CN114172210A - Power grid planning method and system considering power supply uncertainty - Google Patents
Power grid planning method and system considering power supply uncertainty Download PDFInfo
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention provides a power grid planning method and a system considering power supply uncertainty, which comprises the following steps: constructing a risk value model considering energy storage optimization and wind power acceptance conditions, wherein the model comprises an objective function and constraint conditions; taking the sum of the quantized condition risk value cost and the system operation cost as one part of an objective function, simultaneously considering the charge and discharge costs after the energy storage device is configured, and taking the sum of the three parts as the objective function; solving the model and judging: when the actual wind power value accessed by the power system exceeds the maximum wind power value allowed to be accepted by the system, reducing the proportion of wind power integration so as to enable the system to be in a stable operation state; when the actual wind power value accessed by the system is lower than the minimum wind power value allowed to be accepted by the system, the power shortage caused by insufficient wind power output is made up by starting part of thermal power generating units to work. The invention increases the flexible resources of the whole system and improves the allocation capability.
Description
Technical Field
The invention belongs to the technical field of power grids, and particularly relates to a power grid planning method and system considering power supply uncertainty.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In recent years, with rapid economic development, electric utilities have attracted attention, and power generation modes mainly based on thermal power are currently being implemented. But the environmental pollution caused by the pollution is increasingly serious. It has become an important trend to actively seek a power generation mode in which renewable energy is supplied as a power supply to replace the conventional energy. Wind energy is used as clean energy and is in a very important position in renewable energy, and wind power generation has the lowest construction cost and the most mature application technology in current new energy power generation.
However, wind power generation has strong volatility and randomness, and the wind power generation often presents a reverse peak regulation trend in the day scheduling of a power grid, and large-scale new energy grid connection represented by wind power also brings certain challenges to the safe and stable operation of a power system.
In summary, in order to improve the wind power consumption level of the power system and reduce the influence of wind power integration on the stable operation of the power system, it is necessary to quantitatively analyze the influence of the wind power output random characteristic on the operation decision of the power system and to discuss and research how to enhance the robustness of the scheduling decision.
Meanwhile, the energy storage device is reasonably configured in the power grid system containing the wind power plant, so that the adverse effect on the system operation caused by the fluctuation of the wind power can be improved. The flexibility of the energy storage system can be intermodulation with the operation and dispatching of a wind power plant and an electric power system, and the wind power acceptance capability of the system is improved. The economic efficiency of the investment of the energy storage device is also considered, and the economic benefit is ensured to the maximum extent while reasonable resource allocation is realized.
At present, the following three types of methods are commonly adopted for scheduling the power system after wind power integration in the day:
(1) deterministic spare capacity method: and determining the dispatching operation of the unit according to the daily load and the wind power prediction level, and using the reserved rotating reserve capacity to deal with the power imbalance condition caused by the prediction error. This method is widely used because of its simple operation. However, since it is difficult to accurately quantify the influence of errors on the stable operation of the system, it is necessary to reserve sufficient spinning reserve capacity to cope with all uncertain situations, and a large amount of capital is needed, so that the economy is poor.
(2) A stochastic programming method: based on a random optimization technology, a scene method is generally adopted to describe the uncertainty of the internet access load and the power equipment, and optimization decision is made according to the scene. The planning method saves the power generation cost and improves the system stability to a certain extent, but the distribution rule of probability information is difficult to obtain when solving a large-scale power system problem, and a large number of scenes are depicted to ensure the system stability, so that the calculation complexity is increased, and the solving efficiency is reduced.
(3) The robust optimization method comprises the following steps: the method can be used for describing uncertainty in an interval mode under the condition of not considering probability distribution information, namely, acceptable disturbance ranges of all nodes in a system. The robust optimization method carries out decision making at the boundary of the uncertain parameters without specifically analyzing the probability distribution of random variables, and the solving complexity is smaller than that of random planning. The mode of combining the two methods also provides a certain idea for the research of the invention.
In summary, the prior art has the following problems: because of uncertainty of wind power integration, influence of wind power integration on stable operation of a power system cannot be reduced, risk value of wind power condition acceptance cannot be calculated, and influence of wind power output random characteristics on operation decision of the power system cannot be quantitatively analyzed in the prior art.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the power grid dispatching method considering the uncertainty of the power supply, which can well cope with the influence caused by the uncertainty of the power supply and ensure the safe and stable operation of a power system.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
in a first aspect, a power grid dispatching method considering power supply uncertainty is disclosed, which includes:
constructing a risk value model considering energy storage optimization and wind power acceptance conditions, wherein the model comprises an objective function and constraint conditions;
taking the sum of the quantized condition risk value cost and the system operation cost as one part of an objective function, simultaneously considering the charge and discharge costs after the energy storage device is configured, and taking the sum of the three parts as the objective function;
solving the model and judging: when the actual wind power value accessed by the power system exceeds the maximum wind power value allowed to be accepted by the system, reducing the proportion of wind power integration so as to enable the system to be in a stable operation state;
when the actual wind power value accessed by the system is lower than the minimum wind power value allowed to be accepted by the system, the power shortage caused by insufficient wind power output is made up by starting part of thermal power generating units to work.
According to the further technical scheme, the condition risk value of wind power admission is an average loss value caused by the fact that wind power admission exceeds an allowable range due to wind power fluctuation.
According to the further technical scheme, the constraint conditions at least comprise power balance constraint, thermal power unit power limit constraint, wind power unit power limit constraint, thermal power unit climbing rate constraint, system standby capacity constraint, unit minimum on-off time constraint, energy storage system charge-discharge state constraint, energy storage system charge-discharge power constraint and energy storage system capacity constraint.
According to a further technical scheme, the system spare capacity constraint comprises a system positive spare capacity constraint and a system negative spare capacity constraint.
According to the further technical scheme, the positive reserve capacity constraint of the system comprehensively considers the influence of overestimated wind power output on a power grid, at the moment, the actual output of the wind turbine generator is lower than the planned output, and the sum of the system load prediction error and the wind power prediction error is selected as the requirement lower limit value of the positive reserve capacity constraint.
According to the further technical scheme, the system negative spare capacity constraint comprehensively considers the influence of underestimated wind power output on a power grid, at the moment, the actual output of the wind generation set is higher than the planned output, and the difference between the upper limit value of the wind generation set output and the planned output value of each wind generation set is selected as the lower limit value of the requirement of the negative spare capacity constraint.
In a further technical solution, the objective function is:
in the formula (I), the compound is shown in the specification,
for the power of the energy storage system m in the charging and discharging process in the time period t,
charging and discharging cost coefficients of the energy storage system m in a time period t;
t is the number of time segments, N is the number of thermal power generating units put into the system, f (p)i，t) The coal consumption characteristic cost curve of the thermal power generating unit is a quadratic function.
In a second aspect, a power grid dispatching system considering power supply uncertainty is disclosed, comprising:
a model building module configured to: constructing a risk value model considering energy storage optimization and wind power acceptance conditions, wherein the model comprises an objective function and constraint conditions;
taking the sum of the quantized condition risk value cost and the system operation cost as one part of an objective function, simultaneously considering the charge and discharge costs after the energy storage device is configured, and taking the sum of the three parts as the objective function;
a grid scheduling module configured to: solving the model and judging: when the actual wind power value accessed by the power system exceeds the maximum wind power value allowed to be accepted by the system, reducing the proportion of wind power integration so as to enable the system to be in a stable operation state;
when the actual wind power value accessed by the system is lower than the minimum wind power value allowed to be accepted by the system, the power shortage caused by insufficient wind power output is made up by starting part of thermal power generating units to work.
The above one or more technical solutions have the following beneficial effects:
the invention aims at realizing the optimal planning of the power transmission network of the energy storage device, and the main research idea can be divided into the aim of taking economy as the priority and the aim of taking applicability as the priority. Aiming at the priority of economy, the minimum value of the investment and construction cost of the energy storage device is taken as a target function, the random output characteristic of the power grid with a large new energy grid-connected proportion represented by wind power is taken into consideration, a model is built, and the optimal energy storage device configuration is solved; and aiming at the application priority, the method mainly aims at the application scene of the energy storage device, and determines the appropriate capacity of the energy storage device according to the decided dispatching plan.
In a time period with large load demand, the energy storage system discharges to meet the demand of the power grid for increasing the load; and in the time period with small load demand, the energy storage system utilizes the flexible characteristic of the energy storage system to play the roles of weakening the peak value and filling the valley value for responding to the back peak regulation characteristic of the wind power, so that the load curve becomes gentle.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
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 wind power probability density distribution diagram according to an embodiment of the present invention;
FIG. 2 is a single wind power plant wiring diagram of the node system in the embodiment 6 of the present invention;
fig. 3 is a diagram illustrating a scheduling result of the energy storage system according to the embodiment of the present invention.
Detailed Description
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.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The embodiment discloses a power grid dispatching method considering power supply uncertainty, which comprises the following steps:
firstly, a stochastic programming method and a robust optimization method are combined, and a condition risk value (cVaR) is set, namely the average loss of a system caused by the fact that wind power admitted by the system due to the uncertainty of wind power output exceeds an allowed receiving range. Taking the sum of the quantified cVaR cost and the system operation cost as a part of an objective function, simultaneously considering the charge and discharge cost after the energy storage device is configured, taking the sum of the three parts as the objective function, setting related constraint conditions, and comprehensively constructing an optimization model taking the wind power condition risk value into consideration by utilizing a robust optimization thought. Finally, the validity of the scheme is verified through simple 6-node system test analysis.
Optimization model for accounting for conditional risk value
And considering the condition risk value of wind power admission, and setting the objective function of the optimization model as the minimum value of the sum of the thermal power unit operation cost and the condition risk value cost of wind power admission, namely:
in the formula, T is the number of time periods, N is the number of thermal power generating units put into the system, and f (p)i，t) The coal consumption characteristic cost curve of the thermal power generating unit is a quadratic function. Mu.supAnd mudownThe condition risk value coefficient of the wind power is overestimated or underestimated.
In order to exceed the wind power acceptable upper bound wind power,
the wind power is lower than the wind power acceptable lower bound.
Meanwhile, the following aspects need to be considered in the optimization model construction process.
1) Power balance constraint
In the model, the power generated by the fire generator set and the power generated by the wind generator set in grid-connected operation are kept balanced with the required power of the system load at any moment, and the constraint conditions are described as follows:
in the formula, pf,tIs the planned output value p of the wind turbine generator in the dayLoad,tThe demand of the internet surfing load in the day.
2) Thermal power unit power limit constraints
An output interval exists in the actual operation process of the thermal power generating unit, the lower limit value of the allowed output is generally the starting critical power of the thermal power generating unit in normal starting operation, the upper limit value of the allowed output is generally the specified rated output power, and the constraint conditions are described as follows:
uitpi,min≤pi,t≤uitpi,max (3)
in the formula, pi,minIs the lower limit value of the output of the thermal power generating unit i, pi,maxIs the upper limit value u of the output of the thermal power generating unit igtAnd (4) determining a variable for the start-stop state of the unit, wherein the value is 0 or 1.
3) Wind turbine power limit constraints
The research of the invention mainly aims at the short-term scheduling of the power grid, the wind power probability density function based on Beta distribution takes the short-term wind power actual output data as reference, and the variable value of the Beta function is always between 0 and 1, so that the actual situation can be more reasonably depicted. Therefore, the wind power probability density function is described by selecting a Beta function.
In order to describe the wind power output limit constraint more accurately, the planned output value of the wind power plant is limited by referring to the probability constraint in the opportunity constraint planning, and the constraint condition expressed in an uncertain mode is enabled to be established on a given confidence level value.
P{Wt≥Pft}≥ρ (4)
In the formula, rho is a given confidence level under probability constraint, and the higher the confidence level rho is, the higher the possibility that the wind power plant can realize planned output is, and the higher the probability that the corresponding system operates stably is.
The actual wind power output probability density function based on Beta distribution is as follows:
in the formula (f)r(p) a probability density function representing wind power output; α and β are two parameters of the probability density function.
The integral is obtained by the formula (5), and the actual wind power output probability distribution function obtained by deduction is as follows:
in combination with formula (7), formula (4) can be written as
P{Wt≥Pf,t}＝Fr(K)≥ρ (8)
Wherein K is Pf,t/PfmaxEquation (8) represents the probability within this probability distribution that the wind farm can execute a given scheduling plan, and it is apparent that K is a probability distribution function FrAnd (p) dividing the upper side, so that the upper limit of the wind power plant planning force value is not the maximum planning force value of the wind power plant any more, and under the given confidence level, obtaining the corresponding wind power planning force value through a wind power probability distribution function negation function. To Fr(p) solving an inverse function, wherein the constraint conditions of the optimized wind power planned output upper and lower limits are as follows:
0≤Pf,t≤PfmaxFr -1(1-ρ) (9)
in the formula, Fr -1Is Fr(P) inverse function, PfmaxThe maximum output power of the wind turbine generator is obtained.
4) Ramp rate constraint of thermal power generating unit
The constraint condition of the ramp rate of the thermal power generating unit is generally described as follows:
-Δi,dT60ui(t-1)≤pi,t-pi,t-1≤Δi,uT60uit (10)
in the formula,. DELTA.i,uIs the upward climbing speed, Delta, of the thermal power generating unit ii,dIs the downward climbing speed, T, of the thermal power generating unit i60The time value is 60 minutes generally for the ramp response time of the thermal power generating unit.
5) System spare capacity constraint
After large-scale wind power integration, wind power self characteristics are considered, the accuracy of wind power prediction according to the existing method is far lower than the accuracy of system load prediction, so that the influence of wind power integration on power grid scheduling needs to be considered in a standby constraint condition, and a certain positive and negative standby constraint capacity is specified in a common method.
(1) System positive spare capacity constraint
In the formula, SUThe positive spare capacity is reserved for the system to guarantee the stable power supply of the power grid to carry out peak shaving in the period t; sU，iTotal positive spare capacity within 10min response time provided for the ith unit system for time t; l% is the percentage of demand for positive and spare capacity due to the prediction error of the internet load; u% is the percentage of demand on the positive and backup capacity due to wind power prediction error;
setting an output upper limit value for a thermal power generating unit i; t is10To give the response time of the positive standby, the inventionThe Ming is taken for 10 min; and N is the number of thermal power generating units.
And (3) for the equation (11), the influence of the overestimated wind power output on the power grid is comprehensively considered, at the moment, the actual output of the wind turbine generator is lower than the planned output, and the sum of the system load prediction error and the wind power prediction error is selected as the requirement lower limit value of the positive spare capacity constraint.
(2) System negative spare capacity constraint
In the formula, SDReserving negative reserve total capacity for the system in the t period under the condition that the actual wind power output is overlarge; sD，iThe total negative spare capacity of 10min response time provided for the ith unit system in the t period; p is a radical offmaxThe maximum output of the wind turbine generator i is obtained; d% is the percentage of demand for negative reserve capacity due to wind power prediction error; deltai，dThe downward climbing speed of the thermal power generating unit i,
and (4) setting a lower output limit value for the thermal power generating unit i.
And for the formula (12), the influence of underestimated wind power output on the power grid is comprehensively considered, at the moment, the actual output of the wind generation set is higher than the planned output, and the difference between the upper limit value of the wind generation set output and the planned output value of each wind generation set is selected as the lower limit value of the requirement of negative spare capacity constraint.
6) Minimum on-off time constraint of unit
(Xi on-Ti on)(ui(t-1)-uit)≥0
(Xi off-Ti off)(uit-ui(t-1))≥0
In the formula (I), the compound is shown in the specification,
respectively the starting time and the stopping time of the unit i at the initial moment;
the minimum start-up and shut-down time of the unit i are respectively.
Based on the above, the risk value model for the comprehensive consideration, the energy storage optimization and the wind power acceptance condition is introduced as follows:
aiming at the operation risk brought to the power system after new energy such as wind power and the like is combined with the grid, the method can evaluate the output uncertainty of renewable energy by quantifying the risk value of the condition, reduce the influence of the output uncertainty on the stable operation of the power grid, and also consider the optimization by combining and utilizing an energy storage device. The energy storage system can be charged and discharged, can be used as an internet load to absorb power from the system, and can also be used as a power supply to provide power for the system. Due to the flexibility of the energy storage device, the energy storage device can be well matched with a thermal power generating unit in a system so as to reduce the influence of wind power grid connection uncertainty on system operation. In practical application, an optimization model related to an energy storage system is considered to be constructed and is combined with a wind power acceptance condition risk value model for application.
The objective function of the comprehensive optimization model considers the operating cost of the thermal power generating unit and the risk value of the wind power acceptance condition, and also needs to take the cost generated by the energy storage device by using electric energy in the charging and discharging processes into consideration, so that the cost optimization of the energy storage device also needs to be a component of the objective function. The integrated model objective function is represented as:
in the formula (I), the compound is shown in the specification,
for the power of the energy storage system m in the charging and discharging process in the time period t,
and (4) the charge and discharge cost coefficient of the energy storage system m in the t period.
Newly adding constraint conditions:
1) energy storage system charge-discharge state constraint
In the formula (I), the compound is shown in the specification,
the state variables for charging or discharging the energy storage system m describe the state variables, i.e. the energy storage system can only be charged or discharged in one state during operation.
2) Energy storage system charge and discharge power constraints
In the formula (I), the compound is shown in the specification,
the maximum power of the energy storage system m in the charging and discharging processes.
3) Energy storage system capacity constraints
Em,end＝Em,start (18)
In the formula, Em,tIs the energy value T which can be stored by the energy storage system m in the time period T60The value is 60 min;
the charging and discharging efficiency of the energy storage system m is obtained;
the maximum value and the minimum value of the energy stored by the energy storage system m; em,start、Em,endFor storing energyThe system m stores the energy value in the initial period and the energy value in the ending period. And considering that the energy storage system can work normally in the next working period, so that the initial period energy value is equal to the end period energy value.
Example 1:
and summarizing condition risk value summary of wind power admission. The risk value is also called risk income, and the concept is applied to the economic field at the earliest and means the corresponding maximum possible loss value of a certain financial asset in a specific time period in the future under a given confidence level. By extending based on the concept, a quantitative index of conditional risk value can be obtained, and the index is in the form of a conditional mean value of risk value and is defined as an average loss value of the investment portfolio under the condition that the assets exceed a given VaR value in the investment process.
The concept of condition risk value is applied to the scheduling problem of the power system including wind power in the day, and CVaR is expressed as the maximum interval [ W (variable maximum) of wind power uncertainty capable of being accepted by exceeding the power griddown，Wup]The possible average loss value caused in the scenario of (a), i.e., the shaded portion in fig. 1. The conditional risk value of wind admission, when fully accounting for the maximum scheduling capability of the system, may be expressed in this disclosure as the average loss value beyond the system admission range due to wind volatility.
In FIG. 1, WtOutputting a force value in real time for the wind power, wherein the force value is a random value; wup，WdownRespectively is an upper limit value and a lower limit value of wind power which can be accepted by the system; wY，tRepresenting the predicted value of wind power, WmaxFor the upper limit value of the output power of the wind farm, frAnd (p) is a probability density function of wind power output.
For a power grid system after wind power integration, if the injected wind power is always maintained in a wind power value range which can be allowed to be accepted, the accessed wind power value is located in [ W ]down，Wup]In the interval, the safe and stable operation of the system is not influenced; when the actual wind power value accessed by the system exceeds the maximum wind power value W allowed to be accepted by the systemupWhen is WH≥WupAt this time, overtakeThe difference value of the system critical upper limit value is delta W ═ WH-WupThe system needs to reduce the proportion of wind power integration by means of wind abandon and the like so as to ensure that the system is in a stable operation state; when the actual wind power value of the system access is lower than the minimum wind power value W allowed to be accepted by the systemdownWhen is WH≤WdownWhen the difference is smaller than the system critical lower limit value, the difference is that W is equal to Wdown-WHAnd the system needs to start part of thermal power generating units to work through a certain scheduling means so as to make up for the power shortage caused by insufficient wind power output. As can be known from the above, the average loss value caused by the fact that the wind power tolerance causes the admitted wind power to exceed the allowable range is the condition risk value CVaR of the wind power admission of the system.
Example 2:
the effectiveness of the proposed model is analyzed in this example using a simple 6-node system as an example.
(1) Parameter setting
A6-node test system accessed to a single wind power plant is shown in figure 2, the system comprises 3 thermal generator sets, a wind power plant with the capacity set to be 75MW and 1 energy storage system, and the parameter setting of the energy storage system is shown in table 1. The simulation time scale was 1 day for a total of 24 hours. The cost coefficient corresponding to the CVaR accepted by the wind power is set as follows: when the condition of wind abandon occurs, the cost coefficient is 200 yuan/MWh, and when the condition of dispatching a thermal power generating unit or cutting load occurs, the cost coefficient is 1000/MWh; the demand percentage u% of the positive spare capacity is set to 10% and the demand percentage u% of the negative spare capacity is set to 30%.
TABLE 1
(2) Comprehensive optimization model scheduling result and wind power acceptance CVaR scheduling result comparison analysis
Taking a 6-node test system as an example, the confidence level ρ is set to 0.5, and the obtained scheduling results are shown in tables 2 and 3, respectively.
TABLE 2
TABLE 3
From the table 2 and the table 3, it can be obtained that, in the comprehensive optimization model, the unit G1 with the strongest economical efficiency always operates in the scheduling time period and is basically at the rated operation upper limit value in the time period of 8:00-15: 00; the unit G2 with the lowest economical efficiency and the largest cost consumption does not operate all the time in the scheduling time period, compared with the wind power admission CVaR optimization model unit G2 which is out of operation in the time period of 14:00-15:00, the unit G3 is in an operation state in the time period of 15:00-16:00 and 18:00-20:00, the corresponding system total cost is 612540.18 yuan, and compared with the wind power admission CVaR optimization model which is calculated, the cost is reduced by 53127.60 yuan. On one hand, the energy storage system can play an effective role in weakening peak values and making up valley values, so that the thermal power generating unit can be flexibly scheduled to cope with the influence of wind power uncertainty on system scheduling, and the wind power consumption degree of a power grid is improved; on the other hand, the demand of the standby capacity of the thermal power generating unit in the peak period of power utilization is reduced, the starting and stopping times of the thermal power generating unit are reduced, the working life of the thermal power generating unit is indirectly prolonged, the operating efficiency of the thermal power generating unit is improved, and the operating cost of the unit combination is reduced.
(3) Comprehensive cost comparison analysis of optimization model considering different wind power confidence levels
Confidence level values ρ of 0.1, 0.3, 0.5, 0.7, and 0.9 are set, respectively, and for the backup constraint values, a demand percentage u% of positive backup capacity of 10% and a demand percentage u% of negative backup capacity of 30% are set. And calculating the comprehensive cost of the optimization model under different wind power confidence levels, wherein a cost result line graph is shown in FIG. 3.
TABLE 4
As can be seen from table 4, the overall power generation cost is increasing with increasing specified wind power confidence level. The confidence level represents the probability of realizing planned output of the wind power, the higher the given confidence level is, the higher the requirement of the system on the safe and stable operation capacity is, the less the wind power allowed to be connected to the grid is, namely, the output of the thermal power generating unit is increased, and the comprehensive cost is increased; on the contrary, the lower the given confidence level is, the lower the system has requirements on safe and stable operation capacity, and the wind power allowed to be connected to the grid is increased, namely the thermal power generating unit can reduce output power, and the comprehensive cost is reduced at the moment. The large-scale wind power access can reduce the comprehensive scheduling cost, but the cost of influencing the stability of a power grid is also used.
(4) Comprehensive efficiency analysis of charging and discharging modes of energy storage system
And (3) taking the 6-node system as a simulation example, and performing solving analysis to obtain an energy storage system operation diagram shown in the figure 3.
Fig. 3 shows the scheduling operation result of the energy storage system, and it can be seen that in a time period with a large load demand, the energy storage system discharges to meet the demand of the power grid for load increase; and in the time period with small load demand, the energy storage system utilizes the flexible characteristic of the energy storage system to play the roles of weakening the peak value and filling the valley value for responding to the back peak regulation characteristic of the wind power, so that the load curve becomes gentle.
Example simulation analysis is carried out through a 6-node system, and the following results are obtained:
(1) based on a robust optimization thought, the minimum sum of the condition risk value punishment cost, the operation cost and the charging and discharging cost of the energy storage system of the wind power admission is taken as a target function, a robust optimized unit combination model is comprehensively constructed, and the comprehensive optimization model is obtained through comparison and analysis with the condition risk value optimization model only containing the wind power admission, so that the starting and stopping times of the thermal power unit can be reduced, the comprehensive economic cost is reduced, and the operation efficiency of the unit is enhanced.
(2) By setting the wind power confidence level as a parameter, the comprehensive operation cost after wind power integration is evaluated and analyzed, the comprehensive cost of the system is obtained to increase along with the increase of the confidence level, but the safe and stable operation capacity of the system at the moment is increased along with the increase of the confidence level, and the confidence level can be reasonably evaluated in practical application.
(3) Aiming at the optimization problem of the power transmission network, the energy storage system is introduced, the operation flexibility of the energy storage system is utilized, the energy storage system is matched with a thermal power generating unit in the system to be used together to deal with the influence of uncertainty on the system after large-scale wind power is accessed, the discharging amount is increased in the peak period of power utilization to meet the load requirement, the peak value is weakened in the low peak period of power utilization, the valley value is filled, the flexible resources of the whole system are increased, and the allocation capacity is improved.
Example two
It is an object of this embodiment to provide a computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the program.
EXAMPLE III
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
Example four
An object of this embodiment is to provide a power grid planning system considering power uncertainty, including:
a model building module configured to: constructing a risk value model considering energy storage optimization and wind power acceptance conditions, wherein the model comprises an objective function and constraint conditions;
taking the sum of the quantized condition risk value cost and the system operation cost as one part of an objective function, simultaneously considering the charge and discharge costs after the energy storage device is configured, and taking the sum of the three parts as the objective function;
a grid scheduling module configured to: solving the model and judging: when the actual wind power value accessed by the power system exceeds the maximum wind power value allowed to be accepted by the system, reducing the proportion of wind power integration so as to enable the system to be in a stable operation state;
when the actual wind power value accessed by the system is lower than the minimum wind power value allowed to be accepted by the system, the power shortage caused by insufficient wind power output is made up by starting part of thermal power generating units to work.
According to the method, the condition risk value is set as a part of the objective function to reduce the possibility that the wind power is positioned outside the acceptable interval, the flexibility of the energy storage device is combined with the scheduling of the thermal power generating unit, the optimal planning of the power transmission network is realized by weakening the peak value and filling the valley value, and the feasibility of the comprehensive optimization model is verified by performing example simulation by using a 6-node system.
The steps involved in the apparatuses of the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A power grid planning method considering power supply uncertainty is characterized by comprising the following steps:
constructing a risk value model considering energy storage optimization and wind power acceptance conditions, wherein the model comprises an objective function and constraint conditions;
taking the sum of the quantized condition risk value cost and the system operation cost as one part of an objective function, simultaneously considering the charge and discharge costs after the energy storage device is configured, and taking the sum of the three parts as the objective function;
solving the model and judging: when the actual wind power value accessed by the power system exceeds the maximum wind power value allowed to be accepted by the system, reducing the proportion of wind power integration so as to enable the system to be in a stable operation state;
when the actual wind power value accessed by the system is lower than the minimum wind power value allowed to be accepted by the system, the power shortage caused by insufficient wind power output is made up by starting part of thermal power generating units to work.
2. The method as claimed in claim 1, wherein the conditional risk value of wind power admission is an average loss value caused by wind power fluctuation causing the admitted wind power to exceed an allowable range.
3. The method according to claim 1, wherein the constraint conditions at least include a power balance constraint, a thermal power unit power limit constraint, a wind power unit power limit constraint, a thermal power unit ramp rate constraint, a system backup capacity constraint, a unit minimum on-off time constraint, an energy storage system charge-discharge state constraint, an energy storage system charge-discharge power constraint and an energy storage system capacity constraint.
4. A method for power grid planning that takes into account power supply uncertainty as recited in claim 3, wherein the system backup capacity constraints comprise system positive backup capacity constraints and system negative backup capacity constraints.
5. The power grid planning method considering power supply uncertainty as claimed in claim 4, wherein the positive reserve capacity constraint of the system comprehensively considers the influence of overestimated wind power output on the power grid, and when the actual output of the wind turbine is lower than the planned output, the sum of the system load prediction error and the wind power prediction error is selected as the lower limit value of the demand of the positive reserve capacity constraint.
6. The method according to claim 4, wherein the negative reserve capacity constraint of the system comprehensively considers the influence of underestimated wind power output on the power grid, and when the actual output of the wind turbine is higher than the planned output, the difference between the upper limit of the wind turbine output and the planned output of each wind turbine is selected as the lower limit of the demand of the negative reserve capacity constraint.
7. A method for power grid planning that takes into account power supply uncertainty as claimed in claim 1, wherein the objective function is:
in the formula (I), the compound is shown in the specification,
for the power of the energy storage system m in the charging and discharging process in the time period t,
charging and discharging cost coefficients of the energy storage system m in a time period t;
t is the number of time segments, N is the number of thermal power generating units put into the system, f (p)i，t) The coal consumption characteristic cost curve of the thermal power generating unit is a quadratic function.
8. A power grid planning system that accounts for power supply uncertainty, comprising:
a model building module configured to: constructing a risk value model considering energy storage optimization and wind power acceptance conditions, wherein the model comprises an objective function and constraint conditions;
taking the sum of the quantized condition risk value cost and the system operation cost as one part of an objective function, simultaneously considering the charge and discharge costs after the energy storage device is configured, and taking the sum of the three parts as the objective function;
a grid scheduling module configured to: solving the model and judging: when the actual wind power value accessed by the power system exceeds the maximum wind power value allowed to be accepted by the system, reducing the proportion of wind power integration so as to enable the system to be in a stable operation state;
when the actual wind power value accessed by the system is lower than the minimum wind power value allowed to be accepted by the system, the power shortage caused by insufficient wind power output is made up by starting part of thermal power generating units to work.
9. A computing 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 performs the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of the preceding claims 1 to 7.
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