CN115713197A - Power system load-storage combined optimization scheduling method considering wind power uncertainty - Google Patents
Power system load-storage combined optimization scheduling method considering wind power uncertainty Download PDFInfo
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Abstract
The invention discloses a power system load-storage combined optimization scheduling method considering wind power uncertainty, which comprises the following steps: analyzing the uncertainty of the wind power output, and establishing a wind power prediction model; establishing a load-storage combined optimization scheduling model of the power system; constructing peak-shaving effect indexes of the power system, wherein the peak-shaving effect indexes comprise load peak-valley difference, load peak-valley difference rate and abandoned wind power quantity; and solving the load-storage combined optimization scheduling model of the power system to obtain an optimal scheduling scheme. According to the method, through the power system load-storage combined optimization scheduling model, the minimum cost and the minimum peak-valley difference rate are taken as optimization targets, the energy storage operation constraint, the flexible load response constraint and the rotation standby constraint are increased, the peak regulation effect index of the power system is constructed, the optimization scheduling model is further solved to obtain the optimal power system scheduling scheme, and the smaller wind curtailment loss and the load peak-valley difference rate are realized.
Description
Technical Field
The invention belongs to the field of optimal scheduling of power systems, and particularly relates to a power system load-storage combined optimal scheduling method considering wind power uncertainty.
Background
The basic principle that the flexible load and the energy storage system participate in peak regulation optimization of the power system is that peak shifting and valley filling are carried out, energy in the energy storage system has the characteristic of bidirectional fluidity, and when the load of the power system is low, the surplus power is stored; when the load of the power system is high, the energy is released to generate electricity so as to relieve the load pressure of the power grid. The flexible load contributes to the peak load regulation of the power grid mainly through active response participating in peak load shifting.
Compared with the traditional thermal power generating unit peak regulation, the peak regulation mode based on the coordination of flexible load response and the energy storage system can reduce the output of the thermal power generating unit, reduce carbon emission and improve the wind power consumption capability of the system. With the rapid development of novel electric power products, the flexible load scale represented by an electric automobile keeps high-speed growth within a certain time, and the flexible load and the energy storage system are combined to adjust the peak so as to have considerable prospects in promoting wind power consumption and optimizing the operation of an electric power system.
Wind power output has the characteristics of randomness, volatility and intermittence, along with the continuous increase of power peak load and the increasing enlargement of wind power scale, the uncertainty of the output seriously threatens the safety and stability of the operation of a power system, and simultaneously, the wind power anti-peak regulation characteristic and the wind abandon phenomenon caused by the insufficient peak regulation capacity of the system cause huge economic loss.
Most of the existing peak regulation modes are energy storage system assisted thermal power generating units to participate in deep peak regulation, although a peak regulation system taking the thermal power generating units as a main body is mature, the actual peak regulation depth and the economical efficiency are not ideal, and the problem of extra carbon emission caused by the thermal power peak regulation units is also contrary to the aim of converting a power system into clean low carbon. Therefore, a new peak shaving mode optimized scheduling meeting the power load development trend is needed.
Disclosure of Invention
Aiming at the problems, the invention provides a load-storage combined optimization scheduling method for a power system, which considers wind power uncertainty, and combines a flexible load and an energy storage system to improve the peak load regulation capacity of the power system; a time-sharing response control strategy is adopted, so that the flexibility of the flexible load in the power system is improved; energy storage operation constraint, flexible load response constraint and rotation standby constraint are added in a power system load-storage combined optimization scheduling model, peak-load-regulation effect indexes of a power system are constructed, an optimal power system scheduling scheme is obtained by solving the optimization scheduling model, and smaller wind curtailment loss and load peak-valley difference rate are achieved.
The technical scheme of the invention is that the load-storage joint optimization scheduling method of the power system considering the wind power uncertainty comprises the following steps:
step 1: analyzing the uncertainty of the wind power output, and establishing a wind power prediction model;
and 2, step: establishing a load and storage combined optimization scheduling model of the power system;
step 2.1: quantifying the scheduling cost of the flexible load and the energy storage system, and determining a target function of a power system load and storage combined optimization scheduling model;
step 2.2: constructing constraint conditions of the optimized scheduling model, wherein the constraint conditions comprise power balance constraint, unit output constraint, energy storage operation constraint, flexible load response constraint and rotation standby constraint;
and 3, step 3: constructing peak-shaving effect indexes of the power system, wherein the peak-shaving effect indexes comprise load peak-valley difference, load peak-valley difference rate and abandoned wind power quantity;
and 4, step 4: and solving the load-storage combined optimization scheduling model of the power system to obtain an optimal scheduling scheme.
Preferably, the power system load storage joint optimization scheduling method adopts a time-sharing response control strategy, the flexible load users and the power grid company sign a power utilization protocol, a response time interval is appointed in the power utilization protocol, the flexible load users reduce the power of the power load when meeting a load peak of the power system in the response time interval, and the flexible load users increase the power of the power load when meeting a valley of the power system, so that the flexibility of the flexible load in the power system is improved.
Preferably, the uncertainty of the wind power output is described by using a wind power model based on Beta distribution, the Beta distribution can be approximate to various biased distributions and symmetric distributions by setting parameters, the wind power prediction error can be more accurately described when the installed capacity of the wind power is large, and the cumulative probability distribution function is as follows:
wherein x represents a random variable, B (. Alpha.) w ，β w ) Is at α w And beta w Beta function as a parameter, alpha w 、β w Are all shape parameters;
the mean value E (x) of x, which affects the property of the Beta function distribution, is expressed as:
the variance D (x) is expressed as:
shape parameter alpha w Can be expressed as:
shape parameter beta w Can be expressed as:
preferably, the objective function of the power system load-storage joint optimization scheduling model comprises a cost minimum objective and a peak-to-valley difference rate minimum objective.
The dual targets are converted into single targets by a linear weighting method as follows:
minF＝f 1 +αf 2 (30)
in the formula f 1 Is a minimum function of the combined cost of the power system, f 2 Is a minimum function of the peak-to-valley rate of daily load, and alpha isA weight coefficient;
complex cost f of electric power system 1 The expression of (a) is as follows:
f 1 ＝f 11 +f 12 +f 13 (31)
f 11 for the unit operating costs, f 12 Represents the energy storage system cost; f. of 13 Representing flexible load scheduling costs;
in the formula of alpha i 、β i 、γ i All are thermal power generating unit operation cost parameters; sigma is a wind curtailment penalty factor; PC (personal computer) wind The unit wind power operation and maintenance cost; n is a radical of 1 The number of time segments divided in one day; Δ t is the length of each time interval; p ij,tpu Outputting power for the thermal power generating unit; p ij,wind Output P for wind turbine ij,qwind The power is the wind curtailment power.
When the wind power exceeds the wind power receiving space provided by the energy storage system and the thermal power generating unit, a wind abandoning phenomenon is generated, and the calculation mode of the wind abandoning power is as follows:
in the formula P j,qwind The wind curtailment power is j time period; p j,wind Planning output power of the wind power for the period of j; CY j,wind A wind power receiving space in a period of j; p j,load Load demand for period j; ST (ST) ij,H A state vector for the incentive load consumer; p ij,H The excitation power of the interruptible user i in the j period; ST (ST) ij,I A state vector for the incentive load consumer; n is a radical of 2 The number of users capable of interrupting the load for the system; n is a radical of 3 The number of users of the system incentive load; p ij,I The interruption power of the interruptible user i in the j period; p j,c Charging efficiency of the energy storage system in j time period; p j,d Discharging power for the energy storage system in j time period; eta ess For energy storage systemsCharge-discharge efficiency;
the minimum value of the j time interval output of the ith thermal power generating unit is obtained; n is a radical of 4 Indicating the number of thermal power generating units, N 5 Representing the number of wind turbines.
Energy storage system cost f 12 The expression of (a) is as follows:
in the formula C inv_e Investment cost per unit capacity of the energy storage system; c inv_p Investment cost per unit power; m is the service life of the energy storage system; p ess To invest in rated power, CY ess To invest in rated capacity; c om_p The daily operation and maintenance cost of unit power; c om_q The operation and maintenance cost is the unit charge and discharge capacity; p j,ess The output power of the energy storage device in the j time period; PC (personal computer) j,ele The price of electricity in the jth time interval; PC (personal computer) grt Is the subsidy price per discharge amount; Δ t is the length of each period.
Flexible load scheduling cost f 13 The expression of (a) is as follows:
wherein Coe i,I An interruption load compensation coefficient for the i user; coe ij,H Increasing the excitation factor of the load for the i user.
Peak-to-valley rate of daily load f 2 Expressed as:
in the formula P loadmax The optimized daily load peak value is obtained; p is loadmin For the optimized daily load valley, the optimized daily load calculation mode is as follows:
in the formula P jload0 To optimize the load of the previous day, P j,ess Output for energy storage system, P ij,I To interruptible load responsive power, P ij,H Responding to power for the excitation load.
Further, in step 2.2, the power balance constraint expression is as follows:
in the formula P j,load0 Is the day-ahead load demand.
In step 2.2, the generating set comprises a thermal power generating set and a wind power generating set, the thermal power generating set constraint comprises thermal power generating set climbing constraint and wind power output constraint, the wind power output constraint is given by Beta distribution,
(1) And (3) constraining the upper and lower output limits of the thermal power generating unit:
in the formula
And respectively representing the lower limit and the upper limit of the output of the ith thermal power generating unit.
(2) And (3) the climbing rate of the thermal power generating unit is restrained:
-60SPD down ≤P j,tpu -P j-1,tpu ≤60SPD up (16)
SPD in the formula down 、SPD up Respectively representing the maximum descending speed and the maximum ascending speed of the active output of the unit per minute.
(3) Wind power output restraint:
the uncertainty of the actual output of the wind power is represented by probability constraint:
wherein PR represents the probability of an event; p wind,j Planned wind power output for the period of j; w is a j The output is a random variable and represents the actual output of wind power in a period of j; rho is a confidence level and represents the probability that the planned wind power output can be realized; CY wind And the installed capacity of the wind power plant.
Preferably, in step 2.2, the energy storage system operation constraints include:
(1) Energy storage capacity and rated power constraint:
the energy storage system is restricted by factors such as site, investment scale and the like, and the capacity and rated power of the energy storage system are limited:
in the formula P max The maximum value of the rated power of the energy storage system; CY max The maximum value of the capacity of the energy storage system;
(2) Charge and discharge power constraint:
assuming that the energy storage system uses a bidirectional power converter, the maximum power can reach the rated power when the energy storage system is charged and discharged, the output of the energy storage system meets the following requirements:
-P ess ≤P j,ess ≤P ess (19)
(3) Charge-discharge balance constraint:
in order to ensure the stability of the operation of the energy storage system, the daily charge quantity and the daily discharge quantity of the energy storage system need to be kept consistent, otherwise, the charge state of the battery rises or falls day by day to reach the charge and discharge limit.
The daily charge and discharge capacity of the energy storage system meets the following requirements:
(4) And (3) state of charge constraint:
charge of energy storage batteryElectric state S j The set upper and lower limits cannot be exceeded:
SOC min ≤SOC j ≤SOC max (21)
in the formula SOC min 、SOC max Respectively representing the lower limit and the upper limit of the state of charge of the energy storage battery; SOC j And the state of charge of the energy storage battery after the jth time interval is ended.
SOC j Is calculated as follows:
in the formula SOC 0 The initial value of the SOC of the energy storage battery every day is obtained; eta ess The charging and discharging efficiency of the energy storage system is improved.
Further, the compliant load response constraint includes:
1) The interruptible load response power satisfies:
0≤P ij,I ≤P ij,Imax (23)
in the formula P ij,Imax Represents an upper interruptible power limit;
2) The excitation load response power satisfies the following conditions:
0≤P ij,H ≤P ij,Hmax (24)
in the formula P ij,Hmax Representing the excitation power upper limit.
Preferably, in step 2.2, the spinning reserve constraints are as follows:
in the formula
Respectively representing the positive and negative rotation reserve capacities provided by the system for wind power, and the reserve capacities are borne by the thermal power generating unit; e denotes the mathematical expectation operator.
Preferably, in step 2.2, the peak shaving effect index includes:
(1) The difference between the peak and the valley of the load,
load peak to valley difference Δ P load The difference between the maximum load and the minimum load of the power system in a dispatching day is defined as the following calculation formula:
ΔP load ＝P load,max -P load,min (26)
in the formula P load,max The optimized daily load peak value is obtained; p load,min The optimized daily load valley value.
The optimized daily load calculation mode is as follows:
in the formula P j,load0 For day-ahead load power, P j,ess For the energy storage system to exert a force, P ij,I To interruptible load responsive power, P ij,H Responding to power for the excitation load.
(2) The peak-to-valley difference rate of the load,
load peak-to-valley difference rate eta load The peak-to-valley difference of the power system in a dispatching day is defined as the ratio of the peak-to-valley difference to the maximum load, and the calculation formula is as follows:
(3) The wind power is abandoned,
wind power abandon E qwind The calculation formula of (c) is as follows:
in the formula P j,qwind The wind curtailment power for period j.
Preferably, a Cplex solver is adopted to solve the model through the power system load-storage combined optimization scheduling model.
Compared with the prior art, the invention has the beneficial effects that:
1) According to the method, through the power system load-storage combined optimization scheduling model, the minimum cost and the minimum peak-valley difference rate are taken as optimization targets, the energy storage operation constraint, the flexible load response constraint and the rotation standby constraint are increased, the peak regulation effect index of the power system is constructed, the optimization scheduling model is further solved to obtain the optimal power system scheduling scheme, and the smaller wind curtailment loss and the load peak-valley difference rate are realized.
2) The method is based on a time-sharing response control strategy, the internet-surfing time period of the flexible load is controlled, the flexible load and the energy storage system are simultaneously used as peak-shaving resources to participate in peak shaving and valley filling of the system, and the method has high application potential under the background of high-speed increase of the flexible load.
3) Compared with the existing peak regulation mode with high carbon emission, the peak load shifting method realizes peak load shifting with zero carbon emission on the premise of not increasing the cost, is more environment-friendly while digging the peak regulation potential of a flexible load, and is favorable for accelerating the transformation of an electric power system to clean low carbon.
4) Compared with single target scheduling, the method has the advantages that the system operation cost and the peak regulation effect are considered under the premise of considering the wind power uncertainty, the balance between the cost control and the peak regulation effect is obtained, and the method has practical significance.
5) According to the method, the wind power uncertainty model based on Beta distribution corrects the system rotation standby, so that the system rotation standby is considered more comprehensively under the condition of high-proportion wind power grid connection, and the actual requirements are met.
6) According to the invention, under the condition of high-proportion wind power network access, the wind power receiving space of the power system is enlarged by combining the flexible load and the energy storage system, the consumption of the power system on wind power generation is increased, and the phenomenon of wind abandon is reduced.
Drawings
The invention is further illustrated by the following figures and examples.
Fig. 1 is a flowchart illustrating a system load-store joint optimization scheduling method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of the principle of peak shaving by combining the flexible load and the energy storage system according to the embodiment of the present invention.
Fig. 3 is a graph of time of use electricity prices for an embodiment of the present invention.
Fig. 4 is a graph of predicted wind power output and daily load according to the embodiment of the present invention.
Fig. 5a is a histogram of the crew contribution of the scheduling scheme of scenario one in the embodiment of the present invention.
Fig. 5b is a graph of the energy storage system state of charge of the scheduling scheme of scenario one in the embodiment of the present invention.
Fig. 5c is a power curve diagram of the energy storage system of the scheduling scheme of scenario one in the embodiment of the present invention.
Fig. 6 is a histogram of the unit contribution of the scheduling scheme of scenario two in the embodiment of the present invention.
Fig. 7a is a histogram of the crew contribution of the scheduling scheme of scenario three in the embodiment of the present invention.
Fig. 7b is a graph of the energy storage system state of charge of the scheduling scheme of scenario three in the embodiment of the present invention.
Fig. 7c is a power curve diagram of the energy storage system of the scheduling scheme of scenario three in the embodiment of the present invention.
Detailed Description
As shown in fig. 1 to 4, the power system load-storage joint optimization scheduling method considering wind power uncertainty includes:
step 1: analyzing the uncertainty of the wind power output, and establishing a wind power prediction model;
step 2: establishing a load-storage combined optimization scheduling model of the power system;
step 2.1: quantifying the scheduling cost of the flexible load and the energy storage system, and determining a target function of a power system load and storage combined optimization scheduling model;
step 2.2: constructing constraint conditions of the optimized scheduling model, wherein the constraint conditions comprise power balance constraint, unit output constraint, energy storage operation constraint, flexible load response constraint and rotation standby constraint;
and step 3: constructing peak regulation effect indexes of the power system, wherein the peak regulation effect indexes comprise load peak-valley difference, load peak-valley difference rate and abandoned wind power quantity;
and 4, step 4: and solving the load-storage combined optimization scheduling model of the power system to obtain an optimal scheduling scheme.
In the embodiment, the interruptible load is adopted to reduce or interrupt the load during the peak period of power utilization, so as to participate in peak clipping of the power system; the use of the excitation load can increase the power load during the power consumption valley period and participate in the valley filling of the power system, as shown in fig. 2. The embodiment adopts time-of-use electricity price, as shown in figure 3. In an embodiment, the predicted wind power output and daily load are shown in fig. 4.
In the embodiment, through comparison of 3 peak shaving scenes, namely scene one, scene two and scene three, the model is proved to have better peak shaving effect, lower wind power abandonment, reduced comprehensive scheduling cost and more practical benefit compared with the peak shaving of the energy storage system and the peak shaving of the flexible load.
Scenario description of the embodiments:
scene one: and the power system only takes the energy storage system as peak shaving resource optimization scheduling.
Scene two: and the power system only takes the flexible load as peak shaving resource for optimal scheduling.
Scene three: and the power system simultaneously takes the energy storage system and the flexible load as peak shaving resource joint optimization scheduling.
The calculation example of the embodiment comprises an 8-machine system with thermal power, wind power, energy storage and flexible load. The basic situation is 6 thermal power generating units, and the installed capacity is 2100MW;2 wind power units, installed capacity 1100MW. The total installed capacity is 3200MW, wherein the wind power installed capacity accounts for 34.38 percent of the total capacity. The wind curtailment penalty factor is 50 Rm/MW, the confidence level rho is set to be 0.97, and specific parameters of the thermal power generating unit are shown in the table 1.
TABLE 1 thermal power generating unit parameter table
Flexible loads can be classified into interruptible loads and excitation loads according to different management modes, and the interruptible loads and the excitation loads have the following remarkable characteristics: firstly, the response is fast, and the user scheduling measures such as load control and the like can play a role of fast standby; secondly, the dispatching cost is low, and the investment pressure of power plant extension and peak shaving power supply increase can be relieved; third, the power utilization will of the user can be reflected, the power utilization mode is changed according to the user's will, and the power utilization resource allocation is reasonably scheduled.
In the embodiment, a time-sharing response control strategy is used as one of means for controlling the flexible loads to participate in power grid peak shaving, when a load peak or a load valley is detected to be about to come through a control center of a power system, peak shaving requirements are transmitted to distributed flexible load users, then a power controller equipped for each flexible load user can judge whether the current requirement time period is in an appointed response time period which is agreed with a power grid company, if the current requirement time period is in the response time period, the power consumption of the users is increased or reduced according to an agreement, and if the current requirement time period is not in the response time period, the response is refused. Since each flexible load user has a different contract with the grid company, the total power that the flexible load can respond to at different time periods varies.
The flexible load parameters comprise an interruption upper limit, an interruption compensation coefficient, an excitation upper limit and an excitation coefficient which are agreed by the interruptible user and the excitation user and the power grid signing agreement, and the flexible load parameters are shown in the table 2 in the response period.
TABLE 2 Flexible load parameter Table
The energy storage system can store electric energy through a certain medium, can absorb energy from a power grid in advance and store the energy, and releases the stored energy to generate electricity when needed, and the comprehensive cost of the energy storage system comprises investment cost, operation and maintenance cost and energy storage investment return.
The investment cost of the energy storage system is positively correlated with the rated capacity of the energy storage system; the number of auxiliary equipment is related to the scale of the energy storage system, and the investment cost of the part can be approximately positively related to the rated capacity of the energy storage system; the investment cost of the power converter is approximately positively correlated with the rated (maximum) power thereof; the operation and maintenance costs of the energy storage system comprise relatively fixed manpower and technical costs related to the rated power of the energy storage system, maintenance costs related to the charge and discharge amount of the energy storage system and the like.
The energy storage system parameters include the rated capacity of the energy storage system, the power of the energy storage system, the SOC upper and lower limits of the battery and the like, and the specific parameters are shown in Table 3. TABLE 3 energy storage System parameter Table
And (3) establishing a multi-objective optimization model by utilizing matlab, and solving the model by using a Cplex solver.
Analysis of calculation results of the examples: the daily scheduling result of scenario one is shown in fig. 5a, 5b, 5 c; the daily scheduling result of scenario two is shown in fig. 6; the daily scheduling results for scenario three are shown in fig. 7a, 7b, and 7c, and the calculation result data are shown in table 4.
TABLE 4 optimization results table
Therefore, in the third scene, the total cost is reduced by 3.54% compared with the second scene, and the energy storage cost is reduced by 12.24% compared with the first scene, because the flexible load participates in scheduling, the peak load regulation pressure of part of energy storage systems is shared, and the output of the thermal power generating unit is also reduced. The scheduling scheme system jointly optimized by the flexible load and the energy storage system has the lowest running total cost, the optimized load peak-valley difference is reduced to 643MW from 1088MW, the reduction range is 40.9%, and the effect is obvious. Meanwhile, the electric power system has the minimum wind abandoning power, and the flexible load and the energy storage system are optimized in a combined mode to improve the peak regulation capacity range of the system more obviously.
In conclusion, the scheduling method of the invention, namely the scheduling method of scene three, has the peak shaving benefits of the flexible load and the energy storage system, can effectively reduce the influence of the wind power anti-peak shaving effect on the wind power consumption capability of the system, reduces the wind power abandonment quantity of the system, and simultaneously more effectively finishes peak shaving and valley filling.
Claims (9)
1. The power system load-storage combined optimization scheduling method considering wind power uncertainty is characterized by comprising the following steps of:
step 1: analyzing the uncertainty of the wind power output, and establishing a wind power prediction model;
step 2: establishing a load-storage combined optimization scheduling model of the power system;
step 2.1: quantifying the scheduling cost of the flexible load and the energy storage system, and determining a target function of a power system load and storage combined optimization scheduling model;
step 2.2: constructing constraint conditions of the optimized scheduling model, wherein the constraint conditions comprise power balance constraint, unit output constraint, energy storage operation constraint, flexible load response constraint and rotation standby constraint;
and step 3: constructing peak-shaving effect indexes of the power system, wherein the peak-shaving effect indexes comprise load peak-valley difference, load peak-valley difference rate and abandoned wind power quantity;
and 4, step 4: and solving the load-storage combined optimization scheduling model of the power system to obtain an optimal scheduling scheme.
2. The power system load-storage joint optimization scheduling method of claim 1, wherein in step 1, the wind power model based on the Beta distribution is used to describe the uncertainty of the wind power output, and by setting parameters, the Beta distribution can approximate various biased distributions and symmetric distributions, so that the wind power prediction error can be more accurately described when the installed wind power capacity is large, and the cumulative probability distribution function is as follows:
wherein x represents a random variable, B (. Alpha.) w ，β w ) Is at α w And beta w Beta function as a parameter, alpha w 、β w Are all shape parameters;
the mean value E (x) of x, which affects the property of the Beta function distribution, is expressed as:
the variance D (x) is expressed as:
shape parameter alpha w Can be expressed as:
shape parameter beta w Can be expressed as:
3. the power system load-store combined optimization scheduling method according to claim 2, wherein in step 2, the objective function of the power system load-store combined optimization scheduling model includes a cost minimum objective and a peak-to-valley difference rate minimum objective;
converting the double targets into single targets by a linear weighting method, wherein the target function is as follows:
min F＝f 1 +αf 2 (6)
in the formula f 1 Is a minimum function of the combined cost of the power system, f 2 Is a minimum function of daily load peak-valley difference rate, and alpha is a weight coefficient;
complex cost f of electric power system 1 The expression of (a) is as follows:
f 1 ＝f 11 +f 12 +f 13 (7)
f 11 for the unit operating cost, f 12 Indicating stored energyThe cost of the system; f. of 13 Representing flexible load scheduling costs;
in the formula of alpha i 、β i 、γ i All the parameters are the operating cost parameters of the thermal power generating unit; sigma is a wind curtailment penalty factor; PC (personal computer) wind The unit wind power operation and maintenance cost; n is a radical of 1 The number of time segments divided in one day; Δ t is the length of each time interval; p ij，tpu Outputting power for the thermal power generating unit; p ij，wind Output P for wind turbine ij，qwind The wind power is abandoned;
when the wind power exceeds the wind power receiving space provided by the energy storage system and the thermal power generating unit, a wind abandoning phenomenon is generated, and the calculation mode of the wind abandoning power is as follows:
in the formula P j，qwind The wind curtailment power is j time period; p j，wind Planning output power of the wind power for the period of j; CY j，wind A wind power receiving space in a period of j; p j，load Load demand for period j; ST (ST) ij，H A state vector for the incentive load consumer; p ij，H The excitation power of the interruptible user i in the j period; ST (ST) ij，I A state vector for the incentive load consumer; n is a radical of 2 The number of users capable of interrupting the load for the system; n is a radical of 3 The number of users of the system incentive load; p ij，I Interrupt power for interruptible user i during period j; p j，c Charging efficiency of the energy storage system in j time period; p j，d Discharging power for the energy storage system in the j time period; eta ess The charge-discharge efficiency of the energy storage system is obtained;
the minimum value of the j time interval output of the ith thermal power generating unit is obtained; n is a radical of 4 Indicating the number of thermal power generating units, N 5 Representing the number of the wind turbine generators;
energy storage system cost f 12 The expression of (a) is as follows:
in the formula C inv_e Investment cost per unit capacity of the energy storage system; c inv_p Investment cost per unit power; m is the service life of the energy storage system; p ess To invest in rated power, CY ess Investment rated capacity; c om_p The daily operation and maintenance cost of unit power; c om_q The operation and maintenance cost is the unit charge and discharge capacity; p j，ess Is the output power of the energy storage device in the jth time period; PC (personal computer) j，ele The price of electricity in the jth time interval; PC (personal computer) grt Is the subsidy price per discharge amount; Δ t is the length of each time interval;
flexible load scheduling cost f 13 The expression of (a) is as follows:
wherein Coe i，I An interruption load compensation coefficient for the i user; coe ij，H Increasing the excitation coefficient of the load for the i user;
peak-to-valley rate of daily load f 2 Expressed as:
in the formula P loadmax The optimized daily load peak value is obtained; p loadmin For the optimized daily load valley, the optimized daily load calculation mode is as follows:
in the formula P jload0 To optimize the load of the previous day, P j，ess For the energy storage system to exert a force, P ij，I To interruptible load responsive power, P ij，H Responding to power for the excitation load.
4. The power system load-storage joint optimization scheduling method according to claim 3, wherein in step 2.2, the power balance constraint expression is as follows:
in the formula P j，load0 Is the day-ahead load demand.
5. The power system load-storage combined optimal scheduling method of claim 4, wherein in step 2.2, the generator set comprises a thermal power unit and a wind power unit, the thermal power unit constraints comprise thermal power unit ramp-up constraints and wind power output constraints, the wind power output constraints are given by Beta distribution,
(1) And (3) constraining the upper and lower output limits of the thermal power generating unit:
in the formula
Respectively representing the lower limit and the upper limit of the output of the ith thermal power generating unit;
(2) And (3) the climbing rate of the thermal power generating unit is restrained:
-60SPD down ≤P j，tpu -P j-1，tpu ≤60SPD up (16)
SPD in the formula down 、SPD up Respectively representing the maximum descending speed and the maximum ascending speed of the active output of the unit per minute;
(3) Wind power output restraint:
the uncertainty of the actual output of the wind power is represented by probability constraint:
wherein PR represents the probability of an event; p wind，j Planned wind power output for the period of j; w is a j The output is a random variable and represents the actual output of wind power in a period of j; rho is a confidence level and represents the probability that the planned wind power output can be realized; CY wind And the installed capacity of the wind power plant.
6. The power system load-storage combined optimization scheduling method according to claim 5, wherein in step 2.2, the energy storage system operation constraints include:
(1) Energy storage capacity and rated power constraint:
the energy storage system is restricted by site and investment scale, and the capacity and rated power of the energy storage system are limited:
in the formula P max The maximum value of the rated power of the energy storage system; CY max The maximum value of the capacity of the energy storage system; CY ess Investment rated capacity;
(2) And (3) charge and discharge power constraint:
assuming that the energy storage system uses a bidirectional power converter, the maximum power can reach the rated power when the energy storage system is charged and discharged, the output of the energy storage system meets the following requirements:
-P ess ≤P j，ess ≤P ess (19)
(3) And (3) charge and discharge balance constraint:
in order to ensure the stability of the operation of the energy storage system, the daily charge quantity and the daily discharge quantity of the energy storage system need to be kept consistent, otherwise, the charge state of the battery rises or falls day by day to reach the charge and discharge limit;
the daily charge and discharge capacity of the energy storage system meets the following requirements:
(4) And (3) state of charge constraint:
state of charge S of energy storage battery j The upper limit and the lower limit set can not be exceeded:
SOC min ≤SOC j ≤SOC max (21)
SOC in the formula min 、SOC max Respectively representing the lower limit and the upper limit of the state of charge of the energy storage battery; SOC j The state of charge of the energy storage battery after the jth time interval is ended;
SOC j is calculated as follows:
in the formula SOC 0 The initial value of the SOC of the energy storage battery every day is obtained; eta ess The charge-discharge efficiency of the energy storage system.
7. The power system load-storage joint optimization scheduling method of claim 6, wherein in step 2.2, the flexible load response constraints comprise:
1) The interruptible load response power satisfies:
0≤P ij，I ≤P ij，Imax (23)
in the formula P ij，Imax Represents an upper interruptible power limit;
2) The excitation load response power satisfies the following conditions:
0≤P ij，H ≤P ij，Hmax (24)
in the formula P ij，Hmax Representing the excitation power upper limit.
8. The power system load-store joint optimization scheduling method according to claim 7, wherein in step 2.2, the rotating standby constraints are as follows:
in the formula
Respectively representing the positive and negative rotation reserve capacity provided by the system for wind power, and the reserve capacity is borne by a thermal power generating unit; e denotes the mathematical expectation operator.
9. The power system load-storage joint optimization scheduling method of claim 8, wherein in step 2.2, the peak shaving effect index comprises:
(1) The difference between the peak and the valley of the load,
load peak to valley difference Δ P load For the difference between the maximum load and the minimum load of the power system in a dispatching day, the calculation formula is as follows:
ΔP load ＝P load，max -P load，min (26)
in the formula P load，max The optimized daily load peak value is obtained; p load，min The optimized daily load valley value;
the optimized daily load calculation mode is as follows:
in the formula P j，load0 For day-ahead load power, P j，ess Output for energy storage system, P ij，I Responding to power for interruptible loads, P ij，H Responding power to the excitation load;
(2) The peak-to-valley difference rate of the load,
load peak-to-valley difference rate eta load For the ratio of the peak-valley difference to the maximum load of the power system in a scheduling day, the calculation formula is as follows:
(3) The wind power is abandoned,
wind power abandon E qwind The calculation formula of (a) is as follows:
in the formula P j，qwind The wind curtailment power for period j.
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| CN117239843A (en) * | 2023-11-13 | 2023-12-15 | 国网山东省电力公司东营供电公司 | Wind power plant peak regulation optimization scheduling method considering energy storage |
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| CN116073448A (en) * | 2023-03-22 | 2023-05-05 | 国网山东省电力公司临沂供电公司 | Low-carbon benefit-based power distribution system source network load storage collaborative peak shaving method |
| CN116073448B (en) * | 2023-03-22 | 2024-03-08 | 国网山东省电力公司临沂供电公司 | Low-carbon benefit-based power distribution system source network load storage collaborative peak shaving method |
| CN117239843A (en) * | 2023-11-13 | 2023-12-15 | 国网山东省电力公司东营供电公司 | Wind power plant peak regulation optimization scheduling method considering energy storage |
| CN117239843B (en) * | 2023-11-13 | 2024-01-26 | 国网山东省电力公司东营供电公司 | Wind power plant peak regulation optimization scheduling method considering energy storage |
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