CN113869608A - Method for optimizing source network load storage flexible resources day-ahead - Google Patents

Method for optimizing source network load storage flexible resources day-ahead Download PDF

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CN113869608A
CN113869608A CN202111267031.XA CN202111267031A CN113869608A CN 113869608 A CN113869608 A CN 113869608A CN 202111267031 A CN202111267031 A CN 202111267031A CN 113869608 A CN113869608 A CN 113869608A
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time period
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周倩
许晓川
许道林
肖文浩
谢颜斌
朱元
程超
牟凡
肖强
宿晓岚
张航
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State Grid Corp of China SGCC
Chongqing City Power Supply Co of State Grid Chongqing Electric Power Co Ltd
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Chongqing City Power Supply Co of State Grid Chongqing Electric Power Co Ltd
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Abstract

The invention discloses a method for optimizing flexible resources of source network load storage in the day ahead, which comprises the following steps: s1, acquiring source side conventional unit operation parameters, network side topological structure and line parameters, load side electricity utilization information, demand response load scale and operation parameters of an energy storage side battery, and constructing a source network load storage flexible resource day-ahead optimization model according to short-term prediction information of renewable energy power generation and load; and S2, converting the day-ahead optimization model into a mixed integer linear optimization model for solving, and calculating to obtain a day-ahead optimization scheme of the source network load and storage flexible resources. The method can optimize the consumption interval of the renewable energy and the load demand response fluctuation interval, and ensures the feasibility, the safety and the economy of the day-ahead optimization strategy.

Description

Method for optimizing source network load storage flexible resources day-ahead
Technical Field
The invention relates to the field of power systems, in particular to a method for optimizing source network load storage flexible resources day by day.
Background
Under the development goal of double carbon, the development and utilization of renewable energy power generation represented by wind power and photovoltaic still keep a rapid development situation. However, as the penetration rate of wind power and photovoltaic power generation in the power grid is continuously increased, the uncertainty of the generated power makes the safe and economic operation of the power system face a severe test. Researches show that the flexibility resource of the power system is an important guarantee for improving the wind power and photovoltaic absorption capacity. Flexibility modification and automatic power generation control technology of thermal power generating units and input of energy storage devices are common ways for improving flexibility of power systems.
In recent years, electrochemical energy storage power stations represented by lithium ion batteries have the advantages of flexible site selection, short construction period, high energy and power density and high charging and discharging efficiency, and are concerned in research and application of peak-load regulation and frequency modulation on the side of a power grid. Meanwhile, due to the shortage of power transmission corridors and the increase of large-scale wind power and photovoltaic grid-connected capacity, the dynamic capacity increasing technology of the power transmission line is used as an important flexible resource on the power grid side, so that the power transmission line has high economic benefit and provides support for efficient operation of the power transmission line. In addition, research has shown that wind power and photovoltaic absorption capacity can be improved by responding and adjusting user side load demand through load demand. The load-side demand response mainly includes both an excitation-type demand response (IDR) and a price-type demand response (PDR). Since the IDR has contract mandatory, the uncertainty is small; and the PDR is based on the principle that the user participates voluntarily, and the randomness is strong. Under the background of an energy internet, the coordinated operation of the source network load and storage flexible resources needs to consider the uncertainty caused by wind power and photovoltaic and also needs to deal with the uncertainty of a load side PDR.
Aiming at the research of the source-load double-end uncertainty day-ahead optimization problem, at present, multiple sides are more important in the coordination optimization operation aspect of source-load or source-load-storage flexible resources, and intensive research is urgently needed in the coordination operation strategy aspect of source network load-storage multi-type flexible resources. The peak-shaving frequency modulation capability of the source network load-storage flexible resources is related to the operation characteristics of the source network load-storage flexible resources, on the premise of considering uncertainty of source-load ends, the economic efficiency and safety of renewable energy consumption need to be balanced according to the operation characteristics of the source network load-storage flexible resources, the consumption interval of the renewable energy and the load demand response fluctuation interval are reasonably optimized, and the feasibility of a day-ahead optimization strategy is ensured.
Disclosure of Invention
In view of this, the present invention aims to overcome the defects in the prior art, and provides a method for optimizing a source network load storage flexible resource day ahead, which can optimize a consumption interval of renewable energy and a load demand response fluctuation interval, and ensure feasibility, safety and economy of a day ahead optimization strategy.
The invention relates to a method for optimizing flexible resources of source network load storage in the future, which comprises the following steps:
s1, constructing a source network load storage flexible resource day-ahead optimization model F:
Figure BDA0003327111010000021
wherein T is a day-ahead scheduling period; n is a radical ofTThe number of the thermal power generating units is;
Figure BDA0003327111010000022
the starting state coefficient is the starting state coefficient of the thermal power generating unit i;
Figure BDA0003327111010000023
the coefficient is the stop state coefficient of the thermal power generating unit i;
Figure BDA0003327111010000024
adjusting the consumption coefficient of the thermal power generating unit i upwards;
Figure BDA0003327111010000025
downward adjustment consumption coefficient of the thermal power generating unit i; u. ofi,tStarting state variables of the thermal power generating unit i in a time period t; v. ofi,tA stopping state variable of the thermal power generating unit i in a time period t;
Figure BDA0003327111010000026
predicting the difference between the fluctuation amount and the allowable fluctuation amount for the PDR;
Figure BDA0003327111010000027
predicting a difference between the fluctuation amount and the allowable fluctuation amount for the PDR downward; p is a radical ofi,tGenerating power of the thermal power generating unit i in a time period t; x is the number ofi,tThe method comprises the following steps of (1) setting the running state of a thermal power generating unit i in a time period t; a isi、bi、ciRespectively representing each time coefficient of fuel consumption of the thermal power generating unit; n is a radical ofWThe total number of renewable energy stations;
Figure BDA0003327111010000028
the wind/light abandonment penalty coefficient is the wind/light abandonment penalty coefficient of the renewable energy station w;
Figure BDA0003327111010000029
respectively are the load shedding penalty coefficients of the renewable energy station w;
Figure BDA00033271110100000210
the wind/light curtailment for the renewable energy station w at time t;
Figure BDA0003327111010000031
the load shedding amount of the renewable energy station w in the time period t; n is a radical ofNThe number of the transmission lines is;
Figure BDA0003327111010000032
dynamically increasing the equipment and operation and maintenance consumption of unit capacity for the transmission line m;
Figure BDA0003327111010000033
capacity increasing power of the transmission line m in a time period t; n is a radical ofPResponding to the load node set for the electricity price type demand;
Figure BDA0003327111010000034
a penalty coefficient is subjected to upward fluctuation deviation of the demand response load n;
Figure BDA0003327111010000035
a penalty coefficient is set for the downward fluctuation deviation of the demand response load n;
Figure BDA0003327111010000036
an amount of upward fluctuating deviation for the demand responsive load n over time period t;
Figure BDA0003327111010000037
a downward fluctuation deviation amount for the demand response load n at the time period t; n is a radical ofBThe number of energy storage power stations;
Figure BDA0003327111010000038
the operation and maintenance consumption of unit capacity of the energy storage power station b is realized;
Figure BDA0003327111010000039
the operation and maintenance consumption of unit power of the energy storage power station b is realized;
Figure BDA00033271110100000310
the rated capacity of the energy storage power station b;
Figure BDA00033271110100000311
the rated power of the energy storage power station b; alpha is alphabTo recover the coefficients, the
Figure BDA00033271110100000312
r is the rate of the current pasting,
Figure BDA00033271110100000313
the actual cycle life of the energy storage power station b;
Figure BDA00033271110100000314
adjusting the yield of unit power upwards for the energy storage power station;
Figure BDA00033271110100000315
adjusting the yield of unit power downwards for the energy storage power station;
Figure BDA00033271110100000316
upward regulated power for energy storage in a charged state;
Figure BDA00033271110100000317
downward regulated power for stored energy in a charged state;
Figure BDA00033271110100000318
upward regulated power for energy storage in the discharge state;
Figure BDA00033271110100000319
downward regulated power for energy storage in a discharge state;
s2, adjusting each parameter value in a source network load storage flexible resource day-ahead optimization model F to enable the optimization model F to obtain a minimum value, and calculating by using the parameter set when the optimization model F obtains the minimum value to obtain a day-ahead optimization scheme of the source network load storage flexible resource; the day-ahead optimization scheme comprises a conventional unit benchmark output plan, renewable energy power generation power, an allowable fluctuation interval of load demand response, a time-of-use electricity price strategy, a power transmission line dynamic capacity-increasing strategy and an energy storage charging and discharging strategy.
Furthermore, the constraint conditions of the source network charge storage flexible resource day-ahead optimization model F comprise conventional unit constraint, renewable energy station power constraint, power transmission line dynamic capacity increase constraint, PDR response constraint, energy storage power station operation constraint, system power balance constraint, system rotation standby constraint and network transmission capacity safety constraint.
Further, the system power balance constraint is:
Figure BDA00033271110100000320
wherein N isLThe number of load nodes;
Figure BDA00033271110100000321
predicted power for renewable energy station w for time period t;
Figure BDA00033271110100000322
regulating power for storing energy in a discharge state;
Figure BDA00033271110100000323
regulating power for storing energy in a charging state;
Figure BDA00033271110100000324
a predicted value of the load at the node l in the time period t;
Figure BDA0003327111010000041
the predicted total response of the PDR at node n over time period t.
Further, the system rotates the standby constraint:
Figure BDA0003327111010000042
wherein the content of the first and second substances,
Figure BDA0003327111010000043
upward rotation is provided for the adjustable unit a in a time period t for standby;
Figure BDA0003327111010000044
downward rotation is provided for the adjustable unit a in a time period t for standby; n is a radical ofSRepresents a conventional unit andthe total number of energy storage power stations;
Figure BDA0003327111010000045
an upper limit of a tolerable power prediction error for the renewable energy station w at time period t;
Figure BDA0003327111010000046
a lower limit of a tolerable power prediction error for the renewable energy station w for a time period t; binary variable
Figure BDA0003327111010000047
And
Figure BDA0003327111010000048
the method is used for constraining the degree of conservation of the uncertain set of the renewable energy station w in the time period t;
Figure BDA0003327111010000049
respectively, the allowable response upper limit of the PDR at the node n in the period t;
Figure BDA00033271110100000410
a lower allowable response limit for the PDR at node n for time period t; binary variable
Figure BDA00033271110100000411
And
Figure BDA00033271110100000412
all the control variables are the conservative degree of the PDR at the node n in an uncertain set at a time period t; p is a radical ofa,tThe planned output of the adjustable unit a in the t-th time period is provided; x is the number ofa,tIs used for adjusting the running state of the unit a in the t-th time period, and
Figure BDA00033271110100000413
representing the charging state of the energy storage unit b in the time period t,
Figure BDA00033271110100000414
representing the discharge state of the energy storage unit b in a time period t;
Figure BDA00033271110100000415
the technical output upper limit of the adjustable unit a in the t-th time period is set;
Figure BDA00033271110100000416
the technical output lower limit of the adjustable unit a in the t-th time period is set;
Figure BDA00033271110100000417
the climbing rate of the unit a can be adjusted;
Figure BDA00033271110100000418
the gradient rate of the unit a can be adjusted; Δ taA release time reserved for spinning.
Further, the network transmission capacity security constraint is:
Figure BDA00033271110100000419
wherein λ isa,sTransferring distribution factors for the output power of the adjustable unit; lambda [ alpha ]n,sOutputting a power transfer distribution factor for the PDR; lambda [ alpha ]w,sOutputting a power transfer distribution factor for the renewable energy source;
Figure BDA00033271110100000420
and
Figure BDA00033271110100000421
respectively representing the upper limit of the bidirectional transmission power of the branch s-e; Δ pa,tThe adjustment quantity of the adjustable unit a in the time period t is obtained;
Figure BDA00033271110100000422
the fluctuation amount of the PDR at the node n in the time period t;
Figure BDA00033271110100000423
the fluctuation amount of the renewable energy station w in the time period t;
further, carrying out linearization processing on the system rotation standby constraint to obtain a new system rotation standby constraint:
Figure BDA0003327111010000051
wherein the content of the first and second substances,
Figure BDA0003327111010000052
and
Figure BDA0003327111010000053
are all introduced auxiliary variables.
Further, the network transmission capacity security constraint is converted to obtain a new network transmission capacity security constraint:
Figure BDA0003327111010000054
wherein the content of the first and second substances,
Figure BDA0003327111010000055
Hε,t、Kε,tand Gε,tAre all introduced auxiliary variables.
The invention has the beneficial effects that: the invention discloses a method for optimizing a source network load storage flexible resource day ahead, which takes into account the uncertainty of source load and both ends, optimizes the consumption interval of renewable energy and the load demand response fluctuation interval, and ensures the feasibility of a day ahead optimization strategy; meanwhile, based on an equivalent full-cycle frequency model of energy storage charging and discharging, the service life constraint of energy storage operation is considered, and the economical efficiency and feasibility of an energy storage charging and discharging strategy are ensured; in addition, dynamic capacity increasing constraint and transmission capacity safety constraint of the power transmission line are considered, and the safety of network operation is ensured.
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Detailed Description
The invention is further illustrated below:
the invention relates to a method for optimizing flexible resources of source network load storage in the future, which comprises the following steps:
s1, acquiring source side conventional unit operation parameters, network side topological structure and line parameters, charge side electricity utilization information, demand response load scale and operation parameters of an energy storage side battery, and constructing a source network charge storage flexible resource day-ahead optimization model F according to short-term prediction information of renewable energy power generation and load:
Figure BDA0003327111010000061
wherein T is a day-ahead scheduling period; n is a radical ofTThe number of the thermal power generating units is;
Figure BDA0003327111010000062
the starting state coefficient is the starting state coefficient of the thermal power generating unit i;
Figure BDA0003327111010000063
the coefficient is the stop state coefficient of the thermal power generating unit i;
Figure BDA0003327111010000064
adjusting the consumption coefficient of the thermal power generating unit i upwards;
Figure BDA0003327111010000065
downward adjustment consumption coefficient of the thermal power generating unit i; u. ofi,tStarting state variables of the thermal power generating unit i in a time period t; v. ofi,tA stopping state variable of the thermal power generating unit i in a time period t;
Figure BDA0003327111010000066
predicting the difference between the fluctuation amount and the allowable fluctuation amount for the PDR;
Figure BDA0003327111010000067
predicting a difference between the fluctuation amount and the allowable fluctuation amount for the PDR downward; p is a radical ofi,tGenerating power of the thermal power generating unit i in a time period t; x is the number ofi,tThe method comprises the following steps of (1) setting the running state of a thermal power generating unit i in a time period t; a isi、bi、ciRespectively representing each time coefficient of fuel consumption of the thermal power generating unit; n is a radical ofWThe total number of renewable energy stations;
Figure BDA0003327111010000068
the wind/light abandonment penalty coefficient is the wind/light abandonment penalty coefficient of the renewable energy station w;
Figure BDA0003327111010000069
respectively are the load shedding penalty coefficients of the renewable energy station w;
Figure BDA00033271110100000610
the wind/light curtailment for the renewable energy station w at time t;
Figure BDA00033271110100000611
the load shedding amount of the renewable energy station w in the time period t; n is a radical ofNThe number of the transmission lines is;
Figure BDA0003327111010000071
dynamically increasing the equipment and operation and maintenance consumption of unit capacity for the transmission line m;
Figure BDA0003327111010000072
capacity increasing power of the transmission line m in a time period t; n is a radical ofPResponding to the load node set for the electricity price type demand;
Figure BDA0003327111010000073
a penalty coefficient is subjected to upward fluctuation deviation of the demand response load n;
Figure BDA0003327111010000074
a penalty coefficient is set for the downward fluctuation deviation of the demand response load n;
Figure BDA0003327111010000075
an amount of upward fluctuating deviation for the demand responsive load n over time period t;
Figure BDA0003327111010000076
a downward fluctuation deviation amount for the demand response load n at the time period t; n is a radical ofBThe number of energy storage power stations;
Figure BDA0003327111010000077
the operation and maintenance consumption of unit capacity of the energy storage power station b is realized;
Figure BDA0003327111010000078
the operation and maintenance consumption of unit power of the energy storage power station b is realized;
Figure BDA0003327111010000079
the rated capacity of the energy storage power station b;
Figure BDA00033271110100000710
the rated power of the energy storage power station b; alpha is alphabTo recover the coefficients, the
Figure BDA00033271110100000711
r is the rate of the current pasting,
Figure BDA00033271110100000712
the actual cycle life of the energy storage power station b;
Figure BDA00033271110100000713
adjusting the yield of unit power upwards for the energy storage power station;
Figure BDA00033271110100000714
adjusting the yield of unit power downwards for the energy storage power station;
Figure BDA00033271110100000715
upward regulated power for energy storage in a charged state;
Figure BDA00033271110100000716
downward regulated power for stored energy in a charged state;
Figure BDA00033271110100000717
upward regulated power for energy storage in the discharge state;
Figure BDA00033271110100000718
downward regulated power for energy storage in a discharge state; the renewable energy source comprisesWind power and photovoltaic; the renewable energy station comprises a wind power plant and a photovoltaic power station;
s2, adjusting each parameter value in a source network load storage flexible resource day-ahead optimization model F to enable the optimization model F to obtain a minimum value, and calculating by using the parameter set when the optimization model F obtains the minimum value to obtain a day-ahead optimization scheme of the source network load storage flexible resource; the day-ahead optimization scheme comprises a conventional unit benchmark output plan, renewable energy power generation power, an allowable fluctuation interval of load demand response, a time-of-use electricity price strategy, a power transmission line dynamic capacity-increasing strategy and an energy storage charging and discharging strategy. The source network load and storage flexible resource day-ahead optimization model F is converted into a mixed integer linear optimization model, and then a commercial solver is called to solve the linear programming problem of the mixed integer linear optimization model, so that the day-ahead optimization scheme of the source network load and storage flexible resources can be obtained through calculation.
In this embodiment, in order to enable the source network charge storage flexible resource day-ahead optimization model F to better calculate a day-ahead optimization scheme, the constraint conditions of the source network charge storage flexible resource day-ahead optimization model F include conventional unit constraint, renewable energy station power constraint, power transmission line dynamic capacity increase constraint, PDR response constraint, energy storage power station operation constraint, system power balance constraint, system rotation standby constraint, and network transmission capacity safety constraint; the conventional unit constraints comprise minimum start-stop time constraints, technical output constraints, unit climbing constraints and rotary standby release constraints. It should be noted that, when the resource day-ahead optimization model F is converted into the mixed integer linear optimization model, the constraint conditions are also correspondingly converted, thereby ensuring the solution of the linear programming problem.
In this embodiment, the system power balance constraint is:
Figure BDA0003327111010000081
wherein N isLThe number of load nodes;
Figure BDA0003327111010000082
predicted power for renewable energy station w for time period t;
Figure BDA0003327111010000083
regulating power for storing energy in a discharge state;
Figure BDA0003327111010000084
regulating power for storing energy in a charging state;
Figure BDA0003327111010000085
a predicted value of the load at the node l in the time period t;
Figure BDA0003327111010000086
the predicted total response of the PDR at node n over time period t.
In this embodiment, the system rotation standby constraint:
Figure BDA0003327111010000087
wherein the content of the first and second substances,
Figure BDA0003327111010000088
upward rotation is provided for the adjustable unit a in a time period t for standby;
Figure BDA0003327111010000089
downward rotation is provided for the adjustable unit a in a time period t for standby; n is a radical ofSRepresenting the total number of conventional units and energy storage power stations;
Figure BDA00033271110100000810
an upper limit of a tolerable power prediction error for the renewable energy station w at time period t;
Figure BDA00033271110100000811
a lower limit of a tolerable power prediction error for the renewable energy station w for a time period t; binary variable
Figure BDA00033271110100000812
And
Figure BDA00033271110100000813
the method is used for constraining the degree of conservation of the uncertain set of the renewable energy station w in the time period t;
Figure BDA00033271110100000814
respectively, the allowable response upper limit of the PDR at the node n in the period t;
Figure BDA00033271110100000815
a lower allowable response limit for the PDR at node n for time period t; binary variable
Figure BDA00033271110100000816
And
Figure BDA00033271110100000817
all the control variables are the conservative degree of the PDR at the node n in an uncertain set at a time period t; p is a radical ofa,tThe planned output of the adjustable unit a in the t-th time period is provided; x is the number ofa,tIs used for adjusting the running state of the unit a in the t-th time period, and
Figure BDA00033271110100000818
representing the charging state of the energy storage unit b in the time period t,
Figure BDA00033271110100000819
representing the discharge state of the energy storage unit b in a time period t;
Figure BDA00033271110100000820
the technical output upper limit of the adjustable unit a in the t-th time period is set;
Figure BDA00033271110100000821
the technical output lower limit of the adjustable unit a in the t-th time period is set;
Figure BDA00033271110100000822
the climbing rate of the unit a can be adjusted;
Figure BDA00033271110100000823
the gradient rate of the unit a can be adjusted; Δ taRelease time for spinning reserve, Δ taTypically 5-10 minutes.
In this embodiment, the network transmission capacity security constraint is:
Figure BDA0003327111010000091
wherein λ isa,sTransferring distribution factors for the output power of the adjustable unit; lambda [ alpha ]n,sOutputting a power transfer distribution factor for the PDR; lambda [ alpha ]w,sOutputting a power transfer distribution factor for the renewable energy source;
Figure BDA0003327111010000092
and
Figure BDA0003327111010000093
respectively representing the upper limit of the bidirectional transmission power of the branch s-e; Δ pa,tThe adjustment quantity of the adjustable unit a in the time period t is obtained;
Figure BDA0003327111010000094
the fluctuation amount of the PDR at the node n in the time period t;
Figure BDA0003327111010000095
the amount of fluctuation of the renewable energy station w in the time period t.
In this embodiment, the system rotation standby constraint is linearized to obtain a new system rotation standby constraint:
Figure BDA0003327111010000096
wherein the content of the first and second substances,
Figure BDA0003327111010000097
and
Figure BDA0003327111010000098
are all introduced auxiliary variables.
In this embodiment, the network transmission capacity security constraint is converted to obtain a new network transmission capacity security constraint:
Figure BDA0003327111010000101
wherein the content of the first and second substances,
Figure BDA0003327111010000102
Hε,t、Kε,tand Gε,tAre all introduced auxiliary variables.
The feasibility and effectiveness of the invention are analyzed and verified below by taking a modified IEEE 30 node test system as an example:
taking wind power access as an example, the installed capacities of the thermal power generating units access nodes 1, 2, 22, 23 and 27 are respectively 160MW, 100MW, 60MW and 110 MW. And the wind power plant is accessed to the node 5, and the installed capacity is 400 MW. The load demand has a minimum value of 280.69MW, a maximum value of 731.62MW, and an average value of 513.34 MW. The PDR load is located at node 8 and the response prediction error does not exceed 5%. The maximum charge-discharge power of the energy storage battery is 180MW, and the capacity of the energy storage battery is 450 MWh. The energy storage installation cost is 2 ten thousand yuan/MW and 3 ten thousand yuan/MWh respectively. The wind curtailment power penalty cost coefficient and the load demand response downward fluctuation deviation penalty cost coefficient are 400 yuan/MW, the load shedding power penalty cost coefficient, the load demand response upward fluctuation deviation penalty cost coefficient and the power transmission line dynamic capacity increase cost coefficient are 600 yuan/MW.
The following three operational scenarios are defined:
scene 1: source load uncertainty is considered, and source network load storage coordination optimization
Scene 2: source network load storage coordination optimization without considering source load uncertainty
Scene 3: source load uncertainty is considered, and source load storage coordination optimization
As can be seen from table 1, in the operating mode without considering source load uncertainty, the thermal power generating unit has the lowest operating cost and the weakest load demand response capacity, the energy storage power station needs to cope with wind power prediction errors and load demand response fluctuation through frequent charge and discharge cycles, and the wind power absorption capacity is the weakest of three operating scenarios. Comparing the scene 1 with the scene 3, it can be known that, in the scene of source network charge-storage coordinated operation, the operation cost of the thermal power generating unit is slightly increased compared with that in the source charge-storage coordinated operation mode, and the operation life of the energy storage power station is slightly reduced; however, compared with the scenario 3, the wind power consumption capability and the capability of coping with load demand response fluctuation of the scenario 1 are both obviously improved, the optimized target total cost is further reduced, and the feasibility and the effectiveness of the method are verified.
TABLE 1 comparison of the results of calculations in different scenarios
Figure BDA0003327111010000111
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.

Claims (7)

1. A method for optimizing source network load storage flexible resources day-ahead is characterized by comprising the following steps: the method comprises the following steps:
s1, constructing a source network load storage flexible resource day-ahead optimization model F:
Figure FDA0003327111000000011
wherein T is a day-ahead scheduling period; n is a radical ofTThe number of the thermal power generating units is;
Figure FDA0003327111000000012
for starting state of thermal power generating unit iA coefficient;
Figure FDA0003327111000000013
the coefficient is the stop state coefficient of the thermal power generating unit i;
Figure FDA0003327111000000014
adjusting the consumption coefficient of the thermal power generating unit i upwards;
Figure FDA0003327111000000015
downward adjustment consumption coefficient of the thermal power generating unit i; u. ofi,tStarting state variables of the thermal power generating unit i in a time period t; v. ofi,tA stopping state variable of the thermal power generating unit i in a time period t;
Figure FDA0003327111000000016
predicting the difference between the fluctuation amount and the allowable fluctuation amount for the PDR;
Figure FDA0003327111000000017
predicting a difference between the fluctuation amount and the allowable fluctuation amount for the PDR downward; p is a radical ofi,tGenerating power of the thermal power generating unit i in a time period t; x is the number ofi,tThe method comprises the following steps of (1) setting the running state of a thermal power generating unit i in a time period t; a isi、bi、ciRespectively representing each time coefficient of fuel consumption of the thermal power generating unit; n is a radical ofWThe total number of renewable energy stations;
Figure FDA0003327111000000018
the wind/light abandonment penalty coefficient is the wind/light abandonment penalty coefficient of the renewable energy station w;
Figure FDA0003327111000000019
respectively are the load shedding penalty coefficients of the renewable energy station w;
Figure FDA00033271110000000110
the wind/light curtailment for the renewable energy station w at time t;
Figure FDA00033271110000000111
the load shedding amount of the renewable energy station w in the time period t; n is a radical ofNThe number of the transmission lines is;
Figure FDA00033271110000000112
dynamically increasing the equipment and operation and maintenance consumption of unit capacity for the transmission line m;
Figure FDA00033271110000000113
capacity increasing power of the transmission line m in a time period t; n is a radical ofPResponding to the load node set for the electricity price type demand;
Figure FDA00033271110000000114
a penalty coefficient is subjected to upward fluctuation deviation of the demand response load n;
Figure FDA00033271110000000115
a penalty coefficient is set for the downward fluctuation deviation of the demand response load n;
Figure FDA00033271110000000116
an amount of upward fluctuating deviation for the demand responsive load n over time period t;
Figure FDA00033271110000000117
a downward fluctuation deviation amount for the demand response load n at the time period t; n is a radical ofBThe number of energy storage power stations;
Figure FDA00033271110000000118
the operation and maintenance consumption of unit capacity of the energy storage power station b is realized;
Figure FDA00033271110000000119
the operation and maintenance consumption of unit power of the energy storage power station b is realized;
Figure FDA00033271110000000120
the rated capacity of the energy storage power station b;
Figure FDA00033271110000000121
the rated power of the energy storage power station b; alpha is alphabTo recover the coefficients, the
Figure FDA0003327111000000021
r is the rate of the current pasting,
Figure FDA0003327111000000022
the actual cycle life of the energy storage power station b;
Figure FDA0003327111000000023
adjusting the yield of unit power upwards for the energy storage power station;
Figure FDA0003327111000000024
adjusting the yield of unit power downwards for the energy storage power station;
Figure FDA0003327111000000025
upward regulated power for energy storage in a charged state;
Figure FDA0003327111000000026
downward regulated power for stored energy in a charged state;
Figure FDA0003327111000000027
upward regulated power for energy storage in the discharge state;
Figure FDA0003327111000000028
downward regulated power for energy storage in a discharge state;
s2, adjusting each parameter value in a source network load storage flexible resource day-ahead optimization model F to enable the optimization model F to obtain a minimum value, and calculating by using the parameter set when the optimization model F obtains the minimum value to obtain a day-ahead optimization scheme of the source network load storage flexible resource; the day-ahead optimization scheme comprises a conventional unit benchmark output plan, renewable energy power generation power, an allowable fluctuation interval of load demand response, a time-of-use electricity price strategy, a power transmission line dynamic capacity-increasing strategy and an energy storage charging and discharging strategy.
2. The source network load-store flexible resource day-ahead optimization method according to claim 1, characterized in that: the constraint conditions of the source network charge storage flexible resource day-ahead optimization model F comprise conventional unit constraint, renewable energy station power constraint, power transmission line dynamic capacity increase constraint, PDR response constraint, energy storage power station operation constraint, system power balance constraint, system rotation standby constraint and network transmission capacity safety constraint.
3. The source network load-store flexible resource day-ahead optimization method according to claim 2, characterized in that: the system power balance constraint is as follows:
Figure FDA0003327111000000029
wherein N isLThe number of load nodes;
Figure FDA00033271110000000210
predicted power for renewable energy station w for time period t;
Figure FDA00033271110000000211
regulating power for storing energy in a discharge state;
Figure FDA00033271110000000212
regulating power for storing energy in a charging state;
Figure FDA00033271110000000213
a predicted value of the load at the node l in the time period t;
Figure FDA00033271110000000214
prediction of PDR at node n over time period tAnd (4) total response quantity.
4. The source network load-store flexible resource day-ahead optimization method according to claim 2, characterized in that: the system rotates the standby constraint:
Figure FDA0003327111000000031
wherein the content of the first and second substances,
Figure FDA0003327111000000032
upward rotation is provided for the adjustable unit a in a time period t for standby;
Figure FDA0003327111000000033
downward rotation is provided for the adjustable unit a in a time period t for standby; n is a radical ofSRepresenting the total number of conventional units and energy storage power stations;
Figure FDA0003327111000000034
an upper limit of a tolerable power prediction error for the renewable energy station w at time period t;
Figure FDA0003327111000000035
a lower limit of a tolerable power prediction error for the renewable energy station w for a time period t; binary variable
Figure FDA0003327111000000036
And
Figure FDA0003327111000000037
the method is used for constraining the degree of conservation of the uncertain set of the renewable energy station w in the time period t;
Figure FDA0003327111000000038
respectively, the allowable response upper limit of the PDR at the node n in the period t;
Figure FDA0003327111000000039
a lower allowable response limit for the PDR at node n for time period t; binary variable
Figure FDA00033271110000000310
And
Figure FDA00033271110000000311
all the control variables are the conservative degree of the PDR at the node n in an uncertain set at a time period t; p is a radical ofa,tThe planned output of the adjustable unit a in the t-th time period is provided; x is the number ofa,tIs used for adjusting the running state of the unit a in the t-th time period, and
Figure FDA00033271110000000312
Figure FDA00033271110000000313
representing the charging state of the energy storage unit b in the time period t,
Figure FDA00033271110000000314
representing the discharge state of the energy storage unit b in a time period t;
Figure FDA00033271110000000315
the technical output upper limit of the adjustable unit a in the t-th time period is set;
Figure FDA00033271110000000316
the technical output lower limit of the adjustable unit a in the t-th time period is set;
Figure FDA00033271110000000317
the climbing rate of the unit a can be adjusted;
Figure FDA00033271110000000318
the gradient rate of the unit a can be adjusted; Δ taA release time reserved for spinning.
5. The source network load-store flexible resource day-ahead optimization method according to claim 2, characterized in that: the network transmission capacity security constraint is as follows:
Figure FDA00033271110000000319
wherein λ isa,sTransferring distribution factors for the output power of the adjustable unit; lambda [ alpha ]n,sOutputting a power transfer distribution factor for the PDR; lambda [ alpha ]w,sOutputting a power transfer distribution factor for the renewable energy source;
Figure FDA00033271110000000320
and
Figure FDA00033271110000000321
respectively representing the upper limit of the bidirectional transmission power of the branch s-e; Δ pa,tThe adjustment quantity of the adjustable unit a in the time period t is obtained;
Figure FDA00033271110000000322
the fluctuation amount of the PDR at the node n in the time period t;
Figure FDA00033271110000000323
the amount of fluctuation of the renewable energy station w in the time period t.
6. The source network load storage flexibility resource day-ahead optimization method according to claim 4, characterized in that: carrying out linearization processing on the system rotation standby constraint to obtain a new system rotation standby constraint:
Figure FDA0003327111000000041
wherein the content of the first and second substances,
Figure FDA0003327111000000042
and
Figure FDA0003327111000000043
are all introduced auxiliary variables.
7. The source network load storage flexibility resource day-ahead optimization method of claim 5, characterized in that: and converting the network transmission capacity safety constraint to obtain a new network transmission capacity safety constraint:
Figure FDA0003327111000000044
wherein the content of the first and second substances,
Figure FDA0003327111000000045
Hε,t、Kε,tand Gε,tAre all introduced auxiliary variables.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114548757A (en) * 2022-02-21 2022-05-27 三峡大学 Thermal power generating unit flexibility modification planning method considering source load uncertainty
CN115238992A (en) * 2022-07-21 2022-10-25 南方电网科学研究院有限责任公司 Power system source load storage coordination optimization method and device and electronic equipment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114548757A (en) * 2022-02-21 2022-05-27 三峡大学 Thermal power generating unit flexibility modification planning method considering source load uncertainty
CN115238992A (en) * 2022-07-21 2022-10-25 南方电网科学研究院有限责任公司 Power system source load storage coordination optimization method and device and electronic equipment

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