CN110417048B - Direct-current power grid transmitting and receiving end combined peak regulation optimization method considering source grid load constraint - Google Patents
Direct-current power grid transmitting and receiving end combined peak regulation optimization method considering source grid load constraint Download PDFInfo
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Abstract
A direct-current power grid transmitting and receiving end combined peak regulation optimization method considering source grid load constraint can solve the technical problem that a 'straight line' or 'inverse peak regulation' transmission plan frequently appears in the prior art, and great difficulty is brought to peak regulation, frequency regulation and operation mode arrangement of a receiving end power grid. According to the method, day-ahead output prediction data of a wind power plant and a photovoltaic power station are firstly obtained, the surplus electric quantity generated by a power grid sending end power supply is transmitted to a power grid receiving end through a high-voltage direct-current connecting line on the basis of meeting local load power utilization, the minimum total operation cost of a power grid system is taken as an optimization target, and an optimization model is established by taking power balance constraint, conventional generator set constraint, wind and light abandoning constraint, direct-current connecting line constraint and excitation type demand side response constraint as constraint conditions, so that operation power planning of energy supply equipment of a transmitting end and receiving end connected with direct current, the high-voltage direct-current connecting line and excitation type demand side response of the receiving end is obtained. The invention not only reduces the energy cost of users, but also improves the complementation and cooperativity among various energy sources.
Description
Technical Field
The invention relates to the technical field of peak shaving of a power system, in particular to a direct-current power grid transmitting and receiving end combined peak shaving optimization method considering source grid load constraint.
Background
With the rapid development of new energy sources such as wind power, photovoltaic power generation and the like, a power supply pattern with reverse distribution and centralized grid connection is gradually formed in China, the consumption and the delivery of the new energy sources are severely restricted, and the high-voltage direct current trans-regional delivery is urgently needed to expand the consumption range of the new energy sources and realize the national resource optimization configuration. The new energy power generation has the characteristics of randomness, intermittence and volatility, the power generation level of a new energy base has a strong coupling relation with the direct current transmission power, the direct current transmission power plan mainly considers system constraint and less considers the load demand of a receiving-end power grid or coordinately considers the output fluctuation and the delivery demand of the new energy according to a transmission quantity protocol at present, a 'straight line' or 'reverse peak regulation' transmission plan is frequently generated, and great difficulty is brought to peak regulation, frequency regulation and operation mode arrangement of the receiving-end power grid. In order to fully exploit the peak regulation potential of the power grid, expand the consumption of new energy and promote the continuous and healthy development of the new energy, it is more and more important to establish a direct-current power grid transmitting and receiving end combined peak regulation optimization operation model which fully considers the source grid load constraint.
In the current research result, aiming at a peak regulation model of a transmission end and a receiving end of a direct-current power grid, only traditional constraints such as traditional thermal power unit constraints and active power balance constraints of the transmission end and the receiving end of the power grid are simply considered, and constraint conditions such as direct-current connecting line constraints, new energy power generation constraints, pumped storage unit constraints and demand side response constraints are not considered sufficiently. The demand side response technology and the pumped storage power station are used as effective new energy consumption means and are verified by engineering practice, but at present, a direct-current power grid transmitting and receiving end combined peak regulation optimization operation model comprehensively considering combined operation benefits is rarely available.
Disclosure of Invention
The invention provides a direct current power grid transmitting and receiving end combined peak regulation optimization method considering source grid load constraint, which can solve the technical problem that in the prior art, load requirements of a receiving end power grid are less considered or new energy output fluctuation and outward transmission requirements are coordinately considered, so that a 'straight line' or 'reverse peak regulation' transmission plan often appears, and great difficulty is brought to peak regulation, frequency modulation and operation mode arrangement of the receiving end power grid.
In order to achieve the purpose, the invention adopts the following technical scheme:
a direct current power grid transmitting and receiving end combined peak regulation optimization method considering source grid load constraint comprises the following steps:
s100, establishing power output models of a transmitting end and a receiving end of a power grid;
s200, acquiring load day-ahead prediction data of a transmitting end and a receiving end of the direct-current power grid;
s300, establishing a power grid receiving end excitation type demand side response model;
s400, establishing a direct current tie line power transmission model;
s500, establishing a transmitting-receiving end combined peak regulation optimization operation model, on the basis of meeting the constraint conditions and active power balance constraint, wind curtailment and light curtailment constraint of the transmitting-receiving end power output model in the step S100, the receiving-end excitation type demand side response model in the step S300 and the direct-current tie line transmission power model in the step S400, solving the day-ahead optimization operation results of the transmitting-receiving end power output plan, the direct-current tie line power transmission plan and the receiving-end excitation type demand side response regulation plan by using day-ahead prediction data of the load of the receiving end in the step S200 as an optimization target, determining the optimal operation plan of each energy supply device from the day-ahead optimization operation results, and establishing a peak regulation power distribution scheme with optimal decision.
Further, the power output model of the grid sending end and the receiving end in the step S100 includes: the system comprises a new energy power generation power output model of a power grid sending end and a receiving end, a traditional thermal power generating unit output model of the power grid sending end and the receiving end, and a pumped storage unit output model of the power grid receiving end.
Further, in step S100, new energy power generation power output models of the grid sending end and the grid receiving end are established as follows:
acquiring and sorting historical output data of a wind power plant and a photovoltaic power station to obtain daily output data sets of the wind power plant and the photovoltaic power station, performing clustering analysis on the daily output data sets of each month by using a k-means clustering algorithm, dividing the data sets into k clusters, wherein the clustering center of each cluster is called a typical daily output state, and the number of data samples contained in each cluster represents the occurrence probability of the state;
therefore, by integrating all the historical samples, the probability distribution value of each state, i.e. the state probability value, is calculated by the formula (1.1):
where N represents the number of samples in the dataset, ljRepresenting the number of samples in the cluster j;
thus, there are
Dividing the state S1，S2，…,SkCorresponding to [0, 1 ]]The length of the interval is the state probability value; extraction of [0, 1 ] by random sampling]And (4) determining the typical daily output state according to the random numbers R uniformly distributed, and obtaining a random output model of the new energy.
Further, in step S100, the output models of the traditional thermal power generating units at the transmitting end and the receiving end of the power grid are established as follows:
the constraint of the upper limit and the lower limit of the active power of the thermal power generating unit is shown as a formula (2.1):
ui,tPi min≤Pi,t≤ui,tPi max (2.1)
in the formula, Pi max、Pi minRespectively the upper and lower active output limits u of the ith conventional uniti,tStarting and stopping the ith conventional unit at the time t;
it is subject to the climbing constraint as shown in equation (2.2):
-RDi≤(Pi,t-Pi,t-1)/Δt≤RUi (2.2)
in the formula, RDi、RUiRespectively limiting the up-down climbing rate of the ith conventional generator set, wherein delta t is the duration of the t period;
the start and stop constraints suffered by the method are as shown in a formula (2.3):
in the formula, Di、OiThe minimum shutdown time and the minimum startup time of the ith conventional generator set are respectively.
Further, in step S100, a pumped storage group output model of the receiving end of the power grid is established according to the following method:
the active power upper and lower limits of the pumped storage generator set and the water pump set are restricted as shown in formulas (3.1) and (3.2):
Pr min≤Pr,t≤Pr max (3.1)
in the formula, Pr,tOutput of pumped storage generator set at time t, Pr max、Pr minRespectively representing the active output upper limit and the active output lower limit of the pumped storage generator set; ppld,tIs the output of the water pump set at the moment t, Ppld max、Ppld minRespectively the upper limit and the lower limit of the active output of the water pump unit;
wherein the output of water pump unit is the step value:
Ppld,t＝pi×n (3.3)
pithe water pumping power of a single water pump;
the water balance constraint of the pumped storage unit is shown as the formula (3.4):
in the formula, Vt,Vpld,t,Vr,tRespectively the water storage amount of the reservoir at the time t, the water pumping amount of the water pump and the water consumption of the generator, Vt-ΔtThe water storage capacity of the reservoir at the previous moment Vmin，VmaxRespectively the minimum and maximum water storage capacity of the reservoir.
Further, in step S200, day-ahead load prediction data of the transmitting end and the receiving end of the dc power grid are obtained as follows:
and selecting a typical daily load curve according to seasons, carrying out equal-ratio amplification on the load curve according to the power, and obtaining the day-ahead prediction data of the loads of the transmitting end and the receiving end of the power grid by using an interpolation method.
Further, in step S300, a power grid receiving end excitation type demand side response model is established as follows:
the demand side deployment cost is as shown in equation (4.1):
wherein T is the total number of time segments, NmResponding to the user quantity, rho, for the incentive type demand sidemCompensating the price per unit of electricity, P, for user mm,tTransfer load value, Δ t, for user mmScheduling a time length for a unit;
the response quantity constraint satisfied by the incentive type demand side response is shown as equation (4.2):
in the formula, qm1，qm2，…，qmnFor a fixed load-value-shift gear, Q, of user mmThe maximum response capacity value for user m;
the load transfer balance constraint satisfied by the incentive type demand side response is as shown in equation (4.3):
further, in step S400, a dc link power transmission model is established as follows:
the direct current transmission electric quantity constraint satisfied by the direct current tie line is shown as a formula (5.1):
in the formula: t ═ 1,2, …, T; pdc,tActive power of the DC link during time period t, Edc,maxAnd Edc,minAre respectively a DC lineMaximum and minimum trading electricity quantities within the planning period T;
the switching power stepping constraint satisfied by the dc link is as shown in equation (5.2):
Pdc,t∈{Pdc1,Pdc2,...,Pdcn} (5.2)
in the formula: pdc1,Pdc2,…,PdcnAdjusting the gear for the fixed power of the direct current tie line;
the adjustment interval constraint satisfied by the dc link is as shown in equation (5.3):
in the formula, ctIs a 0-1 state variable indicating whether the direct current tie line starts to adjust or not in a time period t, and J is a minimum adjustment interval of the direct current tie line;
the regulation rate constraint that the dc link needs to satisfy is as shown in equation (5.4):
in the formula: rdc +And Rdc -Respectively the ascending rate limit value and the descending rate limit value of the direct current connecting line plan; Δ t is the duration of the t period.
Further, the step S500 is to establish a transmitting-receiving end joint peak shaving optimization operation model, based on the constraint conditions and active power balance constraint, wind curtailment and light curtailment constraint of the transmitting-receiving end power output model in the step S100, the receiving end excitation type demand side response model in the step S300, and the direct-current connecting line transmission power model in the step S400, the total operation cost of the power grid system is the lowest as an optimization target, the transmitting-receiving end load day-ahead prediction data in the step S200 is utilized, day-ahead optimization operation results of the transmitting-receiving end power output plan, the direct-current connecting line power transmission plan and the receiving end excitation type demand side response regulation and control plan are obtained through solving, the optimal operation plan of each energy supply device is determined, and a peak shaving power distribution scheme with the optimal decision is established;
the method specifically comprises the following steps:
the objective function of the run optimization model is characterized by equation (6.1):
in the formula (I), the compound is shown in the specification,
t represents the total time period number, and n is the number of thermal power generating units;
fi(. is a power generation cost function of the ith thermal power generating unit, Pi,tThe optimal output of the ith unit at the time t is obtained;
mifor the start-stop loss of the ith thermal power generating unit, ciStarting and stopping times of the ith unit in an operation period;
Gwtfor the wind-abandon cost of the wind power plant at time t, GstThe light abandoning cost of the photovoltaic power plant at the moment t is obtained;
Mdcadjusting the cost for the power of the direct current tie line at the time t;
the size constraints of the wind curtailment and the light curtailment of the receiving end of the power grid are expressed by an equation (6.2):
in the formula, gw,t、gs,tRespectively representing the abandoned wind and the abandoned light quantity of the wind power plant at the receiving end and the photovoltaic power station at the time t;
the active power balance constraint of the transmitting end is characterized by an equation (6.3):
Pgld,t≤Pgc,t+Pgw,t+Pgs,t-Pdc,t≤(1+α)Pgld,t (6.3)
in the formula Pgc,tThe output of the conventional power plant at the sending end represents the sum P of the outputs of the thermal power generating unitsgw,tFor output of wind power plant at delivery end, Pgs,tFor the output of the photovoltaic power plant at the delivery end, Pgld,tFor the load of the sending end system, alpha is the bearing capacity of the power grid system in the range meeting the safety standardThe maximum margin of (c);
the active power balance constraint of the receiving end is characterized by an equation (6.4):
Pald,t≤Pac,t+Paw,t-gw,t+Pas,t-gs,t+Pdc,t+Pr,t-Ppld,t-Pm,t≤(1+α)Pald,t (6.4)
in the formula Pac,tThe output of a receiving-end conventional power plant represents the sum P of the outputs of the thermal power generating unitsaw,tG for the output of the receiving wind power plantw,tAbandoning wind volume for wind power plant at receiving end, Pas,tG is the output of the receiving end photovoltaic power plants,tFor the amount of light rejected by the receiving photovoltaic power station, Pr,tFor pumped storage generator set output, Ppld,tFor the pumping load of the water pump set, the value is the step value, Pm,tFor receiver-excited demand-side response, Pald,tIs the receiving end system load;
the energy supply equipment constraint is represented by formulas (1.1), (1.2), (1.3), (2.1), (2.2), (2.3), (2.4) (3.1) (3.2) (3.3) (3.4);
the demand side response constraint is represented by formulas (4.1), (4.2) and (4.3);
the direct current tie line constraint is represented by formulas (5.1), (5.2), (5.3) and (5.4);
and solving to obtain the day-ahead operation optimization results of each energy supply device, the power of the direct current connecting line and the excited demand side response of the receiving end by using the obtained day-ahead prediction data of the new energy of the transmitting and receiving ends and the day-ahead prediction data of the load, determining the optimal operation plan of each energy supply device from the day-ahead operation optimization results, and establishing a peak shaving power supply distribution scheme with the optimal decision.
According to the technical scheme, the invention discloses a combined peak regulation optimization method of a direct current power grid transmitting and receiving end, which fully considers source and grid load constraints.
The direct current power grid transmitting and receiving end combined peak regulation optimization method considering source grid load constraint has the following beneficial effects:
the direct-current power grid transmitting and receiving end combined peak shaving model constructed by the invention fully considers the constraint conditions of the direct-current connecting line, pumped storage, demand side response and the like, realizes the optimal scheduling of the transmission power of the generator set and the direct-current connecting line in the direct-current power grid transmitting and receiving end combined peak shaving system, and enables the generator set to operate in the optimal working condition.
On the basis of ensuring the safe operation of a power grid at a transmitting end and a receiving end, the direct current delivery plan is flexibly adjusted, the direct current plan is arranged to undertake part of peak shaving tasks, the consumption of new energy is promoted, and the coordination optimization of the direct current delivery plan, the day-ahead unit combination of the new energy and the conventional energy and the power generation plan is realized.
The invention effectively improves the economy of the combined peak regulation system of the transmitting end and the receiving end of the direct current power grid, simultaneously fully excavates the peak regulation potential of the response of the receiving end demand side of the direct current power grid, realizes the peak clipping and valley filling and the load reduction of the electric load, reduces the energy cost of users, and improves the complementation and the cooperativity among various energy sources.
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FIG. 1 is a schematic flow diagram of the process of the present invention;
fig. 2 is a schematic structural diagram of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention.
As shown in fig. 1, the method for optimizing peak shaving at the transmitting and receiving ends of the dc power grid in consideration of source-grid load constraints in this embodiment includes:
s100, establishing power output models of a transmitting end and a receiving end of a power grid;
s200, acquiring load day-ahead prediction data of a transmitting end and a receiving end of the direct-current power grid;
s300, establishing a power grid receiving end excitation type demand side response model;
s400, establishing a direct current tie line power transmission model;
s500, establishing a transmitting-receiving end combined peak regulation optimization operation model, on the basis of meeting the constraint conditions and active power balance constraint, wind curtailment and light curtailment constraint of the transmitting-receiving end power output model in the step S100, the receiving-end excitation type demand side response model in the step S300 and the direct-current tie line transmission power model in the step S400, solving the day-ahead optimization operation results of the transmitting-receiving end power output plan, the direct-current tie line power transmission plan and the receiving-end excitation type demand side response regulation plan by using day-ahead prediction data of the load of the receiving end in the step S200 as an optimization target, determining the optimal operation plan of each energy supply device from the day-ahead optimization operation results, and establishing a peak regulation power distribution scheme with optimal decision.
The power output model of the power grid sending end and receiving end in step S100 includes: the method comprises the following steps that a new energy power generation power output model of a power grid sending end and a receiving end, a traditional thermal power generating unit output model of the power grid sending end and the receiving end and a pumped storage unit output model of the power grid receiving end are obtained;
step S200, acquiring load day-ahead prediction data of a transmitting end and a receiving end of a direct-current power grid; the load day-ahead prediction data of the transmitting end and the receiving end of the direct-current power grid are obtained by utilizing historical load data and through an interpolation method;
step S300, establishing a power grid receiving end excitation type demand side response model; the incentive type demand side response plan refers to that the high-load industry participates in system optimization scheduling by signing an agreement with a power grid, reduces the power demand in the peak period, increases the power demand in the valley period, relieves the pressure of the power grid and ensures safe operation.
Step S400, establishing a direct current tie line power transmission model; the direct current connecting line transmission plan is characterized in that on the premise of ensuring safe operation of a transmitting/receiving end power grid, a direct current delivery plan is flexibly adjusted, the direct current plan is arranged to undertake partial peak shaving tasks, new energy consumption is promoted, and coordination and optimization of the direct current delivery plan, the day-ahead unit combination of new energy and conventional energy and a power generation plan are achieved.
Step S500, establishing and solving a transmitting-receiving end combined peak regulation optimization operation model; the transmitting and receiving end combined peak regulation optimization operation model takes the lowest total operation cost of a power grid system as an optimization target, and takes active power balance constraint, wind abandonment, light abandonment quantity constraint, energy supply equipment constraint, demand side response constraint and direct current connecting line constraint as constraint conditions, and the day-ahead power optimization results of each energy supply equipment and each direct current connecting line of the transmitting and receiving end are obtained through solving, so that the optimized operation of the direct current power grid transmitting and receiving end combined peak regulation model is realized.
The following is described in detail with reference to fig. 2:
in specific implementation, new energy power generation power output models of a power grid transmitting end and a receiving end are established as follows:
the method comprises the steps of sorting historical output data of the wind power plant and the photovoltaic power station to obtain a daily output data set of the wind power plant and the photovoltaic power station, carrying out clustering analysis on the daily output data set of each month by using a k-means clustering algorithm, dividing the data set into k clusters, wherein the clustering center of each cluster is called a typical daily output state, and the number of data samples contained in each cluster represents the occurrence probability of the state.
Therefore, by integrating all the historical samples, the probability distribution value of each state, i.e. the state probability value, can be calculated by the following formula (1.1):
where N represents the number of samples in the dataset, ljRepresenting the number of samples in cluster j.
Thus, there are
Dividing the state S1，S2，…,SkCorresponding to [0, 1 ]]The interval length is the state probability value. Extraction of [0, 1 ] by random sampling]And (4) determining the typical daily output state according to the random numbers R uniformly distributed, and obtaining a random output model of the new energy. According to the simulation of the random number R, a large amount of forecast data of the day-ahead output of the wind power plant and the photovoltaic power station can be obtained.
The output conditions of the wind power generation and the photovoltaic power generation of the sending end and the receiving end are given by a new energy output model according to seasons and power.
The method comprises the following steps of establishing output models of the traditional thermal power generating units at the sending end and the receiving end of the power grid as follows:
the constraint of the upper limit and the lower limit of the active power of the thermal power generating unit is shown as a formula (2.1):
ui,tPi min≤Pi,t≤ui,tPi max (2.1)
in the formula, Pi max、Pi minRespectively the upper and lower active output limits u of the ith conventional uniti,tAnd the starting and stopping states of the ith conventional unit at the moment t.
It is subject to the climbing constraint as shown in equation (2.2):
-RDi≤(Pi,t-Pi,t-1)/Δt≤RUi (2.2)
in the formula, RDi、RUiAnd respectively limiting the up-down climbing speed of the ith conventional generator set, wherein delta t is the duration of the t period.
The start and stop constraints suffered by the method are as shown in a formula (2.3):
in the formula, Di、OiThe minimum shutdown time and the minimum startup time of the ith conventional generator set are respectively.
Establishing a pumped storage unit output model of a receiving end of a power grid in the following way:
the active power upper and lower limits of the pumped storage generator set and the water pump set are restricted as shown in formulas (3.1) and (3.2):
Pr min≤Pr,t≤Pr max (3.1)
in the formula, Pr,tOutput of pumped storage generator set at time t, Pr max、Pr minRespectively representing the active output upper limit and the active output lower limit of the pumped storage generator set; ppld,tIs the output of the water pump set at the moment t, Ppld max、Ppld minRespectively the active output upper and lower limits of the water pump unit.
Wherein the output of water pump unit is the step value:
Ppld,t＝pi×n (3.3)
pithe water pumping power of a single water pump.
The water balance constraint of the pumped storage unit is shown as the formula (3.4):
in the formula, Vt,Vpld,t,Vr,tRespectively the water storage amount of the reservoir at the time t, the water pumping amount of the water pump and the water consumption of the generator, Vt-ΔtThe water storage capacity of the reservoir at the previous moment Vmin，VmaxThe minimum and maximum water storage amounts of the reservoir are respectively obtained, the water storage amount of the reservoir can be obtained through iterative calculation, and for the reservoir adjusted according to the period, the water pumping amount and the water using amount in one period generally need to be basically balanced.
Acquiring load day-ahead prediction data of a transmitting end and a receiving end of a direct-current power grid as follows:
and selecting a typical daily load curve according to seasons, carrying out equal-ratio amplification on the load curve according to the power, and obtaining the day-ahead prediction data of the loads of the transmitting end and the receiving end of the power grid by using an interpolation method.
Establishing a power grid receiving end excitation type demand side response model as follows:
the demand side deployment cost is as shown in equation (4.1):
wherein T is the total number of time segments, NmResponding to the user quantity, rho, for the incentive type demand sidemCompensating the price per unit of electricity, P, for user mm,tTransfer load value, Δ t, for user mmThe duration is scheduled in units.
In order to ensure the normal production of the industrial users, the response quantity constraint that the incentive type demand side response needs to meet is shown as the formula (4.2):
in the formula, qm1，qm2，…，qmnFor a fixed load-value-shift gear, Q, of user mmThe maximum response capacity value for user m.
The load transfer balance constraint that the incentive type demand side response needs to satisfy is as shown in equation (4.3):
establishing a direct current connecting wire power transmission model according to the following modes:
on the basis of meeting the local load requirement, the sending end power supply transmits abundant electric quantity to the receiving end through a direct current connecting line to assist the sending end energy source to be absorbed and the receiving end to adjust the peak.
At present, a direct current outgoing power plan is mainly determined by trans-regional power market trading. In order to ensure the execution of the trade, the total direct current output electric quantity in the planning period is within the contract agreed range of the market trade.
The direct current transmission electric quantity constraint which needs to be met by the direct current tie line is shown as a formula (5.1):
in the formula: t ═ 1,2, …, T; pdc,tActive power of the DC link during time period t, Edc,maxAnd Edc,minThe maximum and minimum transaction electric quantity of the direct current line in the planning period T are respectively.
In actual dispatching operation, the day-ahead plan has the function of cross-region and cross-provincial large-range resource optimization configuration, frequency modulation requirements and power fluctuation of power grids at two sides do not need to be considered too much, the day-ahead transmission power plan of the direct-current connecting line is relatively stable, and frequent reciprocating adjustment is not needed; meanwhile, considering the limitation of factors such as direct current operation reliability, control feasibility and equipment operation life, the direct current connecting line should filter out factors such as burrs, sawteeth and frequent reciprocating fluctuation in a normal operation mode, that is, the transmission power should be in a step shape.
The switching power stepping constraint that the dc link needs to satisfy is as shown in equation (5.2):
Pdc,t∈{Pdc1,Pdc2,...,Pdcn} (5.2)
in the formula: pdc1,Pdc2,…,PdcnThe gear is adjusted for the fixed power of the dc link.
To maintain the stability of the dc plan, the dc plan is run smoothly for at least a minimum interval after a single adjustment (single or multiple successive periods of time up or down).
The adjustment interval constraint that the dc link needs to satisfy is as shown in equation (5.3):
in the formula, ctIs a 0-1 state variable indicating whether the DC link starts to adjust during a time period t, and J isThe dc link line has a minimum adjustment interval.
The planned adjustment rate of the direct current tie line in the adjacent time period cannot exceed the limit value of the direct current operation mode, and the adjustment rate constraint which needs to be met by the direct current tie line is as shown in a formula (5.4):
in the formula: rdc +And Rdc -Respectively the ascending rate limit value and the descending rate limit value of the direct current connecting line plan; Δ t is the duration of the t period.
Establishing and solving a direct-current power grid transmitting and receiving end combined peak regulation optimization operation model according to the following modes:
the transmitting and receiving end combined peak regulation optimization operation model takes the lowest total operation cost of a power grid system as an optimization target, and takes active power balance constraint, wind abandonment, light abandonment quantity constraint, energy supply equipment constraint, demand side response constraint and direct current connecting line constraint as constraint conditions, and the day-ahead power optimization results of each energy supply equipment and each direct current connecting line of the transmitting and receiving end are obtained through solving, so that the optimized operation of the direct current power grid transmitting and receiving end combined peak regulation model is realized.
The objective function of the run optimization model is characterized by equation (6.1):
in the formula (I), the compound is shown in the specification,
t represents the total time period number, and n is the number of thermal power generating units;
fi(. is a power generation cost function of the ith thermal power generating unit, Pi,tThe optimal output of the ith unit at the time t is obtained;
mifor the start-stop loss of the ith thermal power generating unit, ciStarting and stopping times of the ith unit in an operation period;
Gwtfor the wind-abandon cost of the wind power plant at time t, GstThe light abandoning cost of the photovoltaic power plant at the moment t is obtained;
Mdcadjusting the cost for the power of the direct current tie line at the time t;
the size constraints of the wind curtailment and the light curtailment of the receiving end of the power grid are expressed by an equation (6.2):
in the formula, gw,t、gs,tAnd respectively representing the abandoned wind and the abandoned light quantity of the wind power plant at the receiving end and the photovoltaic power station at the time t.
The active power balance constraint of the transmitting end is characterized by an equation (6.3):
Pgld,t≤Pgc,t+Pgw,t+Pgs,t-Pdc,t≤(1+α)Pgld,t (6.3)
in the formula Pgc,tThe output of the conventional power plant at the sending end represents the sum P of the outputs of the thermal power generating unitsgw,tFor output of wind power plant at delivery end, Pgs,tFor the output of the photovoltaic power plant at the delivery end, Pgld,tFor the load of the sending end system, α is the maximum margin that the grid system can bear within the range that meets the safety standard.
The active power balance constraint of the receiving end is characterized by an equation (6.4):
Pald,t≤Pac,t+Paw,t-gw,t+Pas,t-gs,t+Pdc,t+Pr,t-Ppld,t-Pm,t≤(1+α)Pald,t (6.4)
in the formula Pac,tThe output of a receiving-end conventional power plant represents the sum P of the outputs of the thermal power generating unitsaw,tG for the output of the receiving wind power plantw,tAbandoning wind volume for wind power plant at receiving end, Pas,tG is the output of the receiving end photovoltaic power plants,tFor the amount of light rejected by the receiving photovoltaic power station, Pr,tFor pumped storage generator set output, Ppld,tFor the pumping load of the water pump set, the value is the step value, Pm,tFor receiver-excited demand-side response, Pald,tIs the load of the receiving end system.
The energy supply equipment constraint is represented by formulas (1.1), (1.2), (1.3), (2.1), (2.2), (2.3), (2.4) (3.1) (3.2) (3.3) (3.4);
the demand side response constraints are represented by equations (4.1), (4.2) and (4.3).
The direct current tie line constraints are represented by formulas (5.1), (5.2), (5.3) and (5.4).
And solving to obtain the day-ahead operation optimization results of each energy supply device, the power of the direct current connecting line and the excited demand side response of the receiving end by using the obtained day-ahead prediction data of the new energy of the transmitting and receiving ends and the day-ahead prediction data of the load, determining the optimal operation plan of each energy supply device from the day-ahead operation optimization results, and establishing a peak shaving power supply distribution scheme with the optimal decision.
In conclusion, the method provided by the embodiment of the invention can effectively improve the economy of the peak shaving operation of the direct-current-accessed transmitting and receiving end power grid and the consumption rate of new energy.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A direct current power grid transmitting and receiving end combined peak regulation optimization method considering source grid load constraint is characterized by comprising the following steps: the method comprises the following steps:
s100, establishing power output models of a transmitting end and a receiving end of a power grid;
s200, acquiring load day-ahead prediction data of a transmitting end and a receiving end of the direct-current power grid;
s300, establishing a power grid receiving end excitation type demand side response model;
s400, establishing a direct current tie line power transmission model;
s500, establishing a transmitting-receiving end combined peak regulation optimization operation model, on the basis of meeting the constraint conditions and active power balance constraint, wind curtailment and light curtailment constraint of the transmitting-receiving end power output model in the step S100, the receiving-end excitation type demand side response model in the step S300 and the direct-current connecting line transmission power model in the step S400, solving the day-ahead optimization operation results of the transmitting-receiving end power output plan, the direct-current connecting line power transmission plan and the receiving-end excitation type demand side response regulation plan by using day-ahead prediction data of the load of the receiving end in the step S200 as an optimization target, determining the optimal operation plan of each energy supply device, and establishing a peak regulation power distribution scheme with optimal decision;
wherein, S500 specifically includes:
the objective function of the run optimization model is characterized by equation (6.1):
in the formula (I), the compound is shown in the specification,
t represents the total time period number, and n is the number of thermal power generating units;
fi(. is a power generation cost function of the ith thermal power generating unit, Pi,tThe optimal output of the ith unit at the time t is obtained;
mifor the start-stop loss of the ith thermal power generating unit, ciStarting and stopping times of the ith unit in an operation period;
Gwtfor the wind-abandon cost of the wind power plant at time t, GstThe light abandoning cost of the photovoltaic power plant at the moment t is obtained;
Mdcadjusting the cost for the power of the direct current tie line at the time t;
the size constraints of the wind curtailment and the light curtailment of the receiving end of the power grid are expressed by an equation (6.2):
in the formula, gw,t、gs,tRespectively representing the abandoned wind and the abandoned light quantity of the wind power plant at the receiving end and the photovoltaic power station at the time t;
the active power balance constraint of the transmitting end is characterized by an equation (6.3):
Pgld,t≤Pgc,t+Pgw,t+Pgs,t-Pdc,t≤(1+α)Pgld,t (6.3)
in the formula Pgc,tThe output of the conventional power plant at the sending end represents the sum P of the outputs of the thermal power generating unitsgw,tFor output of wind power plant at delivery end, Pgs,tFor the output of the photovoltaic power plant at the delivery end, Pgld,tFor the load of the sending end system, alpha is the maximum margin that the power grid system can bear in the range meeting the safety standard;
the active power balance constraint of the receiving end is characterized by an equation (6.4):
Pald,t≤Pac,t+Paw,t-gw,t+Pas,t-gs,t+Pdc,t+Pr,t-Ppld,t-Pm,t≤(1+α)Pald,t (6.4)
in the formula Pac,tThe output of a receiving-end conventional power plant represents the sum P of the outputs of the thermal power generating unitsaw,tG for the output of the receiving wind power plantw,tAbandoning wind volume for wind power plant at receiving end, Pas,tG is the output of the receiving end photovoltaic power plants,tFor the amount of light rejected by the receiving photovoltaic power station, Pr,tFor pumped storage generator set output, Ppld,tFor the pumping load of the water pump set, the value is the step value, Pm,tFor receiver-excited demand-side response, Pald,tIs the receiving end system load;
and solving to obtain the day-ahead operation optimization results of each energy supply device, the power of the direct current connecting line and the excited demand side response of the receiving end by using the obtained day-ahead prediction data of the new energy of the transmitting and receiving ends and the day-ahead prediction data of the load, determining the optimal operation plan of each energy supply device from the day-ahead operation optimization results, and establishing a peak shaving power supply distribution scheme with the optimal decision.
2. The direct-current power grid transmitting and receiving end joint peak regulation optimization method considering source grid load constraint according to claim 1, characterized by comprising the following steps:
in step S100, the power output model of the transmitting end and the receiving end of the power grid includes: the system comprises a new energy power generation power output model of a power grid sending end and a receiving end, a traditional thermal power generating unit output model of the power grid sending end and the receiving end, and a pumped storage unit output model of the power grid receiving end.
3. The direct-current power grid transmitting and receiving end joint peak regulation optimization method considering source grid load constraint according to claim 2, characterized in that: in the step S100, new energy power generation power output models of a power grid transmitting end and a receiving end are established according to the following method:
acquiring and sorting historical output data of a wind power plant and a photovoltaic power station to obtain daily output data sets of the wind power plant and the photovoltaic power station, performing clustering analysis on the daily output data sets of each month by using a k-means clustering algorithm, dividing the data sets into k clusters, wherein the clustering center of each cluster is called a typical daily output state, and the number of data samples contained in each cluster represents the occurrence probability of the state;
therefore, by integrating all the historical samples, the probability distribution value of each state, i.e. the state probability value, is calculated by the formula (1.1):
where N represents the number of samples in the dataset, ljRepresenting the number of samples in the cluster j;
thus, there are
Dividing the state S1，S2，…,SkCorresponding to [0, 1 ]]The length of the interval is the state probability value; extraction of [0, 1 ] by random sampling]And (4) determining the typical daily output state according to the random numbers R uniformly distributed, and obtaining a random output model of the new energy.
4. The direct current power grid transmitting and receiving end joint peak regulation optimization method considering source grid load constraint according to claim 3, characterized in that: step S100 is to establish a traditional thermal power generating unit output model of the grid transmitting end and receiving end as follows:
the constraint of the upper limit and the lower limit of the active power of the thermal power generating unit is shown as a formula (2.1):
ui,tPi min≤Pi,t≤ui,tPi max (2.1)
in the formula, Pi max、Pi minRespectively the upper and lower active output limits u of the ith conventional uniti,tStarting and stopping the ith conventional unit at the time t;
it is subject to the climbing constraint as shown in equation (2.2):
-RDi≤(Pi,t-Pi,t-1)/Δt≤RUi (2.2)
in the formula, RDi、RUiRespectively limiting the up-down climbing rate of the ith conventional generator set, wherein delta t is the duration of the t period;
the start and stop constraints suffered by the method are as shown in a formula (2.3):
in the formula, Di、OiThe minimum shutdown time and the minimum startup time of the ith conventional generator set are respectively.
5. The direct current power grid transmitting and receiving end joint peak regulation optimization method considering source grid load constraint according to claim 4, characterized in that:
the step S100 is to establish a pumped storage unit output model of the receiving end of the power grid according to the following method:
the active power upper and lower limits of the pumped storage generator set and the water pump set are restricted as shown in formulas (3.1) and (3.2):
Pr min≤Pr,t≤Pr max (3.1)
in the formula, Pr,tOutput of pumped storage generator set at time t, Pr max、Pr minRespectively representing the active output upper limit and the active output lower limit of the pumped storage generator set; ppld,tIs the output of the water pump set at the moment t, Ppld max、Ppld minRespectively the upper limit and the lower limit of the active output of the water pump unit;
wherein the output of water pump unit is the step value:
Ppld,t＝pi×n (3.3)
pithe water pumping power of a single water pump;
the water balance constraint of the pumped storage unit is shown as the formula (3.4):
in the formula, Vt,Vpld,t,Vr,tRespectively the water storage amount of the reservoir at the time t, the water pumping amount of the water pump and the water consumption of the generator, Vt-ΔtThe water storage capacity of the reservoir at the previous moment Vmin，VmaxRespectively the minimum and maximum water storage capacity of the reservoir.
6. The direct current power grid transmitting and receiving end joint peak regulation optimization method considering source grid load constraint according to claim 5, characterized in that:
step S200 is to obtain the load day-ahead prediction data of the transmitting end and the receiving end of the dc power grid as follows:
and selecting a typical daily load curve according to seasons, carrying out equal-ratio amplification on the load curve according to the power, and obtaining the day-ahead prediction data of the loads of the transmitting end and the receiving end of the power grid by using an interpolation method.
7. The direct current power grid transmitting and receiving end joint peak regulation optimization method considering source grid load constraint according to claim 6, characterized in that:
step S300 is to establish a power grid receiving end excitation type demand side response model as follows:
the demand side deployment cost is as shown in equation (4.1):
wherein T is the total number of time segments, NmResponding to the user quantity, rho, for the incentive type demand sidemCompensating the price per unit of electricity, P, for user mm,tTransfer load value, Δ t, for user mmScheduling a time length for a unit;
the response quantity constraint satisfied by the incentive type demand side response is shown as equation (4.2):
in the formula, qm1，qm2，…，qmnFor a fixed load-value-shift gear, Q, of user mmThe maximum response capacity value for user m;
the load transfer balance constraint satisfied by the incentive type demand side response is as shown in equation (4.3):
8. the direct current power grid transmitting and receiving end joint peak regulation optimization method considering source grid load constraint according to claim 7, characterized in that:
in step S400, a dc link power transmission model is established as follows:
the direct current transmission electric quantity constraint satisfied by the direct current tie line is shown as a formula (5.1):
in the formula: t ═ 1,2, …, T; pdc,tActive power of the DC link during time period t, Edc,maxAnd Edc,minMaximum and minimum transaction electric quantity of the direct current line in a planning period T respectively;
the switching power stepping constraint satisfied by the dc link is as shown in equation (5.2):
Pdc,t∈{Pdc1,Pdc2,...,Pdcn} (5.2)
in the formula: pdc1,Pdc2,…,PdcnAdjusting the gear for the fixed power of the direct current tie line;
the adjustment interval constraint satisfied by the dc link is as shown in equation (5.3):
in the formula, ctIs a 0-1 state variable indicating whether the direct current tie line starts to adjust or not in a time period t, and J is a minimum adjustment interval of the direct current tie line;
the regulation rate constraint that the dc link needs to satisfy is as shown in equation (5.4):
in the formula: rdc +And Rdc -Respectively the ascending rate limit value and the descending rate limit value of the direct current connecting line plan; Δ t is the duration of the t period.
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