CN117595398A - Power system flexibility optimization method and device - Google Patents

Power system flexibility optimization method and device Download PDF

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CN117595398A
CN117595398A CN202311555451.7A CN202311555451A CN117595398A CN 117595398 A CN117595398 A CN 117595398A CN 202311555451 A CN202311555451 A CN 202311555451A CN 117595398 A CN117595398 A CN 117595398A
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power
generator set
period
power system
scheduling
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刘云
张英杰
郭经韬
蔡颖倩
刘军伟
郑幸
余欣梅
余正峰
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China Energy Engineering Group Guangdong Electric Power Design Institute Co Ltd
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    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
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    • H—ELECTRICITY
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    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
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    • H—ELECTRICITY
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    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
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    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving

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Abstract

The invention relates to the technical field of power systems and discloses a power system flexibility optimization method and device. The method comprises the steps of aggregating all subarea power grids of a power system to form an equivalent aggregation network; establishing a flexible resource adjustment capability evaluation index of the power system; based on an equivalent aggregation network, combining with an evaluation index, running a robust optimization model aiming at the lowest power generation cost of the power system in a scheduling period to obtain tie line power and climbing capacity in each subarea; and determining a power deviation range of the tie line according to the power of the tie line, and operating a decisive scheduling model aiming at the lowest power generation cost of the subareas in the scheduling period to obtain a scheduling optimization scheme of the power system. The invention can effectively and timely reflect the actual running state and the flexible resource condition of the power system, and improves the applicability, practicality and effectiveness of the evaluation model.

Description

Power system flexibility optimization method and device
Technical Field
The invention relates to the technical field of power systems, in particular to a power system flexibility optimization method and device.
Background
Wind energy and solar energy become important struts for power supply in a novel power system, wherein randomness and fluctuation of wind and light resources can cause significant changes of power system characteristics. High proportion of wind photovoltaic access presents a significant challenge to the flexibility of the system. The traditional units (hydroelectric and thermal power) of the power system have good regulation performance, and the system flexibility focuses on the uncertainty of power supply to match load fluctuation. After the high-proportion wind power photovoltaic is connected, due to inherent volatility and randomness of the wind power photovoltaic, the conventional unit is regulated more frequently, even started and stopped, the regulating depth is also greatly increased, namely the system is required to have stronger flexibility to match the connected operation of the wind power photovoltaic. If the conventional power supply is not flexible enough, the change of the net load (namely, the difference between the load and the fluctuating power supply) cannot be followed, and in order to ensure the safety and stability of the power system, the wind is properly abandoned or the load is cut. However, the abandoned wind breaks against the original purpose of developing new energy, and the load shedding causes larger social influence and economic loss. Therefore, the power system is ensured to have enough flexibility, and the method has great significance for accommodating more wind power and achieving the aim of cleaning the power system in the future.
Under the novel power system, the random fluctuation of a high proportion of new energy sources brings about huge uncertainty, so that the flexibility requirement of the power system is mostly the regulation requirement from the new energy sources except from the load fluctuation. In the novel power system, the system demand is not only load, the new energy power generation fluctuation is more severe, and the flexibility adjustment capability demand is gradually highlighted.
The existing power system evaluation method only uses a random optimization model, and the flexibility of the power system is not fully considered, so that the difficulty and uncertainty of model solving are increased; the wind-solar complementary characteristics in a specific space-time range are directly used, and the scale and the characteristics of a large-scale interconnected power grid are not considered, so that the characteristics and the characteristics of some power grids are ignored; only the multi-objective random optimization unit combination model of offline evaluation is used, and the flexible resource measurement model and method for providing scheduling decision support in real time are not considered, so that the practicability and effectiveness of the model are reduced.
Disclosure of Invention
The invention provides a power system flexibility optimization method and device, which can effectively and timely reflect the actual running state and the flexible resource condition of a power system and improve the applicability, practicability and effectiveness of an evaluation model.
In order to solve the technical problems, the invention provides a power system flexibility optimization method, which comprises the following steps:
aggregating all subarea power grids of the power system to form an equivalent aggregation network;
establishing a flexible resource adjustment capability evaluation index of the power system;
based on the equivalent aggregation network, operating a robust optimization model with the lowest power generation cost of the power system in the scheduling period as a target by combining the evaluation indexes to obtain the power of the connecting line and the climbing capacity in each subarea;
and determining a power deviation range of the tie line according to the power of the tie line, and operating a decisive scheduling model which aims at the lowest power generation cost of the subareas in the scheduling period to obtain a scheduling optimization scheme of the power system.
Further, the aggregation of the sub-area grids of the power system is performed to form an equivalent aggregation network, which specifically includes:
establishing a subarea power grid aggregation model by taking the minimum power deviation on the tie lines before and after subarea node aggregation as a target, and aggregating all subarea power grids of the power system by utilizing the subarea power grid aggregation model;
the objective function of the subarea power grid aggregation model is as follows:
Wherein,is inter-zone link power before aggregation; />Is the inter-zone link power after aggregation.
Further, the inter-area link power after aggregation is specifically:
wherein,the inter-region link power after aggregation; phi R A reduced network sensitivity factor matrix; (P) inj ) R The injection power of the node after aggregation; diag (1/x) R ) Is a diagonal matrix, the diagonal element of which is x R Is the reciprocal of (2); c (C) R To reduce the pre-network sensitivity factor matrix.
Further, the establishing the flexible resource adjustment capability evaluation index of the power system specifically includes
The flexible resource adjustment capability evaluation indexes comprise power up-regulation deficiency probability of the generator set in the range of the power system, power down-regulation deficiency probability of the generator set in the range of the power system, climbing rate of the generator set in the range of a subarea of the power system and power up-regulation deficiency probability of the generator set in the range of the power system;
the power up-regulation insufficient probability of the generator set in the range of the power system is as follows:
wherein,the power up-regulation shortage probability of the generator set in the dispatching period t in the power system range is provided; τ is the scheduling period interval; p (P) i g [t]The output of the generator set i is scheduled for a period t; r is R i The climbing speed of the generator set i is set;for a predicted power system payload; p (P) i max Maximum output of the generator set i; p (P) i [t]The actual output power of the generator set i at the moment of time t is obtained;
the probability of power down-regulation deficiency of the generator set in the power system range is as follows:
wherein,the power of the generator set in the dispatching period t in the range of the power system is downwards adjusted to be insufficient; τ is the scheduling period interval; p (P) i g [t]The output of the generator set i is scheduled for a period t; r is R i The climbing speed of the generator set i is set; />For a predicted power system payload; p (P) i min The minimum output of the generator set i; p (P) i [t]The actual output power of the generator set i at the moment of time t is obtained;
the climbing rate of the generator set in the power system subarea range is as follows:
RC z [t]＝ZGRP[t]+(Im[t+1]-Im[t])-(Ex[t+1]-Ex[t])
wherein RC is provided with z Scheduling a ramp rate of a period t for the generator set in the region z; ZGRP [ t ]]The climbing power of the generator set in the period t is obtained; im is the inlet power, representing the power introduced from other areas; ex is the outlet power, representing the power to the outlets of other regions;
the power up-regulation insufficient probability of the power system range regional generator set is as follows:
wherein,the power up-regulation shortage probability of the generator set in the dispatching period t in the power system range region z is provided; p (P) i g [t]The output of the generator set i is scheduled for a period t; RC (resistor-capacitor) z Scheduling a ramp rate of a period t for the generator set in the region z; />Is the predicted net load of the power system.
Further, the running of the robust optimization model with the minimum power generation cost of the power system in the scheduling period as a target obtains the power of the tie line and the climbing capacity in each subarea, and the method specifically comprises the following steps:
acquiring historical data of the power system, and determining parameters contained in constraint conditions of the robust optimization model; parameters contained in constraint conditions of the robust optimization model comprise the net load of each node of the power system, an uncertainty scene set of each subarea, the upper and lower limits of the output of the generator set in a scheduling period, the climbing rate of the generator set and the maximum power of a connecting line;
establishing an objective function of a robust optimization model by taking the lowest power generation cost of the power system in a scheduling period as a target;
and inputting parameters contained in constraint conditions of the robust optimization model and an objective function of the robust optimization model into the robust optimization model, and operating the robust optimization model to obtain the power of the connecting line and the climbing rate in each subarea.
Further, the inputting parameters contained in the constraint condition of the robust optimization model and the objective function of the robust optimization model into the robust optimization model specifically comprises:
The objective function of the robust optimization model is specifically:
wherein,a generating cost function of the generator set i; p (P) i g [t]The output of the generator set i is scheduled for a period t; t is the scheduling period length;
setting constraint conditions of the robust optimization model according to parameters contained in the constraint conditions of the robust optimization model and the evaluation index; the constraint conditions of the robust optimization model comprise supply and demand balance constraint, power constraint of future tie line power mutual aid, upper and lower limit constraint of unit output, climbing capacity constraint, tide constraint and random fluctuation range of net load in a future scheduling period.
Further, constraint conditions of the robust optimization model are specifically as follows:
the supply-demand balance constraint is as follows:
wherein,is the payload of node j; p (P) i g [t]The output of the generator set i is scheduled for a period t;
the power constraint for future tie power mutual savings is shown in the following equation:
wherein u is z [t]A scene set for region z uncertainty;is the payload of node j; p (P) i g [t]The output of the generator set i is scheduled for a period t; u (U) z [t]Is the random fluctuation range of the payload;
the upper and lower limits of the set output force are defined as follows:
wherein P is i min The lower output limit of the generator set i is set for a scheduling period t; p (P) i max The upper output limit of the generator set i is set for a scheduling period t; p (P) i g [t]The output of the generator set i is scheduled for a period t;
the climbing capacity constraint is as follows:
wherein R is i Ramp rate for genset i；P i g [t]The output of the generator set i is scheduled for a period t; p (P) i g [t-1]The output of the generator set i is scheduled for a period t-1;
the flow constraint is as follows:
wherein,maximum power for tie k; p (P) i g [t]The output of the generator set i is scheduled for a period t; u (u) z [t]A scene set for region z uncertainty; />A payload of region z; />Is a transmission loss coefficient;
the random fluctuation range of the payload in the future scheduling period is shown as follows:
wherein U is z [t]Is the random fluctuation range of the payload;a payload of region z at time t; />The net load change amount of the region z at the time t; />Is the payload of region z.
Further, determining a power deviation range of the tie line according to the power of the tie line, and operating a decisive scheduling model with the lowest power generation cost of the subareas in the scheduling period as a target to obtain a scheduling optimization scheme of the power system, wherein the method specifically comprises the following steps:
establishing an objective function of a decisive scheduling model by taking the lowest power generation cost of the subareas in the scheduling period as a target;
The objective function of the deterministic scheduling model is specifically:
wherein P is i g [t]The output of the generator set i is scheduled for a period t;a generating cost function of the generator set i;
determining a tie line power deviation range according to the tie line power, and setting constraint conditions of a decisive scheduling model; constraint conditions of the decisive scheduling model comprise supply and demand balance constraint, unit output upper and lower limit constraint, climbing capacity constraint, support power constraint provided by subareas, inter-area interconnecting line power constraint and intra-area power transmission line power constraint;
inputting the objective function of the deterministic scheduling model and the constraint condition of the deterministic scheduling model into the deterministic scheduling model, operating the deterministic scheduling model, calculating the flexible resource scheduling capability assessment index, obtaining an assessment result, and generating a scheduling optimization scheme of the power system.
Further, determining a tie power deviation range according to the tie power, and setting constraint conditions of a deterministic scheduling model, specifically:
the supply-demand balance constraint is as follows:
wherein,is the payload of node j; p (P) i g [t]The output of the generator set i is scheduled for a period t;
The upper and lower limits of the set output force are defined as follows:
wherein P is i min The lower output limit of the generator set i is set for a scheduling period t; p (P) i max The upper output limit of the generator set i is set for a scheduling period t; p (P) i g [t]The output of the generator set i is scheduled for a period t;
the climbing capacity constraint is as follows:
wherein R is i The climbing speed of the generator set i is set; p (P) i g [t]The output of the generator set i is scheduled for a period t; p (P) i g [t-1]The output of the generator set i is scheduled for a period t-1;
the support power constraint provided by the sub-region is as follows:
wherein R is i The climbing speed of the generator set i is set; RC (resistor-capacitor) z Is the climbing rate in the subarea; p (P) i g [t]The output of the generator set i is scheduled for a period t; p (P) i max Maximum output of the generator set i;
the inter-zone link power constraint is shown as follows:
wherein F is k [t]Link power for link k during scheduling period t; epsilon is a preset short-term tie line deviation;the output vector of the n generator sets in the period t comprises the output of all the generator sets; />The vector of the net load of n nodes comprises the net load of all nodes;
the power constraint of the transmission line in the area is shown as follows:
wherein F is m [t]A link power for link m in scheduling period t;the output of the generator set 1-n for the scheduled period t; / >The output of the generator set 1-n for the scheduled period t; />Is the payload of nodes 1-n.
The invention provides a power system flexibility optimization method, which is used for aggregating all subarea power grids of a power system to form an equivalent aggregation network; establishing a flexible resource adjustment capability evaluation index of the power system; based on an equivalent aggregation network, combining with an evaluation index, running a robust optimization model aiming at the lowest power generation cost of the power system in a scheduling period to obtain tie line power and climbing capacity in each subarea; and determining a power deviation range of the tie line according to the power of the tie line, and operating a decisive scheduling model aiming at the lowest power generation cost of the subareas in the scheduling period to obtain a scheduling optimization scheme of the power system. The invention can effectively and timely reflect the actual running state and the flexible resource condition of the power system, and improves the applicability, practicality and effectiveness of the evaluation model.
Correspondingly, the invention provides a flexibility optimizing device of an electric power system, which comprises the following components: the system comprises an aggregation module, an index establishment module, a first optimization module and a second optimization module;
the aggregation module is used for aggregating all subarea power grids of the power system to form an equivalent aggregation network;
The index establishing module is used for establishing a flexible resource adjustment capability assessment index of the power system;
the first optimization module is used for operating a robust optimization model which aims at the lowest power generation cost of the power system in the scheduling period based on the equivalent aggregation network and combining the evaluation index to obtain the power of the connecting line and the climbing capacity in each subarea;
and the second optimization module is used for determining a power deviation range of the tie line according to the power of the tie line, and operating a decisive scheduling model which aims at the lowest power generation cost of the subareas in the scheduling period to obtain a scheduling optimization scheme of the power system.
The invention provides a flexibility optimizing device of a power system, which is used for aggregating all subarea power grids of the power system based on organic combination among modules to form an equivalent aggregation network; establishing a flexible resource adjustment capability evaluation index of the power system; based on an equivalent aggregation network, combining with an evaluation index, running a robust optimization model aiming at the lowest power generation cost of the power system in a scheduling period to obtain tie line power and climbing capacity in each subarea; and determining a power deviation range of the tie line according to the power of the tie line, and operating a decisive scheduling model aiming at the lowest power generation cost of the subareas in the scheduling period to obtain a scheduling optimization scheme of the power system. The invention can effectively and timely reflect the actual running state and the flexible resource condition of the power system, and improves the applicability, practicality and effectiveness of the evaluation model.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of a power system flexibility optimization method provided by the present invention;
FIG. 2 is a flow chart of an embodiment of a method for calculating a sensitivity factor according to the present invention;
fig. 3 is a schematic diagram of a regional power grid node aggregation network topology of the power system provided by the present invention;
FIG. 4 is a flow chart of an embodiment of a LORP adequacy assessment method based on two-stage robust optimization scheduling provided by the invention;
fig. 5 is a schematic structural diagram of an embodiment of a power system flexibility optimization device provided by the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a flow chart of an embodiment of a power system flexibility optimization method provided by the present invention is shown, where the method includes steps 101 to 104, and the steps are specifically as follows:
Step 101: and aggregating all the subarea power grids of the power system to form an equivalent aggregation network.
Further, in the first embodiment of the present invention, each sub-area grid of the power system is aggregated to form an equivalent aggregation network, which specifically is:
establishing a subarea power grid aggregation model by taking the minimum power deviation on the tie lines before and after subarea node aggregation as a target, and aggregating all subarea power grids of the power system by utilizing the subarea power grid aggregation model;
the objective function of the subarea power grid aggregation model is as follows:
wherein,is inter-zone link power before aggregation; />Is the inter-zone link power after aggregation.
Further, in the first embodiment of the present invention, the inter-area link power after aggregation is specifically:
wherein,the inter-region link power after aggregation; phi R A reduced network sensitivity factor matrix; (P) inj ) R The injection power of the node after aggregation; diag (1/x) R ) Is a diagonal matrix, the diagonal element of which is x R Is the reciprocal of (2); c (C) R Is contracted intoThe network sensitivity factor matrix is subtracted.
In the first embodiment of the present invention, referring to fig. 2, a flow chart of an embodiment of a sensitivity factor calculation method provided by the present invention is shown. After network data is input, base state power flow calculation is carried out, each branch of the power system takes the actual power flow direction as a positive direction, a node-branch correlation matrix is formed, so that a node admittance matrix is formed, and sensitivity factors of two branches for switching on and off distribution factors are calculated according to specified requirements. The linear power flow equation is as follows: p=bδ, P being a column vector representing the active power of each node in the power system; b is a node admittance matrix, which comprises admittance values (or equivalent resistance and reactance values) among all nodes in the power system and is used for reflecting the topological structure and parameters of the power system; delta is the relative electrical phase angle of each node in the power system and represents the phase difference of the voltage waveforms.
As an example of the first embodiment of the present invention, the linear power flow equation p=bδ may be written as ap=δ, where a=b -1 . Suppose a i 、a j Respectively the ith row and the jth row, x of the matrix A ij Representing the reactance value between node i and node j. According to line powerCan get +.>Suppose a ii And a ji A is respectively a i 、a j Is the ith element, sk ,i Injecting power pi into line L for node i ij The calculation formula of the sensitivity factor of the tide is as follows: />
In a first embodiment of the present invention, the regional link power under the original network before aggregation isWherein (1)>Tie-line power flow between subareas; pi (II) flow Is L R A x L matrix; LR is the number of nodes, L is the number of lines; phi is a sensitivity factor matrix; p (P) inj Power is injected for the node. By establishing a subarea power grid aggregation model with the minimum power deviation on the interconnecting lines before and after subarea node aggregation as a target, all subarea power grids of the power system can be aggregated by using the model, so that the power of the interconnecting lines among the areas after aggregation is calculated. After the sub-area power grids of the power system are aggregated by using the sub-area power grid aggregation model, a regional power grid node aggregation network topology diagram of the power system is shown in fig. 3, wherein the left side of fig. 3 is the regional power grid node aggregation network topology diagram of the power system, and the right side is a simplified picture of the left side network topology diagram.
In the first embodiment of the invention, by using the regional power grid aggregation method with high precision, the link power and impedance among nodes of the aggregated network can be ensured to be basically consistent with those of the original network, so that an economic dispatch model for developing all dispatch periods which take the most serious net load change into account is provided with a feasible solution.
Step 102: and establishing a flexible resource adjustment capability evaluation index of the power system.
Further, in the first embodiment of the present invention, a flexible resource adjustment capability evaluation index of the power system is established, specifically:
the flexible resource adjustment capability evaluation indexes comprise power up-regulation deficiency probability of the generator set in the range of the power system, power down-regulation deficiency probability of the generator set in the range of the power system, climbing rate of the generator set in the range of a subarea of the power system and power up-regulation deficiency probability of the generator set in the range of the power system;
the power up-regulation insufficient probability of the generator set in the range of the power system is as follows:
wherein,the power up-regulation shortage probability of the generator set in the dispatching period t in the power system range is provided; τ is the scheduling period interval; p (P) i g [t]The output of the generator set i is scheduled for a period t; r is R i The climbing speed of the generator set i is set;for a predicted power system payload; p (P) i max Maximum output of the generator set i; p (P) i [t]The actual output power of the generator set i at the moment of time t is obtained;
the probability of power down-regulation deficiency of the generator set in the power system range is as follows:
wherein,the power of the generator set in the dispatching period t in the range of the power system is downwards adjusted to be insufficient; τ is the scheduling period interval; p (P) i g [t]The output of the generator set i is scheduled for a period t; r is R i The climbing speed of the generator set i is set; />For a predicted power system payload; p (P) i min The minimum output of the generator set i; p (P) i [t]The actual output power of the generator set i at the moment of time t is obtained;
the climbing rate of the generator set in the power system subarea range is as follows:
RC z [t]＝ZGRP[t]+(Im[t+1]-Im[t])-(Ex[t+1]-Ex[t])
wherein RC is provided with z Scheduling gensets in zone zRamp rate for time period t; ZGRP [ t ]]The climbing power of the generator set in the period t is obtained; im is the inlet power, representing the power introduced from other areas; ex is the outlet power, representing the power to the outlets of other regions;
the power up-regulation insufficient probability of the power system range regional generator set is as follows:
wherein,the power up-regulation shortage probability of the generator set in the dispatching period t in the power system range region z is provided; p (P) i g [t]The output of the generator set i is scheduled for a period t; RC (resistor-capacitor) z Scheduling a ramp rate of a period t for the generator set in the region z; />Is the predicted net load of the power system.
In the first embodiment of the present invention, the lorep index may be divided into a system-level loreps and a sub-region-level lorepz, which represent the probability of insufficient flexible resources in the system and the sub-region, respectively. The invention utilizes the full-system flexibility adjustment capacity deficiency probability index LORPS and the sub-region flexibility adjustment capacity deficiency probability index LORPz to be used as the real-time running state quantity of the system, so that a system dispatcher can know the conditions of the flexible resources in the system and each sub-region in real time. The system-wide underclimbing capacity probability LORPS is the probability that a system dispatcher cannot meet the change of the net load by calling the power generation resource to rise/fall power under the current dispatching mode. The loppz may be used to evaluate the flexibility resource adjustment adequacy of a particular sub-region. If the LORPz index is too high, the scheduler will consider temporarily increasing the power headroom for the next scheduling period in order to maintain the intra-area flexibility adjustment capability within a safe range.
Step 103: and based on the equivalent aggregation network, operating a robust optimization model with the minimum power generation cost of the power system in the scheduling period as a target by combining the evaluation indexes to obtain the tie line power and the climbing capacity in each subarea.
Further, in the first embodiment of the present invention, a robust optimization model targeting the lowest power generation cost of the power system in the scheduling period is run to obtain the link power and the climbing capacity in each sub-area, specifically:
acquiring historical data of the power system, and determining parameters contained in constraint conditions of the robust optimization model; parameters contained in constraint conditions of the robust optimization model comprise the net load of each node of the power system, an uncertainty scene set of each subarea, the upper and lower limits of the output of the generator set in a scheduling period, the climbing rate of the generator set and the maximum power of a connecting line;
establishing an objective function of a robust optimization model by taking the lowest power generation cost of the power system in a scheduling period as a target;
and inputting parameters contained in constraint conditions of the robust optimization model and an objective function of the robust optimization model into the robust optimization model, and operating the robust optimization model to obtain the power of the connecting line and the climbing rate in each subarea.
Further, in the first embodiment of the present invention, parameters included in constraint conditions of the robust optimization model and an objective function of the robust optimization model are input to the robust optimization model, specifically:
The objective function of the robust optimization model is specifically:
wherein,a generating cost function of the generator set i; p (P) i g [t]The output of the generator set i is scheduled for a period t; t is the scheduling period length;
setting constraint conditions of the robust optimization model according to parameters contained in the constraint conditions of the robust optimization model and the evaluation index; the constraint conditions of the robust optimization model comprise supply and demand balance constraint, power constraint of future tie line power mutual aid, upper and lower limit constraint of unit output, climbing capacity constraint, tide constraint and random fluctuation range of net load in a future scheduling period.
Further, in the first embodiment of the present invention, constraint conditions of the robust optimization model are specifically:
the supply-demand balance constraint is as follows:
wherein,is the payload of node j; p (P) i g [t]The output of the generator set i is scheduled for a period t;
the power constraint for future tie power mutual savings is shown in the following equation:
wherein u is z [t]A scene set for region z uncertainty;is the payload of node j; p (P) i g [t]The output of the generator set i is scheduled for a period t; u (U) z [t]Is the random fluctuation range of the payload;
the upper and lower limits of the set output force are defined as follows:
wherein P is i min For the dispatch period t, the output of the generator set i isLimiting; p (P) i max The upper output limit of the generator set i is set for a scheduling period t; p (P) i g [t]The output of the generator set i is scheduled for a period t;
the climbing capacity constraint is as follows:
wherein R is i The climbing speed of the generator set i is set; p (P) i g [t]The output of the generator set i is scheduled for a period t; p (P) i g [t-1]The output of the generator set i is scheduled for a period t-1;
the flow constraint is as follows:
wherein,maximum power for tie k; p (P) i g [t]The output of the generator set i is scheduled for a period t; u (u) z [t]A scene set for region z uncertainty; />A payload of region z; />Is a transmission loss coefficient;
the random fluctuation range of the payload in the future scheduling period is shown as follows:
wherein U is z [t]Is the random fluctuation range of the payload;a payload of region z at time t; />The net load change amount of the region z at the time t; />Is the payload of region z.
In a first embodiment of the present invention,
step 104: and determining a power deviation range of the tie line according to the power of the tie line, and operating a decisive scheduling model which aims at the lowest power generation cost of the subareas in the scheduling period to obtain a scheduling optimization scheme of the power system.
Further, in the first embodiment of the present invention, according to the link power, a link power deviation range is determined, and a deterministic scheduling model targeting the lowest power generation cost of the sub-area in the scheduling period is operated, so as to obtain a scheduling optimization scheme of the power system, which specifically includes:
Establishing an objective function of a decisive scheduling model by taking the lowest power generation cost of the subareas in the scheduling period as a target;
the objective function of the deterministic scheduling model is specifically:
wherein P is i g [t]The output of the generator set i is scheduled for a period t;a generating cost function of the generator set i;
determining a tie line power deviation range according to the tie line power, and setting constraint conditions of a decisive scheduling model; constraint conditions of the decisive scheduling model comprise supply and demand balance constraint, unit output upper and lower limit constraint, climbing capacity constraint, support power constraint provided by subareas, inter-area interconnecting line power constraint and intra-area power transmission line power constraint;
inputting the objective function of the deterministic scheduling model and the constraint condition of the deterministic scheduling model into the deterministic scheduling model, operating the deterministic scheduling model, calculating the flexible resource scheduling capability assessment index, obtaining an assessment result, and generating a scheduling optimization scheme of the power system.
Further, in the first embodiment of the present invention, according to the link power, a link power deviation range is determined, and constraint conditions of a deterministic scheduling model are set, specifically:
The supply-demand balance constraint is as follows:
wherein,is the payload of node j; p (P) i g [t]The output of the generator set i is scheduled for a period t;
the upper and lower limits of the set output force are defined as follows:
wherein P is i min The lower output limit of the generator set i is set for a scheduling period t; p (P) i max The upper output limit of the generator set i is set for a scheduling period t; p (P) i g [t]The output of the generator set i is scheduled for a period t;
the climbing capacity constraint is as follows:
wherein R is i The climbing speed of the generator set i is set; p (P) i g [t]The output of the generator set i is scheduled for a period t; p (P) i g [t-1]The output of the generator set i is scheduled for a period t-1;
the support power constraint provided by the sub-region is as follows:
wherein R is i The climbing speed of the generator set i is set; RC (resistor-capacitor) z Is the climbing rate in the subarea; p (P) i g [t]The output of the generator set i is scheduled for a period t; p (P) i max Maximum output of the generator set i;
the inter-zone link power constraint is shown as follows:
wherein F is k [t]Link power for link k during scheduling period t; epsilon is a preset short-term tie line deviation;the output vector of the n generator sets in the period t comprises the output of all the generator sets; />The vector of the net load of n nodes comprises the net load of all nodes;
the power constraint of the transmission line in the area is shown as follows:
Wherein F is m [t]A link power for link m in scheduling period t;the output of the generator set 1-n for the scheduled period t; />The output of the generator set 1-n for the scheduled period t; />Is the payload of nodes 1-n.
In a first embodiment of the present invention, referring to fig. 4, a flowchart of an embodiment of a method for evaluating the LORP adequacy based on two-stage robust optimization scheduling according to the present invention is shown. The scheduling mechanism receives quotation and report of the real-time market generator set, selects the most serious net load fluctuation scene in a scheduling period according to historical data, operates a robust optimization model in a first stage based on an equivalent aggregation network after an electric power system is aggregated, takes the tie line power and the climbing rate in a subarea output by the robust optimization model as the input of a decisive scheduling model in a second stage, sets a tie line power deviation range in the decisive scheduling model, operates the decisive scheduling model to obtain a system scheduling scheme and a regional marginal electricity price, implements the scheduling scheme in the current scheduling period, updates the net load information predicted in the next period, and determines the system scheduling scheme in the next period by using the models in the first stage and the second stage.
In a first embodiment of the present invention, a two-stage robust optimization scheduling model is provided to measure the abundance of flexible adjustment resources of a multi-region power system, where the robust optimization model in the first stage is used to determine the optimal power generation cost in all scheduling periods, and considers the most serious payload fluctuation scenario that may occur in the full period; based on the optimized output of the first stage model, the decisive scheduling model in the second stage considers short-time deviation of the power tolerance of the tie line on the premise of determining the power of each scheduling period of each sub-region, calculates flexible resource adjustment adequacy indexes of the current scheduling period of the system and each sub-region, and forms a system scheduling scheme. The beneficial effects of the invention include:
(1) By screening the uncertainty scene set, the problem that the number of scenes is exponentially increased and difficult to solve due to the existing random sampling technology is avoided, so that the model solving is more convenient, and the result is easier to trace.
(2) Through the network equivalent aggregation technology, the scale of the interconnected large-scale power grid is greatly reduced, and meanwhile, the aggregation network and the original network are guaranteed to have the same power flow characteristic, so that the whole evaluation and the flexibility adjustment capability evaluation of any specific subarea can be carried out.
(3) The two-stage robust optimization deterministic scheduling model is adopted to rapidly calculate the probability index of the insufficient capacity of the flexibility adjustment of the whole system and the subareas, and the index can be used as the actual running state quantity of the system, so that a system dispatcher knows the conditions of the flexible resources in the system and each subarea in real time, and measures are taken to ensure the safe and stable running of the power grid.
In summary, the first embodiment of the invention provides a power system flexibility optimization method, which is to aggregate all subarea power grids of a power system to form an equivalent aggregation network; establishing a flexible resource adjustment capability evaluation index of the power system; based on an equivalent aggregation network, combining with an evaluation index, running a robust optimization model aiming at the lowest power generation cost of the power system in a scheduling period to obtain tie line power and climbing capacity in each subarea; and determining a power deviation range of the tie line according to the power of the tie line, and operating a decisive scheduling model aiming at the lowest power generation cost of the subareas in the scheduling period to obtain a scheduling optimization scheme of the power system. The invention can effectively and timely reflect the actual running state and the flexible resource condition of the power system, and improves the applicability, practicality and effectiveness of the evaluation model.
Example 2
Referring to fig. 5, a schematic structural diagram of an embodiment of a power system flexibility optimization device provided by the present invention, where the device includes an aggregation module 201, an index establishment module 202, a first optimization module 203, and a second optimization module 204;
the aggregation module 201 is used for aggregating all the subarea power grids of the power system to form an equivalent aggregation network;
the index establishing module 202 is used for establishing a flexible resource adjustment capability assessment index of the power system;
the first optimization module 203 is configured to operate a robust optimization model targeting the lowest power generation cost of the power system in the scheduling period based on the equivalent aggregation network in combination with the evaluation index, so as to obtain tie line power and climbing capacity in each sub-area;
the second optimizing module 204 is configured to determine a link power deviation range according to the link power, and run a deterministic scheduling model that targets a lowest power generation cost of the sub-region in the scheduling period, so as to obtain a scheduling optimization scheme of the power system.
Further, in the second embodiment of the present invention, each sub-area grid of the power system is aggregated to form an equivalent aggregation network, specifically:
establishing a subarea power grid aggregation model by taking the minimum power deviation on the tie lines before and after subarea node aggregation as a target, and aggregating all subarea power grids of the power system by utilizing the subarea power grid aggregation model;
The objective function of the subarea power grid aggregation model is as follows:
wherein,is a poly (ethylene-propylene-butyleneInter-zone link power before closing; />Is the inter-zone link power after aggregation.
Further, in the second embodiment of the present invention, the inter-area link power after aggregation is specifically:
wherein,the inter-region link power after aggregation; phi R A reduced network sensitivity factor matrix; (P) inj ) R The injection power of the node after aggregation; diag (1/x) R ) Is a diagonal matrix, the diagonal element of which is x R Is the reciprocal of (2); c (C) R To reduce the pre-network sensitivity factor matrix.
Further, in the second embodiment of the present invention, a flexible resource adjustment capability assessment index of the power system is established, specifically:
the flexible resource adjustment capability evaluation indexes comprise power up-regulation deficiency probability of the generator set in the range of the power system, power down-regulation deficiency probability of the generator set in the range of the power system, climbing rate of the generator set in the range of a subarea of the power system and power up-regulation deficiency probability of the generator set in the range of the power system;
the power up-regulation insufficient probability of the generator set in the range of the power system is as follows:
/>
wherein, Is a generator set in the range of a power systemAdjusting the power up-regulation deficiency probability in the scheduling period t; τ is the scheduling period interval; p (P) i g [t]The output of the generator set i is scheduled for a period t; r is R i The climbing speed of the generator set i is set;for a predicted power system payload; p (P) i max Maximum output of the generator set i; p (P) i [t]The actual output power of the generator set i at the moment of time t is obtained;
the probability of power down-regulation deficiency of the generator set in the power system range is as follows:
wherein,the power of the generator set in the dispatching period t in the range of the power system is downwards adjusted to be insufficient; τ is the scheduling period interval; p (P) i g [t]The output of the generator set i is scheduled for a period t; r is R i The climbing speed of the generator set i is set; />For a predicted power system payload; p (P) i min The minimum output of the generator set i; p (P) i [t]The actual output power of the generator set i at the moment of time t is obtained;
the climbing rate of the generator set in the power system subarea range is as follows:
RC z [t]＝ZGRP[t]+(Im[t+1]-Im[t])-(Ex[t+1]-Ex[t])
wherein RC is provided with z Scheduling a ramp rate of a period t for the generator set in the region z; ZGRP [ t ]]The climbing power of the generator set in the period t is obtained; im is the inlet power, representing the power introduced from other areas; ex is the outlet power, representing the power to the outlets of other regions;
The power up-regulation insufficient probability of the power system range regional generator set is as follows:
wherein,the power up-regulation shortage probability of the generator set in the dispatching period t in the power system range region z is provided; p (P) i g [t]The output of the generator set i is scheduled for a period t; RC (resistor-capacitor) z Scheduling a ramp rate of a period t for the generator set in the region z; />Is the predicted net load of the power system.
Further, in the second embodiment of the present invention, a robust optimization model targeting the lowest power generation cost of the power system in the scheduling period is run to obtain the link power and the climbing capacity in each sub-area, specifically:
acquiring historical data of the power system, and determining parameters contained in constraint conditions of the robust optimization model; parameters contained in constraint conditions of the robust optimization model comprise the net load of each node of the power system, an uncertainty scene set of each subarea, the upper and lower limits of the output of the generator set in a scheduling period, the climbing rate of the generator set and the maximum power of a connecting line;
establishing an objective function of a robust optimization model by taking the lowest power generation cost of the power system in a scheduling period as a target;
and inputting parameters contained in constraint conditions of the robust optimization model and an objective function of the robust optimization model into the robust optimization model, and operating the robust optimization model to obtain the power of the connecting line and the climbing rate in each subarea.
Further, in the second embodiment of the present invention, parameters included in constraint conditions of the robust optimization model and an objective function of the robust optimization model are input to the robust optimization model, specifically:
the objective function of the robust optimization model is specifically:
/>
wherein,a generating cost function of the generator set i; p (P) i g [t]The output of the generator set i is scheduled for a period t; t is the scheduling period length;
setting constraint conditions of the robust optimization model according to parameters contained in the constraint conditions of the robust optimization model and the evaluation index; the constraint conditions of the robust optimization model comprise supply and demand balance constraint, power constraint of future tie line power mutual aid, upper and lower limit constraint of unit output, climbing capacity constraint, tide constraint and random fluctuation range of net load in a future scheduling period.
Further, in the second embodiment of the present invention, constraint conditions of the robust optimization model are specifically:
the supply-demand balance constraint is as follows:
wherein,is the payload of node j; p (P) i g [t]The output of the generator set i is scheduled for a period t;
the power constraint for future tie power mutual savings is shown in the following equation:
wherein u is z [t]Is not in zone z A deterministic scene set;is the payload of node j; p (P) i g [t]The output of the generator set i is scheduled for a period t; u (U) z [t]Is the random fluctuation range of the payload;
the upper and lower limits of the set output force are defined as follows:
wherein P is i min The lower output limit of the generator set i is set for a scheduling period t; p (P) i max The upper output limit of the generator set i is set for a scheduling period t; p (P) i g [t]The output of the generator set i is scheduled for a period t;
the climbing capacity constraint is as follows:
wherein R is i The climbing speed of the generator set i is set; p (P) i g [t]The output of the generator set i is scheduled for a period t; p (P) i g [t-1]The output of the generator set i is scheduled for a period t-1;
the flow constraint is as follows:
wherein,maximum power for tie k; p (P) i g [t]For the dispatch period t the output of the generator set i；u z [t]A scene set for region z uncertainty; />A payload of region z; />Is a transmission loss coefficient;
the random fluctuation range of the payload in the future scheduling period is shown as follows:
wherein U is z [t]Is the random fluctuation range of the payload;a payload of region z at time t; />The net load change amount of the region z at the time t; />Is the payload of region z.
Further, in the second embodiment of the present invention, according to the link power, a link power deviation range is determined, and a deterministic scheduling model targeting the lowest power generation cost of the sub-area in the scheduling period is operated, so as to obtain a scheduling optimization scheme of the power system, which specifically includes:
Establishing an objective function of a decisive scheduling model by taking the lowest power generation cost of the subareas in the scheduling period as a target;
the objective function of the deterministic scheduling model is specifically:
wherein P is i g [t]The output of the generator set i is scheduled for a period t;a generating cost function of the generator set i;
determining a tie line power deviation range according to the tie line power, and setting constraint conditions of a decisive scheduling model; constraint conditions of the decisive scheduling model comprise supply and demand balance constraint, unit output upper and lower limit constraint, climbing capacity constraint, support power constraint provided by subareas, inter-area interconnecting line power constraint and intra-area power transmission line power constraint;
inputting the objective function of the deterministic scheduling model and the constraint condition of the deterministic scheduling model into the deterministic scheduling model, operating the deterministic scheduling model, calculating the flexible resource scheduling capability assessment index, obtaining an assessment result, and generating a scheduling optimization scheme of the power system.
Further, in the second embodiment of the present invention, according to the link power, a link power deviation range is determined, and constraint conditions of a deterministic scheduling model are set, specifically:
The supply-demand balance constraint is as follows:
wherein,is the payload of node j; p (P) i g [t]The output of the generator set i is scheduled for a period t;
the upper and lower limits of the set output force are defined as follows:
wherein P is i min The lower output limit of the generator set i is set for a scheduling period t; p (P) i max The upper output limit of the generator set i is set for a scheduling period t; p (P) i g [t]The output of the generator set i is scheduled for a period t;
the climbing capacity constraint is as follows:
wherein R is i The climbing speed of the generator set i is set; p (P) i g [t]The output of the generator set i is scheduled for a period t; p (P) i g [t-1]The output of the generator set i is scheduled for a period t-1;
the support power constraint provided by the sub-region is as follows:
wherein R is i The climbing speed of the generator set i is set; RC (resistor-capacitor) z Is the climbing rate in the subarea; p (P) i g [t]The output of the generator set i is scheduled for a period t; p (P) i max Maximum output of the generator set i;
the inter-zone link power constraint is shown as follows:
wherein F is k [t]Link power for link k during scheduling period t; epsilon is a preset short-term tie line deviation;the output vector of the n generator sets in the period t comprises the output of all the generator sets; />The vector of the net load of n nodes comprises the net load of all nodes;
the power constraint of the transmission line in the area is shown as follows:
Wherein F is m [t]A link power for link m in scheduling period t;the output of the generator set 1-n for the scheduled period t; />The output of the generator set 1-n for the scheduled period t; />Is the payload of nodes 1-n.
In summary, the second embodiment of the present invention provides a power system flexibility optimization device, which aggregates all sub-area power grids of a power system based on organic combination among modules to form an equivalent aggregation network; establishing a flexible resource adjustment capability evaluation index of the power system; based on an equivalent aggregation network, combining with an evaluation index, running a robust optimization model aiming at the lowest power generation cost of the power system in a scheduling period to obtain tie line power and climbing capacity in each subarea; and determining a power deviation range of the tie line according to the power of the tie line, and operating a decisive scheduling model aiming at the lowest power generation cost of the subareas in the scheduling period to obtain a scheduling optimization scheme of the power system. The invention can effectively and timely reflect the actual running state and the flexible resource condition of the power system, and improves the applicability, practicality and effectiveness of the evaluation model.
Example 3
The invention can divide the large-scale interconnected power grid into a plurality of subregions with similar characteristics by adopting the region division method based on fuzzy clustering so as to reduce the complexity and the calculated amount of the network. The fuzzy clustering-based region dividing method can establish fuzzy similarity relations among grid nodes according to factors such as topological structures, load characteristics and generator set types of the power grid, and then cluster the grid nodes into different regions through a certain algorithm.
The region dividing method based on fuzzy clustering mainly comprises the following steps:
1. data preprocessing: and carrying out operations such as normalization, standardization, dimension reduction and the like on the original data so as to enable the original data to meet the requirement of fuzzy clustering.
2. And (3) establishing a similarity relation: and calculating the similarity between nodes according to various attributes, such as voltage, power, impedance and the like, between the nodes of the power grid, and constructing a fuzzy similarity matrix.
3. And (3) selecting a clustering algorithm: and selecting a proper fuzzy clustering algorithm, such as a transfer closure method, a maximum support tree method, an objective function method and the like, according to different targets and conditions, and performing clustering analysis on fuzzy similarity matrixes to obtain a partitioning scheme of each region.
4. And (3) evaluating a clustering result: and evaluating and optimizing the clustering result according to some evaluation indexes such as the clustering effectiveness, the region balance, the region stability and the like so as to achieve the optimal region division effect.
In a third embodiment of the present invention, after aggregating the subregions of the power system, the system flexibility can be evaluated to adjust the resource adequacy by generating a plurality of different payload fluctuation scenarios using an economic dispatch model based on random programming. The economic dispatch model based on random programming aims at maximizing or minimizing the expected value of the objective function and meets certain probability constraint, and the method can process different types of objective functions and constraint conditions such as linearity, nonlinearity, convexity, non-convexity and the like.
The economic dispatch model based on random programming mainly comprises the following steps:
1. data preprocessing: and carrying out normalization, standardization, dimension reduction and other operations on the original data to ensure that the original data meets the requirement of random planning.
2. Modeling random variables: according to the characteristics of uncertain factors in the power system, a proper probability distribution function is selected to describe the statistical characteristics of random variables, such as mean, variance, covariance and the like.
3. Random optimization problem construction: and selecting a proper random programming model, such as opportunistic constraint programming, risk measurement programming, sample average approximation method and the like, according to the form of the objective function and the constraint condition, and establishing a mathematical model.
4. Solving a random optimization problem: according to the characteristics of the random programming model, a proper algorithm such as a Monte Carlo method, a scene method, a decomposition method and the like is selected, the random optimization problem is converted into a deterministic problem or an approximate problem, and the optimal solution or the approximate solution is obtained by utilizing mathematical programming software or self-programming solution.
5. Evaluation of random optimization results: and according to some evaluation indexes, such as expected values, variances, confidence intervals and the like, evaluating and analyzing the random optimization result so as to achieve the optimal economic dispatching effect.
In the third embodiment of the invention, in an economic dispatch model based on random programming, short-time deviation of the power tolerance of the tie line can be considered, long-time deviation of the power of the tie line can also be considered, and the flexible resource adjustment adequacy index of the system and each subarea is balanced by introducing a penalty factor.
In summary, the power system flexibility optimization method provided by the third embodiment of the invention adopts a fuzzy clustering-based region division method to aggregate all subregions of the power system, adopts an economic dispatch model based on random programming, and evaluates the system flexibility to adjust the resource adequacy by generating a plurality of different net load fluctuation scenes. In the economic dispatch model based on random programming, short-time deviation of the power tolerance of the tie line or long-time deviation of the power of the tie line can be considered, and the flexibility resource adjustment adequacy index of the system and each subarea is balanced by introducing a penalty factor, so that the actual running state and the flexibility resource condition of the power system are effectively and timely reflected, and the optimization degree of the flexibility of the power system is improved.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention, and are not to be construed as limiting the scope of the invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.

Claims (10)

1. A method for optimizing flexibility of an electrical power system, comprising:
aggregating all subarea power grids of the power system to form an equivalent aggregation network;
establishing a flexible resource adjustment capability evaluation index of the power system;
based on the equivalent aggregation network, operating a robust optimization model with the lowest power generation cost of the power system in the scheduling period as a target by combining the evaluation indexes to obtain the power of the connecting line and the climbing capacity in each subarea;
and determining a power deviation range of the tie line according to the power of the tie line, and operating a decisive scheduling model which aims at the lowest power generation cost of the subareas in the scheduling period to obtain a scheduling optimization scheme of the power system.
2. The power system flexibility optimization method according to claim 1, wherein the aggregating all the sub-area power grids of the power system to form an equivalent aggregation network specifically comprises:
establishing a subarea power grid aggregation model by taking the minimum power deviation on the tie lines before and after subarea node aggregation as a target, and aggregating all subarea power grids of the power system by utilizing the subarea power grid aggregation model;
the objective function of the subarea power grid aggregation model is as follows:
Wherein,is inter-zone link power before aggregation; />Is the inter-zone link power after aggregation.
3. The power system flexibility optimization method according to claim 2, wherein the aggregated inter-zone link power is specifically:
wherein,the inter-region link power after aggregation; phi R A reduced network sensitivity factor matrix; (P) inj ) R The injection power of the node after aggregation; diag (1/x) R ) Is a diagonal matrix, the diagonal element of which is x R Is the reciprocal of (2); c (C) R To reduce the pre-network sensitivity factor matrix.
4. The power system flexibility optimization method according to claim 1, wherein the establishing a power system flexibility resource adjustment capability assessment index, in particular
The flexible resource adjustment capability evaluation indexes comprise power up-regulation deficiency probability of the generator set in the range of the power system, power down-regulation deficiency probability of the generator set in the range of the power system, climbing rate of the generator set in the range of a subarea of the power system and power up-regulation deficiency probability of the generator set in the range of the power system;
the power up-regulation insufficient probability of the generator set in the range of the power system is as follows:
Wherein,the power up-regulation shortage probability of the generator set in the dispatching period t in the power system range is provided; τ is the scheduling period interval; p (P) i g [t]The output of the generator set i is scheduled for a period t; r is R i The climbing speed of the generator set i is set;for a predicted power system payload; p (P) i max Maximum output of the generator set i; p (P) i [t]The actual output power of the generator set i at the moment of time t is obtained;
the probability of power down-regulation deficiency of the generator set in the power system range is as follows:
wherein LORP s dn,τ [t]The power of the generator set in the dispatching period t in the range of the power system is downwards adjusted to be insufficient; τ is the scheduling period interval; p (P) i g [t]The output of the generator set i is scheduled for a period t; r is R i Climbing for generating set iA rate;for a predicted power system payload; p (P) i min The minimum output of the generator set i; p (P) i [t]The actual output power of the generator set i at the moment of time t is obtained;
the climbing rate of the generator set in the power system subarea range is as follows:
RC z [t]＝ZGRP[t]+(Im[t+1]-Im[t])-(Ex[t+1]-Ex[t])
wherein RC is provided with z Scheduling a ramp rate of a period t for the generator set in the region z; ZGRP [ t ]]The climbing power of the generator set in the period t is obtained; im is the inlet power, representing the power introduced from other areas; ex is the outlet power, representing the power to the outlets of other regions;
The power up-regulation insufficient probability of the power system range regional generator set is as follows:
wherein,the power up-regulation shortage probability of the generator set in the dispatching period t in the power system range region z is provided; p (P) i g [t]The output of the generator set i is scheduled for a period t; RC (resistor-capacitor) z Scheduling a ramp rate of a period t for the generator set in the region z; />Is the predicted net load of the power system.
5. The power system flexibility optimization method according to claim 1, wherein the running of the robust optimization model targeting the lowest power generation cost of the power system in the scheduling period obtains link power and climbing capacity in each sub-area, specifically:
acquiring historical data of the power system, and determining parameters contained in constraint conditions of the robust optimization model; parameters contained in constraint conditions of the robust optimization model comprise the net load of each node of the power system, an uncertainty scene set of each subarea, the upper and lower limits of the output of the generator set in a scheduling period, the climbing rate of the generator set and the maximum power of a connecting line;
establishing an objective function of a robust optimization model by taking the lowest power generation cost of the power system in a scheduling period as a target;
and inputting parameters contained in constraint conditions of the robust optimization model and an objective function of the robust optimization model into the robust optimization model, and operating the robust optimization model to obtain the power of the connecting line and the climbing rate in each subarea.
6. The power system flexibility optimization method according to claim 5, wherein the parameters contained in the constraint condition of the robust optimization model and the objective function of the robust optimization model are input to the robust optimization model, specifically:
the objective function of the robust optimization model is specifically:
wherein,a generating cost function of the generator set i; p (P) i g [t]The output of the generator set i is scheduled for a period t; t is the scheduling period length;
setting constraint conditions of the robust optimization model according to parameters contained in the constraint conditions of the robust optimization model and the evaluation index; the constraint conditions of the robust optimization model comprise supply and demand balance constraint, power constraint of future tie line power mutual aid, upper and lower limit constraint of unit output, climbing capacity constraint, tide constraint and random fluctuation range of net load in a future scheduling period.
7. The power system flexibility optimization method according to claim 6, wherein the constraint conditions of the robust optimization model are specifically:
the supply-demand balance constraint is as follows:
wherein,is the payload of node j; p (P) i g [t]The output of the generator set i is scheduled for a period t;
The power constraint for future tie power mutual savings is shown in the following equation:
wherein u is z [t]A scene set for region z uncertainty;is the payload of node j; p (P) i g [t]The output of the generator set i is scheduled for a period t; u (U) z [t]Is the random fluctuation range of the payload;
the upper and lower limits of the set output force are defined as follows:
wherein P is i min The lower output limit of the generator set i is set for a scheduling period t; p (P) i max The upper output limit of the generator set i is set for a scheduling period t; p (P) i g [t]For adjustingThe output of the generator set i is in a degree period t;
the climbing capacity constraint is as follows:
wherein R is i The climbing speed of the generator set i is set; p (P) i g [t]The output of the generator set i is scheduled for a period t; p (P) i g [t-1]The output of the generator set i is scheduled for a period t-1;
the flow constraint is as follows:
wherein,maximum power for tie k; p (P) i g [t]The output of the generator set i is scheduled for a period t; u (u) z [t]A scene set for region z uncertainty; />A payload of region z; />Is a transmission loss coefficient;
the random fluctuation range of the payload in the future scheduling period is shown as follows:
wherein U is z [t]Is the random fluctuation range of the payload;a payload of region z at time t; />The net load change amount of the region z at the time t; />Is the payload of region z.
8. The power system flexibility optimization method according to claim 1, wherein the determining a power deviation range of the tie line according to the power of the tie line, and operating a deterministic scheduling model targeting the lowest power generation cost of the sub-area in the scheduling period, to obtain a scheduling optimization scheme of the power system, specifically comprises:
Establishing an objective function of a decisive scheduling model by taking the lowest power generation cost of the subareas in the scheduling period as a target;
the objective function of the deterministic scheduling model is specifically:
wherein P is i g [t]The output of the generator set i is scheduled for a period t;a generating cost function of the generator set i;
determining a tie line power deviation range according to the tie line power, and setting constraint conditions of a decisive scheduling model; constraint conditions of the decisive scheduling model comprise supply and demand balance constraint, unit output upper and lower limit constraint, climbing capacity constraint, support power constraint provided by subareas, inter-area interconnecting line power constraint and intra-area power transmission line power constraint;
inputting the objective function of the deterministic scheduling model and the constraint condition of the deterministic scheduling model into the deterministic scheduling model, operating the deterministic scheduling model, calculating the flexible resource scheduling capability assessment index, obtaining an assessment result, and generating a scheduling optimization scheme of the power system.
9. The power system flexibility optimization method according to claim 8, wherein the determining a link power deviation range according to the link power and setting constraint conditions of a deterministic scheduling model specifically includes:
The supply-demand balance constraint is as follows:
wherein,is the payload of node j; p (P) i g [t]The output of the generator set i is scheduled for a period t; the method comprises the steps of carrying out a first treatment on the surface of the
The upper and lower limits of the set output force are defined as follows:
wherein P is i min The lower output limit of the generator set i is set for a scheduling period t; p (P) i max The upper output limit of the generator set i is set for a scheduling period t; p (P) i g [t]The output of the generator set i is scheduled for a period t;
the climbing capacity constraint is as follows:
wherein R is i The climbing speed of the generator set i is set; p (P) i g [t]The output of the generator set i is scheduled for a period t; p (P) i g [t-1]The output of the generator set i is scheduled for a period t-1;
the support power constraint provided by the sub-region is as follows:
wherein R is i The climbing speed of the generator set i is set; RC (resistor-capacitor) z Is the climbing rate in the subarea; p (P) i g [t]The output of the generator set i is scheduled for a period t; p (P) i max Maximum output of the generator set i;
the inter-zone link power constraint is shown as follows:
wherein F is k [t]Link power for link k during scheduling period t; epsilon is a preset short-term tie line deviation;the output vector of the n generator sets in the period t comprises the output of all the generator sets; />The vector of the net load of n nodes comprises the net load of all nodes;
the power constraint of the transmission line in the area is shown as follows:
Wherein F is m [t]A link power for link m in scheduling period t;the output of the generator set 1-n for the scheduled period t; />The output of the generator set 1-n for the scheduled period t; />Is the payload of nodes 1-n.
10. An electric power system flexibility optimization device, characterized by comprising: the system comprises an aggregation module, an index establishment module, a first optimization module and a second optimization module;
the aggregation module is used for aggregating all subarea power grids of the power system to form an equivalent aggregation network;
the index establishing module is used for establishing a flexible resource adjustment capability assessment index of the power system;
the first optimization module is used for operating a robust optimization model which aims at the lowest power generation cost of the power system in the scheduling period based on the equivalent aggregation network and combining the evaluation index to obtain the power of the connecting line and the climbing capacity in each subarea;
and the second optimization module is used for determining a power deviation range of the tie line according to the power of the tie line, and operating a decisive scheduling model which aims at the lowest power generation cost of the subareas in the scheduling period to obtain a scheduling optimization scheme of the power system.
CN202311555451.7A 2023-11-20 2023-11-20 Power system flexibility optimization method and device Pending CN117595398A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118074236A (en) * 2024-04-17 2024-05-24 浙江大学 Electric power system flexibility evaluation method and system based on generalized polynomial chaos

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118074236A (en) * 2024-04-17 2024-05-24 浙江大学 Electric power system flexibility evaluation method and system based on generalized polynomial chaos

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