CN112580872A - Opportunity constraint planning-based short-term optimization scheduling method for water-light combined system - Google Patents

Opportunity constraint planning-based short-term optimization scheduling method for water-light combined system Download PDF

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CN112580872A
CN112580872A CN202011505067.2A CN202011505067A CN112580872A CN 112580872 A CN112580872 A CN 112580872A CN 202011505067 A CN202011505067 A CN 202011505067A CN 112580872 A CN112580872 A CN 112580872A
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苏承国
原文林
王新奇
卢璐
王沛霖
刘哲
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Abstract

The invention discloses a short-term optimized dispatching method of a water-light combined system based on opportunity constraint planning, which is characterized in that the minimum energy abandonment of the combined system and the maximum power generation amount of the combined system are set as dispatching targets, based on the uncertainty of a photovoltaic output process, a hydroelectric generating set is used as a basic dispatching unit, the on-off duration of the set and the constraint of a vibration region of the set are considered, a set of controllable system for short-term optimized dispatching of the water-light combined system based on opportunity constraint planning is established, so that a refined dispatching operation system with strong operability is obtained, the system forms a typical multivariable, high-dimensional and multi-complex-constraint mixed integer nonlinear planning MINLP, the MINLP is converted into a set of mixed integer linear planning MILP by using a series linearization method, and a high-efficiency solver is used for solving. The invention has pioneering progress significance in both basic theory and practical application.

Description

Opportunity constraint planning-based short-term optimization scheduling method for water-light combined system
Technical Field
The invention relates to the technical field of electric power, in particular to a short-term optimization scheduling method for a water-light combined system.
Background
Because the traditional fossil energy seriously pollutes the environment during power generation and is non-renewable, the development of clean renewable energy sources such as photovoltaic energy, wind power and the like is gradually accelerated in recent years. The photovoltaic power generation has great development potential due to mature technology, wide distribution and low operation and maintenance cost. However, the photovoltaic power generation is random and intermittent, huge pressure is applied to a power grid in the grid connection process, other power sources with good adjusting performance are required to adjust the power grid, and water and electricity can quickly respond to the fluctuation of photovoltaic output through good adjusting capacity, so that the photovoltaic power generation and the hydraulic power generation are combined into the power grid, and the method is an effective and wide-prospect combined grid connection mode. But the renewable energy market in China is not perfect at present, so that the power grid needs to consume clean energy power as much as possible on the basis of ensuring the safe and stable operation of the power grid. Most of the previous research on the water-light combined system only considers the power generation requirement of the combined system, but neglects the consumption requirement of a power grid, so that the combined dispatching strategies are difficult to be directly applied to the water-light combined power generation system in China. Therefore, it is an urgent problem to provide an effective and feasible short-term optimal scheduling method for a water-light combined system.
Disclosure of Invention
The invention aims to provide a short-term optimization scheduling method of a water-light combined system based on opportunity constraint planning.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
The method comprises the steps of firstly setting minimum energy abandonment of a combined system and maximum generated energy of the combined system as scheduling targets, setting a set of water-light combined system short-term optimization scheduling controllable system based on opportunity constraint planning based on uncertainty of a photovoltaic output process and taking a hydroelectric generating set as a basic scheduling unit, considering on-off duration of the set and constraint of a vibration region of the set, so as to obtain a refined scheduling operation system with strong operability, wherein the system forms a typical multivariable, high-dimensional and multi-complex-constraint mixed integer nonlinear programming MINLP, converting the MINLP into a set of mixed integer linear programming MILP by using a series linearization method, and solving by using a high-efficiency solver; the series of linear optimization methods include, but are not limited to: the method comprises a scene simulation linearization method for a linearization opportunity constraint planning principle problem and a piecewise linearization method for a linearization unit vibration region constraint problem.
As a preferred technical scheme of the invention, the mixed integer nonlinear programming MINLP is established based on a LHS and k-means clustering scene generation method, and is based on a photovoltaic output prediction error sequence
Figure BDA0002844640390000011
Obeying a normal distribution N (mu, sigma)2), wherein
Figure BDA0002844640390000012
Due to the fact that the Latin hypercube sampling LHS can obtain better sampling results in few scenes, the LHS method is adopted for sampling and generating multiple photovoltaic output prediction error scenes, and in order to further reduce the calculation burden, the k-means method is used for further reducing the multiple scenes generated by the LHS method; the construction steps comprise two steps of setting an objective function and setting constraint conditions.
As a preferred technical solution of the present invention, the construction method of the objective function is as follows: two objective functions are set: the total electric quantity abandoned by the combined system is minimum, and the total electric energy production of the combined system is maximum;
(1) objective function 1: the total electric quantity discarded by the combined system is minimum; because the uncertain photovoltaic output is a key factor for solving the minimum total electricity discard quantity of the combined system, the uncertainty is processed by adopting opportunity constraint planning, and an objective function 1 is expressed as follows:
Figure BDA0002844640390000021
wherein, T and T respectively represent a scheduling period and a certain scheduling period; at represents the time interval h in which the time interval,
Figure BDA0002844640390000022
representing the minimum electric quantity MWh of the combined system; alpha is alpha1Representing a confidence level that the power curtailment of the combined system does not exceed the target power curtailment;
Figure BDA0002844640390000023
and
Figure BDA0002844640390000024
respectively representing the energy abandon MW of the hydropower station and the photovoltaic power station at the moment t;
(2) the objective function 2: the total power generation capacity of the combined system is maximum; in the opportunistic constraint planning model, the objective function 2 is expressed as:
Figure BDA0002844640390000025
wherein M represents the number of hydropower station units;
Figure BDA0002844640390000026
representing the output MW of the mth hydroelectric generating set at the moment t;
Figure BDA0002844640390000027
representing grid-connected output MW of the photovoltaic power station; 2frepresenting the target total power generation MWh of the water-light combined system; alpha is alpha2Indicating a confidence level that the total power generation amount is not lower than the target power generation amount.
As a preferred technical scheme of the invention, the objective function 1 is taken as a main objective function, and the objective function 2 is taken as a secondary objective, so that the proposed objective function has self-adaptability, when the combined system inevitably generates energy abandon, the minimum energy abandon is taken as a target, otherwise, the maximum power generation amount is taken as a target; the final objective function is expressed as follows:
Figure BDA0002844640390000028
as a preferred technical scheme of the invention, the constraint conditions comprise reservoir and power station constraints, hydroelectric generating set constraints, photovoltaic output constraints and power balance constraints.
As a preferred technical solution of the present invention, the reservoir and power station constraints include:
(1) water balance constraint
vt+1＝vt-3600·(It-Qt)·Δt 4
Figure BDA0002844640390000031
Wherein, ItIndicating the warehousing flow m of the reservoir at the time t3/s；
Figure BDA0002844640390000032
Representing the generating flow m of the hydroelectric generating set m at the moment t3/s；QtShowing the discharge m of the reservoir at time t3/s；vtIndicating the storage capacity m of the reservoir at the end of the t period3；
(2) Water level restraint
Figure BDA0002844640390000033
Figure BDA0002844640390000034
Wherein z istRepresents the upstream water level m at time t;zand
Figure BDA0002844640390000035
respectively representing the upper limit m and the lower limit m of the water level of the upstream reservoir; z is a radical ofbAnd zeRespectively representing the initial water level and the final water level m of the upstream reservoir;
(3) outbound flow constraint
Figure BDA0002844640390000036
Wherein the content of the first and second substances,Qand
Figure BDA0002844640390000037
respectively representing the lower and upper limit values m of the flow out of the hydropower station3/s。
As a preferred technical solution of the present invention, the hydroelectric generating set constraint includes:
(1) unit output constraint
Figure BDA0002844640390000038
Wherein the content of the first and second substances,
Figure BDA0002844640390000039
and
Figure BDA00028446403900000310
respectively representing the upper limit MW and the lower limit MW of the unit m output; u. ofm,tWhen the auxiliary variable is 1, the unit is in a starting state, otherwise, the unit is in a stopping state;
(2) output function of hydroelectric generating set
Figure BDA00028446403900000311
Wherein f ism,pgh(. -) represents a hydroelectric generating set output function; h ism,tRepresenting the water head m of the hydroelectric generating set m at the moment t;
(3) energy curtailment function of hydropower station
For the convenience of solution, assuming that all the abandoned water flows through the No. 1 unit, and the corresponding output force is the abandoned energy of the hydropower station;
Figure BDA00028446403900000312
wherein,
Figure BDA00028446403900000313
indicating the reject flow rate m at time t3/s；
(4) Head restraint
hm,t＝(zt-1+zt)/2-zdt-hl m,t 12
zt＝fzv(vt) 13
zdt＝fzq(Qt) 14
Figure BDA0002844640390000041
Wherein zd istIndicating the tail water level m of the reservoir; hl (high pressure chemical vapor deposition)m,tRepresenting the head loss m of the unit m at the time t; f. ofzv() represents a level reservoir capacity relationship; f. ofzq(-) represents the tail water level versus bleed down flow; f. oflqThe power generation flow and the head loss relation of the unit m are represented;
(5) unit generated current restriction
Figure BDA0002844640390000042
Wherein the content of the first and second substances,
Figure BDA0002844640390000043
andq mrespectively represent the upper limit and the lower limit m of the generating flow of the unit m3/s；
(6) Unit vibration zone restraint
Figure BDA0002844640390000044
Wherein the content of the first and second substances,
Figure BDA0002844640390000045
and
Figure BDA00028446403900000412
respectively representing the upper limit and the lower limit of the kth vibration area of the unit m;
(7) unit on-off duration constraint
Figure BDA0002844640390000046
Figure BDA0002844640390000047
Wherein ξmAnd psimRespectively representing the minimum startup and shutdown duration h of the unit m; x is the number ofm,tIs an auxiliary variable, and when 1 is taken, the unit is started, ym,tAnd the auxiliary variable is 1, and the shutdown of the unit is indicated.
As a preferred solution of the present invention, the photovoltaic output needs to satisfy the following constraints:
Figure BDA0002844640390000048
wherein,
Figure BDA0002844640390000049
representing the actual output MW of the photovoltaic power station at the moment t;
Figure BDA00028446403900000410
and
Figure BDA00028446403900000411
and respectively representing the predicted output and the corresponding predicted output deviation MW of the photovoltaic power station at the time t.
As a preferred technical scheme of the invention, for the power balance constraint, the uncertainty of photovoltaic output threatens the load process submitted to the power grid by the water-light system and the generated energy tracking, which can cause the serious safety problem of the power grid; to control these problems, opportunity constraints are applied to limit the risk of power imbalance for the combined system;
Figure BDA0002844640390000051
wherein ω represents the power imbalance rate in the contract between the power generation company and the power grid;
Figure BDA0002844640390000052
representing the load value MW of the combined system at the t moment; beta represents a preset confidence level that the water-light combined output does not exceed the upper limit and the lower limit of the allowable power deviation and meets the power balance constraint; the probabilistic expression 21 ensures that the probability that the total output of the combined system does not satisfy the load process is less than 1-beta, so as to control the influence caused by the uncertainty of the photovoltaic output.
As a preferred technical solution of the present invention, the method for establishing the mixed integer linear programming MILP includes, but is not limited to: the method comprises the steps of opportunity constraint planning model linearization and unit vibration area linearization.
As a preferred technical solution of the present invention, for the opportunistic constraint programming model linearization, a scene simulation method is adopted to solve:
(1) n types of photovoltaic output prediction error representative scenes are generated by applying a scene generation method, and the actual output of a photovoltaic power station and the photovoltaic output of a photovoltaic power grid under a given scene can be obtained by the following two formulas:
Figure BDA0002844640390000053
Figure BDA0002844640390000054
wherein,
Figure BDA0002844640390000055
and
Figure BDA0002844640390000056
respectively representing the actual output and the output prediction deviation of the photovoltaic power station at the t moment in the nth scene;
Figure BDA0002844640390000057
and
Figure BDA0002844640390000058
respectively representing grid-connected output and corresponding energy abandon values of the photovoltaic power station at the moment t under the nth scene;
(2) judging whether the generated scenes meet the formula (24), superposing the number of the scenes meeting the formula, and recording the total number as S, wherein if the S/N is more than or equal to beta, the chance constraint formula 21 is established, otherwise, the chance constraint formula is not established;
Figure BDA0002844640390000059
(3) based on the above analysis, four 0-1 auxiliary variables a were introducedn,t，bn,t，cn,tAnd dnThen the opportunity constraint can be transformed into the following deterministic expression:
Figure BDA0002844640390000061
Figure BDA0002844640390000062
Figure BDA0002844640390000063
Figure BDA0002844640390000064
Figure BDA0002844640390000065
cn,t＝an,t+bn,t-1 30
Figure BDA0002844640390000066
Figure BDA0002844640390000067
Figure BDA0002844640390000068
wherein, cn,tThe water-light combined system is an auxiliary variable, 1 is selected when the nth scene water-light combined system meets power balance constraint, and 0 is selected otherwise; dnTaking 1 when the combined output of the nth scene meets the power balance constraint, or taking 0; l is a very large constant; pbnRepresenting the probability of scene n;
from formula 25, an,tAnd bn,tTake a value of 1 or 0 if an,t0, according to formula 26,
Figure BDA0002844640390000069
otherwise, as can be seen from equation 27,
Figure BDA00028446403900000610
therefore, as can be seen from formulas 25 and 26, if
Figure BDA00028446403900000611
Then an,tThe value is 1; also, from 28 and 29, if
Figure BDA00028446403900000612
Then b isn,tThe value is 1; only when an,tAnd bn,tWhen both are 1, based on the expressions 26 to 29, the expression 24 holds, and in this case, the expressions 30, cn,tTaking 1; and the formulae 31 to 32 show the results only when
Figure BDA00028446403900000613
I.e. when the total output of the combined system meets the electric power balance constraint in the whole scheduling period, dnTaking 1, otherwise, taking 0; equation 33 ensures that the confidence in the predetermined opportunity constraint plan is met; by equations 25-33, probabilistic opportunity constraints can be converted into a scene-based deterministic expression;
similarly, two objective functions with uncertainty factors, equations 1 and 2, are transformed into the following forms;
Figure BDA0002844640390000071
Figure BDA0002844640390000072
Figure BDA0002844640390000073
wherein, γnAn auxiliary variable is used for deciding that the total power curtailment of the nth scene of the water-light combined system does not exceed a target value; lambda [ alpha ]nAn auxiliary variable is used for deciding that the total power generation amount of the nth scene of the water-light combined system is not lower than the target power generation amount; in formula 35, if the auxiliary variable γ n1, is of the same type
Figure BDA0002844640390000074
Can satisfy the two formula
Figure BDA0002844640390000075
Also satisfies; at this time, the three formulas
Figure BDA0002844640390000076
Ensuring that the probability that the total power curtailment of the combined system does not exceed the target power curtailment is alpha1(ii) a Likewise, equation 36 ensures that the probability that the total power generation of the combined system is not less than the target total power generation is α2。
As a preferred technical scheme of the invention, for the linearization of the vibration region of the unit, a linearization method table is adopted to solve the constraint problem of the vibration region of the unit, and considering the maximum and minimum output limits of the unit, the output of the unit is divided into K +1 feasible unit operation regions by K vibration regions, and the expression of the formula 17 is the following linear expression;
Figure BDA0002844640390000077
Figure BDA0002844640390000078
Figure BDA0002844640390000079
wherein,
Figure BDA00028446403900000710
the index variable is an index variable, and when 1 is taken, the unit is in the kth feasible operation area in the time period;
Figure BDA00028446403900000711
and
Figure BDA00028446403900000712
respectively representing the upper limit and the lower limit of the kth feasible operation area; meanwhile, based on the corresponding relation between the vibration area and the feasible area,the following formula is required;
Figure BDA0002844640390000081
from equations 37-38, when the unit is in operation, the unit output must be in a feasible region; from formula 39, if
Figure BDA0002844640390000082
And the unit m is in the kth feasible region.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in: the invention considers the combined operation of hydraulic power generation and photovoltaic power generation, utilizes the good regulating capacity of the hydropower station to stabilize the uncertainty of photovoltaic output, and then combines the uncertainty of photovoltaic output into a grid so as to improve the utilization rate of renewable energy and ensure the safe and stable operation of a power grid. In the above background, the present invention provides a short-term optimal scheduling method for a water-light combined system based on opportunity constrained programming (CCP). Firstly, in order to improve the energy utilization rate, the scheduling method provided by the invention aims at scheduling the combined system with the minimum energy abandonment and the maximum generated energy of the combined system; in addition, in order to obtain a fine and highly operable scheduling operation scheme, the method firstly considers the uncertainty of the photovoltaic output process, takes the hydroelectric generating set as a basic scheduling unit, gives consideration to the starting and stopping duration of the generating set, the vibration region of the generating set and other constraints, and establishes a CCP-based short-term optimization scheduling model of the water-light combined system. The model is a typical multivariable, high-dimensionality and multi-complex-constraint Mixed integer nonlinear programming (MINLP) model, and if a conventional dynamic programming algorithm and a bionic algorithm are adopted for solving, problems of 'dimensionality disaster', easy falling into local optimal solution and the like can be faced, so that in order to improve the solving efficiency, the invention provides a method for constructing series linearization to convert an original model into a Mixed Integer Linear Programming (MILP) model, and the efficient solver is used for solving.
The invention provides a combined scheduling model of a photovoltaic power station and a high-capacity hydropower station based on a coordination mechanism between an electric power company and a power grid. In the model, a scene analysis method combining an LHS method and k-means clustering is adopted to process the photovoltaic output prediction deviation, and in order to improve the precision of the model, the model takes a hydroelectric generating set as a basic scheduling unit and considers the corresponding constraint of the hydroelectric generating set. In addition, in order to improve the solving efficiency of the SMILLP model, the invention provides a series of linearization methods to convert the original model into the MILP model and solve the MILP model by using a solver. Based on the case research of the Wujiang river crossing water-light combined system in the southwest of China, the progressive significance is shown as follows: (1) the negative effects caused by uncertainty and fluctuation of photovoltaic output can be reduced by combined operation of the photovoltaic power station and the hydropower station, and in addition, the generated energy of the combined system in each period tracks a given load process line of the power grid, so that safe and stable operation of the power grid is guaranteed. Meanwhile, the total electricity abandonment of the combined system in different typical days in different seasons is reduced, and the total generated energy is increased, so that the combined system can improve the grid-connected electric quantity of the renewable energy as much as possible. (2) The model provided by the invention takes the hydroelectric generating set as a basic scheduling unit, fully considers various complex constraints of the generating set, including start-up and shut-down duration constraints, unit vibration region constraints and the like, and the constraints can ensure the safe operation of the generating set, so that the obtained water optical system scheduling scheme has more practicability and performability. (3) The lower the confidence level in the power balance constraint, the higher the load unbalance rate, which is more beneficial to the grid connection of photovoltaic power generation and hydraulic power generation, but at the same time, the safety and stability of the power grid are reduced. Therefore, the power grid dispatching center and the power generation company should negotiate a proper power imbalance rate in advance, and the operator should balance the electricity abandonment, the power generation income and the related dispatching risk and determine the operation dispatching strategy of the water-light combined power generation system.
Drawings
FIG. 1 is a schematic diagram of a combined operation model of a hydropower station and a photovoltaic power station.
FIG. 2 is a schematic illustration of a vibration region of a hydroelectric generating set.
FIG. 3 is a flow of SMILP model solution based on the MILP model.
FIG. 4 is a graph of typical solar photovoltaic plant output for different incoming water seasons.
Fig. 5 is a scheduling process line of the Wujiang river crossing water light mixing system in different incoming water seasons.
Fig. 6 is a force diagram of a typical Ri Wu river crossing water and light combined system in different flood seasons.
Fig. 7 is a diagram of an upstream water level of the Wujiang river crossing hydropower station in rainy days in flood season.
Fig. 8 is a force diagram of each unit of the Wujiang river crossing hydropower station in rainy days in flood season.
Fig. 9 shows the combined output process of typical ri-wu-jiang river crossing water and light mixing systems in the dry period.
Fig. 10 is a force diagram of each unit of the Wujiang river crossing hydropower station in the rainy day of the dry period.
Detailed Description
The following examples illustrate the invention in detail. The raw materials and various devices used in the invention are conventional commercially available products, and can be directly obtained by market purchase.
In the following description of embodiments, for purposes of explanation and not limitation, specific details are set forth, such as particular system architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
Example 1 modeling method framework
The embodiment first provides a scene analysis method to process the uncertainty of photovoltaic output and model the uncertainty of photovoltaic output. On the basis, the invention establishes a short-term multi-objective optimization scheduling method of the water-light combined system based on opportunity constraint programming so as to minimize the total electricity abandonment quantity and maximize the total electricity generation quantity of the water-light combined system; the hydropower station unit is a basic scheduling unit, and the scheduling operation of the combined system is finely researched by comprehensively considering the output limit constraint of the hydropower station unit, the generating flow constraint of the unit, the vibration region constraint of the unit, the on-off duration constraint of the unit and the like. Aiming at the problems of high solving difficulty and the like, a series of linearization methods are provided, a random nonlinear model is converted into a scene-based deterministic MILP problem, and then a high-efficiency mature commercial solver Gurobi is used for solving the problem. The specific process is as follows: 1) and predicting the day-ahead output process of the photovoltaic power station according to the historical operating data and the next-day weather forecast data. 2) In order to reduce the operation risk, the power grid dispatching center generally requires that the water-light combined power station submit a day-ahead output curve hours before power supply. Therefore, 4-6 hours before the next day, the power company needs to determine the combined output power of the water-light system and report the combined output power to the power grid dispatching center, and the combined output power needs to comprehensively consider the technical characteristics of the hydropower station (such as reservoir capacity, minimum on-off duration of a unit and the like) and factors such as deviation between actual output and predicted output of the photovoltaic power station and the like. 3) According to the day-ahead predicted load curve of the power system and the transmission capacity of the power grid line, the power grid operator modifies the load curve submitted by the power generation company, so that the actual output process of the combined system tracks the load change trend, and the peak load regulation pressure of the power grid is reduced as much as possible. 4) The power generation company and the power grid dispatching center repeatedly negotiate the load plan until both parties can accept the load plan. And then, an electric power contract is made, wherein the electric power contract comprises the load process line of the specific water-light combined system, the acceptable power imbalance rate (namely the deviation ratio between the actual output power and the given load) and the electricity price. If the actual output power deviates from the given load by more than an acceptable rate of power imbalance, the power generation company is penalized. 5) And the water-light combined system control center determines the water storage amount of the reservoir and the output of each hydroelectric generating set. And then the power generation plan of other power sources in the system is arranged by the power grid operator according to the determined water-light combined power generation plan.
According to the coordination mechanism, a combined operation plan of the hydropower station and the photovoltaic power station is made to deal with frequent fluctuation of photovoltaic power generation output, so that the combined output of the water-light system tracks the given load process of the power grid. As shown in fig. 1, the hydropower station compensates the photovoltaic power generation process by using its regulation performance, so that the power generated by the water-light system is jointly incorporated into the power grid. During the combined operation of the water-light system, if the photovoltaic output power is very small, the combined system control center can rapidly adjust the power of the hydropower station to compensate the photovoltaic output power, including increasing the generating flow of the hydropower unit or starting part of other hydropower units. And when the photovoltaic power generation capacity is large, the control center can reduce the output of the hydroelectric generating set or close part of the running hydroelectric generating set. In addition, under some extreme weather conditions, the control center also arranges a hydropower station to abandon water, or a photovoltaic power station abandons electricity in a low electricity demand period, and the combined output power of the system is enabled to track the set load curve as much as possible. The coordination mode of the photovoltaic power station and the hydraulic power plant can promote the grid-connected consumption of the electric quantity of the water-light combined system on the premise of improving the controllability of photovoltaic power generation and ensuring the safe and stable operation of a power grid.
The model provided by the invention is applied to the flood home water-crossing optical combined power station in Guizhou province in China. Case research shows that the water-light combined operation can enable the total power generation amount of a combined system to track a given load process of a power grid by researching operation conditions under different confidence levels and different power imbalance rates in an opportunity constraint planning model, and helps to improve the safety and stability of the power grid. In different typical days and incoming seasons, the combined system can improve the grid-connected consumption of renewable energy sources as much as possible by reducing electricity abandonment and improving total power generation.
The output of the photovoltaic power station is easily affected by climatic factors, and the accurate prediction cannot be carried out on the output of the photovoltaic power station, so that the grid-connected consumption of the photovoltaic power station poses certain threat to the safe and stable operation of a power grid. Therefore, by introducing the confidence level parameter as a probability constraint, the invention establishes a CCP-based optimization model which takes the uncertainty of the output power of the photovoltaic power station into account. In addition, in addition to the traditional hydraulic constraint, the invention also considers the start-up and shut-down duration constraint of the hydroelectric generating set and the vibration region constraint of the hydroelectric generating set so as to carry out more refined research and obtain a scheduling scheme with stronger operability.
In addition, the model established in the method is a random mixed integer nonlinear programming (SMINLP) model, and has the characteristics of nonlinearity, uncertainty and the like, and if a conventional algorithm is adopted for solving, the solving difficulty is often high, so that the method provides a series of linearization strategies, converts an original model into an MILP model, and solves the problem by using an efficient and mature solver.
Example 2 photovoltaic output uncertainty
The photovoltaic output has certain randomness, and the current photovoltaic output prediction technology is not mature and has certain limitation, so that the predicted value and the actual value of the photovoltaic output often have certain deviation. In order to solve the problem, the invention provides a scene generation method based on LHS and k-means clustering, which comprises the following specific steps:
1) firstly, a photovoltaic output prediction error sequence is assumed
Figure BDA0002844640390000111
Obeying a normal distribution N (mu, sigma)2) Wherein
Figure BDA0002844640390000112
2) When the sample is large enough, the traditional Monte Carlo sampling (MC) can obtain a better result, but too many scenes can influence the solving efficiency, and compared with the MC method, Latin Hypercube Sampling (LHS) can obtain a better sampling result in fewer scenes. Therefore, the method adopts an LHS method to sample and generate a plurality of photovoltaic output prediction error scenes.
3) However, the number of scenes obtained by the LHS sampling method is still large, and in order to further reduce the calculation load, the k-means method which is widely applied is adopted to further reduce the multiple scenes generated by the LHS method.
Example 3 objective function
As mentioned above, the objective of the proposed model of the invention is to allow the renewable energy to be consumed by the grid as much as possible, while tracking a given load course and meeting other operational constraints in combination with the total power production of the system over time. Therefore, in order to adapt to different operating conditions, the invention proposes two objective functions: the total electric quantity abandoned by the combined system is minimum, and the total electric energy production of the combined system is maximum.
(1) Objective function 1: minimum total electric power discard of combined system
Because the uncertain photovoltaic output is a key factor for solving the minimum total electricity discard quantity of the combined system, the invention adopts a CCP model to process the uncertainty, and the target function 1 is expressed as follows:
Figure BDA0002844640390000121
wherein, T and T respectively represent a scheduling period and a certain scheduling period; at represents the time interval (h),
Figure BDA0002844640390000122
represents the minimum electric curtailment (MWh) of the combined system; alpha is alpha1Representing a confidence level that the power curtailment of the combined system does not exceed the target power curtailment;
Figure BDA0002844640390000123
and
Figure BDA0002844640390000124
respectively, the curtailment (MW) of the hydropower station and the photovoltaic station at time t.
(2) The objective function 2: maximum total power generation of combined system
In the CCP model, the objective function 2 can be expressed as follows:
Figure BDA0002844640390000125
wherein M represents the number of hydropower station units;
Figure BDA0002844640390000126
representing the output (MW) of the mth hydroelectric generating set at the time t;
Figure BDA0002844640390000127
representing the grid-connected output (MW) of the photovoltaic power station; 2frepresenting the target total power generation (MWh) of the water-light combined system; alpha is alpha2Indicating a confidence level that the total power generation amount is not less than the target power generation amount.
On the basis of ensuring safe and stable operation of a power grid, the government requires that the power generated by the combined system is consumed by the power grid as much as possible, so that in order to avoid energy waste, the invention takes the objective function 1 as a main objective function and takes the objective function 2 as a secondary objective. In other words, the objective function proposed by the present invention is adaptive, and when the combined system inevitably generates energy abandon, the energy abandon is the minimum, otherwise, the power generation amount is the maximum. The final objective function can be expressed as follows:
Figure BDA0002844640390000131
example 4, constraint Condition
The constraints considered in the model can be reservoir and power station constraints, hydroelectric generating set constraints, photovoltaic output related constraints and electric power balance constraints. See the examples below for details.
Example 5 reservoir and station constraints
(1) Water balance constraint
vt+1＝vt-3600·(It-Qt)·Δt (4)
Figure BDA0002844640390000132
Wherein, ItIndicates the warehousing flow (m) of the reservoir at the time t3/s)；
Figure BDA0002844640390000133
Shows the power generation flow (m) of the hydroelectric generating set m at the time t3/s)；QtIndicating the discharge (m) of the reservoir at time t3/s)；vtIndicates the storage capacity (m) of the reservoir at the end of the t period3)。
(2) Water level restraint
Figure BDA0002844640390000134
Figure BDA0002844640390000135
Wherein z istRepresents the upstream water level (m) at time t;zand
Figure BDA0002844640390000136
respectively representing the upper limit and the lower limit (m) of the water level of the upstream reservoir; z is a radical ofbAnd zeRespectively representing the initial and final water levels (m) of the upstream reservoir.
(3) Outbound flow constraint
Figure BDA0002844640390000137
Wherein the content of the first and second substances,Qand
Figure BDA0002844640390000138
respectively representing the lower and upper limits (m) of the hydropower station's outbound flow3/s)。
Example 6 hydroelectric generating set constraints
(1) Unit output constraint
Figure BDA0002844640390000139
Wherein the content of the first and second substances,
Figure BDA00028446403900001310
and
Figure BDA00028446403900001311
respectively representing the upper limit (MW) and the lower limit (MW) of the m output of the unit; u. ofm,tTaking 1 as an auxiliary variable indicates the unit isAnd starting up the computer, otherwise, stopping the computer.
(2) Output function of hydroelectric generating set
Figure BDA0002844640390000141
Wherein f ism,pgh(. -) represents a hydroelectric generating set output function; h ism,tRepresenting the head (m) of the hydroelectric generating set m at time t.
(3) Energy curtailment function of hydropower station
For the convenience of solution, it is assumed that all the abandoned water flows through the unit No. 1, and the corresponding output force is the energy abandoned by the hydropower station.
Figure BDA0002844640390000142
Wherein the content of the first and second substances,
Figure BDA0002844640390000143
indicates the flow rate (m) of reject water at time t3/s)。
(4) Head restraint
hm,t＝(zt-1+zt)/2-zdt-hlm,t (12)
zt＝fzv(vt) (13)
zdt＝fzq(Qt) (14)
Figure BDA0002844640390000144
Wherein zd istIndicating the tail water level (m) of the reservoir; hl (high pressure chemical vapor deposition)m,tRepresenting the head loss (m) of the unit m at the time t; f. ofzv() represents a water level reservoir capacity relationship; f. ofzq(-) represents the tail water level versus bleed down flow; f. oflqAnd (h) represents the relationship between the generating flow and the head loss of the unit m.
(5) Unit generated current restriction
Figure BDA0002844640390000145
Wherein the content of the first and second substances,
Figure BDA0002844640390000146
andq mrespectively represent the upper and lower limits (m) of the generating flow of the unit m3/s)。
(6) Unit vibration zone restraint
Figure BDA0002844640390000147
Wherein the content of the first and second substances,
Figure BDA0002844640390000148
and
Figure BDA0002844640390000149
respectively representing the upper limit and the lower limit of the kth vibration area of the unit m.
(7) Unit on-off duration constraint
Figure BDA00028446403900001410
Figure BDA00028446403900001411
Wherein ξmAnd psimRespectively representing the minimum startup and shutdown duration (h) of the unit m; x is the number ofm,tIs an auxiliary variable, and when 1 is taken, the unit is started, ym,tAnd the auxiliary variable is 1, and the shutdown of the unit is indicated.
Example 7 photovoltaic output restraint
Photovoltaic output needs to meet the following constraints
Figure BDA0002844640390000151
Wherein the content of the first and second substances,
Figure BDA0002844640390000152
representing the actual output (MW) of the photovoltaic power plant at time t;
Figure BDA0002844640390000153
and
Figure BDA0002844640390000154
respectively representing the predicted output force and the corresponding predicted deviation (MW) of the output force of the photovoltaic power station at the time t.
Example 8 Power balance constraints
The uncertainty of the photovoltaic output causes certain threat to the load process submitted to the power grid by the joint power generation tracking of the water and light system, and even can cause serious safety problems of the power grid. To control these problems, the present model applies an opportunity constraint to limit the risk of power imbalance for the federated system.
Figure BDA0002844640390000155
Wherein ω represents the power imbalance rate in the contract between the power generation company and the power grid;
Figure BDA0002844640390000156
representing the load value (MW) of the combined system at time t; beta represents a predetermined confidence level that the water-light combined output does not exceed the allowable upper and lower power deviation limits (meeting the power balance constraint).
The probabilistic expression (21) ensures that the probability that the total output of the combined system does not satisfy the load process is less than 1-beta so as to control the influence caused by the uncertainty of the photovoltaic output.
Example 9 MILP model building
Due to the introduction of the opportunistic constraints (equations (1), (2) and (21)) and the four non-linear constraints (equations (10) - (15) and (17)), the original model is a SMINLP model, and it is difficult to obtain the optimal solution by directly solving the model. Therefore, the original SMINLP model needs to be converted into a deterministic MILP model for solving, and then a mature and efficient commercial solver is used for solving.
The current research has tended to mature for the linearization of the net head constraints (equations (12), (13), (14) and (15)), the plant power generation function and the plant curtailment function (equations (10) and (11)), and therefore the present invention is primarily concerned with CCP and the linearization of the plant vibration region constraints, see in particular the examples below.
Example 10 opportunity constrained planning model linearization
For the deterministic transformation of CCP, analytical and simulation methods are mainly used at present. However, when some practical problems are solved, the analytic method is often easily limited and the solving difficulty is high, so the method adopts a scene simulation method to solve.
(1) The scene generation method provided by the above is used for generating N types of photovoltaic output prediction error representative scenes, and the actual output of the photovoltaic power station and the photovoltaic output of the photovoltaic power station merged into the power grid under a given scene can be obtained by the following two formulas:
Figure BDA0002844640390000161
Figure BDA0002844640390000162
wherein,
Figure BDA0002844640390000163
and
Figure BDA0002844640390000164
respectively representing the actual output and the output prediction deviation of the photovoltaic power station at the t moment in the nth scene;
Figure BDA0002844640390000165
and
Figure BDA0002844640390000166
and respectively representing grid-connected output and corresponding energy abandon values of the photovoltaic power station under the nth scene at the moment t.
(2) And judging whether the generated scenes meet the formula (24), superposing the number of scenes meeting the formula (the total number is marked as S), if the S/N is larger than or equal to beta, establishing the opportunity constraint formula (21), and if not, establishing the opportunity constraint formula.
Figure BDA0002844640390000167
(3) Based on the above analysis, four 0-1 auxiliary variables a were introducedn,t，bn,t，cn,tAnd dnThen the opportunity constraint can be transformed into the following deterministic expression:
Figure BDA0002844640390000168
Figure BDA0002844640390000169
Figure BDA00028446403900001610
Figure BDA00028446403900001611
Figure BDA00028446403900001612
cn,t＝an,t+bn,t-1 (30)
Figure BDA00028446403900001613
Figure BDA00028446403900001614
Figure BDA00028446403900001615
wherein, cn,tThe water-light combined system is an auxiliary variable, 1 is selected when the nth scene water-light combined system meets power balance constraint, and 0 is selected otherwise; dnTaking 1 when the combined output of the nth scene meets the power balance constraint, or taking 0; l is a very large constant; pbnRepresenting the probability of scene n.
As shown in formula (25), an,tAnd bn,tTake a value of 1 or 0 if an,t(ii) 0, according to formula (26),
Figure BDA0002844640390000171
otherwise, as can be seen from equation (27),
Figure BDA0002844640390000172
therefore, as can be seen from the formulae (25) and (26)
Figure BDA0002844640390000173
Then an,tThe value is 1. Similarly, from (28) and (29), if
Figure BDA0002844640390000174
Then b isn,tThe value is 1. Therefore, only when an,tAnd bn,tWhen all 1 is taken, the formula (24) is satisfied from the formulas (26) to (29), and in this case, c is expressed from the formula (30)n,tShould take 1. And the formulae (31) to (32) are indicated only when
Figure BDA0002844640390000175
(i.e., the total output of the combined system meets the power balance constraint in the whole scheduling period), dnTake 1, otherwise 0. Formula (33) ensuresThe predetermined confidence in the CCP is satisfied. By equations (25) - (33), probabilistic opportunity constraints can be converted into a scene-based deterministic expression.
Also, two objective functions with uncertainty factors (equations (1) and (2)) can be converted into the following form.
Figure BDA0002844640390000176
Figure BDA0002844640390000177
Figure BDA0002844640390000178
Wherein, γnAn auxiliary variable is used for deciding that the total power curtailment of the nth scene of the water-light combined system does not exceed a target value; lambda [ alpha ]nAn auxiliary variable is used for deciding that the total power generation amount of the nth scene of the water-light combined system is not lower than the target power generation amount; in the formula (35), if the auxiliary variable γ n1, is of the same type
Figure BDA0002844640390000181
Can satisfy the two formula
Figure BDA0002844640390000182
Also satisfies; at this time, the three formulas
Figure BDA0002844640390000183
Ensuring that the probability that the total power curtailment of the combined system does not exceed the target power curtailment is alpha1. Also, the formula (36) ensures that the probability that the total power generation amount of the combined system is not lower than the target total power generation amount is α2。
Example 11 Unit vibration region linearization
For large-scale daily scheduling research of a water-light combined system, constraint solving of the vibration region of the unit is extremely important and extremely challenging, and the difficulty is mainly due to the fact that a plurality of non-operable running regions which cannot be met simultaneously are solved, so that a linearization method table is provided in the section to solve the constraint problem of the vibration region of the unit.
Considering the maximum and minimum output limits of the unit, the K vibration regions divide the unit output into K +1 feasible unit operation regions, as shown in fig. 2 in particular, equation (17) can be expressed as a linear expression as follows.
Figure BDA0002844640390000184
Figure BDA0002844640390000185
Figure BDA0002844640390000186
Wherein the content of the first and second substances,
Figure BDA0002844640390000187
the index variable is an index variable, and when 1 is taken, the unit is in the kth feasible operation area in the time period;
Figure BDA0002844640390000188
and
Figure BDA0002844640390000189
the upper and lower limits of the kth feasible region are respectively expressed, and in addition, the following formula needs to be satisfied because of the corresponding relationship between the vibration region and the feasible region.
Figure BDA00028446403900001810
As can be seen from equations (37) to (38), the unit output must lie in a feasible region when the unit is in the operating state. According to the formula (39), if
Figure BDA00028446403900001811
And the unit m is in the kth feasible region.
Example 12 model solution procedure
Through the above linearization technique, the model proposed by the present invention can be converted into an MILP model, and the solving process is shown in fig. 3.
Example 13 case study
The model developed by the invention is applied to the flood ferry light complementary power station of Guizhou province in China, and the combined system comprises the flood ferry power station and a large photovoltaic power station nearby the flood ferry power station. The Wujiang river hydropower station is located in the Wujiang river basin in southwest China and is one of the most important power sources in Guizhou province. The power station has an annual regulation reservoir and 5 generator sets, and the total installed capacity is 1250 MW. Although the capacity of each hydroelectric power unit is the same (250MW), some units do not have exactly the same operating characteristics, since they are produced by two different manufacturing companies, and the main characteristic parameters of the hydroelectric power station and its units are shown in tables 1 and 2, respectively. In addition, the grid-connected installed capacity of the photovoltaic power station is 300MW, and the photovoltaic power station is located at about 40 km east of the hydropower station and is planned to operate for 30 years.
TABLE 1 characteristic parameters of Wujiang river crossing hydropower station
Figure BDA0002844640390000191
TABLE 2 Main parameters of each unit of Wujiang river-crossing hydropower station
Figure BDA0002844640390000192
The photovoltaic power station output and the reservoir warehousing flow are easily affected by the weather and seasons, so in order to establish an optimization model with robustness, the model selects the flood season, the dry season and the corresponding sunny days, cloudy days and rainy days as typical research objects to research, and the photovoltaic power station daily typical output in different seasons is shown in fig. 4. In practical engineering, the predetermined combined power generation amount of the wujiang river crossing water optical system is generally given in a power contract made between the power grid and a power generation company having the wujiang river crossing system, and this example assumes that it has already been given, and is shown in fig. 5.
For any typical day, the photovoltaic output prediction error generated by the LHS method represents 500 groups of scenes, and is further reduced to 200 groups by using a k-means method. In the case, the scheduling operation period of the combined system is selected to be 1 day, and the model provided by the invention is solved by adopting an efficient Gurobi commercial solver.
Basic case study
In this case, α1、α2Beta and omega were taken as 0.9, 0.9 and 0.05 respectively and the operating conditions of the combined system on different typical days were analyzed in detail next.
Operating conditions in flood season
The operation results of the Wujiang river crossing water optical system in the flood season are shown in table 3, and the electricity abandoning rate represents the ratio of the electricity abandoning amount to the total generated energy. It can be seen that the power consumptions of the combined system on the sunny day, the cloudy day and the rainy day are 240,0 and 49MWh respectively, the total power generation amounts on the sunny day, the cloudy day and the rainy day are 19188,18483 and 18474MWh respectively, and the power consumptions on the three corresponding typical days are 1.25%, 0 and 0.27% respectively. Although the power abandon rate in rainy days is larger than that in other typical days, the total power generation amount of the combined system in rainy days is still larger due to larger warehousing flow in rainy days; the difference between the warehousing flow rates in a sunny day and a cloudy day is not large, however, as the photovoltaic output in the sunny day is large, part of electric quantity of the combined system has to be abandoned in certain time intervals to ensure the safe and stable operation of the power grid. In addition, because the electric quantity is not abandoned in the cloudy days, the total electric energy production of the combined system is as large as possible, so that the total electric energy production of the combined system in the cloudy days is similar to that in the sunny days.
TABLE 3 Ujiang river crossing water and light combined system flood season optimization result
Figure BDA0002844640390000201
Fig. 6 shows the combined output of the combined system at different typical days of the flood season. It can be seen that, first, the combined system total output strictly tracks the grid load process. The flow rate of the storage in a rainy day is large, so that the combined system generates electricity as much as possible in order to reduce the energy abandonment of the combined system and increase the total generated energy of the combined system, and reaches the upper limit of the load at most of the time, and at 12-13 hours, the generated energy of the combined system does not reach the upper limit of the output because the adjusting capacity of the hydropower station is limited and is at the peak value of the load at the moment. Compared with the rainy days, the warehousing flow of the power station is relatively small in the sunny days and the cloudy days, the power generation capacity of the power station is relatively poor, in order to meet the power balance constraint, the power station needs to generate more power in the time period with higher load (such as 10-16 hours), and generates less power in the other time periods with smaller power demand. However, due to the large photovoltaic output and the large fluctuation in sunny days, the combined system may cause the electricity abandonment phenomenon. And no matter in any typical weather scene, the photovoltaic output scene meets the power balance constraint by more than 90%, which shows that the proposed formulas (25) to (33) can meet the opportunity constraint.
Fig. 7 shows the reservoir water level in each time period of the rainy day in the flood season, and it can be seen that the change of the water level in the whole scheduling period is only 0.17m, because the reservoir in the embodiment has good regulation performance. Furthermore, the schedule end-of-term water level is 742.85m, which is the same as the expected given target water level.
The output process of each unit of the Wujiang river crossing hydropower station in rainy days in flood season is shown in fig. 8, and it can be seen that the research model based on the units ensures that the output of each unit effectively avoids the vibration area of the corresponding unit. In addition, due to the fact that the warehousing flow rate is large in rainy days in flood season, except that the No. 4 unit is in a shutdown state at the first moment, the other moments are all in an operating state, and the other units are all in a startup state at all moments.
Running status in dry period
The optimal operation results of the Wujiang river crossing water-light combined system in the dry period are shown in table 4, and it can be seen that no electricity is abandoned in the whole scheduling process because the flow of the hydropower station entering the reservoir in the dry period is small and the regulation capacity of the hydropower station is strong, and the generated energy in rainy days, cloudy days and sunny days is 11084,10620 MWh and 10572MWh respectively.
TABLE 4 Subtraction optimization results for Wujiang river crossing water optical system
Figure BDA0002844640390000211
The output processes of the combined system on different typical days in the dry period are shown in fig. 9, in rainy days, the power generation amount of the combined system reaches the upper limit of the load at most of the time, and in cloudy days and sunny days, the warehousing flow is small and the power balance constraint must be met, so that the combined system generates more power when the load demand is high, and generates less power when the load demand is low.
Fig. 10 shows the output of each unit of the wujiang river crossing hydropower station in the rainy day in the dry period, and in order to respond to the change of the photovoltaic output, although the output of each unit of the hydropower station has some fluctuation, the output process of each unit meets the constraint of the duration time of the start-up and shutdown of the unit (set to 2h in this case), and in addition, the output process of the unit also effectively avoids the vibration area of each unit.
Deterministic model comparisons
In order to verify the correctness of the model, the invention establishes a model without considering uncertainty of photovoltaic output and compares the model with the previous model. In the deterministic model, it is assumed that the photovoltaic contribution process is given and that the other parameters are the same as in the previous model. Table 5 and table 6 show the comparison of the optimization results of the deterministic model in the flood season and the dry season and the stochastic model, respectively, and it can be seen that the deterministic model does not generate electricity in each typical day, and the total power generation amount of the deterministic model is greater than that of the stochastic model, because the deterministic model only considers one deterministic photovoltaic output scene, and the stochastic model considers a plurality of different photovoltaic scenes, the total power generation amount of the combined system obtained by the stochastic model is smaller. However, due to the limitation of the current photovoltaic output prediction technology, the photovoltaic output prediction value is inevitably subjected to error generation, and at the moment, the deterministic model may bring some problems, and even bring threats to the safe and stable operation of the power grid. Because the electric quantities generated by the deterministic model and the stochastic model are not greatly different, the stochastic model is a better choice for the combined operation of the water-optical system from the viewpoints of the safety and stability of the power grid and the like. In addition, it can be seen that improving the photovoltaic output prediction technology is also an effective way to reduce energy waste.
TABLE 5 flood season randomness model and certainty model optimization result comparison
Figure BDA0002844640390000212
Figure BDA0002844640390000221
Note that TPC is the total power consumption (total power consumption), TPG is the total power generation (total power generation), and the confidence level represents the probability that the operating decision satisfies the power balance constraint.
TABLE 6 comparison of optimization results for random model and deterministic model in withering period
Figure BDA0002844640390000222
Note that TPC is the total power consumption (total power consumption), TPG is the total power generation (total power generation), and the confidence level represents the probability that the operating decision satisfies the power balance constraint.
CCP model confidence impact analysis
The influence of the CCP model confidence coefficient on the operation scheduling of the water-light combined system is mainly researched in the part, and the sensitivity of the CCP model is researched by taking the CCP model confidence coefficient as a typical research object in the part in flood season rainy days and dry season rainy days. The total power generation amount and the total power curtailment amount of the combined system at different confidence degrees are shown in table 7. As can be seen from table 7, due to the large flow rate of the warehouse in the flood season, the combined system always generates electricity abandons in different degrees, the electricity abandon amount of the combined system increases correspondingly with the increasing of the confidence level in the opportunity constraint, when the confidence level increases from 0.7 to 0.9, the corresponding electricity abandon amount increases from 117MWh to 240MWh, because the higher the confidence level is, the higher the adjustment capacity requirement of the corresponding reservoir is, and because the photovoltaic output has uncertainty, when the confidence value increases, the number of photovoltaic output scenes required to meet the constraint increases correspondingly. However, the increase of the confidence coefficient correspondingly increases the energy rejection in rainy days and reduces the total power generation amount of the combined system, in other words, in rainy days in flood season, the higher the operation reliability is, the larger the energy rejection is correspondingly caused. In addition, because the maximum power generation amount of the combined system is not a main target of the water-light system scheduling in the flood season, the influence of the confidence level in the opportunity constraint model on the total power generation amount of the combined system is not large. On the other hand, although the confidence level is continuously increased, the electricity abandon amount in the rainy day in the dry period is still 0, and the total electricity generation amount of the combined system is correspondingly reduced. Therefore, when determining the operation plan of the water-light combined power generation system, the operator of the water-light combined power generation system needs to balance the electricity abandonment, the power generation income and the related scheduling risk.
TABLE 7 Effect of confidence level on operating characteristics of the combination System
Figure BDA0002844640390000223
Figure BDA0002844640390000231
Power imbalance rate impact analysis
In order to research the influence of the power imbalance rate on the water-light joint scheduling, based on the previous research, four different power imbalance rates are selected in the part to continuously carry out research. The amount of power curtailment and the total amount of power generation in the case of different power imbalance rates in rainy days at different water periods are shown in table 8. In the flood season, when the power unbalance rate is increased from 0.05 to 0.1, the corresponding energy abandonment is reduced from 240MWh to 98MWh, in addition, when the load unbalance rate is larger than or equal to 0.2, no electricity abandonment is generated in the combined system, and when the load unbalance rate reaches 0.3 and above, the total power generation amount of the combined system tends to be stable and is 20434 MWh. In the dry period, the total power generation of the combined system continuously increases along with the increase of the power unbalance rate, but the increase is gradually slow until the total power generation is kept stable. The smaller the power imbalance rate is, the greater the negative impact of photovoltaic output fluctuation on the safe and stable operation of the power grid is, and therefore, the power grid dispatching center needs to set a proper power imbalance rate so as to balance the requirements for meeting the operation of the power grid and the consumption of renewable energy.
TABLE 8 Combined system operation characteristics for different incoming water seasons, rainy days and different power unbalance rates
Figure BDA0002844640390000232
To sum up, the invention provides a combined scheduling model of a photovoltaic power station and a high-capacity hydropower station based on a coordination mechanism between a power company and a power grid, and based on the scheduling model, the invention provides an opportunity constraint planning model to decide the operation optimization scheduling of a water-light combined system so as to improve the consumption level of renewable energy. In the model, a scene analysis method combining an LHS method and k-means clustering is adopted to process the photovoltaic output prediction deviation, and in order to improve the accuracy of the model, the model takes a hydroelectric generating set as a basic scheduling unit and considers the corresponding constraint of the hydroelectric generating set. In addition, in order to improve the solving efficiency of the SMILLP model, the invention provides a series of linearization methods to convert the original model into the MILP model and solve the MILP model by using a solver. Based on the case study of the Wujiang river crossing water-light combined system in the southwest of China, the following conclusions can be obtained:
(1) the negative effects caused by uncertainty and fluctuation of photovoltaic output can be reduced by combined operation of the photovoltaic power station and the hydropower station, and in addition, the generated energy of the combined system at each period tracks the given load process line of the power grid, so that safe and stable operation of the power grid is ensured. Meanwhile, the total electricity abandonment of the combined system in different typical days in different seasons is reduced, and the total generated energy is increased, so that the combined system can improve the grid-connected electric quantity of the renewable energy as much as possible.
(2) The model provided by the invention takes the hydroelectric generating set as a basic scheduling unit, fully considers various complex constraints of the generating set, including start-up and shut-down duration constraints, unit vibration region constraints and the like, and the constraints can ensure the safe operation of the generating set, so that the obtained water optical system scheduling scheme has more practicability and performability.
(3) The lower the confidence level in the power balance constraint, the higher the load unbalance rate, which is more beneficial to the grid connection of photovoltaic power generation and hydroelectric power generation, but at the same time, the safety and stability of the power grid are reduced. Therefore, the power grid dispatching center and the power generation company should negotiate a proper power imbalance rate in advance, and the operator should balance the electricity abandonment, the power generation income and the related dispatching risks and determine the operation dispatching strategy of the water-light combined power generation system.
The hardware implementation of the invention can directly adopt the existing intelligent equipment, including but not limited to industrial personal computers, PC machines, smart phones, handheld single machines, floor type single machines and the like. The input device preferably adopts an on-screen keyboard, the data storage and calculation module adopts the existing memory, calculator and controller, the internal communication module adopts the existing communication port and protocol, and the remote communication adopts the existing gprs network, the ten-thousand-dimensional internet and the like.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.

Claims (8)

1. The short-term optimization scheduling method of the water-light combined system based on opportunity constraint planning is characterized by comprising the following steps: firstly, setting minimum abandoned energy of a combined system and maximum generated energy of the combined system as a scheduling target, establishing a set of short-term optimized scheduling controllable system of the water-light combined system based on opportunity constraint planning based on uncertainty of a photovoltaic output process and taking a hydroelectric generating set as a basic scheduling unit, considering on-off duration of the unit and constraint of a vibration area of the unit, so as to obtain a refined scheduling operation system with strong operability, wherein the system forms a typical multivariable mixed integer nonlinear programming MINLP with high dimensionality and multiple complex constraints, converts the MINLP into a set of mixed integer linear programming MILP by using a series linearization method, and solves the problems by using a high-efficiency solver; the series of linear optimization methods include, but are not limited to: the method comprises a scene simulation linearization method for a linearization opportunity constraint planning principle problem and a piecewise linearization method for a linearization unit vibration region constraint problem.
2. The opportunity constraint planning-based short-term optimal scheduling method for the water-light combined system according to claim 1, wherein the method comprises the following steps: the mixed integer nonlinear programming MINLP is established based on a LHS and k-means clustering scene generation method, and is based on a photovoltaic output prediction error sequence
Figure FDA0002844640380000011
Obeying a normal distribution N (mu, sigma)2) Wherein, the ratio of mu to 0,
Figure FDA0002844640380000012
due to the fact that the Latin hypercube sampling LHS can obtain better sampling results in few scenes, the LHS method is adopted for sampling and generating multiple photovoltaic output prediction error scenes, and in order to further reduce the calculation burden, the k-means method is used for further reducing the multiple scenes generated by the LHS method; the construction steps comprise two steps of setting an objective function and setting constraint conditions.
3. The opportunity constraint planning-based short-term optimal scheduling method for the water-light combined system according to claim 2, wherein the method comprises the following steps: the construction method of the objective function comprises the following steps: two objective functions are set: the total electric quantity abandoned by the combined system is minimum, and the total electric energy production of the combined system is maximum;
(1) objective function 1: the total electric quantity discarded by the combined system is minimum; because the uncertain photovoltaic output is a key factor for solving the minimum total electricity discard of the combined system, the uncertainty is processed by adopting opportunity constraint planning, and an objective function 1 is expressed as follows:
Figure FDA0002844640380000013
wherein, T and T respectively represent a scheduling period and a certain scheduling period; at represents the time interval h in which the time interval,
Figure FDA0002844640380000014
representing the minimum electric quantity MWh of the combined system; alpha is alpha1Representing a confidence level that the power curtailment of the combined system does not exceed the target power curtailment;
Figure FDA0002844640380000015
and
Figure FDA0002844640380000016
respectively representing the energy abandon MW of the hydropower station and the photovoltaic power station at the moment t;
(2) the objective function 2: the total power generation capacity of the combined system is maximum; in the opportunistic constraint planning model, the objective function 2 is expressed as:
Figure FDA0002844640380000017
wherein M represents the number of hydropower station units;
Figure FDA0002844640380000021
representing the output MW of the mth hydroelectric generating set at the moment t;
Figure FDA0002844640380000022
representing grid-connected output MW of the photovoltaic power station; 2frepresenting the target total power generation MWh of the water-light combined system; alpha is alpha2Indicating a confidence level that the total power generation amount is not lower than the target power generation amount.
4. The opportunity constraint planning-based short-term optimal scheduling method for the water-light combined system according to claim 3, wherein the method comprises the following steps: taking the target function 1 as a main target function and the target function 2 as a secondary target, so that the proposed target function has self-adaptability, and when the combined system inevitably generates energy abandon, the minimum energy abandon is taken as a target, otherwise, the maximum power generation amount is taken as a target; the final objective function is expressed as follows:
Figure FDA0002844640380000023
5. the opportunity constraint planning-based short-term optimal scheduling method for the water-light combined system according to claim 4, wherein the method comprises the following steps: the constraint conditions comprise reservoir and power station constraints, hydroelectric generating set constraints, photovoltaic output constraints and power balance constraints;
the reservoir and power station constraints include:
(1) water balance constraint
vt+1＝vt-3600·(It-Qt)·Δt 4
Figure FDA0002844640380000024
Wherein, ItIndicating the warehousing flow m of the reservoir at the time t3/s；
Figure FDA0002844640380000025
Representing the generating flow m of the hydroelectric generating set m at the moment t3/s；QtShowing the discharge m of the reservoir at time t3/s；vtIndicating the storage capacity m of the reservoir at the end of the t period3；
(2) Water level restraint
Figure FDA0002844640380000026
Figure FDA0002844640380000027
Wherein z istRepresents the upstream water level m at time t;zand
Figure FDA0002844640380000028
respectively representing the upper limit m and the lower limit m of the water level of the upstream reservoir; z is a radical ofbAnd zeRespectively representing the initial water level and the final water level m of the upstream reservoir;
(3) outbound flow constraint
Figure FDA0002844640380000029
Wherein the content of the first and second substances,Qand
Figure FDA00028446403800000210
respectively representing the lower and upper limit values m of the flow out of the hydropower station3/s；
The hydro-power generating unit constraint comprises:
(1) unit output constraint
Figure FDA0002844640380000031
Wherein the content of the first and second substances,
Figure FDA0002844640380000032
and
Figure FDA0002844640380000033
respectively representing the upper limit MW and the lower limit MW of the unit m output; u. ofm,tWhen the auxiliary variable is 1, the unit is in a starting state, otherwise, the unit is in a stopping state;
(2) output function of hydroelectric generating set
Figure FDA0002844640380000034
Wherein f ism,pgh(. -) represents a hydroelectric generating set output function; h ism,tRepresenting the water head m of the hydroelectric generating set m at the moment t;
(3) energy curtailment function of hydropower station
For the convenience of solution, assuming that all the abandoned water flows through the No. 1 unit, and the corresponding output force is the abandoned energy of the hydropower station;
Figure FDA0002844640380000035
wherein,
Figure FDA0002844640380000036
indicating the reject flow rate m at time t3/s；
(4) Head restraint
hm,t＝(zt-1+zt)/2-zdt-hlm,t 12
zt＝fzv(vt) 13
zdt＝fzq(Qt) 14
Figure FDA0002844640380000037
Wherein zd istIndicating the tail water level m of the reservoir; hl (high pressure chemical vapor deposition)m,tRepresenting the head loss m of the unit m at the time t; f. ofzv() represents a level reservoir capacity relationship; f. ofzq(-) represents the tail water level versus bleed down flow; f. oflqThe power generation flow and the head loss relation of the unit m are represented;
(5) unit generated current restriction
Figure FDA0002844640380000038
Wherein the content of the first and second substances,
Figure FDA0002844640380000039
andq mrespectively represent the upper limit and the lower limit m of the generating flow of the unit m3/s；
(6) Unit vibration zone restraint
Figure FDA00028446403800000310
Wherein the content of the first and second substances,
Figure FDA00028446403800000311
and
Figure FDA00028446403800000312
respectively representing the upper limit and the lower limit of the kth vibration area of the unit m;
(7) unit on-off duration constraint
Figure FDA0002844640380000041
Figure FDA0002844640380000042
Wherein ξmAnd psimRespectively representing the minimum startup and shutdown duration h of the unit m; x is the number ofm,tIs an auxiliary variable, when 1 is taken, the unit is started, ym,tThe auxiliary variable is an auxiliary variable, and the shutdown of the unit is indicated when 1 is taken;
the photovoltaic output needs to satisfy the following constraints:
Figure FDA0002844640380000043
wherein,
Figure FDA0002844640380000044
representing the actual output MW of the photovoltaic power station at the moment t;
Figure FDA0002844640380000045
and
Figure FDA0002844640380000046
respectively representing the predicted output and the corresponding predicted output deviation MW of the photovoltaic power station at the time t;
for the power balance constraint, the uncertainty of the photovoltaic output poses a threat to the combined power generation tracking of the water and light system to the load process submitted to the power grid, and can cause a serious safety problem of the power grid; to control these problems, opportunity constraints are applied to limit the risk of power imbalance for the combined system;
Figure FDA0002844640380000047
wherein ω represents the power imbalance rate in the contract between the power generation company and the power grid;
Figure FDA0002844640380000048
representing the load value MW of the combined system at the time t; beta represents a predetermined confidence level that the water-light combined output does not exceed the allowable power deviation upper limit and lower limit and meets the power balance constraint; the probabilistic expression 21 ensures that the probability that the total output of the combined system does not meet the load process is less than 1-beta, so as to control the influence caused by the uncertainty of the photovoltaic output.
6. The opportunistic constraint planning based water-light combined system short-term optimization scheduling method of claim 59, wherein: the mixed integer linear programming MILP establishing method includes but is not limited to: the method comprises the steps of opportunity constraint planning model linearization and unit vibration area linearization.
7. The opportunity constraint planning-based short-term optimal scheduling method for the water-light combined system according to claim 6, wherein the method comprises the following steps: for the opportunity constraint programming model linearization, a scene simulation method is adopted for solving:
(1) n types of photovoltaic output prediction error representative scenes are generated by applying a scene generation method, and the actual output of a photovoltaic power station and the photovoltaic output of the photovoltaic power station merged into a power grid under a given scene can be obtained by the following two formulas:
Figure FDA0002844640380000051
Figure FDA0002844640380000052
wherein,
Figure FDA0002844640380000053
and
Figure FDA0002844640380000054
respectively representing the actual output and the output prediction deviation of the photovoltaic power station at the t moment in the nth scene;
Figure FDA0002844640380000055
and
Figure FDA0002844640380000056
respectively representing grid-connected output and corresponding energy abandon values of the photovoltaic power station at the moment t under the nth scene;
(2) judging whether the generated scenes meet the formula (24), superposing the number of the scenes meeting the formula, and recording the total number as S, wherein if the S/N is more than or equal to beta, the chance constraint formula 21 is established, otherwise, the chance constraint formula is not established;
Figure FDA0002844640380000057
(3) based on the above analysis, four 0-1 auxiliary variables a were introducedn,t，bn,t，cn,tAnd dnThen the opportunity constraint can be translated into the following deterministic expression:
Figure FDA0002844640380000058
Figure FDA0002844640380000059
Figure FDA00028446403800000510
Figure FDA00028446403800000511
Figure FDA00028446403800000512
cn,t＝an,t+bn,t-1 30
Figure FDA00028446403800000513
Figure FDA00028446403800000514
Figure FDA00028446403800000515
wherein, cn,tThe water-light combined system is an auxiliary variable, 1 is selected when the nth scene water-light combined system meets power balance constraint, and 0 is selected otherwise; dnTaking 1 when the combined output of the nth scene meets the power balance constraint, or taking 0; l is a very large constant; pbnRepresenting the probability of scene n;
from formula 25, an,tAnd bn,tTake a value of 1 or 0 if an,t0, according to formula 26,
Figure FDA0002844640380000061
otherwise, as can be seen from equation 27,
Figure FDA0002844640380000062
therefore, as can be seen from formulas 25 and 26, if
Figure FDA0002844640380000063
Then an,tThe value is 1; also, from 28 and 29, if
Figure FDA0002844640380000064
Then b isn,tThe value is 1; only when an,tAnd bn,tWhen both are 1, based on the expressions 26 to 29, the expression 24 holds, and in this case, the expressions 30, cn,tShould take 1; and the formulae 31 to 32 show the results only when
Figure FDA0002844640380000065
I.e. when the total output of the combined system meets the power balance constraint in the whole scheduling period, dnTaking 1, otherwise, taking 0; equation 33 ensures that the confidence in the predetermined opportunity constraint plan is satisfied; by equations 25-33, probabilistic opportunity constraints can be converted into a scene-based deterministic expression;
similarly, two objective functions with uncertainty factors, equations 1 and 2, are transformed into the following forms;
Figure FDA0002844640380000066
Figure FDA0002844640380000067
Figure FDA0002844640380000068
wherein, γnAn auxiliary variable is used for deciding that the total power curtailment of the nth scene of the water-light combined system does not exceed a target value; lambda [ alpha ]nAn auxiliary variable is used for deciding that the total power generation amount of the nth scene of the water-light combined system is not lower than the target power generation amount; in formula 35, if the auxiliary variable γn1, is of the same type
Figure FDA0002844640380000069
Can satisfy the two formula
Figure FDA00028446403800000610
Also satisfies; at this time, the three formulas
Figure FDA00028446403800000611
Ensuring that the probability that the total power curtailment of the combined system does not exceed the target power curtailment is alpha1(ii) a Likewise, equation 36 ensures that the probability that the total power generation of the combined system is not less than the target total power generation is α2。
8. The opportunity constraint planning-based short-term optimal scheduling method for the water-light combined system according to claim 7, wherein: for the linearization of the vibration region of the unit, a linearization method is provided to solve the constraint problem of the vibration region of the unit, the maximum and minimum output limits of the unit are considered, the output of the unit is divided into K +1 feasible unit operation regions by K vibration regions, and the expression of the formula 17 is the following linear expression;
Figure FDA0002844640380000071
Figure FDA0002844640380000072
Figure FDA0002844640380000073
wherein,
Figure FDA0002844640380000074
the index variable is an index variable, and when 1 is taken, the unit is in the kth feasible operation area in the time period;
Figure FDA0002844640380000075
and
Figure FDA0002844640380000076
respectively representing the upper limit and the lower limit of the kth feasible operation area; meanwhile, based on the corresponding relation between the vibration area and the feasible area, the following formula is required to be satisfied;
Figure FDA0002844640380000077
from equations 37-38, when the unit is in operation, the unit output must be in a feasible region; from formula 39, if
Figure FDA0002844640380000078
And the unit m is in the kth feasible region.
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