CN113705913A - Power transmission line and energy storage joint planning method and device - Google Patents

Power transmission line and energy storage joint planning method and device Download PDF

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Publication number
CN113705913A
CN113705913A CN202111017779.4A CN202111017779A CN113705913A CN 113705913 A CN113705913 A CN 113705913A CN 202111017779 A CN202111017779 A CN 202111017779A CN 113705913 A CN113705913 A CN 113705913A
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energy storage
planning
layer
unit
transmission line
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陈中飞
白杨
宋慧
王龙
赵越
张兰
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Guangdong Power Grid Co Ltd
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
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Guangdong Power Grid Co Ltd
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/15—Correlation function computation including computation of convolution operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/16—Cables, cable trees or wire harnesses

Abstract

The invention discloses a power transmission line and energy storage joint planning method and device. According to the method, a double-layer planning model is established, the positions and the quantity of newly-built power transmission lines and energy storage are planned according to actual investment cost and actual system operation cost by using a joint planning layer to obtain an initial planning result, the initial planning result is optimized according to the operation states of the corresponding set and the energy storage when the initial planning result is simulated to operate, then the optimized initial planning result is optimized according to the operation states of the corresponding set and the energy storage when the initial planning result is simulated to operate, the initial planning result is iteratively optimized to obtain the optimal planning result, so that the joint planning of the power transmission lines and the energy storage can be carried out by considering the operation states of the set and the energy storage, and the reliability, the economy and the flexibility of system operation are ensured.

Description

Power transmission line and energy storage joint planning method and device
Technical Field
The invention relates to the technical field of planning and construction of power systems, in particular to a power transmission line and energy storage combined planning method and device.
Background
The existing market reform changes the traditional power transmission network planning method of the power grid, and the traditional minimum investment cost planning method considering the reliability of power supply is changed into a planning method taking the system operation economy as a main target. In addition, the introduction of market mechanisms also brings great uncertainty to the operation of the power system, and energy storage needs to be built to provide flexibility for the power system. However, in order to reduce the complexity of calculation, most of the existing planning methods do not consider the start-stop state of the unit, cannot truly reflect the operation condition of the unit, and are difficult to ensure the operation reliability, economy and flexibility of the power system after market transformation.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a power transmission line and energy storage joint planning method and a power transmission line and energy storage joint planning device, which can carry out power transmission line and energy storage joint planning by considering the operation states of a unit and energy storage, and ensure the reliability, economy and flexibility of system operation.
In order to solve the foregoing technical problem, in a first aspect, an embodiment of the present invention provides a power transmission line and energy storage joint planning method, including:
establishing a double-layer planning model; the upper layer of the double-layer planning model is a joint planning layer, and the lower layer of the double-layer planning model is a simulation operation layer; the combined planning layer performs combined planning on the power transmission line and the energy storage by taking the minimized investment cost and the system operation cost as objective functions and combining the operation states of the unit and the energy storage transmitted by the simulated operation layer to obtain a planning result, and the simulated operation layer simulates and operates the planning result and updates the operation states of the unit and the energy storage by taking the minimized system operation cost as the objective function;
inputting the obtained actual investment cost and the actual system operation cost into the joint planning layer, enabling the joint planning layer to plan the positions and the quantity of newly-built transmission lines and energy storage according to the actual investment cost and the actual system operation cost to obtain an initial planning result, and performing iterative optimization on the initial planning result according to the operation states of the set and the energy storage obtained by simulating the operation of the initial planning result by the simulation operation layer, and outputting an optimal planning result.
Further, the iterative optimization of the initial planning result and the output of an optimal planning result specifically include:
and iteratively optimizing the initial planning result according to an improved genetic algorithm, and outputting the optimal planning result.
Further, the objective function of the joint planning layer is:
Figure BDA0003239623180000021
wherein, L is the transmission corridor number, L1, 2lFor the number, x, of newly built transmission lines in the transmission corridor llIs an integer of ClInvestment cost for newly building a transmission line in the transmission corridor l; s is the number of the energy storage construction node, and S is 1,2sBuilding a new energy storage quantity, x, on a node s for energy storagesIs an integer of CsBuilding a new one on a node s for energy storageThe investment cost of individual energy storage; i is a unit number, i is 1,2,., N, T is a time interval number, T is 1,2,., T, M is an quotation interval number, M is 1,2i,t,mQuoted price, P, for unit i in the mth period of time ti,t,mThe power of the unit i winning the bid in the price quoted in the mth section of the time period t;
Figure BDA0003239623180000022
starting cost of the unit i in a time period t; d is the load number, D1, 2, D, penalty is the penalty factor for lost load, LLd,tThe load d is the loss of load during the time period t.
Furthermore, the joint planning layer takes the quantity constraints of the transmission lines and the stored energy as constraint conditions.
Further, the objective function of the simulation run layer is:
Figure BDA0003239623180000023
wherein, i is a unit number, i 1,2,., N, T is a time interval number, T1, 2., T, M is a quotation section number, M1, 2., M, Ci,t,mQuoted price, P, for unit i in the mth period of time ti,t,mThe power of the unit i winning the bid in the price quoted in the mth section of the time period t;
Figure BDA0003239623180000024
starting cost of the unit i in a time period t; d is the load number, D1, 2, D, penalty is the penalty factor for lost load, LLd,tIs the magnitude of the loss of load of the load d in the time period t.
Further, the simulation operation layer takes unit constraint, energy storage constraint and system constraint as constraint conditions.
Furthermore, the unit constraints comprise unit output power constraints, thermal power unit startup and shutdown time constraints and unit total output power constraints.
Further, the energy storage constraint comprises energy storage energy constraint, energy storage charging and discharging power constraint and energy storage standby capacity constraint.
Further, the system constraints comprise system capacity constraints, system node constraints and system spare capacity constraints.
In a second aspect, an embodiment of the present invention provides a power transmission line and energy storage joint planning apparatus, including:
the double-layer planning model establishing module is used for establishing a double-layer planning model; the upper layer of the double-layer planning model is a joint planning layer, and the lower layer of the double-layer planning model is a simulation operation layer; the combined planning layer performs combined planning on the power transmission line and the energy storage by taking the minimized investment cost and the system operation cost as objective functions and combining the operation states of the unit and the energy storage transmitted by the simulated operation layer to obtain a planning result, and the simulated operation layer simulates and operates the planning result and updates the operation states of the unit and the energy storage by taking the minimized system operation cost as the objective functions;
and the optimal planning result acquisition module is used for inputting the acquired actual investment cost and the actual system operation cost into the joint planning layer, so that the joint planning layer plans the positions and the quantity of newly-built power transmission lines and stored energy according to the actual investment cost and the actual system operation cost to obtain an initial planning result, and iteratively optimizes the initial planning result according to the operation states of the set and the stored energy obtained by simulating the operation of the initial planning result by the simulated operation layer, and outputs an optimal planning result.
The embodiment of the invention has the following beneficial effects:
by establishing a double-layer planning model, wherein the upper layer of the double-layer planning model is a combined planning layer, the lower layer of the double-layer planning model is a simulation operation layer, the combined planning layer takes the minimized investment cost and the system operation cost as objective functions, and performs combined planning on a power transmission line and energy storage by combining the operation states of a unit and energy storage transmitted by the simulation operation layer to obtain a planning result, the simulation operation layer takes the minimized system operation cost as the objective function, simulates the operation planning result, updates the operation states of the unit and the energy storage, inputs the obtained actual investment cost and the obtained actual system operation cost into the combined planning layer, so that the combined planning layer plans the positions and the number of newly-built power transmission lines and energy storage according to the actual investment cost and the actual system operation cost to obtain an initial planning result, and simulates the operation states of the unit and the energy storage obtained by operating the initial planning result according to the simulation operation layer, and (4) iteratively optimizing the initial planning result, outputting the optimal planning result, and realizing the combined planning of the power transmission line and the energy storage. Compared with the prior art, the embodiment of the invention plans the positions and the quantity of newly-built power transmission lines and energy storage according to the actual investment cost and the actual system operation cost by establishing a double-layer planning model and utilizing a joint planning layer to obtain an initial planning result, optimizes the initial planning result according to the operation states of the corresponding set and the energy storage when the initial planning result is simulated to operate after the initial planning result is obtained, optimizes the optimized initial planning result according to the operation states of the corresponding set and the energy storage when the initial planning result is simulated to operate and iteratively optimizes the initial planning result to obtain the optimal planning result, thereby performing joint planning on the power transmission lines and the energy storage in consideration of the operation states of the set and the energy storage and ensuring the reliability, economy and flexibility of system operation.
Drawings
Fig. 1 is a flow chart illustrating a power transmission line and energy storage joint planning method according to a first embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a two-layer planning model according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram of an exemplary triangular probability distribution-based mutation process according to a first embodiment of the present invention;
FIG. 4 is a schematic diagram of an optimization process of an improved genetic algorithm exemplified in a first embodiment of the present invention;
fig. 5 is a schematic illustration of exemplary case-investment results in a first embodiment of the present invention;
fig. 6 is a schematic diagram illustrating the relationship between the energy storage charging and discharging conditions and the load according to an exemplary case of the first embodiment of the present invention;
fig. 7 is a schematic structural diagram of a power transmission line and energy storage joint planning according to a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
It should be noted that, the step numbers in the text are only for convenience of explanation of the specific embodiments, and do not serve to limit the execution sequence of the steps.
The first embodiment:
as shown in fig. 1, a first embodiment provides a power transmission line and energy storage joint planning method, which includes steps S1-S2:
s1, establishing a double-layer planning model; the upper layer of the double-layer planning model is a joint planning layer, and the lower layer is a simulation operation layer; the combined planning layer performs combined planning on the power transmission line and the energy storage by taking the minimized investment cost and the system operation cost as objective functions and combining the operation states of the unit and the energy storage transmitted by the simulated operation layer to obtain a planning result, and the simulated operation layer simulates the operation planning result and updates the operation states of the unit and the energy storage by taking the minimized system operation cost as the objective function;
and S2, inputting the obtained actual investment cost and the actual system operation cost into a joint planning layer, planning the positions and the quantity of newly-built power transmission lines and stored energy by the joint planning layer according to the actual investment cost and the actual system operation cost to obtain an initial planning result, and iteratively optimizing the initial planning result according to the operation states of the set and the stored energy obtained by simulating the operation of the initial planning result by the simulation operation layer to output an optimal planning result.
As shown in fig. 2, as an example, in step S1, a double-layer planning model is established, that is, an upper-layer joint planning layer and a lower-layer simulation operation layer are designed, so that the joint planning layer performs joint planning of the power transmission line and the energy storage by combining the operation states of the units and the energy storage transmitted by the simulation operation layer with the minimized investment cost and the minimized system operation cost as objective functions, and a planning result is obtained, so that the simulation operation layer simulates the operation planning result and updates the operation states of the units and the energy storage with the minimized system operation cost as the objective function.
In step S2, after obtaining the double-layer planning model, obtaining an actual investment cost and an actual system operation cost, inputting the actual investment cost and the actual system operation cost into the joint planning layer, so that the joint planning layer plans the positions and the amounts of the newly-built power transmission line and the stored energy according to the actual investment cost and the actual system operation cost to obtain an initial planning result, and optimizes the initial planning result according to the operation state of the set and the stored energy obtained by simulating the operation of the initial planning result by the simulation operation layer, and then iteratively optimizes the initial planning result according to the operation state of the set and the stored energy updated by the simulated operation layer according to the initial planning result after the simulation operation optimization, and outputs an optimal planning result. For example, the joint planning layer plans the positions and the quantity of newly-built power transmission lines and stored energy according to the actual investment cost and the actual system operation cost to obtain an initial planning result A, the simulation operation layer simulates and operates the initial planning result A to obtain the operation state a of the unit and the stored energy, the joint planning layer optimizes the initial planning result A according to the operation state a of the unit and the stored energy to obtain an optimized initial planning result B, the simulation operation layer simulates and operates the optimized initial planning result B to update the operation state a of the unit and the stored energy into the operation state B of the unit and the stored energy, the joint planning layer optimizes the optimized initial planning result B according to the operation state B of the unit and the stored energy to obtain an optimized planning result C, and the initial planning result A is iteratively optimized according to the process to obtain an optimal planning result.
According to the embodiment, a double-layer planning model is established, the positions and the quantity of newly-built power transmission lines and energy storage are planned by a joint planning layer according to actual investment cost and actual system operation cost to obtain an initial planning result, the initial planning result is optimized according to the operation states of the corresponding set and the energy storage when the initial planning result is simulated to operate, the optimized initial planning result is optimized, and the optimal planning result is obtained by iteratively optimizing the initial planning result, so that the joint planning of the power transmission lines and the energy storage can be performed by considering the operation states of the set and the energy storage, and the reliability, the economy and the flexibility of system operation are ensured.
In a preferred embodiment, the objective function of the joint planning layer is:
Figure BDA0003239623180000061
wherein, L is the transmission corridor number, L1, 2lFor the number, x, of newly built transmission lines in the transmission corridor llIs an integer of ClInvestment cost for newly building a transmission line in the transmission corridor l; s is the number of the energy storage construction node, and S is 1,2sBuilding a new energy storage quantity, x, on a node s for energy storagesIs an integer of CsInvestment cost for newly building an energy storage node s for energy storage; i is a unit number, i is 1,2,., N, T is a time interval number, T is 1,2,., T, M is an quotation interval number, M is 1,2i,t,mQuoted price, P, for unit i in the mth period of time ti,t,mThe power of the unit i winning the bid in the price quoted in the mth section of the time period t;
Figure BDA0003239623180000062
starting cost of the unit i in a time period t; d is the load number, D1, 2, D, penalty is the penalty factor for lost load, LLd,tThe load d is the loss of load during the time period t.
In a preferred embodiment, the joint planning layer takes the number constraints of the transmission lines and the stored energy as constraints.
In a preferred embodiment of this embodiment, the constraints on the number of the transmission lines and the energy storage are as follows:
0≤xl≤xl max；
0≤xs≤xs max；
wherein x isl maxMaximum number of newly built transmission lines, x, in a transmission corridors maxAnd building the maximum amount of the newly built energy storage on the node s for the energy storage.
In the embodiment, the number constraint of the power transmission line and the energy storage is taken as the constraint condition of the joint planning layer, so that the actual situation that the number of the newly-built power transmission line and the newly-built energy storage is limited by factors such as environmental conditions, policies and the like can be considered, and the fact that the planning result of the power transmission line and the energy storage determined by the joint planning layer meets the actual situation is favorably ensured.
In a preferred embodiment, the objective function of the simulation run layer is:
Figure BDA0003239623180000071
wherein, i is a unit number, i 1,2,., N, T is a time interval number, T1, 2., T, M is a quotation section number, M1, 2., M, Ci,t,mQuoted price, P, for unit i in the mth period of time ti,t,mThe power of the unit i winning the bid in the price quoted in the mth section of the time period t;
Figure BDA0003239623180000072
starting cost of the unit i in a time period t; d is the load number, D1, 2, D, penalty is the penalty factor for lost load, LLd,tIs the magnitude of the loss of load of the load d in the time period t.
In a preferred embodiment, the simulation run layer takes the unit constraints, the energy storage constraints and the system constraints as constraints.
In a preferred embodiment of this embodiment, the unit constraints include a unit output power constraint, a live power unit on-off time constraint, and a unit total output power constraint.
The output power of the unit is influenced by the start-stop state on one hand and limited by physical conditions, such as the ramp rate on the other hand, namely the difference between the output powers of the unit at the front moment and the rear moment has an upper limit value.
The output power constraint of the unit is as follows:
αi,tPi,t min≤Pi,t≤αi,tPi,t max；
Pi,t-Pi,t-1≤ΔPi Uαi,t-1+Pi,t min(αi,t-αi,t-1)+Pi,t max(1-αi,t)；
Pi,t-1-Pi,t≤ΔPi Dαi,t+Pi,t min(αi,t-αi,t-1)+Pi,t max(1-αi,t-1)；
wherein alpha isi,tIs the starting and stopping state of the unit i in the time period t, alpha i,t1 indicates that the unit i is in the on state for a time period t, α i,t0 indicates that the unit i is in a shutdown state for a time period t, Pi,tFor the power of the unit i in the time period t, Pi,t minIs the minimum power, P, of the unit i in the time period ti,t maxThe maximum power of the unit i in the time period t is set; delta Pi UMaximum upward ramp Rate, Δ P, for Unit ii DThe maximum ramp-down rate for unit i.
In the embodiment, the output power of the unit can be considered to be influenced by the factors such as the start-stop state, the climbing speed and the like by using the unit output power constraint as the constraint condition on the simulation operation layer, so that the operation state of the unit and the energy storage determined by the simulation operation layer can be ensured to be in accordance with the actual condition.
Due to the physical properties and actual operation requirements of the thermal power generating unit, the thermal power generating unit is required to meet the minimum continuous startup/shutdown time, that is,
the thermal power generating unit is restricted by the starting and stopping time:
Figure BDA0003239623180000081
Figure BDA0003239623180000082
wherein the content of the first and second substances,
Figure BDA0003239623180000083
for the continuous start-up time of unit i in time period t,
Figure BDA0003239623180000084
TUthe minimum continuous starting time of the unit is set;
Figure BDA0003239623180000085
for the continuous down time of the unit i during the time period t,
Figure BDA0003239623180000086
TDis the minimum continuous down time of the unit.
In the embodiment, the simulation operation layer takes the constraint of the on-off time of the thermal power generating unit as the constraint condition, the physical property of the thermal power generating unit and the actual condition required by actual operation can be considered, and the operation state of the thermal power generating unit and the energy storage determined by the simulation operation layer is favorably ensured to meet the actual condition.
The total output power of the unit is the sum of all the output powers in each quoted section, namely,
the total output power constraint of the unit is as follows:
Figure BDA0003239623180000087
Figure BDA0003239623180000088
wherein, Pi,t,mFor the power of the unit i, i 1,2,.., N, M1, 2.., M,
Figure BDA0003239623180000089
the minimum total power quoted in the mth section for unit i,
Figure BDA00032396231800000810
for unit i at mThe maximum total power quoted for the segment.
In a preferred implementation manner of this embodiment, the energy storage constraint includes an energy storage energy constraint, an energy storage charging and discharging power constraint, and an energy storage spare capacity constraint.
It can be understood that the energy in the time period t can be obtained after the initial energy of the stored energy is charged and discharged in the time periods 1,2, 3, …, t, and the energy of the stored energy is connected together through the charging and discharging power of each time period, that is,
Figure BDA0003239623180000091
wherein e iss,tFor the energy of the energy storage newly built on the energy storage building node s in the time period t,
Figure BDA0003239623180000092
for the initial energy of the energy storage newly built on the energy storage building node s,
Figure BDA0003239623180000093
respectively the charging efficiency and the discharging efficiency of the energy storage newly built on the energy storage building node s,
Figure BDA0003239623180000094
charging power and discharging power x of the newly built energy storage on the energy storage construction node s in the time interval tau respectivelysBuilding a new energy storage quantity, x, on a node s for energy storagesAre integers.
In order to ensure the safe operation of the system, the energy of the stored energy should be set to an upper limit and a lower limit, and in order to ensure the good operation of the stored energy in the next time period, the energy of the stored energy in the beginning and the end time periods should be equal, that is,
the energy storage energy constraint is as follows:
Figure BDA0003239623180000095
Figure BDA0003239623180000096
wherein the content of the first and second substances,
Figure BDA0003239623180000097
respectively establishing minimum energy and maximum energy of the newly established energy storage on the energy storage construction node s; t is 1, 2.
In actual operation, the stored energy cannot be charged and discharged simultaneously, i.e.
The energy storage charging and discharging constraints are as follows:
Figure BDA0003239623180000098
wherein the content of the first and second substances,
Figure BDA0003239623180000099
the charging state of the energy storage newly built on the energy storage building node s in the time period t,
Figure BDA00032396231800000910
indicating that the newly-built energy storage at the energy storage construction node s is charged in the time period t,
Figure BDA00032396231800000911
indicating that the newly-built energy storage at the energy storage construction node s is not charged during the time period t,
Figure BDA00032396231800000912
for the discharge state of the energy storage newly built on the energy storage building node s in the time period t,
Figure BDA00032396231800000913
indicating that the newly created energy storage at the energy storage construction node s is discharged in the time period t,
Figure BDA00032396231800000914
showing that the newly built energy storage on the energy storage construction node s is not discharged in the time period t, XsFor establishing new energy storage, X, at node ss1 represents energy storage constructionNewly-built energy storage, X, on node ssAnd 0 represents that no energy is newly built on the energy storage building node s.
In order to ensure the safe operation of the system, the charge and discharge power of the stored energy should set an upper limit, that is,
the energy storage charge and discharge power constraint is as follows:
Figure BDA0003239623180000101
Figure BDA0003239623180000102
wherein, Ps maxAnd the maximum charge and discharge power of the energy storage newly built on the energy storage building node s is obtained.
The capacity of the stored energy to provide backup is constrained by both the total power limit and the remaining stored energy level, i.e.,
the energy storage reserve capacity constraint is:
Figure BDA0003239623180000103
0≤rs,t≤Ps max；
Figure BDA0003239623180000104
wherein r iss,tAnd the standby capacity of the newly-built energy storage at the energy storage construction node s in the time period t is shown.
In the embodiment, the simulation operation layer takes the energy storage energy constraint, the energy storage charge-discharge power constraint and the energy storage reserve capacity constraint as constraint conditions, so that the actual condition of safe and good operation of the system can be considered, and the operation state of the unit and the energy storage determined by the simulation operation layer is favorably ensured to meet the actual condition.
In a preferred embodiment of this embodiment, the system constraints include system capacity constraints, system node constraints, and system spare capacity constraints.
The operation of the system is represented by a direct current power flow model, namely:
fl,t＝Bl(θns(l),t-θnr(l),t)；
-Ml·(1-Xl)≤Nfl,t-xl·Bl(θns(l),t-θnr(l),t)≤Ml·(1-Xl)；
wherein f isl,t＝Bl(θns(l),t-θnr(l),t) Is a direct current power flow model of the existing transmission line, -Ml·(1-Xl)≤Nfl,t-xl·Bl(θns(l),t-θnr(l),t)≤Ml·(1-Xl) A direct current power flow model of the newly-built power transmission line is created; f. ofl,tFor the power flow of the transmission line in the transmission corridor l during a time period t, BlFor admittance of power lines in power transmission corridors l, thetans(l),tFor the voltage phase angle theta of the transmission line transmitting end node in the transmission corridor l in the time period tnr(l),tThe voltage phase angle of a receiving end node of the power transmission line in the power transmission corridor l in a time period t; nfl,tFor power flow, M, on newly built transmission lines in transmission corridor llIs a larger number, relaxing the constraint.
The power flow of the transmission line is limited by the capacity of the transmission line, i.e.,
the system capacity constraints are:
-fl max≤fl≤fl max；
-Nfl max·Xl≤Nfl≤Nfl max·Xl；
wherein f isl maxThe capacity of the transmission line in the transmission corridor l; nfl maxFor the capacity of the newly built transmission line in the transmission corridor l.
The system node constraints are:
the voltage phase angle of each node of the system has a range limit:
-θmax≤θn≤θmax；
wherein, thetanIs the voltage phase angle of node n, thetamaxThe maximum value of the voltage phase angle is 180.
The voltage phase angle of the relaxation node is 0:
θslack＝0；
each node of the system needs to satisfy power balance:
Figure BDA0003239623180000111
wherein G isnSet of all units at node n, SnL | nr (L) ═ n is a set of all lines with node n as the receiving end, L | ns (L) ═ n is a set of all lines with node n as the sending end, and D is a set of all lines with node n as the sending endnIs the set of all loads at node n.
The system also needs a certain amount of spare capacity to prevent emergency situations, to ensure that the system operates safely and reliably, i.e.,
the system spare capacity constraints are:
Figure BDA0003239623180000121
wherein R isDAs a percentage of spare capacity.
In the embodiment, the simulation operation layer takes the system capacity constraint, the system node constraint and the system standby capacity constraint as constraint conditions, so that the actual condition of safe and good operation of the system can be considered, and the operation state of the unit and the energy storage determined by the simulation operation layer is favorably ensured to meet the actual condition.
In a preferred embodiment, the iteratively optimizing the initial planning result and outputting an optimal planning result specifically includes: and iteratively optimizing the initial planning result according to the improved genetic algorithm, and outputting an optimal planning result.
Due to the integer variable xl、αi,tThe introduction of the method and the system leads the computational complexity of the whole problem to be exponentially increased, and for a large-scale system, a classical solving method is difficult to solve a double-layer planning model, so that the double-layer planning model is solved according to an improved genetic algorithm.
The essence of genetic algorithm is "win or loss and survival of the fittest". One feasible solution of the upper-layer problem is that a line and energy storage investment scheme is an individual, each individual comprises two chromosomes and respectively represents the line investment scheme and the energy storage investment scheme, each gene on the chromosome represents the investment scheme of a corresponding position, alleles represent the investment quantity of the corresponding position, and a plurality of individuals form a group. And for each individual, solving the corresponding lower layer clearing problem so as to obtain a target function of the corresponding upper layer problem, wherein the smaller the target function is, the higher the fitness of the corresponding individual is, and the easier the gene is kept.
And selecting superior individuals as parents by a tournament method and putting the parents into a mating pool. Randomly selecting s individuals from the population, and selecting the individual with the minimum objective function from the s individuals as a parent. Repeat N times (N is the number of individuals in the population). In addition, in order to avoid the loss of excellent individual genes in the selection process, a part of the most excellent individuals in each generation of population needs to be directly reserved to the next generation.
Parents are selected from the mating pool for crossover to increase chromosomal diversity. Randomly selecting two individuals from a mating pool as parents, if a random number between 0 and 1 is smaller than a cross rate, randomly selecting two breakpoints on two chromosomes of the two parents, dividing the corresponding chromosomes into three segments, and exchanging the middle segments to generate two new filial individuals.
The offspring individuals are required to be mutated after crossing so as to increase gene diversity, so that the algorithm searches for areas which are not searched yet, and the local optimal solution is avoided. For each offspring individual, if a random number between 0 and 1 is less than the variation rate, a gene is randomly selected for variation on both chromosomes of the individual. As shown in fig. 3, the variation is based on a triangular probability density distribution. The abscissa of the three vertices of the triangle is 0, the current allele (i.e., the number of line/tank investments at the location before mutation), and the maximum allele (i.e., the upper limit of the number of line/tank investments at the location), respectively. For a random number between 0 and 1, the abscissa corresponding to the corresponding area is rounded to obtain the new allele after mutation.
A simulated annealing method is adopted to improve the effect of variation. Objective function value f of the varied individual2Less than the value of the objective function f before variation1When it is, the mutated individuals are used. When the objective function value of the mutated individual is larger than the objective function value before the mutation, the mutated individual is adopted with a certain probability, otherwise, the mutated individual is adopted. This probability P is:
Figure BDA0003239623180000131
wherein T is the temperature of the current generation, T ═ T0·rng，T0The temperature of the initial generation (generally higher), r is the decay rate of the temperature (less than 1, generally close to 1), and ng is the generation number of the current population.
After the variation is finished, a new generation of population is generated, and the four processes of calculating the objective function value, selecting, crossing and varying are repeated until the condition of cycle exit is met.
According to the embodiment, the initial planning result is iteratively optimized according to the improved genetic algorithm, so that the optimal planning result of the power transmission line and the energy storage can be quickly and accurately obtained.
As an example, to more clearly explain the first embodiment, a certain modification is made on the basis of the IEEE 24 bus test system. The testing system is divided into an upper area and a lower area through a transformer, the number of generator sets in the upper area is large, and the load in the lower area is large. In order to meet the requirements of power grid planning, transmission blockage is artificially manufactured. Expanding the load peak value and the unit capacity to be 3 times of the original value; the operating costs of each unit are shown in table 1; each power transmission corridor has 6 alternative lines, the admittance of the alternative lines is equal to the admittance of the existing lines of the corresponding corridor, the capacity of each alternative line is equal to the capacity of the existing lines of the corresponding corridor, and the annual investment cost of the lines is shown in table 2; each node has 6 alternative stored energies, all stored energy parameters are the same, and corresponding parameters are shown in table 3; the load curve for the first tuesday in the IEEE 24 bus test system was chosen. The operating cost of the day is multiplied by 365 to be comparable to the annual investment cost of the lines and energy storage. 70 initial individuals were randomly generated to make up the initial population, and the loop was ended over 100 iterations.
TABLE 1
Type of unit Capacity of unit (MW) Operating costs ($/MWh)
1 400 12
2 20,76,155,350 25
3 12，50，100，197 45
TABLE 2
Figure BDA0003239623180000141
TABLE 3
Figure BDA0003239623180000142
The genetic algorithm process is shown in FIG. 4, and the optimal individual is unchanged from the 97 th generation, and the total cost is 1.2335 × 109A process for the preparation of a catalyst is disclosed. The investment results for the transmission line are shown in table 4. The investment results for energy storage are shown in table 5.
TABLE 4
Figure BDA0003239623180000143
Figure BDA0003239623180000151
TABLE 5
Bus bar 1 Bus bar 2 Number of investment lines Bus bar 1 Bus bar 2 Number of investment lines
1 5 7 1 19 4
2 1 9 1 20 1
3 3 10 1 21 1
4 2 14 3 22 3
5 2 16 1 23 1
6 4 18 3 24 1
As can be seen from table 4, 29 lines among the 38 existing lines were newly built. Due to the random generation of individuals, the genetic algorithm may not be constructed for the lines with smaller investment. For lines with a number of investments equal to or greater than 3, which have been marked with a dashed line in fig. 5, the enlargement of these lines may be more important. As can be seen from fig. 5, these lines mainly enhance the power transmission supply capacity of the bus with strong power generation capacity to the large load bus (bus with load peak minus generator capacity greater than 100 MW), such as lines 21-15, 23-20, etc.; and the power transmission supply capacity of the large load bus of the upper area to the lower area is enhanced, such as lines 23-12-10, 11-10-8 and the like. Similarly, the energy storage with an investment number of 3 or more is indicated in fig. 5. It can be seen that the stored energy tends to build on large load busbars, such as busbars 3, 6, 14, 19. In fig. 6, the power is positive during the energy storage discharge and negative during the charging. As can be seen from fig. 6, the electric energy is stored in the load valley time, and is released in the load peak time, so that the fluctuation of the net load (load minus discharge power) is reduced, and the flexibility of the system operation is improved. The result accords with general cognition, and because the start-stop state of the unit has been considered, the result accords with actual law more.
Second embodiment:
as shown in fig. 7, a second embodiment provides a power transmission line and energy storage joint planning apparatus, including: a double-layer planning model establishing module 21, configured to establish a double-layer planning model; the upper layer of the double-layer planning model is a combined planning layer, and the lower layer is a simulation operation layer; the combined planning layer performs combined planning on the power transmission line and the energy storage by taking the minimized investment cost and the system operation cost as target functions and combining the operation states of the unit and the energy storage transmitted by the simulation operation layer to obtain a planning result, and the simulation operation layer simulates the operation planning result and updates the operation states of the unit and the energy storage by taking the minimized system operation cost as a target function; and the optimal planning result acquisition module 22 is configured to input the acquired actual investment cost and the actual system operation cost into the joint planning layer, so that the joint planning layer plans the positions and the quantities of the newly-built power transmission line and the stored energy according to the actual investment cost and the actual system operation cost to obtain an initial planning result, and iteratively optimizes the initial planning result according to the operation states of the unit and the stored energy obtained by simulating the operation of the initial planning result by the simulation operation layer, and outputs an optimal planning result.
Illustratively, a double-layer planning model is established through a double-layer planning model establishing module 21, that is, an upper-layer combined planning layer and a lower-layer simulation operation layer are designed, so that the combined planning layer performs combined planning of a power transmission line and an energy storage by combining operation states of a unit and the energy storage transmitted by the simulation operation layer with a minimized investment cost and a system operation cost as objective functions to obtain a planning result, the simulation operation layer simulates the operation planning result, and the operation states of the unit and the energy storage are updated.
After a double-layer planning model is obtained through the optimal planning result obtaining module 22, the actual investment cost and the actual system operation cost are obtained, the actual investment cost and the actual system operation cost are input into the joint planning layer, the joint planning layer plans the positions and the quantity of newly-built power transmission lines and stored energy according to the actual investment cost and the actual system operation cost to obtain an initial planning result, the operation states of the set and the stored energy obtained by simulating the operation of the initial planning result are obtained according to the simulation operation layer, the initial planning result is optimized, then the operation states of the set and the stored energy updated according to the simulation operation layer simulation operation result after the operation optimization are performed, the initial planning result is iteratively optimized, and the optimal planning result is output. For example, the joint planning layer plans the positions and the quantity of newly-built power transmission lines and stored energy according to the actual investment cost and the actual system operation cost to obtain an initial planning result A, the simulated operation layer simulates and operates the initial planning result A to obtain the operation state a of the unit and the stored energy, the joint planning layer optimizes the initial planning result A according to the operation state a of the unit and the stored energy to obtain an optimized initial planning result B, the simulated operation layer simulates and operates the optimized initial planning result B to update the operation state a of the unit and the stored energy into the operation state B of the unit and the stored energy, the joint planning layer optimizes the optimized initial planning result B according to the operation state B of the unit and the stored energy to obtain an optimized planning result C, and the initial planning result A is iteratively optimized according to the process to obtain an optimal planning result.
According to the embodiment, a double-layer planning model is established, the positions and the quantity of newly-built power transmission lines and energy storage are planned by a joint planning layer according to actual investment cost and actual system operation cost to obtain an initial planning result, the initial planning result is optimized according to the operation states of the corresponding set and the energy storage when the initial planning result is simulated to operate, the optimized initial planning result is optimized, and the optimal planning result is obtained by iteratively optimizing the initial planning result, so that the joint planning of the power transmission lines and the energy storage can be performed by considering the operation states of the set and the energy storage, and the reliability, the economy and the flexibility of system operation are ensured.
In a preferred embodiment, the objective function of the joint planning layer is:
Figure BDA0003239623180000171
wherein, L is the transmission corridor number, L1, 2lFor the number, x, of newly built transmission lines in the transmission corridor llIs an integer of ClInvestment cost for newly building a transmission line in the transmission corridor l; s is the number of the energy storage construction node, and S is 1,2sBuilding a new energy storage quantity, x, on a node s for energy storagesIs an integer of CsInvestment cost for newly building an energy storage node s for energy storage; i is a unit number, i is 1,2,., N, T is a time interval number, T is 1,2,., T, M is an quotation interval number, M is 1,2i,t,mQuoted price, P, for unit i in the mth period of time ti,t,mThe power of the unit i winning the bid in the price quoted in the mth section of the time period t;
Figure BDA0003239623180000172
starting cost of the unit i in a time period t; d is the load number, D1, 2Factor, LLd,tThe load d is the loss of load during the time period t.
In a preferred embodiment, the joint planning layer takes the number constraints of the transmission lines and the stored energy as constraints.
In a preferred embodiment of this embodiment, the constraints on the number of the transmission lines and the energy storage are as follows:
0≤xl≤xl max；
0≤xs≤xs max；
wherein x isl maxMaximum number of newly built transmission lines, x, in a transmission corridors maxAnd building the maximum amount of the newly built energy storage on the node s for the energy storage.
In the embodiment, the number constraint of the power transmission line and the energy storage is taken as the constraint condition of the joint planning layer, so that the actual situation that the number of the newly-built power transmission line and the newly-built energy storage is limited by factors such as environmental conditions, policies and the like can be considered, and the fact that the planning result of the power transmission line and the energy storage determined by the joint planning layer meets the actual situation is favorably ensured.
In a preferred embodiment, the objective function of the simulation run layer is:
Figure BDA0003239623180000181
wherein, i is a unit number, i 1,2,., N, T is a time interval number, T1, 2., T, M is a quotation section number, M1, 2., M, Ci,t,mQuoted price, P, for unit i in the mth period of time ti,t,mThe power of the unit i winning the bid in the price quoted in the mth section of the time period t;
Figure BDA0003239623180000182
starting cost of the unit i in a time period t; d is the load number, D1, 2, D, penalty is the penalty factor for lost load, LLd,tIs the magnitude of the loss of load of the load d in the time period t.
In a preferred embodiment, the simulation run layer takes the unit constraints, the energy storage constraints and the system constraints as constraints.
In a preferred embodiment of this embodiment, the unit constraints include a unit output power constraint, a live power unit on-off time constraint, and a unit total output power constraint.
The output power of the unit is influenced by the start-stop state on one hand and limited by physical conditions, such as the ramp rate on the other hand, namely the difference between the output powers of the unit at the front moment and the rear moment has an upper limit value.
The output power constraint of the unit is as follows:
αi,tPi,t min≤Pi,t≤αi,tPi,t max；
Pi,t-Pi,t-1≤ΔPi Uαi,t-1+Pi,t min(αi,t-αi,t-1)+Pi,t max(1-αi,t)；
Pi,t-1-Pi,t≤ΔPi Dαi,t+Pi,t min(αi,t-αi,t-1)+Pi,t max(1-αi,t-1)；
wherein alpha isi,tIs the starting and stopping state of the unit i in the time period t, alpha i,t1 indicates that the unit i is in the on state for a time period t, α i,t0 indicates that the unit i is in a shutdown state for a time period t, Pi,tFor the power of the unit i in the time period t, Pi,t minIs the minimum power, P, of the unit i in the time period ti,t maxThe maximum power of the unit i in the time period t is set; delta Pi UMaximum upward ramp Rate, Δ P, for Unit ii DThe maximum ramp-down rate for unit i.
In the embodiment, the output power of the unit can be considered to be influenced by the factors such as the start-stop state, the climbing speed and the like by using the unit output power constraint as the constraint condition on the simulation operation layer, so that the operation state of the unit and the energy storage determined by the simulation operation layer can be ensured to be in accordance with the actual condition.
Due to the physical properties and actual operation requirements of the thermal power generating unit, the thermal power generating unit is required to meet the minimum continuous startup/shutdown time, that is,
the thermal power generating unit is restricted by the starting and stopping time:
Figure BDA0003239623180000191
Figure BDA0003239623180000192
wherein the content of the first and second substances,
Figure BDA0003239623180000193
for the continuous start-up time of unit i in time period t,
Figure BDA0003239623180000194
TUthe minimum continuous starting time of the unit is set;
Figure BDA0003239623180000195
for the continuous down time of the unit i during the time period t,
Figure BDA0003239623180000196
TDis the minimum continuous down time of the unit.
In the embodiment, the simulation operation layer takes the constraint of the on-off time of the thermal power generating unit as the constraint condition, the physical property of the thermal power generating unit and the actual condition required by actual operation can be considered, and the operation state of the thermal power generating unit and the energy storage determined by the simulation operation layer is favorably ensured to meet the actual condition.
The total output power of the unit is the sum of all the output powers in each quoted section, namely,
the total output power constraint of the unit is as follows:
Figure BDA0003239623180000197
Figure BDA0003239623180000198
wherein, Pi,t,mFor the power of the unit i, i 1,2,.., N, M1, 2.., M,
Figure BDA0003239623180000199
the minimum total power quoted in the mth section for unit i,
Figure BDA00032396231800001910
and (4) the maximum total power quoted for the unit i in the mth section.
In a preferred implementation manner of this embodiment, the energy storage constraint includes an energy storage energy constraint, an energy storage charging and discharging power constraint, and an energy storage spare capacity constraint.
It can be understood that the energy in the time period t can be obtained after the initial energy of the stored energy is charged and discharged in the time periods 1,2, 3, …, t, and the energy of the stored energy is connected together through the charging and discharging power of each time period, that is,
Figure BDA00032396231800001911
wherein e iss,tFor the energy of the energy storage newly built on the energy storage building node s in the time period t,
Figure BDA00032396231800001912
for the initial energy of the energy storage newly built on the energy storage building node s,
Figure BDA00032396231800001913
respectively the charging efficiency and the discharging efficiency of the energy storage newly built on the energy storage building node s,
Figure BDA00032396231800001914
respectively charging power and discharging power of the energy storage newly built on the energy storage building node s in the time interval tau,xsbuilding a new energy storage quantity, x, on a node s for energy storagesAre integers.
In order to ensure the safe operation of the system, the energy of the stored energy should be set to an upper limit and a lower limit, and in order to ensure the good operation of the stored energy in the next time period, the energy of the stored energy in the beginning and the end time periods should be equal, that is,
the energy storage energy constraint is as follows:
Figure BDA0003239623180000201
Figure BDA0003239623180000202
wherein the content of the first and second substances,
Figure BDA0003239623180000203
respectively establishing minimum energy and maximum energy of the newly established energy storage on the energy storage construction node s; t is 1, 2.
In actual operation, the stored energy cannot be charged and discharged simultaneously, i.e.
The energy storage charging and discharging constraints are as follows:
Figure BDA0003239623180000204
wherein the content of the first and second substances,
Figure BDA0003239623180000205
the charging state of the energy storage newly built on the energy storage building node s in the time period t,
Figure BDA0003239623180000206
indicating that the newly-built energy storage at the energy storage construction node s is charged in the time period t,
Figure BDA0003239623180000207
indicating that the newly-built energy storage at the energy storage construction node s is not charged during the time period t,
Figure BDA0003239623180000208
for the discharge state of the energy storage newly built on the energy storage building node s in the time period t,
Figure BDA0003239623180000209
indicating that the newly created energy storage at the energy storage construction node s is discharged in the time period t,
Figure BDA00032396231800002010
showing that the newly built energy storage on the energy storage construction node s is not discharged in the time period t, XsFor establishing new energy storage, X, at node ss1 represents newly-built energy storage on the energy storage construction node s, and XsAnd 0 represents that no energy is newly built on the energy storage building node s.
In order to ensure the safe operation of the system, the charge and discharge power of the stored energy should set an upper limit, that is,
the energy storage charge and discharge power constraint is as follows:
Figure BDA00032396231800002011
Figure BDA00032396231800002012
wherein, Ps maxAnd the maximum charge and discharge power of the energy storage newly built on the energy storage building node s is obtained.
The capacity of the stored energy to provide backup is constrained by both the total power limit and the remaining stored energy level, i.e.,
the energy storage reserve capacity constraint is:
Figure BDA0003239623180000211
0≤rs,t≤Ps max；
Figure BDA0003239623180000212
wherein r iss,tAnd the standby capacity of the newly-built energy storage at the energy storage construction node s in the time period t is shown.
In the embodiment, the simulation operation layer takes the energy storage energy constraint, the energy storage charge-discharge power constraint and the energy storage reserve capacity constraint as constraint conditions, so that the actual condition of safe and good operation of the system can be considered, and the operation state of the unit and the energy storage determined by the simulation operation layer is favorably ensured to meet the actual condition.
In a preferred embodiment of this embodiment, the system constraints include system capacity constraints, system node constraints, and system spare capacity constraints.
The operation of the system is represented by a direct current power flow model, namely:
fl,t＝Bl(θns(l),t-θnr(l),t)；
-Ml·(1-Xl)≤Nfl,t-xl·Bl(θns(l),t-θnr(l),t)≤Ml·(1-Xl)；
wherein f isl,t＝Bl(θns(l),t-θnr(l),t) Is a direct current power flow model of the existing transmission line, -Ml·(1-Xl)≤Nfl,t-xl·Bl(θns(l),t-θnr(l),t)≤Ml·(1-Xl) A direct current power flow model of the newly-built power transmission line is created; f. ofl,tFor the power flow of the transmission line in the transmission corridor l during a time period t, BlFor admittance of power lines in power transmission corridors l, thetans(l),tFor the voltage phase angle theta of the transmission line transmitting end node in the transmission corridor l in the time period tnr(l),tThe voltage phase angle of a receiving end node of the power transmission line in the power transmission corridor l in a time period t; nfl,tFor power flow, M, on newly built transmission lines in transmission corridor llIs a larger number, relaxing the constraint.
The power flow of the transmission line is limited by the capacity of the transmission line, i.e.,
the system capacity constraints are:
-fl max≤fl≤fl max；
-Nfl max·Xl≤Nfl≤Nfl max·Xl；
wherein f isl maxThe capacity of the transmission line in the transmission corridor l; nfl maxFor the capacity of the newly built transmission line in the transmission corridor l.
The system node constraints are:
the voltage phase angle of each node of the system has a range limit:
-θmax≤θn≤θmax；
wherein, thetanIs the voltage phase angle of node n, thetamaxThe maximum value of the voltage phase angle is 180.
The voltage phase angle of the relaxation node is 0:
θslack＝0；
each node of the system needs to satisfy power balance:
Figure BDA0003239623180000221
wherein G isnSet of all units at node n, SnL | nr (L) ═ n is a set of all lines with node n as the receiving end, L | ns (L) ═ n is a set of all lines with node n as the sending end, and D is a set of all lines with node n as the sending endnIs the set of all loads at node n.
The system also needs a certain amount of spare capacity to prevent emergency situations, to ensure that the system operates safely and reliably, i.e.,
the system spare capacity constraints are:
Figure BDA0003239623180000222
wherein R isDTo spare capacityPercentage of (c).
In the embodiment, the simulation operation layer takes the system capacity constraint, the system node constraint and the system standby capacity constraint as constraint conditions, so that the actual condition of safe and good operation of the system can be considered, and the operation state of the unit and the energy storage determined by the simulation operation layer is favorably ensured to meet the actual condition.
In a preferred embodiment, the iteratively optimizing the initial planning result and outputting an optimal planning result specifically includes: and iteratively optimizing the initial planning result according to the improved genetic algorithm, and outputting an optimal planning result.
Due to the integer variable xl、αi,tThe introduction of the method and the system leads the computational complexity of the whole problem to be exponentially increased, and for a large-scale system, a classical solving method is difficult to solve a double-layer planning model, so that the double-layer planning model is solved according to an improved genetic algorithm.
The essence of genetic algorithm is "win or loss and survival of the fittest". One feasible solution of the upper-layer problem is that a line and energy storage investment scheme is an individual, each individual comprises two chromosomes and respectively represents the line investment scheme and the energy storage investment scheme, each gene on the chromosome represents the investment scheme of a corresponding position, alleles represent the investment quantity of the corresponding position, and a plurality of individuals form a group. And for each individual, solving the corresponding lower layer clearing problem so as to obtain a target function of the corresponding upper layer problem, wherein the smaller the target function is, the higher the fitness of the corresponding individual is, and the easier the gene is kept.
And selecting superior individuals as parents by a tournament method and putting the parents into a mating pool. Randomly selecting s individuals from the population, and selecting the individual with the minimum objective function from the s individuals as a parent. Repeat N times (N is the number of individuals in the population). In addition, in order to avoid the loss of excellent individual genes in the selection process, a part of the most excellent individuals in each generation of population needs to be directly reserved to the next generation.
Parents are selected from the mating pool for crossover to increase chromosomal diversity. Randomly selecting two individuals from a mating pool as parents, if a random number between 0 and 1 is smaller than a cross rate, randomly selecting two breakpoints on two chromosomes of the two parents, dividing the corresponding chromosomes into three segments, and exchanging the middle segments to generate two new filial individuals.
The offspring individuals are required to be mutated after crossing so as to increase gene diversity, so that the algorithm searches for areas which are not searched yet, and the local optimal solution is avoided. For each offspring individual, if a random number between 0 and 1 is less than the variation rate, a gene is randomly selected for variation on both chromosomes of the individual. The variation is based on a triangular probability density distribution. The abscissa of the three vertices of the triangle is 0, the current allele (i.e., the number of lines/tank investments at the location before mutation), and the maximum allele (i.e., the upper limit of the number of lines/tank investments at the location), respectively. For a random number between 0 and 1, the abscissa corresponding to the corresponding area is rounded to obtain the new allele after mutation.
A simulated annealing method is adopted to improve the effect of variation. Objective function value f of the varied individual2Less than the value of the objective function f before variation1When it is, the mutated individuals are used. When the objective function value of the mutated individual is larger than the objective function value before the mutation, the mutated individual is adopted with a certain probability, otherwise, the mutated individual is adopted. This probability P is:
Figure BDA0003239623180000241
wherein T is the temperature of the current generation, T ═ T0·rng，T0The temperature of the initial generation (generally higher), r is the decay rate of the temperature (less than 1, generally close to 1), and ng is the generation number of the current population.
After the variation is finished, a new generation of population is generated, and the four processes of calculating the objective function value, selecting, crossing and varying are repeated until the condition of cycle exit is met.
According to the embodiment, the initial planning result is iteratively optimized according to the improved genetic algorithm, so that the optimal planning result of the power transmission line and the energy storage can be quickly and accurately obtained.
In summary, the embodiment of the present invention has the following advantages:
by establishing a double-layer planning model, wherein the upper layer of the double-layer planning model is a combined planning layer, the lower layer of the double-layer planning model is a simulation operation layer, the combined planning layer takes the minimized investment cost and the system operation cost as objective functions, and performs combined planning on a power transmission line and energy storage by combining the operation states of a unit and energy storage transmitted by the simulation operation layer to obtain a planning result, the simulation operation layer takes the minimized system operation cost as the objective function, simulates the operation planning result, updates the operation states of the unit and the energy storage, inputs the obtained actual investment cost and the obtained actual system operation cost into the combined planning layer, so that the combined planning layer plans the positions and the number of newly-built power transmission lines and energy storage according to the actual investment cost and the actual system operation cost to obtain an initial planning result, and simulates the operation states of the unit and the energy storage obtained by operating the initial planning result according to the simulation operation layer, and (4) iteratively optimizing the initial planning result, outputting the optimal planning result, and realizing the combined planning of the power transmission line and the energy storage. According to the embodiment of the invention, a double-layer planning model is established, the positions and the quantity of newly-built transmission lines and energy storage are planned according to the actual investment cost and the actual system operation cost by using a combined planning layer to obtain an initial planning result, the initial planning result is optimized according to the operation state of the corresponding set and the energy storage when the initial planning result is simulated to operate after the initial planning result is obtained, the optimized initial planning result is optimized according to the operation state of the corresponding set and the energy storage when the initial planning result is simulated to operate and optimized, and the optimal planning result is obtained by iteratively optimizing the initial planning result, so that the operation state of the set and the energy storage can be considered to carry out the combined planning of the transmission lines and the energy storage, and the reliability, the economy and the flexibility of the system operation are ensured.
The foregoing is directed to the preferred embodiment of the present invention, and it is understood that various changes and modifications may be made by one skilled in the art without departing from the spirit of the invention, and it is intended that such changes and modifications be considered as within the scope of the invention.
It will be understood by those skilled in the art that all or part of the processes of the above embodiments may be implemented by a computer program, which can be stored in a computer readable storage medium and can include the processes of the above embodiments when executed. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.

Claims (10)

1. A power transmission line and energy storage joint planning method is characterized by comprising the following steps:
establishing a double-layer planning model; the upper layer of the double-layer planning model is a joint planning layer, and the lower layer of the double-layer planning model is a simulation operation layer; the combined planning layer performs combined planning on the power transmission line and the energy storage by taking the minimized investment cost and the system operation cost as objective functions and combining the operation states of the unit and the energy storage transmitted by the simulated operation layer to obtain a planning result, and the simulated operation layer simulates and operates the planning result and updates the operation states of the unit and the energy storage by taking the minimized system operation cost as the objective functions;
inputting the obtained actual investment cost and the actual system operation cost into the joint planning layer, enabling the joint planning layer to plan the positions and the quantity of newly-built power transmission lines and stored energy according to the actual investment cost and the actual system operation cost to obtain an initial planning result, and performing iterative optimization on the initial planning result according to the operation states of the set and the stored energy obtained by simulating the operation of the initial planning result by the simulation operation layer, and outputting an optimal planning result.
2. The power transmission line and energy storage joint planning method according to claim 1, wherein the iterative optimization of the initial planning result and the output of an optimal planning result specifically include:
and iteratively optimizing the initial planning result according to an improved genetic algorithm, and outputting the optimal planning result.
3. The power transmission line and energy storage joint planning method of claim 1, wherein the objective function of the joint planning layer is:
Figure FDA0003239623170000011
wherein, L is the transmission corridor number, L1, 2lFor the number, x, of newly built transmission lines in the transmission corridor llIs an integer of ClInvestment cost for newly building a transmission line in the transmission corridor l; s is the number of the energy storage construction node, and S is 1,2sBuilding a new energy storage quantity, x, on a node s for energy storagesIs an integer of CsInvestment cost for newly building an energy storage node s for energy storage; i is a unit number, i is 1,2,., N, T is a time interval number, T is 1,2,., T, M is an quotation interval number, M is 1,2i,t,mQuoted price, P, for unit i in the mth period of time ti,t,mThe power of the unit i winning the bid in the price quoted in the mth section of the time period t;
Figure FDA0003239623170000021
starting cost of the unit i in a time period t; d is the load number, D1, 2, D, penalty is the penalty factor for lost load, LLd,tIs the magnitude of the loss of load of the load d in the time period t.
4. The power transmission line and energy storage planning method according to claim 1, wherein the joint planning layer takes quantity constraints of the power transmission line and the energy storage as constraint conditions.
5. The power transmission line and energy storage joint planning method of claim 1, wherein the objective function of the simulation operation layer is:
Figure FDA0003239623170000022
wherein, i is a unit number, i 1,2,., N, T is a time interval number, T1, 2., T, M is a quotation section number, M1, 2., M, Ci,t,mQuoted price, P, for unit i in the mth period of time ti,t,mThe power of the unit i winning the bid in the price quoted in the mth section of the time period t;
Figure FDA0003239623170000023
starting cost of the unit i in a time period t; d is the load number, D1, 2, D, penalty is the penalty factor for lost load, LLd,tIs the magnitude of the loss of load of the load d in the time period t.
6. The power transmission line and energy storage joint planning method of claim 1, wherein the simulation operation layer takes unit constraint, energy storage constraint and system constraint as constraint conditions.
7. The power transmission line and energy storage combined planning method according to claim 6, wherein the unit constraints include a unit output power constraint, a thermal power unit on-off time constraint and a unit total output power constraint.
8. The power transmission line and energy storage joint planning method according to claim 6, wherein the energy storage constraints include energy storage constraints, energy storage charge and discharge power constraints, and energy storage reserve capacity constraints.
9. The joint planning method for transmission line and energy storage according to claim 6, wherein the system constraints include system capacity constraints, system node constraints, and system spare capacity constraints.
10. The utility model provides a planning device is united with energy storage to transmission line which characterized in that includes:
the double-layer planning model establishing module is used for establishing a double-layer planning model; the upper layer of the double-layer planning model is a joint planning layer, and the lower layer of the double-layer planning model is a simulation operation layer; the combined planning layer performs combined planning on the power transmission line and the energy storage by taking the minimized investment cost and the system operation cost as objective functions and combining the operation states of the unit and the energy storage transmitted by the simulated operation layer to obtain a planning result, and the simulated operation layer simulates and operates the planning result and updates the operation states of the unit and the energy storage by taking the minimized system operation cost as the objective functions;
and the optimal planning result acquisition module is used for inputting the acquired actual investment cost and the actual system operation cost into the joint planning layer, so that the joint planning layer plans the positions and the quantity of newly-built power transmission lines and stored energy according to the actual investment cost and the actual system operation cost to obtain an initial planning result, and iteratively optimizes the initial planning result according to the operation states of the set and the stored energy obtained by simulating the operation of the initial planning result by the simulated operation layer to output an optimal planning result.
CN202111017779.4A 2021-08-31 2021-08-31 Power transmission line and energy storage joint planning method and device Pending CN113705913A (en)

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