CN110323785B - Multi-voltage-level direct-current power distribution network optimization scheduling method for source network load storage interaction - Google Patents
Multi-voltage-level direct-current power distribution network optimization scheduling method for source network load storage interactionDownload PDFInfo
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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
- H02J3/008—Circuit arrangements for AC mains or AC distribution networks involving trading of energy or energy transmission rights
- 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
- H02J3/383—
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
Translated fromChinese
Description
Technical Field
The invention relates to the technical field of direct current power distribution networks, in particular to a source-network-load-storage interaction-based multi-voltage-level direct current power distribution network optimal scheduling method.
Background
With the development of power generation technologies of clean renewable energy sources such as solar energy, the proportion of direct current sources and loads connected into a power grid in a power system is increased day by day. Under the background, compared with an alternating-current power distribution network, the direct-current power distribution network is convenient for the access of a direct-current source and a load, the use of a current converter can be reduced, the operation efficiency of the system is improved, and the direct-current power distribution network becomes the focus of domestic and foreign research. Typical renewable energy photovoltaic power generation can be directly connected into a direct current distribution network through a DC/DC converter, so that the input cost and the operation loss of the system are greatly improved. However, the fluctuation and randomness of the output power of the photovoltaic power generation system also cause the problem of photovoltaic power generation consumption, so that the problem of optimal scheduling of the direct-current power distribution network is more complicated. How to improve the operation of the system and design an optimization model suitable for a direct current power distribution network so as to enable the system to achieve the optimal operation is a problem to be solved urgently at present.
Disclosure of Invention
The invention discloses a source-network-charge-storage interaction-based multi-voltage level direct current distribution network optimal scheduling method, which aims to solve the problems that the random fluctuation of photovoltaic output power in a photovoltaic-containing multi-voltage level direct current distribution network is strong and photovoltaic absorption exists, introduces a source-network-charge-storage interaction optimization concept into the direct current distribution network, and fully exerts the photovoltaic absorption effect of an interaction load and energy storage technology on a system on the basis of deeply analyzing the interaction load characteristics; establishing a direct-current power distribution network multi-target optimization scheduling model with the operation cost, the network loss rate and the voltage deviation as targets, and solving the optimization model by adopting a multi-target particle swarm optimization algorithm; and the multi-objective optimization result is compared, analyzed and verified to enable the system to operate more optimally through the source-network-load-storage interaction model, and meanwhile, the interactive load and the energy storage system are utilized to perform collaborative optimization, so that the system operation cost, the network loss rate and the voltage deviation can be greatly reduced, the photovoltaic consumption level is effectively improved, and the safe and reliable operation of the direct-current power distribution network is realized.
The technical scheme adopted for realizing the aim of the invention is as follows: 1. a multi-voltage-level direct-current power distribution network optimal scheduling method based on source-network-load-storage interaction is characterized by comprising the following steps:
1) establishing an optimized dispatching system model of a multi-voltage-level direct-current power distribution network, wherein the direct-current power distribution network comprises a voltage source type converter, a DC/DC converter, a photovoltaic power generation unit, an energy storage system and a load;
2) establishing a source-network-load-storage interaction optimization system model in the multi-voltage-level direct-current power distribution network, as shown in formulas (1) to (8);
in the formula (1), VAIn order to be able to transfer the load characteristics of the load,
is the minimum power at which the load a can be transferred,
is the maximum power at which the load a can be transferred,
in order to be able to transfer the initial running time of the load a,
in order to be able to transfer the end run time of the load a,
minimum power consumption to transferable load A, CTLRepresenting a transferable load; in the formulae (2) to (4),
power for transferable loads during time T; equation (2) indicates that the power of the transferable load is within the allowable range during operation; formula (3) shows that at the end of the run, the power should meet the minimum power requirement to indicate that the equipment has completed work; the formula (4) shows that the transferable load participates in load scheduling to respond to the power grid requirement, and the power is transferred in a certain time and the work requirement is ensured to be completed; in the formula (5), VBIn order to be able to interrupt the load's load characteristics,
for the minimum power at which the load B can be interrupted,
for the maximum power at which the load B can be interrupted,
in order to be able to interrupt the initial running time of the load B,
in order to interrupt the termination run time of the load B,
minimum satisfaction requirement for interruptible load B, CILRepresenting an interruptible load; in the formulae (6) to (8),
in order to interrupt the load power for a period T,
for the actual satisfaction value of the interruptible load B at time T, equation (6) represents that the minimum power is 0 when the actual state of the device meets the satisfaction requirement of the user during the operation of the interruptible load; formula (7) shows that when the actual state of the device is not higher than the minimum satisfaction requirement of the user, the minimum power is the rated power of the device; the formula (8) shows that the interruptible load participates in load scheduling to meet the power grid requirement in the range of meeting the comfort level of the user;
3) taking the minimum total operation cost, voltage deviation and network loss rate of the multi-voltage-level direct-current power distribution network as a target function of the direct-current power distribution network, as shown in formulas (9) to (11), and obtaining an optimal scheduling model of the multi-voltage-level direct-current power distribution network by taking power balance constraint, node voltage constraint, line transmission power constraint, energy storage system constraint, photovoltaic power generation constraint, distribution network and external network interaction power constraint and interaction load constraint as constraint conditions as shown in formulas (12) to (21);
in formula (9), T is the scheduling period, Fpv(t) photovoltaic cost in DC distribution network operating cost, Fess(t) energy storage cost in DC distribution network operating cost, Fgrid(t) cost of purchasing electricity from a large power grid, Floss(t) is the loss cost of the DC distribution network in the operating cost, Fil(t) Interactive load cost in DC distribution network operating cost, NiAs number of devices, cpvFor the unit cost of electricity generation of the photovoltaic system, cessFor the unit power generation cost of the energy storage system, cbuyTo purchase electricity, csellFor selling electricity, clossTo the loss cost of the network, clsCompensating costs for transferable loads, ccuCompensating costs for interruptible loads, Ppv,iFor photovoltaic power generation, Pess,iFor charging and discharging power, P, of the energy storage systembuyTo purchase electric power, PsellFor selling electricity, PlossFor total loss of the network, Pls,subFor transferable load power, Pcu,subIs interruptible load power; in the formula (10), Ui,tIs the voltage amplitude, U, of node i during a period te,iIs the nominal voltage of node i; in formula (11), PLi,lossFor the loss of line i, PTj,lossFor losses in transformer j, PtotalThe total active power input for the whole network;
Ppv+Pess+Pgrid＝Pload+Ploss+Pil (12)
SOCmin≤SOC(t)≤SOCmax (17)
RPV≥Rpv,min (18)
mload≤Mload (21)
in the formula (13), Ui,tFor the duration of time t the voltage magnitude at node i,
for the lower limit value of the voltage of the node i for the period t,
the upper limit value of the voltage of the node i in the period t; in formula (14), Pij,tFor the transmission power on line i, j for period t,
maximum transmission power on line ij for time period t; in the formulae (15) to (17),
for the charging power of the energy storage system i at time t,
for the discharge power of the energy storage system i at time t,
is the maximum charging power of the energy storage system i,
is the maximum discharge power, SOC, of the energy storage system iminIs the minimum value of the state of charge, SOC, of the energy storage systemmaxThe maximum value of the state of charge of the energy storage system; in the formulae (18) and (19), RPVThe photovoltaic absorption rate; rpv,minIs the minimum value of the photovoltaic absorption rate, Ppv(t) is the photovoltaic system output power at time t,
outputting the maximum value of the power of the photovoltaic system in the t time period; in the formula (20), PgridFor the power interaction between the direct current distribution network and the external network,
is the minimum value of the interactive power of the direct current distribution network and the external network,
the maximum value of the interaction power of the direct-current power distribution network and the external network is obtained; in the formula (21), mloadTo be able to transfer the amount of load transfer, MloadIs a transferable load capacity;
4) solving the multi-voltage-level direct-current power distribution network optimized dispatching model in the step 3) through a multi-objective particle swarm optimization algorithm to obtain an optimal solution.
The invention provides a source-network-load-storage interaction-based multi-voltage-class direct-current power distribution network optimal scheduling method aiming at the problems that the random fluctuation of photovoltaic output power in a photovoltaic-containing multi-voltage-class direct-current power distribution network is strong and photovoltaic consumption exists, and the method is characterized in that a source-network-load-storage interaction optimization concept is introduced into the direct-current power distribution network, and the photovoltaic consumption effect of an interaction load and energy storage technology on a system is fully exerted on the basis of deep analysis of interaction load characteristics; establishing a direct-current power distribution network multi-target optimization scheduling model taking operation cost, network loss rate and voltage deviation as targets, and solving the optimization model by adopting a multi-target particle swarm optimization algorithm; and the multi-objective optimization result is compared, analyzed and verified to enable the system to operate more optimally through the source-network-load-storage interaction model, and meanwhile, the interactive load and the energy storage system are utilized to perform collaborative optimization, so that the system operation cost, the network loss rate and the voltage deviation can be greatly reduced, the photovoltaic consumption level is effectively improved, and the safe and reliable operation of the direct-current power distribution network is realized. Has the advantages of scientific and reasonable method, strong applicability and good effect.
Drawings
FIG. 1 is a schematic diagram of a source-grid-load-storage interaction principle of an optimal scheduling method of a multi-voltage-class direct-current power distribution network based on source-grid-load-storage interaction;
fig. 2 is a diagram of a dc distribution network structure according to the present invention;
FIG. 3 is a schematic view of a photovoltaic prediction curve according to the present invention;
FIG. 4 is a schematic view of a load prediction curve according to the present invention;
FIG. 5 is a diagram illustrating the charging and discharging operation of an energy storage system according to the present invention;
FIG. 6 is a schematic diagram of load curves before and after a transferable load transfer in accordance with the present invention;
fig. 7 is a schematic diagram of a comparison curve of transmission power of a low voltage class interconnection branch according to the present invention.
Detailed Description
The following describes a source-grid-load-storage interaction-based multi-voltage-class direct-current distribution network optimal scheduling method in the present invention with reference to the accompanying drawings and embodiments.
The invention discloses a source-network-load-storage interaction-based multi-voltage-level direct-current power distribution network optimal scheduling method, which comprises the following steps of:
1) establishing an optimized dispatching system model of a multi-voltage-level direct-current power distribution network, wherein the direct-current power distribution network comprises a voltage source type converter, a DC/DC converter, a photovoltaic power generation unit, an energy storage system and a load;
2) establishing a source-network-load-storage interaction optimization system model in the multi-voltage-level direct-current power distribution network, as shown in formulas (1) to (8);
in the formula (1), VAIn order to be able to transfer the load characteristics of the load,
is the minimum power at which the load a can be transferred,
is the maximum power at which the load a can be transferred,
in order to be able to transfer the initial running time of the load a,
in order to be able to transfer the end run time of the load a,
minimum power consumption to transferable load A, CTLRepresenting a transferable load; in the formulae (2) to (4),
power for transferable loads during time T; equation (2) indicates that the power of the transferable load is within the allowable range during operation; formula (3) shows that at the end of the run, the power should meet the minimum power requirement to indicate that the equipment has completed work; the formula (4) shows that the transferable load participates in load scheduling to respond to the power grid requirement, and the power is transferred in a certain time and the work requirement is ensured to be completed; in the formula (5), VBIn order to be able to interrupt the load's load characteristics,
for the minimum power at which the load B can be interrupted,
for the maximum power at which the load B can be interrupted,
in order to be able to interrupt the initial running time of the load B,
in order to interrupt the termination run time of the load B,
minimum satisfaction requirement for interruptible load B, CILRepresenting an interruptible load; in the formulae (6) to (8),
in order to interrupt the load power for a period T,
for the actual satisfaction value of the interruptible load B at time T, equation (6) represents that the minimum power is 0 when the actual state of the device meets the satisfaction requirement of the user during the operation of the interruptible load; formula (7) shows that when the actual state of the device is not higher than the minimum satisfaction requirement of the user, the minimum power is the rated power of the device; the formula (8) shows that the interruptible load participates in load scheduling to meet the power grid requirement in the range of meeting the comfort level of the user;
3) taking the minimum total operation cost, voltage deviation and network loss rate of the multi-voltage-level direct-current power distribution network as a target function of the direct-current power distribution network, as shown in formulas (9) to (11), and obtaining an optimal scheduling model of the multi-voltage-level direct-current power distribution network by taking power balance constraint, node voltage constraint, line transmission power constraint, energy storage system constraint, photovoltaic power generation constraint, distribution network and external network interaction power constraint and interaction load constraint as constraint conditions as shown in formulas (12) to (21);
in formula (9), T is the scheduling period, Fpv(t) photovoltaic cost in DC distribution network operating cost, Fess(t) energy storage cost in DC distribution network operating cost, Fgrid(t) cost of purchasing electricity from a large power grid, Floss(t) is the loss cost of the DC distribution network in the operating cost, Fil(t) Interactive load cost in DC distribution network operating cost, NiAs number of devices, cpvFor the unit cost of electricity generation of the photovoltaic system, cessFor the unit power generation cost of the energy storage system, cbuyTo purchase electricity, csellFor selling electricity, clossTo the loss cost of the network, clsCompensating costs for transferable loads, ccuCompensating costs for interruptible loads, Ppv,iFor photovoltaic power generation, Pess,iFor charging and discharging power, P, of the energy storage systembuyTo purchase electric power, PsellFor selling electricity, PlossFor total loss of the network, Pls,subFor transferable load power, Pcu,subIs interruptible load power; in the formula (10), Ui,tIs the voltage amplitude, U, of node i during a period te,iIs the nominal voltage of node i; in formula (11), PLi,lossFor the loss of line i, PTj,lossFor losses in transformer j, PtotalThe total active power input for the whole network;
Ppv+Pess+Pgrid＝Pload+Ploss+Pil (12)
SOCmin≤SOC(t)≤SOCmax (17)
RPV≥Rpv,min (18)
mload≤Mload (21)
in the formula (13), Ui,tFor the duration of time t the voltage magnitude at node i,
for the lower limit value of the voltage of the node i for the period t,
the upper limit value of the voltage of the node i in the period t; in formula (14), Pij,tFor the transmission power on line i, j for period t,
maximum transmission power on line ij for time period t; in the formulae (15) to (17),
for the charging power of the energy storage system i at time t,
for the discharge power of the energy storage system i at time t,
is the maximum charging power of the energy storage system i,
is the maximum discharge power, SOC, of the energy storage system iminIs the minimum value of the state of charge, SOC, of the energy storage systemmaxThe maximum value of the state of charge of the energy storage system; in the formulae (18) and (19), RPVThe photovoltaic absorption rate; rpv,minIs the minimum value of the photovoltaic absorption rate, Ppv(t) is the photovoltaic system output power at time t,
outputting the maximum value of the power of the photovoltaic system in the t time period; in the formula (20), PgridFor the power interaction between the direct current distribution network and the external network,
is the minimum value of the interactive power of the direct current distribution network and the external network,
the maximum value of the interaction power of the direct-current power distribution network and the external network is obtained; in the formula (21), mloadTo be able to transfer the amount of load transfer, MloadIs a transferable load capacity;
4) solving the multi-voltage-level direct-current power distribution network optimized dispatching model in the step 3) through a multi-objective particle swarm optimization algorithm to obtain an optimal solution.
Referring to fig. 1, the invention realizes bidirectional transmission of energy and information at the source side, the multi-voltage-level direct current distribution network side, the load side and the energy storage side in the direct current distribution system based on source-network-load-storage interaction, and realizes active management decision and optimal control.
Referring to fig. 2, the direct current distribution network adopts a double-end ring network structure, operates under two voltage levels of +/-10 kV and +/-0.375 kV, and comprises 19 nodes in total. Thenodes nodes 9 and 10 are subjected to voltage grade conversion through a DC/DC converter; thenode 3 and the node 9 are respectively an electric automobile power change station and a cell load, wherein the electric automobile power change station and the cell load comprise transferable loads;node 8 is an industrial campus that contains interruptible load nodes. The SOC regulation range of the energy storage system is 2% -95%, and the charge-discharge efficiency is 95%. The node types and parameters are shown in table 1, the other nodes are load nodes, and the load values are shown in fig. 1.Region 1 andregion 2 are two different regions in the low voltage class, and nodes 13-17 are interconnected by a DC/DC converter. The resistance of each branch of the direct-current distribution network is shown in table 2, and the voltage deviation in the objective function is within +/-7% of a standard limit value. The electricity rate information is shown in table 3.
TABLE 1 DC distribution network parameters
TABLE 2 DC POWER DISTRIBUTION NETWORK RESISTANCE
TABLE 3 time-of-use electricity price situation of DC distribution network
Referring to fig. 3, a predicted 24-hour photovoltaic power generation output curve in a dc power distribution grid in accordance with an embodiment of the present invention.
Referring to fig. 4, a 24-hour load prediction curve in a dc distribution network according to an embodiment of the present invention.
And analyzing a direct current distribution network source-network-load-storage interaction optimization system, and setting three different optimization scheduling schemes.Scheme 1 is that a source-load-storage interaction optimization system participates in optimization scheduling; in thescheme 2, a DC/DC converter is added on the basis of thescheme 1 to regulate the voltage of the fixed voltage node; in thescheme 3, interconnection interaction optimization among different areas with low voltage grades is added on the basis of thescheme 2, and the resistance of an interconnection branch is 1.0398 omega.
Referring to fig. 5, in the energy storage system according to the three schemes of the embodiment of the present invention, it can be seen from the figure that when the photovoltaic power generation amount is greater than the load demand in the daytime, the stored energy is charged to ensure the photovoltaic power consumption; after the photovoltaic system stops generating power at night and is also in the peak period of power utilization, the energy storage system discharges, the electric quantity transferred from the external network to the direct-current power distribution network is reduced, and the system cost and the network loss are improved.
Referring to fig. 6, the three schemes of the embodiment of the present invention can transfer load before and after transfer, and as can be seen from the figure, the transferable load transfers part of the load during night (18 to 22 o 'clock) to the middle (10 to 14 o' clock) to operate, so that the load during the high peak time period is transferred, which not only relieves the power supply pressure, but also promotes photovoltaic absorption during the day, can coordinate with the energy storage system, avoids the light rejection phenomenon, relieves the charging and discharging conditions of the energy storage, and can delay the aging speed of the storage battery.
The voltage of each node after system optimization in the three schemes is shown in table 6, the rated voltage of the node 1-11 before optimization is 10kV, and the rated voltage of the node 12-19 before optimization is 0.375 kV. The results show that the network side participates in interaction through the DC/DC converter, the voltage of each node is corrected, the reference value of the fixed voltage is adjusted, and the system is optimally scheduled. In the case of thescheme 2 and thescheme 3, the fixedvoltage nodes
TABLE 6 optimized three schemes
Referring to fig. 7, before and after the optimization of the interactive transmission power of the low voltage network according to an embodiment of the present invention, it can be seen from the figure that the line can adjust the transmission power in real time after the optimization of the interconnection branch in thescheme 3, which more meets the system requirements. Thescheme 1 and thescheme 2 are not provided with interconnection interaction optimization, and the transmission power is consistent with the optimized front curve in thescheme 3.
The results after optimization are shown in table 7.Scheme 1 is source-load-storage interactive optimization scheduling result, comparesscheme 1 with two other schemes and can reachs, and the interaction that has increased the net side can play obvious improvement effect to the objective function of optimization model, and the DC/DC converter that increases inscheme 2 is to the voltage regulation of fixed voltage node, and rising node voltage makes the net rate of loss can obviously reduce, and although the voltage deviation improves slightly, total operating cost obtains reducing under the circumstances of net loss cost reduction. Further comparing the optimization results inscheme 3, it can be seen that increasing the power of the interconnection line can significantly improve the system voltage deviation, the network loss rate, and the total operating cost compared to the two schemes. In summary, the source-network-load-storage overall interactive system in thescheme 3 can optimize the objective function.
TABLE 7 comparison of the results of the optimization of the three protocols
The embodiments of the present invention are not exhaustive, and those skilled in the art will still fall within the scope of the present invention as claimed without simple duplication and modification by the inventive efforts.
Claims (1)
1. A multi-voltage-level direct-current power distribution network optimal scheduling method based on source-network-load-storage interaction is characterized by comprising the following steps:
1) establishing an optimized dispatching system model of a multi-voltage-level direct-current power distribution network, wherein the direct-current power distribution network comprises a voltage source type converter, a DC/DC converter, a photovoltaic power generation unit, an energy storage system and a load;
2) establishing a source-network-load-storage interaction optimization system model in the multi-voltage-level direct-current power distribution network, as shown in formulas (1) to (8);
in the formula (1), VAIn order to be able to transfer the load characteristics of the load,
is the minimum power at which the load a can be transferred,
is the maximum power at which the load a can be transferred,
in order to be able to transfer the initial running time of the load a,
in order to be able to transfer the end run time of the load a,
minimum power consumption to transferable load A, CTLRepresenting a transferable load; in the formulae (2) to (4),
power for transferable loads during time T; equation (2) indicates that the power of the transferable load is within the allowable range during operation; formula (3) shows that at the end of the run, the power should meet the minimum power requirement to indicate that the equipment has completed work; the formula (4) shows that the transferable load participates in load scheduling to respond to the power grid requirement, and the power is transferred in a certain time and the work requirement is ensured to be completed; in the formula (5), VBIn order to be able to interrupt the load's load characteristics,
for the minimum power at which the load B can be interrupted,
for the maximum power at which the load B can be interrupted,
in order to be able to interrupt the initial running time of the load B,
in order to interrupt the termination run time of the load B,
is the lowest satisfaction of interruptible load BDegree required, CILRepresenting an interruptible load; in the formulae (6) to (8),
in order to interrupt the load power for a period T,
for the actual satisfaction value of the interruptible load B at time T, equation (6) represents that the minimum power is 0 when the actual state of the device meets the satisfaction requirement of the user during the operation of the interruptible load; formula (7) shows that when the actual state of the device is not higher than the minimum satisfaction requirement of the user, the minimum power is the rated power of the device; the formula (8) shows that the interruptible load participates in load scheduling to meet the power grid requirement in the range of meeting the comfort level of the user;
3) taking the minimum total operation cost, voltage deviation and network loss rate of the multi-voltage-level direct-current power distribution network as a target function of the direct-current power distribution network, as shown in formulas (9) to (11), and obtaining an optimal scheduling model of the multi-voltage-level direct-current power distribution network by taking power balance constraint, node voltage constraint, line transmission power constraint, energy storage system constraint, photovoltaic power generation constraint, distribution network and external network interaction power constraint and interaction load constraint as constraint conditions as shown in formulas (12) to (21);
in formula (9), T is the scheduling period, Fpv(t) photovoltaic cost in DC distribution network operating cost, Fess(t) is direct currentEnergy storage cost in distribution network operating cost, Fgrid(t) cost of purchasing electricity from a large power grid, Floss(t) is the loss cost of the DC distribution network in the operating cost, Fil(t) Interactive load cost in DC distribution network operating cost, NiAs number of devices, cpvFor the unit cost of electricity generation of the photovoltaic system, cessFor the unit power generation cost of the energy storage system, cbuyTo purchase electricity, csellFor selling electricity, clossTo the loss cost of the network, clsCompensating costs for transferable loads, ccuCompensating costs for interruptible loads, Ppv,iFor photovoltaic power generation, Pess,iFor charging and discharging power, P, of the energy storage systembuyTo purchase electric power, PsellFor selling electricity, PlossFor total loss of the network, Pls,subFor transferable load power, Pcu,subIs interruptible load power; in the formula (10), Ui,tIs the voltage amplitude, U, of node i during a period te,iIs the nominal voltage of node i; in formula (11), PLi,lossFor the loss of line i, PTj,lossFor losses in transformer j, PtotalThe total active power input for the whole network;
Ppv+Pess+Pgrid＝Pload+Ploss+Pil (12)
SOCmin≤SOC(t)≤SOCmax (17)
RPV≥Rpv,min (18)
in the formula (13), Ui,tFor the duration of time t the voltage magnitude at node i,
for the lower limit value of the voltage of the node i for the period t,
the upper limit value of the voltage of the node i in the period t; in formula (14), Pij,tFor the transmission power on line i, j for period t,
maximum transmission power on line ij for time period t; in the formulae (15) to (17),
for the charging power of the energy storage system i at time t,
for the discharge power of the energy storage system i at time t,
is the maximum charging power of the energy storage system i,
is the maximum discharge power, SOC, of the energy storage system iminIs the minimum value of the state of charge, SOC, of the energy storage systemmaxThe maximum value of the state of charge of the energy storage system; in the formulae (18) and (19), RPVThe photovoltaic absorption rate; rpv,minIs the minimum value of the photovoltaic absorption rate, Ppv(t) is the photovoltaic system output power at time t,
outputting the maximum value of the power of the photovoltaic system in the t time period; in the formula (20), PgridFor the power interaction between the direct current distribution network and the external network,
is the minimum value of the interactive power of the direct current distribution network and the external network,
the maximum value of the interaction power of the direct-current power distribution network and the external network is obtained; in the formula (21), mloadTo be able to transfer the amount of load transfer, MloadIs a transferable load capacity;
4) solving the multi-voltage-level direct-current power distribution network optimized dispatching model in the step 3) through a multi-objective particle swarm optimization algorithm to obtain an optimal solution.
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| CN114938040B (en)* | 2022-06-10 | 2023-12-29 | 国网浙江省电力有限公司电力科学研究院 | Comprehensive optimization regulation and control method and device for source-network-load-storage alternating current-direct current system |
| CN115498664A (en)* | 2022-09-28 | 2022-12-20 | 国网浙江省电力有限公司杭州供电公司 | Hydrogen-electricity coupling comprehensive energy system combined with flexible direct-current power distribution network |
| CN115622052B (en)* | 2022-11-18 | 2023-03-28 | 国网天津市电力公司电力科学研究院 | Centralized optimization dispatching method, equipment, system and storage medium of distribution network side |
| CN115940299B (en)* | 2023-02-27 | 2023-06-09 | 国网江西省电力有限公司经济技术研究院 | A collaborative optimization method for active power and reactive power of power distribution system source network load storage |
| CN117239768B (en)* | 2023-10-31 | 2024-11-26 | 国网北京市电力公司 | A method, system, device and medium for controlling voltage of AC/DC hybrid distribution network |
| CN117638912A (en)* | 2023-12-04 | 2024-03-01 | 国网北京市电力公司 | Multi-element equipment coordination control method and device, electronic equipment and storage medium thereof |
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