CN115313438B - AC/DC power transmission network and energy storage collaborative planning method and medium - Google Patents

AC/DC power transmission network and energy storage collaborative planning method and medium Download PDF

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CN115313438B
CN115313438B CN202210788431.3A CN202210788431A CN115313438B CN 115313438 B CN115313438 B CN 115313438B CN 202210788431 A CN202210788431 A CN 202210788431A CN 115313438 B CN115313438 B CN 115313438B
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power
planning
line
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CN115313438A (en
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方家琨
吴云芸
艾小猛
薛熙臻
崔世常
姚伟
陈霞
文劲宇
严道波
赵红生
蔡杰
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Huazhong University of Science and Technology
Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J4/00—Circuit arrangements for mains or distribution networks not specified as ac or dc
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously

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Abstract

The invention discloses an alternating current-direct current power transmission network and energy storage collaborative planning method and medium, belonging to the field of electrical engineering, wherein the method comprises the following steps: based on the uncertainty of new energy output, the adjustment capacity of a direct-current line, the voltage fluctuation of a node of an electric power system and reactive power flow, establishing a two-stage robust optimization model and dividing the two-stage robust optimization model into a main problem model and a sub-problem model by taking the minimum sum of daily chemical investment cost and daily operation cost as a target; determining a limit scene based on historical data of new energy output; solving the main problem model based on the limit scene to obtain a planning scheme of the AC/DC line and the energy storage, substituting the planning scheme into the sub-problem model to solve to verify the feasibility of the planning scheme, and iteratively solving the main and sub-problem models until a feasible planning scheme is obtained to configure the AC/DC line and the energy storage. The operation condition of the power system is simulated more truly, and the obtained planning scheme ensures the safe and stable operation of the power system while promoting the consumption of new energy.

Description

AC/DC power transmission network and energy storage collaborative planning method and medium
Technical Field
The invention belongs to the field of electrical engineering, and particularly relates to an alternating current-direct current power transmission network and energy storage collaborative planning method and medium.
Background
In recent years, high proportion of new energy grid connection becomes a remarkable feature of future power systems. The fluctuation and randomness of new energy sources bring great challenges to the power and electric quantity balance of the power system. Meanwhile, the construction of the relatively lagged conveying channel also gradually highlights the contradiction of new energy consumption in local areas. The transmission capability of the transmission line can be improved and the line congestion can be relieved through the expansion of the transmission network. The energy storage equipment is used as a flexible adjusting resource and has the characteristic of quick charge and discharge, the system power supply structure can be improved and the system adjusting capacity can be improved by configuring energy storage in the power grid, and the expansion capacity of the power transmission grid and the output resistor plug can be reduced by means of peak clipping, valley filling and the like.
The synergistic relation between the power transmission network expansion and the energy storage planning is fully considered, the new energy consumption capability can be improved, the optimal economic benefit and the optimal planning scheme can be obtained, excessive investment is avoided, and the efficiency maximization of the power transmission network and the energy storage is realized. Therefore, the research on the cooperative planning of the power transmission network and the energy storage is of great significance. The conventional power transmission network and energy storage collaborative planning technology mainly ignores the influence of reactive power flow on an electric power system aiming at active power flow, so that the planning result possibly cannot ensure the safe and stable operation of the electric power system.
Disclosure of Invention
Aiming at the defects and improvement requirements of the prior art, the invention provides an alternating current-direct current power transmission network and energy storage collaborative planning method and medium, and aims to solve the problem that the safe and stable operation of a power system cannot be ensured as a planning result is caused by the fact that the actual operation condition of the power system is difficult to simulate more truly by the conventional power transmission network and energy storage collaborative planning.
To achieve the above object, according to an aspect of the present invention, there is provided an ac/dc power transmission network and energy storage collaborative planning method, including: s1, establishing a two-stage robust optimization model of the AC/DC power transmission network and energy storage collaborative planning based on the uncertainty of new energy output, the DC line regulation capability, the node voltage fluctuation of the power system and the reactive power flow and with the aim of minimizing the sum of the daily chemical investment cost and the daily operation cost; s2, carrying out convex relaxation on non-convex constraints in the two-stage robust optimization model, and dividing the two-stage robust optimization model after the convex relaxation into a main problem model and a sub problem model; s3, generating a new energy output set based on historical data of new energy output, and setting a scene corresponding to the top point of the new energy output set as an extreme scene; and S4, solving the main problem model based on the limit scene to obtain a planning scheme of the AC/DC line and the stored energy, substituting the planning scheme into the sub-problem model to solve to verify the feasibility of the planning scheme, iteratively solving the main problem model and the sub-problem model until a feasible planning scheme is obtained, and configuring the AC/DC line and the stored energy according to the feasible planning scheme.
Still further, the S4 includes: s41, solving the main problem model based on the first m worst limit scenes in the limit scenes to obtain a planning scheme of an alternating current-direct current line and energy storage, wherein the initial value of m is 1; s42, substituting the planning scheme into the sub-problem model to solve so as to verify the feasibility of the planning scheme; s43, if the planning scheme is not feasible, updating m to m +1, and repeatedly executing the S41-S42 until a feasible planning scheme is obtained; and S44, configuring the positions and the number of the AC/DC lines and the positions and the capacity of stored energy according to a feasible planning scheme.
Further, the planning scheme is feasible when the following conditions are met, otherwise, the planning scheme is not feasible:
|UB (m) -LB (m) |/UB (m) <ε
wherein, UB (m) For the mth iteration to solve the target value, LB, of the neutron problem model in the process (m) And e is a preset error threshold value for the target value of the main problem model in the mth iteration solving process.
Further, the two-stage robust optimization model is:
Figure BDA0003729495380000031
wherein x is BS,i The number of energy storage units planned for the node i, N is the number of system nodes, eta P Cost per unit power capacity, η, for energy storage E Cost per energy capacity for energy storage, P BS For power capacity of energy storage unit, E BS For energy capacity of energy storage unit, T total,BS Designed service life for energy storage, x AC,ij,k For the state of construction of the kth line of the AC transmission corridor i-j, N lAC Is the number of AC transmission corridors, eta LAC Cost per unit length of AC line, D AC,ij Length of AC transmission corridor i-j, T total,LAC Design life, x, for AC candidate lines DC,ij,l Is the construction state of the first return line of the DC transmission corridor i-j, N lDC Is the number of DC transmission corridors, eta LDC Cost per unit length of the DC line, D DC,ij Is the length, T, of the DC transmission corridor i-j total,LDC Design lifetime, x, for DC candidate lines vsc,i Is the construction state, η, of the VSC device at node i vsc Cost per unit power of VSC device, F vsc,i Is the power capacity, T, of the VSC device at node i total,vsc For the design service life of the VSC device, T is the simulation duration, P G,i,t The output of the thermal power generating unit at a node i in the t period, C (-) is the coal consumption cost of the thermal power generating unit, C SU,i,t 、C SD,i,t The starting cost and the shutdown cost of the thermal power generating unit at the node i in the period t are respectively.
Further, the first-stage constraint conditions in the two-stage robust optimization model include: the method comprises the following steps of alternating current power grid expansion constraint, direct current power grid expansion constraint and energy storage configuration constraint.
Further, the second stage constraint conditions in the two-stage robust optimization model include: the method comprises the following steps of (1) carrying out energy storage system operation constraint, alternating current power grid operation constraint, direct current power grid operation constraint and unit combination constraint; the energy storage system operating constraints include: energy storage charging and discharging power constraint, energy storage charging and discharging state constraint, energy storage energy level constraint and energy storage end energy constraint; the AC grid operating constraints include: the method comprises the following steps of carrying out AC node power balance constraint, AC line active loss and reactive loss constraint and AC line two-terminal voltage amplitude and phase angle relation constraint; the direct current grid operation constraints include: the method comprises the following steps of carrying out direct current node power balance constraint, direct current active loss constraint, voltage amplitude relation constraint at two ends of a direct current line, power balance constraint at two ends of a VSC device and power loss constraint of the VSC device; the unit combination constraints include: spinning reserve constraints, hill climb constraints, start/stop maximum force limit constraints, and minimum start/stop time constraints.
Further, the non-convex constraints include ac line loss constraints and dc line loss constraints, and the convex relaxation is:
Figure BDA0003729495380000041
Figure BDA0003729495380000042
wherein R is lAC,ij Resistance, P, of one circuit line of an AC transmission corridor i-j AC,ij,k,t 、Q AC,ij,k,t 、P lsAC,ij,k,t Receiving end active power, receiving end reactive power and active power loss of the kth circuit of the alternating-current transmission corridor i-j respectively, and Y AC,j,t Is the square of the voltage at AC node j, R lDC,ij Resistance, P, of one circuit for a DC transmission corridor i-j DC,ij,l,t 、P lsDC,ij,l,t Active power of receiving end of the first return line of the direct current transmission corridor i-jPower, active power loss, Y DC,j,t Which is the square of the voltage at dc node j.
Further, the new energy output set is as follows:
Figure BDA0003729495380000043
wherein χ is the new energy output set,
Figure BDA0003729495380000044
for describing a random variable of the new energy contribution, a>
Figure BDA0003729495380000045
Is N W Vector of dimension T, N W For the number of new energy sites, T is the simulation duration, N E Is a limit number of scenes, p e Is a positive coefficient, ω s,e The new energy output condition under the e-th limit scene.
Further, the main problem model is:
Figure BDA0003729495380000046
Figure BDA0003729495380000047
the sub-problem model is as follows:
Figure BDA0003729495380000051
Figure BDA0003729495380000052
wherein f is 1 (. Is the daily chemical investment cost, x (m) Relating to AC/DC power transmission network expansion and energy storage planning in the mth iteration solving processThe planning variables of (a) are,
Figure BDA0003729495380000053
as an intermediate variable related to the daily operating cost, f 2 (. Cndot.) is the daily operating cost, y is an operating variable associated with operation of the power system, ω b (m) For the new energy output scene set of the main problem model in the mth iteration solving process, u (-) is the first-stage constraint in the two-stage robust optimization model, v 1 (·)、v 2 (. To) equality constraint, inequality constraint, omega in the second stage constraint in the two-stage robust optimization model s For the new energy output situation, omega, in all extreme scenarios s,e Is the new energy output condition N in the e-th limit scene E Is the limit number of scenes.
According to another aspect of the present invention, there is provided a computer readable storage medium, having stored thereon a computer program, which when executed by a processor, implements the ac/dc grid and energy storage co-planning method as described above.
Generally, by the above technical solution conceived by the present invention, the following beneficial effects can be obtained:
(1) The AC/DC power transmission network and energy storage collaborative planning method is provided, and the collaborative relationship among AC power transmission network expansion, DC power transmission network expansion and energy storage planning is fully considered, so that the efficiency maximization of the power transmission network and the energy storage is realized while the new energy consumption is promoted, and the economic benefit of the system is ensured;
(2) The method provides a two-stage robust optimization model for the AC/DC power transmission network and energy storage collaborative planning, and considers the influence of uncertainty of new energy output, the adjustment capability of a DC line, node voltage fluctuation of an electric power system and reactive power flow, so that the actual operation condition of the electric power system is simulated more accurately, and the safe and stable operation of the electric power system is ensured.
Drawings
Fig. 1 is a flowchart of a method for collaborative planning of an ac/dc power transmission network and energy storage according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an example topology provided by the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In the present application, the terms "first," "second," and the like (if any) in the description and the drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Fig. 1 is a flowchart of an ac/dc power transmission network and energy storage co-planning method according to an embodiment of the present invention. Referring to fig. 1 and fig. 2, the method for collaborative planning of the ac/dc power transmission network and the stored energy in this embodiment is described in detail, and the method includes operations S1 to S4.
And operation S1, establishing a two-stage robust optimization model of the AC/DC power transmission network and energy storage collaborative planning based on the new energy output uncertainty, the DC line regulation capability, the node voltage fluctuation of the power system and the reactive power flow and with the aim of minimizing the sum of the daily chemical investment cost and the daily operation cost.
According to the embodiment of the invention, the established two-stage robust optimization model is as follows:
Figure BDA0003729495380000061
wherein x is BS,i The number of energy storage units planned for the node i, N is the number of system nodes, eta P Cost per unit power capacity, η, for energy storage E Cost per energy capacity, P, for energy storage BS For power capacity of energy storage unit, E BS For energy capacity of the energy storage unit, T total,BS Designed service life for energy storage, x AC,ij,k For the state of construction of the kth line of the AC transmission corridor i-j, N lAC Is the number of AC transmission corridors, eta LAC Cost per unit length of AC line, D AC,ij Length, T, of AC transmission corridor i-j total,LAC Design life, x, for AC candidate lines DC,ij,l Is the construction state of the first return line of the DC transmission corridor i-j, N lDC Is the number of DC transmission corridors, eta LDC Cost per unit length of the DC line, D DC,ij Is the length, T, of the DC transmission corridor i-j total,LDC Design life time, x, for DC candidate lines vsc,i For the construction state of the VSC device at node i, η vsc Cost per unit power of VSC device, F vsc,i Is the power capacity, T, of the VSC device at node i total,vsc For the design service life of the VSC device, T is the simulation duration, P G,i,t The output of the thermal power generating unit at a node i in the t period, C (-) is the coal consumption cost of the thermal power generating unit, C SU,i,t 、C SD,i,t The starting cost and the shutdown cost of the thermal power generating unit at the node i in the period t are respectively.
According to the embodiment of the invention, the first-stage constraint conditions in the two-stage robust optimization model comprise: the method comprises the following steps of alternating current power grid expansion constraint, direct current power grid expansion constraint and energy storage configuration constraint.
The AC power grid expansion constraint is as follows:
|P AC,ij,k,t |≤x AC,ij,k F AC,ij
|Q AC,ij,k,t |≤x AC,ij,k F AC,ij
|P lsAC,ij,k,t |≤x AC,ij,k F AC,ij
|Q lsAC,ij,k,t |≤x AC,ij,k F AC,ij
wherein, P AC,ij,k,t 、Q AC,ij,k,t 、P lsAC,ij,k,t 、Q lsAC,ij,k,t Receiving end active power, receiving end reactive power, active power loss and reactive power loss of the kth circuit of the alternating-current power transmission corridor i-j are respectively; f AC And ij is the transmission capacity of one circuit of the alternating-current transmission corridor i-j.
The direct current power grid expansion constraint is as follows:
|P DC,ij,l,t |≤x DC,ij,l F DC,ij
|P lsDC,ij,l,t |≤x DC,ij,l F DC,ij
|P vsc,i,t |≤x vsc,i F vsc,i
wherein, P DC,ij,l,t 、P lsDC,ij,l,t The active power loss and the active power loss of the receiving end of the first return line of the direct-current transmission corridor i-j are respectively; f DC,ij The power transmission capacity of one circuit of the i-j line of the direct current power transmission corridor; p vsc,i,t And the active power of the VSC device at the node i at the moment t.
The energy storage configuration constraints are:
Figure BDA0003729495380000081
Figure BDA0003729495380000082
Figure BDA0003729495380000083
wherein the content of the first and second substances,
Figure BDA0003729495380000084
for the maximum number of energy storage units allowed to be configured at node i, <' >>
Figure BDA0003729495380000085
For the energy storage capacity configured at node i ″, in>
Figure BDA0003729495380000086
The energy storage power capacity configured at node i.
According to the embodiment of the invention, the second-stage constraint conditions in the two-stage robust optimization model comprise: the method comprises the following steps of energy storage system operation constraint, alternating current power grid operation constraint, direct current power grid operation constraint and unit combination constraint.
1) Energy storage system operating constraints include:
1.1 Energy storage charge-discharge power constraints:
Figure BDA0003729495380000087
Figure BDA0003729495380000088
wherein the content of the first and second substances,
Figure BDA0003729495380000089
and &>
Figure BDA00037294953800000810
The powers charged and discharged respectively for the stored energy arranged at node i during the period t->
Figure BDA00037294953800000811
And
Figure BDA00037294953800000812
the states of stored energy charging and discharging configured at node i during the t period, respectively.
1.2 Energy storage charge-discharge state constraints:
Figure BDA00037294953800000813
1.3 Energy storage energy level constraints:
Figure BDA00037294953800000814
wherein E is BS0,i For the initial energy level stored at node i,
Figure BDA00037294953800000815
the conversion efficiency of energy storage charging and discharging is achieved.
1.4 Energy storage end energy restraint:
Figure BDA0003729495380000091
2) Ac grid operating constraints include:
2.1 Ac node power balance constraint:
Figure BDA0003729495380000092
Figure BDA0003729495380000093
wherein, P G,i,t And Q G,i,t The active and reactive power output conditions P of the thermal power generating unit at the node i are respectively RE,i,t For the new energy output situation at node i, P D,i,t And Q D,i,t Active and reactive loads at node i, P, respectively vsc,i,t And Q vsc,i,t Active and reactive power, B, respectively, of the VSC device at the AC node i i For line susceptance matrix, V AC,i,t Is the voltage amplitude at the ac node i.
2.2 Active and reactive loss constraints for ac lines:
Figure BDA0003729495380000094
X lAC,ij P lsAC,ij,k,t ＝R lAC,ij Q lsAC,ij,k,t
wherein R is lAC,ij Resistance, X, of one circuit line of an AC transmission corridor i-j lAC,ij The reactance of one circuit of the alternating current transmission corridor i-j is obtained.
2.3 Amplitude and phase angle relationship constraints for ac line two terminal voltages:
Figure BDA0003729495380000095
|θ AC,i,t -θ AC,j,t -X lAC,ij P AC,ij,k,t +R lAC,ij Q AC,ij,k,t |≤M(1-x AC,ij,k )
Figure BDA0003729495380000096
θ AC,i,t -θ AC,j,t ＝X lAC,ij P AC,ij,0,t -R lAC,ij Q AC,ij,0,t
V ACmin ≤V AC,i,t ≤V ACmax
θ ACmin ≤θ AC,i,t ≤θ ACmax
where M is a sufficiently large number, θ AC,i,t Is the phase angle of the voltage at the AC node i, P AC,ij,0,t Receiving end active power Q of established line of AC transmission corridor i-j AC,ij,0,t Receiving end reactive power P of established line of AC transmission corridor i-j lsAC,ij,0,t Active power loss, Q, for established lines of an AC transmission corridor i-j lsAC,ij,0,t Reactive power loss, V, for established lines of AC transmission corridors i-j ACmax And V ACmin Respectively, the maximum and minimum values of the amplitude of the AC node voltage, theta ACmax And theta ACmin The maximum value and the minimum value of the voltage phase angle of the alternating-current node are respectively.
3) The direct current power grid operation constraints include:
3.1 Direct current node power balance constraint:
Figure BDA0003729495380000101
wherein, P DC,i,t And the active power flowing into the VSC device from the direct current node i at the time t.
3.2 Direct current active loss constraint:
Figure BDA0003729495380000102
wherein R is lDC,ij Resistance of one circuit i-j for DC transmission corridor, V DC,i,t The voltage amplitude of the dc node i at time t.
3.3 Voltage magnitude relationship constraint across a dc line:
Figure BDA0003729495380000103
V DCmin ≤V DC,i,t ≤V DCmax
wherein, V DCmax And V DCmin The maximum value and the minimum value of the voltage amplitude of the direct-current node are respectively.
3.4 Power balance constraints across VSC devices:
P DC,i,t ＝P vsc,i,t +P lsvsc,i,t
wherein, P lsvsc,i,t The active power loss of the VSC device is time t.
3.5 VSC device power loss constraints:
P lsvsc,i,t ＝β|P vsc,i,t |
where β is a power loss coefficient of the VSC device.
4) The unit combination constraint comprises:
4.1 Rotational standby constraint:
Figure BDA0003729495380000111
Figure BDA0003729495380000112
wherein the content of the first and second substances,
Figure BDA0003729495380000113
P G,i,t dynamic system of thermal power generating units at node i respectivelyThe upper limit of active power output and the lower limit of dynamic active power output, and gamma is a rotation standby coefficient.
4.2 Hill climbing restraint:
Figure BDA0003729495380000114
Figure BDA0003729495380000115
Figure BDA0003729495380000116
Figure BDA0003729495380000117
wherein the content of the first and second substances,
Figure BDA0003729495380000118
for the uphill speed of the thermal power unit at node i, is>
Figure BDA0003729495380000119
Is the downhill speed u of the thermal power generating unit i G,i,t For the on-off state of the thermal power unit at node i, the on-off state is judged>
Figure BDA00037294953800001110
And &>
Figure BDA00037294953800001111
Respectively the maximum and minimum technical output of the thermal power generating unit at the node i.
4.3 Start/stop maximum output limit constraints:
Figure BDA00037294953800001112
Figure BDA00037294953800001113
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00037294953800001114
and &>
Figure BDA00037294953800001115
The maximum output is respectively the starting-up and stopping-up.
4.4 Minimum on/off time constraints:
Figure BDA00037294953800001116
Figure BDA00037294953800001117
wherein the content of the first and second substances,
Figure BDA00037294953800001118
and &>
Figure BDA00037294953800001119
Minimum continuous operation and continuous downtime, T, of the thermal power generating unit at node i Gon,i,t And T Goff,i,t Respectively the continuous operation and the continuous shutdown time of the thermal power generating unit at the node i.
And operation S2, carrying out convex relaxation on non-convex constraints in the two-stage robust optimization model, and dividing the two-stage robust optimization model after the convex relaxation into a main problem model and a sub problem model.
According to an embodiment of the invention, the non-convex constraints comprise ac line loss constraints and dc line loss constraints, and the ac line loss constraints and dc line loss constraints after convex relaxation are:
Figure BDA0003729495380000121
Figure BDA0003729495380000122
wherein R is lAC,ij Resistance, P, of one circuit line of an AC transmission corridor i-j AC,ij,k,t 、Q AC,ij,k,t 、P lsAC,ij,k,t The active power of the receiving end, the reactive power of the receiving end and the active power loss of the kth return line of the alternating-current power transmission corridor i-j are Y AC,j,t Is the square of the voltage of the AC node j, R lDC,ij Resistance, P, of one circuit line of a DC transmission corridor i-j DC,ij,l,t 、P lsDC,ij,l,t The active power and the active power loss of the receiving end of the first return line of the direct-current transmission corridor i-j, Y DC,j,t Which is the square of the voltage at dc node j.
The two-stage robust optimization model in this embodiment can be written in the following simplified form:
Figure BDA0003729495380000123
Figure BDA0003729495380000124
the model can be reconstructed using extreme scenarios as:
Figure BDA0003729495380000125
Figure BDA0003729495380000126
decomposing the model into a main problem model and a sub problem model, wherein the decomposed main problem model is as follows:
Figure BDA0003729495380000127
Figure BDA0003729495380000128
the sub-problem model is:
Figure BDA0003729495380000131
Figure BDA0003729495380000132
/>
wherein f is 1 (. Is the daily chemical investment cost, x (m) Planning variables related to AC/DC power transmission network expansion and energy storage planning in the mth iterative solution process,
Figure BDA0003729495380000133
as an intermediate variable related to the daily running cost, f 2 (. H) is the daily operating cost, y is an operating variable associated with the operation of the power system, ω b (m) For the new energy output scene set of the main problem model in the mth iteration solving process, u (-) is the first-stage constraint in the two-stage robust optimization model, v 1 (·)、v 2 (. To) equality constraint, inequality constraint, omega in the second stage constraint in the two-stage robust optimization model s For the new energy output situation, omega, in all extreme scenarios s,e Is the new energy output condition N in the e-th limit scene E Is the limit number of scenes.
And operation S3, generating a new energy output set based on the historical data of the new energy output, and setting a scene corresponding to the vertex of the new energy output set as an extreme scene.
According to an embodiment of the invention, any scenario in the set of new energy outputs may be linearly represented by an extreme scenario:
Figure BDA0003729495380000134
wherein χ is a new energy output set,
Figure BDA0003729495380000135
for describing a random variable of the new energy contribution, in conjunction with a predetermined criterion>
Figure BDA0003729495380000136
Is N W Vector of dimension T, N W For the number of new energy stations, T is the simulation duration, N E Is a limit number of scenes, p e Is a positive coefficient, ω s,e The new energy output condition under the e-th limit scene.
And operation S4, solving the main problem model based on the extreme scene to obtain a planning scheme of the AC/DC line and the stored energy, substituting the planning scheme into the sub-problem model to solve to verify the feasibility of the planning scheme, iteratively solving the main problem model and the sub-problem model until a feasible planning scheme is obtained, and configuring the AC/DC line and the stored energy according to the feasible planning scheme.
According to an embodiment of the invention, operation S4 comprises sub-operation S41-sub-operation S44.
In sub-operation S41, the main problem model is solved based on the first m worst extreme scenes in the extreme scenes to obtain a planning scheme for the ac/dc lines and the energy storage, where the initial value m is 1.
When m =0, the target value LB of the main problem model (0) Target value UB of = - ∞ subproblem model (0) ＝+∞。
In sub-operation S42, the planning solution is substituted into the sub-problem model to be solved to verify the feasibility of the planning solution.
Specifically, plan x (m) Substituting all limit scenes into the sub-problem model to solve so as to verify the planning scheme x (m) Whether it is feasible.
According to the embodiment of the invention, when the following conditions are met, the planning scheme is feasible, otherwise, the planning scheme is not feasible, and the conditions required to be met when the planning scheme is feasible are as follows:
|UB (m) -LB (m) |/UB (m) <ε
wherein, UB (m) For the mth iteration to solve the target value, LB, of the neutron problem model in the process (m) And e is a preset error threshold value for the target value of the main problem model in the mth iteration solving process.
In sub-operation S43, if the planning solution is not feasible, m is updated to m +1, and sub-operation S41-sub-operation S42 are repeatedly performed until a feasible planning solution is obtained.
In sub-operation S44, the locations and number of ac and dc lines, and the locations and capacities of the stored energy are configured according to the feasible planning scheme.
Taking the modified Garver-6 node system shown in fig. 2 as an example, the effect of the alternating current/direct current transmission network and energy storage collaborative planning method in the embodiment is verified. The modified Garver-6 node system comprises a thermal power generating unit, a new energy source unit, an alternating current circuit and the like. Before the method for performing the alternating-current/direct-current power transmission network and energy storage collaborative planning is executed, collecting technical parameters of each element in a system, specifically comprising the following steps: (1) the rated voltage of the system and the allowable fluctuation range of the node voltage; (2) the method comprises the following steps of (1) installing capacity, coal consumption coefficient, minimum continuous operation time, minimum continuous shutdown time, starting cost and shutdown cost of a thermal power generating unit; (3) capacity of a new energy machine assembling machine; (4) the energy storage unit power capacity, the energy storage unit energy capacity, the charge-discharge efficiency, the maximum allowable configuration energy storage unit number of each node, the energy storage design service life, the unit power capacity cost and the unit energy capacity cost of the energy storage unit; (5) the unit length cost of the alternating current candidate line, the unit length cost of the direct current candidate line, the unit capacity cost of the VSC device, the design service life of the alternating current candidate line, the design service life of the direct current candidate line, the design service life of the VSC device and the loss coefficient beta of the VSC device.
Energy storage configuration is carried out on nodes 1-6 in the system shown in fig. 2, each node is allowed to be configured with 30 energy storage units at most, the capacity of each energy storage unit is 1MW/3MWh, alternating current and direct current transmission network expansion is carried out on 15 transmission corridors, 4 lines are allowed to be built in each transmission corridor at most, the rated voltage of the system is 500kV, and the allowable fluctuation range of the node voltage is 0.95p.u. -1.05p.u. The planning scheme and cost of each equipment are shown in table 1.
TABLE 1
Figure BDA0003729495380000151
An embodiment of the present invention further provides a computer readable medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for collaborative planning of ac/dc power transmission network and energy storage shown in fig. 1-2.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (9)

1. An alternating current-direct current transmission network and energy storage collaborative planning method is characterized by comprising the following steps:
s1, establishing a two-stage robust optimization model of the AC/DC power transmission network and energy storage collaborative planning based on the uncertainty of new energy output, the DC line regulation capability, the node voltage fluctuation of the power system and the reactive power flow and with the aim of minimizing the sum of the daily chemical investment cost and the daily operation cost;
s2, carrying out convex relaxation on non-convex constraints in the two-stage robust optimization model, and dividing the two-stage robust optimization model after the convex relaxation into a main problem model and a sub problem model;
s3, generating a new energy output set based on historical data of new energy output, and setting a scene corresponding to the top point of the new energy output set as an extreme scene;
s4, solving the main problem model based on the limit scene to obtain a planning scheme of an AC/DC line and energy storage, substituting the planning scheme into the sub-problem model to solve to verify the feasibility of the planning scheme, iteratively solving the main problem model and the sub-problem model until a feasible planning scheme is obtained, and configuring the AC/DC line and the energy storage according to the feasible planning scheme;
the two-stage robust optimization model is as follows:
Figure FDA0004069877200000011
wherein x is BS,i The number of energy storage units planned for the node i, N is the number of system nodes, eta P Cost per unit power capacity, η, for energy storage E Cost per energy capacity for energy storage, P BS For power capacity of energy storage unit, E BS For energy capacity of energy storage unit, T total,BS Designed service life for energy storage, x AC,ij,k For the state of construction of the kth line of the AC transmission corridor i-j, N lAC Is the number of AC transmission corridors, eta LAC Cost per unit length of AC line, D AC,ij Length of AC transmission corridor i-j, T total,LAC Design life, x, for AC candidate lines DC,ij,l Is the construction state of the first return line of the DC transmission corridor i-j, N lDC Is the number of DC transmission corridors, eta LDC Cost per unit length of the DC line, D DC,ij Is the length, T, of the DC transmission corridor i-j total,LDC Design life time, x, for DC candidate lines vsc,i Is the construction state, η, of the VSC device at node i vsc Cost per unit power of VSC device, F vsc,i Is the power capacity, T, of the VSC device at node i total,vsc For the design service life of the VSC device, T is the simulation duration, P G,i,t The output of the thermal power generating unit at a node i in the t period, C (-) is the coal consumption cost of the thermal power generating unit, C SU,i,t 、C SD,i,t The starting cost and the shutdown cost of the thermal power generating unit at the node i in the period t are respectively.
2. The ac/dc power transmission network and energy storage co-planning method according to claim 1, wherein the S4 comprises:
s41, solving the main problem model based on the first m worst limit scenes in the limit scenes to obtain a planning scheme of an AC/DC line and energy storage, wherein the initial value of m is 1;
s42, substituting the planning scheme into the sub-problem model to solve so as to verify the feasibility of the planning scheme;
s43, if the planning scheme is not feasible, updating m to m +1, and repeatedly executing the S41-S42 until a feasible planning scheme is obtained;
and S44, configuring the positions and the number of the AC/DC lines and the positions and the capacity of stored energy according to a feasible planning scheme.
3. The ac-dc power transmission network and energy storage co-planning method according to claim 2, wherein the planning scheme is feasible when the following conditions are met, otherwise the planning scheme is not feasible:
|UB (m) -LB (m) |/UB (m) <ε
wherein, UB (m) For the mth iteration to solve the target value, LB, of the neutron problem model in the process (m) And e is a preset error threshold value for the target value of the main problem model in the mth iteration solving process.
4. The method according to claim 1, wherein the first-stage constraint conditions in the two-stage robust optimization model comprise: the method comprises the following steps of alternating current power grid expansion constraint, direct current power grid expansion constraint and energy storage configuration constraint.
5. The method according to claim 1, wherein the second-stage constraint conditions in the two-stage robust optimization model comprise: the method comprises the following steps of (1) carrying out energy storage system operation constraint, alternating current power grid operation constraint, direct current power grid operation constraint and unit combination constraint;
the energy storage system operating constraints include: energy storage charging and discharging power constraint, energy storage charging and discharging state constraint, energy storage energy level constraint and energy storage end energy constraint;
the AC grid operating constraints include: the method comprises the following steps of alternating current node power balance constraint, alternating current line active loss and reactive loss constraint and alternating current line two-terminal voltage amplitude and phase angle relation constraint;
the direct current grid operation constraints include: the method comprises the following steps of carrying out direct-current node power balance constraint, direct-current active loss constraint, voltage magnitude relation constraint at two ends of a direct-current line, power balance constraint at two ends of a VSC device and power loss constraint of the VSC device;
the unit combination constraints include: spinning reserve constraints, hill climb constraints, start/stop maximum force limit constraints, and minimum start/stop time constraints.
6. The ac-dc power transmission network and energy storage co-planning method according to claim 1, wherein the non-convex constraints include ac line loss constraints and dc line loss constraints, and the convex relaxation is:
Figure FDA0004069877200000031
Figure FDA0004069877200000032
wherein R is lAC,ij Resistance, P, of one circuit line of an AC transmission corridor i-j AC,ij,k,t 、Q AC,ij,k,t 、P lsAC,ij,k,t Receiving end active power, receiving end reactive power and active power loss of the kth circuit of the alternating-current transmission corridor i-j respectively, and Y AC,j,t Is the square of the voltage at AC node j, R lDC,ij Resistance, P, of one circuit line of a DC transmission corridor i-j DC,ij,l,t 、P lsDC,ij,l,t The active power and the active power loss of the receiving end of the first return line of the direct-current transmission corridor i-j, Y DC,j,t Which is the square of the voltage at dc node j.
7. The ac/dc power transmission network and energy storage co-planning method according to claim 1, wherein the new energy output set is:
Figure FDA0004069877200000033
wherein χ is the new energy output set,
Figure FDA0004069877200000034
for describing a random variable of the new energy contribution, in conjunction with a predetermined criterion>
Figure FDA0004069877200000035
Is N W Vector of dimension T, N W For the number of new energy sites, T is the simulation duration, N E Is a limit number of scenes, p e Is a positive coefficient, ω s,e The new energy output condition under the e-th limit scene.
8. The ac-dc power transmission network and energy storage co-planning method according to claim 1, wherein the main problem model is:
Figure FDA0004069877200000041
/>
Figure FDA0004069877200000042
the sub-problem model is as follows:
Figure FDA0004069877200000043
Figure FDA0004069877200000044
wherein, f 1 (. Is the daily chemical investment cost, x (m) For m iterative solving process and AC/DCThe transmission network extends planning variables related to energy storage planning,
Figure FDA0004069877200000045
as an intermediate variable related to the daily operating cost, f 2 (. Cndot.) is the daily operating cost, y is an operating variable associated with operation of the power system, ω b (m) For the new energy output scene set of the main problem model in the mth iteration solving process, u (-) is the first-stage constraint in the two-stage robust optimization model, v 1 (·)、v 2 (. To) equality constraint, inequality constraint, omega in the second stage constraint in the two-stage robust optimization model s For new energy output situation in all extreme scenarios, ω s,e Is the new energy output situation in the e-th limit scene, N E Is the limit number of scenes.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for ac/dc grid and energy storage co-planning according to any one of claims 1 to 8.
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