CN115954952A - Flexible resource planning method based on time sequence operation simulation - Google Patents

Flexible resource planning method based on time sequence operation simulation Download PDF

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CN115954952A
CN115954952A CN202211559868.6A CN202211559868A CN115954952A CN 115954952 A CN115954952 A CN 115954952A CN 202211559868 A CN202211559868 A CN 202211559868A CN 115954952 A CN115954952 A CN 115954952A
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flexible resource
flexible
power
new energy
resource planning
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李博
邵先琪
朱元
温力力
周佳鑫
杨知方
向明旭
程曹阳
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Chongqing University
State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Chongqing Electric Power Co Ltd
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Chongqing University
State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Chongqing Electric Power Co Ltd
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Abstract

The invention discloses a flexible resource planning method based on time sequence operation simulation, which comprises the following steps: s1, constructing a flexible resource planning model; and S2, adjusting each parameter value in the flexible resource planning model to enable the flexible resource planning model to obtain the minimum value, and taking the parameter set when the planning model obtains the minimum value as the optimal parameter of the flexible resource planning. The flexible resource planning method based on the time sequence operation simulation can combine different operation scenes to accurately and reliably plan and analyze various flexible resources with different time scales, and provides technical reference for scientific planning of the flexible resources.

Description

Flexible resource planning method based on time sequence operation simulation
Technical Field
The invention relates to the field of power system planning, in particular to a flexible resource planning method based on time sequence operation simulation.
Background
At the present stage, the flexible adjustment task of the power system is still mainly performed by the traditional thermal power generating units and hydroelectric generating units. Under the condition of high-proportion renewable energy grid connection, the installed capacity and the power generation ratio of the thermal power generating unit are continuously replaced by wind power and photovoltaic power generation, and the development of hydropower is limited by natural resources. In recent years, technologies of flexible resources such as energy storage with high efficiency and strong regulation capacity are mature continuously, and a breakthrough space is still left in the aspects of technology and consumption in the future. Therefore, in consideration of the challenge of flexible supply and demand matching of a future power system, it is important to reasonably plan flexible resources such as energy storage.
The energy storage categories such as electrochemical energy storage used in the existing power system are mainly suitable for tasks such as peak shaving and frequency modulation of the power system. However, as the renewable energy grid-connected scale gradually expands, the source-load balance problem becomes prominent on a longer time scale, and daily, weekly and seasonal power supply and demand mismatch poses a challenge to the safety and stability of the power system. In the face of the flexibility requirements of different time scales, various flexible resources such as short-term and long-term energy storage need to be reasonably planned in the power system planning.
The energy storage regulation capacity depends on the planning capacity and the maximum charge-discharge efficiency to a great extent, the capacity planning is researched in a large amount by the existing scheme, and the energy storage configuration capacity of the corresponding planning year is obtained mainly by solving the optimization problem. On a planned time scale, existing planning methods typically consider a few typical scenarios or extreme scenarios. However, the charging and discharging process of the flexible resources cannot be accurately considered by using a few scenes for planning, and the effect that the flexible resources can obtain in the operation process cannot be considered, which is not favorable for the large-scale application of the flexible resources such as energy storage with good peak clipping and valley filling effects.
In addition, due to the high penetration ratio of renewable energy, the time characteristics of various flexible resources to be planned are different, for example, the charging and discharging processes and the life decay of energy storage equipment influence the discharging power of the energy storage equipment, and compared with a thermal power unit, the load response speed is higher than the climbing constraint, and the like, which all bring burden to the selection of typical situations. In the background that both ends of the source-load fluctuate with the timing sequence, it is difficult to describe the operation scene of the future power system by using some typical scenes.
Therefore, a flexible resource planning method based on time sequence operation simulation is needed, which can solve the above problems.
Disclosure of Invention
In view of this, the present invention aims to overcome the defects in the prior art, and provide a flexible resource planning method based on time sequence operation simulation, which can combine different operation scenes to accurately and reliably plan and analyze various flexible resources at different time scales, and provide technical reference for scientific planning of the flexible resources.
The invention discloses a flexible resource planning method based on time sequence operation simulation, which comprises the following steps:
s1, constructing a flexible resource planning model; the objective function of the flexible resource planning model is:
minC sys ＝C inv +C oper ；
wherein, C sys Is the total power consumption of the power system; c inv Is the investment and consumption of flexible resources;C oper is the operational consumption of the power system;
and S2, adjusting each parameter value in the flexible resource planning model to enable the flexible resource planning model to obtain a minimum value, and taking a parameter set when the planning model obtains the minimum value as an optimal parameter for flexible resource planning.
Further, the investment consumption C of the flexible resource is determined according to the following formula inv ：
Figure BDA0003984205970000021
Wherein s is 1 Is the power consumption coefficient of the flexible resource; s 2 Is the energy consumption coefficient of the flexible resource;
Figure BDA0003984205970000022
is the planned power capacity of the type b flexible resource; />
Figure BDA0003984205970000023
Is the projected energy capacity of the class b flexible resource; gamma is the operation and maintenance consumption conversion coefficient.
Further, the operation consumption C of the power system is determined according to the following formula oper ：
Figure BDA0003984205970000031
Wherein, c t Is the power consumption for the t period;
Figure BDA0003984205970000032
and &>
Figure BDA0003984205970000033
Charging and discharging efficiency of the b-type flexible resource in the t-th time period respectively; />
Figure BDA0003984205970000034
And &>
Figure BDA0003984205970000035
Charging and discharging power of the b-th type flexibility resource in the t-th period respectively; t is the total planning time period number; n is a radical of G Is the total number of thermal power units; />
Figure BDA0003984205970000036
Is the power generation consumption coefficient of the g-th thermal power generating unit; />
Figure BDA0003984205970000037
The output of the g-th thermal power generating unit in the t-th time period; c VoLL Is a power system load shedding penalty coefficient; n is a radical of hydrogen n Is the total node number of the power system; />
Figure BDA0003984205970000038
Is the load shedding amount of the nth node in the t period; c Cur The penalty coefficient of wind abandoning and light abandoning of the power system is shown; n is a radical of W Is the total new energy unit number; />
Figure BDA0003984205970000039
The power of the new energy abandoned by the w-th new energy unit in the t-th time period.
Further, the flexible resource planning model includes energy policy constraints;
the energy policy constraints include:
the new energy permeability requirement is as follows:
Figure BDA00039842059700000310
wherein T is the total planning time period number; n is a radical of hydrogen W Is the total new energy unit number;
Figure BDA00039842059700000311
the output of the w new energy source unit in the t time period; beta is new energy permeability requirement coefficient; n is a radical of n Is the total number of nodes; d n,t Is the nth node at the tLoad demand for a time period;
the new energy reduction requirement is as follows:
Figure BDA00039842059700000312
/>
wherein,
Figure BDA00039842059700000313
is the installed capacity of the w-th new energy unit; />
Figure BDA00039842059700000314
Is the available output coefficient of the w new energy machine set in the t time interval; gamma is a new energy allowed reduction coefficient;
the upper limit requirement of the load shedding:
Figure BDA0003984205970000041
wherein,
Figure BDA0003984205970000042
is the load shedding amount of the nth node in the t period; α is the load shedding proportional limiting coefficient.
Further, the flexible resource planning model includes systematic constraints;
the systematic constraints include:
node power balance:
Figure BDA0003984205970000043
wherein,
Figure BDA0003984205970000044
the output of the g-th thermal power generating unit in the t-th time period; />
Figure BDA0003984205970000045
Is the i-th class flexibility resourceThe discharge power of the source in the t period; />
Figure BDA0003984205970000046
Charging power of the ith type of flexible resource in the tth time period; b is a set of flexible resources; />
Figure BDA0003984205970000047
The output of the w new energy source unit in the t time period; w is the set of new energy banks; />
Figure BDA0003984205970000048
Is the transmission power of the first line of the power system in the t-th period; lS represents a line set for transmitting power from the node n to the outside; lE represents a set of lines injecting power to the node n;
node load shedding constraint:
Figure BDA0003984205970000049
line transmission capacity constraint:
Figure BDA00039842059700000410
Figure BDA00039842059700000411
wherein, F l max Is the upper limit of transmission power allowed for line l;
Figure BDA00039842059700000412
is the susceptance of line l; theta +,t Is the phase angle of the head end node of the line l in the t-th time period; theta.theta. -,t Is the phase angle of the end node of line l at the t-th epoch.
Further, the flexible resource planning model includes generator-side constraints;
the generator side constraint includes:
and (3) restricting the upper and lower limits of the new energy output:
Figure BDA0003984205970000051
wherein,
Figure BDA0003984205970000052
the maximum output of the w-th new energy unit;
and (3) constraining the upper and lower output limits of the thermal power generating unit:
Figure BDA0003984205970000053
wherein,
Figure BDA0003984205970000054
is the lower limit of the output of the g-th thermal power generating unit; />
Figure BDA0003984205970000055
Is the upper limit of the output of the g-th thermal power generating unit;
and (3) climbing restraint of the thermal power generating unit:
Figure BDA0003984205970000056
wherein,
Figure BDA0003984205970000057
and &>
Figure BDA0003984205970000058
The climbing speed of the thermal power generating unit is the climbing speed of the thermal power generating unit.
Further, the flexible resource planning model includes flexible resource-side constraints;
the flexible resource side constraints include:
and (3) limiting the upper and lower limits of charge and discharge power:
Figure BDA0003984205970000059
wherein,
Figure BDA00039842059700000510
is the charging power of the type b flexible resource in the period t; />
Figure BDA00039842059700000511
Is the discharge power of the b-th type flexible resource in the t period; />
Figure BDA00039842059700000512
Is the maximum input capacity of the type b flexible resource;
adjacent time period charge state coupling constraint:
Figure BDA00039842059700000513
wherein,
Figure BDA00039842059700000514
is the state of charge of the b-th type flexible resource in the t-th period; />
Figure BDA00039842059700000515
The charge-discharge efficiency of the b-type flexible resource;
and (3) restriction of upper and lower limits of the charge state:
Figure BDA0003984205970000061
wherein,
Figure BDA0003984205970000062
is the continuous charge-discharge duration of the b-type flexible resource;
state of charge balance constraint in cycle:
Figure BDA0003984205970000063
wherein,
Figure BDA0003984205970000064
is the initial state of charge coefficient for the class b flexible resource; n is a radical of T Is the cycle period.
Furthermore, the flexible resource planning model is solved based on a multi-parameter planning algorithm, so that the flexible resource planning model obtains the minimum value.
The beneficial effects of the invention are: the invention discloses a flexible resource planning method based on time sequence operation simulation, which is characterized in that a flexible resource planning model taking the minimum sum of various consumptions such as the input and the operation of flexible resources as an objective function is constructed, and a multi-parameter planning theory is adopted to solve and analyze the planning model under the constraint of an energy strategy, the constraint of a power system, the constraint of a generator side and the constraint of the flexible resource side, so that various flexible resources with different time scales such as short term and long term are considered in different operation scenes, the analytic relation between the permeability of new energy and the input of the flexible resources can be quantitatively represented, and the optimal configuration capacity of the flexible resources under the requirement of the consumption of the new energy in the future can be rapidly determined.
Drawings
The invention is further described below with reference to the following figures and examples:
FIG. 1 is a schematic flow chart of a planning method according to the present invention;
FIG. 2 is a schematic view of a typical daily load curve of the present invention;
FIG. 3 is a schematic diagram of an exemplary net load curve of the present invention;
FIG. 4 is a schematic diagram of the short-term energy storage daily charge and discharge process of the present invention;
FIG. 5 is a schematic diagram of the long-term energy storage daily charge and discharge process of the present invention;
FIG. 6 is a schematic diagram of the short-term energy storage day SOC change process of the present invention;
FIG. 7 is a schematic diagram of the change of the long-term storage day SOC according to the present invention;
FIG. 8 is a diagram illustrating the relationship between the planning capacity of different types of flexible resources and the total consumption of the system according to the present invention;
fig. 9 is a schematic diagram illustrating an analytic change relationship between total system consumption and new energy permeability according to the present invention.
Detailed Description
The invention is further described in the following description with reference to the drawings, in which:
the invention discloses a flexible resource planning method based on time sequence operation simulation, which comprises the following steps:
s1, constructing a flexible resource planning model; the objective function of the flexible resource planning model is:
minC sys ＝C inv +C oper ；
wherein, C sys Is the total power system consumption; c inv Is the investment and consumption of flexible resources; c oper Is the operational consumption of the power system;
and S2, adjusting each parameter value in the flexible resource planning model to enable the flexible resource planning model to obtain the minimum value, and taking the parameter set when the planning model obtains the minimum value as the optimal parameter of the flexible resource planning.
In this embodiment, the investment consumption C of the flexible resource is determined according to the following formula inv ：
Figure BDA0003984205970000071
Wherein s is 1 Is the power consumption coefficient of the flexible resource; s 2 Is the energy consumption coefficient of the flexible resource;
Figure BDA0003984205970000072
is the planned power capacity of the type b flexible resource; />
Figure BDA0003984205970000073
Is the projected energy capacity of the class b flexible resource; gamma is the operation and maintenance consumption conversion coefficient.
The operation consumption of the power system comprises the operation consumption of the thermal power generating unit, the operation consumption of flexible resources, a load shedding penalty and a wind and light abandoning penalty. Determining an operational consumption C of the power system according to the following formula oper ：
Figure BDA0003984205970000074
Wherein, c t Is the power consumption for the t period;
Figure BDA0003984205970000075
and &>
Figure BDA0003984205970000076
Charging and discharging efficiency of the b-type flexible resource in the t-th time period respectively; />
Figure BDA0003984205970000081
And &>
Figure BDA0003984205970000082
Charging and discharging power of the b-th type flexibility resource in the t-th period respectively; t is the total planning time period number; n is a radical of G Is the total number of thermal power units; />
Figure BDA0003984205970000083
The power generation consumption coefficient of the g-th thermal power generating unit; />
Figure BDA0003984205970000084
The output of the g thermal power generating unit in the t time period is obtained; c VoLL Is the power system load shedding punishment coefficient; n is a radical of hydrogen n Is the total node number of the power system; />
Figure BDA0003984205970000085
Is the load shedding amount of the nth node in the t period; c Cur The penalty coefficient of wind abandoning and light abandoning of the power system is shown; n is a radical of W Is the total new energy unit number; />
Figure BDA0003984205970000086
The power of the new energy abandoned by the w-th new energy unit in the t-th time period. The nodes of the power system are bus bars.
In this embodiment, the flexible resource planning model includes energy policy constraints;
the energy policy constraints include:
the permeability of new energy is required:
Figure BDA0003984205970000087
wherein T is the total planning time period number; n is a radical of W Is the total new energy unit number;
Figure BDA0003984205970000088
the output of the w new energy source unit in the t time period; beta is new energy permeability requirement coefficient; n is a radical of hydrogen n Is the total number of nodes; d n,t Is the load demand of the nth node at the t-th time period;
the new energy reduction requirement is as follows:
Figure BDA0003984205970000089
wherein,
Figure BDA00039842059700000810
is the installed capacity of the w new energy unit; />
Figure BDA00039842059700000811
Is the available output coefficient of the w new energy source unit in the t time period; gamma is a new energy allowed reduction coefficient;
the upper limit requirement of the load shedding:
Figure BDA00039842059700000812
wherein,
Figure BDA00039842059700000813
is the load shedding amount of the nth node in the t period; α is the load shedding proportional limiting coefficient.
The flexible resource planning model includes systematic constraints;
the systematic constraint relates to the constraint of all generator sets in the power system, and specifically comprises the following steps:
node power balance:
Figure BDA0003984205970000091
wherein,
Figure BDA0003984205970000092
the output of the g-th thermal power generating unit in the t-th time period; />
Figure BDA0003984205970000093
Is the discharge power of the i-th type flexible resource in the t-th period; />
Figure BDA0003984205970000094
Charging power of the ith type of flexible resource in the t period; b is a set of flexible resources; />
Figure BDA0003984205970000095
The output of the w new energy source unit in the t time period; w is the set of new energy banks; />
Figure BDA0003984205970000096
Is the transmission power of the first line of the power system in the t-th period; lS represents a line set for transmitting power from the node n to the outside; lE represents a set of lines injecting power to the node n;
node load shedding constraint:
Figure BDA0003984205970000097
line transmission capacity constraint:
Figure BDA0003984205970000098
Figure BDA0003984205970000099
wherein, F l max Is the upper limit of transmission power allowed for line l;
Figure BDA00039842059700000910
is the susceptance of line l; theta +,t Is the phase angle of the head end node of the line l in the t-th time period; theta -,t Is the phase angle of the end node of line l at the t-th epoch.
The flexible resource planning model includes generator-side constraints;
the generator side constraint includes:
and (3) restricting the upper and lower limits of the new energy output:
Figure BDA00039842059700000911
wherein,
Figure BDA00039842059700000912
the maximum output of the w-th new energy unit;
and (3) constraining the upper and lower output limits of the thermal power generating unit:
Figure BDA0003984205970000101
wherein,
Figure BDA0003984205970000102
is the lower limit of the output of the g-th thermal power generating unit; />
Figure BDA0003984205970000103
Is the upper limit of the output of the g-th thermal power generating unit;
and (3) climbing restraint of the thermal power generating unit:
Figure BDA0003984205970000104
wherein,
Figure BDA0003984205970000105
and &>
Figure BDA0003984205970000106
The climbing speed of the thermal power generating unit is the climbing speed of the thermal power generating unit.
The flexible resource planning model includes flexible resource-side constraints;
the flexible resource side constraints include:
and (3) limiting the upper and lower limits of charge and discharge power:
Figure BDA0003984205970000107
wherein,
Figure BDA0003984205970000108
is the charging power of the type b flexible resource in the period t; />
Figure BDA0003984205970000109
Is the discharge power of the b-th type flexible resource in the t period; />
Figure BDA00039842059700001010
Is the maximum input capacity of the type b flexible resource;
adjacent time period charge state coupling constraint:
Figure BDA00039842059700001011
wherein,
Figure BDA00039842059700001012
is the state of charge of the class b flexible resource at the tth time period; />
Figure BDA00039842059700001013
The charge-discharge efficiency of the b-type flexible resource;
and (3) constraint of upper and lower limits of the charge state:
Figure BDA00039842059700001014
wherein,
Figure BDA00039842059700001015
is the continuous charge-discharge duration of the b-type flexible resource;
state of charge balance constraint over cycle:
Figure BDA0003984205970000111
wherein,
Figure BDA0003984205970000112
is the initial state of charge coefficient for the class b flexible resource; n is a radical of T Is the cycle period, N T Typically for 24 hours.
In the embodiment, in step S2, the flexible resource planning model is solved based on a multi-parameter planning algorithm, so that the flexible resource planning model obtains a minimum value; and taking the parameter set when the planning model obtains the minimum value as the optimal planning parameter of the flexible resource.
The multi-parameter planning algorithm or theory may analyze the change of the optimal solution set and feasibility of the problem when one or more parameters in the optimization problem change.
For the optimization problem of the flexible resource planning model of the invention, the following standard form can be written:
Figure BDA0003984205970000113
s.t.
Ax≤Bw+D. (2)
in the formula: x is an optimization variable; w is a planning parameter; a and B are coefficient matrixes; c and D are coefficient vectors.
For this standard form, the optimality condition for the problem is written first. Next, the constraints are classified according to whether the constraints are active or not, and an active constraint set and an inactive constraint set are obtained. All active constraints and inactive constraints are enumerated and are substituted into an optimality condition which obtains equivalence, and the optimal solution (flexible resource planning) can be represented by planning parameters (new energy permeability and the like) through the active constraints.
And then, the analytical representation relationship is substituted into the objective function, so that the analytical relationship between the objective function and the planning parameters can be obtained, and the change of the total consumption of the system when the planning parameters such as the permeability of new energy and the like are changed is quantitatively analyzed, so that technical support is provided for a scientific, safe and reasonable flexible resource planning scheme.
The specific solution is as follows:
firstly, defining the optimal solution of the optimization problems (1) - (2) as x * Then the constraints in equation (2) can be divided into active constraints and inactive constraints:
and (4) function constraint:
A J x * ＝B J w+D J , (3)
non-functional constraints:
A K x * ＜B K w+D K , (4)
next, optimality conditions for optimization problems (1) - (2) are written, including:
the stagnation point condition is as follows:
C+A T λ＝0, (5)
complementary relaxation conditions:
λ⊥(Ax-Bw-D)＝0, (6)
the feasibility conditions of the original problems are as follows:
Ax-Bw-D≤0. (7)
and dual problem feasibility conditions:
λ≥0. (8)
and respectively bringing the acting constraint and the non-acting constraint into the optimality condition of the optimization problem to obtain an equivalent optimality condition:
Figure BDA0003984205970000121
A J x * ＝B J w+D J , (10)
λ K ＝0, (11)
A K x * ＜B K w+D K , (12)
λ J ≥0, (13)
through the equivalence optimality condition (10), the analytic representation relation between the optimal solution and the planning parameters can be obtained:
Figure BDA0003984205970000122
and substituting the formula (14) into the objective function (1), so as to obtain the analytic relationship between the objective function and the planning parameters:
Figure BDA0003984205970000123
it should be noted that the above-mentioned multi-parameter planning algorithm or theory is the prior art, and when performing model solution, the related parameters, variables, coefficient matrices, coefficient vectors, and the like may be replaced or set correspondingly in combination with a specific flexible resource planning model, which is not described herein again.
To better understand the flexible resource planning method of the present invention, the validity of the method of the present invention is now verified using the IEEE-9 node system:
two types of flexible resources, namely short-time energy storage with the continuous charging and discharging duration of 2h and long-time energy storage with the continuous charging and discharging duration of 24h are considered. And a new energy machine set with the capacity of 180MW is arranged on the node 4, and the output of the new energy machine set is considered by adopting a new energy output prediction coefficient. Wherein the input of short-term energy storage and long-term energy storage is respectively 3 × 10 5 M/MWh and 2X 10 5 Yuan/MWh.
When the permeability of new energy is set to be 0.3, the planning model provided by the invention is applied to obtain two types of flexible resource planning capacities of short-term energy storage and long-term energy storage, namely: the short-term energy storage is 83.491MW, and the long-term energy storage is 10.8870MW.
The load and net load curves are respectively shown in fig. 2 and fig. 3, the charging and discharging powers of different types of flexible resources are shown in fig. 4 and fig. 5, the states of charge of different types of flexible resources are shown in fig. 6 and fig. 7, and fig. 8 shows the relationship between the planning capacity of different types of flexible resources and the total consumption of the system.
By applying a multi-parameter planning algorithm, the change situation of the total consumption of the system when the new energy permeability is changed from 0.1 to 0.8 is quantitatively analyzed, as shown in fig. 9. It can be seen that the system consumption is increased after being decreased with the permeability of new energy, and the reason that the new energy is decreased before being decreased is that the new energy has lower operation consumption compared with the conventional unit; the latter is due to the high uncertainty caused by the high new energy, which threatens the safe operation of the power system and requires more resources to be invested.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.

Claims (8)

1. A flexible resource planning method based on time sequence operation simulation is characterized in that: the method comprises the following steps:
s1, constructing a flexible resource planning model; the objective function of the flexible resource planning model is:
minC sys ＝C inv +C oper ；
wherein, C sys Is the total power system consumption; c inv Flexible resources are invested and consumed; c oper Is the operational consumption of the power system;
and S2, adjusting each parameter value in the flexible resource planning model to enable the flexible resource planning model to obtain the minimum value, and taking the parameter set when the planning model obtains the minimum value as the optimal parameter of the flexible resource planning.
2. The method of claim 1, wherein the resource planning is performed by a sequential execution simulation based method, comprising: determining the projected consumption C of flexible resources according to the following formula inv ：
Figure FDA0003984205960000011
Wherein s is 1 Is the power consumption coefficient of the flexible resource; s 2 Is the energy consumption coefficient of the flexible resource;
Figure FDA0003984205960000012
is the planned power capacity of the type b flexible resource; />
Figure FDA0003984205960000013
Is the projected energy capacity of the class b flexible resource; gamma is the operation and maintenance consumption conversion coefficient.
3. The method of claim 2 for flexible resource planning based on time-series operational simulation, wherein: determining an operational consumption C of the power system according to the following formula oper ：
Figure FDA0003984205960000014
Wherein, c t Is the power consumption for the t period;
Figure FDA0003984205960000015
and &>
Figure FDA0003984205960000016
Charging and discharging efficiency of the b-type flexible resource in the t-th time period respectively; />
Figure FDA0003984205960000017
And &>
Figure FDA0003984205960000018
Charging and discharging power of the b-th type flexibility resource in the t-th period respectively; t is the total planning time period number; n is a radical of G Is the total number of thermal power units; />
Figure FDA0003984205960000019
The power generation consumption coefficient of the g-th thermal power generating unit; />
Figure FDA0003984205960000021
The output of the g-th thermal power generating unit in the t-th time period; c VoLL Is a power system load shedding penalty coefficient; n is a radical of n Is the total node number of the power system; />
Figure FDA0003984205960000022
Is the load shedding amount of the nth node in the t period; c Cur The penalty coefficient of wind abandoning and light abandoning of the power system is shown; n is a radical of W Is the total new energy unit number; />
Figure FDA0003984205960000023
Is the w-th stageAnd (4) abandoning the new energy power of the new energy unit in the t-th time period.
4. The method of claim 1 for flexible resource planning based on time-series operational simulation, wherein: the flexible resource planning model includes energy policy constraints;
the energy policy constraints include:
the new energy permeability requirement is as follows:
Figure FDA0003984205960000024
wherein T is the total planning time period number; n is a radical of W Is the total new energy unit number;
Figure FDA0003984205960000025
the output of the w new energy source unit in the t time period; beta is new energy permeability requirement coefficient; n is a radical of n Is the total number of nodes; d n,t Is the load demand of the nth node during the t-th period;
the new energy reduction requirement is as follows:
Figure FDA0003984205960000026
wherein,
Figure FDA0003984205960000027
is the installed capacity of the w new energy unit; />
Figure FDA0003984205960000028
Is the available output coefficient of the w new energy source unit in the t time period; gamma is a new energy allowed reduction coefficient;
the upper limit requirement of the load shedding:
Figure FDA0003984205960000029
wherein,
Figure FDA00039842059600000210
is the load shedding amount of the nth node in the t period; α is the load shedding proportional limiting coefficient.
5. The method of claim 4 for flexible resource planning based on time-series operational simulation, wherein: the flexible resource planning model includes systematic constraints;
the systematic constraints include:
node power balance:
Figure FDA0003984205960000031
wherein,
Figure FDA0003984205960000032
the output of the g-th thermal power generating unit in the t-th time period; />
Figure FDA0003984205960000033
Is the discharge power of the i-th type flexible resource in the t-th period; />
Figure FDA0003984205960000034
Charging power of the ith type of flexible resource in the tth time period; b is a set of flexible resources; />
Figure FDA0003984205960000035
The output of the w new energy source unit in the t time period; w is the set of new energy banks; />
Figure FDA0003984205960000036
Is the transmission power of the first line of the power system in the t-th period; lS denotes slave nodeA line set of point n transmitting power outwards; lE represents a set of lines injecting power to node n;
node load shedding constraint:
Figure FDA0003984205960000037
line transmission capacity constraint:
Figure FDA0003984205960000038
/>
Figure FDA0003984205960000039
wherein,
Figure FDA00039842059600000310
is the upper limit of transmission power allowed for line l; />
Figure FDA00039842059600000311
Is the susceptance of line l; theta +,t Is the phase angle of the head end node of the line l in the t-th time period; theta -,t Is the phase angle of the end node of line l at the t-th epoch.
6. The method of claim 5 for flexible resource planning based on time-series operational simulation, wherein: the flexible resource planning model includes generator-side constraints;
the generator side constraint includes:
and (3) restricting the upper and lower limits of the new energy output:
Figure FDA00039842059600000312
wherein,
Figure FDA00039842059600000313
the maximum output of the w-th new energy unit;
and (3) constraining the upper and lower output limits of the thermal power generating unit:
Figure FDA0003984205960000041
wherein,
Figure FDA0003984205960000042
is the lower limit of the output of the g-th thermal power generating unit; />
Figure FDA0003984205960000043
Is the upper limit of the output of the g-th thermal power generating unit;
and (3) climbing restraint of the thermal power generating unit:
Figure FDA0003984205960000044
wherein,
Figure FDA0003984205960000045
and &>
Figure FDA0003984205960000046
The climbing speed of the thermal power generating unit is the climbing speed of the thermal power generating unit.
7. The method of claim 6, wherein the resource planning step comprises: the flexible resource planning model includes flexible resource-side constraints;
the flexible resource side constraints include:
and (3) upper and lower limits of charge and discharge power constraint:
Figure FDA0003984205960000047
wherein,
Figure FDA0003984205960000048
is the charging power of the b-th type flexibility resource in the t period; />
Figure FDA0003984205960000049
Is the discharge power of the b-th type flexibility resource in the t period; />
Figure FDA00039842059600000410
Is the maximum projected capacity of class b flexible resources;
adjacent period state of charge coupling constraint:
Figure FDA00039842059600000411
wherein,
Figure FDA00039842059600000412
is the state of charge of the class b flexible resource at the tth time period; />
Figure FDA00039842059600000413
The charge-discharge efficiency of the b-type flexible resource; />
And (3) restriction of upper and lower limits of the charge state:
Figure FDA00039842059600000414
wherein,
Figure FDA0003984205960000051
is the continuous charge-discharge duration of the b-type flexible resource;
state of charge balance constraint in cycle:
Figure FDA0003984205960000052
wherein,
Figure FDA0003984205960000053
is the initial state of charge coefficient for the class b flexible resource; n is a radical of hydrogen T Is the cycle period.
8. The method of claim 1 for flexible resource planning based on time-series operational simulation, wherein: and solving the flexible resource planning model based on a multi-parameter planning algorithm so that the flexible resource planning model obtains a minimum value.
CN202211559868.6A 2022-12-06 2022-12-06 Flexible resource planning method based on time sequence operation simulation Pending CN115954952A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116523279A (en) * 2023-07-05 2023-08-01 国网湖北省电力有限公司经济技术研究院 Determination method of flexible resource allocation scheme considering frequency modulation requirement

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116523279A (en) * 2023-07-05 2023-08-01 国网湖北省电力有限公司经济技术研究院 Determination method of flexible resource allocation scheme considering frequency modulation requirement
CN116523279B (en) * 2023-07-05 2023-09-22 国网湖北省电力有限公司经济技术研究院 Determination method of flexible resource allocation scheme considering frequency modulation requirement

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