CN112465323B - Cascade hydropower station short-term robust scheduling method coupled with daily electricity quantity decomposition and day-ahead market bidding - Google Patents
Cascade hydropower station short-term robust scheduling method coupled with daily electricity quantity decomposition and day-ahead market bidding Download PDFInfo
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Abstract
The invention provides a cascade hydropower station short-term robust scheduling method coupling daily electric quantity decomposition and daily market bidding, which aims at maximizing total income of the cascade hydropower station and establishes a deterministic hydropower station short-term optimal scheduling model; converting the deterministic short-term optimized scheduling model into a robust scheduling model considering the uncertainty of electricity price; and then solving by adopting a mixed integer nonlinear programming method. The method can scientifically and reasonably decompose the daily electricity quantity of the cascade hydropower station into an electric power curve, ensure the effective execution of daily contracts, obtain the declaration price pairs participating in the daily spot market, obtain the scheduling result of the cascade hydropower station with robustness, ensure that the expected target is not worse than a certain minimum preset result when the clearing price is in the estimated price information gap, quantitatively describe the relation between the uncertain parameter change range and the minimum acceptable target, and provide a more flexible and visual scheduling and transaction decision basis for the decision maker of the cascade hydropower enterprise with averse risks.
Description
Technical Field
The invention relates to the field of electric power markets and the field of hydropower dispatching operation, in particular to a short-term robust dispatching method of a cascade hydropower station, which is coupled with daily electricity quantity decomposition and daily market bidding.
Background
Since the reform of a new power system, the process of the Chinese power marketing is rapidly advanced, medium-long-term transactions are gradually mature, spot market test points are orderly developed, and 8 test point units enter into simulated transaction and settlement test operation. The gradual establishment of spot market environments tends to bring more theoretical and practical challenges to the participation of the hydroelectric enterprises in marketized transactions.
Under the influence of uncertainty of runoff and uneven space-time distribution, under the electric power spot market environment, hydropower enterprises need to fully utilize reservoir regulation performance, and the aims of realizing flood withered electric quantity transfer, guaranteeing hydropower absorption, avoiding water abandoning or underdevelopment by participating in medium-long term transactions in different stages are fulfilled, so that benefits are locked and risks are avoided. Meanwhile, in order to stabilize the prediction deviation of the incoming water, hydropower enterprises can utilize the advantages of flexible starting of units and high load changing speed, participate in spot market and track market price signals, and achieve greater benefits. However, the following two problems are also faced: 1) The middle-long term transaction contract in the spot mode is formed by signing a power curve by a market main body or defining a decomposition mode in advance, and a time-sharing power curve is formed before a specified time, so that market organization development and financial settlement are facilitated in the future, and the comprehensive benefits of the power generation enterprises are influenced by the curve decomposition result. 2) The spot market price reflects the short-term supply and demand relation and the space-time value of the electric energy, and has strong uncertainty. The hydropower plant is often far away from the load center, needs long-distance transmission and even is in trans-regional digestion, is greatly influenced by the delivery line of the region or the node where the hydropower plant is located, and the uncertainty of runoff is further increased, so that the strong uncertainty of the spot price of hydropower plant is increased, and the uncertainty of the spot price in the past is not ignored.
How to formulate a step hydropower station short-term optimization scheduling scheme for coupling daily electricity quantity decomposition and daily market bidding, to cope with uncertainty of daily spot electricity price, coordinate benefits and risks to meet expected benefits is one of key problems which are urgently needed to be solved by step hydropower enterprises in spot market environments.
The achievement is cut in from the coupling daily electricity quantity decomposition and the day-ahead market bidding connection angle, firstly, each short-term operation constraint and daily electricity quantity contract-to-time-sharing power curve decomposition constraint of a cascade hydropower station are considered, and a deterministic short-term optimization scheduling model aiming at maximizing the generation income is constructed; and modeling uncertainty of spot market price by adopting a non-probabilistic information gap decision theory, constructing a robust model adapting to risk aversion decision makers, and finally solving by adopting a nonlinear programming method. The invention provides a short-term robust scheduling method for coupling daily electricity quantity decomposition and daily market bidding in a spot market environment for a cascade hydropower station, which can enable a scheduling scheme to meet a preset income target within a certain fluctuation range of the daily market electricity price and provide decision support for daily electricity quantity decomposition and daily market bidding.
Disclosure of Invention
The invention aims to: the technical problem to be solved by the invention is to provide the short-term robust scheduling method of the cascade hydropower station, which is coupled with solar power decomposition and day-ahead market bidding, so that a scheduling scheme can meet a preset income target within a certain fluctuation range of the day-ahead market price, and decision support is provided for solar power decomposition and day-ahead market bidding.
The technical scheme of the invention is as follows: a robust scheduling method of a cascade hydropower station, which is coupled with solar power decomposition and day-ahead market bidding, establishes a short-term scheduling scheme of the cascade hydropower station according to the following steps (1) - (4).
(1) Deterministic short-term scheduling model construction, including objective functions, daily power decomposition constraints, and short-term operation constraints.
First, an objective function is constructed. Aiming at short-term optimized scheduling of the cascade hydropower station in the electric power market environment, the invention aims at maximizing the total income of a long-term market and a spot market in the cascade hydropower station, and the expression is as follows:
in the method, in the process of the invention,decomposing the electric quantity for the power station i day electric quantity and outputting power in the period t; />Contract electricity price for power station i; p (P) i,t The total output of the power station i in the period t is obtained; />Predicting electricity clearing price for the market before the period t, wherein the price is Yuan/MWh; t is a scheduling period time period set; i is a cascade hydropower station set; Δt is the period length.
And secondly, constructing a solar electricity quantity decomposition constraint.
The contract decomposition has various typical decomposition curves, and the daily electric quantity decomposition selects a peak-valley curve mode: dividing a day into peak sections, flat sections and valley sections, and determining the load electric quantity of the peak sections, the flat sections and the valley sections by negotiating by the historical load characteristics of a user side or other modes. In the aboveBidding output for the day-ahead market of the power station i in the t period; c (C) i The daily contract electric quantity of the power station i; x is a time period set, wherein p, f and v respectively represent peak, flat and valley time periods; c x,i Contract electricity quantity for the x-period power station i; t (T) x A set of period indicators included for period x; gamma ray x The proportion of the daily charge taken up by the period x.
And thirdly, constructing short-term operation constraint. The method comprises a water quantity balance equation, a reservoir capacity constraint, a power station outlet flow constraint, a hydropower station output constraint, a hydropower station vibration area constraint, a reservoir water level constraint and water level reservoir capacity relation, a water head constraint, a tailwater level lower discharge flow relation and a power generation function relation.
(2) Robust model construction based on information gap decision theory
First, uncertainty set construction
The invention adopts envelope limiting model to construct an uncertain set model by taking the current market price as an uncertain parameter, and the mathematical expression is as follows:
wherein: lambda (lambda) t The actual discharging clear electricity price of the spot market in the period t is represented; alpha represents the fluctuation range of electricity price, and the above represents that the actual price is around the predicted value within the range of (1-alpha, 1+alpha)And fluctuates up and down.
Second step, robust model target and constraint construction
The robust model is an IGDT robust model taking the uncertainty of electricity price into consideration by taking the fluctuation amplitude of the maximum uncertainty variable as a target on the premise of meeting the expected target, and the target function is as follows:
max α (6)
the constraints include minimum benefit constraints in addition to the short-term operational constraints described above, expressed as follows:
π r ＝(1-β r )π 0 ,β r ∈[0,1) (8)
π 0 obtaining optimal benefits for the deterministic model of step (1); pi r Can be based on self-determinationSetting an acceptable minimum profit target by the body demand; beta r For the benefit deviation factor, i.e. the degree of deviation between the expected benefit and the optimal benefit of the deterministic pattern, a larger value indicates a greater degree of evasion of the risk by the decision solution.
(3) And (5) 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, the invention constructs a daily contract time-sharing power curve and a bidding strategy of a daily market.
First, based on the distribution output of the contract electric quantity of each time period obtained by the modelA daily contract time-sharing power curve is formed.
Second, the distribution output of the market in the day-ahead of the time period obtained by the modelThe product can be used as declaration output of market in the day-ahead; the objective function value alpha obtained by the model is the maximum fluctuation range of the current market price under the expected income target, the lower boundary of the fluctuation range is taken as the declaration price of the current market, so that the current price is ensured to be the winning bid when the current price is within the information gap of the estimated price, the bidding decision of the current market is formed, and the output-price declaration decision of the period t can be expressed as
(4) And (5) solving a model. Firstly, predicting running parameters of running water and cascade hydropower stations and predicting current price of market in the pastTaking solar power decomposition constraint and cascade hydropower station short-term operation constraint into consideration, and obtaining deterministic cascade hydropower short-term optimal scheduling optimal profit result pi 0 . Then, the acceptable gain deviation coefficient beta is given by a decision maker of risk aversion of hydropower enterprises r Combined with basic income pi 0 Determining the minimum expected benefit pi acceptable r ＝(1-β r )π 0 Substituting a robust scheduling model that takes into account electricity price 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 resistant electricity price fluctuation amplitude, and forming a short-term optimal scheduling scheme of the cascade hydropower station. The deterministic short-term scheduling model constructed by the invention and the robust model based on the information gap decision theory are all 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 robust scheduling method of a cascade hydropower station for coupling daily electricity quantity decomposition and day-ahead market bidding. Firstly, taking the maximum total income of a cascade hydropower station as a target, and taking into consideration each short-term operation constraint and daily electric quantity contract decomposition requirement of the cascade hydropower station, establishing a deterministic hydropower short-term optimization scheduling model; then, an information gap decision theory is applied to convert a deterministic short-term optimization scheduling model into a robust scheduling model considering uncertainty of electricity price, so that decision solutions can meet minimum income requirements in the information gap range of market estimated prices in the day-ahead; and then 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 be hour by hour, ensure the effective execution of the daily electric quantity, obtain the declaration price pair participating in the daily spot market, obtain the scheduling result of the cascade hydropower station with robustness, ensure that the expected target is not worse than a certain minimum preset result when the clear price is in the estimated price information gap, quantitatively describe the relation between the uncertain parameter change range and the minimum acceptable target, and provide a more flexible and visual scheduling and transaction decision basis for the decision-maker of the cascade hydropower enterprise with averse risks.
Drawings
FIG. 1 is a flow chart of a method implementation of the present invention;
FIG. 2 is a current market share price;
FIG. 3 is a natural runoff of a cascading hydropower station;
FIG. 4 is a graph showing the output and water level process for each plant;
FIG. 5 is a graph of daily power split output for each plant;
FIG. 6 is a revenue distribution diagram in a multiple electricity price scenario;
fig. 7 is a graph of the variation of the price change with preset yields.
Detailed Description
The gradual establishment of the current market environment of the Chinese electric power brings a plurality of new challenges to the short-term optimal scheduling of the cascade hydropower stations. Hydropower enterprises need to formulate a trading strategy for participating in the market in the future based on the short-term optimal scheduling result, and face the following two problems: firstly, uncertainty of current price of market in the past will lead to a great market benefit risk; and secondly, the signed daily electricity quantity contract decomposition mode directly influences the space of participating in daily market bidding. Aiming at the problems, the method of the invention optimizes the daily electricity quantity decomposition and the daily market bidding coordination to take both influences into consideration, and adopts the information gap decision theory to process the uncertainty of the daily market price so as to obtain the robust scheduling result which can meet the preset income target.
The invention is further described below with reference to the drawings and examples. The implementation flow diagram of the invention is shown in fig. 1, and the implementation steps are as follows:
(1) And constructing a deterministic short-term scheduling model, wherein the model comprises an objective function, a daily electricity quantity 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 electric power market environment, the invention aims at maximizing the total income of a long-term market and a spot market in the cascade hydropower station, and the expression is as follows:
in the method, in the process of the invention,the power is output and MW in a period t of decomposing the electric quantity of the power station i day electric quantity; />The contract electricity price of the power station i is yuan/MWh; p (P) i,t Total output of the power station i in the t period is MW; />Predicting electricity clearing price for the market before the period t, wherein the price is Yuan/MWh; t is a scheduling period time period set; i is a cascade hydropower station set; Δt is the duration of 1 period, h. Wherein (1)>And P i,t Is a decision variable of the model.
And secondly, constructing a solar electricity quantity decomposition constraint.
The contract decomposition has various typical decomposition curves, and the daily electric quantity decomposition selects a peak-valley curve mode: dividing a day into peak sections, flat sections and valley sections, and determining the load electric quantity of the peak sections, the flat sections and the valley sections by negotiating by the historical load characteristics of a user side or other modes. In the aboveBidding output and MW for the day-ahead market of the power station i in the t period; c (C) i The daily contract electric quantity of the power station i is MWh; x is a time period set, wherein p, f and v respectively represent peak, flat and valley time periods; c x,i For the contract electrical quantity of the x-period power station i, MWh；T x A set of period indicators included for period x; gamma ray x The proportion of the daily charge taken up by the period x.
And thirdly, constructing short-term operation constraint. The method comprises a water quantity balance equation, a reservoir capacity constraint, a power station outlet flow constraint, a hydropower station output constraint, a hydropower station vibration area constraint, a reservoir water level constraint and water level reservoir capacity relation, a water head constraint, a tailwater level lower discharge flow relation and a power generation function relation.
1) Equation of water balance
Wherein V is i,t For the storage capacity of the power station i in the period t, m 3 ；I i,t For the flow rate of warehouse entry, m 3 /s；Q i,t For the delivery flow of the power station i in the period t, m 3 /s；At t- τ for the kth immediately upstream hydropower station of station i k,i Time period of delivery flow, m 3 /s；K i An upstream water power station set for power station i; τ k,i And h is the time of flow lag.
2) Storage capacity constraint
V i,min ≤V i,t ≤V i,max (14)
V in i,min 、V i,max And respectively restricting the minimum and maximum storage capacity of the power station i.
3) Power station ex-warehouse flow constraints
Q i,min ≤Q i,t ≤Q i,max (15)
In which Q i,min 、Q i,max Respectively the minimum and maximum delivery flow limit of the power station, m 3 /s；For the power generation flow of the power station i in the period t, m 3 /s；s i,t For the reject flow of the power station i in the period t, m 3 /s。
4) Hydropower station output constraint
P i,min ≤P i,t ≤P i,max (17)
Wherein P is i,min 、P i,max Is the minimum and maximum power limit of the power station, MW.
5) Hydropower station vibration zone restraint
Wherein Z is i,m 、The lower limit and the upper limit of the mth vibration area of the power station i are respectively; m is M i The number of vibration areas of the power station i.
6) Reservoir level constraint and level-reservoir capacity relationship
Zf i,min ≤Zf i,t ≤Zf i,max (19)
Zf i,0 ＝Zf i,begin ,Zf i,T ＝Zf i,end (20)
Zf i,t ＝f i,ZV (V i,t ) (21)
In the formula Zf i,t For the dam water level of the power station i in the period t, zf i,max 、Zf i,min Upper and lower limit constraint of water level on a dam of the power station is defined, and m is defined; zf i,begin ,Zf i,end The water levels are respectively the beginning water level and the end water level of the power station i, and the end water level can be determined by the medium-term scheduling of the cascade hydropower station; f (f) i,ZV (V i,t ) Fitting can be performed by using a fourth-order polynomial for the water level-reservoir capacity function on the dam of the power station.
7) Water head restraint
H i,min ≤H i,t ≤H i,max (22)
H i,t ＝(Zf i,t-1 +Zf i,t )/2-Zd i,t -ε i,t (23)
Wherein H is i,t The water head of the power station i in the period t, m; h i,max 、H i,min Respectively restricting the upper and lower limits of the water head of the power station i, and m; zd i,t The tail water level of the power station i in the period t, m; epsilon i,t Is the head loss, m.
8) Tail water level-downdraft flow relationship
Zd i,t ＝f i,ZQ (Q i,t ) (24)
Wherein f i,ZQ (Q i,t ) Fitting can be performed using a fourth order polynomial for the tailwater-downdraft flow function of the power station i.
9) Power generation function relationship
In the method, in the process of the invention,the power generation function of the power station i is represented, the output 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
Firstly, constructing an uncertainty set, namely selecting current market price in the day old as an uncertainty parameter, and constructing an uncertainty set model by adopting an envelope limiting model, wherein the mathematical expression is as follows:
wherein: lambda (lambda) t The actual discharging clear electricity price of the spot market in the period t is represented; alpha represents the fluctuation amplitude of electricity priceIndicating that the actual price of the product is around the predicted value in the range of (1-alpha, 1+ alpha)And fluctuates up and down.
Secondly, constructing a robust model target and a constraint, wherein the robust model is based on the condition that an expected target is met and the fluctuation range of an uncertain variable is maximized, and the IGDT robust model with the uncertainty of electricity price is considered, and an objective function is as follows:
maxα (27)
the constraints include minimum benefit constraints in addition to the short-term operational constraints described above, expressed as follows:
π r ＝(1-β r )π 0 ,β r ∈[0,1) (29)
π 0 obtaining optimal benefits for the deterministic model of step (1); pi r An acceptable minimum profit target can be set for a decision maker according to the self demand; beta r For the benefit deviation factor, i.e. the degree of deviation between the expected benefit and the optimal benefit of the deterministic pattern, a larger value indicates a greater degree of evasion of the risk by the decision solution.
(3) And (5) 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, the invention constructs a daily contract time-sharing power curve and a bidding strategy of a daily market.
First, based on the distribution output of the contract electric quantity of each time period obtained by the modelA daily contract time-sharing power curve is formed.
Second, the distribution output of the market in the day-ahead of the time period obtained by the modelThe product can be used as declaration output of market in the day-ahead; the objective function value alpha obtained by the model is the maximum fluctuation range of the current market price under the expected income target, the lower boundary of the fluctuation range is taken as the declaration price of the current market, so that the current price is ensured to be the winning bid when the current price is within the information gap of the estimated price, the bidding decision of the current market is formed, and the output-price declaration decision of the period t can be expressed as
And (5) solving a model. Firstly, predicting running parameters of running water and cascade hydropower stations and predicting current price of market in the pastTaking solar power decomposition constraint and cascade hydropower station short-term operation constraint into consideration, and obtaining deterministic cascade hydropower short-term optimal scheduling optimal profit result pi 0 . Then, the acceptable gain deviation coefficient beta is given by a decision maker of risk aversion of hydropower enterprises r Combined with basic income pi 0 Determining the minimum expected benefit pi acceptable r ＝(1-β r )π 0 Substituting a robust scheduling model that takes into account electricity price 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 resistant electricity price fluctuation amplitude, and 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 both solved by adopting a mixed integer nonlinear programming method, and a global solver in LINGO software is selected to realize in consideration of difficulty and stability. The invention aims to verify the effect of the model in solving the short-term optimized scheduling aspect of the cascade hydropower station for coupling daily electricity quantity decomposition and daily market bidding. The calculation analysis is carried out by taking a 4-library cascade hydropower station in southwest of China as a research object, wherein the upstream to downstream power stations are A, B, C, D respectively, and each power station is provided with a plurality of power stationsThe characteristic parameters of the station are shown in table 1.
TABLE 1 Main characteristic parameters of step hydropower station
The detailed implementation steps and effect analysis of the method of the invention for the above example scene are as follows.
(1) And constructing a deterministic short-term scheduling model, wherein the model comprises an objective function, a daily electricity quantity 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 electric power market environment, the invention aims at maximizing the total income of a long-term market and a spot market in the cascade hydropower station, and the expression is as follows:
in the method, in the process of the invention,the power is output and MW in a period t of decomposing the electric quantity of the power station i day electric quantity; />The contract electricity price of the power station i is yuan/MWh; p (P) i,t Total output of the power station i in the t period is MW; />Predicting electricity clearing price for the market before the period t, wherein the price is Yuan/MWh; t is a scheduling period time period set; i is a cascade hydropower station set; Δt is the duration of 1 period, h.
And secondly, constructing a solar electricity quantity decomposition constraint.
The contract decomposition has various typical decomposition curves, and the daily electricity quantity decomposition selects peak-to-valley curve modes: dividing a day into peak sections, flat sections and valley sections, and determining the load electric quantity of the peak sections, the flat sections and the valley sections by negotiating by the historical load characteristics of a user side or other modes. In the aboveBidding output and MW for the day-ahead market of the power station i in the t period; c (C) i The daily contract electric quantity of the power station i is MWh; x is a time period set, wherein p, f and v respectively represent peak, flat and valley time periods; c x,i The contract electric quantity of the power station i in the x period is MWh; t (T) x A set of period indicators included for period x; gamma ray x The proportion of the daily charge taken up by the period x.
And thirdly, constructing short-term operation constraint. The method comprises a water quantity balance equation, a reservoir capacity constraint, a power station outlet flow constraint, a hydropower station output constraint, a hydropower station vibration area constraint, a reservoir water level constraint and water level reservoir capacity relation, a water head constraint, a tailwater level lower discharge flow relation and a power generation function relation.
1) Equation of water balance
Wherein V is i,t For the storage capacity of the power station i in the period t, m 3 ；I i,t For the flow rate of warehouse entry, m 3 /s；Q i,t For the delivery flow of the power station i in the period t, m 3 /s；At t- τ for the kth immediately upstream hydropower station of station i k,i Time period of delivery flow, m 3 /s；K i An upstream water power station set for power station i; τ k,i And h is the time of flow lag.
2) Storage capacity constraint
V i,min ≤V i,t ≤V i,max (35)
V in i,min 、V i,max And respectively restricting the minimum and maximum storage capacity of the power station i.
3) Power station ex-warehouse flow constraints
Q i,min ≤Q i,t ≤Q i,max (36)
In which Q i,min 、Q i,max Respectively the minimum and maximum delivery flow limit of the power station, m 3 /s；For the power generation flow of the power station i in the period t, m 3 /s；s i,t For the reject flow of the power station i in the period t, m 3 /s。
4) Hydropower station output constraint
P i,min ≤P i,t ≤P i,max (38)
Wherein P is i,min 、P i,max Is the minimum and maximum power limit of the power station, MW.
5) Hydropower station vibration zone restraint
In the method, in the process of the invention,Z i,m 、the lower limit and the upper limit of the mth vibration area of the power station i are respectively; m is M i I vibration for the power stationThe number of the dynamic areas.
6) Reservoir level constraint and level-reservoir capacity relationship
Zf i,min ≤Zf i,t ≤Zf i,max (40)
Zf i,0 ＝Zf i,begin ,Zf i,T ＝Zf i,end (41)
Zf i,t ＝f i,ZV (V i,t ) (42)
In the formula Zf i,t For the dam water level of the power station i in the period t, zf i,max 、Zf i,min Upper and lower limit constraint of water level on a dam of the power station is defined, and m is defined; zf i,begin ,Zf i,end The water levels are respectively the beginning water level and the end water level of the power station i, and the end water level can be determined by the medium-term scheduling of the cascade hydropower station; f (f) i,ZV (V i,t ) Fitting can be performed by using a fourth-order polynomial for the water level-reservoir capacity function on the dam of the power station.
7) Water head restraint
H i,min ≤H i,t ≤H i,max (43)
H i,t ＝(Zf i,t-1 +Zf i,t )/2-Zd i,t -ε i,t (44)
Wherein H is i,t The water head of the power station i in the period t, m; h i,max 、H i,min Respectively restricting the upper and lower limits of the water head of the power station i, and m; zd i,t The tail water level of the power station i in the period t, m; epsilon i,t Is the head loss, m.
8) Tail water level-downdraft flow relationship
Zd i,t ＝f i,ZQ (Q i,t ) (45)
Wherein f i,ZQ (Q i,t ) Fitting can be performed using a fourth order polynomial for the tailwater-downdraft flow function of the power station i.
9) Power generation function relationship
In the method, in the process of the invention,the power generation function of the power station i is represented, the output 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
Firstly, constructing an uncertainty set, namely selecting current market price in the day old as an uncertainty parameter, and constructing an uncertainty set model by adopting an envelope limiting model, wherein the mathematical expression is as follows:
wherein: lambda (lambda) t The actual discharging clear electricity price of the spot market in the period t is represented; alpha represents the fluctuation range of electricity price, and the above represents that the actual price is around the predicted value within the range of (1-alpha, 1+alpha)And fluctuates up and down.
Secondly, constructing a robust model target and a constraint, wherein the robust model is based on the condition that an expected target is met and the fluctuation range of an uncertain variable is maximized, and the IGDT robust model with the uncertainty of electricity price is considered, and an objective function is as follows:
maxα (48)
the constraints include minimum benefit constraints in addition to the short-term operational constraints described above, expressed as follows:
π r ＝(1-β r )π 0 ,β r ∈[0,1) (50)
π 0 for the determination of step (1)Obtaining optimal benefits by the qualitative model; pi r An acceptable minimum profit target can be set for a decision maker according to the self demand; beta r For the benefit deviation factor, i.e. the degree of deviation between the expected benefit and the optimal benefit of the deterministic pattern, a larger value indicates a greater degree of evasion of the risk by the decision solution.
(3) And (5) 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, the invention constructs a daily contract time-sharing power curve and a bidding strategy of a daily market.
First, based on the distribution output of the contract electric quantity of each time period obtained by the modelA daily contract time-sharing power curve is formed.
Second, the distribution output of the market in the day-ahead of the time period obtained by the modelThe product can be used as declaration output of market in the day-ahead; the objective function value alpha obtained by the model is the maximum fluctuation range of the current market price under the expected income target, the lower boundary of the fluctuation range is taken as the declaration price of the current market, so that the current price is ensured to be the winning bid when the current price is within the information gap of the estimated price, the bidding decision of the current market is formed, and the output-price declaration decision of the period t can be expressed as
(4) And (5) solving a model.
The actual runoff data and the current market electricity price are adopted to refer to the Nordic electric power market electricity price setting, and the electricity price setting is shown in figures 2 and 3 respectively. The daily contract electric quantity and the electricity price refer to the historical electric energy generation quantity of the cascade hydropower station and the medium-long contract electricity price setting, and are shown in table 2. The peak-to-valley period and the ratio setting of the daily electricity contract are shown in Table 3. The day is selected as a scheduling period, and 1 hour is a scheduling period.
Table 2 long term daily scale contract
TABLE 3 Peak Flat Valley period and electric quantity proportional setting
Firstly, a deterministic cascade hydropower station power generation gain maximization model is solved, and the obtained gain is 1317.079 ten thousand yuan, and consists of 897.255 ten thousand yuan of daily contract gain and 419.824 ten thousand yuan of daily market gain, which are taken as basic gain of a subsequent robust model. If the minimum income pi acceptable by cascade hydroelectric enterprises r 1280 ten thousand yuan (than pi) 0 2.82% less, i.e. beta r =0.0282), solving a robust model considering uncertainty of electricity prices, and obtaining a defensive spot electricity price fluctuation range α of 0.0877. Meaning that the gain of the cascade hydropower station is not lower than 1280 ten thousand yuan when the fluctuation range of the actual electricity price of the spot market around the predicted electricity price is not more than 8.77%.
Figure 4 shows the process of the output and water level change of the cascade hydropower station. The power station B is incompletely regulated for many years, the fluctuation range of the water level is smaller, the rest power stations are daily regulated, and the fluctuation of the water level is relatively larger. The output and the water level change of each power station are in a reasonable range, and various constraints are met, so that the physical operation is ensured.
For each power station, the corresponding output of 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 electric power curve is shown in figure 5; the time-interval output of the daily market allocation can be used as the bidding power quantity of the daily market corresponding to the time interval, the lower boundary of the fluctuation range of the bearable electricity price of the corresponding time interval can be used as the corresponding declaration electricity price, and the bidding information of the daily market of each power station in part of the time interval is shown in a table 3.
TABLE 3 Bid information for daily market for each station for partial period
In conclusion, the robust model can obtain a step short-term optimal scheduling scheme under the condition of minimum income, and the decomposing output of the peak, flat and valley contract electric quantity of each power station in a time-by-time period and the bidding output of the corresponding time period participating in the day-ahead market and the electric price fluctuation range which can be resisted, so that a daily electric quantity decomposing curve and a 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, the power price fluctuation range obtained by the robust model is simulated by adopting Latin hypercube sampling with even distribution to simulate 1000 power price scenes. And substituting scenes into the short-term optimized scheduling scheme obtained by the robust model one by one for inspection, and obtaining the total income of the cascade hydropower station under the corresponding scenes, wherein the income distribution is shown in figure 6. From this, it can be seen that the benefits approximately follow the normal distribution, and are not lower than the preset benefits (1280 ten thousand yuan). The method means that when the spot market electricity price fluctuates in a robust area, the short-term scheduling scheme of the cascade hydropower station obtained based on the robust model considering the uncertainty of the electricity price can ensure that the power generation income is not lower than an expected value, and the robust effect of the model provided by the invention is verified.
Further, the minimum revenue that market bodies can afford is different resulting in different decision making processes. And then, by changing the expected income target of a decision maker, solving a robust model considering the uncertainty of the electricity price, and obtaining the maximum fluctuation amplitude of the current market electricity price which can be resisted under the corresponding target, wherein the change condition is shown in the figure 7. As can be seen from FIG. 7, the negative correlation exists between the current market price and the current market price, and the fluctuation range of the current market price can be resisted, and increases with the reduction of expected benefits of a decision maker. In other words, the lower the expected income of the cascade hydropower station is, the better the robustness of the obtained short-term optimized scheduling scheme for coupling daily contract electric quantity decomposition and daily market bidding is, and the larger spot market electric price fluctuation can be resisted.
The model result can quantitatively describe the relation between the uncertain parameter change range and the lowest acceptable target, and a more flexible and visual scheduling and transaction decision basis is provided for risk aversion decision makers.
The embodiments described above are intended to provide those skilled in the art with a means of making or using the invention, and those skilled in the art may make various modifications or alterations to the embodiments described above without departing from the inventive concept thereof, and thus the scope of the invention is not limited by the embodiments described above, but is to be accorded the broadest scope consistent with the innovative features set forth in the claims.
Claims (1)
1. A cascade hydropower station short-term robust scheduling method coupled with daily electricity quantity decomposition and daily 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 an objective function, a daily electricity quantity decomposition constraint and a short-term operation constraint;
first, constructing an objective function: aiming at short-term optimized scheduling of the cascade hydropower station in the power market environment, the total income of a long-term market and a spot market in the cascade hydropower station is the maximum, and the expression is as follows:
in the method, in the process of the invention,decomposing the electric quantity for the power station i day electric quantity and outputting power in the period t; />Contract electricity price for power station i; p (P) i,t The total output of the power station i in the period t is obtained; />Predicting electricity clearing prices for markets before t time periods; t is a scheduling period time period set; i is a cascade hydropower station set; Δt is the time period length;
secondly, constructing a solar electricity quantity decomposition constraint;
the contract decomposition has various typical decomposition curves, and the daily electricity quantity decomposition selects peak-to-valley curve modes: dividing a day into peak sections, flat sections and valley sections, and negotiating and determining three sections of load electric quantity of the peak sections, the flat sections and the valley sections by historical load characteristics of a user side or other modes; in the aboveBidding output for the day-ahead market of the power station i in the t period; c (C) i The daily contract electric quantity of the power station i; x is a time period set, wherein p, f and v respectively represent peak, flat and valley time periods; c x,i Contract electricity quantity for the x-period power station i; t (T) x A set of period indicators included for period x; gamma ray x The ratio of the solar energy occupied by the period x is the ratio of the solar energy occupied by the period x;
thirdly, constructing short-term operation constraint including a water balance equation, a reservoir capacity constraint, a power station ex-reservoir flow constraint, a hydropower station output constraint, a hydropower station vibration area constraint, a reservoir water level constraint and water level reservoir capacity relationship, a water head constraint, a tail water level lower drainage flow relationship and a power generation function relationship;
(2) Robust model construction based on information gap decision theory
First, uncertainty set construction
The current market price is selected as an uncertain parameter, an envelope limiting model is adopted to construct an uncertain set model, and the mathematical expression is as follows:
wherein: lambda (lambda) t The actual discharging clear electricity price of the spot market in the period t is represented; alpha represents the fluctuation range of electricity price, and the above represents that the actual price is around the predicted value within the range of (1-alpha, 1+alpha)Fluctuation up and down;
second step, robust model target and constraint construction
The robust model is an IGDT robust model taking the fluctuation amplitude of an uncertain variable as a target and considering the uncertainty of the electricity price on the premise of meeting an expected target, and the objective function is as follows:
maxα (6)
the constraints include minimum benefit constraints in addition to the short-term operational constraints described above, expressed as follows:
π r ＝(1-β r )π 0 ,β r ∈[0,1) (8)
wherein pi 0 Obtaining optimal benefits for the deterministic model of step (1); pi r An acceptable minimum profit target can be set for a decision maker according to the self demand; beta r For the profit deviation factor, namely the deviation degree between the expected profit and the optimal profit of the deterministic mode, the larger the value of the profit deviation factor is, the larger the evasion degree of the decision solution to the risk is;
(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 daily market;
first, based on the distribution output of the contract electric quantity of each time period obtained by the modelForming a daily contract time-sharing power curve;
second, the distribution output of the market in the day-ahead of the time period obtained by the modelDeclaration output as a market in the day-ahead; the objective function value alpha obtained by the model is the maximum fluctuation range of the current market price under the expected income target, the lower boundary of the fluctuation range is taken as the declaration price of the current market, so that the current price is ensured to be the winning bid when the current price is within the information gap of the estimated price, the bidding decision of the current market is formed, and the output-price declaration decision of the period t is expressed as->
(4) Solving a model;
firstly, predicting running parameters of running water and cascade hydropower stations and predicting current price of market in the pastTaking solar power decomposition constraint and cascade hydropower station short-term operation constraint into consideration, and obtaining deterministic cascade hydropower short-term optimal scheduling optimal profit result pi 0 The method comprises the steps of carrying out a first treatment on the surface of the Then, the acceptable gain deviation coefficient beta is given by a decision maker of risk aversion of hydropower enterprises r Combined with basic income pi 0 Determining the minimum expected benefit pi acceptable r ＝(1-β r )π 0 Substituting a robust scheduling model considering the uncertainty of 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 resistant electricity price fluctuation amplitude, and forming a short-term optimal scheduling scheme of the cascade hydropower station.
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