CN112580872B - Short-term optimization scheduling method of water-light combined system based on opportunity constraint planning - Google Patents
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Abstract
The invention discloses a short-term optimized scheduling method of a water-light combined system based on opportunistic constraint planning, which is characterized in that the minimum energy rejection of the combined system and the maximum power generation capacity of the combined system are set as scheduling targets, a hydroelectric generating set is used as a basic scheduling unit based on uncertainty of a photovoltaic output process, the start-stop duration time of the generating set and the constraint of a vibration area of the generating set are considered, a set of short-term optimized scheduling controllable system of the water-light combined system based on opportunistic constraint planning is established, so that a fine and high-operability scheduling operation system is obtained, the system forms a typical multi-variable, high-dimensional and multi-complex constraint mixed integer nonlinear programming MINLP, the MINLP is converted into a set of mixed integer linear programming MILP by utilizing a series linearization method, and a high-efficiency solver is used for solving. The invention has pioneering progressive significance in the aspect of basic theory and practical application.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a short-term optimization scheduling method of a water-light combined system.
Background
Since the conventional fossil energy causes serious environmental pollution and is not renewable when generating electricity, the development of clean renewable energy sources such as photovoltaic, wind power and the like has been accelerated in recent years. Among them, photovoltaic power generation has great development potential because of its technical maturity, extensive distribution, operation maintenance cost are low. However, photovoltaic power generation has randomness and intermittence, huge pressure is caused to a power grid in the grid connection process, other power supplies with good adjustment performance are needed to adjust the photovoltaic power generation, and hydropower can rapidly respond to fluctuation of photovoltaic output through good adjustment capability, so that the combination of photovoltaic power generation and hydroelectric power generation into the power grid is an effective and wide-prospect combined grid connection mode. However, the renewable energy market in China is not perfect at present, so that the power grid can consume clean energy power as much as possible on the basis of ensuring safe and stable operation of the power grid. Most previous researches on the water-light combined system only consider the power generation requirement of the combined system, but neglect the consumption requirement of a power grid, so that the combined scheduling strategies are difficult to directly apply to the water-light combined power generation system in China. Therefore, the effective and feasible short-term optimization scheduling method of the water-light combined system is provided to be the current urgent problem to be solved.
Disclosure of Invention
The invention aims to provide a short-term optimization scheduling method of a water-light combined system based on opportunistic 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 the minimum energy rejection of a combined system and the maximum power generation capacity of the combined system as scheduling targets, taking a hydroelectric generating set as a basic scheduling unit based on uncertainty of a photovoltaic output process, taking the start-stop duration time and the unit vibration area constraint of the generating set into consideration, establishing a set of water-light combined system short-term optimization scheduling steerable system based on opportunistic constraint planning so as to obtain a refined and high-operability scheduling operation system, forming a typical multi-variable, 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 an efficient solver; the series of linearization methods include, but are not limited to: a scene simulation linearization method for linearization opportunity constraint programming principle problems and a piecewise linearization method for linearization unit vibration area constraint problems.
As a preferable technical scheme of the invention, the mixed integer nonlinear programming MINLP is established based on a scene generation method of LHS and k-means clustering and is based on a photovoltaic output prediction error sequenceObeys a normal distribution N (mu, sigma) 2 ) Wherein μ=0, < >>Because Latin hypercube sampling LHS can obtain better sampling results in fewer scenes, the LHS method is adopted to sample and generate various photovoltaic output prediction error scenes, and in order to further reduce the calculation load, the k-means method is utilized to further reduce various scenes generated by the LHS method; the construction step comprises two steps of setting an objective function and setting constraint conditions.
As a preferable technical scheme of the invention, the construction method of the objective function is as follows: two objective functions are set: the minimum total electric quantity of the combined system and the maximum total electric energy generation of the combined system;
(1) Objective function 1: the total waste amount of the combined system is minimum; because the uncertain photovoltaic output is a key factor for solving the minimum total power loss of the combined system, the uncertainty is processed by adopting opportunistic constraint planning, and the objective function 1 is expressed as follows:
wherein T and T are eachRepresenting a scheduling period and a certain scheduling period; Δt represents the time interval h over which, Representing the minimum power rejection MWh of the combined system; alpha 1 A confidence level indicating that the combined system power rejection does not exceed the target power rejection; />And->The energy discarding MW of the hydropower station and the photovoltaic power station at the time t is respectively represented;
(2) Objective function 2: the total power generation of the combined system is maximum; in the opportunistic constraint planning model, objective function 2 is expressed as:
wherein M represents the number of hydropower station units;the output MW of the mth hydroelectric generating set at the time t is shown; />Representing grid-connected output MW of the photovoltaic power station; f (f) 2 Representing a target total power generation amount MWh of the water-light combined system; alpha 2 A confidence level indicating 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 energy rejection inevitably occurs in the combined system, the objective is taken as the minimum energy rejection, otherwise, the objective is taken as the maximum energy generation; the final objective function is expressed as follows:
as a preferable technical scheme of the invention, the constraint conditions comprise reservoir and power station constraint, hydroelectric generating set constraint, photovoltaic output constraint and power balance constraint.
As a preferred embodiment of the present invention, the reservoir and power station constraints include:
(1) Water balance constraint
v t+1 ＝v t -3600·(I t -Q t )·Δt 4
Wherein I is t Represents the storage flow m of the reservoir at the time t 3 /s；Represents the power generation flow m of the hydroelectric generating set m at the time t 3 /s；Q t Represents the discharge flow m of the reservoir at the time t 3 /s；v t Storage capacity m of the reservoir at the end of period t 3 ；
(2) Water level constraint
Wherein z is t The upstream water level m at time t is represented; z andrespectively representing the upper limit m and the lower limit m of the water level of an upstream water reservoir; z b And z e Respectively representing the initial water level and the final water level m of the upstream reservoir;
(3) Delivery flow constraints
Wherein Q andrespectively representing the lower limit value and the upper limit value m of the outlet flow of the hydropower station 3 /s。
As a preferred technical solution of the present invention, the hydropower unit constraint includes:
(1) Unit output constraint
Wherein, and->Respectively representing upper and lower limits MW of the m output of the unit; u (u) m,t Taking 1 as an auxiliary variable to indicate that the unit is in a starting state, otherwise, taking 1 as a stopping state;
(2) Output function of hydroelectric generating set
Wherein f m,pgh (. Cndot.) represents the hydroelectric generating set output function; h is a m,t The water head m of the hydroelectric generating set m at the time t is shown;
(3) Hydropower station energy rejection function
For convenience in solution, assume that all the abandoned water flows through a No. 1 unit, and the corresponding generated output is the abandoned energy of the hydropower station;
wherein, represents the reject flow m at time t 3 /s；
(4) Water head restraint
h m,t ＝(z t-1 +z t )/2-zd t -hl m,t 12
z t ＝f zv (v t ) 13
zd t ＝f zq (Q t ) 14
Wherein zd t Representing the tail water level m of the reservoir; hl (hl) m,t The head loss m of the unit m at the time t is represented; f (f) zv (. Cndot.) represents the water level storage capacity relationship; f (f) zq (. Cndot.) represents the tailwater level versus downdraft flow; f (f) lq (. Cndot.) represents the relation between the power generation flow and the head loss of the unit m;
(5) Unit power generation flow constraint
Wherein, and q m Respectively represent the upper limit m and the lower limit m of the generating flow of the unit m 3 /s；
(6) Unit vibration zone restraint
Wherein, and->Respectively representing the upper limit and the lower limit of a kth vibration zone of the unit m;
(7) Unit start-stop duration constraints
Wherein, xi m Sum phi m Respectively representing the minimum on-off duration h of the unit m; x is x m,t Is an auxiliary variable, when 1 is taken, the machine set is started, y m,t And taking 1 as an auxiliary variable to represent shutdown of the unit.
As a preferred embodiment of the present invention, the photovoltaic output needs to meet the following constraints:
wherein, representing the actual output MW of the photovoltaic power station at the time t; />And->The predicted output of the photovoltaic power station at the time t and the corresponding output prediction deviation MW are respectively shown.
As a preferable technical scheme of the invention, for the power balance constraint, uncertainty of the photovoltaic output threatens the load process submitted to the power grid by the combined generating capacity tracking of the water-light system, and can cause serious safety problem of the power grid; to control these problems, opportunistic constraints are applied to limit the risk of power imbalance of the combined system;
Wherein ω represents the power unbalance rate in the contract between the power generation company and the grid;a load value MW representing the moment t of the combined system; beta represents a predetermined confidence level that the combined water-light output does not exceed the upper and lower allowable power deviation limits to meet the power balance constraint; the probability expression 21 ensures that the probability that the total output of the combined system does not meet the load process is smaller 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 opportunity constrains the planning model to linearize and the unit vibration area to linearize.
As a preferable technical scheme of the invention, for linearization of the opportunistic constraint planning model, a scene simulation method is adopted for solving:
(1) Generating N photovoltaic output prediction error representative scenes by using a scene generation method, wherein the actual output of a photovoltaic power station under a given scene and the photovoltaic output integrated into a power grid can be obtained by the following two formulas:
wherein, and->Respectively representing actual output and output prediction deviation of the photovoltaic power station at the moment t under the nth scene; />And->Respectively representing grid-connected output and corresponding energy rejection values of the photovoltaic power station at the time t under the nth scene;
(2) Judging whether the generated scene meets the formula (24), superposing the scene numbers meeting the formula, and recording the total number as S, wherein if S/N is greater than or equal to beta, the opportunity constraint formula 21 is established, otherwise, the opportunity constraint formula is not established;
(3) Based on the above analysis, four 0-1 auxiliary variables a were introduced n,t ，b n,t ，c n,t And d n The opportunity constraint may translate into the following deterministic expression:
c n,t ＝a n,t +b n,t -1 30
wherein c n,t Taking 1 as an auxiliary variable when the nth scene water-light combined system meets the power balance constraint, otherwise taking 0; d, d n Taking 1 when the combined output of the nth scene meets the power balance constraint as another auxiliary variable, otherwise taking 0; l is a very large constant; pb n A probability representing scene n;
as can be seen from 25, a n,t And b n,t Take a value of 1 or 0, if a n,t =0, according to equation 26,otherwise, as can be seen from formula 27 +.>Thus, from formulae 25 and 26, if +.>Then a n,t The value is 1; also, as known from 28 and 29, ifThen b n,t The value is 1; only when a n,t And b n,t When 1 is taken, the formula 24 is established based on the formulas 26 to 29, and the formulas 30 and c are used in this case n,t 1 should be taken; and formulae 31 to 32 show that onlyOnly when->I.e. when the total output of the combined system meets the power balance constraint in the whole dispatching period, d n Taking 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 transformed into a scene-based deterministic expression;
Also, the two objective functions with uncertainty factors, equations 1 and 2, are converted into the following form;
wherein, gamma n The total power rejection of the nth scene of the water-light combined system is determined to be not more than a target value by using an auxiliary variable; lambda (lambda) n The method is used for deciding that the total power generation amount of an 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 gamma n =1, in one formCan meet the two formulas->Also satisfies; at this time, three formulas->The probability of ensuring that the total power discarding quantity of the combined system does not exceed the target power discarding quantity is alpha 1 The method comprises the steps of carrying out a first treatment on the surface of the Likewise, 36 ensures that the combined system total power generation is not less than the target totalProbability of power generation amount alpha 2 。
As a preferable technical scheme of the invention, for linearization of a unit vibration area, a linearization method table is provided to solve the constraint problem of the unit vibration area, and in consideration of the maximum and minimum output limit of the unit, K vibration areas divide the unit output into K+1 feasible unit operation areas, and the expression of formula 17 is expressed as the following linear expression;
wherein, taking 1 as an indicating variable to indicate that the unit is in a kth feasible operation area in the time period; />Andrespectively representing the upper limit and the lower limit of a 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;
As can be seen from equations 37-38, the unit output must be in a viable area when the unit is in operation; from formula 39, ifThe crew m is in the kth feasible region.
The beneficial effects of adopting above-mentioned technical scheme to produce lie in: the invention considers the combined operation of hydroelectric power generation and photovoltaic power generation, utilizes the good regulation capability of the hydropower station to stabilize the uncertainty of photovoltaic output, and combines the combination of the photovoltaic output and grid connection to improve the utilization rate of renewable energy sources and ensure the safe and stable operation of a power grid. Under the background, the invention provides a water-light combined system short-term optimization scheduling method based on opportunity constraint planning (Chance constraint programming, CCP). Firstly, in order to improve the energy utilization rate, the scheduling method provided by the invention takes the minimum energy rejection of the combined system and the maximum power generation amount of the combined system as scheduling targets; in addition, in order to obtain a fine scheduling operation scheme with strong operability, the invention firstly considers the uncertainty of the photovoltaic output process, takes the hydroelectric generating set as a basic scheduling unit, takes the constraints of the start-stop duration time, the vibration area and the like of the generating set into consideration, and establishes a CCP-based water-light combined system short-term optimization scheduling model. The model is a typical multi-variable, high-dimensionality and multi-complex constraint mixed integer nonlinear programming (Mixed integer nonlinearprogramming, MINLP) model, if a traditional dynamic programming algorithm and a bionic algorithm are adopted for solving, the problems of dimension disaster and easy sinking into a local optimal solution are possibly faced, so that in order to improve the solving efficiency, the invention proposes to convert an original model into a mixed integer linear programming (Mixed integer linearprogramming, MILP) model by constructing a series of linearization methods, and the efficient solver is used for solving.
The invention provides a combined dispatching 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, and provides an opportunity constraint planning model based on the dispatching model to decide the operation optimization dispatching of a water-light combined system so as to improve the level of the consumption of renewable energy sources. In the model, a scene analysis method combining an LHS method and k-means clustering is adopted to process photovoltaic output prediction deviation, and in order to improve the precision of the model, the model uses a hydroelectric generating set as a basic scheduling unit and considers corresponding constraint. In addition, in order to improve the solving efficiency of the SMILLP model, the invention provides a series of linearization methods for converting the original model into the MILP model and solving by using a solver. Based on the case study of the water-light combined system of the river in the southwest of China, the progress significance is as follows: (1) The combined operation of the photovoltaic power station and the hydropower station can reduce negative effects caused by uncertainty and fluctuation of the photovoltaic output, and in addition, the generated energy of each period of the combined system tracks a given load process line of the power grid, so that safe and stable operation of the power grid is ensured. Meanwhile, the combined system can improve the grid-connected electric quantity of renewable energy sources as much as possible by reducing the total power rejection of the combined system in different seasons and different typical days and increasing the total power generation. (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 the start-stop duration time constraint, the generating set vibration area constraint and the like, and ensures the safe operation of the generating set, so that the obtained hydro-optical system scheduling scheme has more reality and executable performance. (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 performance of the power grid are reduced. Therefore, the power grid dispatching center and the power generation company should negotiate proper power unbalance rate in advance, and the operators should balance the power rejection, the power generation income and related dispatching risks to determine the operation dispatching strategy of the water-light combined power generation system.
Drawings
FIG. 1 is a schematic diagram of a combined hydropower station and photovoltaic power station operation model.
Fig. 2 is a schematic view of the vibration area of the hydroelectric generating set.
FIG. 3 is a SMILP model solution flow based on the MILP model; in the figure, (a) flood period, (b) withered period.
Fig. 4 is a graph of typical solar power plant output for different water seasons.
FIG. 5 is a dispatching process line of the Wujiang river transition water light mixing system in different water supply seasons; in the figure, (a) 40 th scene in rainy days, (b) 59 th scene in cloudy days, and (c) 92 th scene in sunny days.
FIG. 6 is a graph showing the light and water combination system of various typical Niujiang river water in flood season.
FIG. 7 is a map of the water level upstream of the Wujiang hydropower station in rainy days of flood season; in the figure, a) a No. 1 unit, (b) a No. 2 unit, (c) a No. 3 unit, (d) a No. 4 unit, and (e) a No. 5 unit.
FIG. 8 is a graph showing the power plant output of the Wujiang river hydropower station in rainy days of flood season; in the figure, (a) a 19 th scene in a rainy day, (b) a 67 th scene in a cloudy day, and (c) a 151 th scene in a sunny day.
FIG. 9 is a graph showing the joint output process of the light mixing system for river water transition in different typical days in the dry period; in the figure, (a) a No. 1 unit, (b) a No. 2 unit, (c) a No. 3 unit, (d) a No. 4 unit, and (e) a No. 5 unit.
FIG. 10 is a graph of the plant output of the Wujiang river hydropower station in the rainy days of the dead period.
Detailed Description
The following examples illustrate the application in detail. The raw materials and the equipment used by the application are conventional commercial products, and can be directly obtained through 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 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 should 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 the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Reference in the 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 application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified 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 firstly provides a scene analysis method for processing uncertainty of photovoltaic output and modeling the uncertainty. On the basis, the invention establishes a short-term multi-objective optimization scheduling method of the water-light combined system based on opportunity constraint planning so as to minimize the total electric quantity abandoned by the water-light combined system and maximize the total electric energy generation; the hydropower station unit is a basic scheduling unit, and the scheduling operation of the combined system is studied in a refined mode by comprehensively considering the output limit constraint of the hydropower station unit, the power generation flow constraint of the unit, the vibration area constraint of the unit, the duration constraint of the start-stop 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 deterministic MILP problem based on a scene, and then the problem is solved by using a commercial solver Gurobi with high efficiency. The specific process is as follows: 1) And predicting the day-ahead output process of the photovoltaic power station according to the historical operation data and the next day weather forecast data. 2) To reduce operational risk, grid dispatching centers typically require hydro-optical cogeneration plants to submit day-ahead output curves hours before powering. Therefore, 4-6 hours before the next day, the electric power company needs to determine the combined output power of the hydro-optical 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 time of a unit and the like) and factors such as deviation of actual output and predicted output of the photovoltaic power station. 3) According to the daily 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 as to enable the actual output process of the combined system to track the load change trend and reduce the peak shaving pressure of the power grid as much as possible. 4) The power generation company and the power grid dispatching center repeatedly negotiate the load plan until the two parties can accept the load plan. A power contract is then entered into which includes the explicit hydro-optical combination system load process lines, acceptable power imbalance rates (i.e., the deviation ratio between actual output power and a given load), and electricity prices. If the actual output power deviates from a given load by more than an acceptable power imbalance rate, the power generation company will be penalized. 5) The control center of the water-light combined system determines the water storage capacity of the reservoir and the output of each hydroelectric generating set. And then, the power grid operators arrange power generation plans of other power sources in the system according to the determined water-light combined power generation plan.
According to the coordination mechanism, a hydropower station and photovoltaic power station combined operation plan is formulated so as to cope with frequent fluctuation of photovoltaic power generation output, and the combined output of the hydro-optical system is enabled to track a given load process of a power grid. As shown in fig. 1, the hydropower station compensates the photovoltaic power generation process by using the regulation performance of the hydropower station, so that the generated energy of the hydro-optical system is combined and integrated into a power grid. During the combined operation of the hydro-optical system, if the photovoltaic output power is small, the control center of the combined system can quickly adjust the power of the hydropower station to compensate the photovoltaic output power, including increasing the power generation flow of the hydropower unit or starting part of other hydropower units. When the photovoltaic power generation amount is large, the control center can reduce the output of the hydroelectric generating set or shut down a part of the hydroelectric generating set which runs. In addition, under certain extreme weather conditions, the control center also needs to arrange the hydropower station to discard water or the photovoltaic power station to discard electricity in a period of low electricity consumption requirement, so that the combined output power of the system tracks a set load curve as much as possible. The coordination mode of the photovoltaic power station and the hydropower plant can promote 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 guaranteeing safe and stable operation of a power grid.
The model provided by the invention is applied to the flood household water-light combined power station in Guizhou province of China. The case study shows that the running conditions under different confidence levels and different power unbalance rates in the opportunity constraint planning model are studied, so that the combined operation of water and light can enable the total power generation amount of the combined system to track the given load process of the power grid, and the safety and stability of the power grid are improved. The combined system can improve the grid-connected consumption of renewable energy sources as much as possible by reducing the power rejection and improving the total power generation during different typical days and water supply seasons.
The output of the photovoltaic power station is easily influenced by climate factors, and the accurate prediction cannot be performed, so that the grid connection of the photovoltaic power station causes a certain threat to the safe and stable operation of the power grid. Therefore, by introducing confidence level parameters as probability constraints, the invention establishes a CCP-based optimization model that takes into account the uncertainty of the output power of the photovoltaic power plant. Besides the traditional hydraulic constraint, the method also considers the start-stop duration constraint of the hydroelectric generating set and the vibration area constraint of the generating set so as to conduct more detailed research and obtain a scheduling scheme with stronger operability.
In addition, the model established in the method is a random mixed integer nonlinear programming (stochastic mixed integer nonlinear programming, SMILLP) model, has the characteristics of nonlinearity, uncertainty and the like, and is often difficult to solve if a conventional algorithm is adopted for solving, so that the method provides a series of linearization strategies to convert an original model into an MILP model, and utilizes an efficient and mature solver for solving.
Example 2 photovoltaic output uncertainty
Photovoltaic output has certain randomness, and the current photovoltaic output prediction technology is not mature and has certain limitation, so that a predicted value and an actual value of the photovoltaic output are always in 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) First assume a photovoltaic output prediction error sequenceObeys a normal distribution N (mu, sigma) 2 ) Wherein μ=0,
2) When the sample is large enough, the traditional Monte Carlo sampling (MC) can obtain better results, but too many scenes can affect the solving efficiency, and compared with the MC method, latin Hypercube Sampling (LHS) can obtain better sampling results in fewer scenes. Therefore, the invention adopts the LHS method to sample and generate various 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 computational burden, the k-means method widely applied is adopted to further reduce the number of scenes generated by the LHS method.
Example 3 objective function
As mentioned above, the purpose of the proposed model of the invention is to make as much of the renewable energy source as possible be consumed by the grid, while the total power generation of the combined system at each time period tracks a given load process and meets other operational constraints. Therefore, to adapt to different operating conditions, the present invention proposes two objective functions: the total waste power of the combined system is minimum and the total power generation of the combined system is maximum.
(1) Objective function 1: minimum total power loss of combined system
Because the uncertain photovoltaic output is a key factor for solving the minimum total waste capacity of the combined system, the uncertainty is processed by adopting the CCP model, and the objective function 1 is expressed as follows:
wherein, T and T respectively represent a scheduling period and a certain scheduling period; Δt represents the time interval (h),representing a minimum power reject (MWh) of the combined system; alpha 1 A confidence level indicating that the combined system power rejection does not exceed the target power rejection; />And->The energy rejection (MW) at time t is shown for hydropower stations and photovoltaic power stations, respectively.
(2) Objective function 2: maximum total power generation of combined system
In the CCP model, the objective function 2 can be expressed as follows:
wherein M represents the number of hydropower station units;the output (MW) of the mth hydroelectric generating set at the time t is shown; />Representing grid-connected output (MW) of the photovoltaic power station; f (f) 2 Representing a target total power generation (MWh) of the water-light combined system; alpha 2 A confidence level indicating that the total power generation amount is not lower than the target power generation amount.
On the basis of ensuring the safe and stable operation of the power grid, the government requires that the power generated by the combined system should be consumed by the power grid as much as possible, so that the invention takes the objective function 1 as a main objective function and the objective function 2 as a secondary objective in order to avoid the waste of energy. In other words, the objective function proposed by the present invention is adaptive, when the combined system inevitably fails, the objective is to minimize the fail, otherwise the objective is to maximize the power generation. The final objective function may be expressed as follows:
example 4 constraint
The constraints considered in the model can be divided into reservoir constraints, power station constraints, hydroelectric generating set constraints, photovoltaic output related constraints and power balance constraints. See the examples below.
Example 5 reservoir and Power station constraints
(1) Water balance constraint
v t+1 ＝v t -3600·(I t -Q t )·Δt (4)
Wherein I is t Represents the flow rate (m) 3 /s)；Represents the power generation flow rate (m 3 /s)；Q t Represents the discharge flow (m 3 /s)；v t Representing the reservoir capacity (m) of the reservoir at the end of period t 3 )。
(2) Water level constraint
Wherein z is t An upstream water level (m) at time t; z andrespectively representing upper and lower limits (m) of the water level of the upstream water reservoir; z b And z e The initial level and final level (m) of the upstream reservoir are indicated, respectively.
(3) Delivery flow constraints
Wherein Q andrespectively represent the lower limit value and the upper limit value (m 3 /s)。
Example 6 hydropower set restraint
(1) Unit output constraint
Wherein, and->Respectively representing upper and lower limits (MW) of the m output of the unit; u (u) m,t And taking 1 as an auxiliary variable to indicate that the unit is in a starting state, otherwise, stopping the unit.
(2) Output function of hydroelectric generating set
Wherein f m,pgh (. Cndot.) represents the hydroelectric generating set output function; h is a m,t The head (m) of the hydroelectric generating set m at time t is shown.
(3) Hydropower station energy rejection function
For convenience of solution, it is assumed that all the waste water flows through the No. 1 unit, and the corresponding generated output is the waste energy of the hydropower station.
Wherein, represents the reject flow (m) at time t 3 /s)。
(4) Water head restraint
h m,t ＝(z t-1 +z t )/2-zd t -hl m,t (12)
z t ＝f zv (v t ) (13)
zd t ＝f zq (Q t ) (14)
Wherein zd t Representing the tail water level (m) of the reservoir; hl (hl) m,t The head loss (m) of the unit m at the time t is represented; f (f) zv (. Cndot.) represents the water level storage capacity relationship; f (f) zq (. Cndot.) represents the tailwater level versus downdraft flow; f (f) lq (. Cndot.) represents the relationship between the power generation flow rate and the head loss of the unit m.
(5) Unit power generation flow constraint
Wherein, and q m Respectively represents the upper and lower limits (m) 3 /s)。
(6) Unit vibration zone restraint
Wherein, and->Respectively represent the upper and lower limits of the kth vibration region of the unit m.
(7) Unit start-stop duration constraints
Wherein, xi m Sum phi m Representing the minimum on-off duration (h) of the unit m, respectively; x is x m,t Is an auxiliary variable, when 1 is taken, the machine set is started, y m,t And taking 1 as an auxiliary variable to represent shutdown of the unit.
Example 7 photovoltaic output restraint
The photovoltaic output needs to meet the following constraints
Wherein, representing the actual output (MW) of the photovoltaic power station at time t; />And->The predicted output and the corresponding output prediction deviation (MW) of the photovoltaic power station at the time t are respectively shown.
Example 8, power Balancing constraint
The uncertainty of the photovoltaic output causes a certain threat to the load process submitted to the power grid by the combined power generation amount tracking of the water-light system, and even can cause serious safety problems of the power grid. To control these problems, the present model uses opportunistic constraints to limit the risk of power imbalance in the combined system.
Wherein ω represents the power unbalance rate in the contract between the power generation company and the grid;a load value (MW) representing the moment of the joint system t; beta represents a predetermined confidence level that the combined water and light output does not exceed the upper and lower allowable power deviation limits (meeting the power balance constraint).
The probability expression (21) ensures that the probability that the total output of the combined system does not meet the load process is smaller than 1-beta so as to control the influence caused by the uncertainty of the photovoltaic output.
Example 9 MILP modeling
Because of the introduction of opportunistic constraints (equations (1), (2) and (21)) and four nonlinear constraints (equations (10) - (15) and (17)), the original model is an SMINLP model, and it is difficult to obtain an optimal solution by directly solving the model. Therefore, the original SMILLP model needs to be converted into a deterministic MILP model for solving, and then a mature and efficient commercial solver is used for solving.
For linearization of the fresh water head constraints (equations (12), (13), (14) and (15)), the unit power generation function and the plant waste energy functions (equations (10) and (11)), the current research has tended to be mature, and therefore the present invention has been developed primarily for CCP and unit vibration zone constraint linearization, see in particular the following examples.
Embodiment 10, opportunistic constraint planning model linearization
For deterministic transformation of CCP, analytical and simulation methods are currently dominant. However, when solving some practical problems, the resolution method is often limited and the solving difficulty is high, so the invention adopts a scene simulation method to solve.
(1) Generating N kinds of photovoltaic output prediction error representative scenes by using the scene generation method, wherein the actual output of the photovoltaic power station and the photovoltaic output integrated into the power grid under a given scene can be obtained by the following two formulas:
wherein, and->Respectively representing actual output and output prediction deviation of the photovoltaic power station at the moment t under the nth scene; />And->And respectively representing grid-connected output and corresponding energy rejection values of the photovoltaic power station at the t moment in the nth scene.
(2) Whether the generated scene satisfies the formula (24) is determined, the number of scenes satisfying the formula is superimposed (total number is denoted by S), if S/N is equal to or larger than beta, the opportunity constraint formula (21) is established, otherwise, the opportunity constraint formula is not established.
(3) Based on the above analysis, four 0-1 auxiliary variables a were introduced n,t ，b n,t ，c n,t And d n The opportunity constraint may translate into the following deterministic expression:
c n,t ＝a n,t +b n,t -1 (30)
wherein c n,t Taking 1 as an auxiliary variable when the nth scene water-light combined system meets the power balance constraint, otherwise taking 0; d, d n Taking 1 when the combined output of the nth scene meets the power balance constraint as another auxiliary variable, otherwise taking 0; l is a very large constant; pb n Representing the probability of scene n.
From the formula (25), a n,t And b n,t Take a value of 1 or 0, if a n,t =0, according to equation (26),otherwise, as can be seen from formula (27), +.>Therefore, as shown in the formulae (25) and (26), if +.>Then a n,t The value is 1. Similarly, as known from (28) and (29), ifThen b n,t The value is 1. Thus, only when a n,t And b n,t When 1 is taken, the formula (24) is established as shown in the formulas (26) to (29), and the formula (30) shows that c n,t 1 should be taken. And formulae (31) - (32) show that only(i.e., the total combined system output satisfies the power balance constraint throughout the schedule period), d n Taken as 1, otherwise 0. Equation (33) ensures that the confidence in the predetermined CCP is satisfied. By equations (25) - (33), probabilistic opportunity constraints can be translated into a deterministic representation based on the scene.
Also, the two objective functions with uncertainty factors (equations (1) and (2)) can be converted into the following form.
/>
Wherein, gamma n The total power rejection of the nth scene of the water-light combined system is determined to be not more than a target value by using an auxiliary variable; lambda (lambda) n The method is used for deciding that the total power generation amount of an 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 gamma n =1, in one formCan meet the two formulas->Also satisfies; at this time, three formulas- >The probability of ensuring that the total power discarding quantity of the combined system does not exceed the target power discarding quantity is alpha 1 . Also, equation (36) ensures that the probability that the combined system total power generation amount is not lower than the target total power generation amount is α 2 。
Example 11 set vibration zone linearization
For the daily scheduling research of a large-scale water-light combined system, the constraint solving of the vibration area of the unit is extremely important and has a great challenge, and the difficulty is mainly due to a plurality of unfeasible operation areas which cannot be met at the same time, so that the problem of constraint of the vibration area of the unit is solved by providing a linearization method table in this section.
Considering the maximum and minimum output limits of the unit, the K vibration areas divide the unit output into k+1 possible unit operation areas, and particularly as shown in fig. 2, equation (17) can be expressed as a linear expression as follows.
Wherein, taking 1 as an indicating variable to indicate that the unit is in a kth feasible operation area in the time period; />Andthe upper and lower limits of the kth feasible operation area are respectively expressed, and in addition, the following formula needs to be satisfied because of the correspondence between the vibration area and the feasible area. />
From equations (37) - (38), it is clear that the unit output must be in a viable area when the unit is in operation. As can be seen from formula (39), ifThe crew m is in the kth feasible region.
Example 12 model solving Process
Through the linearization technique, the model proposed by the invention can be converted into an MILP model, and the solving process is shown in figure 3.
Example 13, case study
The model developed by the invention is applied to a flood household water light complementary power station in Guizhou province in China, and the combined system comprises the flood household water power station and a large photovoltaic power station nearby the flood household water power station. The Wujiang river power station is located in the Wujiang river basin in the southwest of China and is one of the most important power sources in Guizhou province. The power station has annual regulating reservoir, 5 generator sets and 1250MW total capacity. Although the capacity of each hydroelectric generating set is the same (250 MW), since the sets are produced by two different manufacturing companies, the operating characteristics of some sets are not exactly the same, and the main characteristic parameters of the hydroelectric generating set and the sets thereof are shown in tables 1 and 2, respectively. Furthermore, the grid-tied installed capacity of the photovoltaic power plant is 300MW and it is located about 40 km from the east at the hydropower station, intended for 30 years.
TABLE 1 characterization parameters of Wujiang river crossing hydropower station
TABLE 2 main parameters of each unit of Wujiang river hydropower station
The output of the photovoltaic power station and the input flow of the reservoir are easily influenced by weather and seasons, so that in order to establish an optimization model with robustness, the model selects a flood period, a dead period and corresponding sunny days, cloudy days and rainy days as typical research objects to be researched, and the typical daily output of the photovoltaic power station in different seasons is shown in fig. 4. In practical engineering, the predetermined joint power generation amount of the ujiang river crossing water light system is generally given in a power contract made between the power grid and a power generation company having the ujiang river crossing system, which is assumed for example to have 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 the 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 adopts a high-efficiency Gurobi business solver to solve.
Basic case study
In the present case, alpha 1 、α 2 The beta and omega values are taken as 0.9, 0.9 and 0.05, respectively, and the operating conditions of the combined system on different typical days are analyzed in detail next.
Flood season running conditions
The result of the flood season operation of the Ujiang river water light system is shown in table 3, and the electric discarding rate represents the ratio of the electric discarding amount to the total electric generating amount. It can be seen that the power rejection of the combined system on sunny days, cloudy days and rainy days is 240,0 and 49MWh respectively, and the total power generation amounts on sunny days, cloudy days and rainy days are 19188,18483 and 18474MWh respectively, and the power rejection rates under the corresponding three typical days are 1.25%,0 and 0.27% respectively. Although the electricity rejection rate in the rainy day is larger than that in other typical days, the total electricity generation capacity of the combined system in the rainy day is still larger due to the larger warehousing flow in the rainy day; the warehouse-in flow rate in sunny days and cloudy days is not very different, however, due to the large photovoltaic output in sunny days, the combined system has to discard partial electric quantity in certain time periods so as to ensure the safe and stable operation of the power grid. In addition, as no electric quantity is abandoned in the cloudy days, the total electric quantity of the combined system is as large as possible, so that the total electric quantity of the combined system in the cloudy days is similar to that in the sunny days.
TABLE 3 optimization results of flood season of Ujiang river water-light combined system
Figure 6 shows the combined output of the combined system at various typical days of the flood season. It can be seen that, first, the combined system total output tracks the grid load process strictly. The storage flow is large in rainy days, so that the combined system can generate as much power as possible in order to reduce the energy abandonment of the combined system and increase the total power generation capacity of the combined system, and the load upper limit is reached at most moments, and when 12-13, the hydropower station has limited regulating capacity and is at a load peak at the moment, and the power generation capacity of the combined system does not reach the output upper limit at the moment. Compared with the rainy days, the warehouse-in flow in sunny days and cloudy days is relatively smaller, the power generation capacity of the power station is relatively poorer, and in order to meet the power balance constraint, the power station has to generate more power in a period with higher load (such as 10-16 days), and generate less power in other periods with smaller power requirements. However, due to the large photovoltaic output and the large volatility of the photovoltaic output, the combined system may have a power-off phenomenon. And in any typical weather scene, the photovoltaic output scene meets the power balance constraint in more than 90%, which indicates that the invention provides formulas (25) - (33) to meet the opportunity constraint.
Fig. 7 shows the reservoir water level at various periods of the flood season in a rainy day, and it can be seen that the water level changes by only 0.17m throughout the schedule period, because the reservoir has good regulation performance in this case. Furthermore, the schedule end water level is 742.85m, which is the same as the intended given target water level.
The output process of each unit of the Wujiang river hydropower station in the rainy days in the flood season is shown in fig. 8, and it can be seen that the research model based on the unit provided by the invention ensures that the output of each unit effectively avoids the vibration area of the corresponding unit. In addition, because the warehouse-in flow is larger in the rainy days in the flood season, the No. 4 unit is in the running state at other times except the stop state at the first time, and the other units are in the starting state at all times.
Run condition in the dead period
The results of the optimization operation of the Ujiang river water-light combined system in the dead period are shown in table 4, and it can be seen that the hydropower station in the dead period has smaller warehouse-in flow and stronger hydropower station regulating capacity, so that no electricity is abandoned in the whole dispatching process, and the generated energy in rainy days, cloudy days and sunny days is 11084,10620 and 10572MWh respectively.
TABLE 4 results of optimization of the withered period of the Ujiang river transition water optical system
The output process of the combined system on each different typical day in the dead period is shown in fig. 9, the generated energy of the combined system reaches the upper load limit in most moments in rainy days, and in cloudy days and sunny days, the power balance constraint must be met due to the fact that the warehouse-in flow is smaller, therefore, 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 ujiang hydropower station in rainy days in the dead period, in response to the change of the photovoltaic output, the output process of each unit of the power station meets the constraint of the duration of the start-stop time of each unit (set as 2h in the present case), and in addition, the vibration area of each unit is effectively avoided in the output process of each unit.
Deterministic model comparison
In order to verify the correctness of the model, the invention establishes a model which does not consider uncertainty of the photovoltaic output and compares the model with the previous model. In the deterministic model, it is assumed that the photovoltaic output process is given and that other parameters are the same as the previous model. Table 5 and Table 6 show the comparison of the optimal results of the deterministic model and the stochastic model for the flood period and the dead period, respectively, and it can be seen that the deterministic model does not generate electricity in each typical day, and the total power generation of the deterministic model is larger 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 of the combined system obtained by the stochastic model is smaller. However, due to limitations of the current photovoltaic output prediction technology, errors in the photovoltaic output predicted value are unavoidable, and at this time, the deterministic model may cause problems and even threats to safe and stable operation of the power grid. Because the electric quantity generated by the deterministic model and the stochastic model are not different, the stochastic model is a better choice for the joint operation of the water-light system from the viewpoints of safety and stability of a power grid and the like. In addition, it can be seen that improving the photovoltaic output prediction technique is also an effective way to reduce energy waste.
TABLE 5 comparison of the results of the stochastic model and deterministic model optimizations during flood season
Note that TPC is the total power discarded (total power curtailment), TPG is the total power generated (the total power generation), and confidence levels represent the probability that the operating decision satisfies the power balance constraint.
TABLE 6 comparison of the results of the stochastic model and deterministic model optimizations at the dead time
Note that TPC is the total power discarded (total power curtailment), TPG is the total power generated (the total power generation), and confidence levels represent the probability that the operating decision satisfies the power balance constraint.
CCP model confidence impact analysis
The influence of the confidence of the CCP model on the operation scheduling of the water-light combined system is mainly studied in the part, and the sensitivity of the CCP model is studied by taking the rainy days in the flood season and the rainy days in the dead season as typical study objects in the part. The total power generation and total power rejection of the combined system at different confidence levels are shown in table 7. As can be seen from table 7, due to the greater flow rate of the warehouse entry in the flood season, the combined system always has different degrees of electricity discarding, and as the confidence level in the opportunity constraint increases, the amount of electricity discarding of the combined system increases correspondingly, and when the confidence level increases from 0.7 to 0.9, the corresponding amount of electricity discarding increases from 117MWh to 240MWh, because the higher the confidence level, the higher the demand it has on the adjustment capacity of the reservoir, 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 confidence increase can correspondingly increase the abandoned energy in the rainy days and reduce the total power generation of the combined system, in other words, the higher the running reliability in the rainy days in the flood season, the larger the abandoned energy caused by the running reliability. 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 rainy days in the flood season, the confidence in the opportunity constraint model has little influence on the total power generation amount of the combined system. On the other hand, although the confidence level is continuously increased, the abandoned electric quantity in the rainy days in the dead period is still 0, and the total electric quantity of the combined system is correspondingly reduced. Therefore, operators of the cogeneration system need to balance the curtailment of electricity, generation revenue, and associated scheduling risks in determining the operation plan of the cogeneration system.
TABLE 7 influence of confidence levels on combined system operating characteristics
Power imbalance rate impact analysis
To study the effect of the power imbalance rate on the combined water-light scheduling, based on previous studies, this section selects four different power imbalance rates to continue the development of the study. The amount of power discarded and the total power generation in the case of different power unbalance rates under rainy days in different water supply periods are shown in table 8. In the flood season, when the electric power unbalance rate is increased from 0.05 to 0.1, the corresponding energy rejection is reduced from 240MWh to 98MWh, in addition, when the load unbalance rate is more than or equal to 0.2, no electricity rejection is generated by the combined system, and when the load unbalance rate reaches 0.3 and above, the total electric power generation amount of the combined system tends to be stable, and is 20434MWh. In the dead period, the total power generation of the combined system is continuously increased along with the increase of the power unbalance rate, but the increase is gradually slow until the combined system is stable. The smaller the power imbalance ratio, the greater the negative impact of the photovoltaic output fluctuation on the safe and stable operation of the grid, and therefore, the grid dispatching center needs to set a suitable power imbalance ratio in order to balance between meeting the grid operation requirements and the consumption of renewable energy sources.
TABLE 8 operation characteristics of different Power imbalance Rate Joint systems in different coming seasons and raindays
In summary, each embodiment of 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, and based on the scheduling model, the invention provides an opportunity constraint planning model to decide the running optimization scheduling of the water-light combined system so as to improve the level of the consumption of renewable energy sources. In the model, a scene analysis method combining an LHS method and k-means clustering is adopted to process photovoltaic output prediction deviation, and in order to improve the precision of the model, the model uses a hydroelectric generating set as a basic scheduling unit and considers corresponding constraint. In addition, in order to improve the solving efficiency of the SMILLP model, the invention provides a series of linearization methods for converting the original model into the MILP model and solving by using a solver. Based on the case study of the water-light combined system of the river in the southwest of China, the following conclusion can be obtained:
(1) The combined operation of the photovoltaic power station and the hydropower station can reduce negative effects caused by uncertainty and fluctuation of the photovoltaic output, and in addition, the generated energy of each period of the combined system tracks a given load process line of the power grid, so that safe and stable operation of the power grid is ensured. Meanwhile, the combined system can improve the grid-connected electric quantity of renewable energy sources as much as possible by reducing the total power rejection of the combined system in different seasons and different typical days and increasing the total power generation.
(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 the start-stop duration time constraint, the generating set vibration area constraint and the like, and ensures the safe operation of the generating set, so that the obtained hydro-optical system scheduling scheme has more reality and executable performance.
(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 performance of the power grid are reduced. Therefore, the power grid dispatching center and the power generation company should negotiate proper power unbalance rate in advance, and the operators should balance the power rejection, the power generation income and related dispatching risks to 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 computers, PCs, smart phones, handheld single machines, floor type single machines and the like. The input device is preferably a screen keyboard, the data storage and calculation module adopts an existing memory, a calculator and a controller, the internal communication module adopts an existing communication port and protocol, and the remote communication module adopts an existing gprs network, a universal Internet and the like.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (5)
1. The short-term optimization scheduling method of the water-light combined system based on the opportunity constraint planning is characterized by comprising the following steps of: firstly, setting the minimum energy rejection of a combined system and the maximum power generation capacity of the combined system as scheduling targets, taking a hydroelectric generating set as a basic scheduling unit based on uncertainty of a photovoltaic output process, taking the start-stop duration time of the generating set and the constraint of a vibration area of the generating set into consideration, establishing a set of short-term optimized scheduling controllable system of the combined system based on opportunistic constraint planning, so as to obtain a refined and highly-operable scheduling operation system, forming a typical multi-variable, 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 linearization method comprises the following steps: a scene simulation linearization method for linearization opportunity constraint programming principle problem and a piecewise linearization method for linearization unit vibration area constraint problem;
The mixed integer nonlinear programming MINLP is established based on a scene generation method of LHS and k-means clustering, and is based on a photovoltaic output prediction error sequenceObeys a normal distribution N (mu, sigma) 2 ) Wherein->Because Latin hypercube sampling LHS can obtain better sampling results in fewer scenes, the LHS method is adopted to sample and generate various photovoltaic output prediction error scenes, and in order to further reduce the calculation load, the k-means method is utilized to further reduce various scenes generated by the LHS method; the construction step comprises two steps of setting an objective function and setting constraint conditions;
the construction method of the objective function comprises the following steps: two objective functions are set: the minimum total electric quantity of the combined system and the maximum total electric energy generation of the combined system;
(1) Objective function 1: the total waste amount of the combined system is minimum; because the uncertain photovoltaic output is a key factor for solving the minimum total power loss of the combined system, the uncertainty is processed by adopting opportunistic constraint planning, and the objective function 1 is expressed as follows:
wherein, T and T respectively represent a scheduling period and a certain scheduling period; Δt represents a time interval;representing the minimum power rejection MWh of the combined system; alpha 1 A confidence level indicating that the combined system power rejection does not exceed the target power rejection; / >And->Indicating time of hydropower station and photovoltaic power station respectivelyEnergy discarding MW at t;
(2) Objective function 2: the total power generation of the combined system is maximum; in the opportunistic constraint planning model, objective function 2 is expressed as:
wherein M represents the number of hydropower station units;the output MW of the mth hydroelectric generating set at the time t is shown; />Representing grid-connected output MW of the photovoltaic power station; 2 frepresenting a target total power generation amount MWh of the water-light combined system; alpha 2 A confidence level indicating that the total power generation amount is not lower than the target power generation amount.
2. The short-term optimal scheduling method for the water-light combined system based on the opportunity constraint programming according to claim 1, which is characterized by comprising the following steps of: taking the objective function 1 as a main objective function and the objective function 2 as a secondary objective, so that the proposed objective function has self-adaptability, when the energy abandoning inevitably occurs in the combined system, the objective is taken as the minimum energy abandoning, otherwise, the objective is taken as the maximum energy generating capacity; the final objective function is expressed as follows:
3. the short-term optimal scheduling method for the water-light combined system based on the opportunity constraint programming according to claim 2, which is characterized by comprising the following steps: the constraint conditions comprise reservoir constraint, power station constraint, hydroelectric generating set constraint, photovoltaic output constraint and power balance constraint;
The reservoir and power station constraints include:
(1) Water balance constraint
v t+1 ＝v t -3600·(I t -Q t )·Δt 4
Wherein I is t Represents the storage flow m of the reservoir at the time t 3 /s；Represents the power generation flow m of the hydroelectric generating set m at the time t 3 /s；/>Represents the abandoned water flow m of the reservoir at the time t 3 /s；Q t Represents the discharge flow m of the reservoir at the time t 3 /s；v t Storage capacity m of the reservoir at the end of period t 3 ；
(2) Water level constraint
Wherein z is t The upstream water level m at time t is represented; z andrespectively representing the upper limit m and the lower limit m of the water level of an upstream water reservoir; z b And z e Respectively representing the initial water level and the final water level m of the upstream reservoir;
(3) Delivery flow constraints
Wherein, Qandrespectively representing the lower limit value and the upper limit value m of the outlet flow of the hydropower station 3 /s；
The hydroelectric generating set constraint comprises:
(1) Unit output constraint
Wherein, and->Respectively representing upper and lower limits MW of the m output of the unit; u (u) m,t Taking 1 as an auxiliary variable to indicate that the unit is in a starting state, otherwise, taking 1 as a stopping state;
(2) Output function of hydroelectric generating set
Wherein f m,pgh (. Cndot.) represents the hydroelectric generating set output function; h is a m,t The water head m of the hydroelectric generating set m at the time t is shown;
(3) Hydropower station energy rejection function
For convenience in solution, assume that all the abandoned water flows through a No. 1 unit, and the corresponding generated output is the abandoned energy of the hydropower station;
wherein, represents the reject flow m at time t 3 /s；
(4) Water head restraint
h m,t ＝(z t-1 +z t )/2-zd t -hl m,t 12
z t ＝f zv (v t ) 13
zd t ＝f zq (Q t ) 14
Wherein zd t Representing the tail water level m of the reservoir; hl (hl) m,t The head loss m of the unit m at the time t is represented; f (f) zv (. Cndot.) represents the water level storage capacity relationship; f (f) zq (. Cndot.) represents the tailwater level versus downdraft flow; f (f) lq (. Cndot.) represents the relation between the power generation flow and the head loss of the unit m;
(5) Unit power generation flow constraint
Wherein, andq m respectively represent the upper limit m and the lower limit m of the generating flow of the unit m 3 /s；
(6) Unit vibration zone restraint
Wherein, and->Respectively representing the upper limit and the lower limit of a kth vibration zone of the unit m;
(7) Unit start-stop duration constraints
Wherein, xi m Sum phi m Respectively representing the minimum on-off duration of the unit m; x is x m,t Is an auxiliary variable, when 1 is taken, the machine set is started, y m,t Taking 1 as an auxiliary variable to represent shutdown of the unit;
the photovoltaic output needs to meet the following constraints:
wherein, representing the actual output MW of the photovoltaic power station at the time t; />And->Respectively representing the predicted output of the photovoltaic power station at the time t and the corresponding output prediction deviation MW;
for the power balance constraint, uncertainty of the photovoltaic output threatens the load process submitted to the power grid by the combined generating capacity tracking of the water-light system, and serious safety problem of the power grid can be caused; to control these problems, opportunistic constraints are applied to limit the risk of power imbalance of the combined system;
Wherein ω represents the power unbalance rate in the contract between the power generation company and the grid;a load value MW representing the moment t of the combined system; beta represents a predetermined confidence level that the combined water-light output does not exceed the upper and lower allowable power deviation limits to meet the power balance constraint; the probability expression 21 ensures that the probability that the total output of the combined system does not meet the load process is smaller than 1-beta so as to control the influence caused by the uncertainty of the photovoltaic output.
4. The short-term optimal scheduling method for the water-light combined system based on the opportunity constraint programming according to claim 3, wherein the method comprises the following steps of: and linearizing the opportunistic constraint planning model, and solving by adopting a scene simulation method:
(1) Generating N photovoltaic output prediction error representative scenes by using a scene generation method, wherein the actual output of a photovoltaic power station under a given scene and the photovoltaic output integrated into a power grid can be obtained by the following two formulas:
wherein, and->Respectively representing actual output and output prediction deviation of the photovoltaic power station at the moment t under the nth scene;and->Respectively representing grid-connected output and corresponding energy rejection values of the photovoltaic power station at the time t under the nth scene;
(2) Judging whether the generated scene meets the formula 24 or not, superposing the scene numbers meeting the formula, wherein the total number is S, if S/N is more than or equal to beta, the opportunity constraint formula 21 is established, and otherwise, the opportunity constraint formula is not established;
(3) Based on the above analysis, four 0-1 auxiliary variables a were introduced n,t ，b n,t ，c n,t And d n The opportunity constraint may translate into the following deterministic expression:
c n,t ＝a n,t +b n,t -1 30
wherein c n,t Taking 1 as an auxiliary variable when the nth scene water-light combined system meets the power balance constraint, otherwise taking 0; d, d n Taking 1 when the combined output of the nth scene meets the power balance constraint as another auxiliary variable, otherwise taking 0; l is a very large constant; pb n A probability representing scene n;
as can be seen from 25, a n,t And b n,t Take a value of 1 or 0, if a n,t =0, according to equation 26,otherwise, as can be seen from formula 27 +.>Thus, as is known from formulas 25 and 26, ifThen a n,t The value is 1; also, as known from 28 and 29, ifThen b n,t The value is 1; only when a n,t And b n,t When 1 is taken, the formula 24 is established based on the formulas 26 to 29, and the formulas 30 and c are used in this case n,t 1 should be taken; and formulae 31 to 32 show that only +.>I.e. when the total output of the combined system meets the power balance constraint in the whole dispatching period, d n Taking 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 transformed into a scene-based deterministic expression;
also, the two objective functions with uncertainty factors, equations 1 and 2, are converted into the following form;
Wherein, gamma n The total power rejection of the nth scene of the water-light combined system is determined to be not more than a target value by using an auxiliary variable; lambda (lambda) n The method is used for deciding that the total power generation amount of an 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 gamma n =1, in one formCan meet the two formulas->Also satisfy the followingThe method comprises the steps of carrying out a first treatment on the surface of the At this time, three formulas->The probability of ensuring that the total power discarding quantity of the combined system does not exceed the target power discarding quantity is alpha 1 The method comprises the steps of carrying out a first treatment on the surface of the Also, equation 36 ensures that the probability that the combined system total power generation amount is not lower than the target total power generation amount is α 2 。
5. The short-term optimal scheduling method for the water-light combined system based on the opportunity constraint programming according to claim 4, which is characterized by comprising the following steps: for linearization of a unit vibration area, solving the constraint problem of the unit vibration area by providing a linearization method table, and considering the maximum and minimum output limit of the unit, dividing the unit output into K+1 feasible unit operation areas by K vibration areas, wherein the expression 17 is expressed as the following linear expression;
wherein, taking 1 as an indicating variable to indicate that the unit is in a kth feasible operation area in the time period; />And->Respectively represent the upper part of the kth feasible operation areaA lower limit; meanwhile, based on the corresponding relation between the vibration area and the feasible area, the following formula is required to be satisfied;
As can be seen from equations 37-38, the unit output must be in a viable area when the unit is in operation; from formula 39, ifThe crew m is in the kth feasible region.
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