CN113036751A - Renewable energy micro-grid optimization scheduling method considering virtual energy storage - Google Patents

Renewable energy micro-grid optimization scheduling method considering virtual energy storage Download PDF

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CN113036751A
CN113036751A CN202110055907.8A CN202110055907A CN113036751A CN 113036751 A CN113036751 A CN 113036751A CN 202110055907 A CN202110055907 A CN 202110055907A CN 113036751 A CN113036751 A CN 113036751A
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power
energy storage
water heater
electric water
grid
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王爱元
卜玉杭
秦翌菲
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Shanghai Dianji University
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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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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 relates to a renewable energy micro-grid optimization scheduling method considering virtual energy storage, which is characterized by comprising the following steps of: s1: acquiring energy consumption data of a building containing virtual energy storage; s2: establishing a mathematical model of each device in the renewable energy microgrid, a constraint condition and an objective function for optimizing scheduling, and establishing an optimized scheduling model of the renewable energy microgrid system; s3: compared with the prior art, the method has the advantages that the virtual energy storage is effectively realized to replace a storage battery, and the like.

Description

Renewable energy micro-grid optimization scheduling method considering virtual energy storage
Technical Field
The invention relates to the field of microgrid optimization scheduling, in particular to a renewable energy microgrid optimization scheduling method considering virtual energy storage.
Background
In recent years, as the demand for energy increases, the shortage of fossil fuels and deterioration of the environment become more and more serious. The advent of renewable energy sources has effectively addressed the above-mentioned problems. Building integrated photovoltaic renewable energy is a system for generating power by deploying renewable energy devices such as photovoltaic arrays on the surface of buildings, and has been widely accepted.
Renewable energy power generation has stochastic and intermittent characteristics that affect the stable operation and real-time power balance of the main grid. The above problems can be effectively solved by installing a battery energy storage system. The storage battery has the defects of high investment cost and short service life, and in order to solve the problem, virtual energy storage also comes from the beginning as a general form of energy storage, and aims to enable controllable loads to show a regulating function similar to that of a traditional energy storage system by properly distributing the controllable loads, so that the virtual energy storage is continuously applied to a micro-grid.
The redundant electric quantity of the existing renewable energy sources is sold to a power grid or stored in a storage battery, so that the operation cost is increased, and the operation of the micro-grid is not in accordance with the current concept of energy conservation and emission reduction. The concept of virtual energy storage has been in existence for a long time, but it is not well utilized and does not bring due benefits to the operation of the power grid. The current virtual energy storage model is mainly an air conditioner and electric water heater model, but in the little electric wire netting operation process, the influence of virtual energy storage to main power grid fluctuation etc. all has very big influence to the future development of virtual energy storage such as the influence of user's comfort level and cost, and current technique can not better exert the benefit of utilizing virtual energy storage. The existing system does not have a model system for the economic operation of the microgrid with virtual energy storage, which comprehensively considers the supply of hybrid energy power meeting the user requirements, so that the optimal scheduling problem of the microgrid cannot be solved, and the problem that the virtual energy storage replaces a storage battery cannot be solved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a renewable energy microgrid optimization scheduling method considering virtual energy storage.
The purpose of the invention can be realized by the following technical scheme:
a renewable energy micro-grid optimization scheduling method considering virtual energy storage comprises the following steps:
s1: acquiring energy consumption data of a building containing virtual energy storage;
s2: establishing a mathematical model of each device in the renewable energy microgrid, a constraint condition and an objective function for optimizing scheduling, and establishing an optimized scheduling model of the renewable energy microgrid system;
s3: and inputting the energy consumption data containing the virtual energy storage building into an optimized scheduling model for solving, and performing optimized scheduling on the renewable energy micro-grid system.
Furthermore, each device in the renewable energy microgrid comprises a photovoltaic device, a storage battery, an air conditioner and an electric water heater.
Further, in step S2, the mathematical model established specifically includes: the device comprises a photovoltaic power generation output power mathematical model, a storage battery charging/discharging mathematical model, an air conditioner energy consumption model, an electric water heater energy consumption model, a virtual air conditioner model, an air conditioner virtual energy storage model, an electric water heater virtual energy storage model and an electric water heater virtual energy storage and discharge power mathematical model.
Further, the constraint conditions include system power balance constraint, building heat balance constraint, indoor temperature limit constraint, upper and lower power limit constraint of system equipment, and upper and lower capacity limit constraint of system equipment.
Further, the objective function comprises three items, namely an economic cost item, a punishment item based on indoor personnel comfort and an income item based on electricity sale input into a main power grid.
Further, the expression of the system power balance constraint is as follows:
Pgrid+Ppv+Pb＝Pec+Ph+Pload
wherein, PgridFor exchanging power, P, between a microgrid system and a main gridgridNot less than 0 represents that the micro-grid system purchases electric power from the main grid, Pgrid<0 denotes the microgrid system selling power to the main grid, PpvFor photovoltaic power generation output power, PbFor charging/discharging the accumulator, PecFor the power consumption of the air conditioner, PhFor the power consumption of electric water heaters, PloadEnergy consumption power for other loads;
the expression of the building heat balance constraint is as follows:
ρ×C×V×(Fin,t-Fin,t-1)＝Δt×[Qin+Qwall+Qwin+Qsc-Qec]
where ρ is the air density, C is the specific heat capacity of air, V is the indoor air volume, Fin,tIs the room temperature at time t, Fin,t-1Is the indoor temperature at time t-1, Δ t is the simulated time step, QinHeat supply to heat sources in the room, QwallFor heat transfer from the building's outer wall to the room, QwinFor heat transfer from the exterior windows of the building to the interior, QscFor heat transfer by solar radiation, QecHeat output for an air conditioner;
the expression of the indoor temperature limit constraint is as follows:
Fin_min≤Fin≤Fin_max
wherein, FinIs the room temperature, Fin_minLower limit of indoor temperature, Fin_maxIs an upper limit value of the indoor temperature.
Furthermore, the power upper and lower limit constraints of the system equipment respectively comprise power exchange power P between the micro-grid system and the main gridgridCharging/discharging power P of the accumulatorbAnd the air conditioner energy consumption power PecAnd the energy consumption power P of the electric water heaterhThe upper limit and the lower limit of the capacity of the system equipment respectively comprise the stored energy E of the storage batterybAnd heat E in the hot water storage tank of the electric water heaterhUpper and lower limits of.
Further, the expression of the objective function is:
Figure BDA0002900594460000031
Figure BDA0002900594460000032
C2＝cpv×Ppv,t+cb×|Pb,t|+cec×Pec,t+ch×Ph,t
C3＝γ×|Tin,t-Tset,t|
Figure BDA0002900594460000033
wherein, C1Cost of electricity for purchasing the grid, cgridFor real-time electricity prices, C2Maintenance costs for devices within the renewable energy microgrid, cpv、cb、cecAnd chThe operation and maintenance costs of the photovoltaic, the storage battery, the air conditioner and the electric water heater are Ppv,tFor photovoltaic power generation output power at time t, Pb,tFor the charging/discharging power of the accumulator at time t, Pec,tThe power consumption of the air conditioner at time t, Ph,tThe energy consumption power of the electric water heater at the moment t, C3A penalty term for the comfort of the indoor personnel, gamma is a penalty factor, Tin,tFor the actual temperature, T, input to the room after the air conditioner is turned onset,tAir conditioning temperature for human body comfort preset, C4For selling revenue for electricity to the main grid.
Furthermore, the photovoltaic power generation output power PpvThe mathematical model expression of (2) is:
Figure BDA0002900594460000034
wherein, UgFor photovoltaic output voltage, UoFor effective value of photovoltaic grid-connected voltage, RsIs a sampling resistor of TsClock cycle of flip-flop K, Kg、Uc、R1And C1Is a control parameter in one cycle;
the mathematical model expression of the storage battery charging/discharging is as follows:
Figure BDA0002900594460000041
wherein E isb,tFor the energy stored in the accumulator at time t, ηbchFor the charging efficiency of the accumulator, etabdisThe discharge efficiency of the storage battery, wherein lambda is the self-discharge rate of the storage battery;
the expression of the air conditioner energy consumption model is as follows:
Qec＝Pec×ηec
wherein eta isecIs the energy efficiency ratio of the air conditioner;
the expression of the electric water heater energy consumption model is as follows:
Qh＝Ph×ηh
wherein Q ishIs the output heat of the electric water heater, etahIs the energy efficiency ratio of the electric water heater.
Furthermore, the expression of the virtual air-conditioning model is as follows:
Figure BDA0002900594460000042
the expression of the air conditioner virtual energy storage model is as follows:
Pves_ec＝Pec_ne-Pec_re
wherein, Pves_ecCharging/discharging power, P, with virtual energy storage for air-conditioningec_nePower consumed by the air-conditioner without using virtual energy storage, Pec_reThe power consumed by the air conditioner is used for virtual energy storage.
Furthermore, the expression of the virtual energy storage model of the electric water heater is as follows:
Eh,t+1＝(1-δ)×Eh,t
where δ is a loss factor which is affected by the ambient temperature and the temperature of hot water in the water tank, and is set to 2%, Eh,t+1Heat in the hot water storage tank of the electric water heater at time t +1, Eh,tThe heat in the hot water storage tank of the electric water heater at the moment t;
the electric water heater is charged and discharged with power P for virtual energy storageves_hThe mathematical model expression of (2) is:
Pves_h＝Ph_ne-Ph_re
wherein, Pves_hCharging/discharging power, P, for virtual energy storage of electric water heaterh_neFor the power consumed by an electric water heater when virtual energy storage is not used, Ph_reThe power consumed by the electric water heater when virtual energy storage is adopted.
Compared with the prior art, the invention has the following advantages:
1) according to the method, accurate function modeling is carried out on each device in the renewable energy microgrid, an optimal scheduling objective function containing air conditioners and virtual energy storage of electric water heaters is built, an optimal scheduling model is solved through historical data, the problem that only intelligent buildings and virtual energy storage exist in modern science but the optimal scheduling function model containing virtual energy storage does not exist is solved, the economical type of operation of the microgrid containing virtual energy storage is solved, the comfort and the economy of meeting daily requirements of users are also solved, and the implementation mode that virtual energy storage replaces storage batteries is also solved;
2) the photovoltaic power generation output power mathematical model, the storage battery charging/discharging mathematical model, the air conditioner energy consumption model, the electric water heater energy consumption model, the virtual air conditioner model, the air conditioner virtual energy storage model, the electric water heater virtual energy storage model and the electric water heater virtual energy storage charging and discharging power mathematical model are respectively established, the working state and state change of each device in the renewable energy micro-grid system are comprehensively and accurately described, approximate simulation of actual operation in operation is provided, the optimization scheduling model is established on the basis, and the reliability is high;
3) the method fully considers the operation constraints of the renewable energy micro-grid system, including system power balance constraint, building heat balance constraint, indoor temperature limit constraint, power upper and lower limit constraint of system equipment and capacity upper and lower limit constraint of the system equipment, and the constructed optimization scheduling model realizes the target optimization under the constraint conditions, so that the obtained optimization result is in line with the reality and the reliability is high.
Drawings
FIG. 1 is a block diagram of a renewable energy microgrid system;
FIG. 2 is a schematic flow chart of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Examples
As shown in fig. 1, the renewable energy microgrid system comprises a power grid, photovoltaic renewable energy, a storage battery, an air conditioner, an electric water heater and other uncontrollable resources, wherein the air conditioner and the electric water heater form a virtual battery capable of virtually storing energy, and the other uncontrollable resources comprise electric lamps, computers, monitoring and the like.
As shown in fig. 2, the present invention provides a renewable energy microgrid optimization scheduling method considering virtual energy storage, which mainly includes the following three parts:
I. acquiring energy consumption data of a building containing virtual energy storage, specifically: and collecting main energy consumption data information of each season and each day of the intelligent building or the large-scale factory building containing the virtual energy storage to perform optimal scheduling calculation for later use. The energy consumption data mainly comprises real-time electricity price, photovoltaic power generation power, light intensity, building electricity demand, cold/heat load demand and hot water load demand, and the data are properly processed and used for calculating an optimal scheduling strategy.
II. And establishing a mathematical model of each device in the renewable energy microgrid, a constraint condition and an objective function for optimizing scheduling, and establishing an optimized scheduling model of the renewable energy microgrid system.
1. The mathematical model of each device in the renewable energy microgrid specifically comprises:
(1) photovoltaic power generation output power mathematical model
The photovoltaic power generation works in a maximum power point tracking mode, the mode is combined with a single-period control method for use, and an output power mathematical model is established as follows:
Figure BDA0002900594460000061
wherein U isgIs the output voltage of the photovoltaic, UoIs the effective value of the grid-connected voltage of the photovoltaic, RsIs a sampling resistor, TsIs the clock cycle of the flip-flop, K, Kg、Uc、R1、C1Respectively, are parameters of the control in one cycle.
(2) Mathematical model for battery charging/discharging
Figure BDA0002900594460000062
Wherein E isb,tIs the energy stored in the accumulator, Pb,tIs the charge/discharge power of the accumulator, Δ t is the analog time step, ηbchAnd ηbdisIs the charge/discharge efficiency of the battery, λ is the self-discharge rate of the battery, the monthly self-discharge rate of the lead-acid battery is about 2.8%, which can be ignored in the daily optimal scheduling of the microgrid.
(3) Air conditioner energy consumption model
The air conditioner consumes electric energy to meet the cold and hot demands, and the mathematical model can be expressed as follows:
Qec＝Pec×ηec
wherein Q isecIndicating the output heat of the air conditioner, PecIs the energy consumption power of the air conditioner, etaecRepresents the energy efficiency ratio of the air conditioner.
(4) Energy consumption model of electric water heater
The electric water heater consumes electric energy to provide heat for the system. The mathematical model is described as follows:
Qh＝Ph×ηh
wherein Q ishRepresenting the output of an electric water heaterHeat, PhIs the power consumption of the electric water heater, etahIs the energy efficiency ratio of the electric water heater.
(5) Virtual air-conditioning model
The air conditioner sends cool air or warm air into the building, changes the indoor temperature, and the building has certain cold and hot energy storage ability, therefore the air conditioner has the virtual energy storage effect, according to the energy conservation, taking the summer cooling as an example, the heat balance equation of the air conditioner describes:
Figure BDA0002900594460000071
Qwall＝kwall×Fwall×(Fout-Fin)
Qwin＝kwin×Fwin×(Fout-Fin)
Qsc＝I×Fwin×SC
the left side of the heat balance equation of the air conditioner represents a change in indoor heat, and the right side represents various factors causing the change.
ρ is the air density, C is the specific heat capacity of air, V is the indoor air amount, QinIs a heat source in the room, which comes from human and electric equipment, QwallIs the heat transferred from the exterior wall to the interior of the building, kwallIs the heat transfer coefficient of the outer wall, which represents the amount of heat per second passing through the outer wall at steady state for a temperature difference between indoor and outdoor, FwallIs the area of the outer wall of the building, FoutIs the outdoor temperature, FinIs the indoor temperature, QwinIs the transfer of heat from the exterior window to the interior of the building, kwinIs the heat transfer coefficient of the outer window, which means that during stable heat transfer, heat will continuously pass through the outer window when the temperature difference between the inside and the outside is 1 degree centigrade, FwinIs the area of the outer window of the building, QscIs the heat transferred from the solar radiation, I is the solar radiation power, which means the heat received per square meter per second when the outer window of the building is perpendicular to the light, SC is the shading coefficient, the value of which is related to the light-shielding device.
The heat balance equation of the air conditioner shows that the indoor temperature can be adjusted by adjusting the output cold/heat power of the air conditioner. When the system does not employ virtual energy storage, the air conditioner will maintain the indoor temperature at the set point. When the virtual energy storage is used, the working mode of the air conditioner is changed, the indoor temperature of the air conditioner can be adjusted within a certain range, and the temperature is in a temperature area comfortable for a human body. Therefore, the power consumed by the air conditioner is controllable, which enables the air conditioner to express the charge/discharge characteristics of the secondary battery. Therefore, the air conditioner virtual energy storage can participate in economic optimization scheduling of the micro-grid like a storage battery.
(6) Air conditioner virtual energy storage model
The charge/discharge power of the air conditioner virtual energy storage is defined as follows:
Pves_ec＝Pec_ne-Pec_re
wherein, Pves_ecIs a charge/discharge power representing a virtual energy storage of the air conditioner, Pec_nePower consumed by air conditioners not using virtual energy storage method, Pec_reIs the power consumed by the air conditioner when the virtual energy storage method is adopted.
(7) Virtual energy storage model of electric water heater
The electric water heater is an important demand response resource, and a storage tank of the electric water heater has good heat insulation property. Therefore, when electricity prices are low or photovoltaic power generation is large, more water is heated and stored in the water tank, and the water is used when electricity prices are high or power consumption peaks. Thus, the electric water heater functions similarly to a battery. When hot water is stored in the water tank, there is a heat dissipation phenomenon. Suppose the heat in the hot water storage tank at time t is Eh,tAnd the loss coefficient is delta, the next time Eh,t+1The expression of the virtual energy storage model of the electric water heater is as follows:
Eh,t+1＝(1-δ)×Eh,t
the loss factor δ is affected by the ambient temperature and the hot water temperature in the water tank, and its value is set to 2%.
(8) Mathematical model of charge and discharge power of virtual energy storage of electric water heater
The electric water heater is modeled as a virtual energy storage device, and the charging/discharging power of the virtual energy storage of the electric water heater is defined as follows:
Pves_h＝Ph_ne-Ph_re
wherein, Pves_hIs the charge/discharge power of the virtual energy storage of the electric water heater, Ph_neThe power consumed by the electric water heater when the virtual energy storage of the electric water heater is not adopted, Ph_reIs the power consumed by the electric water heater when the virtual energy storage is used.
2. The constraint conditions specifically include:
(1) system power balance constraint:
Pgrid+Ppv+Pb＝Pec+Ph+Pload
wherein, PgridExchange of power between the system and the main grid, PgridNot less than 0 means that the system is buying power, P, from the mainsgrid<0 denotes that the system sells power to the main grid, PloadIs the power consumed by the uncontrollable load.
(2) Building heat balance restraint
Heat dissipation and temperature change in a building are slowly changing processes, and the building heat balance equation can be expressed by a difference equation:
ρ×C×V×(Fin,t-Fin,t-1)＝Δt×[Qin+Qwall+Qwin+Qsc-Qec]
(3) indoor temperature limit restraint
The indoor temperature limit is:
Fin_min≤Fin≤Fin_max
wherein Fin_minAnd Fin_maxThe upper limit and the lower limit of the indoor temperature directly influence the comfort of indoor personnel and the virtual energy storage effect.
(4) Upper and lower power limit constraints for system devices
The upper and lower power limits of the system device are described as:
Figure BDA0002900594460000091
wherein, Pgrid_minAnd Pgrid_maxIs the upper and lower limits of the power exchange between the system and the main grid, Pb_minAnd Pb_maxLower and upper limits of the charging/discharging power of the storage battery, Pec_min、Pec_max、Ph_minAnd Ph_maxThe lower limit and the upper limit of the power consumption of the air conditioner and the electric water heater are respectively.
(5) Upper and lower capacity constraints for system equipment
The upper and lower capacity limits of the system equipment are described as:
Figure BDA0002900594460000092
Eb_min、Eb_max、Eh_minand Eh_maxThe lower limit and the upper limit of the storage battery capacity and the storage capacity of the electric water heater are respectively.
3. Constructing an objective function
The invention aims to reduce the daily running cost of the system, and simultaneously, the comfort level of workers in the building is also considered. Thus, the objective function contains three parts: the first is the economic cost, including the cost of purchasing electricity from the main grid and the maintenance cost of each device in the system, the second is the fine due to the reduced comfort of the staff, and the last is the input of the sold electricity to the main grid, and the expression of the objective function is:
Figure BDA0002900594460000093
wherein, C1Is the cost of purchasing power from the grid; when P is presentgridWhen not less than 0, C1＝cgrid×|Pgrid,tI.times.DELTA.t when PgridWhen < 0, C10, wherein cgridIs the real-time electricity price; c2＝cpv×Ppv,t+cb×|Pb,t|+cec×Pec,t+ch×Ph,tThis is the maintenance cost of each device in the system, cpv、cb、cecAnd chThe operating and maintenance costs of the photovoltaic, storage battery, air conditioner and electric water heater are respectively every kilowatt-hour; c3＝γ×|Tin,t-Tset,tL, this is a penalty for the comfort of the persons in the room, γ is a penalty factor, Tin,tIs the temperature, T, actually input to the room after the air conditioner is startedset,tThe preset air-conditioning temperature which makes human body feel comfortable; c4Is the income for selling electricity to the main grid, when PgridWhen < 0, C4＝0.2×cgrid×|Pgrid, |. times.DELTA.t when PgridWhen not less than 0, C4The electricity rate sold to the main grid is set to 0.2 times the real-time rate.
And III, inputting the energy consumption data containing the virtual energy storage building into an optimized scheduling model for solving, and performing optimized scheduling on the renewable energy micro-grid system.
The method can be used for conventional intelligent buildings and can also be applied to other microgrid dispatching containing virtual energy storage.
In the invention, firstly, a mathematical model of each unit in the system is established according to the working principle of the unit, and the working state of the unit is described. These models describe precisely the changes in their state and provide an approximate simulation of the actual operation in which they are operating. Each cell in the microgrid has physical operational limitations, such as the maximum capacity of the storage battery. At the same time, the operation of the system also has various constraints, such as power balance constraints, heat transfer balance constraints inside and outside the building, and comfort constraints for the indoor personnel. The system must operate under these constraints and achieve its optimization goals. The invention develops an optimal scheduling model of the system, which comprises a group of constraints and an economic objective function. It should be noted that the optimization model proposed by the present invention can also be applied and modified by similar studies. For example, the virtual energy storage model and control strategy may also be applied to a hybrid microgrid containing more dispatchable loads and distributed generators. In addition, auxiliary services such as fast frequency response may also be provided by appropriately controlling the virtual stored energy in the ac microgrid.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and those skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A renewable energy micro-grid optimization scheduling method considering virtual energy storage is characterized by comprising the following steps:
s1: acquiring energy consumption data of a building containing virtual energy storage;
s2: establishing a mathematical model of each device in the renewable energy microgrid, a constraint condition and an objective function for optimizing scheduling, and establishing an optimized scheduling model of the renewable energy microgrid system;
s3: and inputting the energy consumption data containing the virtual energy storage building into an optimized scheduling model for solving, and performing optimized scheduling on the renewable energy micro-grid system.
2. The method according to claim 1, wherein each device in the renewable energy microgrid comprises a photovoltaic device, a storage battery, an air conditioner and an electric water heater, and the mathematical model established in step S2 specifically comprises: the device comprises a photovoltaic power generation output power mathematical model, a storage battery charging/discharging mathematical model, an air conditioner energy consumption model, an electric water heater energy consumption model, a virtual air conditioner model, an air conditioner virtual energy storage model, an electric water heater virtual energy storage model and an electric water heater virtual energy storage and discharge power mathematical model.
3. The method as claimed in claim 2, wherein the constraint conditions include a system power balance constraint, a building thermal balance constraint, an indoor temperature limit constraint, a power upper and lower limit constraint of system equipment, and a capacity upper and lower limit constraint of system equipment.
4. A method for optimizing and scheduling a renewable energy microgrid according to any one of claims 1-3 and taking virtual energy storage into consideration, characterized in that the objective function includes three items, namely an economic cost item, a penalty item based on indoor personnel comfort and an income item input to the main grid based on electricity sales.
5. The method as claimed in claim 3, wherein the system power balance constraint is expressed as:
Pgrid+Ppv+Pb＝Pec+Ph+Pload
wherein, PgridFor exchanging power, P, between a microgrid system and a main gridgridNot less than 0 represents that the micro-grid system purchases electric power from the main grid, Pgrid<0 denotes the microgrid system selling power to the main grid, PpvFor photovoltaic power generation output power, PbFor charging/discharging the accumulator, PecFor the power consumption of the air conditioner, PhFor the power consumption of electric water heaters, PloadEnergy consumption power for other loads;
the expression of the building heat balance constraint is as follows:
ρ×C×V×(Fin,t-Fin,t-1)＝Δt×[Qin+Qwall+Qwin+Qsc-Qec]
where ρ is the air density, C is the specific heat capacity of air, V is the indoor air volume, Fin,tIs the room temperature at time t, Fin,t-1Is the indoor temperature at time t-1, Δ t is the simulated time step, QinHeat supply to heat sources in the room, QwallFor heat transfer from the building's outer wall to the room, QwinTo transfer heat from the exterior windows of the building to the interior,Qscfor heat transfer by solar radiation, QecHeat output for an air conditioner;
the expression of the indoor temperature limit constraint is as follows:
Fin_min≤Fin≤Fin_max
wherein, FinIs the room temperature, Fin_minLower limit of indoor temperature, Fin_maxIs an upper limit value of the indoor temperature.
6. The method as claimed in claim 3, wherein the constraints on the upper and lower power limits of the system devices respectively include power exchange power P between the microgrid system and a main gridgridCharging/discharging power P of the accumulatorbAnd the air conditioner energy consumption power PecAnd the energy consumption power P of the electric water heaterhThe upper limit and the lower limit of the capacity of the system equipment respectively comprise the stored energy E of the storage batterybAnd heat E in the hot water storage tank of the electric water heaterhUpper and lower limits of.
7. The method as claimed in claim 4, wherein the objective function is expressed as:
Figure RE-FDA0003015831930000021
Figure RE-FDA0003015831930000022
C2＝cpv×Ppv,t+cb×|Pb,t|+cec×Pec,t+ch×Ph,t
C3＝γ×|Tin,t-Tset,t|
Figure RE-FDA0003015831930000023
wherein, C1Cost of electricity for purchasing the grid, cgridFor real-time electricity prices, C2Maintenance costs for devices within the renewable energy microgrid, cpv、cb、cecAnd chThe operation and maintenance costs of the photovoltaic, the storage battery, the air conditioner and the electric water heater are Ppv,tFor photovoltaic power generation output power at time t, Pb,tFor the charging/discharging power of the accumulator at time t, Pec,tThe power consumption of the air conditioner at time t, Ph,tThe energy consumption power of the electric water heater at the moment t, C3A penalty term for the comfort of the indoor personnel, gamma is a penalty factor, Tin,tFor the actual temperature, T, input to the room after the air conditioner is turned onset,tAir conditioning temperature for human body comfort preset, C4For selling revenue for electricity to the main grid.
8. The method as claimed in claim 2, wherein the photovoltaic power generation output power P is the optimal scheduling method for the renewable energy microgrid considering virtual energy storagepvThe mathematical model expression of (2) is:
Figure RE-FDA0003015831930000031
wherein, UgFor photovoltaic output voltage, UoFor effective value of photovoltaic grid-connected voltage, RsIs a sampling resistor of TsClock cycle of flip-flop K, Kg、Uc、R1And C1Is a control parameter in one cycle;
the mathematical model expression of the storage battery charging/discharging is as follows:
Figure RE-FDA0003015831930000032
wherein E isb,tFor the energy stored in the accumulator at time t, ηbchFor the charging efficiency of the accumulator, etabdisThe discharge efficiency of the storage battery, wherein lambda is the self-discharge rate of the storage battery;
the expression of the air conditioner energy consumption model is as follows:
Qec＝Pec×ηec
wherein eta isecIs the energy efficiency ratio of the air conditioner;
the expression of the electric water heater energy consumption model is as follows:
Qh＝Ph×ηh
wherein Q ishIs the output heat of the electric water heater, etahIs the energy efficiency ratio of the electric water heater.
9. The method as claimed in claim 2, wherein the virtual air-conditioning model is expressed as:
Figure RE-FDA0003015831930000033
the expression of the air conditioner virtual energy storage model is as follows:
Pves_ec＝Pec_ne-Pec_re
wherein, Pves_ecCharging/discharging power, P, with virtual energy storage for air-conditioningec_nePower consumed by the air-conditioner without using virtual energy storage, Pec_reThe power consumed by the air conditioner is used for virtual energy storage.
10. The renewable energy microgrid optimization scheduling method considering virtual energy storage according to claim 2, characterized in that the expression of the virtual energy storage model of the electric water heater is as follows:
Eh,t+1＝(1-δ)×Eh,t
where δ is a loss factor which is affected by the ambient temperature and the temperature of hot water in the water tank, and is set to 2%, Eh,t+1Heat in the hot water storage tank of the electric water heater at time t +1, Eh,tThe heat in the hot water storage tank of the electric water heater at the moment t;
the electric water heater is charged and discharged with power P for virtual energy storageves_hThe mathematical model expression of (2) is:
Pves_h＝Ph_ne-Ph_re
wherein, Pves_hCharging/discharging power, P, for virtual energy storage of electric water heaterh_neFor the power consumed by an electric water heater when virtual energy storage is not used, Ph_reThe power consumed by the electric water heater when virtual energy storage is adopted.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117175647A (en) * 2023-11-02 2023-12-05 江苏龙擎动力科技股份有限公司 New energy storage method and system applied to micro-grid

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117175647A (en) * 2023-11-02 2023-12-05 江苏龙擎动力科技股份有限公司 New energy storage method and system applied to micro-grid
CN117175647B (en) * 2023-11-02 2024-01-30 江苏龙擎动力科技股份有限公司 New energy storage method and system applied to micro-grid

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