CN114266136A - Clean energy consumption capacity evaluation method based on time sequence simulation and risk measurement - Google Patents

Clean energy consumption capacity evaluation method based on time sequence simulation and risk measurement Download PDF

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CN114266136A
CN114266136A CN202111418213.2A CN202111418213A CN114266136A CN 114266136 A CN114266136 A CN 114266136A CN 202111418213 A CN202111418213 A CN 202111418213A CN 114266136 A CN114266136 A CN 114266136A
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energy consumption
renewable energy
capacity
risk
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罗少杰
张晓波
李红
屠永伟
万灿
邵蓝锌
吴昱德
徐川子
陈奕
罗庆
骆丽杭
楼洁妮
陈慧增
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Zhejiang University ZJU
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Zhejiang University ZJU
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The embodiment of the application provides a clean energy consumption capability evaluation method based on time sequence simulation and risk measurement, which comprises the steps of determining a function expression of various constraint renewable energy consumption factors including power fluctuation rate, output change rate and capacity confidence; establishing a renewable energy consumption capability evaluation model expression based on time sequence production simulation; and constructing a model expression of a short-term clean energy consumption capability evaluation model based on risk measurement, and solving the following double-layer robust optimization problem to determine an optimal solution under the maximum allowable wind power output level. The long-short-term clean energy consumption capability evaluation model based on time sequence simulation and risk measurement can comprehensively evaluate the renewable energy consumption capability in multiple time scales, lays a research foundation for planning and optimizing scheduling of a power system considering high-proportion renewable energy consumption, and provides reference for further researching the effect of renewable energy access on the flexibility and stability of optimizing scheduling of the power system.

Description

Clean energy consumption capacity evaluation method based on time sequence simulation and risk measurement
Technical Field
The application relates to the field of power transmission circuit operation, in particular to a clean energy consumption capacity evaluation method based on time sequence simulation and risk measurement.
Background
Under the background of energy transformation, the global new energy power generation installation is rapidly developed, and the occupation ratio is greatly improved. In 2019, the installed capacity of renewable energy sources of more than 200GW is newly increased all over the world, the average increase rate is more than 8%, and the increase is fastest in nearly 5 years. By the end of 2019, the total installed amount of global renewable energy reaches 2,588GW, and the proportion of renewable energy power generation in many countries and regions is rapidly increasing. The existing installed capacity of renewable energy sources can support 27.3% of the worldwide power demand.
Zhejiang province has a plurality of innovations and attempts in the aspect of renewable energy consumption, and important stage achievements are obtained. In the aspect of power supply, the built installed power supply has larger scale, more types of varieties and higher cleaning proportion and saves internal power supply. In the aspect of power grids, a strong smart power grid which covers urban and rural areas and greatly improves the capacity of accepting extraprovincial electric energy and urban and rural power supply capacity is basically formed. The Zhejiang power system reverses the situation of power shortage which restricts the development of the economic society for a long time, the main indexes are in the forefront of China, and a good foundation is laid for comprehensively developing the transformation construction of renewable energy sources, accelerating the energy revolution and novel electrification. The evaluation of the clean energy consumption capability is an important boundary condition for planning and operating the power system, and the reasonable evaluation model of the clean energy consumption capability can effectively reduce the planning and operating cost of the power grid.
Disclosure of Invention
The embodiment of the application provides a clean energy consumption capacity evaluation method based on time sequence simulation and risk measurement, and provides reference for further researching the effect of renewable energy access on the flexibility and stability of optimal scheduling of a power system.
Specifically, the method for evaluating the clean energy consumption capability based on the time sequence simulation and the risk measurement comprises the following steps:
s1, determining a function expression of various constraint renewable energy consumption factors including power fluctuation rate, output change rate and capacity confidence;
s2, optimally establishing a renewable energy consumption capability evaluation model expression based on time sequence production simulation by using the overall economy of the system containing the external cost of the environment based on the low-carbon and emission-reduction benefits of the renewable energy;
s3, constructing a model expression of the short-term clean energy consumption capability evaluation model based on the risk measurement, and determining an optimal solution under the maximum allowable wind power output level by solving the following double-layer robust optimization problem.
Optionally, the S1 includes constructing a function expression corresponding to the power fluctuation rate, and specifically includes:
daoyiwang (a Chinese medicine for promoting daily life)The rate fluctuation is as follows, and for the statistical time interval of the month, the year and the like, a certain power fluctuation rate interval [ F ] is counteddi,Fdj]The probability of occurrence is as follows:
Figure BDA0003376394770000021
Figure BDA0003376394770000022
Figure BDA0003376394770000023
optionally, the S1 includes constructing a function expression corresponding to the output change rate, and specifically includes:
the ratio of the output change value in a specific time period to the installed capacity is characterized by adopting a first-order difference, namely the output change rate:
Figure BDA0003376394770000024
optionally, the S1 includes constructing a function expression corresponding to the capacity confidence, specifically including:
the method adopts the expectation of insufficient system power as an index for evaluating the risk of the system, and the mathematical expression of the effective load bearing capacity ELCC is as follows:
F(C,L)＝F(C+ΔC,L+ΔL)，
Figure BDA0003376394770000031
in the formula: f is expressed as a function of installed capacity and load and system risk indicators; c represents the original installed capacity of the system; c represents the newly increased renewable energy capacity; l represents the original load of the system; Δ L is expressed as the amount of additional load that is satisfied by the newly added power source, i.e., as the confidence capacity of the renewable energy source, CPVNIs represented byInstalled capacity of renewable energy sources after the network.
Optionally, the S2 includes:
constructing an objective function corresponding to a renewable energy consumption capability evaluation model based on time sequence production simulation,
Figure BDA0003376394770000032
wherein N is the number of power grid partitions, Pc,n,i，Ph,n,i，ac,n,i，bh,n,iThe operation costs of the generated output of thermal power stations and hydropower stations in the n subareas and the unit generated output of the thermal power stations and the hydropower stations are respectively; u. ofi,tAnd SiAnd (4) operating state and starting cost of the thermal power generating unit i in the time period t.
Optionally, the S2 further includes:
the renewable energy region interconnection consumption capability analysis model direct current channel model considers direct current channel upper and lower limit constraints, and the output constraint of the region power grid interconnection direct current channel is described as
|Pline|≤min(Pline.max-Pline.cur,ΔPex.max)，
In the formula, PlineRepresenting the power variation amplitude of the direct current channel; pline.cur，Pline.maxRespectively representing the current power level and the maximum power level of the direct current channel; delta Pex.maxRepresents the maximum power exchange (only output is not received in simulation) range of the transmitting and receiving end area;
for a bipolar DC current with a maximum transmission power Cn, D, the cumulative distribution function of the transmission capacity is
Figure BDA0003376394770000041
In the formula, ql,DProbability of shutdown for bipolar fault; q. q.sl,SProbability of outage for monopolar fault; cl,DAnd Cl,SThe maximum transmission power is respectively the maximum transmission power when the direct current bipolar and the single pole operate; cumulative distribution function of transmission capacity of l-loop lineObtained by over-convolution calculation, expressed as
Figure BDA0003376394770000042
In the formula:
Figure BDA0003376394770000043
a probability density function of the available transmission capacity of the l line;
Figure BDA0003376394770000044
and the cumulative distribution function of the available transmission capacity of the l-1 circuit after the convolution of the l-1 circuits.
Optionally, the S2 further includes:
the unit output constraint expression is
Figure BDA0003376394770000045
In the formula ugtAnd pgtFor the running state and planned output of the unit g at the moment t,
Figure BDA0003376394770000046
and
Figure BDA0003376394770000047
the minimum and maximum output of the unit g at the moment t, emtAnd fjtA corresponding risk cost coefficient, wherein F is the sum of system risk cost caused by inscribing load and abandoned wind in a planning period;
the unit climbing constraint expression is
Figure BDA0003376394770000048
Figure BDA0003376394770000049
In the formula (I), the compound is shown in the specification,
Figure BDA00033763947700000410
and
Figure BDA00033763947700000411
the upper and lower adjustable spare capacity of the unit g is obtained;
the junctor transmission power limit expression is
Figure BDA00033763947700000412
Figure BDA00033763947700000413
Figure BDA0003376394770000051
Wherein B is a grid node admittance matrix, theta is a node voltage phase angle, and FlTransmitting a power limit for the tie line;
the power balance constraint expression is
Figure BDA0003376394770000052
In an economic power generation dispatching mode, thermal generator sets of the same type are sorted from low to high according to unit power generation cost; when the unit generating cost is the same, sorting the unit generating cost from high to low according to the single machine capacity; when the unit power generation cost is the same as the single machine capacity, the unit power generation cost and the single machine capacity are sorted according to the peak shaving capacity of the unit from high to low; in the energy-saving power generation dispatching mode, thermal generator sets of the same type are sorted from low to high according to the energy consumption level; when the energy consumption level is the same, sorting the pollutant emission levels from low to high; when the energy consumption and the pollutant emission level are the same, sorting according to an economic power generation dispatching mode; based on the renewable energy region interconnection consumption analysis model, according to the optimization simulation principle, the renewable energy region interconnection consumption of the power grid at the transmitting end and the receiving end is considered comprehensively.
Optionally, the S3 includes:
the determination of the maximum allowable wind power output level is obtained by solving the following two-layer robust optimization problem:
an objective function of
Figure BDA0003376394770000053
In the formula,. DELTA.omegamtIs the abandoned wind electric quantity delta D of the wind power plant m at the moment tjtIs the load shedding electric quantity at the node j at the time t, emtAnd fjtAnd F is the sum of system risk cost caused by the inscribing load and wind curtailment in the planning period.
Optionally, the S3 includes:
the maximum risk limiting condition is
Figure BDA0003376394770000054
In the formula, DjtAnd ωmtMaximum risk limit corresponding to load shedding and wind abandoning;
constructing a renewable energy consumption capability evaluation model based on risk measurement as follows:
Figure BDA0003376394770000061
Figure BDA0003376394770000062
Figure BDA0003376394770000063
Figure BDA0003376394770000064
Figure BDA0003376394770000065
mathematically, the model is a two-stage robust optimization problem, and the first stage determines the wind power consumption space by taking the minimum operation risk cost as a target
Figure BDA0003376394770000066
Second phase verification
Figure BDA0003376394770000067
Whether the maximum operation risk born by the system under the set unit output plan is met
Figure BDA0003376394770000068
Has the advantages that:
the method lays a research foundation for planning and optimizing and scheduling the power system considering high-proportion renewable energy consumption, provides reference for further researching the effect of renewable energy access on the flexibility and stability of optimizing and scheduling the power system, and accords with the energy transformation development situation under the current domestic 'carbon peak-to-peak, carbon neutral' target.
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In order to more clearly illustrate the technical solutions of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for evaluating clean energy consumption capability based on time series simulation and risk measurement according to an embodiment of the present application;
fig. 2 is a schematic diagram of distribution of factors restricting consumption of renewable energy according to an embodiment of the present application.
Detailed Description
To make the structure and advantages of the present application clearer, the structure of the present application will be further described with reference to the accompanying drawings.
The method for evaluating the consumption capacity of the clean energy based on the time sequence simulation and the risk measurement, as shown in fig. 1, includes:
s1, determining a function expression of various constraint renewable energy consumption factors including power fluctuation rate, output change rate and capacity confidence;
s2, optimally establishing a renewable energy consumption capability evaluation model expression based on time sequence production simulation by using the overall economy of the system containing the external cost of the environment based on the low-carbon and emission-reduction benefits of the renewable energy;
s3, constructing a model expression of the short-term clean energy consumption capability evaluation model based on the risk measurement, and determining an optimal solution under the maximum allowable wind power output level by solving the following double-layer robust optimization problem.
The long-short term clean energy consumption capability evaluation model based on time sequence simulation and risk measurement can comprehensively evaluate the renewable energy consumption capability in multiple time scales, lays a research foundation for planning and optimizing scheduling of a power system considering high-proportion renewable energy consumption, provides reference for further researching the effect of renewable energy access on the flexibility and stability of optimizing scheduling of the power system, and accords with the energy transformation development situation under the current domestic ' carbon peak, carbon neutral and ' targets '.
The specific contents are as follows: (1) analyzing indexes of the renewable energy source such as output change rate, power fluctuation rate, capacity confidence coefficient and peak shaving characteristic, and combining load characteristic, power structure and outgoing condition of the power grid to obtain main factors restricting the renewable energy source absorption capability; (2) a renewable energy consumption capability evaluation model based on time sequence production simulation is established, constraints such as climbing, outgoing power and power balance of a thermal power generating unit are considered, and a certain proportion of renewable energy is allowed to be abandoned to replace for improving the consumption electric quantity of the renewable energy. (3) For a well-established unit combination plan, the maximum allowable clean energy output level of a system in each scheduling period is defined based on the concept of an uncertain set, and a short-term clean energy consumption capability evaluation model based on risk measurement is established.
The analysis of the renewable energy consumption factor of the power grid on the renewable energy consumption capability is not only related to the intermittence and fluctuation of the renewable energy output, but also depends on factors such as load characteristics, long-distance transmission, power grid architecture and the like. Therefore, when the renewable energy consumption capability of a certain area is evaluated, the factors are considered comprehensively, a complete evaluation method for evaluating the renewable energy consumption capability is constructed from the aspects of the self output characteristics of the renewable energy and the operation characteristics of the power grid, and the evaluation target is decomposed, clarified and quantified, so that guidance is provided for improving the power grid consumption capability.
(1) Power fluctuation ratio
The power fluctuation rate is introduced as an index for reflecting the power fluctuation characteristic of the renewable energy source, and the power change characteristic of the renewable energy source can be reflected in a large time scale. The peak regulation condition of the power grid can be seriously influenced by the power fluctuation of the renewable energy source, and further, the capability of the power grid for absorbing the renewable energy source is influenced. The daily power fluctuation is as follows, and for the statistical time interval of month, year and the like, a certain power fluctuation rate interval [ F ] can be counteddi,Fdj]The probability of occurrence is as follows:
Figure BDA0003376394770000081
Figure BDA0003376394770000082
Figure BDA0003376394770000083
(2) rate of change of output
The solar output change rate curves of photovoltaic power and wind power can find that: the wind power has high volatility, and the output power in a long time period cannot be accurately judged; the output change rate value of the photovoltaic power station is far smaller than that of wind power, the output power is relatively gentle, obvious increase and decrease are achieved in one day, and regularity of photovoltaic power generation caused by day-night alternate change of solar radiation is reflected. The method is characterized in that the output change rate is introduced to describe the output change of the clean energy power station within a certain time scale, and the ratio of the output change value of the clean energy power station in a specific time period to the installed capacity is the output change rate by adopting a first-order difference:
Figure BDA0003376394770000091
(3) capacity confidence
Under the operating condition of a power system, the load carrying Capacity of wind power and photovoltaic units with the same Capacity is different from that of conventional thermal power and hydroelectric units, so that the Confidence Capacity (CC) of a wind power plant and a photovoltaic power station is obviously different from that of a traditional power supply. The capacity confidence coefficient is the proportion of the capacity of the renewable energy source which can replace the conventional unit to the installed capacity of the renewable energy source on the premise of equal reliability, and due to the fluctuation and randomness of the renewable energy source, the renewable energy source is generally considered to be a power source which only provides electricity and does not provide capacity value, so that the capacity value of the renewable energy source needs to be evaluated. Previously, scholars at home and abroad make a great deal of research work on the calculation of the confidence coefficient of the renewable energy capacity and put forward a plurality of definitions and methods, and meanwhile, the calculation results of all regions in the world are different and include respective assumptions and trial conditions. An Effective Load Carrying Capacity (ELCC) is used as an evaluation index of the output credible capacity of the renewable energy. The definition ensures that the system maintains the same reliability level before and after the access of the renewable energy sources, and the proportion of the extra load supplied by the renewable energy sources to the installed grid-connected renewable energy sources is calculated as the confidence coefficient of the power generation capacity of the renewable energy sources. And adopting a load of load expectation (LOLE) as an index for evaluating the risk of the system. The mathematical expression for ELCC is:
F(C,L)＝F(C+ΔC,L+ΔL)，
Figure BDA0003376394770000092
in the formula: f is expressed as a function of installed capacity and load and system risk indicators; c represents the original installed capacity of the system; c represents newly added renewable energyCapacity; l represents the original load of the system; Δ L is expressed as the amount of additional load that is satisfied by the newly added power source, i.e., as the confidence capacity of the renewable energy source, CPVNAnd the installed capacity of renewable energy after grid connection is represented.
(4) Daily peak regulation contribution rate of renewable energy
Superposing the renewable energy output on the original load in a negative form to obtain a new load curve, comparing and analyzing the difference between the original load peak and valley and the difference between the new load curve load peak and valley, wherein the renewable energy output is represented as a positive peak regulation when the difference between the original load peak and valley is larger, and the renewable energy output is represented as a reverse peak regulation when the difference between the original load peak and valley is smaller, and the calculation formula is as follows:
Figure BDA0003376394770000101
Figure BDA0003376394770000102
Figure BDA0003376394770000103
Figure BDA0003376394770000104
in the formula (I), the compound is shown in the specification,
Figure BDA0003376394770000105
and
Figure BDA0003376394770000106
expressed as the system original load maximum and minimum on day j,
Figure BDA0003376394770000107
and
Figure BDA0003376394770000108
expressed as day j systemMaximum and minimum values, delta P, of new load after totally superimposing renewable energy sourcesjAnd
Figure BDA0003376394770000109
respectively representing the difference between the peak and valley of the original load and the peak and valley of the new load on the j-th day, CjExpressed as the change value of the system peak-valley difference before and after the j-th day of renewable energy access, GjExpressed as the daily peak shaving contribution rate of the renewable energy source.
(5) Peak shaving rate of conventional unit
Assuming that the peak shaving capacity of the power grid meets the power supply of the low-ebb load before the renewable energy is not accessed, after the renewable energy is accessed, the renewable energy is preferentially consumed as a principle, and in order to meet the power balance condition of the power grid during the low-ebb load, the output of a conventional unit needs to be reduced. When the conventional unit reduces the minimum technical output of the unit, the maximum renewable energy electric quantity which can be consumed by the power of the conventional unit is the maximum renewable energy electric quantity of the power grid. The peak regulation capability of the power grid refers to the capability that the difference between the output of a normally operated unit and the minimum technical output of the operated unit meets the requirement of the peak-valley difference of the system. For a conventional unit, a hydroelectric generating unit participates in main peak regulation of the system in a dry season, and a medium-sized thermal power plant participates in main peak regulation of the system in a rich season. For thermal power generating units, the minimum output is about 60-70%, the minimum output can be reduced to 50% of the rated output value, deep peak regulation is participated, and during the winter heating period, the thermal power generating units need to meet the heating task, and the minimum output is about 75-85% of the rated value. The thermal power generating unit peak regulation rate is shown as the following formula:
Figure BDA0003376394770000111
in the formula, PGminExpressed as the minimum technical output of the unit, PGNExpressed as the unit rated capacity.
(6) Load characteristic of power grid
The load of the power grid is an important link of the power system, and has the characteristic of being unpredictable and uncontrollable, and the consumption of renewable energy sources is seriously influenced. On the load side, renewable energy consumption space is increased by implementing demand response. On one hand, the flexibility of the load is enhanced, the demand side is responded actively, the load peak-valley difference is reduced, and the renewable energy consumption space can be provided in real time. On the other hand, the power is adjusted along with the output of the renewable energy, and the reduction of the power abandon rate is facilitated. In addition, the implementation of electric energy replacement is accelerated, and the absorption of renewable energy sources is promoted. In terms of influencing the consumption of renewable energy sources, the load characteristics of the power grid mainly comprise two aspects of load level and load peak-valley difference. To some extent, an increase in load is beneficial to an increase in the renewable energy consumption capacity, but the degree of increase is not significant. The reason is as follows: the renewable energy consumption capability is mainly determined by the peak regulation margin (the difference between the load and the minimum output of the conventional unit) provided by the system, after the load is increased, in order to realize power balance, the starting capacity of the arranged conventional unit is increased, but the minimum technical output of the conventional unit is also correspondingly increased, and the increase of the load has limited influence on the increase of the peak regulation margin of the system, so that the renewable energy consumption capability is improved to a certain extent but is not obvious; in addition, reducing the load peak-to-valley difference rate helps to improve the renewable energy consumption capability. This is because, under the condition that the maximum load is not changed, reducing the load peak-to-valley difference is equivalent to increasing the peak regulation margin of the system, and further increasing the consumption space of the renewable energy source.
(7) Regional interconnect
For a large receiving end power grid, the uncertainty of an external incoming call can cause additional operation risks of the system, and different external electrical characteristics have different influences on the clean energy consumption capability of the system. If the out-of-range call is pure fire, a higher proportion of the out-of-range calls is more beneficial to the clean energy consumption of the system; if the out-of-range incoming call contains the electric power types with strong uncertainty such as wind power, photovoltaic and the like, the influence on the clean energy of the system cannot be visually evaluated, and simulation analysis needs to be carried out by combining the safe operation criterion of a large receiving end power grid.
The analysis can be combined to obtain the factors for restricting the consumption of the renewable energy sources, which mainly comprise three aspects of a load side, a power grid side and a power supply side, and the specific factors are shown in fig. 2. From the aspect of power grid operation, the output characteristics of the renewable energy unit, the peak regulation capability of the traditional unit and the load peak-valley difference characteristics can be reduced to the restriction of the peak regulation capability of the system. In addition, on the premise of meeting the local energy demand, the renewable energy consumption can transmit rich energy across the regions so as to avoid the waste of the electric quantity of the renewable energy. Therefore, the factors restricting the consumption of renewable energy sources mainly include two aspects of system peak regulation capacity and grid power transmission capacity.
1. Renewable energy consumption capability evaluation model based on time sequence production simulation
The time sequence production simulation is to gradually optimize the output condition of each generator set by taking the step length of each hour under a certain load, and is widely applied to power system scheduling, power generation production plan, power balance and renewable energy consumption calculation at home and abroad at present. And (3) simulating the real operation condition of the system by taking each hour as a unit by adopting a time sequence production simulation method and considering the generation constraint and the operation condition of the unit according to the wind power, photovoltaic and load annual data. The method has good accuracy in calculating the annual consumption value, can obtain the annual optimal consumption value of the renewable energy source, provides an effective basis for energy development planning, and develops and introduces a clean energy consumption capability evaluation model based on time sequence production simulation:
(1) objective function
In order to consider the low-carbon and emission-reduction benefits of renewable energy, a target function is established with the overall economic performance of a system containing environment external cost being optimal and the total operation cost of thermal power, hydropower and renewable energy power generation being the lowest, and can be expressed as:
Figure BDA0003376394770000131
wherein N is the number of power grid partitions, Pc,n,i，Ph,n,i，ac,n,i，bh,n,iThe operation costs of the generated output of thermal power stations and hydropower stations in the n subareas and the unit generated output of the thermal power stations and the hydropower stations are respectively; u. ofi,tAnd SiAnd (4) operating state and starting cost of the thermal power generating unit i in the time period t.
(2) DC channel model
The renewable energy region interconnection consumption capability analysis model direct current channel model considers direct current channel upper and lower limit constraints, and the region power grid interconnection direct current channel output constraint can be described as
|Pline|≤min(Pline.max-Pline.cur,ΔPex.max)，
In the formula, PlineRepresenting the power variation amplitude of the direct current channel; pline.cur，Pline.maxRespectively representing the current power level and the maximum power level of the direct current channel; delta Pex.maxRepresenting the maximum power exchange (only output is not received in the simulation) range of the transmitting-receiving end area. Considering the forced outage rate of the DC path, there are 3 cases of normal operation, unipolar latching and bipolar latching in DC operation. For a bipolar direct current with a maximum transmission power Cn, D, the cumulative distribution function of the available transmission capacity is
Figure BDA0003376394770000132
In the formula, ql,DProbability of shutdown for bipolar fault; q. q.sl,SProbability of outage for monopolar fault; cl,DAnd Cl,SThe maximum transmission power is respectively the maximum transmission power when the direct current bipolar and the single pole operate. The cumulative distribution function of the transmission capacity of the l-loop line can be obtained by convolution calculation and can be expressed as
Figure BDA0003376394770000141
In the formula:
Figure BDA0003376394770000142
a probability density function of the available transmission capacity of the l line; the method is a cumulative distribution function of the available transmission capacity of the l-1 circuit after the convolution of the l-1 circuit.
(3) Unit output restraint:
Figure BDA0003376394770000143
in the formula ugtAnd pgtFor the running state and planned output of the unit g at the moment t,
Figure BDA0003376394770000144
and
Figure BDA0003376394770000145
the minimum and maximum output of the unit g at the moment t, emtAnd fjtAnd F is the sum of system risk cost caused by the inscribing load and wind curtailment in the planning period.
(4) Unit climbing restraint:
Figure BDA0003376394770000146
Figure BDA0003376394770000147
in the formula (I), the compound is shown in the specification,
Figure BDA0003376394770000148
and
Figure BDA0003376394770000149
the upper and lower adjustable spare capacity of the unit g.
(5) Tie line transmit power limit:
Figure BDA00033763947700001410
Figure BDA00033763947700001411
Figure BDA00033763947700001412
wherein B is a grid node admittance matrix, theta is a node voltage phase angle, and FlTransmit power limits for the tie.
(6) And power balance constraint:
Figure BDA00033763947700001413
in an economic power generation dispatching mode, thermal generator sets of the same type are sorted from low to high according to unit power generation cost; when the unit generating cost is the same, sorting the unit generating cost from high to low according to the single machine capacity; when the unit generating cost is the same as the single machine capacity, the peak shaving capacity of the unit is ranked from high to low. In the energy-saving power generation dispatching mode, thermal generator sets of the same type are sorted from low to high according to the energy consumption level; when the energy consumption level is the same, sorting the pollutant emission levels from low to high; and when the energy consumption and the pollutant emission level are the same, sequencing according to an economic power generation dispatching mode. Based on the renewable energy region interconnection consumption analysis model, according to the optimization simulation principle, the renewable energy region interconnection consumption of the power grid at the transmitting end and the receiving end is considered comprehensively.
By combining the analysis, the provided model comprehensively considers factors such as a power supply structure, peak regulation capacity, grid structure constraint, trans-regional power exchange, renewable energy output characteristics and the like, and combines the influence analysis of power grid partition and power supply regulation capacity constraint and an optimized renewable energy power generation output model, the calculation result is accurate, the annual power generation amount of renewable energy can be estimated, and the limited power (namely the renewable energy receiving capacity) under annual and different wind power and solar power generation installed scales can be estimated. In addition, through reasonable design of model objective functions and constraints, comprehensive economy of power generation cost, heat supply cost, power transmission cost and pollutant emission cost can be evaluated.
2. Short-term clean energy consumption capability assessment model based on risk measurement
The system short-term clean energy consumption capability assessment is mainly used for specifying day-ahead and real-time scheduling plans. And taking wind power consumption as an example, a short-term clean energy consumption capability evaluation model based on risk measurement is explained. For a well-established unit combination plan, the maximum allowable wind power output level of each scheduling period system is defined based on the concept of an uncertain set, which means that as long as the actual wind power output is below the maximum allowable wind power output level, the system does not have the operation risks such as load shedding, wind curtailment and the like, and the determination of the maximum allowable wind power output level can be obtained by solving the following double-layer robust optimization problem:
(1) an objective function:
Figure BDA0003376394770000151
in the formula,. DELTA.omegamtIs the abandoned wind electric quantity delta D of the wind power plant m at the moment tjtIs the load shedding electric quantity at the node j at the time t, emtAnd fjtAnd F is the sum of system risk cost caused by the inscribing load and wind curtailment in the planning period.
(2) Considering a direct current channel model, a unit output constraint, a climbing constraint, a tie line transmission power limit, a power balance constraint and the like.
(3) Maximum risk limit:
Figure BDA0003376394770000161
Figure BDA0003376394770000162
in the formula, DjtAnd ωmtMaximum risk limits for tangential loads and wind curtailment.
(4) Constructing a renewable energy consumption capability evaluation model based on risk measurement as follows:
Figure BDA0003376394770000163
Figure BDA0003376394770000164
Figure BDA0003376394770000165
Figure BDA0003376394770000166
Figure BDA0003376394770000167
mathematically, the model is a two-stage robust optimization problem, and the first stage determines the wind power consumption space by taking the minimum operation risk cost as a target
Figure BDA0003376394770000168
Second phase verification
Figure BDA0003376394770000169
Whether the maximum operation risk born by the system under the set unit output plan is met
Figure BDA00033763947700001610
For solving conveniently, the following linear approximation processing is carried out on the objective function to obtain an approximate expression form of the model:
Figure BDA00033763947700001611
first, the second stage sub-problem is solved:
Figure BDA0003376394770000171
s.t.Jp+Kθ+MΔD+P(wov)+Xv≤r，
Sv≤u，
based on the dual theory, solving the problem is equivalent to solving its dual problem:
Figure BDA0003376394770000172
Figure BDA0003376394770000173
λ≤0，
processing bilinear terms in the problem constraint by using a large M rule to obtain the following mixed integer linear programming problem, which can be directly solved by commercial mathematical optimization software CPLEX:
Figure BDA0003376394770000174
-Mbigv≤γ≤0，
-Mbig(1-v)≤λ-γ≤0，
Figure BDA0003376394770000175
therefore, the two-stage robust optimization problem can be solved by a column and constraint generation (C & CG) algorithm, and the renewable energy consumption capability evaluation algorithm based on the risk value has the following flow:
step 1: is provided with
Figure BDA0003376394770000176
G0＝+∞。
Step 2: solving the following main problem to obtain wk+1，
Figure BDA0003376394770000177
Figure BDA0003376394770000178
Figure BDA0003376394770000179
Figure BDA00033763947700001710
And step 3: if | Gk-Gk-1If the | is less than the epsilon, stopping the calculation; otherwise, solving the subproblem, updating eta, and obtaining the optimal solution v of the subproblemk+1，λk+1The following feasibility constraints were added to the main problem:
eTΔwk+1+fTΔDk+1≤max{η,Closs}，
Jpk+1+Kθk+1+MΔwk+1+NΔDk+1+P(wovk+1)+Xvk+1≤r，
Figure BDA0003376394770000181
and returning l ═ l +1 and O ═ O { [ l +1} to step 2.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (9)

1. A method for evaluating clean energy consumption capability based on time sequence simulation and risk measurement is characterized by comprising the following steps:
s1, determining a function expression of various constraint renewable energy consumption factors including power fluctuation rate, output change rate and capacity confidence;
s2, optimally establishing a renewable energy consumption capability evaluation model expression based on time sequence production simulation by using the overall economy of the system containing the external cost of the environment based on the low-carbon and emission-reduction benefits of the renewable energy;
s3, constructing a model expression of the short-term clean energy consumption capability evaluation model based on the risk measurement, and determining an optimal solution under the maximum allowable wind power output level by solving the following double-layer robust optimization problem.
2. The method for evaluating clean energy consumption capability based on time series simulation and risk measurement as claimed in claim 1, wherein the step S1 includes constructing a functional expression corresponding to the power fluctuation rate, specifically including:
the power fluctuation of the solar induction is as follows, and for the statistical time interval of the month, the year and the like, a certain power fluctuation rate interval is counted [ Fdi,Fdj]The probability of occurrence is as follows:
Figure FDA0003376394760000011
Figure FDA0003376394760000012
Figure FDA0003376394760000013
3. the method for evaluating clean energy consumption capability based on time series simulation and risk measure according to claim 1, wherein the step S1 comprises constructing a functional expression corresponding to the output change rate, specifically comprising:
the ratio of the output change value in a specific time period to the installed capacity is characterized by adopting a first-order difference, namely the output change rate:
Figure FDA0003376394760000021
4. the method for evaluating clean energy consumption capability based on time series simulation and risk measure according to claim 1, wherein the step S1 includes constructing a function expression corresponding to the capacity confidence, specifically including:
the method adopts the expectation of insufficient system power as an index for evaluating the risk of the system, and the mathematical expression of the effective load bearing capacity ELCC is as follows:
F(C,L)＝F(C+ΔC,L+ΔL)，
Figure FDA0003376394760000022
in the formula: f is expressed as a function of installed capacity and load and system risk indicators; c represents the original installed capacity of the system; c represents the newly increased renewable energy capacity; l represents the original load of the system; Δ L is expressed as the amount of additional load that is satisfied by the newly added power source, i.e., as the confidence capacity of the renewable energy source, CPVNAnd the installed capacity of renewable energy after grid connection is represented.
5. The method for evaluating clean energy consumption capability based on time series simulation and risk metric of claim 1, wherein the S2 comprises:
constructing an objective function corresponding to a renewable energy consumption capability evaluation model based on time sequence production simulation,
Figure FDA0003376394760000023
wherein N is the number of power grid partitions, Pc,n,i，Ph,n,i，ac,n,i，bh,n,iThe operation costs of the generated output of thermal power stations and hydropower stations in the n subareas and the unit generated output of the thermal power stations and the hydropower stations are respectively; u. ofi,tAnd SiAnd (4) operating state and starting cost of the thermal power generating unit i in the time period t.
6. The method for evaluating clean energy consumption capability based on time series simulation and risk metric of claim 1, wherein the S2 further comprises:
the renewable energy region interconnection consumption capability analysis model direct current channel model considers direct current channel upper and lower limit constraints, and the output constraint of the region power grid interconnection direct current channel is described as
|Pline|≤min(Pline.max-Pline.cur,ΔPex.max)，
In the formula, PlineRepresenting the power variation amplitude of the direct current channel; pline.cur，Pline.maxRespectively representing the current power level and the maximum power level of the direct current channel; delta Pex.maxRepresents the maximum power exchange (only output is not received in simulation) range of the transmitting and receiving end area;
for a bipolar DC current with a maximum transmission power Cn, D, the cumulative distribution function of the transmission capacity is
Figure FDA0003376394760000031
In the formula, ql,DProbability of shutdown for bipolar fault; q. q.sl,SProbability of outage for monopolar fault; cl,DAnd Cl,SThe maximum transmission power is respectively the maximum transmission power when the direct current bipolar and the single pole operate; the cumulative distribution function of the transmission capacity of the l-circuit line is obtained by convolution calculation and is expressed as
Figure FDA0003376394760000032
In the formula:
Figure FDA0003376394760000033
a probability density function of the available transmission capacity of the l line;
Figure FDA0003376394760000034
and the cumulative distribution function of the available transmission capacity of the l-1 circuit after the convolution of the l-1 circuits.
7. The method for evaluating clean energy consumption capability based on time series simulation and risk metric of claim 1, wherein the S2 further comprises:
the unit output constraint expression is
Figure FDA0003376394760000035
In the formula ugtAnd pgtFor the running state and planned output of the unit g at the moment t,
Figure FDA0003376394760000036
and
Figure FDA0003376394760000037
the minimum and maximum output of the unit g at the moment t, emtAnd fjtA corresponding risk cost coefficient, wherein F is the sum of system risk cost caused by inscribing load and abandoned wind in a planning period;
the unit climbing constraint expression is
Figure FDA0003376394760000041
Figure FDA0003376394760000042
In the formula (I), the compound is shown in the specification,
Figure FDA0003376394760000043
and
Figure FDA0003376394760000044
the upper and lower adjustable spare capacity of the unit g is obtained;
the junctor transmission power limit expression is
Figure FDA0003376394760000045
Figure FDA0003376394760000046
Figure FDA0003376394760000047
Wherein B is a grid node admittance matrix, theta is a node voltage phase angle, and FlTransmitting a power limit for the tie line;
the power balance constraint expression is
Figure FDA0003376394760000048
In an economic power generation dispatching mode, thermal generator sets of the same type are sorted from low to high according to unit power generation cost; when the unit generating cost is the same, sorting the unit generating cost from high to low according to the single machine capacity; when the unit power generation cost is the same as the single machine capacity, the unit power generation cost and the single machine capacity are sorted according to the peak shaving capacity of the unit from high to low; in the energy-saving power generation dispatching mode, thermal generator sets of the same type are sorted from low to high according to the energy consumption level; when the energy consumption level is the same, sorting the pollutant emission levels from low to high; when the energy consumption and the pollutant emission level are the same, sorting according to an economic power generation dispatching mode; based on the renewable energy region interconnection consumption analysis model, according to the optimization simulation principle, the renewable energy region interconnection consumption of the power grid at the transmitting end and the receiving end is considered comprehensively.
8. The method for evaluating clean energy consumption capability based on time series simulation and risk metric of claim 1, wherein the S3 comprises:
the determination of the maximum allowable wind power output level is obtained by solving the following two-layer robust optimization problem:
an objective function of
Figure FDA0003376394760000051
In the formula,. DELTA.omegamtIs the abandoned wind electric quantity delta D of the wind power plant m at the moment tjtIs the load shedding electric quantity at the node j at the time t, emtAnd fjtAnd F is the sum of system risk cost caused by the inscribing load and wind curtailment in the planning period.
9. The method for evaluating clean energy consumption capability based on time series simulation and risk metric of claim 1, wherein the S3 comprises:
the maximum risk limiting condition is
Figure FDA0003376394760000052
In the formula, DjtAnd ωmtMaximum risk limit corresponding to load shedding and wind abandoning;
constructing a renewable energy consumption capability evaluation model based on risk measurement as follows:
Figure FDA0003376394760000053
Figure FDA0003376394760000054
s.t.
Figure FDA0003376394760000055
Figure FDA0003376394760000056
Figure FDA0003376394760000057
mathematically, the model is a two-stage robust optimization problem, and the first stage determines the wind power consumption space by taking the minimum operation risk cost as a target
Figure FDA0003376394760000058
Second phase verification
Figure FDA0003376394760000059
Whether the maximum operation risk born by the system under the set unit output plan is met
Figure FDA00033763947600000510
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115882452A (en) * 2023-01-04 2023-03-31 温州电力建设有限公司 New energy consumption capability analysis and evaluation method considering source load uncertainty

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115882452A (en) * 2023-01-04 2023-03-31 温州电力建设有限公司 New energy consumption capability analysis and evaluation method considering source load uncertainty

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