CN111325423A - Regional multi-energy interconnection operation optimization method and computing equipment - Google Patents

Regional multi-energy interconnection operation optimization method and computing equipment Download PDF

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CN111325423A
CN111325423A CN201811531726.2A CN201811531726A CN111325423A CN 111325423 A CN111325423 A CN 111325423A CN 201811531726 A CN201811531726 A CN 201811531726A CN 111325423 A CN111325423 A CN 111325423A
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曾鸣
叶嘉雯
王雨晴
田立燚
霍现旭
赵保国
张剑
***
王剑峰
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North China Electric Power University
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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Abstract

The invention discloses a regional multi-energy interconnection operation optimization method, which is executed in computing equipment and comprises the following steps: establishing a battery operation cost model, wherein the cost model comprises battery electricity quantity price and battery electricity quantity consumption in the charging and discharging process; constructing a random unit combination model of the microgrid based on the cost model, wherein the combination model comprises a constraint condition and an objective function, and the objective function is the minimum expected operation cost in a time period; and solving the combination model by adopting a preset method to obtain optimal unit combination parameters, and combining the units according to an optimal result to realize regional multi-energy interconnection operation optimization. The invention also discloses a computing device for executing the operation optimization method.

Description

Regional multi-energy interconnection operation optimization method and computing equipment
Technical Field
The invention relates to the field of power systems, in particular to a regional multi-energy interconnection operation optimization method and computing equipment.
Background
With the rapid increase of domestic economy, the demand of society on electric power is increasingly vigorous, the investment of the infrastructure of a power grid is increased, and the application and research of the energy interconnection micro-grid are increased. In the energy interconnection microgrid system supply and demand double-side multi-energy collaborative optimization strategy and the solving algorithm thereof, the influence among the supply side, the demand side and the energy conversion is considered, an energy interconnection microgrid system supply and demand double-side multi-energy collaborative optimization strategy model is constructed, and a combined algorithm combining an individual difference ant colony optimization algorithm and a particle swarm optimization algorithm for solving a mixed integer nonlinear programming model is provided. The unified planning model and the Benders decoupling method of the power-natural gas integrated energy system research the unified planning problem of the power-natural gas integrated energy system considering the boundary condition constraints of the power system and the natural gas system, optimize the site selection and the volume fixing of a gas power plant, a power transmission line, a natural gas supply station and a natural gas pipeline, and construct a mixed integer non-convex nonlinear planning model; then, adopting Benders decoupling to simplify the mixed integer non-convex nonlinear programming problem into a double-layer main problem and a double-layer sub problem, and respectively adopting efficient commercial solvers CPLEX and IPOPT to carry out iterative solution; finally, the feasibility of the developed united planning model based on Benders decoupling is demonstrated by adopting the constructed electricity-gas integrated energy system comprising the 54-node power system and the 19-node natural gas network which are mutually coupled.
However, most of the current research is done on a "scenario-based stochastic programming" basis, this approach being based on a replicated deterministic model generated under Monte Carlo simulation scenarios. As the number of survey scenarios increases, the computational burden in this approach grows exponentially. Reducing the scene using different techniques may alleviate the computational burden problem, but this approach may ignore cases of low probability but high impact.
Disclosure of Invention
To this end, the present invention provides a new regional multi-energy interconnect operational optimization method and computing device in an effort to solve, or at least alleviate, the above-identified problems.
According to one aspect of the invention, a regional multi-energy interconnection operation optimization method is provided and executed in computing equipment, and the method comprises the following steps: establishing a battery operation cost model, wherein the cost model comprises battery electricity quantity price and battery electricity quantity consumption in the charging and discharging process; constructing a random unit combination model of the microgrid based on the cost model, wherein the combination model comprises a constraint condition and an objective function, and the objective function is the minimum expected operation cost in a time period; and solving the combination model by adopting a preset method to obtain optimal unit combination parameters, and combining the units according to an optimal result to realize regional multi-energy interconnection operation optimization.
Alternatively, in the method according to the invention, the battery charge price cbatThe calculation formula of (2) is as follows:
Figure BDA0001905822700000021
wherein the content of the first and second substances,
Figure BDA0001905822700000022
represents the price of energy for charging the battery,
Figure BDA0001905822700000023
represents the available cost of battery capacity, which refers to the available cost of having a storage capacity of 1 kilowatt-hour, C∑Is the full life cycle capacity of the battery, crepIs the cost of the reset.
Alternatively, in the method according to the invention, for lead-acid and lithium-ion batteries:
C∑＝CrDODr[Lr-0.2*(1+2+...+Lr)/Lr]＝CrDODr(0.9Lr-0.1)(kWh)
for vanadium redox batteries: c∑＝CrDODrLr(kWh)，
Wherein, DODrIs depth of discharge, CrIs the rated capacity of the battery, LrIs the rated life.
Alternatively, in the method according to the invention, the energy usage H for supplying the load per unit time with battery power consumption during the discharge process isbatThe power consumption of the battery during the charging process is the power loss L of the rechargeable battery per unit timebatThe calculation formulas are respectively as follows:
Figure BDA0001905822700000024
wherein the content of the first and second substances,
Figure BDA0001905822700000025
is the output power of the battery or batteries,
Figure BDA0001905822700000026
is the loss of the power of the discharge,
Figure BDA0001905822700000027
is the input power of the battery and is,
Figure BDA0001905822700000028
is the charging power loss.
Alternatively, in the method according to the invention, for lead-acid and lithium-ion batteries,
Figure BDA0001905822700000031
Figure BDA0001905822700000032
wherein SOC is a state of charge, VrIs the rated voltage, Q, of the batteryrIs the rated capacity of the battery, R is the internal ohmic resistance, and K is a constant calculated from the manufacturer's data.
Alternatively, in the method according to the invention, for a vanadium redox cell,
Figure BDA0001905822700000033
Figure BDA0001905822700000034
wherein, VOCIs the open circuit voltage of the battery cell,
Figure BDA0001905822700000035
and
Figure BDA0001905822700000036
stack currents during discharge and during charge of the battery respectively,
Figure BDA0001905822700000037
are the battery loss model coefficients, which are the coefficients corresponding to the rated voltage VrOr rated current IrThe parameter concerned.
Alternatively, in the method according to the invention,
Figure BDA0001905822700000038
Figure BDA0001905822700000039
optionally, in the method according to the invention, the objective function is
Figure BDA00019058227000000310
Wherein
Figure BDA00019058227000000311
Wherein, FkIs the total expected operating cost, S, over time period kkIs the total transition cost including the startup and shutdown costs of the generator during time period k, N is the time horizon, Fg,k，
Figure BDA00019058227000000312
Representing the total operating costs of the generator, discharged battery and charged battery, respectively, during time period k, Fm,kDue to the cost caused by the power mismatch.
Alternatively, in the method according to the invention,
Figure BDA00019058227000000313
Figure BDA00019058227000000314
where T is the time step, n1And n2Representing the number of generators and batteries, g, respectivelyiAnd biRespectively representing a generator i and a battery i, sgi,k、
Figure BDA00019058227000000315
Representing the binary states of generator i and battery i, respectively, during time period k, cgiIs the fuel price of the generator i, cbiIs the charge price of battery i, FgiIs the fuel cost of the generator i, HgiIs the fuel consumption of the generator i and,
Figure BDA0001905822700000041
and
Figure BDA0001905822700000042
the power consumption of battery i during discharge and charge respectively,
Figure BDA0001905822700000043
and
Figure BDA0001905822700000044
operating costs, P, of the battery i during discharge and charge, respectivelygi,k、
Figure BDA0001905822700000045
Figure BDA0001905822700000046
Representing the transmission power of the generator i, the discharged battery and the charged battery, P, respectively, during the time period km,kIs the cost due to power mismatch in time period k, FmRefers to the cost per unit time due to power mismatch.
Alternatively, in the method according to the invention,
Figure BDA0001905822700000047
Figure BDA0001905822700000048
Figure BDA0001905822700000049
wherein, Pnet,kIs the net load of the k period, pkIs the probability that the payload is less than 0, E (y | x) is the expectation of y under the condition that x is satisfied, cex,kIs the electricity price exported to the grid, cim,kIs the electricity rate imported into the grid, αkAnd βkIs a parameter, wherein αkIs to control the probability level, P, of the input/output of power from the grid to the microgridgen,kIs the total power generation amount, Pchg,kIs the total charge rate of the battery,
Figure BDA00019058227000000410
is Pnet,kIs calculated from the expected value of (c).
Alternatively, in the method according to the invention,
when P is presentnet,k≥0，Pm,k＝Pgen,k-Pnet,k，
Figure BDA00019058227000000411
When P is presentnet,kWhen < 0, Pm,k＝Pchg,k-Pnet,k，
Figure BDA00019058227000000412
Optionally, in the method according to the present invention, the constraint condition includes at least one of:
Figure BDA00019058227000000413
where p (x) is the probability that x satisfies the condition, AND where AND (a, b) ═ 0 means that a AND b cannot be 1 at the same time, SOCbi,kIs the state of charge of battery i over a period of k,
Figure BDA0001905822700000051
and
Figure BDA0001905822700000052
respectively the minimum and maximum value of the state of charge of battery i,
Figure BDA0001905822700000053
and
Figure BDA0001905822700000054
respectively the minimum and maximum value of the transmitted power of the discharged battery i,
Figure BDA0001905822700000055
and
Figure BDA0001905822700000056
respectively the minimum and maximum value of the transmission power of the rechargeable battery i,
Figure BDA0001905822700000057
is the transmit power of the k-period generator i on-line,
Figure BDA0001905822700000058
and
Figure BDA0001905822700000059
respectively minimum and maximum values of the generator i transmitted power,
Figure BDA00019058227000000510
and
Figure BDA00019058227000000511
respectively an on-line time and an off-line time of the k-period generator i,
Figure BDA00019058227000000512
and
Figure BDA00019058227000000513
which are the minimum values of the on-line time and the off-line time of the generator i, respectively.
Alternatively, in the method according to the present invention, wherein the constraint R1 is:
Figure BDA00019058227000000514
where φ is a cumulative distribution function, σ, following a (0,1) standard normal distributionnet,kIs the net load error Δ Pnet,kStandard deviation of (1), Δ Pnet,kIs the actual load error Δ PloadPhotovoltaic power generation error delta PpvAnd wind power generation error delta PWTSum of (a), Δ Pload、ΔPpvAnd Δ PWTIs a prediction error that depends on the prediction method and the prediction range.
Optionally, in the method according to the invention, the predetermined method is a stochastic dynamic programming method, wherein the state space at phase k is
Figure BDA00019058227000000515
Wherein L iskIs the set of feasible states for stage k, mkIs LkThe number of states in the set is,
Figure BDA00019058227000000516
is a unit xiBinary state of (x)iRepresenting a generator, a discharged battery or a charged battery.
Optionally, in the method according to the present invention, the stochastic dynamic programming method is a forward stochastic dynamic programming method, where the minimum cost for the B-phase to reach the state a is:
Figure BDA00019058227000000517
wherein the content of the first and second substances,
Figure BDA00019058227000000518
is an arrival state
Figure BDA00019058227000000519
The minimum cost of the system (c) is,
Figure BDA00019058227000000520
is for the state
Figure BDA00019058227000000521
The operating costs of the system are reduced by the system,
Figure BDA00019058227000000522
is a slave state
Figure BDA00019058227000000523
To the state
Figure BDA00019058227000000524
For a transition feeThe application is as follows.
According to an aspect of the invention, there is provided a computing device comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions comprising instructions for performing the regional multi-energy interconnect operational optimization method as described above.
According to an aspect of the present invention, there is provided a readable storage medium storing program instructions, which when read and executed by a computing device, cause the computing device to execute the regional multi-energy interconnection operation optimization method as described above.
According to the technical scheme of the invention, the invention provides a regional multi-energy interconnection operation optimization operation method based on random dynamic programming. The model takes into account the cycle life and charge-discharge efficiency of the battery. The model can realize the economic dispatching of multiple batteries in the micro-grid system without introducing additional objective functions to improve the efficiency and the cycle life to the maximum extent. Furthermore, a probabilistic constrained approach is proposed to take into account uncertainty in load and renewable energy prediction errors. The method adopts random dynamic programming to find the optimal feedforward scheduling for the typical micro power grid of a natural gas generator, photovoltaic power generation, wind power generation, a vanadium redox battery and a lead-acid storage battery. The result shows that the method can maintain the optimal operation of the system with high probability without investigating a large number of scenes.
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To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which are indicative of various ways in which the principles disclosed herein may be practiced, and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description read in conjunction with the accompanying drawings. Throughout this disclosure, like reference numerals generally refer to like parts or elements.
FIG. 1 shows a block diagram of a computing device 100, according to one embodiment of the invention; and
fig. 2 shows a schematic diagram of a regional multi-energy interconnection operation optimization method 200 according to an embodiment of the invention;
FIG. 3 illustrates a flow diagram of a forward stochastic dynamic programming method according to one embodiment of the invention;
FIG. 4 shows a schematic diagram of an exemplary microgrid according to an embodiment of the present invention;
FIG. 5 shows a schematic diagram of load and renewable energy predictions in the microgrid of FIG. 4; and
fig. 6 and 7 show schematic diagrams of deterministic and stochastic charging results, respectively, in the microgrid of fig. 4.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Fig. 1 is a block diagram of an example computing device 100. In a basic configuration 102, computing device 100 typically includes system memory 106 and one or more processors 104. A memory bus 108 may be used for communication between the processor 104 and the system memory 106.
Depending on the desired configuration, the processor 104 may be any type of processing, including but not limited to: a microprocessor (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. The processor 104 may include one or more levels of cache, such as a level one cache 110 and a level two cache 112, a processor core 114, and registers 116. The example processor core 114 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a digital signal processing core (DSP core), or any combination thereof. The example memory controller 118 may be used with the processor 104, or in some implementations the memory controller 118 may be an internal part of the processor 104.
Depending on the desired configuration, system memory 106 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 106 may include an operating system 120, one or more applications 122, and program data 124. In some embodiments, application 122 may be arranged to operate with program data 124 on an operating system. The program data 124 comprises instructions, and in the computing device 100 according to the present invention, the program data 124 comprises instructions for performing the regional multi-energy interconnect operational optimization method 200.
Computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to the basic configuration 102 via the bus/interface controller 130. The example output device 142 includes a graphics processing unit 148 and an audio processing unit 150. They may be configured to facilitate communication with various external devices, such as a display or speakers, via one or more a/V ports 152. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 158. An example communication device 146 may include a network controller 160, which may be arranged to facilitate communications with one or more other computing devices 162 over a network communication link via one or more communication ports 164.
A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media, such as carrier waves or other transport mechanisms, in a modulated data signal. A "modulated data signal" may be a signal that has one or more of its data set or its changes made in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or private-wired network, and various wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), or other wireless media. The term computer readable media as used herein may include both storage media and communication media.
Computing device 100 may be implemented as a server, such as a file server, a database server, an application server, a WEB server, etc., or as part of a small-form factor portable (or mobile) electronic device, such as a cellular telephone, a Personal Digital Assistant (PDA), a personal media player device, a wireless WEB-watch device, a personal headset device, an application specific device, or a hybrid device that include any of the above functions. Computing device 100 may also be implemented as a personal computer including both desktop and notebook computer configurations. In some embodiments, the computing device 100 is configured to perform a regional multi-energy interconnect operations optimization method 200 in accordance with the present invention.
Fig. 2 shows a schematic diagram of a post-construction evaluation method 200 for an electric power construction project according to an embodiment of the present invention. The method resides in execution in the computing device 100.
As shown in fig. 2, the method is adapted to step S220. In step S220, a battery operation cost model is established, where the cost model includes a battery charge price and a battery charge consumption during charging and discharging.
The battery operating cost model references the operating cost of the microgrid's small-scale natural gas generator, which is mainly reflected in the fuel cost, which is FgenCan be viewed as a function of output power:
Figure BDA0001905822700000081
wherein, cgen(dollars/gallon) as fuel price, Hgen(Pgen) (gallons per hour) is the fuel consumption, PgenFor the generator i outputAnd (4) power.
In contrast to generators, batteries operate without fuel, which makes it challenging to assess the operating costs of the battery. However, during the energy conversion process, the battery and the generator are similar. In the generator, energy is stored in the form of fuel and electrical energy is produced by the combustion process. Also, electricity in the battery is charged and discharged through an electrochemical process. Generally, recharging a battery is similar to refueling a generator; thus, the input electrical charge (kilowatt-hours) can be considered as the battery "fuel". The cost of the input electricity is expressed as "kWhf"to emphasize this analogy relationship. Thus, the operating cost of the battery is determined in the same way as the generator cost function, from the battery charge price (kWh)fPrice) and battery power consumption (kWh)fSpent) was obtained. The battery types studied by the invention are lead-acid batteries, lithium ion batteries and vanadium redox batteries.
Price of fuel c for generatorgenIs composed of two parts:
Figure BDA0001905822700000091
wherein the content of the first and second substances,
Figure BDA0001905822700000092
which represents the cost of the fuel,
Figure BDA0001905822700000093
representing availability costs, including fuel transportation costs and other service costs, such as costs of on-site storage facilities. Considering the location of the generator set, transportation costs and other service costs may result in cgenRatio of
Figure BDA0001905822700000094
Much larger.
According to one embodiment, the battery charge price c is referenced to the generator modelbatCan be as follows:
Figure BDA0001905822700000095
wherein
Figure BDA0001905822700000096
Represents the price of energy for charging the battery,
Figure BDA00019058227000000910
represents the available cost of battery capacity, which is the "available cost of having 1 kilowatt-hour of storage capacity", C∑Is the full life cycle capacity of the battery, crepIs the cost of the reset. In a microgrid, if renewable energy is used to recharge the batteries,
Figure BDA0001905822700000098
may be zero and, therefore,
Figure BDA0001905822700000099
is a major component of price.
Generally, lead-acid, lithium-ion electrochemical cells are generally considered to have degraded to 80% of rated capacity at the end of the life cycle. Assuming that the battery is discharged to a rated discharge depth every cycle, the average capacity degradation rate is (0.2/Lr) CrIn which C isrIs the rated capacity of the battery, LrIs the rated life. Vanadium Redox Batteries (VRBs) have a negligible drop in capacity after repeated deep discharge and charge relative to lead-acid and lithium-ion batteries. The cycle life of a vanadium cell is mainly determined by the life of the proton exchange membrane and the pump. Vanadium cells can be cycled more than 10000 times until their membranes degrade or the pump fails.
Thus, for lead acid and lithium ion batteries, the total life available capacity of the battery is:
C∑＝CrDODr[Lr-0.2*(1+2+...+Lr)/Lr]＝CrDODr(0.9Lr-0.1)(kWh)，
and the total life available capacity of the vanadium redox battery is:
C∑＝CrDODrLr(kWh)
wherein, DODrIs the depth of discharge.
The operating cost model of the battery is based on similarity to the fuel cost model of the generator, and therefore has little additional complexity compared to standard methods. kWh of batteryfThe price does not change so frequently as the fuel price. Price of battery charge (kWh)fPrice) includes the cost of replacement, the rated capacity and the life cycle, which are determined by the time of purchase and need not be upgraded.
Battery power consumption during discharge (kWh)fConsumption) is defined as "energy usage H per unit time to supply the loadbatThe power consumption of the battery during the charging process is the power loss L of the rechargeable battery per unit timebatThe calculation formulas are respectively as follows:
Figure BDA0001905822700000101
Figure BDA0001905822700000102
wherein the content of the first and second substances,
Figure BDA0001905822700000103
is the output power of the battery or batteries,
Figure BDA0001905822700000104
is the loss of the power of the discharge,
Figure BDA0001905822700000105
is the input power of the battery and is,
Figure BDA0001905822700000106
is the charging power loss. According to the battery type, HbatAnd LbatAre respectively
Figure BDA0001905822700000107
And
Figure BDA0001905822700000108
function of (1), in the present invention, HbatAnd LbatRespectively, lead-acid, lithium-ion and vanadium redox battery types.
The power loss of lead-acid or lithium-ion batteries is mainly caused by heat loss during charging or discharging. Heat is generated by ohmic resistance of the electrodes and electrolyte and polarization effects. The power loss is proportional to the voltage drop (polarization) caused by the current PjouleΔ V × I, the voltage drop during discharge and during charge of lead-acid and lithium-ion batteries, respectively, can be determined as,
Figure BDA0001905822700000109
Figure BDA00019058227000001010
where SOC is the state of charge, R is the internal ohmic resistance, K is a constant that can be calculated from the manufacturer's data, and QrIs the rated capacity of the battery.
On this basis, according to one embodiment of the present invention, the battery power consumption of lead-acid and lithium-ion batteries during discharge is:
Figure BDA00019058227000001011
Figure BDA0001905822700000111
the battery charge consumption during charging is:
Figure BDA0001905822700000112
Figure BDA0001905822700000113
wherein, VrIs the nominal voltage of the battery.
For vanadium redox batteries, the power loss during charging and discharging comprises two parts: electrolyte pumping and stack power loss due to internal resistance and electrochemical processes. The open circuit voltage and stack current may be characterized as a function of charge and discharge power:
Figure BDA0001905822700000114
Figure BDA0001905822700000115
Figure BDA0001905822700000116
wherein, VOCIs the open circuit voltage of the battery cell,
Figure BDA0001905822700000117
and
Figure BDA0001905822700000118
stack currents during discharge and during charge of the battery respectively,
Figure BDA0001905822700000119
are the battery loss model coefficients, which are the coefficients corresponding to the rated voltage VrAnd rated current IrThe relevant parameters, all model coefficients, are given in table 1 and can be substituted for the calculation.
TABLE 1 vanadium cell loss model coefficients
Figure BDA00019058227000001110
Based on this, the battery charge consumption (kWh) of the vanadium redox battery during discharge and during charge can be determinedfConsumption) were respectively:
Figure BDA00019058227000001111
Figure BDA00019058227000001112
subsequently, in step S240, a stochastic unit combination model of the microgrid is constructed based on the cost model, the combination model including constraints and an objective function, the objective function being the minimum expected operation cost for a period of time.
For the random unit combination problem of the microgrid, the objective is to reduce the expected operating cost C of the microgrid in a time range, so the objective function is:
Figure BDA0001905822700000121
wherein the content of the first and second substances,
Figure BDA0001905822700000122
Figure BDA0001905822700000123
Figure BDA0001905822700000124
Figure BDA0001905822700000125
Fm,k＝Fm(Pm,k)T
wherein, FkIs the total expected operating cost, S, over time period kkIs the total transition cost including the startup and shutdown costs of the generator during time period k, N is the time horizon, Fg,k，
Figure BDA00019058227000001214
(dollars) respectively represent the generator and the generator in the time period kTotal operating cost of electric and rechargeable batteries, Fm,kDue to the cost caused by the power mismatch. T is the time step, n1And n2Representing the number of generators and batteries, g, respectivelyiAnd biRespectively representing a generator i and a battery i, sgi,k、
Figure BDA0001905822700000127
Respectively representing the binary states of the generator i and the battery i in the time period k, since the battery can be in a state of neither charging nor discharging at the same time
Figure BDA0001905822700000128
May be zero. c. Cgi(dollar/liter) is the fuel price of the generator i, cbi(dollar/kilowatt-hour) is the charge price (kwh) of battery ifPrice), FgiIs the fuel cost of the generator i, Hgi(gallons/hour) is the fuel consumption of the generator i,
Figure BDA0001905822700000129
(kilowatt-hour) and
Figure BDA00019058227000001210
(kwh/hr) is the charge drain on battery i during discharge and charge respectively,
Figure BDA00019058227000001215
and
Figure BDA00019058227000001212
operating costs, P, of the battery i during discharge and charge, respectivelygi,k、
Figure BDA00019058227000001213
(kilowatts) represent the transmission power of the generator i, the discharged battery and the charged battery, P, respectively, during time period km,kIs the cost due to power mismatch in time period k, FmRefers to the cost per unit time due to power mismatch.
To better define this problem, the present invention introduces the following convention:
1) charging power
Figure BDA0001905822700000131
Considered to be a negative value;
2) renewable energy sources (photovoltaic and wind generators) are not dispatchable and are considered loads. Payload P of time period knet,kIs defined as:
Pnet,k＝∑Pload,k-∑PPV,k-∑PW,k
wherein, Pload,k，PPV,k，PW,kRespectively representing the time period k load, the real-time power of the photovoltaic generator and the wind generator, which are all random, so Pnet,kConsidered to be a random variable;
3) only when P isnet,kWhen the voltage is less than zero, the battery is charged;
4) power mismatch P in time period km,kIs the difference between the total power production and the net load,
when P is presentnet,kWhen the content is more than or equal to 0,
Figure BDA0001905822700000132
when P is presentnet,k<At 0, Pm,k＝Pchg,k-Pnet，
Figure BDA0001905822700000133
Wherein P isgen,k>0 is the total power generation, Pchg,k< 0 is the total charge rate.
The realization of grid connection, electricity price and buyback price in the microgrid is deterministic, so that the constraint conditions of the random combination model are defined based on the energy management strategy in the microgrid and the physical limitations of equipment, and the constraint conditions comprise at least one of the following constraint conditions:
r1: the power mismatch is required to be greater than zero at a predetermined probability.
R2: battery discharge is not used for charging other batteries; the generator is not used to charge the battery.
R3: each storage device cannot exceed (or fall below) a maximum (or minimum) charge (or discharge) SOC.
R4: the charge (or discharge) rate of each storage device should not exceed a maximum (or minimum) value.
R5: each generator reaches at least its minimum output set point while online.
R6: when the generator is on line, the minimum set time of on line is ensured; when the generator is powered off, the shortest shutdown time before restarting is ensured.
In small systems such as micro grids, micro grid power is not used to charge the energy storage due to the relatively low round trip efficiency of the energy storage cells ESS. Thus, the energy storage unit should not charge other energy storage units nor use power for energy storage, and the energy storage unit can only be charged using renewable energy, which is reflected in the constraint R2. The constraints are specified as follows:
Figure BDA0001905822700000141
Figure BDA0001905822700000142
Figure BDA0001905822700000143
Figure BDA0001905822700000144
Figure BDA0001905822700000145
Figure BDA0001905822700000146
where p (x) is the probability that x satisfies the condition, AND where AND (a, b) ═ 0 means that a AND b cannot be 1 at the same time, SOCbi,kIs the state of charge of battery i over a period of k,
Figure BDA0001905822700000147
and
Figure BDA0001905822700000148
respectively the minimum and maximum value of the state of charge of battery i,
Figure BDA0001905822700000149
and
Figure BDA00019058227000001410
respectively the minimum and maximum value of the transmitted power of the discharged battery i,
Figure BDA00019058227000001411
and
Figure BDA00019058227000001412
respectively the minimum and maximum value of the transmission power of the rechargeable battery i,
Figure BDA00019058227000001413
is the transmit power of the k-period generator i on-line,
Figure BDA00019058227000001414
and
Figure BDA00019058227000001415
respectively minimum and maximum values of the generator i transmitted power,
Figure BDA00019058227000001416
and
Figure BDA00019058227000001417
respectively an on-line time and an off-line time of the k-period generator i,
Figure BDA00019058227000001418
and
Figure BDA00019058227000001419
minimum values of on-line time and off-line time of generator i, αkAnd βkIs a parameter.
Further, formula R2 may be upgraded as:
Figure BDA00019058227000001420
wherein the content of the first and second substances,
Figure BDA00019058227000001421
and
Figure BDA00019058227000001422
battery power consumption (kwh) of battery i during discharging and during charging, respectively, in time period kfConsumed),
Figure BDA00019058227000001423
battery charge cost (kwh) for battery i over time period kfCost).
By implementing the constraint R1 when Pnet,kWhen > 0, αkWhatever the change, Pm,kAre all non-negative; when P is presentnet,k<At 0, βkWhatever the change, Pm,kAre all non-negative. Based on the formula in convention 4), constraint R1 may be further rewritten as:
Figure BDA0001905822700000151
parameter αkControl the probability level of power input/output from the grid to the microgrid if αkAt 0, the probability of the microgrid generating sufficient power internally is zero, and the required power must be imported from the grid k1, the probability of always generating sufficient power within the microgrid is 1, which is not a realistic situation and therefore αkIs strictly limited to less than 1.0 (but ideally quite close to 1.0.) similarly, if βkAll net renewable energy sources will be used for 0Charging the stored energy, for example, if βk0.5 means that there is a 50% probability that the remaining power of the renewable energy will be exported to the grid, selecting a larger β will reduce the probability that the renewable energy will be used to charge the energy store, thereby enabling more renewable energy to be exported to the gridkAnd βkThe amount of power at the inlet/outlet is determined by the choice of α and β parameters.selecting a smaller α will increase the likelihood that the system will import power from the grid to provide a load, while selecting a larger β will increase the likelihood that the system will export excess renewable energy to the grid. α and β are freedom parameters that determine whether and how much power is imported/exported from the grid based on the required energy management policy.
To implement a cost function FkAnd constraint R1, requiring the specification of a Cumulative Distribution Function (CDF) and an average value Pnet，k. In practice, the actual load, the predicted values of the photovoltaic generator and the wind generator for the time period k can be obtained separately
Figure BDA0001905822700000152
Thus, the implementation of the actual load, PV, wind power generation and net load can be expressed as:
Figure BDA0001905822700000153
Figure BDA0001905822700000154
Figure BDA0001905822700000155
Figure BDA0001905822700000156
wherein, Δ Pload，ΔPpv，ΔPWTRespectively the actual load error,The photovoltaic power generation error and the wind power generation error are prediction errors depending on a prediction method and a prediction range. To model uncertainty in load and renewable energy predictions, Δ Pload，ΔPpv，ΔPWTAre considered to be random variables. While the weber distribution, the cauchy distribution, and the mixed laplacian distribution may more accurately describe the wind power generation prediction error, it may be approximately fit with a zero-mean normal distribution. Furthermore, since the load demand and photovoltaic power generation prediction error are very close to normal distribution, the net load error Δ Pnet,k(the sum of all errors) can be approximated by a zero-mean normal distribution. Delta Pnet,kThe standard deviation of (d) can be calculated as follows:
Figure BDA0001905822700000161
thus, the following expectations and probabilities may be calculated:
Figure BDA0001905822700000162
Figure BDA0001905822700000163
Figure BDA0001905822700000164
Figure BDA0001905822700000165
P(Pnet,k＞0)＝1-pk
Figure BDA0001905822700000166
Figure BDA0001905822700000167
where φ is a normal score obeying the (0,1) criterionCumulative distribution function of cloth, E (P)net,k) And
Figure BDA0001905822700000168
is Pnet,kExpected value of pkIs the probability that the payload is less than 0, and E (y | x) is the expectation of y under the condition that x is satisfied, e.g.
Figure BDA0001905822700000169
When P isnet,k> 0 time Pm,kThe expectation is that. The constraint R1 can therefore be understood as:
Figure BDA00019058227000001610
by selecting αkAnd βkThe system will generate (or charge) more or less power. FkThe formula can be further expressed as:
Figure BDA00019058227000001611
Figure BDA00019058227000001612
Figure BDA00019058227000001613
wherein, cex,kIs the electricity price exported to the grid, cim,kIs the electricity rate imported into the grid.
Subsequently, in step S260, a predetermined method is adopted to solve the combination model to obtain optimal unit combination parameters, and unit combination is performed according to an optimal result to realize regional multi-energy interconnection operation optimization.
According to one embodiment, the predetermined method is a Dynamic Programming (DP) method. The crew composition problem can be classified as a continuous decision problem, with Dynamic Programming (DP) being well known. Dynamic planning is a process of finding the shortest way to reach a destination by breaking it down into a period of timeAnd (4) a routing method. In each step, a possible dominant sequence (route) is determined based on the best possible subsequence in the previous step, and finally the best sequence for the last step is found. The main advantage of DP is that the feasibility of the solution can be maintained by the ability to find the optimal order. The main drawback of DP is computationally burdensome. For example, in a system of N units, there are 2 units per time period N1 combinations, the total number of combinations being (2) for M time periodsN-1)M. For large systems, the computations required to traverse this space may be overwhelming. However, in microgrid applications, the small number of cells and the large number of constraints significantly reduce the search space, so DP can be an appropriate choice for the algorithm.
The cost of each phase is typically a random variable due to the uncertainty associated with the stochastic problem. Therefore, in the random DP technique, the problem is to minimize the expected cost. When applying DP, the state space at stage k is defined as follows:
Figure BDA0001905822700000171
Figure BDA0001905822700000172
wherein L iskIs the set of feasible states for stage k, mkIs LkThe number of states in the set is,
Figure BDA0001905822700000173
is a unit xiBinary state of (x)iRepresenting a generator, a discharged battery or a charged battery. If the constraints R2, R3, R6 and the following conditions are satisfied, then
Figure BDA0001905822700000174
Is the active state:
Figure BDA0001905822700000175
further, using the forward stochastic programming method in the present invention, fig. 3 shows a schematic diagram of the progressive DP algorithm, the algorithm that calculates the minimum cost to reach state a in stage B:
Figure BDA0001905822700000176
wherein the content of the first and second substances,
Figure BDA0001905822700000177
is an arrival state
Figure BDA0001905822700000178
The minimum cost of the system (c) is,
Figure BDA0001905822700000179
is for the state
Figure BDA00019058227000001710
The operating costs of the system are reduced by the system,
Figure BDA00019058227000001711
is a slave state
Figure BDA00019058227000001712
To the state
Figure BDA00019058227000001713
The transition costs of (a). The operating cost may be minimized by performing economic scheduling (ED) for F with constraints R1, R4, and R5kA cost function. Regarding the economic dispatching method, a person skilled in the art may adopt an existing common dispatching method, such as a power grid dispatching method based on a particle swarm algorithm, which is not limited in the present invention. According to one embodiment, the economic scheduling problem may be solved by using a steepest descent algorithm, and details of detailed parameters of the steepest descent algorithm may be set by a person skilled in the art as needed, which is not limited by the invention.
According to the technical scheme of the invention, a novel battery operation cost model considering battery charging/discharging efficiency and cycle life time is provided, and the model enables the battery to be regarded as an equivalent natural gas generator in charging and discharging. In addition, a probability constraint method for introducing uncertainty of renewable energy and load requirements into the charging and discharging problem in the microgrid is also provided, and the unit combination problem in the microgrid is solved by using random dynamic programming.
In a specific practical operation, the present invention tests the proposed method through a case study figure 4 shows a typical microgrid connected to the low voltage side of a distribution transformer to power residential loads, the microgrid comprising a 50kW natural gas generator, 220 kW wind power generator sets, a 50kW photovoltaic array, 10kW/40kWh vanadium cells and 12kW/30kWh AGM lead acid batteries the total load at peak is 50 kW. the cost of AGM cells is estimated to be $ 8000. the reset cost of vanadium cells VRB is estimated to be $ 20000kAnd βkThe parameters were chosen to be 0.9 and 0.1 respectively, high value αkHigh probability of all loads being satisfied internally low value βkIndicating a low likelihood of exporting renewable energy to the grid (i.e., a priority to use excess power to charge the energy storage unit).
Table 2 natural gas generator data
Figure BDA0001905822700000181
TABLE 3 Battery data
Figure BDA0001905822700000182
Figure BDA0001905822700000191
TABLE 4 Standard deviation of Net load prediction error
Figure BDA0001905822700000192
The standard deviation sigma of the calculated hourly net load prediction error is given in table 4net,kThe result of deterministic charging is shown in FIG. 6. by introducing a battery's operating cost function, economic dispatch tends to generate power to a battery with longer cycle life, lower reset cost, and higher efficiency, in this case, vanadium batteries are lower, but lead-acid batteries are more efficient, and therefore the resulting generated power is close to that shown in FIG. 7. in comparison to natural gas generators, the battery operates at a lower cost due to a lower "fuel" price and higher efficiency, but the maximum depth of discharge of the battery is limited, so the battery can only discharge a few hours at night, as shown by the results, the result of random charging in contrast to the deterministic case is shown in FIG. 7. by comparing the random and deterministic cases, α can be seenkAnd βkThe effect of the selection:
1) when the load is high and the renewable energy power generation is low (about 15 to 24 hours), with PDG*>PDGThe deterministic case shown compares, the stochastic algorithm surpasses the natural gas genset because αkA value greater than 0.9 is chosen, which indicates that the load is satisfied internally with a high probability. Since renewable energy is not available within these hours, it is required that the natural gas generator must be able to withstand any potential changes in load.
2) When the load is high and the renewable energy generation is high (about 9 to 14), the stochastic algorithm overcharges the energy storage unit due to βkA value less than 0.1 is chosen, which means that there is little chance of sending the overproduction to the grid, thereby increasing the likelihood of charging the battery.
3) The random combinatorial algorithm is able to closely conform to the deterministic case (0 to 8 hours) when the load is low and there is no renewable energy.
By selecting αkAnd βkThe allowed risk in the system can be adjustedIn this example, αkAnd βkThe values of both remain constant throughout the 24 hours, but in practice, these values may change in order to cope with expected changes in load or renewable energy. Furthermore, although not explicitly described, the two energy storage units are operated in a matched manner according to the respective operating conditions detailed above to maximize their life.
In this case, vanadium batteries and lead-acid batteries are similar, and their better life cycle properties and efficiency characteristics yield similar long-term economic benefits for lead-acid batteries even though vanadium batteries are fairly expensive to install.
A9 the method of A8, wherein,
Figure BDA0001905822700000201
Figure BDA0001905822700000202
Figure BDA0001905822700000203
Fm,k＝Fm(Pm,k)T
where N is the time range, T is the time step, N1And n2Representing the number of generators and batteries, g, respectivelyiAnd biRespectively representing a generator i and a battery i, sgi,k、
Figure BDA0001905822700000204
Representing the binary states of generator i and battery i, respectively, during time period k, cgiIs the fuel price of the generator i, cbiIs the charge price of battery i, FgiIs the fuel cost of the generator i, HgiIs the fuel consumption of the generator i and,
Figure BDA0001905822700000205
and
Figure BDA0001905822700000206
the power consumption of battery i during discharge and charge respectively,
Figure BDA0001905822700000207
and
Figure BDA0001905822700000208
operating costs, P, of the battery i during discharge and charge, respectivelygi,k、
Figure BDA0001905822700000209
Representing the transmission power of the generator i, the discharged battery and the charged battery, P, respectively, during the time period km,kIs the cost due to power mismatch in time period k, FmRefers to the cost per unit time due to power mismatch.
A10 the method of A9, wherein,
Figure BDA00019058227000002010
Figure BDA0001905822700000211
Figure BDA0001905822700000212
wherein, Pnet,kIs the net load of the k period, pkIs the probability that the payload is less than 0, E (y | x) is the expectation of y under the condition that x is satisfied, cex,kIs the electricity price exported to the grid, cim,kIs the electricity rate imported into the grid, αkAnd βkIs a parameter, wherein αkIs to control the probability level, P, of the input/output of power from the grid to the microgridgen,kIs the total power generation amount, Pchg,kIs the total charge rate of the battery,
Figure BDA0001905822700000213
is Pnet,kIs calculated from the expected value of (c).
A11 the method according to A10, wherein when P isnet,kWhen the content is more than or equal to 0,
Figure BDA0001905822700000214
a12 the method according to A10, wherein when P isnet,kWhen the ratio is less than 0, the reaction mixture is,
Figure BDA0001905822700000215
a13, the method of any one of A1-A12, wherein the constraints comprise at least one of the following six constraints:
Figure BDA0001905822700000216
Figure BDA0001905822700000217
Figure BDA0001905822700000218
Figure BDA0001905822700000219
Figure BDA00019058227000002110
Figure BDA00019058227000002111
where p (x) is the probability that x satisfies the condition, AND where AND (a, b) ═ 0 means that a AND b cannot be 1 at the same time, SOCbi,kIs the state of charge of battery i over a period of k,
Figure BDA00019058227000002112
and
Figure BDA00019058227000002113
respectively the minimum and maximum value of the state of charge of battery i,
Figure BDA00019058227000002114
and
Figure BDA00019058227000002115
respectively the minimum and maximum value of the transmitted power of the discharged battery i,
Figure BDA00019058227000002116
and
Figure BDA00019058227000002117
respectively the minimum and maximum value of the transmission power of the rechargeable battery i,
Figure BDA00019058227000002118
is the transmit power of the k-period generator i on-line,
Figure BDA00019058227000002119
and
Figure BDA00019058227000002120
respectively minimum and maximum values of the generator i transmitted power,
Figure BDA0001905822700000221
and
Figure BDA0001905822700000222
respectively an on-line time and an off-line time of the k-period generator i,
Figure BDA0001905822700000223
and
Figure BDA0001905822700000224
which are the minimum values of the on-line time and the off-line time of the generator i, respectively.
A14, the method as claimed in a13, wherein the constraint R1 is:
Figure BDA0001905822700000225
where φ is a cumulative distribution function, σ, following a (0,1) standard normal distributionnet,kIs the net load error Δ Pnet,kStandard deviation of (1), wherein Δ Pnet,kIs the actual load error Δ PloadPhotovoltaic power generation error delta PpvAnd wind power generation error delta PWTSum of (a), Δ Pload、ΔPpv、ΔPWTIs a prediction error that depends on the prediction method and the prediction range.
A15, the method as in any one of a1-a14, wherein the predetermined method is a stochastic dynamic programming method, wherein the state space at phase k is:
Figure BDA0001905822700000226
wherein L iskIs the set of feasible states for stage k, mkIs LkThe number of states in the set is,
Figure BDA0001905822700000227
is a unit xiBinary state of (x)iRepresenting a generator,A discharged battery or a charged battery.
A16, the method of a15, wherein the stochastic dynamic programming method is a forward stochastic dynamic programming method wherein the minimum cost to reach state a during phase B is:
Figure BDA0001905822700000228
wherein the content of the first and second substances,
Figure BDA0001905822700000229
is an arrival state
Figure BDA00019058227000002210
The minimum cost of the system (c) is,
Figure BDA00019058227000002211
is for the state
Figure BDA00019058227000002212
The operating costs of the system are reduced by the system,
Figure BDA00019058227000002213
is a slave state
Figure BDA00019058227000002214
To the state
Figure BDA00019058227000002215
The transition costs of (a).
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into multiple sub-modules.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Furthermore, some of the described embodiments are described herein as a method or combination of method elements that can be performed by a processor of a computer system or by other means of performing the described functions. A processor having the necessary instructions for carrying out the method or method elements thus forms a means for carrying out the method or method elements. Further, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of carrying out the invention.
As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense with respect to the scope of the invention, as defined in the appended claims.

Claims (10)

1. A regional multi-energy interconnect operations optimization method, executed in a computing device, the method comprising:
establishing a battery operation cost model, wherein the cost model comprises battery electricity quantity price and battery electricity quantity consumption in the charging and discharging process;
constructing a random unit combination model of the microgrid based on the cost model, wherein the random unit combination model comprises an objective function and a constraint condition, and the objective function is the minimum of the expected operation cost in a time period;
and solving the combination model by adopting a preset method to obtain optimal unit combination parameters, and combining the units according to an optimal result to realize regional multi-energy interconnection operation optimization.
2. The method of claim 1, wherein the battery charge price cbatThe calculation formula of (2) is as follows:
Figure FDA0001905822690000011
Figure FDA0001905822690000012
wherein the content of the first and second substances,
Figure FDA0001905822690000013
represents the price of energy for charging the battery,
Figure FDA0001905822690000014
represents the available cost of battery capacity, which refers to the available cost of having a storage capacity of 1 kilowatt-hour, C∑Is the full life cycle capacity of the battery, crepIs the cost of the reset.
3. The method of claim 2, wherein, for lead acid and lithium ion batteries,
C∑＝CrDODr[Lr-0.2*(1+2+...+Lr)/Lr]
＝CrDODr(0.9Lr-0.1)(kWh)
for vanadium redox batteries:
C∑＝CrDODrLr(kWh)
wherein, DODrIs depth of discharge, CrIs the rated capacity of the battery, LrIs the rated life.
4. The method of claim 1, wherein the battery power consumption during discharging is the energy usage H supplied to the load per unit timebatThe power consumption of the battery during the charging process is the power loss L of the rechargeable battery per unit timebatThe calculation formulas are respectively as follows:
Figure FDA0001905822690000015
wherein the content of the first and second substances,
Figure FDA0001905822690000024
is the output power of the battery or batteries,
Figure FDA0001905822690000025
is the loss of the power of the discharge,
Figure FDA0001905822690000026
is the input power of the battery and is,
Figure FDA0001905822690000027
is the charging power loss.
5. The method of claim 4, wherein, for lead acid and lithium ion batteries,
Figure FDA0001905822690000021
Figure FDA0001905822690000022
wherein SOC is a state of charge, VrIs the rated voltage, Q, of the batteryrIs electricityThe nominal capacity of the cell, R is the internal ohmic resistance and K is a constant calculated from the manufacturer's data.
6. The method of claim 4, wherein, for a vanadium redox cell,
Figure FDA00019058226900000217
Figure FDA0001905822690000028
wherein, VOCIs the open circuit voltage of the battery cell,
Figure FDA00019058226900000214
and
Figure FDA00019058226900000215
stack currents during discharge and during charge of the battery respectively,
Figure FDA00019058226900000216
are the battery loss model coefficients, which are the coefficients corresponding to the rated voltage VrOr rated current IrThe parameter concerned.
7. The method of claim 6, wherein,
Figure FDA0001905822690000029
Figure FDA00019058226900000210
Figure FDA00019058226900000211
8. the method of any one of claims 1-7, wherein the objective function is:
Figure FDA0001905822690000023
Figure FDA00019058226900000212
wherein, FkIs the total expected operating cost, S, over time period kkIs the total transition cost, F, including the startup and shutdown costs of the generator during time period kg,k，
Figure FDA00019058226900000213
Representing the total operating costs of the generator, discharged battery and charged battery, respectively, during time period k, Fm,kDue to the cost caused by the power mismatch.
9. A computing device, comprising:
at least one processor; and
a memory storing program instructions configured for execution by the at least one processor, the program instructions comprising instructions for performing the method of any of claims 1-8.
10. A readable storage medium storing program instructions that, when read and executed by a computing device, cause the computing device to perform the method of any of claims 1-8.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111967716A (en) * 2020-07-20 2020-11-20 国网湖北省电力有限公司电力科学研究院 Comprehensive energy efficiency calculation method for electric vehicle direct-current charging facility
CN112100871A (en) * 2020-11-19 2020-12-18 清华四川能源互联网研究院 Decoupling method and device of multi-energy coupling system, electronic device and storage medium
CN112285235A (en) * 2020-10-22 2021-01-29 北京理工大学 Passenger car interior trim release characteristic testing method based on air bag

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111967716A (en) * 2020-07-20 2020-11-20 国网湖北省电力有限公司电力科学研究院 Comprehensive energy efficiency calculation method for electric vehicle direct-current charging facility
CN111967716B (en) * 2020-07-20 2022-04-22 国网湖北省电力有限公司电力科学研究院 Comprehensive energy efficiency calculation method for electric vehicle direct-current charging facility
CN112285235A (en) * 2020-10-22 2021-01-29 北京理工大学 Passenger car interior trim release characteristic testing method based on air bag
CN112285235B (en) * 2020-10-22 2022-02-01 北京理工大学 Passenger car interior trim release characteristic testing method based on air bag
CN112100871A (en) * 2020-11-19 2020-12-18 清华四川能源互联网研究院 Decoupling method and device of multi-energy coupling system, electronic device and storage medium
CN112100871B (en) * 2020-11-19 2021-02-19 清华四川能源互联网研究院 Decoupling method and device of multi-energy coupling system, electronic device and storage medium

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