CN112465323A - Short-term robust scheduling method for cascade hydropower station coupling daily electric quantity decomposition and day-ahead market bidding - Google Patents

Short-term robust scheduling method for cascade hydropower station coupling daily electric quantity decomposition and day-ahead market bidding Download PDF

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CN112465323A
CN112465323A CN202011300751.7A CN202011300751A CN112465323A CN 112465323 A CN112465323 A CN 112465323A CN 202011300751 A CN202011300751 A CN 202011300751A CN 112465323 A CN112465323 A CN 112465323A
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李刚
于旭光
张加贝
陈然
孙恒一
赵伟华
程春田
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Kunming Electric Power Transaction Center Co ltd
Dalian University of Technology
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Abstract

The invention provides a cascade hydropower station short-term robust scheduling method coupling daily electric quantity decomposition and day-ahead market bidding, which aims at setting the maximum total income of a cascade hydropower station as a target and establishing a deterministic hydropower station short-term optimization scheduling model; converting the deterministic short-term optimization scheduling model into a robust scheduling model considering the uncertainty of the electricity price; and solving by adopting a mixed integer nonlinear programming method. The method can scientifically and reasonably decompose the daily electric quantity of the cascade hydropower station into an electric power curve, guarantee effective execution of daily contracts, and obtain declared price pairs participating in spot markets before the day, the obtained scheduling result of the cascade hydropower station has robustness, and is embodied in that when the clearing price is in a pre-estimated price information interval, an expected target can be guaranteed not to be worse than a certain minimum preset result, the relation between an uncertain parameter variation range and a minimum acceptable target can be quantitatively depicted, and a more flexible and visual scheduling and transaction decision basis can be provided for a risk aversive cascade hydropower enterprise decision maker.

Description

Short-term robust scheduling method for cascade hydropower station coupling daily electric quantity decomposition and day-ahead market bidding
Technical Field
The invention relates to the field of electric power markets and hydropower dispatching operation, in particular to a short-term robust dispatching method for a cascade hydropower station coupling daily electric quantity decomposition and day-ahead market bidding.
Background
Since a new round of power system innovation, the Chinese power marketization process is rapidly promoted, medium-term and long-term transactions are gradually mature, spot market test is developed in order, and 8 test units enter simulated transaction and settlement test operation. The gradual establishment of the spot market environment inevitably brings more theoretical and practical challenges for the participation of the hydropower enterprises in marketized trading.
Under the influence of runoff uncertainty and uneven distribution of air in time, under the electric power spot market environment, hydropower enterprises need to fully utilize reservoir regulation performance, realize flood withering electric quantity transfer, guarantee hydropower consumption, avoid water abandoning or undermining by participating in medium-long-term transactions in different stages, and achieve the purposes of locking benefits and avoiding risks. Meanwhile, in order to stabilize the forecast deviation of the water, hydropower enterprises can participate in the spot market and track the market price signal by utilizing the advantages of flexible starting and high load change rate of the units, so that the hydropower enterprises can seek greater benefits. However, the following two problems are also faced: 1) in the medium-long term trade contract in the spot mode, a market main body signs a power curve or defines a decomposition mode in advance, and a time-sharing power curve is formed before the specified time, so that the market organization develops and settles financial settlement in the future, and the curve decomposition result influences the comprehensive income of the power generation enterprise. 2) The spot market price reflects the short-term supply-demand relationship and the space-time value of the electric energy, and has strong uncertainty. The hydropower plants are often far away from the load center, long-distance power transmission is needed, even cross-regional consumption is needed, the influence of the sending-out line of the located region or node is great, in addition, the uncertainty of the runoff aggravates the strong uncertainty of the water and electricity spot price, and the uncertainty of the spot price in the future cannot be ignored.
How to formulate a short-term optimization scheduling scheme of the cascade hydropower station coupling daily electric quantity decomposition and day-ahead market bidding, coping with uncertainty of day-ahead spot electricity price, coordinating income and risk to meet expected benefits, and is one of key problems to be solved urgently by cascade hydropower enterprises in the spot market environment.
The achievement of the invention is cut in from the coupling daily electric quantity decomposition and day-ahead market bidding linking angles, firstly, each short-term operation constraint of the cascade hydropower station and the constraint from daily electric quantity contract to time-sharing electric power curve decomposition are considered, and a deterministic short-term optimization scheduling model with the aim of maximizing the generation income is constructed; then, modeling the uncertainty of the spot market price by adopting a non-probabilistic information gap decision theory, constructing a robust model suitable for a risk aversion decision maker, and finally solving by adopting a non-linear programming method. The invention provides a short-term robust scheduling method for coupling daily electric quantity decomposition and day-ahead market bidding in a spot market environment for a cascade hydropower station, which can enable a scheduling scheme to meet a preset income target in a certain fluctuation range of the day-ahead market electric price and provide decision support for the daily electric quantity decomposition and the day-ahead market bidding.
Disclosure of Invention
The purpose of the invention is as follows: the technical problem to be solved by the invention is to provide a short-term robust scheduling method for a cascade hydropower station coupling daily electric quantity decomposition and day-ahead market bidding, which can enable a scheduling scheme to meet a preset income target in a certain fluctuation range of the day-ahead market price and provide decision support for the daily electric quantity decomposition and the day-ahead market bidding.
The technical scheme of the invention is as follows: a cascade hydropower station robust scheduling method coupling daily electric quantity decomposition and day-ahead market bidding formulates a cascade hydropower station short-term scheduling scheme according to the following steps (1) to (4).
(1) And constructing a deterministic short-term scheduling model, wherein the deterministic short-term scheduling model comprises an objective function, a daily electricity decomposition constraint and a short-term operation constraint.
First, an objective function is constructed. Aiming at short-term optimized scheduling of the cascade hydropower station in the power market environment, the invention aims at the maximum total income of the two parts of the medium-term market and the long-term market of the cascade hydropower station as a target, and the expression is as follows:
Figure BDA0002786742110000021
in the formula,
Figure BDA0002786742110000022
the power is output for the power station at the time of i days to decompose the electric quantity at t time;
Figure BDA0002786742110000023
contract electricity price for power station i; pi,tThe total output of the power station i in the time period t;
Figure BDA0002786742110000024
forecasting the clear electricity price, yuan/MWh, for the day-ahead market at the time t; t is a scheduling period time interval set; i is a cascade hydropower station set; Δ t is the period length.
And secondly, constructing daily electric quantity decomposition constraint.
Figure BDA0002786742110000025
Figure BDA0002786742110000026
Figure BDA0002786742110000027
The contract decomposition has a plurality of typical decomposition curves, and the daily electricity decomposition of the invention selects a peak-to-valley curve mode: a day is divided into a peak section, a flat section and a valley section, and load electric quantities of the peak section, the flat section and the valley section can be determined by historical load characteristics of a user side or negotiation in other modes. In the above formula
Figure BDA0002786742110000028
Bidding and outputting the day-ahead market of the power station i at the time t; ciThe daily contract electric quantity of the power station i is obtained; x is a time interval set, wherein p, f and v respectively represent peak time intervals, flat time intervals and valley time intervals; c. Cx,iContract electric quantity of the power station i in x time period; t isxIs the set of period indicators contained in the x period; gamma rayxIs the ratio of the daily electric quantity occupied by the x time period.
And thirdly, constructing short-term operation constraint. The method comprises a water quantity balance equation, reservoir capacity constraint, power station ex-reservoir flow constraint, hydropower station output constraint, hydropower station vibration area constraint, reservoir water level constraint and water level reservoir capacity relation, water head constraint, tail water level downward discharge flow relation and power generation function relation.
(2) Robust model construction based on information gap decision theory
First, uncertainty set construction
Selecting the day-ahead market electricity price as an uncertain parameter, and constructing an uncertain set model by adopting an envelope restriction model, wherein the mathematical expression of the uncertain set model is as follows:
Figure BDA0002786742110000031
in the formula: lambda [ alpha ]tRepresenting the actual clear electricity price of the spot market in the t period; alpha represents the fluctuation range of the electricity price, and the above formula shows that the actual clearing price surrounds the predicted value in the range of (1-alpha, 1+ alpha)
Figure BDA0002786742110000032
Fluctuating up and down.
Second, robust model object and constraint construction
The robust model is an IGDT robust model considering the uncertainty of the electricity price, and the objective function is as follows, wherein the goal is to maximize the fluctuation range of uncertain variables on the premise of meeting the expected goal:
max α (6)
the constraints include a minimum benefit constraint in addition to the short-term operating constraint described above, and the expression is as follows:
Figure BDA0002786742110000033
πr＝(1-βr)π0,βr∈[0,1) (8)
π0obtaining an optimal yield for the deterministic model of step (1); pirAn acceptable minimum income target can be set for a decision maker according to self requirements; beta is arThe larger the value of the yield deviation factor, namely the deviation degree between the expected yield and the optimal yield of the deterministic pattern, the larger the avoidance degree of the risk of the decision solution.
(3) And constructing a contract decomposition curve and a day-ahead market bidding strategy. Based on the decision solution and the target value obtained by the robust model, the invention constructs a daily contract time-sharing power curve and a bidding strategy of a day-ahead market under the condition of meeting the minimum income preset target acceptable by a decision maker.
The first step is that the distributed output of the contract electric quantity by time interval is obtained based on the model
Figure BDA0002786742110000034
And forming a daily contract time-sharing power curve.
Second, the distributed output of the time-interval day-ahead market obtained by the model
Figure BDA0002786742110000035
The device can be used as the declaration output of the market in the day-ahead; the objective function value alpha obtained by the model is the maximum fluctuation range of the spot market electricity price under the condition of meeting the expected income target, and the lower boundary of the fluctuation range is taken as the declaration electricity price of the spot market, so that the current day can be usedThe bid price is guaranteed when the clearing price is in the information gap of the estimated price, so that the bidding decision of the market before the day is formed, and the output-price declaration decision in the period t can be expressed as
Figure BDA0002786742110000036
(4) And (6) solving the model. Firstly, forecasting the running parameters of the incoming water and the cascade hydropower station and forecasting the price of the clear electricity in the market at the day before
Figure BDA0002786742110000037
And the deterministic step hydropower station short-term optimization scheduling optimal profit result pi is obtained by considering daily electric quantity decomposition constraint and step hydropower station short-term operation constraint0. Then, an acceptable income deviation coefficient beta is given by a decision maker of risk aversion of the hydropower enterpriserCombined with basic profit n0Determining the minimum expected yield n acceptabler＝(1-βr)π0And substituting a robust scheduling model considering the uncertainty of the electricity price. And finally, forming a time-sharing power curve through the obtained contract decomposition output, constructing a bidding strategy through the obtained spot market bidding output and the resisted electricity price fluctuation amplitude, and simultaneously forming a short-term optimal scheduling scheme of the cascade hydropower station. The deterministic short-term scheduling model and the robust model based on the information gap decision theory are solved by adopting a mixed integer nonlinear programming method.
Compared with the prior art, the invention has the following beneficial effects: the invention provides a cascade hydropower station robust scheduling method coupling daily electric quantity decomposition and day-ahead market bidding. The method comprises the steps of firstly, taking the maximum total income of the cascade hydropower station as a target, considering each short-term operation constraint and daily electric quantity contract decomposition requirement of the cascade hydropower station, and establishing a deterministic hydropower short-term optimization scheduling model; then, on the basis, an information gap decision theory is used for converting a deterministic short-term optimization scheduling model into a robust scheduling model considering the uncertainty of the electricity price, so that the decision solution can meet the minimum benefit requirement in the information gap range of the estimated price of the market in the future; and solving by adopting a mixed integer nonlinear programming method.
The method can scientifically and reasonably decompose the daily electric quantity of the cascade hydropower station to hour-by-hour, ensure the effective execution of the daily electric quantity, and obtain the declared price-measuring pair participating in the spot market before the day, the obtained scheduling result of the cascade hydropower station has robustness, and the method can ensure that the expected target is not worse than a certain minimum preset result when the clearing price is in the interval of estimated price information, can quantitatively depict the relation between the uncertain parameter variation range and the minimum acceptable target, and can provide a more flexible and intuitive scheduling and transaction decision basis for a risk aversive cascade hydropower enterprise decision maker.
Drawings
FIG. 1 is a flow chart of a method embodying the present invention;
FIG. 2 is a day-ahead market price for electricity;
figure 3 is a step hydropower station natural runoff;
FIG. 4 is a graph of the power output and water level process for each plant;
FIG. 5 is a daily power split output curve for each plant;
FIG. 6 is a revenue distribution plot for a multi-price scenario;
fig. 7 is a graph of variation of the price variation in resistance with a preset profit.
Detailed Description
The gradual establishment of the China's electric power spot market environment brings many new challenges to the short-term optimized scheduling of the cascade hydropower stations. Hydropower enterprises need to make trading strategies for participating in the day-ahead market based on short-term optimized scheduling results, and face the following two problems: firstly, the uncertainty of the price of the fresh electricity in the market at present can cause great market income risk; and secondly, the signed daily electric quantity contract decomposition mode directly influences the bidding space of the day-ahead market participating by the contract. Aiming at the problems, the method of the invention carries out daily electric quantity decomposition and day-ahead market bidding coordination optimization to take both the influences of the daily electric quantity decomposition and day-ahead market bidding into consideration, and adopts an information gap decision theory to process uncertainty of the day-ahead market price so as to obtain a robust scheduling result which can meet a preset profit target.
The invention is further described below with reference to the figures and examples. The implementation flow diagram of the invention is shown in fig. 1, and the specific implementation steps are as follows:
(1) and constructing a deterministic short-term scheduling model, wherein the model comprises an objective function, a daily electricity decomposition constraint and a short-term operation constraint.
First, an objective function is constructed. Aiming at short-term optimized scheduling of the cascade hydropower station in the power market environment, the invention aims at the maximum total income of the two parts of the medium-term market and the long-term market of the cascade hydropower station as a target, and the expression is as follows:
Figure BDA0002786742110000051
in the formula,
Figure BDA0002786742110000052
the power is output for the power station at the i-day electric quantity decomposition electric quantity t period;
Figure BDA0002786742110000053
the contract electricity price of the power station i is Yuan/MWh; pi,tThe total output power, MW, of the power station i in the time period t;
Figure BDA0002786742110000054
forecasting the clear electricity price, yuan/MWh, for the day-ahead market at the time t; t is a scheduling period time interval set; i is a cascade hydropower station set; Δ t is the duration of 1 period, h. Wherein,
Figure BDA0002786742110000055
and Pi,tIs the decision variable of the model.
And secondly, constructing daily electric quantity decomposition constraint.
Figure BDA0002786742110000056
Figure BDA0002786742110000057
Figure BDA0002786742110000058
The contract decomposition has a plurality of typical decomposition curves, and the daily electricity decomposition of the invention selects a peak-to-valley curve mode: a day is divided into a peak section, a flat section and a valley section, and load electric quantities of the peak section, the flat section and the valley section can be determined by historical load characteristics of a user side or negotiation in other modes. In the above formula
Figure BDA0002786742110000059
Bidding output, MW for the day-ahead market of the power station i in the period t; ciThe daily contract electric quantity, MWh, of the power station i; x is a time interval set, wherein p, f and v respectively represent peak time intervals, flat time intervals and valley time intervals; c. Cx,iThe contract electric quantity, MWh, of the power station i in the x time period; t isxIs the set of period indicators contained in the x period; gamma rayxIs the ratio of the daily electric quantity occupied by the x time period.
And thirdly, constructing short-term operation constraint. The method comprises a water quantity balance equation, reservoir capacity constraint, power station ex-reservoir flow constraint, hydropower station output constraint, hydropower station vibration area constraint, reservoir water level constraint and water level reservoir capacity relation, water head constraint, tail water level downward discharge flow relation and power generation function relation.
1) Equation of water balance
Figure BDA00027867421100000510
In the formula, Vi,tIs the storage capacity, m, of the power station i in the time period t3；Ii,tFor warehousing traffic, m3/s；Qi,tIs the ex-warehouse flow m of the power station i in the time period t3/s；
Figure BDA0002786742110000061
The k-th directly upstream hydropower station for station i at t- τk,iTime interval of delivery, m3/s；KiAn upstream hydropower station set which is a power station i; tau isk,iThe flow rate is time lag, h.
2) Capacity constraint
Vi,min≤Vi,t≤Vi,max (14)
In the formula Vi,min、Vi,maxRespectively, the minimum and maximum storage capacity constraints of the power station i.
3) Out-of-warehouse flow constraint of power station
Qi,min≤Qi,t≤Qi,max (15)
Figure BDA0002786742110000062
In the formula, Qi,min、Qi,maxMinimum and maximum ex-warehouse flow limits, m, of the power station, respectively3/s；
Figure BDA0002786742110000063
For the generated flow of the station i in the time period t, m3/s；si,tIs the water discharge of the power station i in the time period t, m3/s。
4) Hydropower station output restriction
Pi,min≤Pi,t≤Pi,max (17)
In the formula, Pi,min、Pi,maxThe minimum and maximum output limits of the plant, MW.
5) Hydropower station vibration zone constraints
Figure BDA0002786742110000064
In the formula, Zi,m、
Figure BDA0002786742110000065
Respectively is the lower limit and the upper limit of the mth vibration area of the power station i; miThe number of vibration areas of the power station i.
6) Reservoir level constraint and level-reservoir capacity relationship
Zfi,min≤Zfi,t≤Zfi,max (19)
Zfi,0＝Zfi,begin,Zfi,T＝Zfi,end (20)
Zfi,t＝fi,ZV(Vi,t) (21)
In the formula, Zfi,tFor the water level above the dam of station i during time t, Zfi,max、Zfi,minM is the restriction of the upper and lower limits of the water level on the dam of the power station; zfi,begin,Zfi,endThe initial water level and the final water level of the power station i, m, and the final water level can be determined by the medium-term dispatching of the cascade hydropower station; f. ofi,ZV(Vi,t) The function of the water level on the dam of the power station and the reservoir capacity can be fitted by using a fourth-order polynomial.
7) Head restraint
Hi,min≤Hi,t≤Hi,max (22)
Hi,t＝(Zfi,t-1+Zfi,t)/2-Zdi,t-εi,t (23)
In the formula, Hi,tWater head of the power station i in a time period t, m; hi,max、Hi,minRespectively limiting the water head upper limit and the water head lower limit of the power station i, m; zdi,tThe tail water level m of the power station i in the time period t; epsiloni,tIs head loss, m.
8) Tailwater level-letdown flow relationship
Zdi,t＝fi,ZQ(Qi,t) (24)
In the formula (f)i,ZQ(Qi,t) For the tail water level-let-down flow function of the power station i, a fourth-order polynomial can be adopted for fitting.
9) Power generation functional relationship
Figure BDA0002786742110000071
In the formula,
Figure BDA0002786742110000072
indicating electricityThe output of the power generation function of the station i is related to the power generation flow and the water head, is a binary nonlinear function of the power generation flow and the water head, and can be fitted by adopting a binary quadratic function.
(2) Robust model construction based on information gap decision theory
The method comprises the following steps of firstly, constructing an uncertainty set, selecting the day-ahead market electricity price as an uncertainty parameter, constructing an uncertainty set model by adopting an envelope constraint model, wherein the mathematical expression of the uncertainty set model is as follows:
Figure BDA0002786742110000073
in the formula: lambda [ alpha ]tRepresenting the actual clear electricity price of the spot market in the t period; alpha represents the fluctuation range of the electricity price, and the above formula shows that the actual clearing price surrounds the predicted value in the range of (1-alpha, 1+ alpha)
Figure BDA0002786742110000074
Fluctuating up and down.
Secondly, constructing a robust model target and a constraint, wherein the robust model is an IGDT robust model considering the uncertainty of the electricity price, and the objective function is as follows, wherein the robust model is based on the maximization of the fluctuation range of the uncertain variable as a target on the premise of meeting an expected target:
maxα (27)
the constraints include a minimum benefit constraint in addition to the short-term operating constraint described above, and the expression is as follows:
Figure BDA0002786742110000075
πr＝(1-βr)π0,βr∈[0,1) (29)
π0obtaining an optimal yield for the deterministic model of step (1); pirAn acceptable minimum income target can be set for a decision maker according to self requirements; beta is arAs yield deviation factors, i.e. expected yield and deterministic patternsThe degree of deviation between the optimal benefits is larger, and the larger the value of the deviation indicates that the decision solution avoids the risk to a greater extent.
(3) And constructing a contract decomposition curve and a day-ahead market bidding strategy. Based on the decision solution and the target value obtained by the robust model, the invention constructs a daily contract time-sharing power curve and a bidding strategy of a day-ahead market under the condition of meeting the minimum income preset target acceptable by a decision maker.
The first step is that the distributed output of the contract electric quantity by time interval is obtained based on the model
Figure BDA0002786742110000081
And forming a daily contract time-sharing power curve.
Second, the distributed output of the time-interval day-ahead market obtained by the model
Figure BDA0002786742110000082
The device can be used as the declaration output of the market in the day-ahead; the objective function value alpha obtained by the model is the maximum fluctuation range of the spot market electricity price under the condition of meeting the expected income target, the lower boundary of the fluctuation range is taken as the declaration electricity price of the spot market, so that the bid price is guaranteed when the date-ahead clearing price is in the information gap of the predicted price, thereby forming the bidding decision of the day-ahead market, and the output-price declaration decision in the period t can be expressed as
Figure BDA0002786742110000083
And (6) solving the model. Firstly, forecasting the running parameters of the incoming water and the cascade hydropower station and forecasting the price of the clear electricity in the market at the day before
Figure BDA0002786742110000084
And the deterministic step hydropower station short-term optimization scheduling optimal profit result pi is obtained by considering daily electric quantity decomposition constraint and step hydropower station short-term operation constraint0. Then, an acceptable income deviation coefficient beta is given by a decision maker of risk aversion of the hydropower enterpriserCombined with basic profit n0Determining the minimum expected yield n acceptabler＝(1-βr)π0Substitution intoA robust scheduling model that accounts for power rate uncertainty. And finally, forming a time-sharing power curve through the obtained contract decomposition output, constructing a bidding strategy through the obtained spot market bidding output and the resisted electricity price fluctuation amplitude, and simultaneously forming a short-term optimal scheduling scheme of the cascade hydropower station. A deterministic short-term scheduling model and a robust model based on an information gap decision theory are both established by adopting a mixed integer nonlinear programming method, the difficulty and the stability are considered, and a global solver in LINGO software is selected for realization. The model provided by the invention has the effect of solving the short-term optimization scheduling of the cascade hydropower station coupling daily electric quantity decomposition and day-ahead market bidding. A certain 4-reservoir cascade hydropower station in southwest of China is adopted as a research object to carry out example analysis, A, B, C, D are respectively arranged from an upstream power station to a downstream power station, and characteristic parameters of each power station are shown in Table 1.
TABLE 1 main characteristic parameters of cascade hydroelectric station
Figure BDA0002786742110000085
The detailed implementation steps and effect analysis of the method of the invention aiming at the above example scenes are as follows.
(1) And constructing a deterministic short-term scheduling model, wherein the model comprises an objective function, a daily electricity decomposition constraint and a short-term operation constraint.
First, an objective function is constructed. Aiming at short-term optimized scheduling of the cascade hydropower station in the power market environment, the invention aims at the maximum total income of the two parts of the medium-term market and the long-term market of the cascade hydropower station as a target, and the expression is as follows:
Figure BDA0002786742110000091
in the formula,
Figure BDA0002786742110000092
the power is output for the power station at the i-day electric quantity decomposition electric quantity t period;
Figure BDA0002786742110000093
the contract electricity price of the power station i is Yuan/MWh; pi,tThe total output power, MW, of the power station i in the time period t;
Figure BDA0002786742110000094
forecasting the clear electricity price, yuan/MWh, for the day-ahead market at the time t; t is a scheduling period time interval set; i is a cascade hydropower station set; Δ t is the duration of 1 period, h.
And secondly, constructing daily electric quantity decomposition constraint.
Figure BDA0002786742110000095
Figure BDA0002786742110000096
Figure BDA0002786742110000097
The contract decomposition has various typical decomposition curves, and the daily electricity decomposition selects a peak-to-valley curve mode: a day is divided into a peak section, a flat section and a valley section, and load electric quantities of the peak section, the flat section and the valley section can be determined by historical load characteristics of a user side or negotiation in other modes. In the above formula
Figure BDA0002786742110000098
Bidding output, MW for the day-ahead market of the power station i in the period t; ciThe daily contract electric quantity, MWh, of the power station i; x is a time interval set, wherein p, f and v respectively represent peak time intervals, flat time intervals and valley time intervals; c. Cx,iThe contract electric quantity, MWh, of the power station i in the x time period; t isxIs the set of period indicators contained in the x period; gamma rayxIs the ratio of the daily electric quantity occupied by the x time period.
And thirdly, constructing short-term operation constraint. The method comprises a water quantity balance equation, reservoir capacity constraint, power station ex-reservoir flow constraint, hydropower station output constraint, hydropower station vibration area constraint, reservoir water level constraint and water level reservoir capacity relation, water head constraint, tail water level downward discharge flow relation and power generation function relation.
1) Equation of water balance
Figure BDA0002786742110000099
In the formula, Vi,tIs the storage capacity, m, of the power station i in the time period t3；Ii,tFor warehousing traffic, m3/s；Qi,tIs the ex-warehouse flow m of the power station i in the time period t3/s；
Figure BDA00027867421100000910
The k-th directly upstream hydropower station for station i at t- τk,iTime interval of delivery, m3/s；KiAn upstream hydropower station set which is a power station i; tau isk,iThe flow rate is time lag, h.
2) Capacity constraint
Vi,min≤Vi,t≤Vi,max (35)
In the formula Vi,min、Vi,maxRespectively, the minimum and maximum storage capacity constraints of the power station i.
3) Out-of-warehouse flow constraint of power station
Qi,min≤Qi,t≤Qi,max (36)
Figure BDA0002786742110000101
In the formula, Qi,min、Qi,maxMinimum and maximum ex-warehouse flow limits, m, of the power station, respectively3/s；
Figure BDA0002786742110000102
For the generated flow of the station i in the time period t, m3/s；si,tIs the water discharge of the power station i in the time period t, m3/s。
4) Hydropower station output restriction
Pi,min≤Pi,t≤Pi,max (38)
In the formula, Pi,min、Pi,maxThe minimum and maximum output limits of the plant, MW.
5) Hydropower station vibration zone constraints
Figure BDA0002786742110000103
In the formula,Z i,m、
Figure BDA0002786742110000104
respectively is the lower limit and the upper limit of the mth vibration area of the power station i; miThe number of vibration areas of the power station i.
6) Reservoir level constraint and level-reservoir capacity relationship
Zfi,min≤Zfi,t≤Zfi,max (40)
Zfi,0＝Zfi,begin,Zfi,T＝Zfi,end (41)
Zfi,t＝fi,ZV(Vi,t) (42)
In the formula, Zfi,tFor the water level above the dam of station i during time t, Zfi,max、Zfi,minM is the restriction of the upper and lower limits of the water level on the dam of the power station; zfi,begin,Zfi,endThe initial water level and the final water level of the power station i, m, and the final water level can be determined by the medium-term dispatching of the cascade hydropower station; f. ofi,ZV(Vi,t) The function of the water level on the dam of the power station and the reservoir capacity can be fitted by using a fourth-order polynomial.
7) Head restraint
Hi,min≤Hi,t≤Hi,max (43)
Hi,t＝(Zfi,t-1+Zfi,t)/2-Zdi,t-εi,t (44)
In the formula, Hi,tThe head of the station i during the time t,m；Hi,max、Hi,minrespectively limiting the water head upper limit and the water head lower limit of the power station i, m; zdi,tThe tail water level m of the power station i in the time period t; epsiloni,tIs head loss, m.
8) Tailwater level-letdown flow relationship
Zdi,t＝fi,ZQ(Qi,t) (45)
In the formula (f)i,ZQ(Qi,t) For the tail water level-let-down flow function of the power station i, a fourth-order polynomial can be adopted for fitting.
9) Power generation functional relationship
Figure BDA0002786742110000111
In the formula,
Figure BDA0002786742110000112
the method is characterized in that the method represents a power generation function of a power station i, the output is related to the power generation flow and the water head, the output is a binary nonlinear function of the power generation flow and the water head, and binary quadratic function fitting can be adopted.
(2) Robust model construction based on information gap decision theory
The method comprises the following steps of firstly, constructing an uncertainty set, selecting the day-ahead market electricity price as an uncertainty parameter, constructing an uncertainty set model by adopting an envelope constraint model, wherein the mathematical expression of the uncertainty set model is as follows:
Figure BDA0002786742110000113
in the formula: lambda [ alpha ]tRepresenting the actual clear electricity price of the spot market in the t period; alpha represents the fluctuation range of the electricity price, and the above formula shows that the actual clearing price surrounds the predicted value in the range of (1-alpha, 1+ alpha)
Figure BDA0002786742110000114
Fluctuating up and down.
Secondly, constructing a robust model target and a constraint, wherein the robust model is an IGDT robust model considering the uncertainty of the electricity price, and the objective function is as follows, wherein the robust model is based on the maximization of the fluctuation range of the uncertain variable as a target on the premise of meeting an expected target:
maxα (48)
the constraints include a minimum benefit constraint in addition to the short-term operating constraint described above, and the expression is as follows:
Figure BDA0002786742110000115
πr＝(1-βr)π0,βr∈[0,1) (50)
π0obtaining an optimal yield for the deterministic model of step (1); pirAn acceptable minimum income target can be set for a decision maker according to self requirements; beta is arThe larger the value of the yield deviation factor, namely the deviation degree between the expected yield and the optimal yield of the deterministic pattern, the larger the avoidance degree of the risk of the decision solution.
(3) And constructing a contract decomposition curve and a day-ahead market bidding strategy. Based on the decision solution and the target value obtained by the robust model, the invention constructs a daily contract time-sharing power curve and a bidding strategy of a day-ahead market under the condition of meeting the minimum income preset target acceptable by a decision maker.
The first step is that the distributed output of the contract electric quantity by time interval is obtained based on the model
Figure BDA0002786742110000121
And forming a daily contract time-sharing power curve.
Second, the distributed output of the time-interval day-ahead market obtained by the model
Figure BDA0002786742110000122
The device can be used as the declaration output of the market in the day-ahead; the objective function value alpha obtained by the model is the maximum fluctuation range of the spot market electricity price under the condition of meeting the expected income target, and the lower boundary of the fluctuation range is taken as the declaration electricity price of the spot market, so that the declaration electricity price is obtainedThe bid price is guaranteed when the coming price is in the information gap of the estimated price before the day, so that the bidding decision of the market before the day is formed, and the output-price declaration decision in the period t can be expressed as
Figure BDA0002786742110000123
(4) And (6) solving the model.
Actual runoff data and the current market price are adopted to refer to the northern Europe power market price setting, which are respectively shown in the attached figures 2 and 3. The daily contract electric quantity and the electricity price refer to the historical generated energy of the cascade hydropower station and the medium and long term contract electricity price setting, and are shown in a table 2. The peak-to-valley period and the proportion of the daily electricity contract are shown in table 3. The selection day is a scheduling period, and 1 hour is a scheduling period.
TABLE 2 Medium and Long term daily scale contract
Figure BDA0002786742110000124
TABLE 3 Peak-to-valley period and quantity of electricity ratio settings
Figure BDA0002786742110000125
Firstly, a deterministic cascade hydropower station power generation profit maximization model is solved, obtained profit is 1317.079 ten thousand yuan, and the model consists of 897.255 ten thousand yuan of daily contract profit and 419.824 ten thousand yuan of daily market profit and serves as basic profit of a subsequent robust model. If the minimum income acceptable by the cascade hydropower enterprises is pi r1280 ten thousand yuan (ratio pi)02.82% smaller, i.e.. beta.r0.0282), solving a robust model considering the uncertainty of the electricity price to obtain a tolerable spot electricity price fluctuation amplitude alpha of 0.0877. Meaning that the yield of the cascade hydropower station is not less than 1280 ten thousand yuan when the fluctuation range of the spot market actual electricity price around the predicted electricity price is not more than 8.77%.
Figure 4 shows the output and water level change process of the cascade hydroelectric station. The power station B is incompletely adjusted for many years, the fluctuation range of the water level is small, the other power stations are adjusted daily, and the fluctuation of the water level is relatively large. The output power and the water level change of each power station are within a reasonable range, various constraints are met, and physical operation is guaranteed.
For each power station, the output corresponding to the contract electric quantity in the whole optimization period can be submitted to a transaction center as a contract decomposition result, and a daily contract time-sharing power curve is shown in the attached figure 5; the time-interval output of the day-ahead market allocation can be used as the bidding electric quantity of the day-ahead market in the corresponding time interval, the lower boundary of the tolerable power price fluctuation range in the corresponding time interval can be used as the corresponding declared power price, and the bidding information of the day-ahead market of each power station in part of the time interval is shown in a table 3.
TABLE 3 Bidding information for each plant day-ahead market for a portion of the time period
Figure BDA0002786742110000131
In conclusion, the robust model can obtain a cascade short-term optimal scheduling scheme meeting the minimum profit condition, and the peak, average and valley contract electric quantity of each power station decomposes the output in time intervals, participates in the bidding output of the day-ahead market in corresponding time intervals and the resisted electric price fluctuation range, so that a daily electric quantity decomposition curve and the day-ahead market bidding strategy are obtained, and the rationality and the effectiveness of the model are verified.
In order to further verify the robust effect of the model, uniformly distributed Latin hypercube sampling is adopted to simulate 1000 electricity price scenes in the electricity price fluctuation range obtained by the robust model. And (3) adopting a short-term optimization scheduling scheme obtained by the robust model, substituting the scenes into the test one by one, and obtaining the total income of the stepped hydropower stations under the corresponding scenes, wherein the income distribution is shown in the attached figure 6. As can be seen, the gains are approximately in accordance with a normal distribution, and are not lower than a preset gain (1280 ten thousand yuan). The power generation yield can be ensured to be not lower than an expected value based on the short-term scheduling scheme of the cascade hydropower station obtained by considering the robust model with uncertain power rates when the power rates of the spot market fluctuate in the robust region, and the robust effect of the model provided by the invention is verified.
Further, the decision-making process varies depending on the lowest revenue that the market subject can afford. And then, by changing the expected income target of a decision maker and solving a robust model considering the uncertainty of the electricity price, the maximum fluctuation range of the electricity price of the current market which can be resisted under the corresponding target is obtained, and the change condition is shown in the attached figure 7. As can be seen from FIG. 7, the two have a negative correlation relationship, and the fluctuation range of the sustainable day-ahead market electricity price increases with the reduction of the expected profit of the decision maker. In other words, the lower the expected revenue of the cascade hydropower station, the better the robustness of the acquired short-term optimized scheduling scheme for coupling daily contract electricity decomposition and daily market bidding is, and the greater the fluctuation of the spot market electricity price can be resisted.
The model result can quantitatively depict the relation between the uncertain parameter variation range and the lowest acceptable target, and a more flexible and visual scheduling and transaction decision basis is provided for a risk aversion decision maker.
The embodiments described above are provided to enable persons skilled in the art to make or use the invention, and persons skilled in the art may make modifications or changes to the embodiments described above without departing from the inventive idea of the invention, so that the protective scope of the invention is not limited by the embodiments described above, but should be accorded the widest scope consistent with the innovative features set forth in the claims.

Claims (1)

1. A short-term robust scheduling method for a cascade hydropower station coupling daily electric quantity decomposition and day-ahead market bidding is characterized by comprising the following steps:
(1) constructing a deterministic short-term scheduling model, wherein the deterministic short-term scheduling model comprises a target function, a daily electric quantity decomposition constraint and a short-term operation constraint;
firstly, constructing an objective function: aiming at short-term optimized scheduling of a cascade hydropower station in an electric power market environment, the total income of a medium-term market and a long-term market and a spot market of the cascade hydropower station is the maximum target, and the expression is as follows:
Figure FDA0002786742100000011
in the formula,
Figure FDA0002786742100000012
the power is output for the power station at the time of i days to decompose the electric quantity at t time;
Figure FDA0002786742100000013
contract electricity price for power station i; pi,tThe total output of the power station i in the time period t;
Figure FDA0002786742100000014
forecasting the price of the clear electricity for the day-ahead market at the time t; t is a scheduling period time interval set; i is a cascade hydropower station set; Δ t is the period length;
secondly, constructing daily electric quantity decomposition constraints;
Figure FDA0002786742100000015
Figure FDA0002786742100000016
Figure FDA0002786742100000017
the contract decomposition has various typical decomposition curves, and the daily electricity decomposition selects a peak-to-valley curve mode: dividing one day into a peak section, a flat section and a valley section, and negotiating by historical load characteristics or other modes at a user side to determine three sections of load electric quantity of the peak section, the flat section and the valley section; in the above formula
Figure FDA0002786742100000018
Bidding and outputting the day-ahead market of the power station i at the time t; ciThe daily contract electric quantity of the power station i is obtained; x is a time interval set, wherein p, f and v respectively represent peak time intervals, flat time intervals and valley time intervals; c. Cx,iContract electric quantity of the power station i in x time period; t isxIs the set of period indicators contained in the x period; gamma rayxThe ratio of the daily electric quantity in the x time period;
thirdly, constructing short-term operation constraints, including a water quantity balance equation, reservoir capacity constraints, power station ex-reservoir flow constraints, hydropower station output constraints, hydropower station vibration area constraints, reservoir water level constraints and water level reservoir capacity relationships, water head constraints, tail water level downward discharge flow relationships and power generation function relationships;
(2) robust model construction based on information gap decision theory
First, uncertainty set construction
Selecting the day-ahead market electricity price as an uncertain parameter, and constructing an uncertain set model by adopting an envelope restriction model, wherein the mathematical expression of the uncertain set model is as follows:
Figure FDA0002786742100000021
in the formula: lambda [ alpha ]tRepresenting the actual clear electricity price of the spot market in the t period; alpha represents the fluctuation range of the electricity price, and the above formula shows that the actual clearing price surrounds the predicted value in the range of (1-alpha, 1+ alpha)
Figure FDA0002786742100000022
Fluctuating up and down;
second, robust model object and constraint construction
The robust model is an IGDT robust model taking the maximum fluctuation range of uncertain variables as the target and considering the uncertainty of the electricity price under the premise of meeting the expected target, and the target function is as follows:
maxα (6)
the constraints include a minimum benefit constraint in addition to the short-term operating constraint described above, and the expression is as follows:
Figure FDA0002786742100000023
πr＝(1-βr)π0,βr∈[0,1) (8)
wherein, pi0Is the step (1)) The deterministic model obtains an optimal yield; pirAn acceptable minimum income target can be set for a decision maker according to self requirements; beta is arThe higher the value of the deviation factor of the profit, namely the deviation degree between the expected profit and the optimal profit of the deterministic pattern, the larger the avoidance degree of the decision solution to the risk;
(3) constructing a contract decomposition curve and a day-ahead market bidding strategy;
based on the decision solution and the target value obtained by the robust model, under the condition of meeting the minimum income preset target acceptable by a decision maker, constructing a daily contract time-sharing power curve and a bidding strategy of a day-ahead market;
the first step is that the distributed output of the contract electric quantity by time interval is obtained based on the model
Figure FDA0002786742100000031
Forming a daily contract time-sharing power curve;
second, the distributed output of the time-interval day-ahead market obtained by the model
Figure FDA0002786742100000032
As a declared force for the day-ahead market; the objective function value alpha obtained by the model is the maximum fluctuation range of the spot market electricity price under the condition of meeting the expected income target, the lower boundary of the fluctuation range is taken as the declaration electricity price of the spot market, so that the bid price is guaranteed when the date-ahead clearing price is in the information gap of the predicted price, thereby forming the bidding decision of the day-ahead market, and the output-price declaration decision in the period t is expressed as
Figure FDA0002786742100000033
(4) Solving the model;
firstly, forecasting the running parameters of the incoming water and the cascade hydropower station and forecasting the price of the clear electricity in the market at the day before
Figure FDA0002786742100000034
And daily electric quantity decomposition constraint and short-term operation constraint of cascade hydropower station are consideredAnd obtaining the deterministic optimal profit result pi of the cascade hydropower short-term optimization scheduling0(ii) a Then, an acceptable income deviation coefficient beta is given by a decision maker of risk aversion of the hydropower enterpriserCombined with basic profit n0Determining the minimum expected yield n acceptabler＝(1-βr)π0Substituting a robust scheduling model considering the uncertainty of the electricity price; and finally, forming a time-sharing power curve through the obtained contract decomposition output, constructing a bidding strategy through the obtained spot market bidding output and the resisted electricity price fluctuation amplitude, and simultaneously forming a short-term optimal scheduling scheme of the cascade hydropower station.
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CN116667356B (en) * 2023-05-29 2024-06-04 南方电网能源发展研究院有限责任公司 Power generation main body behavior control method, device, equipment, medium and product

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