CN114039347B - Wind power pumped storage scheduling method considering conditional risk and uncertainty - Google Patents

Wind power pumped storage scheduling method considering conditional risk and uncertainty Download PDF

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CN114039347B
CN114039347B CN202111351465.8A CN202111351465A CN114039347B CN 114039347 B CN114039347 B CN 114039347B CN 202111351465 A CN202111351465 A CN 202111351465A CN 114039347 B CN114039347 B CN 114039347B
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uncertainty
wind power
pumped storage
storage unit
risk
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CN114039347A (en
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赵文杰
王世谦
李虎军
刘军会
邓方钊
杨钦臣
邓振立
贺明康
吴雄
刘炳文
尹硕
柴喆
郭兴五
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State Grid Corp of China SGCC
Xian Jiaotong University
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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State Grid Corp of China SGCC
Xian Jiaotong University
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Abstract

The invention relates to a wind power pumped storage scheduling method considering condition risk and uncertainty, which comprises the following steps: constructing a nonlinear pumped storage model; converting nonlinearity into linearity, and simplifying a pumped storage model; the uncertainty of the wind power output is processed, and an uncertainty set of the wind power output is obtained; measuring the risk value caused by uncertainty of the day-ahead electricity price; and (5) aiming at maximizing the system benefit, and formulating a corresponding scheduling strategy. The invention can combine pumped storage with new energy to generate power, inhibit fluctuation of wind power output, and formulate corresponding scheduling strategy to ensure system income, and has higher economy.

Description

Wind power pumped storage scheduling method considering conditional risk and uncertainty
Technical Field
The invention belongs to the technical field of scheduling of new energy power systems, and particularly relates to a wind power pumped storage scheduling method considering conditional risk and uncertainty.
Background
At present, as the proportion of new energy sources in a power grid is higher and higher, the influence brought by the new energy sources is more and more remarkable. The wind power output has the characteristics of clearance, randomness, volatility and the like under the influence of wind speed, meanwhile, because the adjustability of the wind power unit is poor, the wind speed at night is generally higher than the wind speed at daytime, the wind power output usually has the inverse peak regulation characteristic, the output of the conventional thermal power unit at night is forced to be reduced, even stopped, and the peak regulation requirement of a power system is increased. Meanwhile, as the power market reforms deeply, the electricity price is used as the core content of the power market, so that the electricity price becomes an uncertain amount and fluctuates frequently like commodities along with the change of supply and demand relations, and the income of the system cannot be ensured.
The pumped storage power station has the characteristics of flexible operation mode, large stored energy, quick start and stop, quick climbing speed and the like, plays roles of black start, accident standby, peak regulation, frequency modulation and the like in a power system, and is mainly used as a peak regulation power supply in the power system. With the increasing of the voltage level of the power grid, the scale is larger and larger, and the high-proportion new energy is connected into the power grid, the peak-valley difference is increased, the demand for high-quality electric energy is higher and higher, but the traditional power supply has weak peak regulation capability, the output change cannot be quickly regulated, and the forced development of the pumped storage power station becomes a necessary trend.
In addition to the effects of uncertainty, the system's ability to withstand risk should also be considered, and methods currently in common use include risk value (VaR) and conditional risk value (CVaR). Because of the monotonic, sub-additive, translational invariance and positive-secondary nature of CVaR, CVaR-based approaches have received great attention as an effective approach to study risk metrics. The Information Gap Decision Theory (IGDT) is a decision method for researching the existence of serious uncertainty factors, and can effectively process uncertainty errors. There are two different strategies in IGDT, one of which considers that the uncertainty is unfavorable for the system target and can reduce the system target value, the purpose of which is to ensure that the system target is not lower than the set worst target all the time, which is called a risk avoidance strategy; another concern is that the uncertainty amount may raise the target value of the system with the purpose of giving the system target an opportunity to exceed the set aggressive target value, referred to as an opportunity seeking strategy. The method is characterized in that: the uncertainty amount may be detrimental to the goals of the system setup, or may be advantageous, and quantifies the impact of two different aspects of uncertainty amount generation. It is therefore significant how to guarantee the benefits of the system taking into account the uncertainty of the power system.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a wind power pumped storage scheduling method considering condition risks and uncertainty, which can be used for generating power by matching pumped storage with new energy, inhibiting fluctuation of wind power output, formulating a corresponding scheduling strategy, ensuring the income of a system and having higher economy.
The invention adopts the technical scheme that: a wind power pumped storage scheduling method considering conditional risk and uncertainty comprises the following steps:
s1: constructing a nonlinear pumped storage model;
s2: converting nonlinearity into linearity, and simplifying a pumped storage model;
s3: the uncertainty of the wind power output is processed, and an uncertainty set of the wind power output is obtained;
s4: measuring the risk value caused by uncertainty of the day-ahead electricity price;
s5: and (5) aiming at maximizing the system benefit, and formulating a corresponding scheduling strategy.
Specifically, the pumped storage model refers to the output power of a pumped storage unit, the output power of the pumped storage unit is a nonlinear function of a water purification head, water flow and unit efficiency, and the output power of the pumped storage unit during pumping and generating is respectively expressed as follows:
in the formula ,pP 、p T Respectively output power when pumping and generating the water of the pumped storage unit, G is gravity acceleration, h net Is water purifying head, q P 、q T Respectively the water flow, eta when the water pump energy storage unit pumps water and generates electricity P 、η T The efficiency and ρ of the pumped storage unit during pumping and generating are respectively that i P 、ρ i T A constant related to a parameter of the pumped-storage unit can be obtained from the efficiency curve of the pumped-storage unit.
Specifically, constraint conditions of the pumped storage unit model are as follows:
wherein ,the maximum value of water flow when the pumped storage unit pumps water and generates electricity is respectively, u P 、u T Respectively pumping and generating of the pumped storage unit, wherein 0 represents stopping, 1 represents starting and +.>The maximum value of the upper water head and the lower water head is +.> V up The upper limit and the lower limit of the upper reservoir capacity are respectively +.> V down The upper limit and the lower limit of the lower reservoir capacity are respectively.
In particular, the stepsIn step S2, a piecewise linearization technique is used to convert the nonlinearity into linearity, specifically: for definition inThe unitary nonlinear function h=f (q) is divided into M subintervals, and the original nonlinear function is replaced by a primary function on the subintervals, and the specific process is as follows:
z m ∈{0,1}m＝0,1,···,M
in the formula ,for the end points of the respective subintervals, Z m A variable of 0-1, so that a section where q is located is determined, and then an approximate value of the water head is calculated;
for definition inP=g (h, q), will first +.>Divided into N sub-intervals, in each sub-interval [ h ] n-1 ,h n ]Upper selection of appropriate->Converting a function into n unitary functions g n (q) for a unitary nonlinear function g n (q) section->Dividing into M subintervals, and replacing the original nonlinear function with a linear function on each subinterval, wherein the method comprises the following steps:
in the formula ,for the end points of the respective subintervals>And the variable is 0-1, so that the interval where the water head h and the water flow q are located is determined, and then the output power of the pumped storage unit is approximately calculated by using a linear function.
Specifically, in the step S3, when the uncertainty of the wind power output is handled, an uncertainty set of the wind power output is obtained by adopting the IGDT, and the expression is as follows:
wherein U is an uncertain set of wind power output,p is the predicted value of wind power output w And alpha is the fluctuation range of the wind power output.
Specifically, in the step S4, the risk value caused by the uncertainty of the current price before day is measured by using CVaR, and the expression is as follows:
wherein F is the risk value caused by the current price before the day, eta is the confidence level, pi s Probability of scene s, N s F is the number of scenes and f is the objective function.
Specifically, the step S5 specifically includes: under the condition of considering wind power output and day-ahead electricity price risk, the system objective function is the maximum benefit, and the expression is as follows:
wherein T is the number of time periods, the number of J pump storage units, lambda t The current price is the current price before the day at the moment t,for the output power of the system at time t, SU i,t 、SD i,t The starting and stopping costs of a water pump i in the pumped storage unit at the moment t are respectively, mu is a risk preference coefficient,/>Auxiliary variables introduced to calculate conditional risk value.
The invention has the beneficial effects that: aiming at the defects of the existing new energy output and day-ahead electricity price, the invention provides a wind power pumped storage scheduling method, considers the nonlinearity of a pumped storage model, converts the pumped storage model into a linear model, utilizes IGDT and CVaR to respectively treat the influence caused by wind power output and day-ahead electricity price, makes a scheduling strategy of a unit under the premise of ensuring the system income, and makes corresponding strategies according to the risk preference of a decision maker to respectively obtain the output plans of each unit, suppresses the fluctuation of wind power output, and ensures that the income of a combined power plant is smoother and has higher economical efficiency and safety.
Drawings
FIG. 1 is a flow chart of the steps of the present invention;
FIG. 2 is a graph of predicted wind power output and day-ahead electricity prices according to the present invention;
FIG. 3 is a graph of system output obtained by three scheduling strategies in the present invention;
FIG. 4 is a graph of water flow over time for the pumped and generated conditions of the pumped storage unit of the present invention;
FIG. 5 is a graph showing the variation of the head of the pumped-storage unit with time;
FIG. 6 is a graph of pump and generator output power of the pump storage unit of the present invention as a function of time;
FIG. 7 is a graph showing the water level of the upper and lower reservoirs of the pumped-storage unit according to the present invention over time;
FIG. 8 is a graph showing the influence of the gain deviation coefficient on wind power uncertainty in the present invention;
FIG. 9 is a graph showing the influence of risk preference coefficients on wind power uncertainty in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. All other embodiments, which can be made by those skilled in the art without making any inventive effort, based on the embodiments of the present invention are within the scope of the present invention, and are specifically described below in connection with the embodiments.
As shown in fig. 1, 2 and 3, the present invention includes the steps of:
s1: the method comprises the following specific processes of constructing a nonlinear pumped storage model:
the water pumping energy storage model refers to the output power of a water pumping energy storage unit, the output power of the water pumping energy storage unit is a nonlinear function of a water purifying head, a water flow and unit efficiency, the water purifying head is related to an upper water head, a lower water head and head loss, the upper water head and the lower water head are respectively a nonlinear function of the upper water flow and the lower water flow, the water quantity of a reservoir is related to the water flow, and the expressions of the upper water head, the lower water head and the head loss are respectively as follows:
h loss ＝c(q P +q T ) 2
in the formula ,hup 、h down Respectively an upper water head (m) and a lower water head (m), q P 、q T Respectively pumping water flow (m) of pumped storage unit during power generation 3 /s)，Obtained from pumped-storage parameters, h loss The head loss is c is the coefficient of friction.
Thus, a clean water head can be obtained:
h net ＝h up -h down -h loss
the efficiency of pumping and generating electricity is related to the water purification head and water flow, and is expressed as follows by a binary nonlinear function:
in the formula ,ηP 、η T The efficiency and ρ of the pumped storage unit during pumping and generating are respectively that i P 、ρ i T A constant related to a parameter of the pumped-storage unit can be obtained from the efficiency curve of the pumped-storage unit.
Therefore, the output power of the pumped storage unit during pumping and generating is respectively expressed as follows:
in the formula ,pP 、p T And the output power and the G are gravity acceleration when the pumped storage unit pumps water and generates electricity respectively.
The water quantity of the upper reservoir and the lower reservoir of the pumped storage power station is related to the water flow, and the water quantity and the water flow are respectively expressed as follows:
in the formula ,Vt up 、V t down The water quantity is respectively the upper and lower reservoir water quantity in t time periods, and delta is the duration of one time period.
Constraint conditions of the pumped storage unit model are as follows:
wherein ,the maximum value of water flow when the pumped storage unit pumps water and generates electricity is respectively, u P 、u T Respectively pumping and generating of the pumped storage unit, wherein 0 represents stopping, 1 represents starting and +.>The maximum value of the upper water head and the lower water head is +.> V up The upper limit and the lower limit of the upper reservoir capacity are respectively +.> V down The upper limit and the lower limit of the lower reservoir capacity are respectively.
S2: the non-linearity is converted into linearity, the pumped storage model is simplified, and the specific process is as follows:
for definition inThe unitary nonlinear function h=f (q) is divided into M subintervals, and the original nonlinear function is replaced by a primary function on the subintervals, and the specific process is as follows:
z m ∈{0,1}m＝0,1,···,M
in the formula ,for the end points of the respective subintervals, Z m A variable of 0-1, so that a section where q is located is determined, and then an approximate value of the water head is calculated;
for definition inP=g (h, q), will first +.>Divided into N sub-intervals, in each sub-interval [ h ] n-1 ,h n ]Upper selection of appropriate->Converting a function into n unitary functions g n (q) for a unitary nonlinear function g n (q) section->Dividing into M subintervals, and replacing the original nonlinear function with a linear function on each subinterval, wherein the specific process is as follows:
in the formula ,for the end points of the respective subintervals>And the variable is 0-1, so that the interval where the water head h and the water flow q are located is determined, and then the output power of the pumped storage unit is approximately calculated by using a linear function.
S3: and (3) responding to the uncertainty of the wind power output to obtain an uncertainty set of the wind power output, wherein the uncertainty set is specifically expressed as follows:
when the uncertainty of the wind power output is dealt with, an IGDT is adopted to obtain an uncertainty set of the wind power output, and the expression is as follows:
wherein U is an uncertain set of wind power output,p is the predicted value of wind power output w And alpha is the fluctuation range of the wind power output.
S4: the risk value caused by uncertainty of the day-ahead electricity price is measured, and the risk value is specifically expressed as follows:
the CVaR is adopted to measure the risk value caused by the uncertainty of the day-ahead electricity price, and the expression is as follows:
wherein F is the risk value caused by the current price before the day, eta is the confidence level, pi s Probability of scene s, N s F is the number of scenes and f is the objective function.
S5: aiming at maximizing system income, a corresponding scheduling strategy is formulated, and the specific process is as follows:
under the condition of considering wind power output and day-ahead electricity price risk, the system objective function is the maximum benefit, and the expression is as follows:
wherein T is the number of time periods, the number of J pump storage units, lambda t The current price is the current price before the day at the moment t,for the output power of the system at time t, SU i,t 、SD i,t The starting and stopping costs of a water pump i in the pumped storage unit at the moment t are respectively, mu is a risk preference coefficient,/>Auxiliary variables introduced to calculate conditional risk value.
And then, according to the risk preference of the decision maker, making different scheduling strategies.
The risk avoidance scheduling policy is:
maxα
the opportunity seeks to schedule the policy:
minα
wherein μ is a risk preference coefficient, in the risk avoidance scheduling model, its value is greater than 0.1, in the opportunity seeking scheduling model, its value is less than 0.05,auxiliary variable, delta, introduced for calculating conditional risk value 1 、Δ 2 Is a gain deviation coefficient.
And finally solving the optimization problem according to the risk preference of the decision maker to obtain the scheduling plans of all the units.
The validity of the present invention is verified as follows:
the predicted wind power output and day-ahead electricity price curves are shown in fig. 2.
Setting the gain deviation coefficient to be 0.2, setting the risk preference coefficient to be 0.1 in a risk avoidance strategy, and setting the risk preference coefficient to be 0.05 in an opportunity seeking strategy to obtain output curves of the system under three scheduling strategies of a deterministic strategy, the risk avoidance strategy and the opportunity seeking strategy, wherein a decision maker is more conservative under the risk avoidance strategy, so that the output of the system is reduced, the gain is reduced, but the capability of avoiding the risk is enhanced; under the opportunity seeking strategy, a decision maker seeks higher benefits, the uncertainty of wind power output is increased, and the bearing risk is increased.
In a deterministic strategy, a water flow rate change curve of the pumped storage unit is shown in fig. 4, a water head change curve of the unit is further obtained, a water head change curve of the unit is shown in fig. 5, output power of the pumped storage unit is obtained, when electricity price is low, for example, 4-9 hours, a pumped storage power station is in a pumped state, a water pump pumps water to an upper reservoir, redundant electric energy of wind power is converted into gravitational potential energy of water to be stored, water pumping flow rate determines an upper water head, pumping power is determined, when electricity price is high, for example, 14-20 hours, the pumped storage power station is in a power generation state, water flows from the upper reservoir to a lower reservoir, a water turbine is driven to generate power, the lower water head is determined by the generated water flow rate, and then the generated power is determined by the generated water flow rate and the water purification head. The pumped storage power station buys electric energy for storage at low electricity price, sells electric energy at high electricity price, obtains benefits, and a change curve of the reservoir water quantity of the pumped storage power station is shown in figure 7.
The gain deviation coefficient represents acceptable expectations of a decision maker on gains, in order to study the influence of the gain deviation coefficient on uncertain parameters, the risk preference coefficient is kept to be 0.1 unchanged in a risk avoidance strategy, the risk preference coefficient is kept to be 0.05 unchanged in an opportunity seeking strategy, the gain deviation coefficient is increased from 0.05 to 0.5 at intervals of 0.05, a fluctuation range of wind power output, namely a change curve of wind power uncertainty is obtained, as shown in fig. 8, in the risk avoidance strategy, as the gain deviation coefficient is continuously increased, the wind power output uncertainty which can be tolerated by the system is also increased, and as the decision maker considers that the wind power output uncertainty is harmful, a relatively conservative robust decision strategy is formulated when the wind power output uncertainty is considered, the actual output of the wind power unit is reduced, the gain is reduced, and as the wind power output uncertainty is larger, the wind power output uncertainty is lower, under the opportunity seeking strategy, as the gain deviation coefficient is continuously increased, the wind power output uncertainty which is expected by the system is also increased. Decision makers consider that uncertainty of wind power output is beneficial, increasing wind power output can improve benefits of the combined power plant, and the uncertainty can have positive influence on the benefits of the combined power plant, and the greater the uncertainty of wind power output is, the greater the benefits of the combined power plant are.
The risk preference coefficient corresponds to a weight coefficient of the conditional risk value, represents the preference level of a decision maker for the risk, and generally represents that the decision maker selects a risk avoidance strategy when the value is more than 0.1, and a certain benefit is used for exchanging smaller risks. When the value is larger than 0.05, a decision maker selects an opportunity seeking strategy, more benefits are obtained by using larger risks, the benefit deviation coefficient is 0.2, the risk preference coefficient is increased from 0 to 0.10 in the risk avoidance strategy, the interval is 0.025, the risk preference coefficient is increased from 0.10 to 0.20 in the opportunity seeking strategy, the interval is 0.025, a change curve of wind power uncertainty is obtained along with the change curve, as shown in fig. 9, and as the risk preference coefficient is increased, the decision maker is more prone to avoid the influence caused by electricity price fluctuation, so that the fluctuation range of acceptable wind power output is increased, and wind power uncertainty is increased.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (4)

1. The wind power pumped storage scheduling method taking condition risk and uncertainty into consideration is characterized by comprising the following steps of:
s1: constructing a nonlinear pumped storage model;
s2: converting nonlinearity into linearity, and simplifying a pumped storage model;
s3: the uncertainty of the wind power output is processed, and an uncertainty set of the wind power output is obtained;
s4: measuring the risk value caused by uncertainty of the day-ahead electricity price;
s5: aiming at maximizing system income and formulating a corresponding scheduling strategy according to the risk preference of a decision maker;
in the step S3, when the uncertainty of the wind power output is to be dealt with, an IGDT is adopted to obtain an uncertainty set of the wind power output, and the expression is as follows:
wherein U is uncertainty of wind power outputFixing device the collection of the liquid-liquid mixture is carried out,p is the predicted value of wind power output w Alpha is the fluctuation range of the wind power output;
in the step S4, the risk value caused by the uncertainty of the current price before day is measured by using CVaR, and the expression is as follows:
wherein F is the risk value caused by the current price before the day, eta is the confidence level, pi s Probability of scene s, N s F is an objective function, which is the number of scenes;
the step S5 specifically comprises the following steps: under the condition of considering wind power output and day-ahead electricity price risk, the system objective function is the maximum benefit, and the expression is as follows:
wherein T is the number of time periods, the number of J pump storage units, lambda t The current price is the current price before the day at the moment t,for the output power of the system at time t, SU i,t 、SD i,t The starting and stopping costs of the water pump i in the pumped storage unit at the moment t are respectively, mu is a risk preference coefficient,auxiliary variables introduced to calculate conditional risk value.
2. A wind power pumped storage scheduling method taking into account conditional risk and uncertainty as defined in claim 1, wherein: the pumped storage model refers to the output power of a pumped storage unit, the output power of the pumped storage unit is a nonlinear function of water purification head, water flow and unit efficiency, and the output power of the pumped storage unit during pumping and generating is respectively expressed as follows:
in the formula ,pP 、p T Respectively output power when pumping and generating the water of the pumped storage unit, G is gravity acceleration, h net Is water purifying head, q P 、q T Respectively the water flow, eta when the water pump energy storage unit pumps water and generates electricity P 、η T The efficiency and ρ of the pumped storage unit during pumping and generating are respectively that i P 、ρ i T A constant related to a parameter of the pumped-storage unit can be obtained from the efficiency curve of the pumped-storage unit.
3. A wind power pumped-storage scheduling method taking into account conditional risk and uncertainty as defined in claim 2, wherein constraints of said pumped-storage group model are:
wherein ,the maximum value of water flow when the pumped storage unit pumps water and generates electricity is respectively, u P 、u T Respectively pumping and generating of the pumped storage unit, wherein 0 represents stopping, 1 represents starting and +.>The maximum value of the upper water head and the lower water head is +.> V up The upper limit and the lower limit of the upper reservoir capacity are respectively +.> V down The upper limit and the lower limit of the lower reservoir capacity are respectively.
4. The wind power pumped storage scheduling method considering conditional risk and uncertainty according to claim 2, wherein in step S2, a piecewise linearization technique is adopted to convert nonlinearity into linearity, specifically: for definition inThe unitary nonlinear function h=f (q) is divided into M subintervals, and the original nonlinear function is replaced by a primary function on the subintervals, and the specific process is as follows:
z m ∈{0,1}m＝0,1,···,M
in the formula ,for the end points of the respective subintervals, Z m A variable of 0-1, so that a section where q is located is determined, and then an approximate value of the water head is calculated;
for definition inP=g (h, q), will first +.>Divided into N sub-intervals, in each sub-interval [ h ] n-1 ,h n ]Upper selection of appropriate->Converting a function into n unitary functions g n (q) for a unitary nonlinear function g n (q) section->Dividing into M subintervals, and replacing the original nonlinear function with a linear function on each subinterval, wherein the method comprises the following steps:
in the formula ,for the end points of the respective subintervals>And the variable is 0-1, so that the interval where the water head h and the water flow q are located is determined, and then the output power of the pumped storage unit is approximately calculated by using a linear function.
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