CN111293688A - Network source collaborative planning modeling method considering new energy output and electricity price factors - Google Patents

Network source collaborative planning modeling method considering new energy output and electricity price factors Download PDF

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CN111293688A
CN111293688A CN202010163502.1A CN202010163502A CN111293688A CN 111293688 A CN111293688 A CN 111293688A CN 202010163502 A CN202010163502 A CN 202010163502A CN 111293688 A CN111293688 A CN 111293688A
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electricity price
power station
planning
construction
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CN111293688B (en
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李旭霞
王皑
邓娇娇
胡迎迎
李佳
童星
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Economic and Technological Research Institute of State Grid Shanxi Electric Power Co Ltd
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国网山西省电力公司经济技术研究院
深圳市橙智科技有限公司
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • 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
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers

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Abstract

The invention discloses a network source collaborative planning modeling method considering new energy output and electricity price factors, which is characterized by comprising the following steps: the method comprises the following steps: constructing a network source collaborative planning objective function; step two: establishing power supply and power grid related constraint conditions according to the objective function; step three: modeling uncertainty of new energy output, and adding corresponding constraint conditions; step four: acquiring data required by planning calculation; step five: constructing an electricity price prediction model, predicting the electricity price in a planning period, and taking the predicted electricity price as electricity price input data of the model; step six: and establishing a network source collaborative planning model. Through reasonable conception and scheme design, the invention solves the problems (such as insufficient power supply or power transmission resistance plug of a power grid, transmission bottleneck and the like) caused by the separation of the traditional network source construction, well solves a plurality of challenges caused by the large-scale access of new energy to the power grid, and provides good guarantee for the access and the application of the new energy.

Description

Network source collaborative planning modeling method considering new energy output and electricity price factors
Technical Field
The invention relates to the technical field of power system planning, in particular to a network source collaborative planning modeling method considering new energy output and electricity price factors.
Background
The power system planning has important significance for guaranteeing the continuous safe, reliable, scientific and sustainable development of the power grid, and the scientific and reasonable power grid planning has important social and economic values. In recent years, with the rapid development of power systems, power planning also faces a plurality of new challenges, and on one hand, the large-scale access of new energy brings a plurality of uncertain factors to power grid planning; on the other hand, due to uncertainty of power trading, the fluctuation of the power price is large, and the future income of the power system is difficult to accurately calculate. The above factors make it important to introduce new energy output characteristics and consideration of electricity price factors in power planning.
The existing power planning method is generally to perform power grid planning and power supply planning separately, and perform power supply planning first and then perform power grid planning on the basis of the power supply planning. The method for the separation of the network source planning is easy to cause the problems of insufficient power supply, power transmission resistance plug of the power grid, power transmission bottleneck and the like, and if the treatment is not proper, the loss of the power industry and the national economy can be brought.
Disclosure of Invention
The invention aims to provide a network source collaborative planning modeling method considering new energy output and electricity price factors, and the obtained power grid and power supply collaborative planning model solves the problem caused by network source construction separation, can effectively coordinate the planning of a power supply and a power grid, and further improves the reliability of the operation of a power system and the scientificity of the construction of the power system.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a network source collaborative planning modeling method considering new energy output and electricity price factors comprises the following steps:
the method comprises the following steps: constructing a network source collaborative planning objective function;
step two: establishing power supply and power grid related constraint conditions according to the objective function;
step three: modeling uncertainty of new energy output, and adding corresponding constraint conditions;
step four: acquiring data required by planning calculation;
step five: constructing an electricity price prediction model, predicting the electricity price in a planning period, and taking the predicted electricity price as electricity price input data of the model;
step six: and establishing a network source collaborative planning model.
Specifically, in the first step, a network source collaborative planning objective function is constructed by taking the maximum profit, the lowest cost or the environmental protection objective priority as a precondition.
Further, in the step one, when the revenue maximization is used as a precondition to construct the network source collaborative planning objective function, the objective function is as follows:
Figure BDA0002406616910000021
in the formula, p1,tAnd p2,tRepresenting the predicted electricity price and coal price of the t year; x is the number ofiThe construction condition of the ith candidate wind power station is represented and is a variable of 0-1; qiAnd QjRespectively representing the construction capacity of the ith candidate wind power station and the jth existing wind power station; hi,tAnd Hj,tRespectively representing the annual utilization hours of the ith candidate wind power station and the jth existing wind power station in the t year; y isi,jThe ith candidate thermal power station is represented, and the construction capacity is CjThe construction condition of (1) is a variable of 0 to 1; ciAnd CjRespectively represent the ith candidate fire power stationAnd the construction capacity of the jth existing fire power station, and the optional capacity set of the fire power station is {0, C1,C2,……,CmWhen the construction capacity is 0, the construction is not carried out; fi,tAnd Fj,tRespectively representing the number of annual utilization hours of the ith candidate thermal power station and the jth existing thermal power station in the t year; riAnd RjRespectively representing the power generation operation cost of unit electric quantity of the ith candidate wind power station and the existing wind power station; gjAnd GiRespectively representing the power generation operation cost of unit electric quantity of the jth candidate thermal power station and the ith existing thermal power station; kjThe j th candidate thermal power station is represented, and the construction capacity is CjThe construction cost of (2); wiThe construction cost of the unit capacity of the ith candidate wind power station is represented; z is a radical ofjRepresenting the construction return number of the jth candidate line; l isjRepresenting the single-circuit construction cost of the jth candidate line; ptotalRepresents the total profit; deltaTIs a set of planned ages; deltah，ΔHRespectively representing the set of the candidate fire power station and the existing fire power station; deltag，ΔGRespectively representing a set of candidate wind power plants and existing wind power plants.
Specifically, in the second step, the power supply and power grid related constraints include power demand constraints, newly added wind power plant quantity constraints, newly added coal-fired thermal power plant quantity constraints, power supply investment total constraints, annual maximum load constraints, transmission line investment total constraints, thermal power plant construction variable value constraints, single candidate line construction return number constraints, generator set output constraints, node power balance constraints, wind power plant output constraints, coal-fired thermal power generator output constraints, node phase angle constraints and line tide upper limit constraints.
Specifically, in the fourth step, the acquired data includes:
(1) planning the year limit and the annual power consumption in the planning period;
(2) capacity list, construction cost, operation cost, to-be-constructed position information and annual utilization hours of a candidate coal-fired thermal power station;
(3) capacity list, construction cost, operation cost, position information to be built and annual utilization hours of the wind power station to be built;
(4) electricity price data and coal price data of nearly 10-20 years;
(5) candidate line construction information and single loop construction cost;
(6) the capacity, construction cost, operation cost, position information to be built and annual utilization hours of the existing coal-fired thermal power station and wind power station.
Preferably, the electricity price prediction model in the fifth step is an estimation model for establishing the electricity price based on the geometric brownian motion.
Further, in the sixth step, a cplex software package is called to improve the calculation efficiency of the model.
Compared with the prior art, the invention has the following beneficial effects:
(1) the power supply and power grid planning is considered comprehensively in a set of model, the optimal planning result is generated at one time, and meanwhile the problems of insufficient power supply capacity or power grid resistance transmission plugs, power transmission bottlenecks and the like caused by improper planning and coordination of the power supply and the power grid are avoided, so that the economy of power system construction and the reliability of operation are improved. It should be noted that the method and the system construct a network source collaborative planning objective function by taking benefit maximization, lowest cost and environment protection objective priority as preconditions, and comprehensively consider power supply and power grid constraint conditions, thereby realizing the optimal configuration of resources and the maximization of total benefit from the global perspective; meanwhile, the randomness of the new energy output is modeled, and corresponding constraint conditions are added to process the output of the change characteristics of the electricity price. Therefore, the network source collaborative planning model established by the invention can output the network source collaborative planning scheme result which can well accord with the development direction of power planning.
(2) The method and the device utilize the geometric Brownian motion to predict the electricity price, and can accurately predict the future electricity price level, thereby bringing the maximum benefit for power planning.
(3) The cplex optimization software package is called, so that the calculation efficiency can be effectively improved, and the output efficiency of the network source collaborative planning scheme result is improved.
(4) The invention has reasonable design and reliable application, and provides good guarantee for large-scale access and application of new energy in a power grid system.
Detailed Description
The present invention is further illustrated by the following examples, which include, but are not limited to, the following examples.
The invention provides a network source collaborative planning modeling method considering new energy output and electricity price factors, which can effectively coordinate a comprehensive construction scheme of a power grid and a power supply. The implementation process of the invention is as follows:
the method comprises the following steps: and constructing a network source collaborative planning objective function.
The objective function can be selected according to practical conditions, such as maximum profit, lowest cost, environmental protection objective priority, etc., as the network source collaborative planning objective function.
The maximum profit is taken as an example, and the profit includes the profit generated by electricity selling, the construction cost of a newly-built thermal power station, the construction cost of a newly-built wind power station, the construction cost of a power grid, the operation cost of a newly-built wind power station, the operation cost of an existing wind power station, the operation cost of a newly-built thermal power station, the operation cost of an existing thermal power station, and the like. The objective function is as follows:
Figure BDA0002406616910000041
in the formula, the definition of each parameter is described in detail in the above description.
Step two: and establishing power supply and power grid related constraint conditions.
The variables of the objective function are constrained by indexes in the planning, and mainly comprise the following types:
1) and electric quantity demand constraint: the sum of the total generated energy of all the units is not less than the requirement of the target annual power consumption:
Figure BDA0002406616910000042
in the formula, EtDenotes the t ∈ ΔtAnnual power demand.
2) Newly adding the quantity constraint of the wind power plant:
Figure BDA0002406616910000043
in the formula, Xw,maxAnd the upper limit of the number of the newly built wind power plants is shown.
3) Newly-increased coal-fired thermal power plant quantity restraint:
Figure BDA0002406616910000044
in the formula, Xf,maxAnd the upper limit of the number of the newly-built coal-fired thermal power stations is shown.
4) Power supply investment total amount constraint:
Figure BDA0002406616910000045
in the formula ImaxAnd the total investment upper limit of power supply construction is shown.
5) Annual maximum load constraint: the total capacity of all units is not less than the annual actual maximum load on the premise of ensuring a certain reserve margin:
Figure BDA0002406616910000046
in the formula, epsilon is a spare coefficient, 5-25% of which is taken as PmaxRepresenting the actual annual maximum load.
6) The total investment of the power transmission line is constrained:
Figure BDA0002406616910000047
in the formula Il,maxAnd the total upper limit of the line construction investment is shown.
7) Construction variable value restriction of a thermal power station:
Figure BDA0002406616910000051
8) and (3) constructing a return number constraint of a single candidate route:
0≤zj≤nj,max
in the formula, nj,maxRepresenting the maximum number of construction returns for line j.
9) And (3) output restraint of the generator set:
Figure BDA0002406616910000052
wherein, ImaxAnd the total investment upper limit of power supply construction is shown.
10) Node power balance constraint:
Figure BDA0002406616910000053
in the formula phii,in、Φi,rRespectively representing the input power and the demand load of the node ij、ΘiRepresenting the phase angles, S, of nodes j and i, respectivelyj,iRepresenting the susceptance of the line between nodes j and i.
11) And (3) output constraint of the wind power station:
Φw,i≤Qi
in the formula phiw,iRepresenting the output of the wind power plant i.
12) Output restraint of the coal-fired thermal power generator:
Φf,i≤Ci
in the formula phif,iRepresents the output of the coal-fired thermal generator i.
13) And (3) node phase angle constraint:
Θj,min≤Θj≤Θj,max
in the formula, thetaj,max、Θj,minRespectively representing the upper and lower limits of the phase angle of the node j.
14) And (3) line tide upper limit constraint:
|Sj,i(Θj-Θi)|≤Φi,j,max
in the formula phii,j,maxRepresenting the upper limit of the line transmission power between nodes j and i.
Step three: and modeling uncertainty of the new energy output, and establishing corresponding constraint conditions.
Taking wind power as an example, the wind power output has a random characteristic, and it cannot be guaranteed that the wind turbine set can provide a load equal to the installed capacity of the wind turbine set at a corresponding time. Therefore, a random variable needs to be provided for the wind power output.
If the output random variable of the wind turbine is P, the probability density function can be expressed as:
Figure BDA0002406616910000061
the constraint (5) in step two is improved by using the random variable as follows:
Figure BDA0002406616910000062
wherein, alpha is a confidence level, and the value is 0.5-1.
Step four: data required for planning calculations are acquired.
The acquired data includes:
(1) planning the year limit and the annual power consumption in the planning period;
(2) capacity list, construction cost, operation cost, to-be-constructed position information and annual utilization hours of a candidate coal-fired thermal power station;
(3) capacity list, construction cost, operation cost, position information to be built and annual utilization hours of the wind power station to be built;
(4) electricity price data and coal price data of nearly 10-20 years;
(5) candidate line construction information and single loop construction cost;
(6) the capacity, construction cost, operation cost, position information to be built and annual utilization hours of the existing coal-fired thermal power station and wind power station.
Step five: and constructing an electricity price prediction model, predicting the electricity price in a planning period, and taking the predicted electricity price as input data of the model.
The value of the power is expressed by the price of the power, the future power price level can be correctly predicted to bring the maximum benefit for power planning, and the geometric Brownian motion is used for predicting the power price.
The model of the geometric brownian motion is as follows:
the electricity price in the t-th year is expressed as μ (t), λ represents the expected profitability in the t-th year, σ represents the standard deviation of the profitability when electricity is sold at the price μ (t), and both λ and σ are constants. The change of the electricity price along with the time is expressed as follows:
dμ(t)＝λμ(t)dt+σμ(t)dz(t)
wherein Z (t) represents a standard Brownian motion,
Figure BDA0002406616910000063
ω follows a standard normal distribution. Initial electricity price set to μ0And the electricity price in the t year is as follows:
Figure BDA0002406616910000064
E(μ(t))＝μ0exp(λt)
Figure BDA0002406616910000065
in the formula, λ and σ can be estimated by the electricity price historical data:
Figure BDA0002406616910000071
Figure BDA0002406616910000072
in the formula, Δ t represents a discrete time interval of the electricity rate history data.
Step six: and establishing a network source collaborative planning model and outputting a network source collaborative planning scheme result.
And developing a network source planning program, inputting the obtained data, establishing a network source collaborative planning model, and outputting a network source collaborative planning scheme result. And a cplex optimization software package can be called in a network source planning program to improve the calculation efficiency, so that the output efficiency of the network source collaborative planning scheme result is improved.
Application example:
the node data of the IEEE-30 node system is selected and researched by utilizing the model obtained by the invention. In order to adapt to the model, a method for planning the target year is adopted. And selecting the node 1 as a balance node, setting a planning period to be 4 years, wherein the load predicted value of each node per year in the planning period is increased by 10%, and the total annual power consumption predicted value is 20GW h.
Table 1 lists candidate power supply unit information.
Figure BDA0002406616910000073
TABLE 1
The model obtained by the invention is used for predicting the average electricity price and the average coal price in the future 4 years to be respectively (0.89 yuan/kw h,0789 yuan/kw h, 0.76 yuan/kw h, 0.82 yuan/kw h) and (450 yuan/ton, 480 yuan/ton, 468 yuan/ton, 501 yuan/ton); taking 15% of a load spare coefficient epsilon in the model; confidence level was taken to be 0.85; the upper limit of the number of newly built generators is 4; the upper limit of the number of the transmission lines is set to 10; the total investment upper limit of the power grid and the power supply is 2000 ten thousand yuan and 5000 ten thousand yuan respectively.
In order to illustrate the advantages of the invention compared with the power supply separate planning of the power grid, the present example simultaneously plans a scene of separate planning of the power supply and the power grid, and firstly carries out power supply planning and then carries out power grid planning. In power supply planning, the objective function is the income obtained by subtracting the investment cost of the power grid, and the constraint conditions are 1) to 5), 7) and 9); in the power grid planning, the objective function is the minimization of the total investment of the power grid line, namely the maximization of the last term of the objective function, and the constraints from 6) to 8), 10) and 14).
The results show that: table 2 shows the planning results in each scenario (where scenario 1 is power supply planning first and then power grid planning, scenario 2 is power grid planning first and then power supply planning, and scenario 3 is network source collaborative planning). Table 3 shows the investment cost and profit for each scenario.
Figure BDA0002406616910000081
TABLE 2
Figure BDA0002406616910000082
TABLE 3
The three scene comparison further proves the significance of network source collaborative planning. The power supply planning is carried out in the scene 1, and the wind turbine generator is greatly adopted in the planning due to the environmental protection characteristic, but the subsequent power grid planning needs higher power grid construction cost. The power grid construction cost of the scene 2 is far lower than that of the scene 1, and the comprehensive income scene 2 is better than that of the scene 1. Scene 1 one reason that the cost of power grid construction is high is that the transmission lines of the nodes where the wind power plants are located in the planning are weak. This illustrates the advantage of wind power in terms of operating costs, but the overall profit for the scenario is still minimal. In the power supply planning of the scene 2, the constraint for determining the newly increased installed capacity is modified, the number of newly-built units in the planning result is more than that of the scene 1, and the power supply construction cost is higher.
The network source collaborative planning model provides a scheme for solving the problem. The result of the scene 3 shows that the grid source collaborative planning can more reasonably coordinate the development of the wind power and thermal power generation power supply and the planning of the power grid. Through collaborative planning, the situation that the construction cost of the matched net rack caused by the excessive construction of the wind power in the scene 1 is too high can be avoided, the situation that the wind power utilization requirement cannot be met due to the fact that the meeting and the transmission of the power consumption requirement are excessively concerned in the scene 2 can be avoided, and the development prospect of a power network is met.
The above-mentioned embodiment is only one of the preferred embodiments of the present invention, and should not be used to limit the scope of the present invention, but all the insubstantial modifications or changes made within the spirit and scope of the main design of the present invention, which still solve the technical problems consistent with the present invention, should be included in the scope of the present invention.

Claims (7)

1. A network source collaborative planning modeling method considering new energy output and electricity price factors is characterized by comprising the following steps:
the method comprises the following steps: constructing a network source collaborative planning objective function;
step two: establishing power supply and power grid related constraint conditions according to the objective function;
step three: modeling uncertainty of new energy output, and adding corresponding constraint conditions;
step four: acquiring data required by planning calculation;
step five: constructing an electricity price prediction model, predicting the electricity price in a planning period, and taking the predicted electricity price as electricity price input data of the model;
step six: and establishing a network source collaborative planning model.
2. The modeling method for grid source collaborative planning considering new energy output and electricity price factors according to claim 1, wherein in the first step, a grid source collaborative planning objective function is constructed with a benefit maximization, a cost minimization or an environmental protection objective priority as a precondition.
3. The modeling method for grid source collaborative planning considering new energy output and electricity price factors according to claim 2, wherein in the first step, when the goal function of the grid source collaborative planning is constructed with the benefit maximization as a precondition, the goal function is as follows:
Figure FDA0002406616900000011
in the formula, p1,tAnd p2,tRepresenting the predicted electricity price and coal price of the t year; x is the number ofiThe construction condition of the ith candidate wind power station is represented and is a variable of 0-1; qiAnd QjRespectively representing the construction capacity of the ith candidate wind power station and the jth existing wind power station; hi,tAnd Hj,tRespectively indicating that the ith candidate wind power station and the jth existing wind power station are at the ththe number of annual hours of use in t years; y isi,jThe ith candidate thermal power station is represented, and the construction capacity is CjThe construction condition of (1) is a variable of 0 to 1; ciAnd CjRespectively representing the construction capacity of the ith candidate thermal power station and the jth existing thermal power station, wherein the selectable capacity set of the thermal power stations is {0, C1,C2,……,CmWhen the construction capacity is 0, the construction is not carried out; fi,tAnd Fj,tRespectively representing the number of annual utilization hours of the ith candidate thermal power station and the jth existing thermal power station in the t year; riAnd RjRespectively representing the power generation operation cost of unit electric quantity of the ith candidate wind power station and the existing wind power station; gjAnd GiRespectively representing the power generation operation cost of unit electric quantity of the jth candidate thermal power station and the ith existing thermal power station; kjThe j th candidate thermal power station is represented, and the construction capacity is CjThe construction cost of (2); wiThe construction cost of the unit capacity of the ith candidate wind power station is represented; z is a radical ofjRepresenting the construction return number of the jth candidate line; l isjRepresenting the single-circuit construction cost of the jth candidate line; ptotalRepresents the total profit; deltaTIs a set of planned ages; deltah，ΔHRespectively representing the set of the candidate fire power station and the existing fire power station; deltag，ΔGRespectively representing a set of candidate wind power plants and existing wind power plants.
4. The modeling method for grid source collaborative planning considering new energy output and electricity price factors according to claim 1, wherein in the second step, the power supply and power grid related constraints comprise power demand constraints, newly added wind power plant quantity constraints, newly added coal-fired thermal power plant quantity constraints, power supply investment total constraints, annual maximum load constraints, transmission line investment total constraints, thermal power plant construction variable value constraints, single candidate line construction back number constraints, generator set output constraints, node power balance constraints, wind power plant output constraints, coal-fired generator output constraints, node phase angle constraints, and line tide upper limit constraints.
5. The grid source collaborative planning modeling method considering new energy output and electricity price factors according to claim 1, wherein in the fourth step, the acquired data includes:
(1) planning the year limit and the annual power consumption in the planning period;
(2) capacity list, construction cost, operation cost, to-be-constructed position information and annual utilization hours of a candidate coal-fired thermal power station;
(3) capacity list, construction cost, operation cost, position information to be built and annual utilization hours of the wind power station to be built;
(4) electricity price data and coal price data of nearly 10-20 years;
(5) candidate line construction information and single loop construction cost;
(6) the capacity, construction cost, operation cost, position information to be built and annual utilization hours of the existing coal-fired thermal power station and wind power station.
6. The grid-source collaborative planning modeling method considering new energy output and electricity price factors according to claim 1, wherein the electricity price prediction model in the fifth step is an estimation model for establishing electricity price based on geometric brownian motion.
7. The network source collaborative planning modeling method considering new energy output and electricity price factors according to claim 1, wherein in the sixth step, a cplex software package is called to improve the model calculation efficiency.
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