CN111245027A - Alternating current-direct current hybrid system optimal scheduling method considering PET loss - Google Patents

Alternating current-direct current hybrid system optimal scheduling method considering PET loss Download PDF

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CN111245027A
CN111245027A CN202010164035.4A CN202010164035A CN111245027A CN 111245027 A CN111245027 A CN 111245027A CN 202010164035 A CN202010164035 A CN 202010164035A CN 111245027 A CN111245027 A CN 111245027A
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pet
power
energy storage
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loss
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CN111245027B (en
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梁刚
穆云飞
戚艳
曹旌
郭铁峰
孙志国
杨要中
赵旭
田圳
马占军
王钰
强军伟
陈文福
夏志兵
庞博
赵宇
赵琳
戈溢
闫帅鹏
孔亚鸣
郭丰瑞
王振法
马子岳
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
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State Grid Tianjin Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • 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
    • 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
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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Abstract

The invention discloses an alternating current-direct current hybrid system optimization scheduling method considering PET loss, which is characterized by establishing an alternating current-direct current hybrid system model considering the PET loss, wherein the alternating current-direct current hybrid system model comprises a loss-considered PET optimization model, a micro gas turbine model, an energy storage battery model, a fan model and a photovoltaic model; and setting system balance constraint by taking the minimum system operation cost as a target, and performing optimal scheduling on the alternating current-direct current hybrid system considering the PET loss. The method can provide reference for the optimization scheduling of the alternating current-direct current hybrid system considering the PET loss, analyzes the influence of the power loss of the PET and the energy storage equipment model in the operation process, and has important significance for researching the influence of the PET electric energy conversion efficiency on the optimization scheduling of the alternating current-direct current hybrid system.

Description

Alternating current-direct current hybrid system optimal scheduling method considering PET loss
Technical Field
The invention belongs to the technical field of optimal scheduling of alternating current and direct current hybrid micro-grids of a power system, and particularly relates to an optimal scheduling method of an alternating current and direct current hybrid system considering PET loss.
Background
The economic and social development can not be kept away from the sustainable and effective energy supply. By 2035 years, the Chinese energy demand is estimated to account for 24% of the world energy demand, and the high-speed growth is continuously kept, the contradiction between energy shortage and economic development is increasingly sharp, and the energy problem becomes the primary problem of the sustainable development in China. The full improvement of the energy utilization efficiency is a necessary way for realizing sustainable development, and the distributed energy is successfully used in a commercial way and is a utilization mode with the highest comprehensive efficiency, so that the great development of the distributed energy has great significance for improving the energy utilization efficiency of China.
The distributed energy access power grid is divided into an alternating current access type and a direct current access type. Compared with an alternating current access mode, the direct current access mode does not need conversion between direct current and alternating current, can save the current conversion process between the direct current access mode and the alternating current access mode, saves the cost of current conversion equipment on one hand, and reduces loss compared with the original structure because of no current conversion link; on the other hand, the direct current access power grid does not need to consider the synchronization of the phase and the frequency in the alternating current access mode, and theoretically, the controllability and the reliability of the system can be enhanced. For the above reasons, the dc access mode is getting more and more attention, and is considered as an ideal access form for distributed energy. However, considering the historical development of power systems, at the present stage, the main form of the power grid is an alternating current grid, it is unlikely that the power grid is completely changed into direct current in a short time, and the main form of distributed energy grid connection is an alternating current form, so that a hybrid structure with alternating current and direct current is a main system form for a long time later.
The large-scale distributed energy is accessed into the power system, the large-scale distributed energy is not simply connected with the power system, the power generation of the distributed energy is limited by various conditions of solar energy and wind energy, the power generation is intermittent, a large amount of access causes disturbance to the power system, the flexible regulation and interconnection mutual aid capability of the conventional power grid is not enough to well solve the problems, and therefore the large-scale access of the distributed energy is blocked, and the phenomenon of wind abandoning and light abandoning is caused. The PET has flexible power regulation and control capability, and can be applied to an alternating current-direct current hybrid system containing distributed energy to solve the problems. The PET is added with a power electronic conversion circuit on the basis of a high-frequency transformer, and the functions of the PET are realized by combining a power electronic device with the high-frequency transformer. The alternating current-direct current hybrid system constructed based on the PET and other flexible devices can improve the structure of a power grid and improve the access flexibility of renewable energy sources; the fast regulation and control capability of the response uncertainty of the power grid is enhanced, and the coordinated complementary consumption of various types of renewable energy sources is realized; the conversion links are reduced, and the energy utilization efficiency is improved.
At present, a series of optimization methods have been proposed by domestic and foreign teams aiming at the optimization operation of an alternating current-direct current hybrid system, however, the operation mode, network constraint and control object of the alternating current-direct current hybrid system are more complex, the optimization scheduling is influenced by a large amount of uncertainty, multidimensional optimization variables and operation constraint, and the optimization scheduling has obvious multi-time scale differences, the overall optimization operation difficulty of the system is increased, and related problems are far from being completely solved. Although the application of the novel power electronic equipment provides a new regulation and control means for the sufficient consumption and the efficient utilization of renewable energy sources, a model for using the equipment in system-level optimized scheduling is still lacked at present, and the flexible regulation capability of the equipment cannot be fully utilized.
Disclosure of Invention
The invention aims to provide an alternating current-direct current hybrid system optimal scheduling method considering PET loss, and aims to solve the problem that the influence of power loss of PET and an energy storage device model in the operation process is not considered in the day-ahead optimal scheduling method of a PET-containing alternating current-direct current hybrid system, realize the operation of novel power electronic equipment and an alternating current-direct current hybrid system so as to realize flexible and efficient consumption of various renewable energy sources, and discuss the influence of PET efficiency on a system optimal scheduling scheme.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
an alternating current-direct current hybrid system optimal scheduling method considering PET loss is characterized by comprising the following steps:
s1, establishing an alternating current-direct current hybrid system model considering the loss of the PET, wherein the alternating current-direct current hybrid system model comprises a loss-considering PET optimization model, a micro gas turbine model, an energy storage battery model, a fan model and a photovoltaic model;
and S2, aiming at the minimum system operation cost, setting system balance constraint and carrying out optimal scheduling on the alternating current-direct current hybrid system considering the PET loss.
Furthermore, in the loss-accounted PET model, the PET serves as an intermediate junction for energy transmission, a certain power loss exists inside the PET, and a physical quantity PET net input power P is introduced by taking three-port PET as an examplePETtExpressed as formulas (1) to (2):
Figure BDA0002406769410000031
Figure BDA0002406769410000032
in the formula (I), the compound is shown in the specification,
Figure BDA0002406769410000033
for PET net input Power at time t, the value at time t is expressedInputting the sum of total power of PET from the main network, the alternating current area and the direct current area, wherein η is the power conversion coefficient of the PET;
Figure BDA0002406769410000034
representing the interaction power of the main network area and the PET at the time t;
Figure BDA0002406769410000035
representing the interaction power of the alternating current area and the PET at the time t;
Figure BDA0002406769410000036
representing the interaction power of the direct current region and the PET at the time t;
the optimization model for PET is represented by formula (3):
Figure BDA0002406769410000037
further, in order to ensure that the PET operates in a safe state, the interaction power of the three ports of the PET has a power upper limit constraint represented by equations (4) to (6):
Figure BDA0002406769410000038
Figure BDA0002406769410000039
Figure BDA00024067694100000310
in the formula, PMFor maximum interaction power of PET with the main network, PACMaximum interaction power, P, for PET and AC regionsDCThe maximum interaction power of PET and a direct current region is obtained.
Furthermore, in the micro gas turbine model, the output power of the micro gas turbine is taken as a control variable of the model, and the power generation amount constraint is expressed by the following formulas (7) to (9):
Figure BDA00024067694100000311
Figure BDA00024067694100000312
Figure BDA0002406769410000041
in the formula (I), the compound is shown in the specification,
Figure BDA0002406769410000042
for the output power of the micro gas turbine at time t,
Figure BDA0002406769410000043
for the output power of the micro gas turbine at time t +1,
Figure BDA0002406769410000044
is the upper limit of output power of micro gas turbine, RUMTUpper limit of output power rise rate, RD, for micro gas turbineMTIs the upper limit of the output power reduction rate of the micro gas turbine.
Furthermore, in the energy storage battery model, the operation loss of the energy storage battery is considered, and the energy storage system has a charging upper limit value and a discharging upper limit value during charging and discharging at each moment; in addition, the energy storage of the energy storage system at the final moment is the same as the energy storage at the initial moment; the operating constraints are expressed as equations (10) - (12):
Figure BDA0002406769410000045
Figure BDA0002406769410000046
Figure BDA0002406769410000047
in the formula (I), the compound is shown in the specification,
Figure BDA0002406769410000048
for the energy storage of the energy storage system at time t,
Figure BDA0002406769410000049
storing energy of the energy storage system at the time t-1;
Figure BDA00024067694100000410
the charge and discharge amount of the energy storage system at the moment t is obtained; σ is the self-discharge coefficient; pDmaxIs the upper limit value of discharge, P, of the energy storage system per unit timeCmaxThe charging upper limit value is the charging upper limit value of the energy storage system at unit moment;
Figure BDA00024067694100000411
for the energy storage of the energy storage system at the initial moment,
Figure BDA00024067694100000412
the energy storage system stores energy at the final moment.
Further, in the wind turbine model, the set wind speed satisfies Weibull distribution, and the probability density function and the mathematical expectation thereof can be expressed as formulas (13) to (14):
Figure BDA00024067694100000413
Figure BDA00024067694100000414
wherein f (·) is a probability density function; v. ofwindIs a wind speed sampling value (unit: m/s); k is a shape parameter; c is a scale parameter, E (-) is a mathematical expectation, and Γ (-) is a Gamma function;
the output power of the fan is expressed by equation (15):
Figure BDA0002406769410000051
in the formula, PWTOutputting power for the fan; v. ofin,vrAnd voutThe cut-in wind speed, the rated wind speed and the cut-out wind speed (unit: m/s) of the fan are obtained; pwtThe rated power of the fan.
Further, in the photovoltaic model, the photovoltaic output power is set to satisfy the Beta distribution, and the probability density function and the mathematical expectation are expressed as the following formulas (16) to (17):
Figure BDA0002406769410000052
Figure BDA0002406769410000053
in the formula, PPVAnd
Figure BDA0002406769410000054
the actual output power and the maximum output power of the photovoltaic equipment, Gamma (·) is a Gamma function, α and β are scale parameters of Beta distribution
Figure BDA0002406769410000055
The sampling variable after being treated as a per unit has a value range of 0,1]Multiplying the variable by the power output of the photovoltaic device
Figure BDA0002406769410000056
Further, the maintenance cost of the equipment, the power generation cost of the micro gas turbine and the energy storage loss cost are considered and expressed as the following formulas (18) to (21):
Figure BDA0002406769410000057
Figure BDA0002406769410000058
Figure BDA0002406769410000059
Figure BDA00024067694100000510
wherein N is the total number of time segments of one operation cycle, mWT、mPVRespectively are the equipment maintenance cost coefficients of a fan and a photovoltaic,
Figure BDA0002406769410000061
the power generation powers of the fan and the photovoltaic at the moment t are respectively; m isMTThe power generation cost coefficient of the micro gas turbine; cbuyRepresenting the total electricity purchase charge, CsellRepresenting the total electricity sales charge, CbuyIs a positive value, CsellThe value of the negative value is the negative value,
Figure BDA0002406769410000062
for the main grid power purchase factor at time t,
Figure BDA0002406769410000063
the major network power selling coefficient at the time t; m isBLoss cost factor for stored energy;
and establishing an objective function of alternating current-direct current hybrid optimization scheduling according to the minimum running cost of the system, wherein the objective function is expressed as an expression (22):
Figure BDA0002406769410000064
further, the power balance constraint of the system is represented by equation (23):
Figure BDA0002406769410000065
in the formula (I), the compound is shown in the specification,
Figure BDA0002406769410000066
in order to exchange the system load at time t,
Figure BDA0002406769410000067
is the dc system load at time t.
The invention has the advantages and positive effects that:
1. the method can provide reference for the optimization scheduling of the alternating current-direct current hybrid system considering the PET loss, analyzes the influence of the power loss of the PET and the energy storage equipment model in the operation process, and has important significance for researching the influence of the PET electric energy conversion efficiency on the optimization scheduling of the alternating current-direct current hybrid system.
2. The calculation results of the invention show that the change of the PET efficiency can possibly cause the change of the running state of the AC/DC system at a certain moment, but the influence of the AC/DC system on the running state of the system is limited in general. Furthermore, the operating costs of the system decrease as the efficiency of the PET increases.
Drawings
FIG. 1 is a system topology of an application example of the present invention;
fig. 2 is a graph of the output power of the PET dc port and ac port over a 24 hour period for an application example 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 further described in detail with reference to the following 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.
Examples
The embodiment provides an ac/dc hybrid system optimal scheduling method considering Power Electronic Transformer (PET) loss, which includes:
step one, establishing an alternating current-direct current hybrid system model considering PET loss
For an alternating current-direct current hybrid system containing distributed energy, PET is used as a communication device between the hybrid system and a main network. The PET optimization model, the micro gas turbine model, the energy storage battery model, the fan model, the photovoltaic model, and the load model, which take losses into consideration, are given first.
1) PET model taking loss into account
PET is used as an intermediate hub for energy transmission, certain power loss exists in the PET, and the net input power of physical quantity PET is introduced by taking three-port PET as an example
Figure BDA0002406769410000071
Expressed as formulas (1) to (2):
Figure BDA0002406769410000072
Figure BDA0002406769410000073
in the formula (I), the compound is shown in the specification,
Figure BDA0002406769410000074
the net input power of the PET at the time t represents the sum of the total power of the PET input from the main network, the alternating current area and the direct current area at the time t, η is the power conversion coefficient of the PET;
Figure BDA0002406769410000075
representing the interaction power of the main network area and the PET at the time t;
Figure BDA0002406769410000076
representing the interaction power of the alternating current area and the PET at the time t;
Figure BDA0002406769410000077
representing the interaction power of the dc region with the PET at time t.
The optimization model for PET is represented by formula (3):
Figure BDA0002406769410000078
in order to ensure that the PET operates in a safe state, the interaction power of three ports of the PET has upper power limit constraints expressed by equations (4) to (6):
Figure BDA0002406769410000079
Figure BDA00024067694100000710
Figure BDA0002406769410000081
in the formula, PMFor maximum interaction power of PET with the main network, PACMaximum interaction power, P, for PET and AC regionsDCThe maximum interaction power of PET and a direct current region is obtained.
2) Miniature gas turbine model
The output power of the micro gas turbine is taken as a control variable of the model, and the generated energy is constrained to be the following formulas (7) to (9):
Figure BDA0002406769410000082
Figure BDA0002406769410000083
Figure BDA0002406769410000084
in the formula, PMTtFor the output power of the micro gas turbine at time t,
Figure BDA0002406769410000085
for the output power of the micro gas turbine at time t +1,
Figure BDA0002406769410000086
is the upper limit of output power of micro gas turbine, RUMTUpper limit of output power rise rate, RD, for micro gas turbineMTIs the upper limit of the output power reduction rate of the micro gas turbine.
3) Energy storage battery model
Considering the operation loss of the energy storage battery, the energy storage system has a charging upper limit value and a discharging upper limit value during charging and discharging at each moment; in addition, the energy storage system stores the same energy at the final moment as at the initial moment. The operating constraints are expressed as equations (10) - (12):
Figure BDA0002406769410000087
Figure BDA0002406769410000088
Figure BDA0002406769410000089
in the formula (I), the compound is shown in the specification,
Figure BDA00024067694100000810
for the energy storage of the energy storage system at time t,
Figure BDA00024067694100000811
storing energy of the energy storage system at the time t-1;
Figure BDA00024067694100000812
the charge and discharge amount of the energy storage system at the moment t is obtained; σ is the self-discharge coefficient; pDmaxIs the upper limit value of discharge, P, of the energy storage system per unit timeCmaxThe charging upper limit value is the charging upper limit value of the energy storage system at unit moment;
Figure BDA00024067694100000813
for the energy storage of the energy storage system at the initial moment,
Figure BDA00024067694100000814
the energy storage system stores energy at the final moment.
4) Fan model
The set wind speed satisfies the Weibull distribution, and the probability density function and mathematical expectation thereof can be expressed as the following equations (13) to (14):
Figure BDA0002406769410000091
Figure BDA0002406769410000092
wherein f (·) is a probability density function; v. ofwindIs a wind speed sampling value (unit: m/s); k is a shape parameter; c is a scale parameter, E (-) is a mathematical expectation, and Γ (-) is a Gamma function.
The output power of the fan is expressed by equation (15):
Figure BDA0002406769410000093
in the formula, PWTOutputting power for the fan; v. ofin,vrAnd voutThe cut-in wind speed, the rated wind speed and the cut-out wind speed (unit: m/s) of the fan are obtained; pwtThe rated power of the fan.
5) Photovoltaic model
And setting the photovoltaic output power to satisfy Beta distribution, wherein the probability density function and the mathematical expectation are expressed as formulas (16) to (17):
Figure BDA0002406769410000094
Figure BDA0002406769410000095
in the formula, PPVAnd
Figure BDA0002406769410000096
the actual output power and the maximum output power of the photovoltaic equipment, Gamma (·) is a Gamma function, α and β are scale parameters of Beta distribution
Figure BDA0002406769410000097
The sampling variable after being treated as a per unit has a value range of 0,1]Multiplying the variable by the power output of the photovoltaic device
Figure BDA0002406769410000098
Step two, the objective function and the constraint condition of the column write optimization model
Considering the operation cost, the equipment maintenance cost, the power generation cost and the energy storage loss cost of the micro gas turbine, the cost is expressed as the following formulas (18) to (21):
Figure BDA0002406769410000099
Figure BDA0002406769410000101
Figure BDA0002406769410000102
Figure BDA0002406769410000103
wherein N is the total number of time segments of one operation cycle, mWT、mPVRespectively are the equipment maintenance cost coefficients of a fan and a photovoltaic,
Figure BDA0002406769410000104
the power generation powers of the fan and the photovoltaic at the moment t are respectively; m isMTThe power generation cost coefficient of the micro gas turbine; cbuyRepresenting the total electricity purchase charge, CsellRepresenting the total electricity sales charge, CbuyIs a positive value, CsellThe value of the negative value is the negative value,
Figure BDA0002406769410000105
for the main grid power purchase factor at time t,
Figure BDA0002406769410000106
the major network power selling coefficient at the time t; m isBIs the loss cost factor of the stored energy.
And establishing an objective function of alternating current-direct current hybrid optimization scheduling according to the minimum running cost of the system, wherein the objective function is expressed as an expression (22):
Figure BDA0002406769410000107
the power balance constraint of the system is represented by equation (23):
Figure BDA0002406769410000108
in the formula (I), the compound is shown in the specification,
Figure BDA0002406769410000109
in order to exchange the system load at time t,
Figure BDA00024067694100001010
for the direct current system load at the time t, the meanings of other parameters refer to the corresponding parameter description.
Application example
Taking an actual alternating current-direct current hybrid system containing PET in Jiangsu province as an example, the topological structure of the system is shown in figure 1, the system consists of an alternating current network and a direct current network, the alternating current network and the direct current network are used for energy transmission by means of the PET, three ports of the PET are connected with the alternating current network and the direct current network, and one port of the PET is directly connected with a main network. When the energy supply in the alternating current-direct current system is insufficient or the electricity purchasing economy from the main grid is better, the system can purchase electricity from the main grid and inject the electricity into the alternating current-direct current system through PET to ensure the energy supply and demand balance.
The alternating current system comprises a micro gas turbine (MT), a fan (WT), alternating current Controllable loads (AC Lctrl) and alternating current uncontrollable loads (Non-Controllable AC loads, AC Lcri), wherein the power generation amount of the micro gas turbine and the reduction amount of the alternating current Controllable loads at each moment are control variables, and the output of the fan and the load amount of the alternating current uncontrollable loads are set values.
The direct current system comprises a photovoltaic power generation unit (PV), a battery energy storage device (BS), a direct current Controllable load (DC Lctrl) and a direct current uncontrollable load (Non-Controllable DC Loads, DC Lcri). The energy storage amount of the battery energy storage device, the photovoltaic power generation amount and the reduction amount of the direct-current controllable load at each moment are control variables, and the photovoltaic output and the load amount of the direct-current uncontrollable load are given values. For the state of the battery, it is decided by the optimized solution whether it is charged or discharged at each moment. The battery may be considered a load when charged; when it is discharged, it can be regarded as a power generation device.
According to the embodiment, the day-ahead optimized scheduling is carried out on the system, the optimized scheduling step length is 1h, the self-discharge coefficient of the energy storage battery is ignored, and the equipment configuration parameters and the cost coefficients of all parts of the system are shown in the table 1.
TABLE 1 AC/DC MIXING SYSTEM PARAMETERS CONTAINING PET IN THE LIGNANCE OF Jiangsu province
Figure BDA0002406769410000111
Figure BDA0002406769410000121
The output power of the PET dc and ac ports over a 24 hour period is shown in fig. 2, with a positive value indicating power flow from the ac/dc system and a negative value indicating power injection into the ac/dc system. Table 2 shows the state values and operating states of the ac system, the dc system and the main network to which the PET is connected within 24 hours.
Table 2 PET three port status values (η ═ 0.95)
Figure BDA0002406769410000122
To discuss the influence of PET efficiency on the optimal scheduling, η is 0.92, 0.95 and 0.98 respectively, the day-ahead optimal scheduling is performed on the system, table 2 in the upper section shows the state values of the ac system, the dc system and the main network connected to PET in 24 hours when η is 0.95, and table 3 and table 4 in the section respectively show the state values of the ac system, the dc system and the main network when η is 0.92 and 0.98.
Table 3 PET three port status values (η ═ 0.92)
Figure BDA0002406769410000131
Table 4 PET three port status values (η ═ 0.98)
Figure BDA0002406769410000132
Figure BDA0002406769410000141
TABLE 5 System operating costs under different PET efficiencies
Figure BDA0002406769410000142
Comparing table 3 with table 2, the state values of the AC system and the DC system are changed only in the AC state at 15h and the DC state at 16 h; comparing table 4 with table 2, the AC system and the dc system have changed AC state only at 15 h. Analyzing the data shows that the data of the AC surplus and the DC surplus are correspondingly floated due to the change of the PET efficiency, so that the value which is originally in the critical state may change after fluctuation.
The total operating cost of the system for the three operating efficiencies is given in table 5, and it can be seen from the data in the table that the total operating cost of the system containing PET decreases as the efficiency of PET increases.
The invention provides an alternating current-direct current hybrid system optimal scheduling method considering Power Electronic Transformer (PET) loss, and the influence of PET operation efficiency on system operation scheduling is analyzed. The calculation result shows that:
1) changes in PET efficiency may result in changes in the operating conditions of the ac/dc system at a given time, but generally have limited effect on the operating conditions of the system.
2) The operating cost of the system decreases as the efficiency of the PET increases.
The PET-containing alternating current and direct current hybrid microgrid optimal scheduling method is researched more deeply, and is a direction to be researched deeply in the future.
Although the embodiments of the present invention and the accompanying drawings are disclosed for illustrative purposes, those skilled in the art will appreciate that: various substitutions, changes and modifications are possible without departing from the spirit and scope of the invention and the appended claims, and therefore the scope of the invention is not limited to the disclosure of the embodiments and the accompanying drawings.

Claims (9)

1. An alternating current-direct current hybrid system optimal scheduling method considering PET loss is characterized by comprising the following steps:
s1, establishing an alternating current-direct current hybrid system model considering the loss of the PET, wherein the alternating current-direct current hybrid system model comprises a loss-considering PET optimization model, a micro gas turbine model, an energy storage battery model, a fan model, a photovoltaic model and a load model;
and S2, aiming at the minimum system operation cost, setting system balance constraint and carrying out optimal scheduling on the alternating current-direct current hybrid system considering the PET loss.
2. The optimal scheduling method for the AC-DC hybrid system considering the PET loss according to claim 1, wherein the optimal scheduling method comprises the following steps: in the loss-accounted PET model, PET is used as an intermediate junction for energy transmission, certain power loss exists in the PET, and the net input power of physical quantity PET is introduced by taking three-port PET as an example
Figure FDA0002406769400000011
Expressed as formulas (1) to (2):
Figure FDA0002406769400000012
Figure FDA0002406769400000013
in the formula (I), the compound is shown in the specification,
Figure FDA0002406769400000014
the net input power of the PET at the time t represents the sum of the total power of the PET input from the main network, the alternating current area and the direct current area at the time t, η is the power conversion coefficient of the PET;
Figure FDA0002406769400000015
representing the interaction power of the main network area and the PET at the time t;
Figure FDA0002406769400000016
representing the interaction power of the alternating current area and the PET at the time t;
Figure FDA0002406769400000017
representing the interaction power of the direct current region and the PET at the time t;
the optimization model for PET is represented by formula (3):
Figure FDA0002406769400000018
3. the optimal scheduling method for the AC-DC hybrid system considering the PET loss as claimed in claim 2, wherein: in order to ensure that the PET operates in a safe state, the interaction power of three ports of the PET has upper power limit constraints expressed by equations (4) to (6):
Figure FDA0002406769400000019
Figure FDA00024067694000000110
Figure FDA00024067694000000111
in the formula, PMFor maximum interaction power of PET with the main network, PACMaximum interaction power, P, for PET and AC regionsDCThe maximum interaction power of PET and a direct current region is obtained.
4. The optimal scheduling method for the AC-DC hybrid system considering the PET loss according to claim 1, wherein the optimal scheduling method comprises the following steps: in the micro gas turbine model, the output power of the micro gas turbine is used as a control variable of the model, and the generated energy constraint is expressed by the following formulas (7) to (9):
Figure FDA0002406769400000021
Figure FDA0002406769400000022
Figure FDA0002406769400000023
in the formula (I), the compound is shown in the specification,
Figure FDA0002406769400000024
for the output power of the micro gas turbine at time t,
Figure FDA0002406769400000025
for the output power of the micro gas turbine at time t +1,
Figure FDA0002406769400000026
is the upper limit of output power of micro gas turbine, RUMTUpper limit of output power rise rate, RD, for micro gas turbineMTIs the upper limit of the output power reduction rate of the micro gas turbine.
5. The optimal scheduling method for the AC-DC hybrid system considering the PET loss according to claim 1, wherein the optimal scheduling method comprises the following steps: in the energy storage battery model, the operation loss of the energy storage battery is considered, and the energy storage system has a charging upper limit value and a discharging upper limit value during charging and discharging at each moment; in addition, the energy storage of the energy storage system at the final moment is the same as the energy storage at the initial moment; the operating constraints are expressed as equations (10) - (12):
Figure FDA0002406769400000027
Figure FDA0002406769400000028
Figure FDA0002406769400000029
in the formula (I), the compound is shown in the specification,
Figure FDA00024067694000000210
for the energy storage of the energy storage system at time t,
Figure FDA00024067694000000211
storing energy of the energy storage system at the time t-1;
Figure FDA00024067694000000212
the charge and discharge amount of the energy storage system at the moment t is obtained; σ is the self-discharge coefficient; pDmaxIs the upper limit value of discharge, P, of the energy storage system per unit timeCmaxThe charging upper limit value is the charging upper limit value of the energy storage system at unit moment;
Figure FDA00024067694000000213
for the energy storage of the energy storage system at the initial moment,
Figure FDA00024067694000000214
the energy storage system stores energy at the final moment.
6. The optimal scheduling method for the AC-DC hybrid system considering the PET loss according to claim 1, wherein the optimal scheduling method comprises the following steps: in the wind turbine model, the set wind speed satisfies Weibull distribution, and the probability density function and the mathematical expectation can be expressed as formulas (13) to (14):
Figure FDA0002406769400000031
Figure FDA0002406769400000032
wherein f (·) is a probability density function; v. ofwindIs a wind speed sampling value (unit: m/s); k is a shape parameter; c is a scale parameter, E (-) is a mathematical expectation, and Γ (-) is a Gamma function.
The output power of the fan is expressed by equation (15):
Figure FDA0002406769400000033
in the formula, PWTOutputting power for the fan; v. ofin,vrAnd voutThe cut-in wind speed, the rated wind speed and the cut-out wind speed (unit: m/s) of the fan are obtained; pwtThe rated power of the fan.
7. The optimal scheduling method for the AC-DC hybrid system considering the PET loss according to claim 1, wherein the optimal scheduling method comprises the following steps: in the photovoltaic model, the photovoltaic output power is set to satisfy Beta distribution, and the probability density function and the mathematical expectation are expressed as formulas (16) to (17):
Figure FDA0002406769400000034
Figure FDA0002406769400000035
in the formula, PPVAnd
Figure FDA0002406769400000036
the actual output power and the maximum output power of the photovoltaic equipment, Gamma (·) is a Gamma function, α and β are scale parameters of Beta distribution
Figure FDA0002406769400000037
The sampling variable after being treated as a per unit has a value range of 0,1]Calculating the work of the photovoltaic deviceMultiplying the variable by the output rate
Figure FDA0002406769400000038
8. The optimal scheduling method for the AC-DC hybrid system considering the PET loss according to claim 1, wherein the optimal scheduling method comprises the following steps: considering the operation cost, the equipment maintenance cost, the power generation cost and the energy storage loss cost of the micro gas turbine, the cost is expressed as the following formulas (18) to (21):
Figure FDA0002406769400000039
Figure FDA0002406769400000041
Figure FDA0002406769400000042
Figure FDA0002406769400000043
wherein N is the total number of time segments of one operation cycle, mWT、mPVRespectively are the equipment maintenance cost coefficients of a fan and a photovoltaic,
Figure FDA0002406769400000044
the power generation powers of the fan and the photovoltaic at the moment t are respectively; m isMTThe power generation cost coefficient of the micro gas turbine; cbuyRepresenting the total electricity purchase charge, CsellRepresenting the total electricity sales charge, CbuyIs a positive value, CsellThe value of the negative value is the negative value,
Figure FDA0002406769400000045
for the main grid power purchase factor at time t,
Figure FDA0002406769400000046
the major network power selling coefficient at the time t; m isBLoss cost factor for stored energy;
and establishing an objective function of alternating current-direct current hybrid optimization scheduling according to the minimum running cost of the system, wherein the objective function is expressed as an expression (22):
Figure FDA0002406769400000047
9. the method for optimally scheduling the AC-DC hybrid system considering the PET loss as recited in claim 8, wherein the power balance constraint of the system is expressed by equation (23):
Figure FDA0002406769400000048
in the formula (I), the compound is shown in the specification,
Figure FDA0002406769400000049
in order to exchange the system load at time t,
Figure FDA00024067694000000410
is the dc system load at time t.
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