CN113346555A - Intraday rolling scheduling method considering electric quantity coordination - Google Patents

Intraday rolling scheduling method considering electric quantity coordination Download PDF

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CN113346555A
CN113346555A CN202110572568.0A CN202110572568A CN113346555A CN 113346555 A CN113346555 A CN 113346555A CN 202110572568 A CN202110572568 A CN 202110572568A CN 113346555 A CN113346555 A CN 113346555A
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张晗
王秀丽
彭巧
赵文成
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Xian Jiaotong University
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Abstract

The invention belongs to the technical field of electric power system dispatching operation, and discloses a day-rolling optimal dispatching method considering electric quantity coordination, by introducing a target planning method, the method takes the maximum consumption of new energy, the tracking of a day-ahead output plan, the tracking of a day-ahead electric quantity plan including contract decomposition electric quantity and the minimum deviation of the electric quantity plan completion rate as multiple targets, comprehensively considers the power grid, the unit state information, the new energy predicted output information and the day-plan electric quantity completion condition which are acquired in real time, the dynamic rolling intraday optimization scheduling model ensures that the rolling modified power generation plan promotes and consumes new energy sources, the method and the device realize the balanced control of the daily electric quantity plan completion progress of the unit, solve the problems that the daily electric quantity control of the unit is difficult to adapt to the change of the power grid operating environment and the dependence degree of manual intervention is high, and ensure that the daily electric quantity plan of the power plant is effectively and fairly executed.

Description

Intraday rolling scheduling method considering electric quantity coordination
Technical Field
The invention belongs to the technical field of electric power system dispatching operation, relates to an electric power system dispatching optimization method in an electric power market transition environment, and particularly relates to a day rolling dispatching method considering electric quantity coordination.
Background
With the large-scale grid connection of new energy such as wind power, photovoltaic and the like, the establishment of a power generation plan adapting to the randomness and the intermittence of the output of the new energy becomes one of important ways for a power grid to absorb the new energy. The new energy ultra-short-term predicted power based on rolling updating is used for carrying out rolling scheduling in the day of the power system, so that the problem of system imbalance caused by new energy fluctuation can be effectively balanced, and new energy consumption is promoted. With the development of the power market, the coordination problem of the planned power quantity and various contract power quantities needs to be considered when scheduling a scheduling plan, and the existing 'three-public' scheduling mode provides a new requirement of power constraint for the formulation of a daily scheduling plan. How to make a day-rolling scheduling strategy suitable for the current electric power market environment on the basis of guaranteeing new energy consumption is an important subject faced by electric power system operators.
In the real-time scheduling operation, due to new energy prediction errors and possible fault conditions such as forced shutdown of the generator set and the like, the actual power generation curve and the day-ahead power generation plan curve generate larger deviation; the fluctuation of new energy power generation also enables the output of the unit to be continuously corrected in the day real-time scheduling process, the workload of real-time scheduling is greatly increased, and the day-ahead electric quantity plan of the power plant has to be changed. However, the dispatching department, as an executor of the power generation plan, needs to ensure that the rolling correction of the power generation plan in the day should track the power generation curve in the day ahead as much as possible, so as to ensure the effective execution of the power generation plan in the day ahead. Meanwhile, under the current electric power market environment of China, the current 'three-public' scheduling has strict requirements on the electric quantity completion progress of a unit (or a power plant), and on the basis of considering the economy and the optimal new energy consumption, how to flexibly and accurately consider the electric quantity progress in a scheduling plan in the day and realize scheduling fairness is the key point of research on the current rolling scheduling of an electric power system in the day.
At present, most of the existing day-rolling power generation plans do not consider dynamic rolling adjustment of the electric quantity completion progress, a dispatching department often depends on manual adjustment of the electric quantity deviation of a unit, the electric quantity deviation of the day is compensated by the future day, the mode lacks effective constraint of fairness, convergence of the electric quantity completion progress of each power plant cannot be guaranteed, and meanwhile, frequent adjustment of the unit is caused, and the operation and maintenance cost is increased.
Disclosure of Invention
The invention aims to solve the problems that how to meet the requirement of electric power market transition period on the fairness of planned electric quantity and contract electric quantity in real-time dispatching operation, flexibly and accurately consider the electric quantity completion progress during the planning of dispatching in the day, realize rolling adjustment, obtain the output plan of a day unit considering multi-type electric quantity coordination, and the control of the electric quantity in the day of the unit is difficult to adapt to the change of a power grid operation environment and the dependence of manual intervention is high.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a rolling scheduling method in day considering electric quantity coordination comprises the following steps:
acquiring a day-ahead scheduling plan of the power system, wherein the day-ahead scheduling plan of the power system comprises unit start-stop, output information and an electric quantity plan arranged day-ahead; the electric quantity plan comprises the daily total electric energy production and contract electric energy production of each power plant scheduled in the day-ahead scheduling plan;
updating the output power prediction and load prediction information of the ultra-short term new energy;
step three, based on the results obtained in the step one and the step two, establishing and solving an in-day optimized scheduling model considering electric quantity coordination, and rolling and correcting the electric quantity plan of a scheduling period to obtain an in-day scheduling scheme considering electric quantity coordination in the remaining period of the day;
taking a unit start-stop and output scheme of a first time period in a day scheduling scheme considering electric quantity coordination in the remaining time period of the day as a correction scheme of the current time;
step five, judging whether 96 points in the whole day are finished according to the correction scheme, if not, entering the next moment, and repeating the step two to the step five; and if the scheduling is finished, the rolling scheduling of the day is finished all day, a day scheduling plan considering the electric quantity coordination is obtained, the day-ahead electric quantity plan is effectively tracked according to the day scheduling plan considering the electric quantity coordination, and the effectiveness and the fairness of the electric quantity completion are guaranteed.
Further, step two includes the following processes: dividing 24 hours in the operating day into 96 points at intervals of 15min, and regarding a certain operating time t in the day0Updating the current operating time t0The unit state and the actual output; for the operating time t0And updating the short-term power prediction and the load prediction of the new energy in the interval of the ending time T of the day.
Further, in the third step, the intra-day optimal scheduling model considering electric quantity coordination is established through the following processes:
and (3) considering the day-by-day optimization scheduling model of electric quantity coordination to minimize the new energy power abandon cost, the day-ahead output plan adjustment cost, the day-ahead electric quantity plan deviation cost and the day-ahead electric quantity plan completion rate deviation as a multi-objective function.
Further, the objective functions of minimizing the cost of new energy power curtailment, the adjustment cost of the day-ahead output plan, the deviation cost of the day-ahead power plan and the deviation of the day-ahead power plan completion rate are as follows:
Figure BDA0003083114950000031
wherein f is1～f4Is a 4-term objective function; f. of4、f2、f3And f4Sequentially performing a target function of minimizing the new energy electricity abandoning cost, the day-ahead output plan adjustment cost, the day-ahead electric quantity plan deviation cost and the day-ahead electric quantity plan completion rate deviation; t is t0Is the current time; t is the end time of the day; Δ T is the rolling scheduling interval; n is a radical ofREThe number of new energy power plants comprises a wind power plant and a photovoltaic power plant;
Figure BDA0003083114950000032
the electricity abandoning cost of the new energy power plant i is low;
Figure BDA0003083114950000033
the electric power is abandoned by the new energy power plant i at the moment t; n is a radical ofHydroIs the number of hydroelectric power plants;
Figure BDA0003083114950000034
the electricity abandonment cost of the hydropower plant i;
Figure BDA0003083114950000035
the electric quantity is the electricity abandoning quantity of the hydraulic power plant i; n is a radical ofThermalIs the number of thermal power plants;
Figure BDA0003083114950000036
is the number of units of the power plant i; k is the type of the output adjustment;
Figure BDA0003083114950000037
respectively outputting an up-regulation cost and a down-regulation cost in a k interval by a unit of the thermal power plant i;
Figure BDA0003083114950000038
the adjustment quantity of the unit j of the thermal power plant i in the k interval at the time t is obtained;
Figure BDA0003083114950000039
respectively adjusting the output up-regulation cost and the output down-regulation cost of the unit of the non-thermal power plant i;
Figure BDA00030831149500000310
the output adjustment amount of the non-thermal power plant i at the time t; n is the number of all power plants; n is a radical ofcontractThe number of medium and long term contract types signed by the power plant;
Figure BDA0003083114950000041
the deviation electric quantity of the contract c generated electricity in the day-ahead plan of the power plant i;
Figure BDA0003083114950000042
is the electricity deviation cost of the power plant i contract c;
Figure BDA0003083114950000043
the deviation electric quantity of the out-of-contract electric generation quantity in the day-ahead plan of the power plant i;
Figure BDA0003083114950000044
the deviation cost of the power generation amount outside the i contract of the power plant;
Figure BDA0003083114950000045
the power generation amount of the power plant i is increased within a day;
Figure BDA0003083114950000046
the cost of increasing the power generation amount in i days of the power plant; n is a radical oftypeIs the power type number;
Figure BDA0003083114950000047
the deviation value of the power generation amount completion rate of the day-ahead planned contract c of the power plant i is obtained;
Figure BDA0003083114950000048
the deviation value of the planned out-of-contract electricity generation completion rate of the power plant i days ago is shown.
Further, the multi-objective optimization of the objective function is converted into single-objective optimization, and the process is as follows:
Figure BDA0003083114950000049
in the formula (I), the compound is shown in the specification,
Figure BDA00030831149500000410
weight factors corresponding to the positive and negative deviations of the g-th target, respectively;
Figure BDA00030831149500000411
positive and negative deviations of the objective function g from the target value, respectively; f. ofgA function g which is a target constraint;
Figure BDA00030831149500000412
as target value of function g, ingAnd (4) performing optimal value of single-target optimization on the objective function.
Further, the day-to-day optimized scheduling model considering electric quantity coordination satisfies power supply operation constraints, which are as follows:
force restraint:
Figure BDA00030831149500000413
Figure BDA00030831149500000414
in the formula, Pi,j,tThe unit output at the moment t after the unit j of the power plant i is subjected to rolling correction; u. ofi,j,tThe starting state of the unit j of the power plant i at the moment t in the rolling optimization within the day is shown; pi,j,max,Pi,j,minRespectively the maximum and minimum technical output of the unit j in the power plant i; RE is a new energy power plant type, including wind power and photovoltaic;
Figure BDA00030831149500000415
the output of the new energy power plant at the moment t after the i is corrected in a rolling mode;
Figure BDA00030831149500000416
the predicted force value of the new energy power plant i at the moment t is obtained;
unit climbing restraint:
Figure BDA00030831149500000417
in the formula (I), the compound is shown in the specification,
Figure BDA0003083114950000051
the upward climbing speed and the downward climbing speed of the unit j in the power plant i are respectively.
Further, the day-to-day optimized scheduling model considering electric quantity coordination satisfies system operation constraints, which are as follows:
and power balance constraint:
Figure BDA0003083114950000052
in the formula, DtIs the system load at time t;
network security constraints:
Figure BDA0003083114950000053
modeling by using direct current power flow, wherein P isl,min,Pl,maxRespectively the active power flow limit of the line l; gl,iIs a generator transfer distribution factor of a unit to a line l; dj,tIs the load at time t of node j;
system standby constraints:
Figure BDA0003083114950000054
in the formula srt,sdtRespectively, the minimum upper and lower rotational reserve capacities of the system at time t.
Further, the day-to-day optimized scheduling model considering electric quantity coordination satisfies the coupling constraint with the day-ahead plan, which is specifically as follows: the output state of the thermal power generating unit is divided into three load states of duty peak regulation and first and second gear peak regulation according to the requirement of a peak regulation auxiliary service market, and the peak regulation with the output of more than 50 percent is duty peak regulation; the peak shaving with the output lower than 50 percent and higher than 40 percent is the first gear peak shaving; the peak shaving when the output is lower than 40 percent is the second gear peak shaving; dividing the difference part between the intraday output and the planned output before the day of the thermal power generating unit into adjustment quantities in 3 intervals according to three peak regulation states, wherein the adjustment quantities are the adjustment quantities;
Figure BDA0003083114950000055
in the formula (I), the compound is shown in the specification,
Figure BDA0003083114950000056
is the output of the unit j of the power plant i at the moment t in the day-ahead plan;
Figure BDA0003083114950000057
set j of power plant i at tThe upper and lower forces of carving;
Figure BDA0003083114950000058
respectively adjusting the unit j of the power plant i in the obligation peak shaving interval at the moment t;
Figure BDA0003083114950000059
respectively adjusting the unit j of the power plant i in a first gear peak regulation interval at the moment t;
Figure BDA00030831149500000510
respectively adjusting the unit j of the power plant i in a second gear peak regulation interval at the moment t;
output coupling constraint of the hydroelectric generating set:
Figure BDA0003083114950000061
output coupling constraint of the new energy unit:
Figure BDA0003083114950000062
in the formula (I), the compound is shown in the specification,
Figure BDA0003083114950000063
and the output of the new energy power plant i at the moment t in the day-ahead plan.
Further, the day-to-day optimized scheduling model considering power coordination satisfies the daily power execution coordination constraint, which is specifically as follows:
total power plant NContractSeed contracts, serial numbers from 1 to NContractDecreasing the priority;
Figure BDA0003083114950000064
Figure BDA0003083114950000065
Figure BDA0003083114950000066
Figure BDA0003083114950000067
in the formula, EiThe daily total power generation of the power plant i comprises the power generation of the operated period of the day and the power generation of a subsequent adjustment plan; Δ T is the scrolling time step;
Figure BDA0003083114950000068
is power plant i at t0Actual operating power at a time t before the time; ei,cIs the power generation amount of the power plant i in the contract c of the day; etai,cThe ratio of the generated energy of the power plant i contract c to the generated energy of the contract c in the day ahead is obtained;
Figure BDA0003083114950000069
is the planned power generation amount of the power plant i contract c in the day ahead; etai,extraIs a linearization parameter of the power plant i contract external power generation quantity;
Figure BDA00030831149500000610
the power generation amount of the power plant i is beyond the contract power amount of the day;
Figure BDA00030831149500000611
the power generation amount of the power plant i outside the contract electric quantity in the day-ahead plan;
Figure BDA00030831149500000612
newly adding electric quantity in the power plant i in the day;
Figure BDA00030831149500000613
the power plant i contracts the external electricity deviation amount before the day of the day; lambda [ alpha ]i,c,λi,extraIs 0-1 auxiliary parameter;
and (3) contract electric quantity completion rate coordination constraint:
Figure BDA0003083114950000071
Figure BDA0003083114950000072
Figure BDA0003083114950000073
in the formula (I), the compound is shown in the specification,
Figure BDA0003083114950000074
the variable is 0-1 auxiliary parameter, if the power generation amount of the c-type contract of the power plant i in the day-ahead plan is 1, otherwise, the variable is 0;
Figure BDA0003083114950000075
respectively positive deviation and negative deviation of contract electric quantity completion rate;
Figure BDA0003083114950000076
the execution rate of the c contract electric quantity arranged in the day ahead of the type power plant is set;
Figure BDA0003083114950000077
the auxiliary parameter is 0-1, if the contract power generation amount of the type c exists in the power supply type in the day-ahead plan of the power plant, the variable is 1, otherwise, the variable is 0;
and (3) coordinating and constraining the completion rate of the power generation amount outside the contract:
Figure BDA0003083114950000078
Figure BDA0003083114950000079
Figure BDA00030831149500000710
in the formula (I), the compound is shown in the specification,
Figure BDA00030831149500000711
the auxiliary parameter is 0-1, if the power plant i has an out-of-contract power generation amount in the day-ahead plan, the variable is 1, otherwise, the variable is 0; etai,fThe execution rate of the power plant i day before the contract external power generation amount;
Figure BDA00030831149500000712
respectively positive deviation and negative deviation of the contract external power generation quantity completion rate;
Figure BDA00030831149500000713
the execution rate of the overall out-of-contract power generation amount of the type power plant;
Figure BDA00030831149500000714
and the auxiliary parameter is 0-1, if the power supply type has the power generation amount out of contract in the day-ahead plan, the variable is 1, and if not, the variable is 0.
Compared with the prior art, the invention has the beneficial effects that: according to the method, the effectiveness and fairness of the day-ahead plan execution in the day-ahead operation are described in a quantitative mode through modeling of the day-ahead electric quantity plan, the electric quantity plan adjustment quantity and the electric quantity completion progress of the unit; by solving an intra-day rolling optimization scheduling model which minimizes the deviation between the power generation adjustment cost and the electric quantity completion rate and maximizes the consumption of new energy, the tracking completion and rolling coordination of various types of electric quantities are realized in an intra-day power generation plan, and an intra-day scheduling decision of the power system is obtained after the economy, the scheduling fairness and the consumption of new energy are optimized, so that the requirement of 'three-way' scheduling in the transition period of the power market is met, and the problem that the intra-day electric quantity control adapts to the change of the power grid operating environment is solved.
Drawings
Fig. 1 is a flowchart of a rolling scheduling method in day considering power coordination according to the present invention.
Fig. 2 is a result graph of scheduling electric quantity in each power plant day and day ahead.
Fig. 3 is a diagram of the results of the fairness of the planned completion of the electric power of each power plant.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
Referring to fig. 1-3, a intraday rolling optimization scheduling method considering electric quantity coordination introduces a target planning method, comprehensively considers unit state information acquired in real time, new energy forecast output information and daily planned electric quantity completion conditions by taking the maximum new energy consumption, tracking a diurnal output plan, tracking a diurnal electric quantity plan including contract decomposed electric quantity and taking the minimum deviation of electric quantity plan completion rate of each power plant as multiple targets, dynamically rolls an intraday optimization scheduling model, determines the start-stop mode of the intraday unit and the active scheduling value of the unit at each moment, adapts to the requirement of 'three public scheduling' in the transition period of an electric power market, and considers the effective and fair execution of the diurnal electric quantity plan.
Referring to fig. 1, the present invention specifically includes the following steps:
step one, acquiring a day-ahead scheduling plan of the power system as input, wherein the day-ahead scheduling plan comprises unit start-stop, output information and an electric quantity plan arranged day-ahead. The electric quantity plan comprises the daily total electric energy production of each power plant and contract electric energy production scheduled in the day-ahead scheduling plan. A certain amount of medium-long term contracts are signed by each power plant, the contract electric quantity is decomposed into executable daily electric quantity, and a day-ahead scheduling plan containing contract electric quantity is obtained after day-ahead scheduling.
And step two, updating the output power prediction and load prediction information of the new energy. Dividing 24 hours in the operating day into 96 points at intervals of 15min, and regarding a certain operating time t in the day0Updating the current operating time t0The unit state and the actual output; for the operating time t0And updating the short-term power prediction and the load prediction of the new energy in the interval of the ending time T of the day.
Step three, based on the electric power system day-ahead scheduling plan obtained in the step one and the updated new energy output power prediction and load prediction information obtained in the step two, aiming at maximum new energy consumption, minimum power generation plan adjustment cost, minimum electric quantity plan adjustment cost and minimum electric quantity completion rate deviation in the remaining time period of the day, comprehensively considering power supply constraint, system operation constraint and dayCoupling constraint of a front plan and daily electric quantity execution coordination constraint, establishing and solving a day-interior optimization scheduling model considering electric quantity coordination, and adjusting a scheduling time interval t according to the real-time execution condition of the day-ahead electric quantity plan on the day0And (3) the electric quantity completion progress at the finishing time T of the day is obtained, an intra-day scheduling scheme with optimal economy, maximum consumption of new energy and balanced electric quantity execution in the remaining target time period of the day is obtained, and the adaptability and execution precision of the unit intra-day electric quantity plan to the change of the power grid operation environment are improved.
The day-to-day optimization scheduling model considering electric quantity coordination is constructed through the following processes:
and (3) considering the day-by-day optimization scheduling model of electric quantity coordination to minimize the new energy power abandon cost, the day-ahead output plan adjustment cost, the day-ahead electric quantity plan deviation cost and the day-ahead electric quantity plan completion rate deviation as a multi-objective function.
Figure BDA0003083114950000091
Wherein f is1～f4Is a 4-term objective function; f. of4、f2、f3And f4Sequentially performing a target function of minimizing the new energy electricity abandoning cost, the day-ahead output plan adjustment cost, the day-ahead electric quantity plan deviation cost and the day-ahead electric quantity plan completion rate deviation; t is t0Is the current time; t is the end time of the day; Δ T is the rolling scheduling interval; n is a radical ofREThe number of new energy power plants comprises a wind power plant and a photovoltaic power plant;
Figure BDA0003083114950000092
the electricity abandoning cost of the new energy power plant i is low;
Figure BDA0003083114950000093
the electric power is abandoned by the new energy power plant i at the moment t; n is a radical ofHydroIs the number of hydroelectric power plants;
Figure BDA0003083114950000101
the electricity abandonment cost of the hydropower plant i;
Figure BDA0003083114950000102
the electric quantity is the electricity abandoning quantity of the hydraulic power plant i; n is a radical ofThermalIs the number of thermal power plants;
Figure BDA0003083114950000103
is the number of units of the power plant i; considering that thermal power participates in deep peak regulation service, the output adjustment quantity of the thermal power is subdivided, k is the type of the output adjustment quantity, and 0,1 and 2 correspond to obligation, first gear and second gear peak regulation respectively;
Figure BDA0003083114950000104
respectively outputting an up-regulation cost and a down-regulation cost in a k interval by a unit of the thermal power plant i;
Figure BDA0003083114950000105
the adjustment quantity of the unit j of the thermal power plant i in the k interval at the time t is obtained;
Figure BDA0003083114950000106
respectively adjusting the output up-regulation cost and the output down-regulation cost of the unit of the non-thermal power plant i;
Figure BDA0003083114950000107
the output adjustment amount of the non-thermal power plant i at the time t; n is the number of all power plants; n is a radical ofcontractThe number of medium and long term contract types signed by the power plant;
Figure BDA0003083114950000108
the deviation electric quantity of the contract c generated electricity in the day-ahead plan of the power plant i;
Figure BDA0003083114950000109
is the electricity deviation cost of the power plant i contract c;
Figure BDA00030831149500001010
the deviation electric quantity of the out-of-contract electric generation quantity in the day-ahead plan of the power plant i;
Figure BDA00030831149500001011
the deviation cost of the power generation amount outside the i contract of the power plant;
Figure BDA00030831149500001012
the power generation amount of the power plant i is increased within a day;
Figure BDA00030831149500001013
the cost of increasing the power generation amount in i days of the power plant; n is a radical oftypeIs the power type number;
Figure BDA00030831149500001014
the deviation value of the power generation amount completion rate of the day-ahead planned contract c of the power plant i is obtained;
Figure BDA00030831149500001015
the deviation value of the planned out-of-contract electricity generation completion rate of the power plant i days ago is shown.
The in-day optimization scheduling model considering electric quantity coordination simultaneously considers a plurality of mutually conflicting objective functions, so a target planning method is introduced, the distance between each objective function and the target value of the objective function is shortened as much as possible, and the multi-objective optimization of the objective functions is converted into single-objective optimization:
Figure BDA00030831149500001016
in the formula (I), the compound is shown in the specification,
Figure BDA00030831149500001017
weight factors corresponding to the positive and negative deviations of the g-th target, respectively;
Figure BDA00030831149500001018
positive and negative deviations of the objective function g from the target value, respectively; f. ofgA function g which is a target constraint;
Figure BDA00030831149500001019
for the target value of the function g, generally taken as fgOptimal value for single-target optimization of objective function。
The day-to-day optimized scheduling model considering electric quantity coordination satisfies the following constraints:
1. power supply operation constraint
Force restraint:
Figure BDA0003083114950000111
Figure BDA0003083114950000112
in the formula, Pi,j,tThe unit output at the moment t after the unit j of the power plant i is subjected to rolling correction; u. ofi,j,tThe starting state of the unit j of the power plant i at the moment t in the rolling optimization within the day is shown; pi,j,max,Pi,j,minRespectively the maximum and minimum technical output of the unit j in the power plant i; RE is a new energy power plant type, including wind power and photovoltaic;
Figure BDA0003083114950000113
the output of the new energy power plant at the moment t after the i is corrected in a rolling mode;
Figure BDA0003083114950000114
and (4) predicting a force value of the new energy power plant i at the moment t.
Unit climbing restraint:
Figure BDA0003083114950000115
in the formula (I), the compound is shown in the specification,
Figure BDA0003083114950000116
the upward climbing speed and the downward climbing speed of the unit j in the power plant i are respectively.
2. System operational constraints
And power balance constraint:
Figure BDA0003083114950000117
in the formula, DtIs the system load at time t.
Network security constraints:
Figure BDA0003083114950000118
modeling by using direct current power flow, wherein P isl,min,Pl,maxRespectively the active power flow limit of the line l; gl,iIs a generator transfer distribution factor of a unit to a line l; dj,tIs the load at time t of node j.
System standby constraints:
Figure BDA0003083114950000119
in the formula srt,sdtRespectively, the minimum upper and lower rotational reserve capacities of the system at time t.
3. Coupling constraints with day-ahead planning
Here primarily the coupling of an in-day power plan with a day-ahead power plan. Firstly, introducing a thermal power generating unit: the output state of the thermal power generating unit can be divided into three load states of obligate peak regulation and first and second gear peak regulation according to the requirements of a peak regulation auxiliary service market, and is generally defined according to a load rate: the peak shaving with the output of more than 50 percent is obligate peak shaving; the peak shaving with the output lower than 50 percent and higher than 40 percent is the first gear peak shaving; and the peak shaving when the output is lower than 40 percent is the second gear peak shaving. Therefore, the difference part between the daytime output and the planned daytime output of the thermal power generating unit, namely the adjustment quantity, is divided into the adjustment quantities in 3 intervals according to the three peak shaving states.
Figure BDA0003083114950000121
In the formula (I), the compound is shown in the specification,
Figure BDA0003083114950000122
is the output of the unit j of the power plant i at the moment t in the day-ahead plan;
Figure BDA0003083114950000123
respectively outputting the up-regulation and the down-regulation of the unit j of the power plant i at the moment t;
Figure BDA0003083114950000124
respectively adjusting the unit j of the power plant i in the obligation peak shaving interval at the moment t;
Figure BDA0003083114950000125
respectively adjusting the unit j of the power plant i in a first gear peak regulation interval at the moment t;
Figure BDA0003083114950000126
the adjustment quantities of the unit j of the power plant i in the second gear peak regulation interval at the moment t are respectively non-negative variables.
The output coupling constraint of the hydroelectric generating set and the new energy generating set does not subdivide the adjustment quantity of the output of the generating set. Output coupling constraint of the hydroelectric generating set:
Figure BDA0003083114950000127
output coupling constraint of the new energy unit:
Figure BDA0003083114950000128
in the formula (I), the compound is shown in the specification,
Figure BDA0003083114950000129
and the output of the new energy power plant i at the moment t in the day-ahead plan.
4. Daily electric quantity execution coordination constraint
Daily power execution coordination should first ensure that tracking completes the day-ahead power plan. The power generation plan in the day includes three types of electric quantity: the contract power generation amount before the day, the contract power generation amount before the day and the planned power generation amount before the day. And the idea of piecewise linearization is adopted for constraint, so that the electric quantity after daily rolling is coupled with the planned electric quantity in the day ahead, and the electric quantity in the day is ensured to follow the order of contract priority when being executed.
Total power plant NContractSeed contracts, serial numbers from 1 to NContractPriority levelAnd (4) decreasing.
Figure BDA00030831149500001210
Figure BDA0003083114950000131
Figure BDA0003083114950000132
Figure BDA0003083114950000133
In the formula, EiThe daily total power generation of the power plant i comprises the power generation of the operated period of the day and the power generation of a subsequent adjustment plan; Δ T is the scrolling time step;
Figure BDA0003083114950000134
is power plant i at t0Actual operating power at a time t before the time; ei,cIs the power generation amount of the power plant i in the contract c of the day; etai,cThe ratio of the generated energy of the power plant i contract c to the generated energy of the contract c in the day ahead is obtained;
Figure BDA0003083114950000135
is the planned power generation amount of the power plant i contract c in the day ahead; etai,extraIs a linearization parameter of the power plant i contract external power generation quantity;
Figure BDA0003083114950000136
the power generation amount of the power plant i is beyond the contract power amount of the day;
Figure BDA0003083114950000137
the power generation amount of the power plant i outside the contract electric quantity in the day-ahead plan;
Figure BDA0003083114950000138
newly adding electric quantity in the power plant i in the day;
Figure BDA0003083114950000139
the power plant i contracts the external electricity deviation amount before the day of the day; lambda [ alpha ]i,c,λi,extraIs a 0-1 auxiliary parameter.
On the basis of completing the day-ahead electricity quantity plan, the electricity quantity completion rates of all power plants are converged, including a day-ahead contract electricity quantity completion rate and a day-ahead contract external electricity quantity completion rate.
And (3) contract electric quantity completion rate coordination constraint:
Figure BDA00030831149500001310
Figure BDA00030831149500001311
Figure BDA00030831149500001312
in the formula (I), the compound is shown in the specification,
Figure BDA00030831149500001313
the variable is 0-1 auxiliary parameter, if the power generation amount of the c-type contract of the power plant i in the day-ahead plan is 1, otherwise, the variable is 0;
Figure BDA0003083114950000141
respectively positive deviation and negative deviation of contract electric quantity completion rate;
Figure BDA0003083114950000142
the execution rate of the c contract electric quantity arranged in the day ahead of the type power plant is set;
Figure BDA0003083114950000143
is an auxiliary parameter of 0-1, and the power supply type has contract power generation amount of type c in the day-ahead plan of the power plant, and the variable is1, otherwise 0.
And (3) coordinating and constraining the completion rate of the power generation amount outside the contract:
Figure BDA0003083114950000144
Figure BDA0003083114950000145
Figure BDA0003083114950000146
in the formula (I), the compound is shown in the specification,
Figure BDA0003083114950000147
the auxiliary parameter is 0-1, if the power plant i has an out-of-contract power generation amount in the day-ahead plan, the variable is 1, otherwise, the variable is 0; etai,fThe execution rate of the power plant i day before the contract external power generation amount;
Figure BDA0003083114950000148
respectively positive deviation and negative deviation of the contract external power generation quantity completion rate;
Figure BDA0003083114950000149
the execution rate of the overall out-of-contract power generation amount of the type power plant;
Figure BDA00030831149500001410
and the auxiliary parameter is 0-1, if the power supply type has the power generation amount out of contract in the day-ahead plan, the variable is 1, and if not, the variable is 0.
The variables to be solved of the intraday optimized scheduling model considering the electric quantity coordination are { u } corresponding to each set at each moment in the operation dayi,j,t,pi,j,t}. The scheduling model is essentially a mixed integer linear programming model, and a solution result of variables is obtained after the solution is carried out by adopting a branch-and-bound method or a secant plane algorithm, wherein u isi,j,tCorrespond to and repairA unit start-stop plan in the direct future; p is a radical ofi,j,tAnd correspondingly considering the active power dispatching values of all the units which are executed in a balanced manner by the power plan.
Step four, taking the unit start-stop and output scheme of the first time period in the remaining time period scheduling scheme determined in the step three as the current time t0The modification of (1).
Step five, according to the current time t0The correction scheme of (3) judges whether 96 points in the whole day are finished, if not, the next time is entered, i.e. t is ordered0＝t0+1, repeating the second step to the fifth step; and if the scheduling is finished, finishing the rolling scheduling of the day all day, and obtaining the intra-day scheduling plan considering the electric quantity coordination. The scheduling scheme in the day is used in the operation of the power system, so that the electric quantity plan before the day can be effectively tracked, the effectiveness and fairness of electric quantity completion are guaranteed, all power plants participate in new energy consumption, and the influence of the output fluctuation of the new energy on the operation of the system is reduced.
Taking an actual power system of a certain province in northwest China as an example to carry out rolling scheduling in all days, the electric quantity scheduling results of each power plant (11-20 are photovoltaic power plants, 21-26 are wind power plants, and 26-31 are thermal power plants) are shown in FIG. 2. All power plants in the day can realize 100% execution of day-ahead contract electric quantity, the completion rate deviation of day-ahead contract external electric quantity of the power plants is controlled within the range of 4 percentage points, the day-ahead electric quantity plan of each power plant is effectively completed, the planned electric quantity completion rate of the day of each power plant is basically at the same level, the completion rate deviation of each contract external electric quantity is shown in figure 3, the average value of the deviation is 2.76 percentage points, the coordination of execution of each type of electric quantity is fully ensured, and the day-ahead electric quantity plan is effectively and fairly executed.
According to the invention, by introducing a target planning method, the maximum consumption of new energy, the tracking of a day-ahead output plan, the tracking of a day-ahead electric quantity plan including contract decomposition electric quantity and the minimum deviation of the completion rate of the electric quantity plan are taken as multiple targets, the power grid, the unit state information, the predicted output information of the new energy and the completion condition of the day-planned electric quantity which are obtained in real time are comprehensively considered, and a day-interior optimization scheduling model is dynamically rolled, so that the rolling modified power generation plan promotes the consumption of the new energy, and meanwhile, the balanced control of the completion progress of the unit day-oriented electric quantity plan is realized, the problems that the unit day-oriented electric quantity control adapts to the change of the power grid operating environment difficultly and the dependence of manual intervention is high are solved, and.

Claims (9)

1. A rolling scheduling method in day considering electric quantity coordination is characterized by comprising the following steps:
acquiring a day-ahead scheduling plan of the power system, wherein the day-ahead scheduling plan of the power system comprises unit start-stop, output information and an electric quantity plan arranged day-ahead; the electric quantity plan comprises the daily total electric energy production and contract electric energy production of each power plant scheduled in the day-ahead scheduling plan;
updating the output power prediction and load prediction information of the ultra-short term new energy;
step three, based on the results obtained in the step one and the step two, establishing and solving an in-day optimized scheduling model considering electric quantity coordination, and rolling and correcting the electric quantity plan of a scheduling period to obtain an in-day scheduling scheme considering electric quantity coordination in the remaining period of the day;
taking a unit start-stop and output scheme of a first time period in a day scheduling scheme considering electric quantity coordination in the remaining time period of the day as a correction scheme of the current time;
step five, judging whether 96 points in the whole day are finished according to the correction scheme, if not, entering the next moment, and repeating the step two to the step five; and if the scheduling is finished, the rolling scheduling of the day is finished all day, a day scheduling plan considering the electric quantity coordination is obtained, the day-ahead electric quantity plan is effectively tracked according to the day scheduling plan considering the electric quantity coordination, and the effectiveness and the fairness of the electric quantity completion are guaranteed.
2. The rolling scheduling method in days taking power coordination into consideration as claimed in claim 1, wherein step two comprises the following processes: dividing 24 hours in the operating day into 96 points at intervals of 15min, and regarding a certain operating time t in the day0Updating the current operating time t0The unit state and the actual output; for theOperating time t0And updating the short-term power prediction and the load prediction of the new energy in the interval of the ending time T of the day.
3. The method according to claim 1, wherein in step three, the intraday optimal scheduling model considering power coordination is established through the following processes:
and (3) considering the day-by-day optimization scheduling model of electric quantity coordination to minimize the new energy power abandon cost, the day-ahead output plan adjustment cost, the day-ahead electric quantity plan deviation cost and the day-ahead electric quantity plan completion rate deviation as a multi-objective function.
4. The rolling scheduling method in days taking into account power coordination of claim 3, wherein minimizing new energy curtailment cost, day-ahead contribution plan adjustment cost, day-ahead power plan deviation cost, and day-ahead power plan completion rate deviation objective function is as follows:
Figure FDA0003083114940000021
wherein f is1～f4Is a 4-term objective function; f. of4、f2、f3And f4Sequentially performing a target function of minimizing the new energy electricity abandoning cost, the day-ahead output plan adjustment cost, the day-ahead electric quantity plan deviation cost and the day-ahead electric quantity plan completion rate deviation; t is t0Is the current time; t is the end time of the day; Δ T is the rolling scheduling interval; n is a radical ofREThe number of new energy power plants comprises a wind power plant and a photovoltaic power plant;
Figure FDA0003083114940000022
the electricity abandoning cost of the new energy power plant i is low;
Figure FDA0003083114940000023
the electric power is abandoned by the new energy power plant i at the moment t; n is a radical ofHydro is the number of hydroelectric power plants；
Figure FDA0003083114940000024
The electricity abandonment cost of the hydropower plant i;
Figure FDA0003083114940000025
the electric quantity is the electricity abandoning quantity of the hydraulic power plant i; n is a radical ofThermalIs the number of thermal power plants;
Figure FDA0003083114940000026
is the number of units of the power plant i; k is the type of the output adjustment;
Figure FDA0003083114940000027
respectively outputting an up-regulation cost and a down-regulation cost in a k interval by a unit of the thermal power plant i;
Figure FDA0003083114940000028
the adjustment quantity of the unit j of the thermal power plant i in the k interval at the time t is obtained;
Figure FDA0003083114940000029
respectively adjusting the output up-regulation cost and the output down-regulation cost of the unit of the non-thermal power plant i;
Figure FDA00030831149400000210
the output adjustment amount of the non-thermal power plant i at the time t; n is the number of all power plants; n is a radical ofcontractThe number of medium and long term contract types signed by the power plant;
Figure FDA00030831149400000211
the deviation electric quantity of the contract c generated electricity in the day-ahead plan of the power plant i;
Figure FDA00030831149400000212
is the electricity deviation cost of the power plant i contract c;
Figure FDA00030831149400000213
the deviation electric quantity of the out-of-contract electric generation quantity in the day-ahead plan of the power plant i;
Figure FDA00030831149400000214
the deviation cost of the power generation amount outside the i contract of the power plant;
Figure FDA00030831149400000215
the power generation amount of the power plant i is increased within a day;
Figure FDA00030831149400000216
the cost of increasing the power generation amount in i days of the power plant; n is a radical oftypeIs the power type number;
Figure FDA00030831149400000217
the deviation value of the power generation amount completion rate of the day-ahead planned contract c of the power plant i is obtained;
Figure FDA00030831149400000218
the deviation value of the planned out-of-contract electricity generation completion rate of the power plant i days ago is shown.
5. The rolling scheduling method in days taking power coordination into consideration as claimed in claim 3, wherein the multi-objective optimization of the objective function is converted into the single-objective optimization, and the process is as follows:
Figure FDA0003083114940000031
in the formula (I), the compound is shown in the specification,
Figure FDA0003083114940000032
weight factors corresponding to the positive and negative deviations of the g-th target, respectively;
Figure FDA0003083114940000033
respectively positive deviation of the objective function g from the target valueAnd a negative offset; f. ofgA function g which is a target constraint;
Figure FDA0003083114940000034
as target value of function g, ingAnd (4) performing optimal value of single-target optimization on the objective function.
6. The method for rolling scheduling in days considering power coordination according to claim 1, wherein the intra-day optimized scheduling model considering power coordination satisfies power supply operation constraints, which are as follows:
force restraint:
Figure FDA0003083114940000035
Figure FDA0003083114940000036
in the formula, Pi,j,tThe unit output at the moment t after the unit j of the power plant i is subjected to rolling correction; u. ofi,j,tThe starting state of the unit j of the power plant i at the moment t in the rolling optimization within the day is shown; pi,j,max,Pi,j,minRespectively the maximum and minimum technical output of the unit j in the power plant i; RE is a new energy power plant type, including wind power and photovoltaic;
Figure FDA0003083114940000037
the output of the new energy power plant at the moment t after the i is corrected in a rolling mode;
Figure FDA0003083114940000038
the predicted force value of the new energy power plant i at the moment t is obtained;
unit climbing restraint:
Figure FDA0003083114940000039
in the formula (I), the compound is shown in the specification,
Figure FDA00030831149400000310
the upward climbing speed and the downward climbing speed of the unit j in the power plant i are respectively.
7. The method for rolling scheduling in days considering power coordination according to claim 1, wherein the intra-day optimized scheduling model considering power coordination satisfies system operation constraints, which are as follows:
and power balance constraint:
Figure FDA00030831149400000311
in the formula, DtIs the system load at time t;
network security constraints:
Figure FDA0003083114940000041
modeling by using direct current power flow, wherein P isl,min,Pl,maxRespectively the active power flow limit of the line l; gl,iIs a generator transfer distribution factor of a unit to a line l; dj,tIs the load at time t of node j;
system standby constraints:
Figure FDA0003083114940000042
in the formula srt,sdtRespectively, the minimum upper and lower rotational reserve capacities of the system at time t.
8. The method for rolling scheduling in days considering power coordination according to claim 1, wherein the day-by-day optimized scheduling model considering power coordination satisfies coupling constraints with a day-ahead plan, specifically as follows: the output state of the thermal power generating unit is divided into three load states of duty peak regulation and first and second gear peak regulation according to the requirement of a peak regulation auxiliary service market, and the peak regulation with the output of more than 50 percent is duty peak regulation; the peak shaving with the output lower than 50 percent and higher than 40 percent is the first gear peak shaving; the peak shaving when the output is lower than 40 percent is the second gear peak shaving; dividing the difference part between the intraday output and the planned output before the day of the thermal power generating unit into adjustment quantities in 3 intervals according to three peak regulation states, wherein the adjustment quantities are the adjustment quantities;
Figure FDA0003083114940000043
in the formula (I), the compound is shown in the specification,
Figure FDA0003083114940000044
is the output of the unit j of the power plant i at the moment t in the day-ahead plan;
Figure FDA0003083114940000045
respectively outputting the up-regulation and the down-regulation of the unit j of the power plant i at the moment t;
Figure FDA0003083114940000046
respectively adjusting the unit j of the power plant i in the obligation peak shaving interval at the moment t;
Figure FDA0003083114940000047
respectively adjusting the unit j of the power plant i in a first gear peak regulation interval at the moment t;
Figure FDA0003083114940000048
respectively adjusting the unit j of the power plant i in a second gear peak regulation interval at the moment t;
output coupling constraint of the hydroelectric generating set:
Figure FDA0003083114940000049
output coupling constraint of the new energy unit:
Figure FDA00030831149400000410
in the formula (I), the compound is shown in the specification,
Figure FDA0003083114940000051
and the output of the new energy power plant i at the moment t in the day-ahead plan.
9. The method according to claim 1, wherein the intra-day optimal scheduling model considering power coordination satisfies a daily power execution coordination constraint, and specifically includes:
total power plant NContractSeed contracts, serial numbers from 1 to NContractDecreasing the priority;
Figure FDA0003083114940000052
Figure FDA0003083114940000053
Figure FDA0003083114940000054
Figure FDA0003083114940000055
in the formula, EiThe daily total power generation of the power plant i comprises the power generation of the operated period of the day and the power generation of a subsequent adjustment plan; Δ T is the scrolling time step;
Figure FDA0003083114940000056
is power plant i at t0Actual operating power at a time t before the time; ei,cIs the power generation amount of the power plant i in the contract c of the day; etai,cThe ratio of the generated energy of the power plant i contract c to the generated energy of the contract c in the day ahead is obtained;
Figure FDA0003083114940000057
is the planned power generation amount of the power plant i contract c in the day ahead; etai,extraIs a linearization parameter of the power plant i contract external power generation quantity;
Figure FDA0003083114940000058
the power generation amount of the power plant i is beyond the contract power amount of the day;
Figure FDA0003083114940000059
the power generation amount of the power plant i outside the contract electric quantity in the day-ahead plan;
Figure FDA00030831149400000510
newly adding electric quantity in the power plant i in the day;
Figure FDA00030831149400000511
the power plant i contracts the external electricity deviation amount before the day of the day; lambda [ alpha ]i,c,λi,extraIs 0-1 auxiliary parameter;
and (3) contract electric quantity completion rate coordination constraint:
Figure FDA0003083114940000061
Figure FDA0003083114940000062
Figure FDA0003083114940000063
in the formula (I), the compound is shown in the specification,
Figure FDA0003083114940000064
is an auxiliary parameter of 0-1, the variable is 1 if the power generation amount of the power plant i with the c-type contract in the day-ahead plan is not the same as the variable, otherwise, the variable is0；
Figure FDA0003083114940000065
Respectively positive deviation and negative deviation of contract electric quantity completion rate;
Figure FDA0003083114940000066
the execution rate of the c contract electric quantity arranged in the day ahead of the type power plant is set;
Figure FDA0003083114940000067
the auxiliary parameter is 0-1, if the contract power generation amount of the type c exists in the power supply type in the day-ahead plan of the power plant, the variable is 1, otherwise, the variable is 0;
and (3) coordinating and constraining the completion rate of the power generation amount outside the contract:
Figure FDA0003083114940000068
Figure FDA0003083114940000069
Figure FDA00030831149400000610
in the formula (I), the compound is shown in the specification,
Figure FDA00030831149400000611
the auxiliary parameter is 0-1, if the power plant i has an out-of-contract power generation amount in the day-ahead plan, the variable is 1, otherwise, the variable is 0; etai,fThe execution rate of the power plant i day before the contract external power generation amount;
Figure FDA00030831149400000612
respectively positive deviation and negative deviation of the contract external power generation quantity completion rate;
Figure FDA00030831149400000613
the execution rate of the overall out-of-contract power generation amount of the type power plant;
Figure FDA00030831149400000614
and the auxiliary parameter is 0-1, if the power supply type has the power generation amount out of contract in the day-ahead plan, the variable is 1, and if not, the variable is 0.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113922734A (en) * 2021-09-18 2022-01-11 南京南瑞继保电气有限公司 Light storage controller and light storage control method for reshaping external characteristics of photovoltaic power station
CN113962553A (en) * 2021-10-22 2022-01-21 国网山东省电力公司经济技术研究院 Real-time correction method and system for comprehensive service station
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CN114792206A (en) * 2022-04-29 2022-07-26 西安交通大学 Uncertainty-considered day-ahead scheduling method for double-tower gravity energy storage power station
CN117096957A (en) * 2023-10-20 2023-11-21 国网辽宁省电力有限公司电力科学研究院 Multi-source collaborative optimization method and system for power distribution network

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102156815A (en) * 2011-04-07 2011-08-17 国电南瑞科技股份有限公司 Method for optimizing power generation plan in mode of tracing scheduling of annual electric quantity progress
CN102738833A (en) * 2012-06-20 2012-10-17 湖北省电力公司 Multi-time-scale rolling coordination scheduling method for electric power system with wind power
CN103886388A (en) * 2014-03-06 2014-06-25 国家电网公司 Multi-cycle generation scheduling coordinated optimization and closed-loop control method
CN106160008A (en) * 2016-07-08 2016-11-23 华北电力大学 A kind of new forms of energy of dissolving be obstructed power He Yuan coordinate control rolling amendment method
CN106160091A (en) * 2016-07-25 2016-11-23 东南大学 Promote the electric automobile charging station discharge and recharge dispatching method that regenerative resource is dissolved
CN107332286A (en) * 2017-07-27 2017-11-07 清华大学 A kind of cogeneration of heat and power containing heat accumulation and wind-powered electricity generation coordinated scheduling method
WO2018229895A1 (en) * 2017-06-14 2018-12-20 株式会社日立製作所 Energy management device and method, energy management system, and energy management system operation planning method
CN112465181A (en) * 2020-09-11 2021-03-09 国网天津市电力公司电力科学研究院 Two-stage optimization scheduling method supporting source-network-load-storage multi-element ubiquitous coordination

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102156815A (en) * 2011-04-07 2011-08-17 国电南瑞科技股份有限公司 Method for optimizing power generation plan in mode of tracing scheduling of annual electric quantity progress
CN102738833A (en) * 2012-06-20 2012-10-17 湖北省电力公司 Multi-time-scale rolling coordination scheduling method for electric power system with wind power
CN103886388A (en) * 2014-03-06 2014-06-25 国家电网公司 Multi-cycle generation scheduling coordinated optimization and closed-loop control method
CN106160008A (en) * 2016-07-08 2016-11-23 华北电力大学 A kind of new forms of energy of dissolving be obstructed power He Yuan coordinate control rolling amendment method
CN106160091A (en) * 2016-07-25 2016-11-23 东南大学 Promote the electric automobile charging station discharge and recharge dispatching method that regenerative resource is dissolved
WO2018229895A1 (en) * 2017-06-14 2018-12-20 株式会社日立製作所 Energy management device and method, energy management system, and energy management system operation planning method
CN107332286A (en) * 2017-07-27 2017-11-07 清华大学 A kind of cogeneration of heat and power containing heat accumulation and wind-powered electricity generation coordinated scheduling method
CN112465181A (en) * 2020-09-11 2021-03-09 国网天津市电力公司电力科学研究院 Two-stage optimization scheduling method supporting source-network-load-storage multi-element ubiquitous coordination

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
ZIHAO LI ET AL.: "An Decomposition Algorithm for Distribution Network Reconfiguration Schedule Considering Demand Response", 《IEEE》 *
张倩文 等: "大用户直购电模式下含大规模风光的电力系统优化调度", 《电力建设》 *
白杨 等: "电量协调与成本控制的日内滚动发电计划", 《电网技术》 *
许传龙 等: "基于预招标的月度偏差电量平衡机制及其多周期发电调度优化模型", 《中国电机工程学报》 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113922734A (en) * 2021-09-18 2022-01-11 南京南瑞继保电气有限公司 Light storage controller and light storage control method for reshaping external characteristics of photovoltaic power station
CN113922734B (en) * 2021-09-18 2023-12-29 南京南瑞继保电气有限公司 Light storage controller for remolding external characteristics of photovoltaic power station and light storage control method
CN113962553A (en) * 2021-10-22 2022-01-21 国网山东省电力公司经济技术研究院 Real-time correction method and system for comprehensive service station
CN114336730A (en) * 2021-11-21 2022-04-12 国网江苏省电力有限公司 Low-carbon optimal scheduling method considering auxiliary service optimization for power system
CN114336730B (en) * 2021-11-21 2023-08-15 国网江苏省电力有限公司 Low-carbon optimal scheduling method for electric power system considering auxiliary service optimization
CN114792206A (en) * 2022-04-29 2022-07-26 西安交通大学 Uncertainty-considered day-ahead scheduling method for double-tower gravity energy storage power station
CN117096957A (en) * 2023-10-20 2023-11-21 国网辽宁省电力有限公司电力科学研究院 Multi-source collaborative optimization method and system for power distribution network
CN117096957B (en) * 2023-10-20 2023-12-26 国网辽宁省电力有限公司电力科学研究院 Multi-source collaborative optimization method and system for power distribution network

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