CN114398777B - Power system flexible resource allocation method based on Yu Bashen game theory - Google Patents
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Abstract
A method for configuring flexible resources of an electric power system based on Yu Bashen game theory includes the steps of firstly regarding a new energy power generation enterprise and flexible resources as two game parties, determining output fluctuation intervals of new energy under different working conditions according to system historical data, calculating to obtain optimal running states of the system corresponding to the output fluctuation intervals of the new energy under each working condition through an optimal power flow model, namely a game expected state set, determining total capacity of flexible resources required by each node under each working condition according to the game expected state set, finally considering characteristics of various flexible resources, and distributing the total capacity of flexible resources required by each node to various flexible resources based on a node flexible demand distribution model with minimum comprehensive cost in a planning period as a target to obtain an optimal flexible resource configuration scheme under each working condition. The design not only realizes accurate and reasonable configuration of flexible resources, but also effectively accelerates the calculation speed.
Description
Technical Field
The invention belongs to the technical field of power, and particularly relates to a power system flexible resource allocation method based on a Bush game theory for a high-proportion renewable energy grid-connected power system.
Background
With the continuous rise of global carbon emissions, in order to ensure clean and sustainable development of the environment while meeting huge power demands, large-scale grid connection of renewable energy sources will be a necessary trend of future power system development. In 2020, the installed capacity and the power generation rate of the renewable energy power supply in China are respectively up to 17.5% and 8.4%, the newly added installed capacity and the accumulated installed capacity are located at the first place in the world, and account for 21% of the total installed capacity of the power supply, and the renewable energy is inferior to thermal power and becomes a second large power supply. The renewable energy grid-connected proportion is continuously improved, the flexibility requirement of the power system caused by uncertainty is gradually increased, and the study on the flexibility of the power system is also gradually in depth.
Flexible resources include all means of regulation that can cope with fluctuations and uncertainties, from the supply side, the energy storage and demand side, such as unit climbing, energy storage, interruptible loads, etc. Due to random change and uncertainty of renewable energy power generation, high-proportion renewable energy grid connection is liable to bring impact to stable operation and safe supply of a system, and how to consider the flexibility requirement of the system and configure flexible resources with enough capacity under the background of high-proportion new energy grid connection, and an optimal flexible resource configuration strategy is formulated, so that the system can reliably and stably operate, and the problem to be solved is urgent. Finally, the method is oriented to the research work of flexible resource planning of the high-proportion renewable energy grid-connected power system, a reasonable flexible resource allocation scheme is provided for the system, the flexibility of the system is effectively improved, the use efficiency of various energy resources is improved, the wind-discarding and light-discarding rate is reduced, and the method has important significance for guaranteeing safe and efficient operation of regional power grids.
Disclosure of Invention
The invention aims to provide a power system flexible resource allocation method capable of realizing a quick and accurate allocation base Yu Bashen game theory aiming at the problems existing in the prior art.
In order to achieve the above object, the technical scheme of the present invention is as follows:
A power system flexible resource allocation method based on Yu Bashen game theory sequentially comprises the following steps:
a, regarding a new energy power generation enterprise and a flexible resource as two game parties according to a base Yu Bashen game theory, and determining output fluctuation intervals of new energy under different working conditions according to system history data;
Step B, calculating to obtain the optimal running state of the system corresponding to the new energy output fluctuation interval under each working condition, namely a game expected state set, through an optimal power flow model based on the new energy output fluctuation interval under each working condition;
Step C, determining the total capacity of flexible resources required by each node under each working condition according to the game expected state set;
And D, considering the characteristics of various flexible resources, and distributing the total capacity of the flexible resources required by each node to various flexible resources based on a node flexible demand distribution model with the minimum comprehensive cost in a planning period as a target to obtain an optimal flexible resource configuration scheme under each working condition, wherein the flexible resources comprise a conventional thermal power generating unit, energy storage equipment and interruptible load.
In step B, the game expected state set θ * is:
θ*＝{θv *,v∈Z}
In the above formula, θ v * is the expected state under the v-th working condition, that is, the maximum and minimum injection power values of each node under the optimal running state of the working condition obtained by calculation of the optimal power flow model, and Z is an integer.
In the step B, the optimal power flow model takes the minimum total cost of active power and reactive power as an objective function:
In the above formula, f i is the active cost function of the ith power generation node, u i is the reactive cost function of the ith power generation node, P gi、Qgi is the active and reactive output of the ith power generation node unit respectively, V is the node voltage amplitude, θ is the node voltage phase, and P g、Qg is the node active and reactive output respectively;
Constraint conditions of the optimal power flow model comprise power balance constraint, node voltage constraint, transmission power constraint, reactive constraint and active constraint;
the power balance constraint is:
In the above formula, P Li、QLi is the load demand active power and reactive power of the ith power generation node, G ij、Bij is the conductance and susceptance of the branch ij, V i is the voltage amplitude of the ith power generation node, and θ ij is the voltage phase angle difference of the branch ij;
the node voltage constraint is:
Vimin≤Vi≤Vimax
In the above formula, V imax、Vimin is the upper limit value and the lower limit value of the voltage of the ith power generation node respectively;
the transmission power constraint is:
Pij≤Pijmax
In the above formula, P ij is the transmission power of the branch ij, and P ijmax is the maximum transmission power allowed by the branch ij;
The reactive constraint is as follows:
Qmin,i≤Qgi≤Qmax,i
In the above formula, Q max,i、Qmin,i is the upper limit value and the lower limit value of reactive power of the ith generating node unit respectively;
the active constraint is as follows:
Pχmin,i-Δf≤Pχi≤Pχmax,i+Δf
In the above formula, χ is the property of the power generation node, when χ is g, it indicates that the node is a conventional power generation node, at this time, P χmin,i、Pχmax,i takes the minimum and maximum power of the power generation of the node unit respectively, when χ is w, it indicates that the node is a fan node, at this time, P χmin,i、Pχmax,i takes the lower limit value or the upper limit value of the fluctuation interval of the wind power at the same time, and Δf is the reserved flexible resource capacity change value.
In step C, the total capacity of the flexible resources required by each node is calculated according to the following formula:
In the above formula, F χit,up、Fχit,down is the upper limit value and the lower limit value of the total capacity of the flexible resource of the ith power generation node at the moment t, max (P χi,t)、min(Pχi,t) is the maximum value and the minimum value of the injection power of the ith power generation node at the moment t under the optimal running state of the working condition calculated by an optimal power flow model, and χ is g, When χ is w,For the fan power of the node, lambda 1、λ2 is the 0-1 variable of the up and down flexibility requirement respectively whenLambda 1 is 1 whenLambda 1 is 0 whenLambda 2 is 1 whenLambda 2 takes 0.
In step D, the objective function g of the node flexibility demand distribution model is:
grun,t,i＝ai·Pt,i 2+bi·Pt,i+c
in the above formula, n p is the number of generating nodes, T is the total number of times, g run,t,i is the generating cost of the ith generating node at time T, when the climbing fee is considered, the climbing rate R t,i of the ith generating node set at time T replaces the active power P t,i,ai、bi and C of the ith generating node set at time T, the active power P t,i,ai、bi and C of the ith generating node set are respectively the quadratic term coefficient, the primary term coefficient and the constant term in the generating cost function, C e,i is the flexible transformation capacity of the ith generating node set, n l is the number of load points, q is the interruptible load unit price, P dl,t,i is the interruptible load of the ith load point at time T, n s is the energy storage number, C set、CArep is the equal annual cost and the replacement cost of the installation cost of energy storage, S i is the total capacity of the ith energy storage, C run is the operation maintenance cost of the energy storage, lambda c、λd is respectively the 0-1 variable representing the charging and discharging of the energy storage, and S c,t,i、Sd,t,i is respectively the charging and discharging power of the ith energy storage at time T.
The minimum generated power constraint after the unit transformation is as follows:
Pmin,i′＝Pmin,i-ΔP
Pmin,i′≥Pmin,i o
In the above formula, P min,i' is the minimum power generated after the modification of the ith power generation node unit, P min,i is the minimum power generated before the modification of the ith power generation node unit, deltaP is the reduction of unit modification capacity, and P min,i o is the theoretical minimum power of the ith power generation node unit;
The unit climbing constraint is as follows:
|Rt,i|≤α1Rmax,i
Pmin,i′≤Pt,i≤Pmax,i
in the above formula, alpha 1 is a climbing margin coefficient, R max,i is the maximum climbing rate of the ith generating node unit, and P max,i is the maximum generating power of the ith generating node unit;
the energy storage charge-discharge power and the state of charge constraint are as follows:
SOCmin,i≤SOCt,i≤SOCmax,i
In the above formula, P c max,i、Pd max,i is the maximum charge and discharge power of the ith energy storage respectively, SOC t,i is the state of charge of the ith energy storage at time t, and SOC max,i、SOCmin,i is the upper limit value and the lower limit value of the state of charge of the ith energy storage respectively;
the maximum interruption capacity and the interruption times of the interruptible load are constrained as follows:
Pd1,ti≤α2PL,t
In the above formula, α 2 is the maximum interruptible load capacity ratio, P L,t is the load value at time t, ε k,t is the number of times of load cut-off at time t, and N d is the maximum number of times of load cut-off;
The flexibility demand balance constraint is:
in the above, F t,i,up、Ft,i,down is the flexibility up-regulating and down-regulating value of the power generation node at the time t, The flexibility of the conventional thermal power generating unit is respectively adjusted up and down,Respectively the flexibility of energy storage is adjusted up and down,For the flexibility up-regulating value of the interruptible load, P max、Pmin is the maximum and minimum power of the unit respectively, P t is the unit output at the moment t, R is the climbing rate of the unit, deltat is the calculation time scale, P c max、Pd max is the maximum charge and discharge power of the stored energy respectively, S c,t、Sd,t is the charge and discharge power of the stored energy at the moment t respectively, SOC (t) is the stored energy charge state at the moment t, SOC max、SOCmin is the upper and lower limit values of the stored energy charge state respectively, eta is the conversion efficiency of the stored energy, E is the rated capacity of the stored energy, and P dl2,t+Δt、Pdl2,t is the load interrupted at the moment t and the moment t+Deltat.
In the step a, the output fluctuation interval of the new energy under different working conditions is determined by converting a plurality of random data points into a limited number of determined interval endpoint data points based on a selected time scale:
In the above formula, bound (P re) is the power generation power fluctuation range of the new energy power generation enterprise, and Δp re is the power generation power fluctuation value of the new energy power generation enterprise.
Compared with the prior art, the invention has the beneficial effects that:
According to the method, a new energy power generation enterprise and flexible resources are regarded as two parties in a game firstly, output fluctuation intervals of new energy under different working conditions are determined according to historical data of the system, then optimal running states of the system corresponding to the output fluctuation intervals of the new energy under each working condition are obtained through calculation of an optimal trend model, namely a game expected state set, then total capacity of flexible resources required by each node under each working condition is determined according to the game expected state set, finally characteristics of various flexible resources are considered, the total capacity of flexible resources required by each node is distributed to various flexible resources based on a node flexibility demand distribution model with minimum comprehensive cost in a planning period as a target, and a flexible resource optimal allocation scheme under each working condition is obtained. Therefore, the invention not only realizes accurate and reasonable configuration of flexible resources, but also effectively accelerates the calculation speed.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a diagram of an IEEE standard 39 node system modified in embodiment 1 of the present invention.
Fig. 3 is a four-season wave diagram of wind power in three scenarios obtained in example 1.
Fig. 4 is a graph of the total capacity of the flexible resources required for each conventional generator node in four seasons for each scenario obtained in example 1.
Fig. 5 is a view of a flexible resource allocation result of each node under a scenario.
Fig. 6 is a diagram of the flexible resource allocation result of each node in scenario two.
Fig. 7 is a diagram of the flexible resource allocation result of each node in scenario three.
FIG. 8 is a diagram of the flexible resource aggregate cost change in each scenario.
Fig. 9 is a diagram showing the comparison of the system network loss before and after the flexible resource allocation in scenario one.
Fig. 10 is a diagram illustrating comparison of system network loss before and after flexible resource allocation in scenario two.
Fig. 11 is a diagram illustrating comparison of system network loss before and after flexible resource allocation in scenario three.
FIG. 12 is a graph comparing flexibility evaluation indexes of the present invention with those of the conventional method.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and the accompanying drawings.
The invention provides a power system flexibility resource allocation method based on Yu Bashen game theory, which converts a flexibility resource allocation problem into two sub-problems of flexibility demand calculation and flexibility demand allocation. Firstly, according to a winning strategy of the Bush game (the problem of uncertainty of new energy resource corresponding to the configuration flexible resource is converted into the problem of the system kept in a determined running state by the configuration flexible resource), a flexibility demand calculation model of the power system is established by combining with optimal power flow calculation; the optimal configuration of each type of flexible resource is then determined based on a flexible demand distribution model that aims at minimizing the overall cost. The invention is oriented to the research work of flexible resource planning of the high-proportion renewable energy grid-connected power system, provides a reasonable flexible resource allocation scheme for the system, effectively improves the flexibility of the system, improves the use efficiency of various energy resources, reduces the wind and light abandoning rate, and finally improves the reliability and economy of the system operation.
Example 1:
The embodiment takes a modified IEEE standard 39 node system as a research object, the specific topology structure of which is shown in fig. 2, and three scenarios are set for the system, wherein, the scenario one: replacing G5 at the 34 node and G9 at the 38 node with fans; scene II: replacing G5 at the 34 node, G9 at the 38 node and G6 at the 36 node with fans; scene III: the G5 at the 34 node, the G9 at the 38 node, the G6 at the 36 node and the G7 at the 35 node are replaced by fans (wind power replaces a regular unit with equal capacity to have positive influence on the system oscillation mode, and the effect on a strong correlation unit is larger than that on a weak correlation unit, so that the strong correlation units G9, G5, G6 and G7 in oscillation are replaced by fans in sequence), and the wind power permeability of a first scene, a second scene and a third scene is 18.7%,28.06% and 35.97% respectively. The energy storage installation node mainly selects: fan nodes G5, G6, G7 and G9; the load level is higher at the node 8 and the generator node G10; a point with poor peak regulation capability, at a generator node G8; weak nodes with high reliability requirements, node 16. The internetworking point selects 39 nodes. And setting the transmission power of the interconnected power grid to be 300MW at each maximum transmission power, and enabling the total daily switching power to be not more than 1000MW. The photovoltaic installation capacity is set to be 100MW or 200MW according to the node load, and the capacity of the total assembly machine is not more than 25% of the peak load. The interruptible load is 10% of the peak load.
Referring to fig. 1, a power system flexible resource allocation method based on Yu Bashen game theory is sequentially carried out according to the following steps:
1. Based on Yu Bashen game theory, new energy power generation enterprises and flexible resources are regarded as two game parties, one year is selected as a research period aiming at each scene, 1h is taken as a time scale, wind power data points of 24h in each day in one month are contained in one section based on collected wind power, photovoltaic and load four-season typical day data and historical data, a wind power fluctuation section of each month in one year is obtained, the upper limit value and the lower limit value of each section are 12 multiplied by 24 multiplied by 2=576 data points, and finally the four-season fluctuation section of wind power in three scenes is determined, wherein a fluctuation diagram is shown in fig. 3.
2. Based on the output fluctuation interval of the new energy source in each scene, calculating to obtain the optimal running state of the system corresponding to the output fluctuation interval of the new energy source in each scene through an optimal power flow model, wherein the optimal power flow model takes the minimum total cost of the active power and the reactive power as an objective function:
In the above formula, f i is the active cost function of the ith power generation node, u i is the reactive cost function of the ith power generation node, P gi、Qgi is the active and reactive output of the ith power generation node unit respectively, V is the node voltage amplitude, θ is the node voltage phase, and P g、Qg is the node active and reactive output respectively;
the constraint conditions of the optimal power flow model comprise:
Power balance constraint:
In the above formula, P Li、QLi is the load demand active power and reactive power of the ith power generation node, G ij、Bij is the conductance and susceptance of the branch ij, V i is the voltage amplitude of the ith power generation node, and θ ij is the voltage phase angle difference of the branch ij;
node voltage constraint:
Vimin≤Vi≤Vimax
In the above formula, V imax、Vimin is the upper limit value and the lower limit value of the voltage of the ith power generation node respectively;
Transmission power constraint:
Pij≤Pijmax
In the above formula, P ij is the transmission power of the branch ij, and P ijmax is the maximum transmission power allowed by the branch ij;
reactive power constraint:
Qmin,i≤Qgi≤Qmax,i
In the above formula, Q max,i、Qmin,i is the upper limit value and the lower limit value of reactive power of the ith generating node unit respectively;
Active constraint:
Pχmin,i-Δf≤Pχi≤Pχmax,i+Δf
In the above formula, x is the property of the power generation node, x is g and indicates that the node is a conventional power generation node, at this moment, P χmin,i、Pχmax,i respectively takes the minimum and maximum power generation of the node unit, χ is w and indicates that the node is a fan node, at this moment, P χmin,i、Pχmax,i simultaneously takes the lower limit value or the upper limit value of the fluctuation interval of the wind power, and Δf is the reserved flexible resource capacity change value.
3. The method comprises the steps of calculating injection power values of all nodes at different moments under the optimal running state of a system of each scene based on an optimal power flow model, taking the maximum and minimum values of the injection power values of all nodes at each moment as game expected state values which are required to be met by flexible resources of up and down adjustment of all nodes at the moment, and obtaining a game expected state set theta *:
θ*＝{θv *,v∈Z}
In the above formula, θ v * is the expected state in the v-th scene, that is, the maximum and minimum injection power values of each node in the optimal running state of the scene obtained by calculating the optimal power flow model, and Z is a positive number.
4. Determining the total capacity of flexible resources required by all nodes in all seasons according to the game expected state set:
In the above formula, F χit,up、Fχit,down is the upper limit value and the lower limit value of the total capacity of the flexible resource of the ith power generation node at the moment t, max (P χi,t)、min(Pχi,t) is the maximum value and the minimum value of the injection power of the ith power generation node at the moment t under the optimal running state of the working condition calculated by an optimal power flow model, and χ is g, When χ is w,For the fan power of the node, lambda 1、λ2 is the 0-1 variable of the up and down flexibility requirement respectively whenLambda 1 is 1 whenLambda 1 is 0 whenLambda 2 is 1 whenLambda 2 takes 0.
The total capacity of the flexible resources required by each conventional generator node in each scene obtained in this embodiment is shown in fig. 4. As can be seen from the figure, overall, the total demand capacity for flexibility increases gradually with increasing wind-electricity permeability, the increase in demand capacity for up-regulation flexibility is relatively small, and the demand capacity for down-regulation flexibility increases greatly. In different seasons, the difference of the up-regulation flexibility capacity demands is smaller, but because the power fluctuation of winter and summer monsoon fluctuates greatly, most of conventional generator nodes have higher down-regulation flexibility demands in winter and summer, and the demand is greatly increased compared with other seasons.
5. Considering the characteristics of various flexible resources, and distributing the total capacity of the flexible resources required by each node to various flexible resources based on a node flexible demand distribution model with the minimum total cost of installation and operation of the flexible resources in a planning period to obtain an optimal flexible resource configuration scheme in each scene, wherein an objective function g of the node flexible demand distribution model is as follows:
grun,t,i＝ai·Pt,i 2+bi·Pt,i+c
In the above formula, n p is the number of generating nodes, T is the total number of times, g run,t,i is the generating cost of the ith generating node at time T, when the climbing fee is considered, the climbing rate R t,i of the ith generating node set at time T replaces the active power P t,i,ai、bi and C of the ith generating node set at time T, the active power P t,i,ai、bi and C of the ith generating node set are respectively the quadratic term coefficient, the primary project coefficient and the constant term in the generating cost function, C e,i is the flexible transformation capacity of the ith generating node set, n l is the number of load points, q is the interruptible load unit price, P dl,t,i is the interruptible load of the ith load point at time T, n s is the energy storage number, C set、CArep is the equal annual cost and replacement cost of the installation cost of energy storage, S i is the total capacity of the ith energy storage, C run is the operation maintenance cost of the energy storage, lambda c、λd is respectively the 0-1 variable representing the charging and discharging of the energy storage, and S c,t,i、Sd,t,i is respectively the charging and discharging power of the ith energy storage at time T;
the constraint conditions of the node flexibility demand distribution model comprise:
and (3) unit climbing constraint:
|Rt,i|≤α1Rmax,i
Pmin,i′≤Pt,i≤Pmax,i
in the above formula, alpha 1 is a climbing margin coefficient, R max,i is the maximum climbing rate of the ith generating node unit, and P max,i is the maximum generating power of the ith generating node unit;
Minimum power generation constraint after unit transformation:
Pmin,i′＝Pmin,i-ΔP
Pmin,i′≥Pmin,i o
In the above formula, P min,i' is the minimum power generated after the modification of the ith power generation node unit, P min,i is the minimum power generated before the modification of the ith power generation node unit, deltaP is the reduction of unit modification capacity, and P min,i o is the theoretical minimum power of the ith power generation node unit;
energy storage charge-discharge power and state of charge constraints:
SOCmin,i≤SOCt,i≤SOCmax,i
In the above formula, P c max,i、Pd max,i is the maximum charge and discharge power of the ith energy storage respectively, SOC t,i is the state of charge of the ith energy storage at time t, and SOC max,i、SOCmin,i is the upper limit value and the lower limit value of the state of charge of the ith energy storage respectively;
interruptible load maximum interrupt capacity and interrupt number constraint:
Pdl,ti≤α2PL,t
In the above formula, α 2 is the maximum interruptible load capacity ratio, P L,t is the load value at time t, ε k,t is the number of times of load cut-off at time t, and N d is the maximum number of times of load cut-off;
flexibility demand balance constraint:
in the above, F t,i,up、Ft,i,down is the flexibility up-regulating and down-regulating value of the power generation node at the time t, The flexibility of the conventional thermal power generating unit is respectively adjusted up and down,Respectively the flexibility of energy storage is adjusted up and down,For the flexibility up-regulating value of the interruptible load, P max、Pmin is the maximum and minimum power of the unit respectively, P t is the unit output at the moment t, R is the climbing rate of the unit, deltat is the calculation time scale, P cmax、Pdmax is the maximum charge and discharge power of the stored energy respectively, S c,t、Sd,t is the charge and discharge power of the stored energy at the moment t respectively, SOC (t) is the stored energy charge state at the moment t, SOC max、SOCmin is the upper and lower limit values of the stored energy charge state respectively, eta is the conversion efficiency of the stored energy, E is the rated capacity of the stored energy, and P dl2,t+Δt、Pdl2,t is the load interrupted at the moment t and the moment t+Deltat.
The flexible resource allocation results of each node in each scene obtained in this embodiment are shown in fig. 5 to fig. 7, and the comprehensive cost of the flexible resource in each scene is shown in fig. 8.
From fig. 5-7, overall, the flexible total demand capacity increases gradually as the wind-electricity permeability increases. The total demand capacity of the up-regulation flexibility is 2718MW,3042MW and 3604MW respectively in three scenes, the trend of small increase is shown, the total demand capacity of the down-regulation flexibility is greatly increased from 2397MW to 3639MW, and then the permeability of the wind power is increased to 4125MW.
It can be seen from fig. 8 that as the wind-electricity permeability increases, the total cost of flexible resources increases from 51.3 to 77.5 billion yuan, and the large increase in energy storage demand is a direct cause of the increase in total cost.
In addition, the embodiment compares the system network losses before and after the flexible resource allocation in each scene (see fig. 9-11), and discovers that the method can effectively reduce the system network loss, thereby improving the economical efficiency of system operation.
To examine the advantages of the method, the method is evaluated and compared with the configuration result of the traditional method (a double-layer planning model is built, the upper layer is a genetic algorithm adopting real number coding, and the lower layer is a genetic algorithm adopting non-dominant order) by taking the probability of insufficient flexibility as a system flexibility evaluation index, and the result is shown in fig. 12.
The result shown in fig. 12 shows that, on the time scale of 24 hours, the method provided by the invention has lower probability of insufficient flexibility, and can effectively improve the reliability of system operation.
Meanwhile, the calculation time of the two are compared, and the result is shown in table 1:
Table 1 comparison of calculated times for the two methods
As can be seen from Table 1, the method provided by the invention effectively shortens the calculation time and avoids the problem of incapability of converging.
In summary, the method of the invention effectively reduces the calculation workload, reduces the calculation error, ensures that the system always operates in an optimal state, and effectively improves the reliability and economy of the system operation.
Claims (5)
1. A power system flexible resource allocation method based on Yu Bashen game theory is characterized in that:
The method sequentially comprises the following steps:
a, regarding a new energy power generation enterprise and a flexible resource as two game parties according to a base Yu Bashen game theory, and determining output fluctuation intervals of new energy under different working conditions according to system history data;
Step B, calculating to obtain the optimal running state of the system corresponding to the new energy output fluctuation interval under each working condition, namely a game expected state set, through an optimal power flow model based on the new energy output fluctuation interval under each working condition;
Step C, determining the total capacity of flexible resources required by each node under each working condition according to the game expected state set;
Step D, considering characteristics of various flexible resources, and based on a node flexible demand allocation model with minimum comprehensive cost in a planning period as a target, allocating total capacity of flexible resources required by each node to various flexible resources to obtain an optimal flexible resource allocation scheme under each working condition, wherein the flexible resources comprise a conventional thermal power generating unit, energy storage equipment and interruptible load, and an objective function g of the node flexible demand allocation model is as follows:
grun,t,i＝ai·Pt,i 2+bi·Pt,i+c
In the above formula, n p is the number of generating nodes, T is the total number of times, g run,t,i is the generating cost of the ith generating node at time T, when the climbing fee is considered, the climbing rate R t,i of the ith generating node set at time T replaces the active power P t,i,ai、bi and C of the ith generating node set at time T, the active power P t,i,ai、bi and C of the ith generating node set are respectively the quadratic term coefficient, the primary project coefficient and the constant term in the generating cost function, C e,i is the flexible transformation capacity of the ith generating node set, n l is the number of load points, q is the interruptible load unit price, P dl,t,i is the interruptible load of the ith load point at time T, n s is the energy storage number, C set、CArep is the equal annual cost and replacement cost of the installation cost of energy storage, S i is the total capacity of the ith energy storage, C run is the operation maintenance cost of the energy storage, lambda c、λd is respectively the 0-1 variable representing the charging and discharging of the energy storage, and S c,t,i、Sd,t,i is respectively the charging and discharging power of the ith energy storage at time T;
Constraint conditions of the node flexibility demand distribution model comprise unit climbing constraint, minimum generation power constraint after unit transformation, energy storage charge and discharge power and charge state constraint, interruptible load maximum interruption capacity and interruption times constraint and flexibility demand balance constraint;
the minimum generated power constraint after the unit transformation is as follows:
Pmin,i′＝Pmin,i-ΔP
Pmin,i′≥Pmin,i O
In the above formula, P min,i' is the minimum power generated after the modification of the ith power generation node unit, P min,i is the minimum power generated before the modification of the ith power generation node unit, deltaP is the reduction of unit modification capacity, and P min,i o is the theoretical minimum power of the ith power generation node unit;
The unit climbing constraint is as follows:
|Rt,i|≤α1Rmax,i
Pmin,i′≤Pt,i≤Pmax,i
in the above formula, alpha 1 is a climbing margin coefficient, R max,i is the maximum climbing rate of the ith generating node unit, and P max,i is the maximum generating power of the ith generating node unit;
the energy storage charge-discharge power and the state of charge constraint are as follows:
SOCmin,i≤SOCt,i≤SOCmax,i
In the above formula, P c max,i、Pd max,i is the maximum charge and discharge power of the ith energy storage respectively, SOC t,i is the state of charge of the ith energy storage at time t, and SOC max,i、SOCmin,i is the upper limit value and the lower limit value of the state of charge of the ith energy storage respectively;
the maximum interruption capacity and the interruption times of the interruptible load are constrained as follows:
Pdl,t,i≤α2PL,t
In the above formula, α 2 is the maximum interruptible load capacity ratio, P L,t is the load value at time t, ε k,t is the number of times of load cut-off at time t, and N d is the maximum number of times of load cut-off;
The flexibility demand balance constraint is:
in the above, F t,i,up、Ft,i,down is the flexibility up-regulating and down-regulating value of the power generation node at the time t, The flexibility of the conventional thermal power generating unit is respectively adjusted up and down,Respectively the flexibility of energy storage is adjusted up and down,For the flexibility up-regulating value of the interruptible load, P max、Pmin is the maximum and minimum power of the unit respectively, P t is the unit output at the moment t, R is the climbing rate of the unit, deltat is the calculation time scale, P cmax、Pdmax is the maximum charge and discharge power of the stored energy respectively, S c,t、Sd,t is the charge and discharge power of the stored energy at the moment t respectively, SOC (t) is the stored energy charge state at the moment t, SOC max、SOCmin is the upper and lower limit values of the stored energy charge state respectively, eta is the conversion efficiency of the stored energy, E is the rated capacity of the stored energy, and P dl2,t+Δt、Pdl2,t is the load interrupted at the moment t and the moment t+Deltat.
2. The power system flexible resource allocation method based on the Bush game theory according to claim 1, wherein the method comprises the following steps:
In step B, the game expected state set θ * is:
θ*＝{θv *,v∈Z}
In the above formula, θ v * is the expected state under the v-th working condition, that is, the maximum and minimum injection power values of each node under the optimal running state of the working condition obtained by calculation of the optimal power flow model, and Z is an integer.
3. The power system flexible resource allocation method based on the Bush game theory according to claim 1 or 2, wherein the method comprises the following steps:
in the step B, the optimal power flow model takes the minimum total cost of active power and reactive power as an objective function:
In the above formula, f i is the active cost function of the ith power generation node, u i is the reactive cost function of the ith power generation node, P gi、Qgi is the active and reactive output of the ith power generation node unit respectively, V is the node voltage amplitude, θ is the node voltage phase, and P g、Qg is the node active and reactive output respectively;
Constraint conditions of the optimal power flow model comprise power balance constraint, node voltage constraint, transmission power constraint, reactive constraint and active constraint;
the power balance constraint is:
In the above formula, P Li、QLi is the load demand active power and reactive power of the ith power generation node, G ij、Bij is the conductance and susceptance of the branch ij, V i is the voltage amplitude of the ith power generation node, and θ ij is the voltage phase angle difference of the branch ij;
the node voltage constraint is:
Vimin≤Vi≤Vimax
In the above formula, V imax、Vimin is the upper limit value and the lower limit value of the voltage of the ith power generation node respectively;
the transmission power constraint is:
Pij≤Pijmax
In the above formula, P ij is the transmission power of the branch ij, and P ijmax is the maximum transmission power allowed by the branch ij;
The reactive constraint is as follows:
Qmin,i≤Qgi≤Qmax,i
In the above formula, Q max,i、Qmin,i is the upper limit value and the lower limit value of reactive power of the ith generating node unit respectively;
the active constraint is as follows:
Pχmin,i-Δf≤Pχi≤Pχmax,i+Δf
In the above formula, x is the property of the power generation node, x is g and indicates that the node is a conventional power generation node, at this moment, P χmin,i、Pχmax,i respectively takes the minimum and maximum power generation of the node unit, χ is w and indicates that the node is a fan node, at this moment, P χmin,i、Pχmax,i simultaneously takes the lower limit value or the upper limit value of the fluctuation interval of the wind power, and Δf is the reserved flexible resource capacity change value.
4. The power system flexible resource allocation method based on the Bush game theory according to claim 1 or 2, wherein the method comprises the following steps:
In step C, the total capacity of the flexible resources required by each node is calculated according to the following formula:
In the above formula, F χit,up、Fχit,down is the upper limit value and the lower limit value of the total capacity of the flexible resource of the ith power generation node at the moment t, max (P χi,t)、min(Pχi,t) is the maximum value and the minimum value of the injection power of the ith power generation node at the moment t under the optimal running state of the working condition calculated by an optimal power flow model, and χ is g, When χ is w,For the fan power of the node, lambda 1、λ2 is the 0-1 variable of the up and down flexibility requirement respectively whenLambda 1 is 1 whenLambda 1 is 0 whenLambda 2 is 1 whenLambda 2 takes 0.
5. The power system flexible resource allocation method based on the Bush game theory according to claim 1 or 2, wherein the method comprises the following steps: in the step a, the output fluctuation interval of the new energy under different working conditions is determined by converting a plurality of random data points into a limited number of determined interval endpoint data points based on a selected time scale:
In the above formula, bound (P re) is the power generation power fluctuation range of the new energy power generation enterprise, and Δp re is the power generation power fluctuation value of the new energy power generation enterprise.
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