CN102855591A - Method and system for optimizing scheduling for short-term combined generation of cascade reservoir group - Google Patents

Abstract

The invention discloses a method and a system for optimizing scheduling for short-term combined generation of a cascade reservoir group. The method comprises the following steps: S1) establishing and storing two short-term optimizing scheduling models, namely, a maximum cascade generating capacity model and a maximum cascade energy storage model, by a scheduling server; S2) selecting a short-term optimizing scheduling model according to a generating optimizing target by the scheduling server; S3) collecting model solving materials by a data collecting device, and selecting an algorithm for solving the short-term optimizing scheduling model by the scheduling server; and S4) generating and outputting a short-term generating scheduling optimizing scheme by the scheduling server. According to the method provided by the invention, the benefit and the speed are both considered while an optimizing scheduling model for a cascade hydropower station and a solving algorithm are searched, and the optimal optimizing scheduling scheme for the short-term combined generation of the cascade reservoir group is achieved.

Description

Cascade Reservoirs short-term cogeneration Optimization Scheduling and system

Technical field

The present invention relates to a kind of Cascade Reservoirs short-term cogeneration Optimization Scheduling and system, belong to Cascade Reservoirs generating Optimized Operation field.

Background technology

Step power station short-term electricity generation Optimized Operation is a very complicated systems engineering problem, its core is on the basis that takes into full account every constraint conditions such as short term scheduling waterpower, electric power, foundation can fully reflect the Optimal Operation Model of system physical feature and operating mechanism, seeks and satisfies the model solution algorithm of dispatching ageing and requirement of reasonableness.The short term scheduling cycle is shorter, more near the hydro plant with reservoir actual operating state.Its task is, considering on the basis of the actual state of the running status of hydroelectric system (each reservoir level, reservoir inflow, unit situation etc.) and electrical network at that time, determine that each power station is in running status or network load the distribution between each power station of a following schedule periods by the period.The maximum model of step generated energy is two kinds of common operational modes of current trapeziodal modulation Comparision with the maximum model of the step accumulation of energy of satisfying the requirement of step load process.For the maximum model of step generated energy, because the nearly all step of current China all is connected to the grid, when sends out and how much exert oneself, all by the electrical network United Dispatching, can not let alone to act.And Economical Operation of Power Systems is generally all distributed thermal power plant tape base lotus, hydroelectric power plant's peak regulation, frequency modulation; better results and the hydroelectric power plant will occur by the optimization of step generated energy maximal criterion; be the hydraulic turbine or just operate in high efficient area; shut down, operation must be sacrificed the economy of electric system like this.Therefore, this optiaml ciriterion is not suitable with the Hydropower Stations Short-term Optimal operation under the China market economic mechanism.And for the maximum model of step accumulation of energy, because stock's water of equal number has more potential energy at upper pond than at lower reservoir, therefore, be optimized scheduling by the accumulation of energy maximal criterion, after after a while operation, the water of lower reservoir will appear preferentially using, so that emptying or low water level operation appear in lower reservoir.Therefore, utilizing step accumulation of energy maximal criterion to be optimized in the scheduling process, if can increase constraint condition lower reservoir water level or the process of exerting oneself are limited, overcome thus the problems referred to above, this criterion is still a kind of good selection.

For the complication system optimization problem, constraint condition means that the artificial interference that system optimized operation is forced is larger more, thereby makes the optimization space of system less.Thus, in the situation that satisfies the constraint conditions such as so numerous step waterpower, electric power, electrical network transmission, whether the Hydropower Stations Short-term Optimal Operation regular following at present even final conclusion not yet.Because the multinomial constraints such as vibrating area, network load transmission and PSS that step power station short-term electricity generation Optimized Operation relates to power station self and unit output distributes, loads, and even requirement is long as scheduling slot with 15min, has greatly affected thus the counting yield of model and algorithm and result's rationality.As seen, how to coordinate the numerous and counting yield of optimal operation of cascade reservoirs optimized variable and constraint condition and the contradiction between the rationality as a result, when taking into account benefit and speed, seek the model of step power station Optimized Operation and the Focal point and difficult point that derivation algorithm is still current optimizing scheduling of reservoir theoretical research.

Summary of the invention

The object of the invention is to, a kind of Cascade Reservoirs short-term cogeneration Optimization Scheduling and system are provided, seek model and the derivation algorithm of step power station Optimized Operation when can take into account benefit and speed, obtain optimum Cascade Reservoirs short-term cogeneration Optimized Operation scheme.

For solving the problems of the technologies described above, the present invention adopts following technical scheme: a kind of Cascade Reservoirs short-term cogeneration Optimization Scheduling may further comprise the steps:

S1, dispatch server set up and the maximum model of storage step generated energy and maximum these the two kinds of Model of Short-term Optimal Dispatchs of model of step accumulation of energy;

S2, dispatch server is selected Model of Short-term Optimal Dispatch according to the generating optimization aim;

S3, the data collector collection model is found the solution material, and the dispatch server selection algorithm is found the solution Model of Short-term Optimal Dispatch;

S4, dispatch server generate and output short-term electricity generation scheduling preferred version.

In the aforesaid Cascade Reservoirs short-term cogeneration Optimization Scheduling, if the generating optimization aim is to make the gross generation of step hydropower station in the following schedule periods maximum, then select the maximum model of step generated energy, and adopt large-scale system decomposition-coordination or acceleration genetic algorithm that this model is found the solution.

In the aforesaid Cascade Reservoirs short-term cogeneration Optimization Scheduling, the objective function of the maximum model of step generated energy:

$\mathrm{MaxE}=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{m=1}{\overset{M}{\mathrm{\Σ}}}N(m,t)\mathrm{\Δt}$

In the formula: E is step gross generation in the schedule periods, and N (m, t) is m step reservoir t period average output, segment length when Δ t is; M is the step power station number, and T is the period number;

Its constraint condition:

(1) each step storage capacity (water level) constraint: $V_{m\min }\≤{\underset{\‾}{V}}_t\≤V(m,t)\≤{\stackrel{\‾}{V}}_t\≤V_{m\max }$

In the formula: V (m, t) is the m reservoir t pondage of period end,

Be respectively the retaining upper and lower limit of m reservoir operation phase, V _Mmax, V _MminBe respectively maximum, the minimum retaining restriction of permission of i step reservoir;

(2) each step units limits: N _Mmin≤ N (m, t)≤N _Mmax

In the formula: the average output of N (m, t) expression m power station t period, N _MminBe technology minimum load, N _MmaxFor the installed capacity of considering each power station, unit anticipation are exerted oneself and the scheduling max cap. of electrical network;

(3) each step traffic constraints: Q (m, t) 〉=Q _{T min}

QF(m,t)≤QD _t?max

In the formula: Q (m, t) and QF (m, t) are respectively average outbound flow and the generating flow of m reservoir t period, Q _{M min}For satisfying the minimum outbound flow of requirements of comprehensive utilization, Q _{M max}Be that i step reservoir power station is bigger than the machine flow most;

(4) water balance constraint: V (m, t+1)=V (m, t)+(Qr (m, t)-Q (m, t)) * Δ t

Q(m,t)＝QF(m,t)+QS(m,t)

Qr(m,t)＝Q(m-1,t)+Qu(m,t)

In the formula: Qr (m, t), Qu (m, t) and QS (m, t) are respectively average reservoir inflow of m reservoir t period, local inflow and abandon discharge, τ _mBe the flow propagation time between m reservoir and the m-1 reservoir.

In the aforesaid Cascade Reservoirs short-term cogeneration Optimization Scheduling, when Model of Short-term Optimal Dispatch is found the solution, if the very short impact that then needs to consider interval current time lag between the different reservoirs of scheduling slot length, it is the time in the letdown flow arrival subordinate power station in higher level power station, Qr (m, t) is expressed as:

Qr(m,t)＝Q(m-1,t-τ _m)+Qu(m,t)。

In the aforesaid Cascade Reservoirs short-term cogeneration Optimization Scheduling, if the generating optimization aim is to make the accumulation of energy of satisfying step after the system loading requirement maximum, for safe, the stable and economical operation of hydroelectric system provides foundation, then select the maximum model of step accumulation of energy, and adopt dynamic search or fast allocation method that this model is found the solution.

In the aforesaid Cascade Reservoirs short-term cogeneration Optimization Scheduling, the objective function of the maximum model of step accumulation of energy:

$O{b}_2:\max \underset{t=1}{\overset{T}{\mathrm{\Σ}}}{F}_m\×\mathrm{\Δt}=\max \underset{t=1}{\overset{T}{\mathrm{\Σ}}}[(K_1{H}_{1t}+K_2{H}_{2t+\mathrm{\τ}1}+L+K_N{H}_{N,t+\mathrm{\τ}1+\mathrm{\τ}2+\mathrm{L\τN}-1})(Q{r}_{1t}-Q_{1t})$

$+(K_2{H}_{2t}+K_3{H}_{3t+\mathrm{\τ}2}+L+K_3{H}_{N,t+\mathrm{\τ}2+\mathrm{\τ}3+\mathrm{\τN}-1})(Q{r}_{2t}-Q_{2t})+L$

$+K_M{H}_{\mathrm{Mt}}(Q{r}_{\mathrm{Mt}}-Q_{\mathrm{Mt}})]\×\mathrm{\Δt}$

In the formula: Fm is total accumulation of energy in m power station, and Qrmt, Qmt and Hmt are respectively the m reservoir reservoir inflow of t period, outbound flowrate and delivery head, and segment length when Δ t is, M are the step power station number, and T is the period number;

Constraint condition:

(1) step hydropower station burden requirement:

In the formula:

Be constantly total the exerting oneself of hydroelectric system of t, P is that the constantly total requirement of hydroelectric system of the t of system is exerted oneself;

(2) each step storage capacity (water level) constraint: $V_{m\min }\≤{\underset{\‾}{V}}_t\≤V(m,t)\≤{\stackrel{\‾}{V}}_t\≤V_{m\max }$

In the formula: V (m, t) is the m reservoir t pondage of period end,

Be respectively the retaining upper and lower limit of m reservoir operation phase, V _Mmax, V _MminBe respectively maximum, the minimum retaining restriction of permission of i step reservoir;

(3) each step units limits: N _Mmin≤ N (m, t)≤N _Mmax

In the formula: the average output of N (m, t) expression m power station t period, N _MminBe technology minimum load, N _MmaxFor the installed capacity of considering each power station, unit anticipation are exerted oneself and the scheduling max cap. of electrical network;

(4) each step traffic constraints: Q (m, t) 〉=Q _{T min}

QF(m,t)≤QD _tmax

In the formula: Q (m, t) and QF (m, t) are respectively average outbound flow and the generating flow of m reservoir t period, Q _MminFor satisfying the minimum outbound flow of requirements of comprehensive utilization, Q _MmaxBe that i step reservoir power station is bigger than the machine flow most;

(5) water balance constraint: V (m, t+1)=V (m, t)+(Qr (m, t)-Q (m, t)) * Δ t

Q(m,t)＝QF(m,t)+QS(m,t)

Qr(m,t)＝Q(m-1,t)+Qu(m,t)

In the formula: Qr (m, t), Qu (m, t) and QS (m, t) are respectively average reservoir inflow of m reservoir t period, local inflow and abandon discharge, τ _mBe the flow propagation time between m reservoir and the m-1 reservoir.

In the aforesaid Cascade Reservoirs short-term cogeneration Optimization Scheduling, Model of Short-term Optimal Dispatch is found the solution needs to consider power constraint, and power constraint comprises different Power Plant vibrating area constraints, electrical network PSS constraint and virtual plant constraint;

(1) unit vibration district constraint: N _i(m, t)≤Nt _Min, N _i(m, t) 〉=Nt _Max, in the formula: the t period i platform power that unit sends of Ni (m, t) expression m power station; Ntmax, Ntmin represent respectively current unit i load vibrating area upper limit value and lower limit value.

(2) electrical network PSS constraint: the power system stabilizer, PSS of electrical network (Power System Stabilizer, PSS) constraint is an optional feature of fence excitation system, is used for low-frequency oscillation, the raising power system damping of inhibition system.

(3) virtual plant constraint: exert oneself when assigning to each unit when the power station period, because the different unit outputs in different power stations belong to different transformer station's unifieds allocation of resources, therefore, namely consist of a virtual plant by each unit of same transformer station United Dispatching; Simultaneously, the unit output sum should satisfy same threshold interval in the same virtual plant:

$N{\left(j\right)}_{\min }\≤\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{N}_i(m,t)\≤N{\left(j\right)}_{\max }(j=\mathrm{1,2}\mathrm{LJ}),$

In the formula: power station m period t i platform power that unit sends in j virtual plant of Ni (m, t) expression; N (i) min, N (j) max be respectively j virtual plant can bearing load upper limit value and lower limit value.

In the aforesaid Cascade Reservoirs short-term cogeneration Optimization Scheduling, the model solution material among the step S3 comprises: schedule periods is reservoir filling position, scheduling end of term reservoir control water level, the interior runoff process of schedule periods, reservoir physical characteristics, reservoir and Power Plant Design parameter, output of power station characteristic and reservoir requirements of comprehensive utilization just.The setting of schedule periods take hour or 15 minutes as the period, time span is free setting according to the actual requirements.

Realize a kind of Cascade Reservoirs short-term cogeneration Optimal Scheduling of preceding method, comprise dispatch server and data collector; Data collector is used for collection model and finds the solution material; Dispatch server is provided with:

The Optimal Operation Model storehouse is used for setting up and the maximum model of storage step generated energy and maximum these the two kinds of Model of Short-term Optimal Dispatchs of model of step accumulation of energy;

The Model Selection module is used for selecting Model of Short-term Optimal Dispatch according to the generating optimization aim;

Algorithms library is used for the algorithm that the Mid-long Term Optimized Scheduling model is found the solution in storage, and algorithm comprises large-scale system decomposition-coordination, accelerates genetic algorithm, dynamic search and fast allocation method;

The scheme generation module is used for generating the mid-long runoff for reservoir power generation run preferred version;

The scheme output module is used for output mid-long runoff for reservoir power generation run preferred version;

Wherein, model bank, Model Selection module, algorithms library, scheme generation module are connected with the scheme output module and are connected; Data collector is connected with algorithms library.

Compared with prior art, the present invention sets up the maximum model of step generated energy and maximum these the two kinds of Model of Short-term Optimal Dispatchs of model of step accumulation of energy, selects suitable scheduling model in conjunction with the generating optimization aim, and comes solving model by the algorithm of optimizing; The flow regulation calculating method such as having adopted is after reservoir inflow is processed through arithmetic mean, as generating flow, this itself just with optimizing thought, so the result of equal flow regulation calculating compares, be equivalent to the comparison of optimization method and relative optimization method; Considered simultaneously the impact of current time lags, so it is inconsistent that step comes the water yield, because the flow hysteresis causes in calculating in a few days not only relevant with the water yield on the same day, simultaneously relevant with the water yield of proxima luce (prox. luc), so also so that a day power benefit not only comprises benefit on the same day, comprise simultaneously the impact of proxima luce (prox. luc) benefit.A day power benefit was affected by both the day before yesterday like this, also affected next day.Therefore such comparative result, compare day Optimized Operation with regard to more complicated, but for convenience's sake, conventional scheduling result carried out certain processing, so can not reflect Optimized Operation and the real difference of conventional scheduling fully.Seek model and the derivation algorithm of step power station Optimized Operation when can take into account benefit and speed, obtain optimum Cascade Reservoirs short-term cogeneration Optimized Operation scheme.Wherein, use the maximum model of short-term step generated energy and formulate a day generation schedule, and by knowing that with routine scheduling comparative analysis Optimized Operation step generated energy increases by 1.22% than optimization routine, generated energy amplification reaches 10.82%, has verified thus the rationality of model.

For conventional Cascade Reservoirs short-term electricity generation Optimization Scheduling because a large amount of waterpower, electric power constraint condition are difficult to realize and the problem of " the dimension calamity " that cause, the present invention takes into account the requirement of dispatcher software computational valid time, at first according to bound variable corresponding to various boundary conditions and the difference of character, to conditional restriction classification, and then the inverse time order generates bound variable and realizes constraint condition, coordinate to the full extent thus the contradiction between the optimum benefit of result and the counting yield, and finally obtained Cascade Reservoirs short-term cogeneration Optimized Operation optimal case.

Description of drawings

Fig. 1 is the workflow diagram of the embodiment of the invention;

Fig. 2 is the software flow pattern of the embodiment of the invention;

Fig. 3 is the structural representation of the embodiment of the invention.

Reference numeral: 1-Optimal Operation Model storehouse, 2-Model Selection module, 3-data acquisition module, 4-algorithms library, 5-scheme generation module, 6-scheme output module, 7-dispatch server.

The present invention is further illustrated below in conjunction with the drawings and specific embodiments.

Embodiment

Embodiments of the invention: a kind of Cascade Reservoirs (Wujiang River Basin) short-term cogeneration Optimization Scheduling as shown in Figure 1 and Figure 2, may further comprise the steps:

S1, dispatch server set up and the maximum model of storage step generated energy and maximum these the two kinds of Model of Short-term Optimal Dispatchs of model of step accumulation of energy;

S2, dispatch server is selected Model of Short-term Optimal Dispatch according to the generating optimization aim;

S3, the data collector collection model is found the solution material, and the dispatch server selection algorithm is found the solution Model of Short-term Optimal Dispatch;

S4, dispatch server generate and output short-term electricity generation scheduling preferred version.

If the generating optimization aim is to make the gross generation of step hydropower station in schedule periods in future maximum, then selects the maximum model of step generated energy, and adopt large-scale system decomposition-coordination or acceleration genetic algorithm that this model is found the solution.

The objective function of the maximum model of step generated energy:

In the formula: E is step gross generation in the schedule periods, and N (m, t) is m step reservoir t period average output, segment length when Δ t is; M is the step power station number, and T is the period number;

Its constraint condition:

(1) each step storage capacity (water level) constraint: $V_{m\min }\≤{\underset{\‾}{V}}_t\≤V(m,t)\≤{\stackrel{\‾}{V}}_t\≤V_{m\max }$

In the formula: V (m, t) is the m reservoir t pondage of period end,

Be respectively the retaining upper and lower limit of m reservoir operation phase, V _Mmax, V _MminBe respectively maximum, the minimum retaining restriction of permission of i step reservoir;

(2) each step units limits: N _Mmin≤ N (m, t)≤N _Mmax

In the formula: the average output of N (m, t) expression m power station t period, N _{M min}Be technology minimum load, N _{M max}For the installed capacity of considering each power station, unit anticipation are exerted oneself and the scheduling max cap. of electrical network;

(3) each step traffic constraints: Q (m, t) 〉=Q _Tmin

QF(m,t)≤QD _tmax

In the formula: Q (m, t) and QF (m, t) are respectively average outbound flow and the generating flow of m reservoir t period, Q _MminFor satisfying the minimum outbound flow of requirements of comprehensive utilization, Q _MmaxBe that i step reservoir power station is bigger than the machine flow most;

(4) water balance constraint: V (m, t+1)=V (m, t)+(Qr (m, t)-Q (m, t)) * Δ t

Q(m,t)＝QF(m,t)+QS(m,t)

Qr(m,t)＝Q(m-1,t)+Qu(m,t)

In the formula: Qr (m, t), Qu (m, t) and QS (m, t) are respectively average reservoir inflow of m reservoir t period, local inflow and abandon discharge, τ _mBe the flow propagation time between m reservoir and the m-1 reservoir.

When Model of Short-term Optimal Dispatch was found the solution, if the very short impact that then needs to consider interval current time lag between the different reservoirs of scheduling slot length, namely the letdown flow in higher level power station arrived the time in subordinate power station, and Qr (m, t) is expressed as:

Qr(m,t)＝Q(m-1,t-τ _m)+Qu(m,t)。

If the generating optimization aim is to make the accumulation of energy of satisfying step after the system loading requirement maximum, for safe, the stable and economical operation of hydroelectric system provides foundation, then select the maximum model of step accumulation of energy, and adopt dynamic search or fast allocation method that this model is found the solution.

The objective function of the maximum model of step accumulation of energy:

$O{b}_2:\max \underset{t=1}{\overset{T}{\mathrm{\Σ}}}{F}_m\×\mathrm{\Δt}=\max \underset{t=1}{\overset{T}{\mathrm{\Σ}}}[(K_1{H}_{1t}+K_2{H}_{2t+\mathrm{\τ}1}+L+K_N{H}_{N,t+\mathrm{\τ}1+\mathrm{\τ}2+\mathrm{L\τN}-1})(Q{r}_{1t}-Q_{1t})$

$+(K_2{H}_{2t}+K_3{H}_{3t+\mathrm{\τ}2}+L+K_3{H}_{N,t+\mathrm{\τ}2+\mathrm{\τ}3+\mathrm{\τN}-1})(Q{r}_{2t}-Q_{2t})+L$

$+K_M{H}_{\mathrm{Mt}}(Q{r}_{\mathrm{Mt}}-Q_{\mathrm{Mt}})]\×\mathrm{\Δt}$

In the formula: Fm is total accumulation of energy in m power station, and Qrmt, Qmt and Hmt are respectively the m reservoir reservoir inflow of t period, outbound flowrate and delivery head, and segment length when Δ t is, M are the step power station number, and T is the period number;

Constraint condition:

(1) step hydropower station burden requirement:

In the formula:

Be constantly total the exerting oneself of hydroelectric system of t, P is that the constantly total requirement of hydroelectric system of the t of system is exerted oneself.

(2) each step storage capacity (water level) constraint: $V_{m\min }\≤{\underset{\‾}{V}}_t\≤V(m,t)\≤{\stackrel{\‾}{V}}_t\≤V_{m\max }$

In the formula: V (m, t) is the m reservoir t pondage of period end, Be respectively the retaining upper and lower limit of m reservoir operation phase, V _Mmax, V _MminBe respectively maximum, the minimum retaining restriction of permission of i step reservoir;

(3) each step units limits: N _Mmin≤ N (m, t)≤N _Mmax

In the formula: the average output of N (m, t) expression m power station t period, N _MminBe technology minimum load, N _MmaxFor the installed capacity of considering each power station, unit anticipation are exerted oneself and the scheduling max cap. of electrical network;

(4) each step traffic constraints: Q (m, t) 〉=Q _Tmin

QF(m,t)≤QD _tmax

In the formula: Q (m, t) and QF (m, t) are respectively average outbound flow and the generating flow of m reservoir t period, Q _MminFor satisfying the minimum outbound flow of requirements of comprehensive utilization, Q _MmaxBe that i step reservoir power station is bigger than the machine flow most;

(5) water balance constraint: V (m, t+1)=V (m, t)+(Qr (m, t)-Q (m, t)) * Δ t

Q(m,t)＝QF(m,t)+QS(m,t)

Qr(m,t)＝Q(m-1,t)+Qu(m,t)

In the formula: Qr (m, t), Qu (m, t) and QS (m, t) are respectively average reservoir inflow of m reservoir t period, local inflow and abandon discharge, τ _mBe the flow propagation time between m reservoir and the m-1 reservoir.

Model of Short-term Optimal Dispatch is found the solution needs to consider power constraint, and power constraint comprises different Power Plant vibrating area constraints, electrical network PSS constraint and virtual plant constraint.

(1) unit vibration district constraint: N _i(m, t)≤Nt _Min, Ni (m, t) 〉=Nt _Max

In the formula: the t period i platform power that unit sends of Ni (m, t) expression m power station; Ntmax, Ntmin represent current unit i load vibrating area upper lower limit value.

(2) electrical network PSS constraint: the power system stabilizer, PSS of electrical network (Power System Stabilizer, PSS) constraint is an optional feature of fence excitation system, is used for low-frequency oscillation, the raising power system damping of inhibition system.

(3) virtual plant constraint: exert oneself when assigning to each unit when the power station period, because the different unit outputs in different power stations belong to different transformer station's unifieds allocation of resources, therefore, namely consist of a virtual plant by each unit of same transformer station United Dispatching; Simultaneously, the unit output sum should satisfy same threshold interval in the same virtual plant: $N{\left(j\right)}_{\min }\≤\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{N}_i(m,t)\≤N{\left(j\right)}_{\max }(j=\mathrm{1,2}\mathrm{LJ}),$

In the formula: power station m period t i platform power that unit sends in j virtual plant of Ni (m, t) expression; N (i) min, N (j) max be respectively j virtual plant can bearing load upper limit value and lower limit value.

Model solution material among the step S3 comprises: schedule periods is reservoir filling position, scheduling end of term reservoir control water level, the interior runoff process of schedule periods, reservoir physical characteristics, reservoir and Power Plant Design parameter, output of power station characteristic and reservoir requirements of comprehensive utilization just.The setting of schedule periods take hour or 15 minutes as the period, time span is free setting according to the actual requirements.

Above-mentioned Model of Short-term Optimal Dispatch combine last water level control, outbound control, the control of exerting oneself, etc. outbound control isotype, be connected contact with water level between each pattern, day part can the different computation schemas of arbitrary disposition.

(1) water level control model:

The period Mo water level value of control day part calculates the outbound flow by water balance, consider that head is obstructed, power station, power station unit can be with situations such as number of units under, whole water yields are used for generating, water is abandoned in unnecessary water conduct; When coming water not satisfy water level control to require, come water to calculate water level by actual.

Step1: according to reservoir inflow with at the beginning of the period, last water level calculation interval outbound flow;

Step2: calculate output of power station according to outbound flow rate calculation mode;

Step3: judge successively whether outbound flow, output of power station, last water level satisfy the constraint condition requirement, if do not satisfy then other index of generating electricity by corresponding binding occurrence inverse, and provide information indicating.

(2) outbound flow control mode:

The outbound flow of control day part is used to generating with whole water yields, and after whole full sending out, the unnecessary water yield is for abandoning water; When water level was broken through the bound constraint, by in fact lower limit water level control, the outbound flow of again drafting this period was used for generating electricity.

Step1: calculate output of power station and last water level according to outbound flow rate calculation mode;

Step2: judge successively whether outbound flow, output of power station, last water level satisfy the constraint condition requirement, if do not satisfy then other index of generating electricity by corresponding binding occurrence inverse, and provide information indicating.

(3) go out force control mode:

The average output of control day part is by without abandoning water principle calculation interval end water level, generating flow, abandoning the statistical indicator such as discharge.

Step1: suppose period Mo water level;

Step2: according to reservoir inflow and water level at the beginning of the period, calculate the outbound flow by the water balance equation;

Step3: calculate power station outbound flow and last water level according to the output calculation mode;

Step4: whether the period Mo water level that check is calculated satisfies accuracy requirement with default, if satisfy, then calculates end, otherwise again supposes period Mo water level, returns Step2, until last water level is restrained;

Step5: judge successively whether outbound flow, output of power station, last water level satisfy the constraint condition requirement, if do not satisfy then other index of generating electricity by corresponding binding occurrence inverse, and provide information indicating.

(4) reservoir operation chart-pattern:

By water level control principle at the beginning of the period, according to exerting oneself of this period of determining positions in scheduling graph of water level at the beginning of the period, if water level is out-of-limit, abandon the output of power station of water principle inverse by nothing.

Step1: look into scheduling graph according to water level at the beginning of the period, get the period average output;

Step2: go out the statistical indicators such as force control mode calculation interval end water level and outbound flow according to aforesaid;

Step3: judge successively whether outbound flow, output of power station, last water level satisfy the constraint condition requirement, if do not satisfy then other index of generating electricity by corresponding binding occurrence inverse, and provide information indicating.

(5) flow control such as:

Its ultimate principle is: choose continuous multi-period in, first water level and the last water level of last period with the first period are controlled water level as the first end of waiting the operation of exerting oneself, reservoir inflow process according to day part, under the prerequisite that satisfies the just last water level requirement of control, so that the day part outbound flow of choosing equates.Calculation procedure is as follows:

Step1: segment number and just end control water level when determine waiting the start-stop of flow control (first water level is the calculating end water level of last period, and last water level is grade that the interface the arranges control water level of exerting oneself);

Step2: according to first last water level and day part reservoir inflow, calculate the average outbound flow of day part by the water balance equation;

Step3: for starting at water level, the average outbound flow of day part that calculates with Step2 by outbound flow rate calculation mode, calculates that day part is exerted oneself and the index such as last water level by the period with first water level.

The power generation dispatching preferred version is: the principle of utilizing the maximum model of accumulation of energy that step short term scheduling process is optimized is to use the less water yield as far as possible, to satisfy the system loading task, holds to the storehouse with the water yield as much as possible, to increase the accumulation of energy of the step end of term.Preferentially generate electricity in the power station that definite principle is the electrical generation water head height, water consumption rate is little of step reservoir generating priority ranking.Such as the station, Goupitan, this station generating water consumption rate is that step is minimum as calculated, about about 2.7m3/kWh, if simply utilize Silin, downstream, Sha Tuo power station completion system load task, then can obviously lower the lower station water level, and station, the Goupitan upper amplification of water level very little (it is larger to regulate storage capacity storage capacity, is 29.02m3) makes the step accumulation of energy not necessarily optimum thus.Thus, taking into account affects head and two key factors of flow that Hydropower Plant is exerted oneself, and it is as follows that summary obtains each power station generating priority ranking rule of Cascade Stations on Wujiang River Short-term Optimal Operation:

(1) station, Goupitan is because its storage capacity is larger, water consumption rate less (about about 2.7m3/kWh), the unit water yield cause SEA LEVEL VARIATION less, preferentially generate electricity thus.

(2) Silin, Sha Tuo power station productive head are suitable, and the generating water consumption rate is substantially quite (about about 6.5m3/kWh) also, consider that thus husky a small bay in a river erect-position in the downstream of step, is the accumulation of energy of increase system, and preferentially generate electricity in the Sha Tuo power station thus, then is the power station, Silin.

(3) for four storehouses, step upstream, because erect-position crosses first of the step power station of upstream, the Wujiang River in flood man, should give full play to its retaining benefit and Runoff Compensation effect.Consider that flood man crosses station and east wind station generating water consumption rate substantially quite (about about 3.2m3/kWh), all less than subordinate power station (Suofengying, Wu Jiangdu) generating water consumption rate, and storage capacity less (about 0.674m3) is regulated at the station, Suofengying, and the SEA LEVEL VARIATION that causes of the unit water yield is possible fairly obvious thus.Consider thus, when power station, downstream (Goupitan, Silin, Sha Tuo) load can not satisfy the system loading task, should preferentially select the generating of east wind station, secondly cross the station as the Runoff Compensation effect of step tap reservoir to all the other power stations for performance carry-over storage flood man, consider to select flood man to cross station completion system load task.

(4) for step rope, Wu Erku, station, Suofengying productive head is less, water consumption rate large (about about 6.2m3/kWh), the Wujiang River cross the station productive head greatly, water consumption rate less (about about 3.9m3/kWh).Therefore, consider the correlationship between two storehouse productive heads and the water consumption rate, suggestion selects the station, Suofengying preferentially to generate electricity, and next selects the Wujiang River to cross the station generating, to satisfy the system loading task.

(5) providing each power station of Cascade Stations on Wujiang River Short-term Optimal Operation (generating) priority ranking that discharges water is followed successively by: Goupitan, Sha Tuo, Silin, east wind, Hong Jiadu, Suofengying, Wu Jiangdu, and for scheduling decision.As seen, for making step hydropower station scheduling end of term accumulation of energy maximum, each power station is not to generate electricity according to step order from the bottom to top fully, but taken into account the rapport that affects between the head of power station unit output factor and the flow factor, the reasonable arrangement step holds to be put, the end of term accumulation of energy of increase system is for step hydropower station later stage synergy provides guarantee.

In addition, scheduling is regulated the reservoir (Hong Jiadu, east wind, Wu Jiangdu, Goupitan) of above performance when carrying out the short-term electricity generation Optimized Operation for incomplete year, if do not consider the electrical network demand, the best scheduling mode in its single storehouse self is rationally utilized head and the more evenly generating of coordinating flow quantity relation in the whole schedule periods.Has the reservoir (Suofengying, Silin, Sha Tuo) of day adjusting function when carrying out dispatching day, if do not consider the electrical network demand, the best scheduling mode in its single storehouse self is for improving as early as possible thus productive head according to the minimum generating capacity generating in power station, the multiple electricity of later-stage utilization high water head, fall back at last and require the position, single storehouse generated energy is maximum thus.

Realize a kind of Cascade Reservoirs short-term cogeneration Optimal Scheduling of preceding method, as shown in Figure 3, comprise dispatch server 7 and data collector 3; Data collector 3 is used for collection model and finds the solution material; Dispatch server 7 is provided with:

Optimal Operation Model storehouse 1 is used for setting up and the maximum model of storage step generated energy and maximum these the two kinds of Model of Short-term Optimal Dispatchs of model of step accumulation of energy;

Model Selection module 2 is used for selecting Model of Short-term Optimal Dispatch according to the generating optimization aim;

Algorithms library 4 is used for the algorithm that the Mid-long Term Optimized Scheduling model is found the solution in storage, and algorithm comprises large-scale system decomposition-coordination, accelerates genetic algorithm, dynamic search and fast allocation method;

Scheme generation module 5 is used for generating the mid-long runoff for reservoir power generation run preferred version;

Scheme output module 6 is used for output mid-long runoff for reservoir power generation run preferred version;

Wherein, model bank 1, Model Selection module 2, algorithms library 4, scheme generation module 5 and scheme output module are connected in turn and are connected; Data collector 3 is connected with algorithms library 4.

Claims (9)

1. a Cascade Reservoirs short-term cogeneration Optimization Scheduling is characterized in that, may further comprise the steps:

S1, dispatch server set up and the maximum model of storage step generated energy and maximum these the two kinds of Model of Short-term Optimal Dispatchs of model of step accumulation of energy;

S2, dispatch server is selected Model of Short-term Optimal Dispatch according to the generating optimization aim;

S3, the data collector collection model is found the solution material, and the dispatch server selection algorithm is found the solution Model of Short-term Optimal Dispatch;

S4, dispatch server generate and output short-term electricity generation scheduling preferred version.

2. Cascade Reservoirs short-term cogeneration Optimization Scheduling according to claim 1, it is characterized in that: if the generating optimization aim is to make the gross generation of step hydropower station in the following schedule periods maximum, then select the maximum model of step generated energy, and adopt large-scale system decomposition-coordination or acceleration genetic algorithm that this model is found the solution.

3. Cascade Reservoirs short-term cogeneration Optimization Scheduling according to claim 2 is characterized in that: the objective function of the maximum model of step generated energy:

In the formula: E is step gross generation in the schedule periods, and N (m, t) is m step reservoir t period average output, segment length when Δ t is; M is the step power station number, and T is the period number;

Its constraint condition:

(1) each step storage capacity (water level) constraint: $V_{m\min }\≤{\underset{\‾}{V}}_t\≤V(m,t)\≤{\stackrel{\‾}{V}}_t\≤V_{m\max }$

In the formula: V (m, t) is the m reservoir t pondage of period end,

Be respectively the retaining upper and lower limit of m reservoir operation phase, V _{M max}, V _{M min}Be respectively maximum, the minimum retaining restriction of permission of i step reservoir;

(2) each step units limits: N _{M min}≤ N (m, t)≤N _{M max}

In the formula: the average output of N (m, t) expression m power station t period, N _{M min}Be technology minimum load, N _{M max}For the installed capacity of considering each power station, unit anticipation are exerted oneself and the scheduling max cap. of electrical network;

(3) each step traffic constraints: Q (m, t) 〉=Q _{T min}

QF(m,t)≤QD _t?max

In the formula: Q (m, t) and QF (m, t) are respectively average outbound flow and the generating flow of m reservoir t period, Q _{M min}For satisfying the minimum outbound flow of requirements of comprehensive utilization, Q _{M max}Be that i step reservoir power station is bigger than the machine flow most;

(4) water balance constraint: V (m, t+1)=V (m, t)+(Qr (m, t)-Q (m, t)) * Δ t

Q(m,t)＝QF(m,t)+QS(m,t)

Qr(m,t)＝Q(m-1,t)+Qu(m,t)

In the formula: Qr (m, t), Qu (m, t) and QS (m, t) are respectively average reservoir inflow of m reservoir t period, local inflow and abandon discharge, τ _mBe the flow propagation time between m reservoir and the m-1 reservoir.

4. Cascade Reservoirs short-term cogeneration Optimization Scheduling according to claim 3, it is characterized in that: when Model of Short-term Optimal Dispatch is found the solution, if the very short impact that then needs to consider interval current time lag between the different reservoirs of scheduling slot length, it is the time in the letdown flow arrival subordinate power station in higher level power station, Qr (m, t) is expressed as:

Qr(m,t)＝Q(m-1,t-τ _m)+Qu(m,t)。

5. Cascade Reservoirs short-term cogeneration Optimization Scheduling according to claim 1, it is characterized in that: if the generating optimization aim is to make the accumulation of energy of satisfying step after the system loading requirement maximum, for safe, the stable and economical operation of hydroelectric system provides foundation, then select the maximum model of step accumulation of energy, and adopt dynamic search or fast allocation method that this model is found the solution.

6. Cascade Reservoirs short-term cogeneration Optimization Scheduling according to claim 5 is characterized in that: the objective function of the maximum model of step accumulation of energy:

$O{b}_2:\max \underset{t=1}{\overset{T}{\mathrm{\Σ}}}{F}_m\×\mathrm{\Δt}=\max \underset{t=1}{\overset{T}{\mathrm{\Σ}}}[(K_1{H}_{1t}+K_2{H}_{2t+\mathrm{\τ}1}+L+K_N{H}_{N,t+\mathrm{\τ}1+\mathrm{\τ}2+\mathrm{L\τN}-1})(Q{r}_{1t}-Q_{1t})$

$+(K_2{H}_{2t}+K_3{H}_{3t+\mathrm{\τ}2}+L+K_3{H}_{N,t+\mathrm{\τ}2+\mathrm{\τ}3+\mathrm{\τN}-1})(Q{r}_{2t}-Q_{2t})+L$

$+K_M{H}_{\mathrm{Mt}}(Q{r}_{\mathrm{Mt}}-Q_{\mathrm{Mt}})]\×\mathrm{\Δt}$

In the formula: Fm is total accumulation of energy in m power station, and Qrmt, Qmt and Hmt are respectively the m reservoir reservoir inflow of t period, outbound flowrate and delivery head, and segment length when Δ t is, M are the step power station number, and T is the period number;

Constraint condition:

(1) step hydropower station burden requirement:

In the formula:

Be constantly total the exerting oneself of hydroelectric system of t, P is that the constantly total requirement of hydroelectric system of the t of system is exerted oneself;

(2) each step storage capacity (water level) constraint: $V_{m\min }\≤{\underset{\‾}{V}}_t\≤V(m,t)\≤{\stackrel{\‾}{V}}_t\≤V_{m\max }$

In the formula: V (m, t) is the m reservoir t pondage of period end,

Be respectively the retaining upper and lower limit of m reservoir operation phase, V _{M max}, V _{M min}Be respectively maximum, the minimum retaining restriction of permission of i step reservoir;

(3) each step units limits: N _{M min}≤ N (m, t)≤N _{M max}

In the formula: the average output of N (m, t) expression m power station t period, N _{M min}Be technology minimum load, N _{M max}For the installed capacity of considering each power station, unit anticipation are exerted oneself and the scheduling max cap. of electrical network;

(4) each step traffic constraints: Q (m, t) 〉=Q _{T min}

QF(m,t)≤QD _t?max

In the formula: Q (m, t) and QF (m, t) are respectively average outbound flow and the generating flow of m reservoir t period, Q _{M min}For satisfying the minimum outbound flow of requirements of comprehensive utilization, Q _{M max}Be that i step reservoir power station is bigger than the machine flow most;

(5) water balance constraint: V (m, t+1)=V (m, t)+(Qr (m, t)-Q (m, t)) * Δ t

Q(m,t)＝QF(m,t)+QS(m,t)

Qr(m,t)＝Q(m-1,t)+Qu(m,t)

In the formula: Qr (m, t), Qu (m, t) and QS (m, t) are respectively average reservoir inflow of m reservoir t period, local inflow and abandon discharge, τ _mBe the flow propagation time between m reservoir and the m-1 reservoir.

7. according to claim 4 or 6 described Cascade Reservoirs short-term cogeneration Optimization Schedulings, it is characterized in that: Model of Short-term Optimal Dispatch is found the solution needs and is considered power constraint, and power constraint comprises different Power Plant vibrating area constraints, electrical network PSS constraint and virtual plant constraint.

8. the medium-term and long-term cogeneration Optimization Scheduling of Cascade Reservoirs according to claim 7, it is characterized in that the model solution material among the step S3 comprises: schedule periods is reservoir filling position, scheduling end of term reservoir control water level, the interior runoff process of schedule periods, reservoir physical characteristics, reservoir and Power Plant Design parameter, output of power station characteristic and reservoir requirements of comprehensive utilization just.

9. realize a kind of Cascade Reservoirs short-term cogeneration Optimal Scheduling of the described method of claim 1～8, it is characterized in that, comprise dispatch server (7) and data collector (3); Data collector (3) is used for collection model and finds the solution material; Dispatch server (7) is provided with:

Optimal Operation Model storehouse (1) is used for setting up and the maximum model of storage step generated energy and maximum these the two kinds of Model of Short-term Optimal Dispatchs of model of step accumulation of energy;

Model Selection module (2) is used for selecting Model of Short-term Optimal Dispatch according to the generating optimization aim;

Algorithms library (4) is used for the algorithm that the Mid-long Term Optimized Scheduling model is found the solution in storage, and algorithm comprises large-scale system decomposition-coordination, accelerates genetic algorithm, dynamic search and fast allocation method;

Scheme generation module (5) is used for generating the mid-long runoff for reservoir power generation run preferred version;

Scheme output module (6) is used for output mid-long runoff for reservoir power generation run preferred version;

Wherein, model bank (1), Model Selection module (2), algorithms library (4), scheme generation module (5) are connected 6 with the scheme output module) connect in turn; Data collector (3) is connected with algorithms library (4).

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