CN111799848A - Power system node electricity price evaluation method containing pumped storage unit under market environment - Google Patents

Power system node electricity price evaluation method containing pumped storage unit under market environment Download PDF

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Publication number
CN111799848A
CN111799848A CN202010688388.4A CN202010688388A CN111799848A CN 111799848 A CN111799848 A CN 111799848A CN 202010688388 A CN202010688388 A CN 202010688388A CN 111799848 A CN111799848 A CN 111799848A
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node
electricity price
power
pumped
pumped storage
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罗佑坤
黎昌杰
林恺
辛晟
乔志园
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Peak and Frequency Regulation Power Generation Co of China Southern Power Grid Co Ltd
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Peak and Frequency Regulation Power Generation Co of China Southern Power Grid Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Abstract

The method for evaluating the node electricity price of the power system with the pumped storage unit in the market environment fully considers the characteristics of quick start and stop of the pumped storage unit and the characteristic of quick conversion of the power generation pumping working condition, and takes the characteristics as a means for inhibiting the node electricity price from being greatly increased; an electric power market optimized electricity purchasing and pumped storage unit optimized calling double-layer model is established, and the output of the pumped storage unit to be called can be quickly determined; the solving method based on the sensitivity method is provided, the generator set and the load which need to be adjusted and the corresponding adjustment amount can be rapidly determined, and the method is suitable for the real-time application scene of the power system; finally, a probability theory method and a law of large numbers are introduced, the duration of each state is obtained by extracting the influence of various determined events and random events of the power system on the operation state of the system, and therefore the expected value of the node electricity price in a certain event is calculated, and effective power market operation information is provided.

Description

Power system node electricity price evaluation method containing pumped storage unit under market environment
Technical Field
The invention relates to the technical field of power market price evaluation, in particular to a power system node price evaluation method containing a pumped storage unit in a market environment.
Background
The pumped storage power station is a large-capacity energy storage device with long service life and the most mature technology in a power system, and plays a very important role in ensuring the safe, stable and economic operation of the power system. The pumped storage unit is flexible in starting and stopping and rapid in response, can realize conversion of different operation conditions in a short time, has good dynamic characteristics, meets the requirements of rapid adjustment under emergency conditions of emergency standby, frequency modulation, phase modulation, black start and the like of a power system, and can effectively improve the power supply quality and the safety and stability level of the power system.
The node electricity price is the marginal cost when the unit load demand is increased at a certain node under the condition of meeting the operating characteristics and constraint conditions of various devices and resources, namely the cost which is increased when the 'one-degree more electricity' is consumed at a certain time and a certain place. Node electricity prices are widely used in the centralized electricity markets in north america, australia, singapore, etc. Generally, "equipment and resources" affecting node electricity prices mainly refer to generator sets and transmission lines, and "operating characteristics and constraints" include power load balancing, maximum and minimum power output and ramp of generator sets, transmission capabilities of transmission lines and transmission sections (which may be understood as a set of lines) in normal and fault states, and the like.
There are currently several categories of forecasting of electricity prices in the electricity market:
(1) and classifying according to the prediction period. The method comprises short-term electricity price prediction and long-term electricity price prediction. The short-term electricity price forecast is 15min, 30min, 1h and the like, and is mainly used for a bidding strategy in a reproduced goods market of a power generation company. Long-term electricity price forecasts, of months to years, are used primarily for investment decisions.
(2) According to the classification of the prediction points, the prediction can be divided into market clearing power price, regional market power price, node marginal power price prediction and the like for the whole system, a specific region or a specific bus.
The market price prediction method comprises an artificial neural network method, an event sequence method, a regression analysis method, a grey theory prediction method and the like. The node electricity price is a real-time electricity price calculation method, and many related researches are carried out at home and abroad, for example, the definition, the composition and the model of the node electricity price are researched, and a learner establishes a new model of the node electricity price in the electric power market environment, and the model is characterized in that: node electricity price and blocking multipliers are embedded into the model as independent variables, so that a scheduling target and a settlement target in the market reach a unified state [1] Yingying, Wangxinnfan, a node electricity price model [ J ] based on the independent electricity price variables, 2006(08): 20-24; the students provide a real-time electricity price calculation method based on the optimal power flow, and can decompose the real-time electricity prices of active power and reactive power into various auxiliary services (rotary standby, network loss supplement, voltage support, network safety and the like), and an interior point method is applied to solve a modified model [2] thank you, songym, Erliang.
The conventional method researches a node electricity price calculation method under the condition of normal operation of a power system, but has the following two problems:
1. in an electric power system with a pumped storage unit, under the condition that the node electricity price is too high due to the fact that an electric power network has faults, the generator unit and a transmission network are overhauled or events such as network blockage and the like occur, how a system dispatcher calls a pumped storage power station to generate electricity or pump water, changes the flow distribution and inhibits the too high node electricity price is not considered, and therefore the situation that the electric power market runs stably is guaranteed.
2. No evaluation method for the node electricity price system in any period is formed, the conventional method only calculates the node electricity price at a single moment, and sufficient market information cannot be provided for market participants to make decisions.
Disclosure of Invention
The invention provides a method for evaluating the node electricity price of the power system with the pumped storage unit under the market environment, aiming at overcoming the technical defects of the existing market electricity price prediction method.
In order to solve the technical problems, the technical scheme of the invention is as follows:
the method for evaluating the electricity price of the power system node containing the pumped storage unit in the market environment comprises the following steps:
s1: establishing a node electricity price calculation model containing a pumped storage power station;
s2: solving the node electricity price calculation model based on a power flow sensitivity method;
s3: and according to the solving result, introducing a probability theory method, calculating the duration expected value of each power state and the electricity price of each node through random sampling, and evaluating the electricity price of the nodes of the power system.
Wherein, in the step S1, the node electricity price calculation model includes an upper layer model and a lower layer model; wherein:
the upper layer model is used for determining the electricity price of each node in the power system;
the lower layer model is used for determining how to call the pumped storage unit to restrain the node electricity price after the node electricity price is greatly increased, and therefore stable operation of the power market is guaranteed.
The upper layer model takes the lowest electricity purchasing cost as an objective function, and determines the node electricity price according to the objective function, and the model is specifically expressed as follows:
market clearing targets: minimizing electricity purchase cost
Figure BDA0002588448330000031
And (4) system constraint: load balancing
Figure BDA0002588448330000032
Branch capacity constraint
Figure BDA0002588448330000033
Unit restraint: upper and lower limit constraints of output
Figure BDA0002588448330000034
Where ρ isiFor the generator price, PgiIs the output of the ith generator, PdiIs the ith load, and S is the line sensitivity factor; solving the models (1) - (4) to obtain a target function and a shadow price of network constraint, namely a node electricity price; let λ, μ, α, β be the lagrangian multipliers corresponding to the constraints, respectively, then the lagrangian function is:
Figure BDA0002588448330000035
the optimality conditions are as follows:
Figure BDA0002588448330000036
the node electricity price is:
Figure BDA0002588448330000037
the lower model is used for calling the pumped storage unit to suppress node electricity prices, the calling target of the pumped storage unit is to eliminate node electricity price imbalance caused by system blockage or generator faults and transmission line faults as much as possible, so that the electricity prices of all nodes tend to be consistent, and the target function is expressed as follows:
Figure BDA0002588448330000038
in the formula, PDrawerFor pumped storage power station node sets, rhoiFor quoted prices, Δ P, of pumped storage power stations in the electricity marketDrawerThe generated energy is increased for the pumped storage power station; wherein the constraints are as follows:
unit restraint: pumped storage group upper and lower limit of output restriction (9)
And (4) system constraint: generator and load power balance (10)
Network constraint: and (3) power transmission line capacity constraint (11).
In step S2, the dc power flow equation defining the power flow sensitivity is as follows:
p ═ B, also denoted AP ═ B, where a ═ B-1(ii) a Let ai、ajI and j rows of the matrix A respectively, according to the line power:
Figure BDA0002588448330000041
obtaining:
Figure BDA0002588448330000042
let aii，ajiAre respectively ai、ajThe ith element of
Figure BDA0002588448330000043
Then Sk,iInjecting power p for node iiTo line LijSensitivity factor of tidal current.
In step S2, power changes of the generator set and the pumped-storage generator set in the power system are calculated for any line LijThe sensitivity of tidal current change is preferentially selected, the unit with high sensitivity is selected to change the output, and the specific solving process is as follows:
s21: executing the electricity purchasing optimization program of the upper layer models (1) - (4) and judging whether feasible solutions exist or not; if not, indicating that the line is overloaded, selecting the branch with the most serious overload as a regulation object, and executing step S22; if the feasible solution exists, executing step S25;
s22: according to the selected adjusting branch, calculating the sensitivity of the power change of each generator in the system to the adjusting branch, and simultaneously calculating the sensitivity of each load point to the adjusting branch;
s23: selecting a generator with positive maximum sensitivity as an adjustment object, selecting a load point with negative maximum sensitivity as a load participating in adjustment, reducing the output delta P of the generator and reducing the load delta P;
s24: executing the step S21 to re-execute the electricity purchasing optimization program;
s25: executing the electricity purchasing optimization program of the upper layer models (1) - (4), calculating lagrangian multipliers of node electricity price and branch flow constraint, simultaneously executing the lower layer model, calling the pumped storage unit model, and calculating the value of the target function (8);
s26: selecting the branch with the largest Lagrange multiplier of the branch power flow constraint (3) as a regulating object from all the branches of the node with the highest electricity price;
s27: selecting a step length delta P, reducing the load delta P at a load node which has the largest influence on the regulating branch according to the magnitude of the tidal current sensitivity of the selected branch, and reducing the output delta P at a generator node which has the largest influence on the regulating branch;
s28: executing an electricity purchasing optimization program according to the formulas (1) to (4), and recalculating the lagrangian coefficient of node electricity price and branch flow constraint;
s29: whether the calculation formula (8) is reduced or not is judged, if not, the system meets the optimization, and a result is output; if yes, go to step S26.
In step S23, the curtailed generated output and load should be equal to maintain the total power balance of the system without considering the grid loss; in step S26, according to the dual principle, the branch with the largest lagrange multiplier represents the largest contribution to the increase in node electricity price due to its capacity limitation.
In step S3, an evaluation model is first constructed, specifically:
set random event group e1，e2，…enAll obedience parameter is lambda1，λ2，…λnAre independent of each other, it can be proved that the first random event e, which occurs is the minejThe time of arrival obeys a parameter of
Figure BDA0002588448330000051
The distribution of indices; when the system is in the state i, the arrival time of the random event which occurs firstly is set as TiNext deterministic event ej0The time of arrival is Tj0(ii) a Comparison TiAnd Tj0The characteristics of the exponential distribution are:
Figure BDA0002588448330000052
whether a deterministic event occurs first can be judged in the stochastic simulation according to the formula (12); the specific method comprises the following steps:
produce [0,1]Random numbers mu distributed uniformly over the interval if
Figure BDA0002588448330000053
Determine event ej0First, the system transitions out of state i, where the dwell time is Tj0(ii) a Otherwise, it is a random event group { e1,e2,...en}iOne random event in (2) occurs first; deterministic event ej0The distribution function and the recurrence formula of the expected duration of any state before the occurrence are shown in (13) - (15):
Figure BDA0002588448330000054
Figure BDA0002588448330000055
Figure BDA0002588448330000056
electricity price expectation value of node i
Figure BDA0002588448330000057
Wherein, the step S3 specifically includes:
s301: inputting basic parameters such as a power transmission network of an electric power system, power of a generator, load and the like, setting the sampling frequency M to be 1, and enabling the system to be in a kth state to be 1;
s302: calculating the parameter F of the state k from the equations (13) to (14)k、Pk；
S303: probability sampling is carried out according to a formula (12) to judge a deterministic event ej0Whether or not it occurs first, e.g. randomlyWhen the event occurs first, step S304 is executed; if a deterministic event occurs first, go to step 309;
s304: if a random event occurs first, the duration D of state k is determined by equation (15)k；
S305: determining a first arriving random event according to the distribution probability;
s306: calculating the node electricity price by using the upper layer model;
s307: judging whether the pumped storage unit needs to be called or not; if yes, jointly solving by an upper layer model and a lower layer model to determine the node electricity price of the state k; otherwise, determining the node electricity price by the upper model;
s308: determining the expected value of the electricity price of the node by the formula (16), and executing the step S310;
s309: if a deterministic event occurs first, the duration D of state k is determined by equation (15)kAnd the next deterministic event occurrence time Tj1；
S310: judging whether the sampling frequency M is greater than a preset sampling frequency N or not by changing k to k +1, and if so, outputting a result; otherwise, executing step S311;
s311: let M be M +1, return to step S302.
Wherein, in the step S3, the random event includes a generator fault and a transmission line fault; the deterministic events comprise generator set overhaul and transmission line overhaul.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the method for evaluating the node electricity price of the power system with the pumped storage unit in the market environment fully considers the characteristics of quick start and stop of the pumped storage unit and the characteristic of quick conversion of the power generation pumping working condition, and takes the characteristics as a means for inhibiting the node electricity price from being greatly increased; an electric power market optimized electricity purchasing and pumped storage unit optimized calling double-layer model is established, and the output of the pumped storage unit to be called can be quickly determined; the solving method based on the sensitivity method is provided, the generator set and the load which need to be adjusted and the corresponding adjustment amount can be rapidly determined, and the method is suitable for the real-time application scene of the power system; finally, a probability theory method and a law of large numbers are introduced, the duration of each state is obtained by extracting the influence of various determined events and random events of the power system on the operation state of the system, and therefore the expected value of the node electricity price in a certain event is calculated, and effective power market operation information is provided.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a schematic flow chart of the tidal current sensitivity method of step S2 according to the present invention;
FIG. 3 is a schematic diagram illustrating the evaluation flow of step S3 according to the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 1, the method for evaluating the electricity price of a node of a power system including a pumped storage group in a market environment includes the following steps:
s1: establishing a node electricity price calculation model containing a pumped storage power station;
s2: solving the node electricity price calculation model based on a power flow sensitivity method;
s3: and according to the solving result, introducing a probability theory method, calculating the duration expected value of each power state and the electricity price of each node through random sampling, and evaluating the electricity price of the nodes of the power system.
In the specific implementation process, the invention establishes a node electricity price calculation model considering the function of the pumped storage unit, and the model is divided into an upper layer and a lower layer. The upper layer model is used for determining the electricity price of each node in the power system, and the lower layer model is used for determining how to call the pumped storage unit to restrain the electricity price of the node after the electricity price of the node is greatly increased, so that the stable operation of the power market is ensured.
In the specific implementation process, the invention provides a method for quickly adjusting a generator, a pumped storage unit and a load based on a tidal current sensitivity method so as to inhibit abnormal node electricity prices. The double-layer models established by the method are all nonlinear models, the number of nodes is large, and the solving difficulty is high. By establishing a sensitivity method, the generator and the load which need to be adjusted when the power system is abnormal can be quickly found, and the size of the adjustment amount can be accurately determined.
In the specific implementation process, the invention further provides an evaluation flow of the node electricity price system of the power system. A probability theory method is introduced, the coming time of deterministic events (such as unit overhaul and line overhaul) and random events (such as unit random trip and transmission line fault) of the power system is extracted through random sampling, and the duration expected value of each state and the power price of each node are calculated. According to the law of large numbers, the expected value of the node electricity price in any period of time can be effectively reflected, and more effective decision information can be provided for market participants.
Example 2
More specifically, the invention is further explained on the basis of the embodiment 1 by combining the attached drawings:
when the generator trips, the power transmission network fails or network blockage occurs, the node electricity price may be greatly increased, and a system dispatcher dispatches a pumped storage power station in the power system to generate power or pump water, so that the overhigh node electricity price is restrained. The pumped storage power station mainly has the following two functions:
1) when the generator trips and the power transmission network fails, the pumped storage power station is called to generate power so as to meet the operation requirement of the system.
2) When the power price of some nodes is overhigh due to the blockage of the power transmission line, the pumped storage power station is called to generate power or pump water, and the flow distribution of the system is changed, so that the power price of the nodes is reduced.
Based on the above analysis, the present invention designs a 2-layer optimization model to implement the above functions. Therefore, in the step S1, the node electricity price calculation model includes an upper layer model and a lower layer model; wherein:
the upper layer model is used for determining the electricity price of each node in the power system;
the lower layer model is used for determining how to call the pumped storage unit to restrain the node electricity price after the node electricity price is greatly increased, and therefore stable operation of the power market is guaranteed.
More specifically, the upper layer model takes the lowest electricity purchasing cost as an objective function, and determines the node electricity price according to the objective function, and the model is specifically expressed as follows:
market clearing targets: minimizing electricity purchase cost
Figure BDA0002588448330000081
And (4) system constraint: load balancing
Figure BDA0002588448330000082
Branch capacity constraint
Figure BDA0002588448330000083
Unit restraint: upper and lower limit constraints of output
Figure BDA0002588448330000084
Where ρ isiFor the generator price, PgiIs the output of the ith generator, PdiIs the ith load, and S is the line sensitivity factor; solving the models (1) - (4) to obtain a target function and a shadow price of network constraint, namely a node electricity price; let λ, μ, α, β be the lagrangian multipliers corresponding to the constraints, respectively, then the lagrangian function is:
Figure BDA0002588448330000085
the optimality conditions are as follows:
Figure BDA0002588448330000086
the node electricity price is:
Figure BDA0002588448330000091
more specifically, the lower model is used for calling the pumped storage unit to suppress node electricity prices, the calling target of the pumped storage unit is to eliminate node electricity price imbalance caused by system blockage or generator faults and transmission line faults as much as possible, so that the electricity prices of all nodes tend to be consistent, and the target function is expressed as:
Figure BDA0002588448330000092
in the formula, PDrawerFor pumped storage power station node sets, rhoiFor quoted prices, Δ P, of pumped storage power stations in the electricity marketDrawerThe generated energy is increased for the pumped storage power station; wherein the constraints are as follows:
unit restraint: pumped storage group upper and lower limit of output restriction (9)
And (4) system constraint: generator and load power balance (10)
Network constraint: and (3) power transmission line capacity constraint (11).
More specifically, in step S2, the dc power flow equation defining the power flow sensitivity is as follows:
p ═ B, also denoted AP ═ B, where a ═ B-1(ii) a Let ai、ajI and j rows of the matrix A respectively, according to the line power:
Figure BDA0002588448330000093
obtaining:
Figure BDA0002588448330000094
let aii，ajiAre respectively ai、ajThe ith element of
Figure BDA0002588448330000095
Then Sk,iInjecting power p for node iiTo line LijSensitivity factor of tidal current.
More specifically, as shown in fig. 2, in the step S2, the power changes of the generator set and the pumped-storage generator set in the power system are first calculated for any line LijThe sensitivity of tidal current change is preferentially selected, the unit with high sensitivity is selected to change the output, and the specific solving process is as follows:
s21: executing the electricity purchasing optimization program of the upper layer models (1) - (4) and judging whether feasible solutions exist or not; if not, indicating that the line is overloaded, selecting the branch with the most serious overload as a regulation object, and executing step S22; if the feasible solution exists, executing step S25;
s22: according to the selected adjusting branch, calculating the sensitivity of the power change of each generator in the system to the adjusting branch, and simultaneously calculating the sensitivity of each load point to the adjusting branch;
s23: selecting a generator with positive maximum sensitivity as an adjustment object, selecting a load point with negative maximum sensitivity as a load participating in adjustment, reducing the output delta P of the generator and reducing the load delta P;
s24: executing the step S21 to re-execute the electricity purchasing optimization program;
s25: executing the electricity purchasing optimization program of the upper layer models (1) - (4), calculating lagrangian multipliers of node electricity price and branch flow constraint, simultaneously executing the lower layer model, calling the pumped storage unit model, and calculating the value of the target function (8);
s26: selecting the branch with the largest Lagrange multiplier of the branch power flow constraint (3) as a regulating object from all the branches of the node with the highest electricity price;
s27: selecting a step length delta P, reducing the load delta P at a load node which has the largest influence on the regulating branch according to the magnitude of the tidal current sensitivity of the selected branch, and reducing the output delta P at a generator node which has the largest influence on the regulating branch;
s28: executing an electricity purchasing optimization program according to the formulas (1) to (4), and recalculating the lagrangian coefficient of node electricity price and branch flow constraint;
s29: whether the calculation formula (8) is reduced or not is judged, if not, the system meets the optimization, and a result is output; if yes, go to step S26.
In step S23, the curtailed generated output and load should be equal to maintain the total power balance of the system without considering the grid loss; in step S26, according to the dual principle, the branch with the largest lagrange multiplier represents the largest contribution to the increase in node electricity price due to its capacity limitation.
More specifically, in step S3, an evaluation model is first constructed, specifically:
set random event group e1，e2，…enAll obedience parameter is lambda1，λ2，…λnAre independent of each other, it can be proved that the first random event e, which occurs is the minejThe time of arrival obeys a parameter of
Figure BDA0002588448330000101
The distribution of indices; when the system is in the state i, the arrival time of the random event which occurs firstly is set as TiNext deterministic event ej0The time of arrival is Tj0(ii) a Comparison TiAnd Tj0The characteristics of the exponential distribution are:
Figure BDA0002588448330000102
whether a deterministic event occurs first can be judged in the stochastic simulation according to the formula (12); the specific method comprises the following steps:
produce [0,1]Random numbers mu distributed uniformly over the interval if
Figure BDA0002588448330000103
Determine event ej0First, the system transitions out of state i, where the dwell time is Tj0(ii) a Otherwise, it is a random event group { e1,e2,...en}iOne random event in (2) occurs first; deterministic event ej0The distribution function and the recurrence formula of the expected duration of any state before the occurrence are shown in (13) - (15):
Figure BDA0002588448330000111
Figure BDA0002588448330000112
Figure BDA0002588448330000113
electricity price expectation value of node i
Figure BDA0002588448330000114
More specifically, as shown in fig. 3, the step S3 specifically includes:
s301: inputting basic parameters such as a power transmission network of an electric power system, power of a generator, load and the like, setting the sampling frequency M to be 1, and enabling the system to be in a kth state to be 1;
s302: calculating the parameter F of the state k from the equations (13) to (14)k、Pk；
S303: probability sampling is carried out according to a formula (12) to judge a deterministic event ej0If the random event occurs first, execute step S304; if a deterministic event occurs first, go to step 309;
s304: if a random event occurs first, the duration D of state k is determined by equation (15)k；
S305: determining a first arriving random event according to the distribution probability;
s306: calculating the node electricity price by using the upper layer model;
s307: judging whether the pumped storage unit needs to be called or not; if yes, jointly solving by an upper layer model and a lower layer model to determine the node electricity price of the state k; otherwise, determining the node electricity price by the upper model;
s308: determining the expected value of the electricity price of the node by the formula (16), and executing the step S310;
s309: if a deterministic event occurs first, the duration D of state k is determined by equation (15)kAnd the next deterministic event occurrence time Tj1；
S310: judging whether the sampling frequency M is greater than a preset sampling frequency N or not by changing k to k +1, and if so, outputting a result; otherwise, executing step S311;
s311: let M be M +1, return to step S302.
More specifically, in the step S3, the random event includes a generator fault and a transmission line fault; the deterministic events comprise generator set overhaul and transmission line overhaul.
In the specific implementation process, the pumped storage unit has good dynamic characteristics, is flexible in starting and stopping and quick in response, can realize the conversion of the power generation and pumping operation conditions in a short time, can effectively improve the power supply quality and the safety and stability level of a power system, and can be used for preventing the power price of a system node from being abnormal due to the reasons of power transmission resistor plugs and the like in the power market environment.
In the specific implementation process, the invention establishes a node electricity price calculation model considering the function of the pumped storage unit, and the model is divided into an upper layer and a lower layer. The upper layer model can be used for determining the electricity price of each node in the power system, and the lower layer model can be used for calling the pumped storage unit to restrain the electricity price of the node under the condition that the electricity price of the node is abnormal, so that the stable operation of the power market is ensured. Based on the double-layer model provided by the invention, a solving method based on a power flow sensitivity method is provided. Because the double-layer models are all nonlinear models, the number of nodes is large, and the solving difficulty is high. By establishing a sensitivity method, the generator, the pumped storage unit and the load which need to be adjusted when the power system is abnormal can be quickly found, and the size of the adjustment amount can be accurately determined.
In the specific implementation process, the invention also establishes an evaluation flow of the node electricity price system of the power system. A probability theory method is introduced, the coming time of deterministic events (such as unit overhaul and line overhaul) and random events (such as unit random trip and transmission line fault) of the power system is extracted through random sampling, and the duration expected value of each state is calculated. According to the law of large numbers, the expected value of the node electricity price in a certain period of time can be effectively reflected, and more effective decision information can be provided for market participants.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. The method for evaluating the electricity price of the power system node containing the pumped storage unit in the market environment is characterized by comprising the following steps of:
s1: establishing a node electricity price calculation model containing a pumped storage power station;
s2: solving the node electricity price calculation model based on a power flow sensitivity method;
s3: and according to the solving result, introducing a probability theory method, calculating the duration expected value of each power state and the electricity price of each node through random sampling, and evaluating the electricity price of the nodes of the power system.
2. The method for evaluating the node electricity price of the power system with the pumped-storage group in the market environment according to claim 1, wherein in the step S1, the node electricity price calculation model comprises an upper layer model and a lower layer model; wherein:
the upper layer model is used for determining the electricity price of each node in the power system;
the lower layer model is used for determining how to call the pumped storage unit to restrain the node electricity price after the node electricity price is greatly increased, and therefore stable operation of the power market is guaranteed.
3. The method for evaluating the node electricity price of the power system with the pumped-storage unit under the market environment according to claim 2, wherein the upper layer model takes the lowest electricity purchasing cost as an objective function, and determines the node electricity price according to the objective function, and the model is specifically represented as follows:
market clearing targets: minimizing electricity purchase cost
Figure FDA0002588448320000011
And (4) system constraint: load balancing
Figure FDA0002588448320000012
Branch capacity constraint
Figure FDA0002588448320000013
Unit restraint: upper and lower limit constraints of output
Figure FDA0002588448320000014
Where ρ isiFor the generator price, PgiIs the output of the ith generator, PdiIs the ith load, and S is the line sensitivity factor; solving the models (1) - (4) to obtain a target function and a shadow price of network constraint, namely a node electricity price; let λ, μ, α, β be the lagrangian multipliers corresponding to the constraints, respectively, then the lagrangian function is:
Figure FDA0002588448320000015
the optimality conditions are as follows:
Figure FDA0002588448320000021
the node electricity price is:
Figure FDA0002588448320000022
4. the method for evaluating the node electricity prices of the electric power system with the pumped storage group in the market environment according to claim 3, wherein the lower layer model is used for calling the pumped storage group to suppress the node electricity prices, the calling goal of the pumped storage group is to eliminate the node electricity price imbalance caused by system blockage or generator fault and transmission line fault as much as possible, so that the electricity prices of all the nodes tend to be consistent, and the objective function is expressed as:
Figure FDA0002588448320000023
in the formula, PDrawerFor pumped storage power station node sets, rhoiFor quoted prices, Δ P, of pumped storage power stations in the electricity marketDrawerThe generated energy is increased for the pumped storage power station; wherein the constraints are as follows:
unit restraint: pumped storage group upper and lower limit of output restriction (9)
And (4) system constraint: generator and load power balance (10)
Network constraint: and (3) power transmission line capacity constraint (11).
5. The method for evaluating the node electricity prices of the power systems with the pumped-storage units in the market environment according to claim 4, wherein in the step S2, the DC power flow equation defining the power flow sensitivity is as follows:
p ═ B, also denoted AP ═ B, where a ═ B-1(ii) a Let ai、ajI and j rows of the matrix A respectively, according to the line power:
Figure FDA0002588448320000024
obtaining:
Figure FDA0002588448320000025
let aii，ajiAre respectively ai、ajThe ith element of
Figure FDA0002588448320000026
Then Sk,iInjecting power p for node iiTo line LijSensitivity factor of tidal current.
6. The method for assessing the electricity prices of nodes of an electric power system with a pumped-storage group under market conditions according to claim 5, wherein in step S2, the power changes of the generator set and the pumped-storage group in the electric power system are first calculated for any line LijThe sensitivity of tidal current change is preferentially selected, the unit with high sensitivity is selected to change the output, and the specific solving process is as follows:
s21: executing the electricity purchasing optimization program of the upper layer models (1) - (4) and judging whether feasible solutions exist or not; if not, indicating that the line is overloaded, selecting the branch with the most serious overload as a regulation object, and executing step S22; if the feasible solution exists, executing step S25;
s22: according to the selected adjusting branch, calculating the sensitivity of the power change of each generator in the system to the adjusting branch, and simultaneously calculating the sensitivity of each load point to the adjusting branch;
s23: selecting a generator with positive maximum sensitivity as an adjustment object, selecting a load point with negative maximum sensitivity as a load participating in adjustment, reducing the output delta P of the generator and reducing the load delta P;
s24: executing the step S21 to re-execute the electricity purchasing optimization program;
s25: executing the electricity purchasing optimization program of the upper layer models (1) - (4), calculating lagrangian multipliers of node electricity price and branch flow constraint, simultaneously executing the lower layer model, calling the pumped storage unit model, and calculating the value of the target function (8);
s26: selecting the branch with the largest Lagrange multiplier of the branch power flow constraint (3) as a regulating object from all the branches of the node with the highest electricity price;
s27: selecting a step length delta P, reducing the load delta P at a load node which has the largest influence on the regulating branch according to the magnitude of the tidal current sensitivity of the selected branch, and reducing the output delta P at a generator node which has the largest influence on the regulating branch;
s28: executing an electricity purchasing optimization program according to the formulas (1) to (4), and recalculating the lagrangian coefficient of node electricity price and branch flow constraint;
s29: whether the calculation formula (8) is reduced or not is judged, if not, the system meets the optimization, and a result is output; if yes, go to step S26.
7. The method for evaluating the node price of the power system with the pumped-storage group in the market environment according to claim 6, wherein in the step S23, the reduced generated output and the load should be equal in order to maintain the total power balance of the system without considering the grid loss; in step S26, according to the dual principle, the branch with the largest lagrange multiplier represents the largest contribution to the increase in node electricity price due to its capacity limitation.
8. The method for evaluating the electricity price of the power system node including the pumped-storage group in the market environment according to claim 7, wherein in the step S3, an evaluation model is first constructed, specifically:
set random event group e1，e2，…enAll obedience parameter is lambda1，λ2，…λnAre independent of each other, it can be proved that the first random event e, which occurs is the minejThe time of arrival obeys a parameter of
Figure FDA0002588448320000041
The distribution of indices; when the system is in the state i, the arrival time of the random event which occurs firstly is set as TiNext deterministic event ej0The time of arrival is Tj0(ii) a Comparison TiAnd Tj0The characteristic of the exponential distribution is：
Figure FDA0002588448320000042
Whether a deterministic event occurs first can be judged in the stochastic simulation according to the formula (12); the specific method comprises the following steps:
produce [0,1]Random numbers mu distributed uniformly over the interval if
Figure FDA0002588448320000043
Determine event ej0First, the system transitions out of state i, where the dwell time is Tj0(ii) a Otherwise, it is a random event group { e1,e2,...en}iOne random event in (2) occurs first; deterministic event ej0The distribution function and the recurrence formula of the expected duration of any state before the occurrence are shown in (13) - (15):
Figure FDA0002588448320000044
Figure FDA0002588448320000045
Figure FDA0002588448320000046
electricity price expectation value of node i
Figure FDA0002588448320000047
9. The method for evaluating the electricity price of the power system node with the pumped-storage group in the market environment according to claim 8, wherein the step S3 is specifically as follows:
s301: inputting basic parameters such as a power transmission network of an electric power system, power of a generator, load and the like, setting the sampling frequency M to be 1, and enabling the system to be in a kth state to be 1;
s302: calculating the parameter F of the state k from the equations (13) to (14)k、Pk；
S303: probability sampling is carried out according to a formula (12) to judge a deterministic event ej0If the random event occurs first, execute step S304; if a deterministic event occurs first, go to step 309;
s304: if a random event occurs first, the duration D of state k is determined by equation (15)k；
S305: determining a first arriving random event according to the distribution probability;
s306: calculating the node electricity price by using the upper layer model;
s307: judging whether the pumped storage unit needs to be called or not; if yes, jointly solving by an upper layer model and a lower layer model to determine the node electricity price of the state k; otherwise, determining the node electricity price by the upper model;
s308: determining the expected value of the electricity price of the node by the formula (16), and executing the step S310;
s309: if a deterministic event occurs first, the duration D of state k is determined by equation (15)kAnd the next deterministic event occurrence time Tj1；
S310: judging whether the sampling frequency M is greater than a preset sampling frequency N or not by changing k to k +1, and if so, outputting a result; otherwise, executing step S311;
s311: let M be M +1, return to step S302.
10. The method for evaluating the electricity price of the node of the power system with the pumped-storage group in the market environment according to claim 9, wherein in the step S3, the random event comprises a generator failure, a transmission line failure; the deterministic events comprise generator set overhaul and transmission line overhaul.
CN202010688388.4A 2020-07-16 2020-07-16 Power system node electricity price evaluation method containing pumped storage unit under market environment Pending CN111799848A (en)

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