CN113937819B - Multi-energy short-term optimization scheduling method - Google Patents
Multi-energy short-term optimization scheduling method Download PDFInfo
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Abstract
The invention discloses a multi-energy short-term optimization scheduling method, which comprises the following steps: step one: firstly, taking minimum energy consumption of system operation as a target, taking system operation constraint conditions in a scheduling period into consideration to establish a multisource coordination short-term optimization scheduling model, and simultaneously evaluating the combination condition of units to judge whether the system capacity is redundant; step two: if the system capacity is redundant, adding the maximum starting number constraint of the thermal power unit, and performing simulation calculation on four aspects of a unit combination scheme, an energy source generating power condition, system energy loss and a thermal power unit output standard deviation. The beneficial effects of the invention are as follows: according to the invention, the peak regulation constraint of the continuous scheduling period is considered in the multi-source coordinated scheduling system, so that the randomness of the new energy output can be adapted, and the peak regulation adequacy of the system is ensured; the method introduces the constraint of the maximum starting number of the units into the optimal scheduling model, can reduce frequent conversion of the running state of the units, and avoids redundancy of starting capacity.
Description
Technical Field
The invention relates to an optimized scheduling method, in particular to a multi-energy short-term optimized scheduling method, and belongs to the technical field of energy scheduling.
Background
In recent years, the new energy power generation ratio of the power system is continuously improved, the clean energy is greatly developed to bring positive influence to the economic operation of the system, the energy shortage problem is relieved, and however, the randomness of wind-light output and the anti-peak shaving characteristic bring great challenges to the economic and stable operation of the power system. Therefore, the multi-energy optimization scheduling model considering peak shaving constraint from the short-term scheduling layer research has important significance for relieving the peak shaving pressure of the system.
The existing multi-energy optimization scheduling research mainly focuses on considering the economic operation problem of the system, the literature research aims at the minimum total operation cost to research the scheduling problem of complex thermal power unit combinations, and a thermal power unit start-stop plan meeting load requirements is formulated. However, aiming at the randomness and the anti-peak regulation characteristic of grid-connected output of new energy sources such as wind and light, the thermal power generating unit has certain peak regulation capability, but has low response speed, and if wind, light and electricity are regulated only by thermal power, the unit is frequently started and stopped, and the starting and stopping cost is high. Therefore, the complementary characteristics among the energy sources are fully utilized, and the regulation effect of the new energy sources on the system operation is exerted. The research of the combined optimization scheduling of three energy sources of wind, water and fire is carried out by taking the prior new energy source elimination as a target, and a heuristic search method is provided to determine the optimal scheduling quantity of the thermal power generating unit. The method is characterized in that the method utilizes pumped storage to make up the valley filling capability which is not possessed by water and electricity, establishes a wind-containing water and fire combined optimization scheduling model, solves the model by adopting a segmentation-sequence-feedback method, realizes multi-energy segmentation coordination scheduling, ignores the influence of uncertainty of new energy output, and is difficult to overcome the peak regulation problem caused by new energy grid connection of wind, light and the like. Therefore, the complementary level between energy sources is evaluated by literature research tracking degree, a virtual power supply configuration strategy is provided, and the peak shaving benefit of the system is increased. However, the above document only considers the reliability of the system operation in a single scheduling period, and does not consider the peak shaving constraint of the system continuous scheduling period and the influence of the system capacity redundancy.
Based on the background, the research content of the chapter fully considers the randomness and the inverse peak regulation characteristic of the new energy output. In order to overcome the peak regulation difficulty brought by new energy grid connection such as wind and light, and coordinate the power generation scheduling of wind, light, water and fire, a multi-energy optimization scheduling model considering the peak regulation constraint of a continuous scheduling period is constructed. Firstly, with the minimum energy consumption of system operation as a target, multiple coupling operation constraints of wind, light, water and fire energy forms are established, and aiming at the uncertainty of new energy output and the problem of larger operation inertia of the traditional thermal power generating unit, the standby constraint, the low-valley wind-abandoning constraint and the light-abandoning constraint of continuous dispatching periodic load peaks are established. Then, in order to alleviate the problems of economy and capacity redundancy of the system, an economy sequencing method is provided, and the constraint of the maximum starting number of the thermal power generating unit is introduced.
Disclosure of Invention
The invention aims to solve the problems and provide a multi-energy short-term optimization scheduling method.
The invention realizes the aim through the following technical scheme, and the multi-energy short-term optimization scheduling method comprises the following steps:
Step one: firstly, taking minimum energy consumption of system operation as a target, taking system operation constraint conditions in a scheduling period into consideration to establish a multisource coordination short-term optimization scheduling model, and simultaneously evaluating the combination condition of units to judge whether the system capacity is redundant;
Step two: if the system capacity is redundant, adding the maximum starting number constraint of the thermal power unit, and performing simulation calculation on four aspects of a unit combination scheme, an energy source power generation condition, system energy loss and a thermal power unit output standard deviation;
Step three: and (3) adding constraint considering peak regulation of the continuous scheduling period on the basis of the second step, performing simulation calculation on the four indexes again, and performing comparison evaluation analysis on respective solving results of system scheduling under three constraint modes.
Preferably, the constraint condition for establishing the multi-energy short-term optimization scheduling model considering the peak shaving constraint of the continuous scheduling period comprises two parts, namely the intra-period constraint and the next scheduling period constraint;
In order to realize the economic benefit of the system through coordination complementation of various energy sources of wind, light, water and fire, the model optimization target is set to minimize the running cost and the starting, starting and stopping cost of the thermal power unit, namely:
Wherein: the energy consumption of the operation of the thermal power unit is a quadratic function of the output of the thermal power unit, expressed as A i is a quadratic term coefficient, b i is a first order term coefficient, and c i is a constant term; To start up energy consumption; Is to start and stop energy consumption.
Preferably, the power balance constraint:
Wherein: n and The total number of the thermal power generating units and the maximum output force in the period t are respectively; n w The total number of the hydroelectric generating sets and the maximum output force in the period t are respectively; And The maximum predicted output force of the photovoltaic power generation and the wind power generation in the t period is respectively; p Dt、R(Pt) and delta P are respectively a t-period load value, a system reserve amount and a maximum power adjustment amount; beta is the confidence level of the system output;
since the constraint equation is in the form of a probability, the equation needs to be subjected to a deterministic process for solving, and the transformation form is as follows:
And then solving by using a dichotomy to obtain:
Wherein: f pt is a joint probability distribution function of wind-light output and load in t period, As an inverse function of this, i.e. the difference between the load predicted force and the maximum predicted force of the wind and light.
Preferably, the system is constrained by the backup:
Reserve:
The following standby is carried out:
Wherein: The upper limits of the output of the thermal power unit and the hydroelectric generating set are respectively set; p Hi、PWm is the lower limit of the output of the thermal power unit and the hydroelectric generating set respectively; alpha is the system load reserve rate; And R (P t) is the standby requirement under wind, light and electricity.
Preferably, the wind power output constraint:
photoelectric output constraint:
thermal power output constraint:
wherein: z i,t is a 0-1 variable, which is expressed as the running state of the thermal power unit;
Climbing constraint of thermal power generating unit:
-vdiΔt≤PHi,t-PHi,t-1≤vuiΔt (10)
wherein: v di and v ui are the regulation rates of the thermal power unit;
and (3) constraint of starting and stopping time of the thermal power generating unit:
(GHsi,(t-1)-THs,i)(zi.(t-1)-zi,t)≥0 (11)
(GHoi,(t-1)-THo,i)(zi.(t-1)-zi,t)≥0 (12)
Wherein: g Hsi,(t-1) and G Hoi,(t-1) are respectively the continuous startup and shutdown hours of the thermal power unit; t Hs,i and T Ho,i are respectively the minimum startup and shutdown hours of the thermal power unit;
hydropower force constraint:
constraint of water energy and electric energy conversion relation:
PWm,t＝ηWmIAmtWmt (14)
wherein: w mt、Amt、I、ηWm is the water purifying head of the reservoir, the water flow required by the hydroelectric generating set and the conversion coefficient and the working efficiency of the hydroelectric generating set in t time intervals respectively;
Water balance constraint:
Xwmin≤Xwt≤Xwmax (16)
Wherein: v wp,t is the storage capacity of the hydropower station in the t period, R wp,t is the interval water inflow of the hydropower station in the t period, X wt is the power generation flow of the hydropower station in the t period, X wmax and X wmin are the upper limit and the lower limit of the power generation flow of the hydropower station respectively, S wp,t is the water discarding amount of the hydropower station in the t period, and S wp1,t and X w1t are the water discarding amount of the upstream power station and the power generation flow of the power generation unit of the upstream power station in the t period;
Reservoir capacity flow constraint:
XSwpmin≤XSwp,t≤XSwpmax (18)
Wherein: XS wp,t is the storage capacity flow of reservoir wp in time t, and XS wpmax、XSwpmin is the upper and lower limits of the storage capacity flow of reservoir wp, respectively.
Preferably, the future schedule period peak load constraint:
wherein: z i,T is the starting and stopping state of the thermal power generating unit at the cycle end, z up,i is the starting and stopping state of the thermal power generating unit at the time of the peak load of the future dispatching cycle, tau is the time of the peak load of the future dispatching cycle, T is the time of the cycle end, P Dτ and Load demands and upper standby demands at peak time of a future scheduling period are respectively;
When the minimum shutdown time meets CT i to be more than or equal to 1-tau, the linearized switching state is as follows:
zup,i≤1-zi,T (20)
Wherein: beta 1 takes 10 4 and epsilon takes 10 -1, and the method is also applicable to the low-valley wind-discarding constraint of future scheduling periods;
When the minimum shutdown time satisfies CT i. Ltoreq.1- τ, the linearized switching state is as follows:
zup,i＝1-zi,T (23)
Future scheduling period valley wind-abandoning constraint:
wherein: z down,i is the start-up start-stop state of the thermal power generating unit when the load is low in the future scheduling period, tau 1 is the moment of the load in the low in the future scheduling period, AndThe load demand and the lower standby demand at the valley time of the future scheduling period are respectively,Acceptable air discarding quantity for the valley time of the future scheduling period:
When the minimum running time satisfies OT i≥1-τ1, the linearized switching state is as follows:
zdown,i≤zi,T (25)
When the minimum run time satisfies OT i≤1-τ1, the linearized switching state is as follows:
zdown,i＝zi,T (28)
future scheduling period light rejection constraints:
wherein: z ups,i is the start-up and stop state of the thermal power generating unit when the photovoltaic output of the future dispatching cycle is maximum, tau 2 is the moment when the photovoltaic output of the valley load of the future dispatching cycle is maximum, AndThe load requirement and the lower standby requirement at the maximum moment of the photovoltaic output of the future dispatching period are respectively,Acceptable amount of waste for future dispatch period photovoltaic maximum power moment; the on-off state of the unit is consistent with the off-peak wind curtailment constraint solving mode of the future scheduling period.
Preferably, the new energy grid connection can cause redundancy of the thermal power unit capacity, and the start-stop number and sequence of the thermal power unit are further optimized in the peak regulation constraint process;
sequencing the economy of the thermal power generating unit:
The calculation formula of the minimum specific consumption lambda min is as follows:
The quadratic function of the thermal power unit output is expressed as Wherein a i is a quadratic term coefficient, b i is a first order term coefficient, and c i is a constant term; p λi is determined by the maximum and minimum output of the thermal power unit and the coefficient of the thermal power unit, and is expressed by the following formula:
calculating the minimum specific consumption of each thermal power generating unit, and determining the number of units participating in peak regulation constraint in order from small to large;
Determining the state of a unit:
The peak load period considers extreme conditions, and the hydroelectric generating set runs at rated power and does not participate in providing positive standby of the system; if the maximum unit combination determined at the moment meets the system requirements for the load and the upper standby, other time periods except the peak load time period are also met; on the basis, determining the calling sequence of the thermal power unit by utilizing the minimum specific consumption; the starting machine set meets the following conditions:
Wherein: p Fτ and P Sτ are the wind power output and the photovoltaic output at peak load, respectively.
The beneficial effects of the invention are as follows:
firstly, the invention considers the peak regulation constraint of the continuous scheduling period in the multi-source coordinated scheduling system, can adapt to the randomness of the new energy output, and ensures the peak regulation adequacy of the system.
Secondly, the maximum starting number constraint of the unit is introduced into the optimal scheduling model, so that frequent conversion of the running state of the unit can be reduced, and redundancy of starting capacity is avoided.
Thirdly, the invention comprehensively considers the continuous scheduling period peak regulation constraint and the unit starting number constraint in the multi-source coordinated scheduling system, thereby not only ensuring the stability of the system output, but also ensuring the running economy of the system.
Drawings
FIG. 1 is a schematic diagram of a solving process of a multi-energy short-term optimization scheduling model;
table 1 shows the operating parameters of the hydropower station of the invention;
FIG. 2 is a schematic diagram of a system load demand curve according to the present invention;
FIG. 3 is a schematic diagram of a predicted output curve according to the present invention;
table 2 shows the combination scheme of the thermal power generating unit;
FIG. 4 is a comparison diagram of the combination scheme of the unit of the present invention;
FIG. 5 is a schematic diagram of the present invention considering the output of each energy source under the constraint of peak shaving in the next period;
FIG. 6 is a schematic diagram of the present invention considering the output of each energy source under the constraint of peak shaving in the next period;
table 3 shows the scheduling results in different constraint modes of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-6 and tables 1-3, a multi-energy short-term optimized scheduling method includes the following steps:
Step one: firstly, taking minimum energy consumption of system operation as a target, taking system operation constraint conditions in a scheduling period into consideration to establish a multisource coordination short-term optimization scheduling model, wherein the constraint conditions correspond to the formulas (2) to (18) listed later, and meanwhile, evaluating the combination condition of a unit to judge whether the system capacity is redundant;
step two: if the system capacity is redundant, adding a maximum starting number constraint of the thermal power unit, wherein the constraint condition corresponds to the formulas (19) to (29) listed below, and performing simulation calculation on four aspects of a unit combination scheme, an energy generating power condition, system energy loss and a thermal power unit output standard deviation;
Step three: and (3) adding constraint considering peak regulation of the continuous scheduling period on the basis of the second step, and performing simulation calculation on the four indexes again according to the formulas (30) to (34) listed below, and performing comparison evaluation analysis on the respective solving results of the system scheduling under the three constraint modes.
As a technical optimization scheme of the invention, the constraint condition for establishing the multi-energy short-term optimization scheduling model taking the peak shaving constraint of the continuous scheduling period into consideration comprises two parts, namely the intra-period constraint and the next scheduling period constraint;
In order to realize the economic benefit of the system through coordination complementation of various energy sources of wind, light, water and fire, the model optimization target is set to minimize the running cost and the starting, starting and stopping cost of the thermal power unit, namely:
Wherein: the energy consumption of the operation of the thermal power unit is a quadratic function of the output of the thermal power unit, expressed as A i is a quadratic term coefficient, b i is a first order term coefficient, and c i is a constant term; To start up energy consumption; Is to start and stop energy consumption.
As a technical optimization scheme of the invention, the power balance constraint:
Wherein: n and The total number of the thermal power generating units and the maximum output force in the period t are respectively; n w The total number of the hydroelectric generating sets and the maximum output force in the period t are respectively; And The maximum predicted output force of the photovoltaic power generation and the wind power generation in the t period is respectively; p Dt、R(Pt) and delta P are respectively a t-period load value, a system reserve amount and a maximum power adjustment amount; beta is the confidence level of the system output;
since the constraint equation is in the form of a probability, the equation needs to be subjected to a deterministic process for solving, and the transformation form is as follows:
And then solving by using a dichotomy to obtain:
Wherein: f pt is a joint probability distribution function of wind-light output and load in t period, As an inverse function of this, i.e. the difference between the load predicted force and the maximum predicted force of the wind and light.
As a technical optimization scheme of the invention, the system is constrained by standby:
Reserve:
The following standby is carried out:
Wherein: The upper limits of the output of the thermal power unit and the hydroelectric generating set are respectively set; p Hi、PWm is the lower limit of the output of the thermal power unit and the hydroelectric generating set respectively; alpha is the system load reserve rate; And R (P t) is the standby requirement under wind, light and electricity.
As a technical optimization scheme of the invention, wind power output constraint:
photoelectric output constraint:
thermal power output constraint:
wherein: z i,t is a 0-1 variable, which is expressed as the running state of the thermal power unit;
Climbing constraint of thermal power generating unit:
-vdiΔt≤PHi,t-PHi,t-1≤vuiΔt (10)
wherein: v di and v ui are the regulation rates of the thermal power unit;
and (3) constraint of starting and stopping time of the thermal power generating unit:
(GHsi,(t-1)-THs,i)(zi.(t-1)-zi,t)≥0 (11)
(GHoi,(t-1)-THo,i)(zi.(t-1)-zi,t)≥0 (12)
Wherein: g Hsi,(t-1) and G Hoi,(t-1) are respectively the continuous startup and shutdown hours of the thermal power unit; t Hs,i and T Ho,i are respectively the minimum startup and shutdown hours of the thermal power unit;
hydropower force constraint:
constraint of water energy and electric energy conversion relation:
PWm,t＝ηWmIAmtWmt (14)
wherein: w mt、Amt、I、ηWm is the water purifying head of the reservoir, the water flow required by the hydroelectric generating set and the conversion coefficient and the working efficiency of the hydroelectric generating set in t time intervals respectively;
Water balance constraint:
Xwmin≤Xwt≤Xwmax (16)
Wherein: v wp,t is the storage capacity of the hydropower station in the t period, R wp,t is the interval water inflow of the hydropower station in the t period, X wt is the power generation flow of the hydropower station in the t period, X wmax and X wmin are the upper limit and the lower limit of the power generation flow of the hydropower station respectively, S wp,t is the water discarding amount of the hydropower station in the t period, and S wp1,t and X w1t are the water discarding amount of the upstream power station and the power generation flow of the power generation unit of the upstream power station in the t period;
Reservoir capacity flow constraint:
XSwpmin≤XSwp,t≤XSwpmax (18)
Wherein: XS wp,t is the storage capacity flow of reservoir wp in time t, and XS wpmax、XSwpmin is the upper and lower limits of the storage capacity flow of reservoir wp, respectively.
As a technical optimization scheme of the invention, the peak load constraint of the future scheduling period is as follows:
wherein: z i,T is the starting and stopping state of the thermal power generating unit at the cycle end, z up,i is the starting and stopping state of the thermal power generating unit at the time of the peak load of the future dispatching cycle, tau is the time of the peak load of the future dispatching cycle, T is the time of the cycle end, P Dτ and Load demands and upper standby demands at peak time of a future scheduling period are respectively;
When the minimum shutdown time meets CT i to be more than or equal to 1-tau, the linearized switching state is as follows:
zup,i≤1-zi,T (20)
Wherein: beta 1 takes 10 4 and epsilon takes 10 -1, and the method is also applicable to the low-valley wind-discarding constraint of future scheduling periods;
When the minimum shutdown time satisfies CT i. Ltoreq.1- τ, the linearized switching state is as follows:
zup,i＝1-zi,T (23)
Future scheduling period valley wind-abandoning constraint:
wherein: z down,i is the start-up start-stop state of the thermal power generating unit when the load is low in the future scheduling period, tau 1 is the moment of the load in the low in the future scheduling period, AndThe load demand and the lower standby demand at the valley time of the future scheduling period are respectively,Acceptable air discarding quantity for the valley time of the future scheduling period:
When the minimum running time satisfies OT i≥1-τ1, the linearized switching state is as follows:
zdown,i≤zi,T (25)
When the minimum run time satisfies OT i≤1-τ1, the linearized switching state is as follows:
zdown,i＝zi,T (28)
future scheduling period light rejection constraints:
wherein: z ups,i is the start-up and stop state of the thermal power generating unit when the photovoltaic output of the future dispatching cycle is maximum, tau 2 is the moment when the photovoltaic output of the valley load of the future dispatching cycle is maximum, AndThe load requirement and the lower standby requirement at the maximum moment of the photovoltaic output of the future dispatching period are respectively,Acceptable amount of waste for future dispatch period photovoltaic maximum power moment; the on-off state of the unit is consistent with the off-peak wind curtailment constraint solving mode of the future scheduling period.
As a technical optimization scheme of the invention, the new energy grid connection can cause redundancy of the thermal power unit capacity, and the start-stop number and sequence of the thermal power unit are further optimized in the peak regulation constraint process;
sequencing the economy of the thermal power generating unit:
The calculation formula of the minimum specific consumption lambda min is as follows:
The quadratic function of the thermal power unit output is expressed as Wherein a i is a quadratic term coefficient, b i is a first order term coefficient, and c i is a constant term; p λi is determined by the maximum and minimum output of the thermal power unit and the coefficient of the thermal power unit, and is expressed by the following formula:
calculating the minimum specific consumption of each thermal power generating unit, and determining the number of units participating in peak regulation constraint in order from small to large;
Determining the state of a unit:
The peak load period considers extreme conditions, and the hydroelectric generating set runs at rated power and does not participate in providing positive standby of the system; if the maximum unit combination determined at the moment meets the system requirements for the load and the upper standby, other time periods except the peak load time period are also met; on the basis, determining the calling sequence of the thermal power unit by utilizing the minimum specific consumption; the starting machine set meets the following conditions:
Wherein: p Fτ and P Sτ are the wind power output and the photovoltaic output at peak load, respectively.
As a technical optimization scheme of the invention, the device unit adopts a 10-machine system for simulation calculation, two cascade hydropower stations, a wind power station and a photovoltaic power station are connected into the system; the data of the hydroelectric generating sets are shown in table 1, wherein 4 sets with the numbers of W1, W2, W3 and W4 belong to a downstream hydroelectric power station; 3 units with the numbers of W5, W6 and W7 belong to an upstream water power station; the load demand curve and the wind power output and photovoltaic power generation output prediction curve of the calculation example are respectively shown in fig. 2 and 3; the power constraint central communication level of the system is 0.7, the load reserve rate is 0.05, and the hydropower conversion coefficient is 9.81. The scheduling model is realized through GAMS software programming, and a CPLEX solver is called for solving.
As a technical optimization scheme of the invention, in order to analyze the influence of the power generation capacity redundancy in the system on the scheduling decision of the system, the model is compared with a unit combination scheme without considering the starting number constraint model of the thermal power unit, and the simulation result is shown in table 2; it can be seen that the thermal power generating units participate in system scheduling in each period of the model under the two constraint modes, the output of the thermal power generating units in the peak load period is 1250MW to 1254MW, and the output of the thermal power generating units in the low-valley period is 652MW to 654MW, so that the effective operation of the system can be ensured; the maximum starting number constraint of the units is introduced, the starting number of the thermal power units is reduced from 10 units to 9 units, the running time period number of the units with low economical efficiency is reduced, the frequent conversion of the running states of the units is reduced, and the redundancy of the system capacity is avoided;
In order to further verify the necessity of taking the peak shaving constraint of the continuous scheduling period into consideration in the modeling type, on the basis of introducing the starting number constraint of the thermal power generating unit, carrying out comparative analysis of an example from the two angles of whether the peak shaving constraint of the future scheduling period is carried out or not; the output conditions of each power generation energy source in the system under two conditions are obtained through optimization calculation, as shown in figures 5 and 6;
As can be seen from fig. 5 and 6, under the constraint of the number of starts of the thermal power generating unit, when the peak regulation constraint is considered, the total power generated by the thermal power generating unit in the 4 time periods of the end and the beginning of the dispatching cycle is 2964MW, the total power generated by the thermal power generating unit in the 4 time periods of the peak load is 4839MW, and when the water and electricity do not participate in dispatching in the 3 time periods of the beginning of the dispatching cycle, and when the peak regulation constraint is not considered, the total power generated by the thermal power generating unit in the 4 time periods of the end and the beginning of the dispatching cycle is 2816MW, and the total power generated by the thermal power generating unit in the 4 time periods of the peak load is 4904MW; the method is characterized in that the peak shaving constraint is added to improve the peak shaving requirement of the unit, so that the thermal power unit needs to meet the coming peak load requirement in real time at the end and beginning of the period to ensure the reliable operation of the system, and the thermal power output is improved to some extent;
In order to verify the lifting effect of continuous scheduling period peak regulation constraint and unit starting number constraint on the economic and stable operation of the system, comparing and analyzing operation schemes under four constraint modes respectively;
scheme 1: modes of avoiding future scheduling period peak shaving constraint and avoiding maximum starting number of units;
scheme 2: a mode of considering the peak shaving constraint of a future dispatching cycle but not considering the maximum starting number of the unit;
scheme 3: modes which do not consider the peak shaving constraint of a future dispatching cycle but consider the maximum starting number of units;
scheme 4: a mode of considering the peak shaving constraint of a future dispatching cycle and the maximum starting number of the unit;
Meanwhile, a quantization index thermal power output standard deviation sigma H is introduced to compare the running stability of the system under each scheme, the smaller the sigma H is, the higher the system output stability is, and the specific expression is as follows:
as can be seen from Table 3, the scheduling results of the scheme 1 and the scheme 2 are the same, 10 thermal power units are all involved in scheduling, and the standard deviation of the coal consumption and the thermal power output of the 10 thermal power units are relatively high, namely, when the constraint of the maximum starting number of the units is not considered, the system is in a power generation capacity redundancy state. And the standard deviation of the system coal consumption and the thermal power output of the scheme 3 is respectively reduced by 0.49% and 25.05% compared with the standard deviation of the system coal consumption and the thermal power output of the scheme 1 and the scheme 2, and the standard deviation of the system coal consumption and the thermal power output of the scheme 4 is respectively reduced by 0.29% and 28.53% compared with the standard deviation of the system coal consumption and the thermal power output of the scheme 1 and the scheme 2, which indicates that whether peak regulation constraint is considered or not, and the stability and the economical efficiency of the multi-source coordinated scheduling system can be improved by considering the maximum starting number constraint of units. In addition, the coal consumption of the scheme 3 is obviously reduced compared with that of the scheme 4, and the standard deviation of the thermal power output is improved, which shows that in order to meet the continuous scheduling period peak regulation constraint of the system, the scheme 4 can reduce the standard deviation of the output of the thermal power unit, namely the stability of the running scheduling of the system is improved although the coal consumption of the system is increased.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
TABLE 1
TABLE 2
TABLE 3 Table 3
Claims (1)
1. The short-term optimal scheduling method for the multiple energy sources is characterized by comprising the following steps of:
Step one: firstly, taking minimum energy consumption of system operation as a target, taking system operation constraint conditions in a scheduling period into consideration to establish a multisource coordination short-term optimization scheduling model, and simultaneously evaluating the combination condition of units to judge whether the system capacity is redundant;
Step two: if the system capacity is redundant, adding the maximum starting number constraint of the thermal power unit, and performing simulation calculation on four aspects of a unit combination scheme, an energy source power generation condition, system energy loss and a thermal power unit output standard deviation;
Step three: adding constraint considering peak regulation of a continuous scheduling period on the basis of the second step, performing simulation calculation on the four indexes again, and performing comparison evaluation analysis on respective solving results of system scheduling under three constraint modes; the constraint condition of establishing a multi-energy short-term optimization scheduling model considering continuous scheduling period peak shaving constraint comprises two parts, namely intra-period constraint and next scheduling period constraint;
In order to realize the economic benefit of the system through coordination complementation of various energy sources of wind, light, water and fire, the model optimization target is set to minimize the running cost and the starting, starting and stopping cost of the thermal power unit, namely:
Wherein: the energy consumption of the operation of the thermal power unit is a quadratic function of the output of the thermal power unit, expressed as A i is a quadratic term coefficient, b i is a first order term coefficient, and c i is a constant term; To start up energy consumption; The energy consumption is for starting and stopping;
Power balance constraint:
Wherein: n and The total number of the thermal power generating units and the maximum output force in the period t are respectively; n w The total number of the hydroelectric generating sets and the maximum output force in the period t are respectively; And The maximum predicted output force of the photovoltaic power generation and the wind power generation in the t period is respectively; p Dt、R(Pt) and delta P are respectively a t-period load value, a system reserve amount and a maximum power adjustment amount; beta is the confidence level of the system output;
since the constraint equation is in the form of a probability, the equation needs to be subjected to a deterministic process for solving, and the transformation form is as follows:
And then solving by using a dichotomy to obtain:
Wherein: f pt is a joint probability distribution function of wind-light output and load in t period, The inverse function of the wind-solar hybrid power generation system is that the difference between the predicted power and the maximum predicted power of wind and light is predicted for the load;
System up and down standby constraints:
Reserve:
The following standby is carried out:
Wherein: The upper limits of the output of the thermal power unit and the hydroelectric generating set are respectively set; p Hi、PWm is the lower limit of the output of the thermal power unit and the hydroelectric generating set respectively; alpha is the system load reserve rate; r (P t) is the standby requirement under wind, light and electricity;
wind power output constraint:
photoelectric output constraint:
thermal power output constraint:
wherein: z i,t is a 0-1 variable, which is expressed as the running state of the thermal power unit;
Climbing constraint of thermal power generating unit:
-vdiΔt≤PHi,t-PHi,t-1≤vuiΔt
(10)
wherein: v di and v ui are the regulation rates of the thermal power unit;
and (3) constraint of starting and stopping time of the thermal power generating unit:
(GHsi,(t-1)-THs,i)(zi.(t-1)-zi,t)≥0
(11)
(GHoi,(t-1)-THo,i)(zi.(t-1)-zi,t)≥0
(12)
wherein: g Hsi,(t-1) and G Hoi,(t-1) are respectively the continuous startup and shutdown hours of the thermal power unit; t Hs,i and T Ho,i are respectively the minimum startup and shutdown hours of the thermal power unit;
hydropower force constraint:
constraint of water energy and electric energy conversion relation:
PWm,t＝ηWmIAmtWmt
(14)
wherein: w mt、Amt、I、ηWm is the water purifying head of the reservoir, the water flow required by the hydroelectric generating set and the conversion coefficient and the working efficiency of the hydroelectric generating set in t time intervals respectively;
Water balance constraint:
Xwmin≤Xwt≤Xwmax
(16)
wherein: v wp,t is the storage capacity of the hydropower station in the t period, R wp,t is the interval water inflow of the hydropower station in the t period, X wt is the power generation flow of the hydropower station in the t period, X wmax and X wmin are the upper limit and the lower limit of the power generation flow of the hydropower station respectively, S wp,t is the water discarding amount of the hydropower station in the t period, and S wp1,t and X w1t are the water discarding amount of the upstream power station and the power generation flow of the power generation unit of the upstream power station in the t period;
Reservoir capacity flow constraint:
XSwpmin≤XSwp,t≤XSwpmax
(18)
Wherein: XS wp,t is the storage capacity flow of the reservoir wp in the period t, and XS wpmax、XSwpmin is the upper limit and the lower limit of the storage capacity flow of the reservoir wp respectively;
future scheduling period peak load constraints:
wherein: z i,T is the starting and stopping state of the thermal power generating unit at the cycle end, z up,i is the starting and stopping state of the thermal power generating unit at the time of the peak load of the future dispatching cycle, tau is the time of the peak load of the future dispatching cycle, T is the time of the cycle end, P Dτ and Load demands and upper standby demands at peak time of a future scheduling period are respectively;
When the minimum shutdown time meets CT i to be more than or equal to 1-tau, the linearized switching state is as follows:
zup,i≤1-zi,T
(20)
Wherein: beta 1 takes 10 4 and epsilon takes 10 -1, and the method is also applicable to the low-valley wind-discarding constraint of future scheduling periods;
When the minimum shutdown time satisfies CT i. Ltoreq.1- τ, the linearized switching state is as follows:
zup,i＝1-zi,T
(23)
Future scheduling period valley wind-abandoning constraint:
wherein: z down,i is the start-up start-stop state of the thermal power generating unit when the load is low in the future scheduling period, tau 1 is the moment of the load in the low in the future scheduling period, AndThe load demand and the lower standby demand at the valley time of the future scheduling period are respectively,Acceptable air discarding quantity for the valley time of the future scheduling period:
When the minimum running time satisfies OT i≥1-τ1, the linearized switching state is as follows:
zdown,i≤zi,T
(25)
When the minimum run time satisfies OT i≤1-τ1, the linearized switching state is as follows:
zdown,i＝zi,T
(28)
future scheduling period light rejection constraints:
wherein: z ups,i is the start-up and stop state of the thermal power generating unit when the photovoltaic output of the future dispatching cycle is maximum, tau 2 is the moment when the photovoltaic output of the valley load of the future dispatching cycle is maximum, AndThe load requirement and the lower standby requirement at the maximum moment of the photovoltaic output of the future dispatching period are respectively,Acceptable amount of waste for future dispatch period photovoltaic maximum power moment; the on-off state of the unit is consistent with the off-peak wind curtailment constraint solving mode of a future scheduling period;
The new energy grid connection can cause redundancy of the thermal power unit capacity, and the starting and stopping number and sequence of the thermal power unit are further optimized in the peak regulation constraint process;
sequencing the economy of the thermal power generating unit:
The calculation formula of the minimum specific consumption lambda min is as follows:
The quadratic function of the thermal power unit output is expressed as Wherein a i is a quadratic term coefficient, b i is a first order term coefficient, and c i is a constant term; p λi is determined by the maximum and minimum output of the thermal power unit and the coefficient of the thermal power unit, and is expressed by the following formula:
calculating the minimum specific consumption of each thermal power generating unit, and determining the number of units participating in peak regulation constraint in order from small to large;
Determining the state of a unit:
The peak load period considers extreme conditions, and the hydroelectric generating set runs at rated power and does not participate in providing positive standby of the system; if the maximum unit combination determined at the moment meets the system requirements for the load and the upper standby, other time periods except the peak load time period are also met; on the basis, determining the calling sequence of the thermal power unit by utilizing the minimum specific consumption; the starting machine set meets the following conditions:
Wherein: p Fτ and P Sτ are the wind power output and the photovoltaic output at peak load, respectively.
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