power generation regulation and control method based on source load uncertainty of claim 7, wherein the method is characterized by comprising the following steps of: the intra-day scheduling period T i For 1 hour, time interval DeltaT of intra-day scheduling period i 15 minutes.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the multi-scenario step-wise optimized power generation regulation method based on source load uncertainty as claimed in any one of claims 4 to 9.
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