CN111555281A - Method and device for simulating flexible resource allocation of power system - Google Patents
Method and device for simulating flexible resource allocation of power system Download PDFInfo
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Abstract
The invention belongs to the field of power system resource allocation, and provides a power system flexible resource allocation simulation method and device. The method comprises the steps of obtaining load data and unit data to be put into production in a planning year, and constructing and simulating a power supply optimization investment decision model by taking the minimum total cost in the planning year as an objective function to obtain various power plant putting-in and building schemes; establishing and simulating a short-term operation simulation model by using the minimum cost as a target function to obtain a typical day unit starting mode; based on typical day-long unit combination, corresponding penalty items are added, the minimum cost is used as a target function, an ultra-short-term operation simulation model is constructed and simulated, the up-down peak-shaving power shortage and the up-down climbing power shortage of the system in a typical time period are obtained and serve as flexibility indexes, whether the flexibility requirement of the power system is met is judged, if the flexibility requirement is not met, the flexible unit is added and tuned, if the flexibility requirement is not met, the commissioning condition of various power plants is simulated and planned again until the flexibility requirement is met.
Description
Technical Field
The invention belongs to the field of power system resource allocation, and particularly relates to a power system flexible resource allocation simulation method and device. Power system flexible resource configuration simulation
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Different from the traditional energy forms of thermal power, hydroelectric power and the like, the method is influenced by random meteorological factors, and the output of renewable energy sources such as photovoltaic power, wind power and the like is measured in a magic way. Therefore, large-scale grid connection of renewable energy is seriously aggravating the variability and uncertainty degree of the operation of the power system, thereby bringing great challenges to the stable operation and power balance of the power system. This requires that, in the resource allocation process of the power system, not only long-term power-electricity balance needs to be ensured, but also short-term or even ultra-short-term power-electricity balance needs to be ensured. The power balance of the power system does not simply mean the balance of long-term operation scheduling, but also includes the short-term power balance when the load demand is suddenly changed, namely the flexibility of the power system.
The inventor finds that most of the existing researches are centered on the aspects of flexible unit modeling, ultra-short-term optimization scheduling methods, flexible resource mining and the like, the comprehensive planning of flexible resources and the multi-level coordination planning of the flexible resources containing high-proportion renewable energy sources are lacked, and the optimal allocation of the power system resources considering both economy and flexibility is difficult to realize.
Disclosure of Invention
In order to solve the problems, the invention provides a simulation method and a device for flexible resource allocation of a power system, which comprehensively consider three layers of a planning layer, an operation layer and a flexible layer, solve a power supply optimization investment decision model and a short-term and ultra-short-term operation simulation model through multiple iterations, combine the multi-layer iteration and relaxation technology on the solution method, improve the robustness of calculation, enable the calculation result to meet the system flexibility requirement and realize the optimal system economy, and are finally suitable for resource optimization allocation of a large power system.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a simulation method for flexible resource allocation of a power system.
A power system flexibility resource configuration simulation method comprises the following steps:
acquiring load data and unit data to be put into production in a planning year, and constructing and simulating a power supply optimization investment decision model by taking the minimum total cost in the planning year as a target function to obtain various power plant putting schemes;
based on various power plant commissioning schemes, a short-term operation simulation model is constructed and simulated and solved by taking the minimum cost as a target function, and the starting mode of the unit in a typical day is obtained;
based on the typical in-day unit combination, corresponding penalty items are added, the minimum cost is used as a target function, an ultra-short-term operation simulation model is constructed and simulated and solved, and the up-down peak-shaving power shortage and the up-down climbing power shortage of the system in a typical time period are solved;
and judging whether the flexibility requirement of the power system is met by taking the power shortage of up-down peak shaving and the power shortage of up-down climbing of the system in a typical time period as flexibility indexes, if not, increasing and adjusting the flexible unit, and if not, re-simulating and planning the commissioning condition of each type of power plant until the flexibility requirement is met.
A second aspect of the present invention provides a power system flexible resource allocation simulation apparatus.
An electric power system flexibility resource configuration simulation apparatus, comprising:
the planning layer model building and solving module is used for obtaining load data and unit data to be put into operation in a planning year, and building and simulating a power supply optimization investment decision model by taking the minimum total cost in the planning year as a target function to obtain various power plant putting-in schemes;
the operation layer model building and solving module is used for building and simulating a short-term operation simulation model based on various power plant commissioning schemes by taking the minimum cost as a target function to obtain a typical day unit starting mode;
the flexible layer model building and solving module is used for building and simulating an ultra-short-term operation simulation model based on typical in-day unit combination by adding corresponding penalty items and taking the minimum cost as a target function, and solving the up-down peak-load power shortage and the up-down slope-climbing power shortage of the system in a typical time period;
and the flexibility requirement judging module is used for judging whether the flexibility requirement of the power system is met by taking the power shortage of the up-down peak regulation and the power shortage of the up-down climbing of the system in a typical time period as flexibility indexes, increasing and regulating the flexible unit if the flexibility requirement is not met, and re-simulating and planning the commissioning condition of each type of power plant if the flexibility requirement is not met after the flexible unit is increased and regulated until the flexibility requirement is met.
A third aspect of the invention provides a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps in the power system flexible resource configuration simulation method as described above.
A fourth aspect of the invention provides a computer apparatus.
A computer apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the power system flexibility resource configuration simulation method as described above when executing the program.
Compared with the prior art, the invention has the beneficial effects that:
in the embodiment, a planning layer, an operation layer and a flexible layer are comprehensively considered, a power supply optimization investment decision model, a short-term operation simulation model and an ultra-short-term operation simulation model are constructed in a simulation mode, the power supply optimization investment decision model, the short-term operation simulation model and the ultra-short-term operation simulation model are solved in a multiple-simulation iteration mode, a multilayer iteration and relaxation technology are combined in a solving method, the robustness of calculation is improved, the calculation result can meet the system flexibility requirement, the system economy is optimal, and the method is finally suitable for resource optimization configuration of a large power system.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of a simulation method for flexible resource allocation of an electric power system according to an embodiment of the present invention;
FIG. 2 illustrates a planned period power balance and load trend of an embodiment of the present invention;
FIG. 3 is a diagram of the accumulated electric power of each power source in the initial ultra-short term scheduling of the power system according to the embodiment of the invention;
FIG. 4 shows the peak shaving power shortage at each time of the initial ultra-short term scheduling of the power system according to the embodiment of the present invention;
FIG. 5 is a graph of accumulated power of each power source in ultra-short scheduling of the power system after iteration according to the embodiment of the present invention;
FIG. 6 shows the power shortage of the up-down peak shaving at each time of the ultra-short scheduling of the power system after iteration according to the embodiment of the present invention;
FIG. 7 is a load curve after an embodiment of the present invention that accounts for interruptible load participation in ultra-short term scheduling.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
As shown in fig. 1, a simulation method for flexible resource allocation of an electric power system in this embodiment includes:
step 1): load data and unit data to be put into production in the planning year are obtained, a power supply optimization investment decision model is constructed and simulated and solved by taking the minimum total cost in the planning year as an objective function, and various power plant putting schemes are obtained.
In the specific implementation, economic factors are considered in the power supply planning process, the objective function is the total cost in the planning year, and a power supply optimization investment decision model is obtained, namely a planning layer model:
wherein:
in the formula, T1The total number of months in the planning period; g1、RES1、H1、BES1Respectively planning the number of conventional, renewable energy, hydropower plants and energy storage power plants to be built in the period;
the new capacity and unit construction cost of the conventional power plant in the Tth month are increased;
newly increased capacity and unit construction cost of the renewable energy power plant in the Tth month;
newly increased capacity and unit construction cost of the hydropower plant in the Tth month;
indicating whether the original investment cost and the service life need to be shared by newly adding conventional, renewable energy, hydropower and energy storage in the Tth month;
newly adding the power capacity and the energy capacity of the energy storage device for the Tth month and the corresponding unit capacity construction cost; zetatThe coefficient of the present value of the t month; σ is annual percentage of discount.
The total installed capacity of the existing various power supplies will be used in the subsequent model, so that:
in the formula:
respectively representing the installed capacity, newly-increased installed capacity, retired capacity and final installed capacity of each thermal power plant in corresponding time periods;
respectively representing the existing installed capacity, the newly-increased installed capacity, the retired capacity and the final installed capacity of the renewable energy power plant in corresponding time periods;
respectively representing the existing installed capacity, the newly-increased installed capacity, the retired capacity and the final installed capacity of the water power plant in corresponding time periods.
And respectively representing the existing installed capacity, the newly-increased installed capacity, the retired capacity and the final installed capacity of the energy storage unit in the corresponding time period.
Constraint conditions are as follows:
the constraint conditions of power supply investment decision mainly comprise power constraint:
in the formula, Pt L,max、
The maximum load value and the static capacity reserve coefficient participating in power balance in the t month.
Maximum and minimum utilization hour constraints of various power plants:
in the formula,
the minimum and maximum utilization hours of the conventional unit;
the actual utilization hours of a conventional unit.
And (3) renewable energy source installation proportion constraint:
in the formula, αtAnd the renewable energy is installed for the t month.
Electric quantity constraint of a hydraulic power plant:
in the formula,
the method comprises the following steps of (1) consuming electric quantity for the tth month of a hydropower plant;
and predicting the electric quantity for the t month horizontal year of the hydropower plant.
Considering the interaction of the power supply investment decision module and the short-term operation simulation module, the negative standby constraint should also be considered:
in the formula,
the maximum and minimum technical output of the thermal power plant i in the t month;
providing a negative spare capacity coefficient for the thermal power plant i in the t month;
forecasting the yield of the hydropower plant in the t-th month in the open water year;
providing a negative spare capacity coefficient for the hydropower plant l;
rated power is generated and stored for a first energy storage power plant k;
providing a negative standby capacity coefficient for an energy storage power plant k; pt L,maxMaximum load at month t;
is the negative spare requirement coefficient for the t month.
Considering the interaction between the power supply investment decision module and the ultra-short-term operation simulation module, the climbing resource constraint should be considered:
in the formula,
the upward and downward climbing rates of the conventional power plant;
the upward and downward climbing speeds of the renewable energy power plant;
the upward and downward climbing rates of the hydropower plant are adopted;
the upward and downward climbing speeds of the energy storage power plant; the delta T is the time interval of the climbing resource constraint response;
in order to consider the prediction error of the new energy, the upward and downward climbing capacity accounts for the installed proportion of the new energy;
in order to consider the prediction error of the load, the upward and downward climbing capacity accounts for the proportion of the peak load.
Step 2): based on various power plant commissioning schemes, a short-term operation simulation model is constructed and simulated and solved by taking the minimum cost as an objective function, and the unit starting mode in a typical day is obtained.
The short-term operation simulation model is an operation layer model, the objective function of the short-term operation simulation model is the minimum cost, and the short-term operation simulation model comprises the coal consumption cost and the unit startup and shutdown cost caused by power generation, the maintenance cost and depreciation cost of an energy storage device, and the wind and light abandoning cost:
in the formula, G2、BES2、RES2The number of conventional, energy storage and renewable energy source units participating in short-term scheduling; t is2The number of time segments in a short-term scheduling period;
respectively representing the coal consumption cost, the starting cost and the shutdown cost of the unit i;
actual output of the conventional unit i at the time t;
starting state variables and stopping state variables of the unit i in a time period t;
the operation and maintenance cost is the unit electric quantity operation and maintenance cost of the kth energy storage unit;
the actual output of the energy storage unit k at the moment t;
rated capacity and rated power of the kth energy storage unit;
the current value of the unit capacity and unit power installation cost of the energy storage unit k is obtained;
the service life loss coefficient of the energy storage unit k is obtained;
the available resource quantity and the actual output of the renewable energy power plant l are obtained; rhoresA light abandoning penalty factor is abandoned for wind abandonment; rhoILAnd Pt ILPrice and interruptible load amount participating in short-term scheduling are compensated for interruptible load.
The coal consumption cost of the unit i in the above formula can be shown as a quadratic function:
in the formula, ai、bi、ciAnd (4) the coal consumption coefficient of the unit i.
Constraint conditions are as follows:
the constraint conditions in the short-term operation simulation model are equality constraint and inequality constraint, and the equality constraint is system active power balance constraint:
in the formula,
the active power output of the conventional, renewable energy, hydropower and energy storage unit at the moment t; pt ILAn interruptible load amount participating in short-term scheduling at time t; pt LThe load demand of the system at time t.
The inequality constraints are:
0≤Pt IL≤Pi IL,max(19)
equations (13) - (19) are respectively set output upper and lower limit constraints, climbing constraints, system hot standby constraints, start-stop cost constraints, start-stop time constraints, renewable energy output constraints, and interruptible load interruption amount constraints. Wherein, Ui,tStarting and stopping states of a conventional unit i at the time t;
the up and down climbing rates of the unit; rho is a hot standby coefficient;
limiting the maximum starting and stopping cost of the unit i; hi、JiSingle start-up, shut-down cost for group i; TS and TO are minimum shutdown and startup time;
the amount of available resources that are renewable energy; pi IL,maxIs the maximum amount of interruption that can interrupt the load.
In addition, the electric quantity constraint of the hydraulic power plant is the same as the formula (6) in the power supply optimization investment decision model, and the constraint in the energy storage participation system short-term scheduling process is as follows:
equations (20) - (23) are respectively the charge and discharge power upper and lower limit constraints of the energy storage, the working state constraints, the charge and discharge electricity storage constraints, and the capacities of the energy storage device at the beginning and the end of the scheduling period are equal. Wherein,
the working states of energy storage charging and discharging are achieved;
charging and discharging power for energy storage; mu.skIs a margin coefficient;
is the rated power of the energy storage device k; ek,tActual stored electric quantity of the energy storage device k at the moment t;
maximum and minimum stored charge for energy storage device k;kis the energy self-loss factor of the energy storage device k,
and the energy conversion efficiency in the charging and discharging processes of the energy storage unit is improved.
Step 3): based on the typical day unit combination, corresponding penalty items are added, the minimum cost is used as a target function, an ultra-short-term operation simulation model is constructed and simulated and solved, and the up-down peak-load power shortage and the up-down slope-climbing power shortage of the system in a typical time period are obtained.
Wherein, the ultra-short period operation simulation model is a flexible layer model.
In order to calculate flexibility indexes such as peak load and shortage of the system conveniently, a minute level is often used as a simulation time scale in the ultra-short-term operation simulation process of the power system. Because the whole simulation period is short, the start-stop state of the conventional unit is not changed in the simulation process, so that the start-stop cost of the unit can be not considered, and the depreciation cost and the maintenance cost of the energy storage device can be ignored. However, in the short-term operation simulation process, the situations of insufficient up-down climbing capacity and insufficient up-down peak-shaving capacity may occur, so that corresponding penalty items need to be added. Slightly modifying the target function in the short-term operation simulation model to obtain the target function of the ultra-short-term operation simulation:
in the formula, T3The number of running time is; g3、RES3Respectively the number of conventional units and the number of renewable energy units participating in ultra-short-term scheduling; pt reserve,U、Pt reserve,DThe peak power is relaxed between the upper and lower regulation; pt ramp,U、Pt ramp,DThe power of the upper and lower climbing is relaxed; rho1～ρ4Is the corresponding penalty factor.
The constraint conditions mainly include:
equations (25) - (28) are respectively system power balance constraint, normal unit operation climbing constraint, new energy unit operation constraint, and interruptible load interruption amount upper and lower limit constraint. Wherein, Pt reserve,DAdjusting peak power relaxation at the time t;
discharging and charging power for the energy storage device k; pt LLoad of the power system at the time t;
the capacity of climbing up and down slopes of the conventional unit.
In the new energy unit, because the fan output has inertia, the up-down climbing constraint of the fan also needs to be considered in the ultra-short-term scheduling:
in the formula,
the active power output of the fan j at the time t and the time t-1 is obtained;
the capacity of the fan for climbing up and down slopes.
Considering that the climbing capacity of the flexible resource is not enough, the climbing constraint needs to be relaxed:
in the formula, Pt FThe output of the flexible unit F at the moment t is obtained;
the up-down climbing capacity of the flexible unit F is achieved.
In addition, the system can still normally operate when any thermal power generating unit of the power system fails, and the constraint is as follows:
in the formula, K represents the number of all generator sets; pi max、Pi,tThe rated power of the generator i and the actual power at the moment t are obtained; pG,maxThe rated power of the maximum thermal power generating unit.
Step 4): and (3) judging whether the flexibility requirement of the power system is met by taking the power shortage of up-down peak shaving and the power shortage of up-down climbing of the system in a typical time period as flexibility indexes, if not, returning to the step 2) to increase and adjust the flexible unit, and if not, returning to the step 1) to re-simulate and plan the commissioning condition of each type of power plant until the flexibility requirement is met.
When the flexibility of the system is insufficient, the startup of a flexible unit needs to be increased, and meanwhile, the startup of a conventional unit needs to be reduced in order not to influence the peak reduction capability of the system. The objective function in the flexible unit tone-increasing model mainly comprises the cost of increasing the startup of the flexible unit and the cost of shutting down the conventional unit:
wherein F, C represents the number of flexible units and the number of conventional units;
the method comprises the steps of obtaining a flexible unit startup penalty coefficient and a conventional unit shutdown penalty coefficient;
the method comprises the steps of obtaining a flexible unit startup state variable and a conventional unit shutdown state variable;
the capacity increment for climbing up and down is relaxed;
the increase of the up-down peak-shaving capacity is relaxed; rho1～ρ4Is the corresponding penalty factor.
The constraint conditions mainly comprise upper and lower peak regulation/slope climbing shortage limit constraints:
in the formula, Pi C,max、Pi C,minIn order to reduce the maximum power and the minimum power of a conventional unit i for starting; pi F,max、Pi F,minIn order to increase the maximum power and the minimum power of a flexible unit i for starting;
in order to reduce the up-down climbing power of the conventional unit i when the unit is started;
in order to increase the up-down climbing power of the flexible unit i when the unit is started.
For convenience of calculation and observation, the up/down peak power shortage and the up/down hill climbing power shortage of the system are directly used as flexibility indexes of the system, the larger the numerical value of the system is, the larger the flexibility shortage of the system is, and the smaller the numerical value is, the smaller the flexibility shortage of the system is.
Coal consumption cost in the short-term operation simulation model and the ultra-short-term operation simulation model is represented in a quadratic function mode, so that the calculated amount of the system is greatly increased when the system is enlarged in scale, and a large amount of time is consumed in solving, so that the coal consumption cost can be subjected to piecewise linearization treatment:
in the formula: c0,i＝ai(Pi G,min)2+biPi G,min+ci；
m is the total number of segments; ki,sThe slope of each section of the coal consumption function after the piecewise linearization is adopted; c0,iCoal consumption cost when the unit operates with the lowest output;
the output of each section of the unit.
After linearization, both the short-term operation simulation model and the ultra-short-term operation simulation model are mixed integer linearization models, and meanwhile, the power optimization investment decision model is also a mixed integer linearization model, so that the solution is carried out by adopting commercial optimization software CPLEX. In order to simplify the program format and improve the program readability, MATLAB is used as a variable programming environment, a YALMIP toolbox is introduced, decision variables, target functions and constraint conditions can be conveniently and visually added, and finally a CPLEX solver is called for solving.
And (3) an iteration process: the solving sequence is that the power supply optimization investment decision model of the planning layer is solved, the solved power supply commissioning condition is transmitted to the operation layer, then the short-term operation simulation model is solved to obtain the unit combination in the short-term scheduling period, the unit combination result is transmitted to the flexible layer, and the ultra-short-term operation simulation model is solved to obtain the system flexibility shortage. Iteration is performed in the whole solving process according to whether the system standby requirement is met or not and whether the system flexibility requirement is met or not, and the specific flow is shown in fig. 1.
The validity of the proposed flexible resource allocation method is verified below with reference to specific examples. In a selected area, 12 conventional power plants exist in the initial year of planning, the total number of the power plants is 162, and the total installed capacity is 7.476 MW; there are 2 renewable energy power plants with a total confidence capacity of 1.502 MW. 9 conventional power plants are to be built in the planning period, and the total installed capacity is 3.74 MW; 4 hydroelectric plants to be built have the total installed capacity of 0.226 MW; 1 energy storage power plant to be built has the rated capacity of 0.02 MW; a renewable energy power plant to be built; the confidence capacity of the renewable energy power plant 12 to be built is 0.3206 MW.
The calculation example is put into the proposed model to be solved, the power balance and the monthly load change trend in the planning period can be obtained as shown in fig. 2, and various power plants can be reasonably built according to the load increase trend in the planning period to meet the power balance.
Selecting a typical day process short-term operation simulation, and selecting a renewable energy output rise time period of 7:00-9:00 in a typical day to perform an ultra-short-term operation simulation, wherein the ultra-short-term operation simulation time interval is selected to be 5 min. The accumulated energy map and the peak shaving deficit at each time in the initial ultra-short-term operation simulation result are shown in fig. 3 and 4, and the accumulated energy map and the peak shaving deficit at each time in the ultra-short-term operation simulation result after iteration are shown in fig. 5 and 6.
Comparing and analyzing fig. 3 and fig. 5, after several iterations, the output of the conventional unit is reduced, the output of the hydroelectric generating unit is increased, and the amount of abandoned wind and abandoned light is obviously reduced compared with the initial simulation. The amount of the abandoned wind light is greatly reduced at each moment, wherein the amount of the abandoned wind light is reduced to zero. This greatly increases the ability of the power system to consume renewable energy.
Comparing and analyzing fig. 4 and fig. 6, it can be seen that the peak-load capacity of the system is no longer insufficient, the peak-load maximum power shortage is reduced from 1.213MW to 0.108MW, and the peak-load power shortage of the system as a whole tends to zero, which illustrates that the flexibility of the power system can be greatly improved by multi-layer iterative solution.
FIG. 7 illustrates an original load curve and an actual load curve of the power system after the interruptible load participates in the scheduling. It can be seen that in the period of 7:35-8:05, a part of interrupt load participates in scheduling, which makes up for the insufficient peak shaving capacity of the power system, and further increases the flexibility of the power system.
Example two
The embodiment provides a power system flexibility resource configuration simulation device, which includes:
(1) the planning layer model building and solving module is used for obtaining load data and unit data to be put into operation in a planning year, and building and simulating a power supply optimization investment decision model by taking the minimum total cost in the planning year as a target function to obtain various power plant putting-in schemes;
(2) the operation layer model building and solving module is used for building and simulating a short-term operation simulation model based on various power plant commissioning schemes by taking the minimum cost as a target function to obtain a typical day unit starting mode;
(3) the flexible layer model building and solving module is used for building and simulating an ultra-short-term operation simulation model based on typical in-day unit combination by adding corresponding penalty items and taking the minimum cost as a target function, and solving the up-down peak-load power shortage and the up-down slope-climbing power shortage of the system in a typical time period;
(4) and the flexibility requirement judging module is used for judging whether the flexibility requirement of the power system is met by taking the power shortage of the up-down peak regulation and the power shortage of the up-down climbing of the system in a typical time period as flexibility indexes, increasing and regulating the flexible unit if the flexibility requirement is not met, and re-simulating and planning the commissioning condition of each type of power plant if the flexibility requirement is not met after the flexible unit is increased and regulated until the flexibility requirement is met.
In specific implementation, in the planning layer model constructing and solving module, the operation layer model constructing and solving module and the flexible layer model constructing and solving module, the corresponding models are solved based on constraint conditions.
The constraint conditions of the power supply optimization investment decision model comprise power constraint, maximum and minimum utilization hour constraint of various power plants, renewable energy installed ratio constraint, hydropower plant electric quantity constraint, negative standby constraint and climbing resource constraint.
The constraint conditions of the short-term operation simulation model comprise equality constraint and inequality constraint, and the equality constraint is system active power balance constraint; the inequality constraints comprise an organic group of upper and lower output limit constraints, climbing constraints, system hot standby constraints, start-stop cost constraints, start-stop time constraints, renewable energy output constraints, interruptible load interruption amount constraints, hydropower plant electric quantity constraints and constraints in the short-term scheduling process of the energy storage participation system.
The constraint conditions of the ultra-short-term operation simulation model comprise system power balance constraint, conventional unit operation climbing constraint, new energy unit operation constraint, interruptible load interruption amount upper and lower limit constraint, relaxed fan up and down climbing constraint and system normal operation constraint when any thermal power unit of the power system fails.
In the flexibility requirement judging module, when the flexible unit is increased and adjusted, the startup of the conventional unit is reduced, and the power shortage of the up-down peak regulation power and the power shortage of the up-down climbing power of the system are both in the limit constraint in a typical time period.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the power system flexible resource configuration simulation method according to the first embodiment.
Example four
The embodiment provides a computer device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the power system flexibility resource configuration simulation method according to the first embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A simulation method for flexible resource allocation of a power system is characterized by comprising the following steps:
acquiring load data and unit data to be put into production in a planning year, and constructing and simulating a power supply optimization investment decision model by taking the minimum total cost in the planning year as a target function to obtain various power plant putting schemes;
based on various power plant commissioning schemes, a short-term operation simulation model is constructed and simulated and solved by taking the minimum cost as a target function, and the starting mode of the unit in a typical day is obtained;
based on the typical in-day unit combination, corresponding penalty items are added, the minimum cost is used as a target function, an ultra-short-term operation simulation model is constructed and simulated and solved, and the up-down peak-shaving power shortage and the up-down climbing power shortage of the system in a typical time period are solved;
and judging whether the flexibility requirement of the power system is met by taking the power shortage of up-down peak shaving and the power shortage of up-down climbing of the system in a typical time period as flexibility indexes, if not, increasing and adjusting the flexible unit, and if not, re-simulating and planning the commissioning condition of each type of power plant until the flexibility requirement is met.
2. The simulation method for flexible resource allocation of an electric power system according to claim 1, wherein the power optimization investment decision model is solved based on constraint condition simulation, and the constraint conditions of the power optimization investment decision model include electric power constraint, maximum and minimum utilization hour constraint of various power plants, renewable energy installed ratio constraint, hydropower plant electric quantity constraint, negative standby constraint and climbing resource constraint.
3. The simulation method for the flexibility resource allocation of the electric power system according to claim 1, wherein the short-term operation simulation model is solved based on the simulation of constraint conditions, the constraint conditions of the short-term operation simulation model include equality constraint and inequality constraint, and the equality constraint is system active power balance constraint; the inequality constraints comprise an organic group of upper and lower output limit constraints, climbing constraints, system hot standby constraints, start-stop cost constraints, start-stop time constraints, renewable energy output constraints, interruptible load interruption amount constraints, hydropower plant electric quantity constraints and constraints in the short-term scheduling process of the energy storage participation system.
4. The simulation method for flexibility resource allocation of the electric power system according to claim 1, wherein the ultra-short-term operation simulation model is solved based on constraint condition simulation, and the constraint conditions of the ultra-short-term operation simulation model include system power balance constraint, conventional unit operation climbing constraint, new energy unit operation constraint, upper and lower limits of interruptible load interruption amount constraint, relaxed upper and lower climbing constraints of a fan, and normal operation constraint of the system when any thermal power unit of the electric power system fails.
5. The simulation method for flexible resource allocation of an electric power system according to claim 1, wherein startup of a conventional unit is reduced while a flexible unit is increased and adjusted, and both the power shortage of up-down peak shaving and the power shortage of up-down climbing of the system are within limit constraints in a typical period.
6. An electric power system flexibility resource allocation simulation apparatus, comprising:
the planning layer model building and solving module is used for obtaining load data and unit data to be put into operation in a planning year, and building and simulating a power supply optimization investment decision model by taking the minimum total cost in the planning year as a target function to obtain various power plant putting-in schemes;
the operation layer model building and solving module is used for building and simulating a short-term operation simulation model based on various power plant commissioning schemes by taking the minimum cost as a target function to obtain a typical day unit starting mode;
the flexible layer model building and solving module is used for building and simulating an ultra-short-term operation simulation model based on typical in-day unit combination by adding corresponding penalty items and taking the minimum cost as a target function, and solving the up-down peak-load power shortage and the up-down slope-climbing power shortage of the system in a typical time period;
and the flexibility requirement judging module is used for judging whether the flexibility requirement of the power system is met by taking the power shortage of the up-down peak regulation and the power shortage of the up-down climbing of the system in a typical time period as flexibility indexes, increasing and regulating the flexible unit if the flexibility requirement is not met, and re-simulating and planning the commissioning condition of each type of power plant if the flexibility requirement is not met after the flexible unit is increased and regulated until the flexibility requirement is met.
7. The power system flexibility resource allocation simulation apparatus of claim 6, wherein in the planning layer model building and solving module, the operation layer model building and solving module, and the flexible layer model building and solving module, respective models are solved based on constraint conditions.
8. The simulation apparatus for flexibility resource allocation of electric power system according to claim 6, wherein in the flexibility requirement determining module, when the flexible unit is increased and adjusted, the startup of the conventional unit is reduced, and both the peak-shaving power shortage and the climbing power shortage of the system are within the limit constraints in the typical time period.
9. A computer readable storage medium having stored thereon a computer program, characterized in that the program, when being executed by a processor, realizes the steps in the power system flexibility resource configuration simulation method according to any of the claims 1-5.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps in the power system flexibility resource configuration simulation method of any of claims 1-5.
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