CN113937819A - Multi-energy short-term optimization scheduling method - Google Patents

Multi-energy short-term optimization scheduling method Download PDF

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CN113937819A
CN113937819A CN202110738594.6A CN202110738594A CN113937819A CN 113937819 A CN113937819 A CN 113937819A CN 202110738594 A CN202110738594 A CN 202110738594A CN 113937819 A CN113937819 A CN 113937819A
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thermal power
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period
output
power generating
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***
吴悦
邵冲
刘丽娟
王定美
刘克权
魏博
徐宏雷
余姣
刘春�
汤文
张烜榕
周强
李津
张金平
吕清泉
高鹏飞
张彦琪
张健美
张珍珍
张睿骁
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STATE GRID GASU ELECTRIC POWER RESEARCH INSTITUTE
State Grid Gansu Electric Power Co Ltd
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STATE GRID GASU ELECTRIC POWER RESEARCH INSTITUTE
State Grid Gansu Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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Abstract

The invention discloses a multi-energy short-term optimization scheduling method, which comprises the following steps: the method comprises the following steps: firstly, establishing a multi-source coordination short-term optimization scheduling model by taking the minimum system operation energy consumption as a target and considering system operation constraint conditions in a scheduling period, and meanwhile, evaluating the unit combination condition and judging whether the system capacity is redundant; step two: and if the system capacity is redundant, adding the maximum starting number constraint of the thermal power generating unit, and performing simulation calculation on the four aspects of the unit combination scheme, the energy generation power condition, the system energy loss and the thermal power generating unit output standard deviation. The invention has the beneficial effects that: according to the invention, the randomness of new energy output can be adapted by considering the peak regulation constraint of the continuous scheduling period in the multi-source coordinated scheduling system, and the abundance of the peak regulation of the system is ensured; the invention introduces the maximum starting number constraint of the unit in the optimized scheduling model, can reduce the frequent switching of the running state of the unit and avoid the redundancy of the starting capacity.

Description

Multi-energy short-term optimization scheduling method
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 an electric power system is continuously improved, clean energy is vigorously developed to bring positive influence to the economic operation of the system, the problem of energy shortage is relieved, and however, the randomness and the reverse peak regulation characteristic of wind and light output bring great challenges to the economic stable operation of the electric power system. Therefore, the multi-energy optimization scheduling model considering the peak load regulation constraint is researched from the short-term scheduling level, and has important significance for relieving the peak load regulation pressure of the system.
The existing multi-energy optimization scheduling research mainly focuses on considering the economic operation problem of a system, and literature researches research researches the scheduling problem of complex thermal power unit combination by taking the lowest total operation cost as a target, and makes a thermal power unit start-stop plan meeting load requirements. However, for the randomness and the anti-peak-shaving characteristics of the grid-connected output of new energy such as wind, light and the like, the thermal power generating unit has certain peak shaving capability but slow response speed, and if the wind, light and electricity are adjusted only by thermal power, the unit is frequently started and stopped, and the starting and stopping cost is very high. Therefore, the complementary characteristics of the energy sources are required to be fully utilized, and the regulation effect of the new energy sources on the system operation is exerted. Literature research aims at preferentially absorbing new energy, and researches on combined optimal scheduling of wind, water and fire three kinds of energy, and a heuristic search method is provided to determine the optimal scheduling quantity of thermal power generating units. According to literature research, pumped storage is utilized to compensate for the valley filling capacity which water and electricity do not have, a wind-containing and fire-storing combined optimization scheduling model is established, the model is solved by a 'segmentation-sequence-feedback' method, multi-energy segmentation coordination scheduling is achieved, the influence of uncertainty of output of new energy is ignored, and the problem of peak shaving caused by grid connection of new energy such as wind and light is difficult to overcome. Therefore, the complementary level among energy sources is evaluated by researching the tracking degree in literature, and a virtual power supply configuration strategy is proposed, so that the peak regulation 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 continuous scheduling period of the system and the influence of the system capacity redundancy.
Based on the background, the research content in this chapter fully considers the randomness and the anti-peak-shaving characteristic of the new energy output. In order to overcome the peak shaving problem brought by the grid connection of new energy such as wind, light, water and fire and coordinate the power generation dispatching of wind, light, water and fire, a multi-energy optimization dispatching model considering the continuous dispatching cycle peak shaving constraint is constructed. Firstly, aiming at the minimum energy consumption of system operation, establishing a plurality of coupled operation constraints of four energy forms of wind, light, water and fire, and aiming at the problems of uncertainty of new energy output and larger operation inertia of the traditional thermal power unit, establishing a standby constraint, a low-valley wind abandoning constraint and a light abandoning constraint of a load peak in a continuous scheduling period. Then, in order to reduce the problems of economy and capacity redundancy of the system, an economical sequencing method is provided, and the maximum starting number constraint of the thermal power generating units 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 a multi-energy short-term optimization scheduling method comprises the following steps:
the method comprises the following steps: firstly, establishing a multi-source coordination short-term optimization scheduling model by taking the minimum system operation energy consumption as a target and considering system operation constraint conditions in a scheduling period, and meanwhile, evaluating the unit combination condition and judging whether the system capacity is redundant;
step two: if the system capacity is redundant, adding the maximum starting number constraint of the thermal power generating unit, and performing simulation calculation on the four aspects of the unit combination scheme, the energy generation power condition, the system energy loss and the thermal power generating unit output standard deviation;
step three: and (4) adding the peak regulation constraint considering the continuous scheduling period on the basis of the step two, performing simulation calculation on the four indexes again, and performing comparative evaluation analysis on the respective solution results of system scheduling in the three constraint modes.
Preferably, the constraint conditions for establishing the multi-energy short-term optimization scheduling model considering the peak load regulation constraint of the continuous scheduling period comprise an intra-period constraint and a next scheduling period constraint;
in order to realize the economic benefit of the system through the coordination and complementation of various energy sources of wind, light, water and fire, the model optimization target is set to minimize the operation cost and the starting and stopping cost of the thermal power generating unit, namely:
Figure RE-GDA0003416736710000031
in the formula:
Figure RE-GDA0003416736710000032
the energy consumption of the thermal power generating unit is a quadratic function of the output of the thermal power generating unit and is expressed as
Figure RE-GDA0003416736710000033
aiIs a coefficient of a quadratic term, biIs a coefficient of a first order term, ciIs a constant term;
Figure RE-GDA0003416736710000034
energy consumption is started;
Figure RE-GDA0003416736710000035
energy consumption is started and stopped.
Preferably, the power balance constraint is:
Figure RE-GDA0003416736710000036
in the formula: n and
Figure RE-GDA0003416736710000037
respectively representing the total number of the thermal power generating units and the maximum output in the t period; n is a radical ofwAnd
Figure RE-GDA0003416736710000038
respectively representing the total quantity of the hydroelectric generating sets and the maximum output in the t period;
Figure RE-GDA0003416736710000039
and
Figure RE-GDA00034167367100000310
respectively predicting the maximum output of photovoltaic power generation and wind power generation in a time period t; pDt、R(Pt) And delta P is a load value, a system spare amount and a maximum power adjustment amount in the t time period respectively; beta is the confidence level of the system output;
since the constraint equation is in a probabilistic form, the equation needs to be determined for solving, and the conversion form is as follows:
Figure RE-GDA00034167367100000311
and then solving by using a dichotomy to obtain:
Figure RE-GDA0003416736710000041
in the formula: fptFor the joint probability distribution function of the wind-solar output and the load in the period t,
Figure RE-GDA0003416736710000042
is the inverse function of the predicted contribution from the load and the maximum predicted contribution from the wind and light.
Preferably, the system is constrained on standby:
and (3) standby:
Figure RE-GDA0003416736710000043
the following is for standby:
Figure RE-GDA0003416736710000044
in the formula:
Figure RE-GDA0003416736710000045
the upper limits of the output of the thermal power unit and the hydroelectric generating set are respectively; HiP、 WmPthe lower limits of the output of the thermal power unit and the hydroelectric generating set are respectively; alpha is the system load spare rate;
Figure RE-GDA0003416736710000046
for the standby requirement of wind, light and electricity,R(Pt) The wind, light and electricity standby requirement is met.
Preferably, the wind power output constraint is as follows:
Figure RE-GDA0003416736710000047
photoelectric output force restraint:
Figure RE-GDA0003416736710000048
thermal power output constraint:
Figure RE-GDA0003416736710000049
in the formula: z is a radical ofi,tThe variable is a 0-1 variable and is expressed as the running state of the thermal power generating unit;
and (3) climbing restraint of the thermal power generating unit:
-vdiΔt≤PHi,t-PHi,t-1≤vuiΔt (10)
in the formula: v. ofdiAnd vuiRegulating the speed of the thermal power generating unit;
and (3) limiting the 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)
in the formula: gHsi,(t-1)And GHoi,(t-1)Respectively the continuous startup and shutdown hours of the thermal power generating unit; t isHs,iAnd THo,iRespectively setting minimum startup and shutdown hours of the thermal power generating unit;
and (3) water and electricity output restraint:
Figure RE-GDA0003416736710000051
constraint of water energy and electric energy conversion relation:
PWm,t＝ηWmIAmtWmt (14)
in the formula: wmt、Amt、I、ηWmThe water flow, the conversion coefficient and the working efficiency of the hydroelectric generating set are respectively the water purifying head of the reservoir and the water flow required by the hydroelectric generating set for generating electricity in the time period t;
and (3) water balance constraint:
Figure RE-GDA0003416736710000052
Xwmin≤Xwt≤Xwmax (16)
in the formula: vwp,tIs the reservoir capacity, R, of the hydropower station in the period of twp,tInterval water inflow of hydropower stations in t time period, XwtThe generated flow of the hydroelectric generating set in the time period of t, XwmaxAnd XwminRespectively the upper and lower limits of the generating flow of the hydroelectric generating set Swp,tFor a period of t time, the water abandoning amount of the hydropower station interval Swp1,tAnd Xw1tThe water abandoning amount of an upstream power station and the power generation flow of a power generating set of the upstream power station in the time period t;
reservoir capacity flow constraint:
Figure RE-GDA0003416736710000053
XSwpmin≤XSwp,t≤XSwpmax (18)
in the formula: XS (Cross site architecture)wp,tIs the reservoir capacity flow, XS, of the reservoir wp during the period twpmax、XSwpminRespectively the upper and lower limits of the reservoir capacity flow of the reservoir wp.
Preferably, the future scheduling period peak load constraint:
Figure RE-GDA0003416736710000061
in the formula: z is a radical ofi,TFor starting and stopping the electric machine set in the period of end fire, zup,iStarting and stopping states of the thermal power generating unit at peak load of a future scheduling period, wherein tau is the peak load moment of the future scheduling period, T is the end moment of the period, and P isDτAnd
Figure RE-GDA0003416736710000062
load requirements and upper standby requirements at peak time of a future scheduling period are respectively set;
wherein the minimum down time satisfies CTiAt > 1- τ, the linearized switch state is as follows:
zup,i≤1-zi,T (20)
Figure RE-GDA0003416736710000063
Figure RE-GDA0003416736710000064
in the formula: beta is a1Get 104In the case of epsilon 10-1The method is also suitable for the low valley wind curtailment constraint of the future scheduling period;
when the minimum down time satisfies CTiAt 1-tau or less, the linearized switch states are as follows:
zup,i＝1-zi,T (23)
and (3) carrying out low-valley wind curtailment constraint on a future scheduling period:
Figure RE-GDA0003416736710000065
in the formula: z is a radical ofdown,iFor the starting and stopping state, tau, of the thermal power generating unit during the low-ebb load of the future scheduling cycle1For the future scheduling of the cycle trough load moments,
Figure RE-GDA0003416736710000071
and
Figure RE-GDA0003416736710000072
load demand and next standby demand at the time of the future scheduling cycle trough are respectively,
Figure RE-GDA0003416736710000073
the acceptable air abandon rate for the low ebb moment of the future scheduling cycle:
wherein OT is satisfied when the minimum operation time is satisfiedi≥1-τ1The linearized switch states are as follows:
zdown,i≤zi,T (25)
Figure RE-GDA0003416736710000074
Figure RE-GDA0003416736710000075
when the minimum operation time satisfies OTi≤1-τ1The linearized switch states are as follows:
zdown,i＝zi,T (28)
and (3) light abandoning constraint of a future scheduling period:
Figure RE-GDA0003416736710000076
in the formula: z is a radical ofups,iFor the starting and stopping state, tau, of the thermal power generating unit when the photovoltaic output is maximum in the future scheduling period2For scheduling the peak load photovoltaic output maximum time of the cycle in the future,
Figure RE-GDA0003416736710000077
and
Figure RE-GDA0003416736710000078
load requirements and lower standby requirements at the moment of maximum photovoltaic output of a future scheduling cycle are respectively obtained,
Figure RE-GDA0003416736710000079
the acceptable light abandon amount of the photovoltaic maximum output moment of the dispatching cycle in the future; and the on-off state of the unit is consistent with the low valley wind curtailment constraint solving mode of the future scheduling period.
Preferably, the capacity of the thermal power generating unit is redundant due to new energy grid connection, and the starting and stopping number and sequence of the thermal power generating unit are further optimized in the peak regulation constraint process;
and (3) economic sequencing of thermal power generating units:
minimum specific consumption lambdaminThe calculation formula of (a) is as follows:
Figure RE-GDA0003416736710000081
the quadratic function of the thermal power unit output is expressed as
Figure RE-GDA0003416736710000082
Wherein a isiIs a coefficient of a quadratic term, biIs a coefficient of a first order term, ciIs a constant term; pλiThe method is determined by the maximum and minimum output of the thermal power generating unit and the coefficient of the thermal power generating unit, and is expressed as follows:
Figure RE-GDA0003416736710000083
Figure RE-GDA0003416736710000084
calculating the minimum specific consumption of each thermal power generating unit, and determining the number of the units participating in peak regulation constraint in a sequence from small to large;
determining the state of the unit:
extreme conditions are considered in the peak load period, and the hydroelectric generating set runs at rated power and does not participate in providing positive standby of the system; if the determined maximum unit combination meets the system requirements for both the load and the upper standby, other time periods except the peak load time period are met; on the basis, determining the calling sequence of the thermal power generating unit by using the minimum specific consumption; the starting unit meets the following conditions:
Figure RE-GDA0003416736710000085
Figure RE-GDA0003416736710000086
in the formula: pFτAnd PSτRespectively wind power output and photoelectric output at peak load.
The invention has the beneficial effects that:
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 abundance of the peak regulation of the system.
Secondly, the invention introduces the maximum starting number constraint of the unit in the optimized scheduling model, can reduce the frequent switching of the unit running state, and avoids the redundancy of the starting capacity.
Thirdly, the continuous dispatching cycle peak regulation constraint and the unit starting number constraint in the multi-source coordinated dispatching system are comprehensively considered, so that the stability of the system output can be guaranteed, and the running economy of the system can be guaranteed.
Drawings
FIG. 1 is a schematic diagram of a solving process of a multi-energy short-term optimization scheduling model according to the present invention;
table 1 shows the operating parameters of the hydropower station according to the invention;
FIG. 2 is a schematic view of a system load demand curve according to the present invention;
FIG. 3 is a schematic diagram of a predicted force curve according to the present invention;
table 2 shows a combined scheme of the thermal power generating unit of the present invention;
FIG. 4 is a comparison of the assembly scheme of the present invention;
FIG. 5 is a schematic diagram of the present invention for restraining the output of each energy in consideration of peak shaving in the next period;
FIG. 6 is a schematic diagram of the present invention for restraining the output of each energy in consideration of peak shaving in the next period;
table 3 shows the scheduling results of the present invention under different constraint modes.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-6 and tables 1-3, a multi-energy short-term optimal scheduling method includes the following steps:
the method comprises the following steps: firstly, establishing a multi-source coordination short-term optimization scheduling model by taking the minimum system operation energy consumption as a target and considering system operation constraint conditions in a scheduling period, wherein the constraint conditions correspond to formulas (2) to (18) listed later, and meanwhile, evaluating the unit combination condition and judging whether the system capacity is redundant;
step two: if the system capacity is redundant, adding the maximum starting number constraint of the thermal power generating unit, wherein the constraint conditions correspond to formulas (19) to (29) listed later, and performing simulation calculation on four aspects of a unit combination scheme, an energy generation power condition, system energy loss and a thermal power generating unit output standard deviation;
step three: and (3) adding continuous scheduling period peak regulation constraints into consideration on the basis of the second step, corresponding to the formulas (30) to (34) listed later, performing simulation calculation on the four indexes, and performing comparative evaluation analysis on the respective solution results of system scheduling in the three constraint modes.
As a technical optimization scheme of the invention, the constraint conditions of the multi-energy short-term optimization scheduling model considering the peak load regulation constraint of the continuous scheduling period comprise an intra-period constraint and a next scheduling period constraint;
in order to realize the economic benefit of the system through the coordination and complementation of various energy sources of wind, light, water and fire, the model optimization target is set to minimize the operation cost and the starting and stopping cost of the thermal power generating unit, namely:
Figure RE-GDA0003416736710000101
in the formula:
Figure RE-GDA0003416736710000102
the energy consumption of the thermal power generating unit is a quadratic function of the output of the thermal power generating unit and is expressed as
Figure RE-GDA0003416736710000103
aiIs a coefficient of a quadratic term, biIs a coefficient of a first order term, ciIs a constant term;
Figure RE-GDA0003416736710000104
energy consumption is started;
Figure RE-GDA0003416736710000105
energy consumption is started and stopped.
As a technical optimization scheme of the present invention, the power balance constraint is:
Figure RE-GDA0003416736710000106
in the formula: n and
Figure RE-GDA0003416736710000107
respectively representing the total number of the thermal power generating units and the maximum output in the t period; n is a radical ofwAnd
Figure RE-GDA0003416736710000108
respectively representing the total quantity of the hydroelectric generating sets and the maximum output in the t period;
Figure RE-GDA0003416736710000109
and
Figure RE-GDA00034167367100001010
respectively predicting the maximum output of photovoltaic power generation and wind power generation in a time period t; pDt、R(Pt) And delta P is a load value, a system spare amount and a maximum power adjustment amount in the t time period respectively; beta is the confidence level of the system output;
since the constraint equation is in a probabilistic form, the equation needs to be determined for solving, and the conversion form is as follows:
Figure RE-GDA0003416736710000111
and then solving by using a dichotomy to obtain:
Figure RE-GDA0003416736710000112
in the formula: fptFor the joint probability distribution function of the wind-solar output and the load in the period t,
Figure RE-GDA0003416736710000113
is the inverse function of the predicted contribution from the load and the maximum predicted contribution from the wind and light.
As a technical optimization scheme of the invention, the system is provided with upper and lower standby constraints:
and (3) standby:
Figure RE-GDA0003416736710000114
the following is for standby:
Figure RE-GDA0003416736710000115
in the formula:
Figure RE-GDA0003416736710000116
the upper limits of the output of the thermal power unit and the hydroelectric generating set are respectively; HiP、 WmPthe lower limits of the output of the thermal power unit and the hydroelectric generating set are respectively; alpha is the system load spare rate;
Figure RE-GDA0003416736710000117
for the standby requirement of wind, light and electricity,R(Pt) The wind, light and electricity standby requirement is met.
As a technical optimization scheme of the invention, the wind power output is constrained as follows:
Figure RE-GDA0003416736710000118
photoelectric output force restraint:
Figure RE-GDA0003416736710000121
thermal power output constraint:
Figure RE-GDA0003416736710000122
in the formula: z is a radical ofi,tThe variable is a 0-1 variable and is expressed as the running state of the thermal power generating unit;
and (3) climbing restraint of the thermal power generating unit:
-vdiΔt≤PHi,t-PHi,t-1≤vuiΔt (10)
in the formula: v. ofdiAnd vuiRegulating the speed of the thermal power generating unit;
and (3) limiting the 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)
in the formula: gHsi,(t-1)And GHoi,(t-1)Respectively the continuous startup and shutdown hours of the thermal power generating unit; t isHs,iAnd THo,iRespectively setting minimum startup and shutdown hours of the thermal power generating unit;
and (3) water and electricity output restraint:
Figure RE-GDA0003416736710000123
constraint of water energy and electric energy conversion relation:
PWm,t＝ηWmIAmtWmt (14)
in the formula: wmt、Amt、I、ηWmThe water flow, the conversion coefficient and the working efficiency of the hydroelectric generating set are respectively the water purifying head of the reservoir and the water flow required by the hydroelectric generating set for generating electricity in the time period t;
and (3) water balance constraint:
Figure RE-GDA0003416736710000124
Xwmin≤Xwt≤Xwmax (16)
in the formula: vwp,tIs the reservoir capacity, R, of the hydropower station in the period of twp,tInterval water inflow of hydropower stations in t time period, XwtThe generated flow of the hydroelectric generating set in the time period of t, XwmaxAnd XwminRespectively the upper and lower limits of the generating flow of the hydroelectric generating set Swp,tFor a period of t time, the water abandoning amount of the hydropower station interval Swp1,tAnd Xw1tThe water abandoning amount of an upstream power station and the power generation flow of a power generating set of the upstream power station in the time period t;
reservoir capacity flow constraint:
Figure RE-GDA0003416736710000131
XSwpmin≤XSwp,t≤XSwpmax (18)
in the formula: XS (Cross site architecture)wp,tIs the reservoir capacity flow, XS, of the reservoir wp during the period twpmax、XSwpminRespectively the upper and lower limits of the reservoir capacity flow of the reservoir wp.
As a technical optimization scheme of the invention, the peak load constraint in the future scheduling period is as follows:
Figure RE-GDA0003416736710000132
in the formula: z is a radical ofi,TFor starting and stopping the electric machine set in the period of end fire, zup,iStarting and stopping states of the thermal power generating unit at peak load of a future scheduling period, wherein tau is the peak load moment of the future scheduling period, T is the end moment of the period, and P isDτAnd
Figure RE-GDA0003416736710000133
load requirements and upper standby requirements at peak time of a future scheduling period are respectively set;
wherein the minimum down time satisfies CTiAt > 1- τ, the linearized switch state is as follows:
zup,i≤1-zi,T (20)
Figure RE-GDA0003416736710000134
Figure RE-GDA0003416736710000141
in the formula: beta is a1 Get 104In the case of epsilon 10-1The method is also suitable for the low valley wind curtailment constraint of the future scheduling period;
when the minimum down time satisfies CTiAt 1-tau or less, the linearized switch states are as follows:
zup,i＝1-zi,T (23)
and (3) carrying out low-valley wind curtailment constraint on a future scheduling period:
Figure RE-GDA0003416736710000142
in the formula: z is a radical ofdown,iFor the starting and stopping state, tau, of the thermal power generating unit during the low-ebb load of the future scheduling cycle1For the future scheduling of the cycle trough load moments,
Figure RE-GDA0003416736710000143
and
Figure RE-GDA0003416736710000144
load demand and next standby demand at the time of the future scheduling cycle trough are respectively,
Figure RE-GDA0003416736710000145
the acceptable air abandon rate for the low ebb moment of the future scheduling cycle:
wherein OT is satisfied when the minimum operation time is satisfiedi≥1-τ1The linearized switch states are as follows:
zdown,i≤zi,T (25)
Figure RE-GDA0003416736710000146
Figure RE-GDA0003416736710000147
when the minimum operation time satisfies OTi≤1-τ1The linearized switch states are as follows:
zdown,i＝zi,T (28)
and (3) light abandoning constraint of a future scheduling period:
Figure RE-GDA0003416736710000148
in the formula: z is a radical ofups,iFor the starting and stopping state, tau, of the thermal power generating unit when the photovoltaic output is maximum in the future scheduling period2For scheduling the peak load photovoltaic output maximum time of the cycle in the future,
Figure RE-GDA0003416736710000151
and
Figure RE-GDA0003416736710000152
load requirements and lower standby requirements at the moment of maximum photovoltaic output of a future scheduling cycle are respectively obtained,
Figure RE-GDA0003416736710000153
the acceptable light abandon amount of the photovoltaic maximum output moment of the dispatching cycle in the future; and the on-off state of the unit is consistent with the low valley wind curtailment constraint solving mode of the future scheduling period.
As a technical optimization scheme of the invention, the capacity of the thermal power generating unit is redundant due to new energy grid connection, and the number and the sequence of starting and stopping the thermal power generating unit are further optimized in the peak regulation constraint process;
and (3) economic sequencing of thermal power generating units:
minimum specific consumption lambdaminThe calculation formula of (a) is as follows:
Figure RE-GDA0003416736710000154
the quadratic function of the thermal power unit output is expressed as
Figure RE-GDA0003416736710000155
Wherein a isiIs a coefficient of a quadratic term, biIs a coefficient of a first order term, ciIs a constant term; pλiThe method is determined by the maximum and minimum output of the thermal power generating unit and the coefficient of the thermal power generating unit, and is expressed as follows:
Figure RE-GDA0003416736710000156
Figure RE-GDA0003416736710000157
calculating the minimum specific consumption of each thermal power generating unit, and determining the number of the units participating in peak regulation constraint in a sequence from small to large;
determining the state of the unit:
extreme conditions are considered in the peak load period, and the hydroelectric generating set runs at rated power and does not participate in providing positive standby of the system; if the determined maximum unit combination meets the system requirements for both the load and the upper standby, other time periods except the peak load time period are met; on the basis, determining the calling sequence of the thermal power generating unit by using the minimum specific consumption; the starting unit meets the following conditions:
Figure RE-GDA0003416736710000161
Figure RE-GDA0003416736710000162
in the formula: pFτAnd PSτRespectively wind power output and photoelectric output at peak load.
As a technical optimization scheme of the invention, the unit of the device adopts a 10-machine system to carry out simulation calculation, and two cascade hydropower stations, a wind power plant and a photovoltaic power station are connected into the system; the data of the hydroelectric generating sets are shown in the table 1, wherein 4 hydroelectric generating sets with the numbers of W1, W2, W3 and W4 belong to a downstream hydropower station; the 3 units numbered W5, W6, W7 belong to upstream hydroelectric power stations; the load demand curve and the wind power output and photovoltaic power generation output prediction curves of the example are respectively shown in fig. 2 and fig. 3; the system power constraint confidence level 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 to solve.
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 system scheduling decision, the model is compared with a unit combination scheme without considering a starting number constraint model of the thermal power unit, and the simulation result is shown in Table 2; it can be seen that thermal power generating units participate in system scheduling in each time period of the models in the two constraint modes, the output of the thermal power generating units in the peak load time period is 1250MW to 1254MW, and the output of the thermal power generating units in the valley time period is 652MW to 654MW, so that the effective operation of the system can be guaranteed; the maximum starting number constraint of the thermal power generating units is introduced, the starting number of the thermal power generating units is reduced from 10 to 9, the number of sections of the thermal power generating units in operation with low economy is reduced, frequent switching of the operation state of the thermal power generating units is reduced, and system capacity redundancy is avoided;
in order to further verify the necessity of considering peak regulation constraint of a continuous scheduling period in the established model, on the basis of introducing the number constraint of starting units of the thermal power generating unit, comparative analysis of an example is carried out from two angles of whether the peak regulation constraint of a future scheduling period is carried out; the output conditions of the power generation energy sources in the system under two conditions are obtained through optimization calculation, and are shown in fig. 5 and 6;
as can be seen from fig. 5 and 6, under the constraint of the number of the thermal power units started, when the peak regulation constraint is considered, the total power generation power of the thermal power unit at the end of the scheduling cycle and in the first 4 periods is 2964MW, the total power generation power of the thermal power unit at the peak load is 4839MW, and hydropower does not participate in scheduling in the first 3 periods, but when the peak regulation constraint is not considered, the total power generation power of the thermal power unit at the end of the scheduling cycle and in the first 4 periods is 2816MW, and the total power generation power of the thermal power unit at the peak load is 4904 MW; the reason is considered, the peak regulation requirement of the unit is improved by adding the peak regulation constraint, and in order to ensure the reliable operation of the system, the thermal power unit at the end and the beginning of the period needs to meet the peak load requirement to be met in real time, so that the thermal power output is improved;
respectively carrying out comparative analysis on the operation schemes under the four constraint modes in order to verify the effect of continuous scheduling period peak regulation constraint and unit starting number constraint on improving the economic and stable operation of the system;
scheme 1: the mode of peak regulation constraint of a future scheduling period and the maximum number of starting units is not considered;
scheme 2: considering the peak regulation constraint of a future scheduling period but not considering the mode of the maximum starting number of the units;
scheme 3: a mode that peak regulation constraint of a future scheduling period is not considered, but the maximum starting number of the units is considered;
scheme 4: considering the peak regulation constraint of a future scheduling period and considering the mode of the maximum starting number of the units;
simultaneously introducing a standard deviation sigma of thermal power output of a quantization indexHComparing the stability of the system operation under each scheme, sigmaHThe smaller the output force, the higher the stability of the system, and the specific expression is as follows:
Figure RE-GDA0003416736710000171
as can be seen from table 3, the scheduling results of the scheme 1 and the scheme 2 are the same, all 10 thermal power units participate in scheduling, and the coal consumption and the thermal power output standard deviation of the two units are relatively high, that is, when the maximum power-on unit number constraint of the units is not considered, the system is in a redundant state of power generation capacity. Compared with the scheme 1 and the scheme 2, 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%, and compared with the scheme 1 and the scheme 2, 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%, which shows that whether peak regulation constraint is considered or not, the stability and the economy of the multi-source coordinated dispatching system can be improved by considering the maximum starting number constraint of the unit. In addition, compared with the scheme 4, the coal consumption of the scheme 3 is reduced more obviously, and the thermal power output standard deviation is improved, which shows that in order to meet the peak regulation constraint of the continuous scheduling period of the system, although the scheme 4 increases the coal consumption of the system, the output standard deviation of the thermal power unit can be reduced, that is, the stability of the operation scheduling of the system is improved.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Figure RE-GDA0003416736710000181
TABLE 1
Figure 4559DEST_PATH_HDA0003140716400000031
TABLE 2
Figure RE-GDA0003416736710000192
Table 3.

Claims (7)

1. A multi-energy short-term optimization scheduling method is characterized by comprising the following steps:
the method comprises the following steps: firstly, establishing a multi-source coordination short-term optimization scheduling model by taking the minimum system operation energy consumption as a target and considering system operation constraint conditions in a scheduling period, and meanwhile, evaluating the unit combination condition and judging whether the system capacity is redundant;
step two: if the system capacity is redundant, adding the maximum starting number constraint of the thermal power generating unit, and performing simulation calculation on the four aspects of the unit combination scheme, the energy generation power condition, the system energy loss and the thermal power generating unit output standard deviation;
step three: and (4) adding the peak regulation constraint considering the continuous scheduling period on the basis of the step two, performing simulation calculation on the four indexes again, and performing comparative evaluation analysis on the respective solution results of system scheduling in the three constraint modes.
2. The multi-energy short-term optimized dispatching method as claimed in claim 1, wherein: establishing constraint conditions of a multi-energy short-term optimization scheduling model considering peak regulation constraints of a continuous scheduling period, wherein the constraint conditions comprise an intra-period constraint and a next scheduling period constraint;
in order to realize the economic benefit of the system through the coordination and complementation of various energy sources of wind, light, water and fire, the model optimization target is set to minimize the operation cost and the starting and stopping cost of the thermal power generating unit, namely:
Figure FDA0003140716380000011
in the formula:
Figure FDA0003140716380000012
the energy consumption of the thermal power generating unit is a quadratic function of the output of the thermal power generating unit and is expressed as
Figure FDA0003140716380000013
aiIs a coefficient of a quadratic term, biIs a coefficient of a first order term, ciIs a constant term;
Figure FDA0003140716380000014
energy consumption is started;
Figure FDA0003140716380000015
energy consumption is started and stopped.
3. The multi-energy short-term optimized dispatching method as claimed in claim 1, wherein:
and (3) power balance constraint:
Figure FDA0003140716380000021
in the formula: n and
Figure FDA0003140716380000022
respectively representing the total number of the thermal power generating units and the maximum output in the t period; n is a radical ofwAnd
Figure FDA0003140716380000023
respectively representing the total quantity of the hydroelectric generating sets and the maximum output in the t period;
Figure FDA0003140716380000024
and
Figure FDA0003140716380000025
respectively predicting the maximum output of photovoltaic power generation and wind power generation in a time period t; pDt、R(Pt) And delta P is a load value, a system spare amount and a maximum power adjustment amount in the t time period respectively; beta is the confidence level of the system output;
since the constraint equation is in a probabilistic form, the equation needs to be determined for solving, and the conversion form is as follows:
Figure FDA0003140716380000026
and then solving by using a dichotomy to obtain:
Figure FDA0003140716380000027
in the formula: fptFor the joint probability distribution function of the wind-solar output and the load in the period t,
Figure FDA0003140716380000028
is the inverse function of the predicted contribution from the load and the maximum predicted contribution from the wind and light.
4. The multi-energy short-term optimized dispatching method as claimed in claim 1, wherein:
and (3) system up-and-down standby constraint:
and (3) standby:
Figure FDA0003140716380000029
the following is for standby:
Figure FDA00031407163800000210
in the formula:
Figure FDA0003140716380000031
the upper limits of the output of the thermal power unit and the hydroelectric generating set are respectively; HiP、 WmPthe lower limits of the output of the thermal power unit and the hydroelectric generating set are respectively; alpha is the system load spare rate;
Figure FDA0003140716380000032
for the standby requirement of wind, light and electricity,R(Pt) The wind, light and electricity standby requirement is met.
5. The multi-energy short-term optimized dispatching method as claimed in claim 1, wherein:
wind power output restraint:
Figure FDA0003140716380000033
photoelectric output force restraint:
Figure FDA0003140716380000034
thermal power output constraint:
Figure FDA0003140716380000035
in the formula: z is a radical ofi,tThe variable is a 0-1 variable and is expressed as the running state of the thermal power generating unit;
and (3) climbing restraint of the thermal power generating unit:
-vdiΔt≤PHi,t-PHi,t-1≤vuiΔt (10)
in the formula: v. ofdiAnd vuiRegulating the speed of the thermal power generating unit;
and (3) limiting the 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)
in the formula: gHsi,(t-1)And GHoi,(t-1)Respectively the continuous startup and shutdown hours of the thermal power generating unit; t isHs,iAnd THo,iRespectively setting minimum startup and shutdown hours of the thermal power generating unit;
and (3) water and electricity output restraint:
Figure FDA0003140716380000036
constraint of water energy and electric energy conversion relation:
PWm,t＝ηWmIAmtWmt (14)
in the formula: wmt、Amt、I、ηWmReservoir water purification head and water in t time period respectivelyThe water flow, the conversion coefficient and the working efficiency of the hydroelectric generating set are required by the power generation of the electric generating set;
and (3) water balance constraint:
Figure FDA0003140716380000041
Xwmin≤Xwt≤Xwmax (16)
in the formula: vwp,tIs the reservoir capacity, R, of the hydropower station in the period of twp,tInterval water inflow of hydropower stations in t time period, XwtThe generated flow of the hydroelectric generating set in the time period of t, XwmaxAnd XwminRespectively the upper and lower limits of the generating flow of the hydroelectric generating set Swp,tFor a period of t time, the water abandoning amount of the hydropower station interval Swp1,tAnd Xw1tThe water abandoning amount of an upstream power station and the power generation flow of a power generating set of the upstream power station in the time period t;
reservoir capacity flow constraint:
Figure FDA0003140716380000042
XSwpmin≤XSwp,t≤XSwpmax (18)
in the formula: XS (Cross site architecture)wp,tIs the reservoir capacity flow, XS, of the reservoir wp during the period twpmax、XSwpminRespectively the upper and lower limits of the reservoir capacity flow of the reservoir wp.
6. The multi-energy short-term optimized dispatching method as claimed in claim 1, wherein:
peak load constraint for future scheduling period:
Figure FDA0003140716380000043
in the formula: z is a radical ofi,TFor starting and stopping the electric machine set in the period of end fire, zup,iScheduling weeks for the futureStarting and stopping states of the thermal power generating unit during peak load period, wherein tau is peak load time of a future scheduling period, T is end time of the period, and P isDτAnd
Figure FDA0003140716380000051
load requirements and upper standby requirements at peak time of a future scheduling period are respectively set;
wherein the minimum down time satisfies CTiAt > 1- τ, the linearized switch state is as follows:
zup,i≤1-zi,T (20)
Figure FDA0003140716380000052
Figure FDA0003140716380000053
in the formula: beta is a1Get 104In the case of epsilon 10-1The method is also suitable for the low valley wind curtailment constraint of the future scheduling period;
when the minimum down time satisfies CTiAt 1-tau or less, the linearized switch states are as follows:
zup,i＝1-zi,T (23)
and (3) carrying out low-valley wind curtailment constraint on a future scheduling period:
Figure FDA0003140716380000054
in the formula: z is a radical ofdown,iFor the starting and stopping state, tau, of the thermal power generating unit during the low-ebb load of the future scheduling cycle1For the future scheduling of the cycle trough load moments,
Figure FDA0003140716380000055
and
Figure FDA0003140716380000056
load demand and next standby demand at the time of the future scheduling cycle trough are respectively,
Figure FDA0003140716380000057
the acceptable air abandon rate for the low ebb moment of the future scheduling cycle:
wherein OT is satisfied when the minimum operation time is satisfiedi≥1-τ1The linearized switch states are as follows:
zdown,i≤zi,T (25)
Figure FDA0003140716380000061
Figure FDA0003140716380000062
when the minimum operation time satisfies OTi≤1-τ1The linearized switch states are as follows:
zdown,i＝zi,T (28)
and (3) light abandoning constraint of a future scheduling period:
Figure FDA0003140716380000063
in the formula: z is a radical ofups,iFor the starting and stopping state, tau, of the thermal power generating unit when the photovoltaic output is maximum in the future scheduling period2For scheduling the peak load photovoltaic output maximum time of the cycle in the future,
Figure FDA0003140716380000064
and
Figure FDA0003140716380000065
load demands at the moment of maximum photovoltaic output for the future scheduling cycle andthe requirement of the next standby is met,
Figure FDA0003140716380000066
the acceptable light abandon amount of the photovoltaic maximum output moment of the dispatching cycle in the future; and the on-off state of the unit is consistent with the low valley wind curtailment constraint solving mode of the future scheduling period.
7. The multi-energy short-term optimized dispatching method as claimed in claim 1, wherein:
the capacity of the thermal power generating unit is redundant due to new energy grid connection, and the starting and stopping number and sequence of the thermal power generating unit are further optimized in the peak regulation constraint process;
and (3) economic sequencing of thermal power generating units:
minimum specific consumption lambdaminThe calculation formula of (a) is as follows:
Figure FDA0003140716380000067
the quadratic function of the thermal power unit output is expressed as
Figure FDA0003140716380000068
Wherein a isiIs a coefficient of a quadratic term, biIs a coefficient of a first order term, ciIs a constant term; pλiThe method is determined by the maximum and minimum output of the thermal power generating unit and the coefficient of the thermal power generating unit, and is expressed as follows:
Figure FDA0003140716380000071
Figure FDA0003140716380000072
calculating the minimum specific consumption of each thermal power generating unit, and determining the number of the units participating in peak regulation constraint in a sequence from small to large;
determining the state of the unit:
extreme conditions are considered in the peak load period, and the hydroelectric generating set runs at rated power and does not participate in providing positive standby of the system; if the determined maximum unit combination meets the system requirements for both the load and the upper standby, other time periods except the peak load time period are met; on the basis, determining the calling sequence of the thermal power generating unit by using the minimum specific consumption; the starting unit meets the following conditions:
Figure FDA0003140716380000073
Figure FDA0003140716380000074
in the formula: pFτAnd PSτRespectively wind power output and photoelectric output at peak load.
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