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CN106026150B - An optimal allocation method for source-storage-load in business parks - Google Patents

An optimal allocation method for source-storage-load in business parks Download PDF

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CN106026150B
CN106026150B CN201610318005.8A CN201610318005A CN106026150B CN 106026150 B CN106026150 B CN 106026150B CN 201610318005 A CN201610318005 A CN 201610318005A CN 106026150 B CN106026150 B CN 106026150B
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load
energy storage
power
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optimal
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CN106026150A (en
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李建林
张婳
修晓青
李蓓
惠东
唐跃中
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Shanghai Electric Power Co Ltd
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Shanghai Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/383—
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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  • Power Engineering (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Air Conditioning Control Device (AREA)

Abstract

The invention relates to a source-storage-load optimal configuration method for a commercial park, which comprises the following steps: (1) establishing a controllable air conditioner cold and heat load optimization regulation model; (2) acquiring a photovoltaic power generation system output curve of the commercial park; (3) determining a charging and discharging strategy of the energy storage system; (4) establishing a source-storage-load optimization configuration model of the commercial park; (5) obtaining the charging and discharging power value of the energy storage battery meeting the requirement at each moment; the invention utilizes the controllability of the air conditioner load as virtual energy storage to reduce the capacity of an energy storage system required to be configured, and simultaneously realizes the timely photovoltaic consumption to the maximum extent by taking the least electricity purchasing cost of a user from a large power grid as a target function, thereby reducing the investment cost of energy storage, saving the electricity cost of the user and having better economic feasibility.

Description

Commercial park source-storage-load optimal configuration method
Technical Field
The invention relates to an optimal configuration method, in particular to a source-storage-load optimal configuration method for a commercial park.
Background
The large-scale battery energy storage system can realize the peak clipping and valley filling functions of the load by discharging at the peak of the load and charging at the valley of the load. For the power grid, the peak clipping and valley filling can delay the upgrading of the equipment capacity, improve the utilization rate of the equipment and save the updating cost of the equipment; for users, the peak clipping and valley filling electricity price difference can be utilized to obtain economic benefits.
The peak clipping and valley filling optimization is to optimize an optimal charging and discharging strategy for 24 hours before a new day starts according to a predicted daily load curve, namely whether a battery is charged and discharged at each moment and the charging and discharging power. And during real-time control, calculating a charging and discharging power instruction according to a charging and discharging strategy optimally given at present, and data such as a load value, a battery state and the like at the current moment, and issuing the charging and discharging power instruction to each group of power electronic converters.
In recent years, the rapid increase of air conditioning load becomes a main cause of seasonal power shortage, so that the load peak-valley difference is further enlarged, the load supply gap of the power grid peak is increased, and the safe and stable operation of the power grid is influenced. The energy storage system is an effective means for solving the above problems, the energy storage device can reduce the load of the peak power by dynamically absorbing and releasing the electric energy, the stored energy is charged to store the electric energy in the valley of the load, and the electric energy is released to meet the load demand when the power supply of the power grid is insufficient in the peak power of the load. However, the existing energy storage equipment has high cost and limited energy storage capacity, and the economy of load peak clipping and valley filling by only using an energy storage system is poor.
Therefore, a method is urgently needed, the optimal regulation and control of the air conditioning equipment is matched with the energy storage equipment, the energy storage capacity required to be configured can be effectively reduced, and the economical efficiency of the whole system is improved.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a commercial park source-storage-load optimal configuration method.
The technical scheme provided by the invention is as follows: a method for source-storage-load optimal configuration of a commercial park, the method comprising the steps of:
(1) establishing a controllable air conditioner load optimization regulation model;
(2) acquiring the output power of each month of the photovoltaic power generation system in the commercial park;
(3) determining a charging and discharging strategy of the energy storage system;
(4) establishing a source-storage-load optimization configuration model of the commercial park;
(5) and obtaining the charging and discharging power value of the energy storage battery meeting the requirement at each moment.
Preferably, the step (1) of establishing the controllable air-conditioning load optimization regulation and control model includes: setting the temperature of an air conditioner, and establishing a dynamic process simplified model of single air conditioning equipment;
the temperatures of the air conditioner in the on and off states are represented by the following equations, respectively:
Tin,t+Δt＝Tout,t+Δt-QR-(Tout,t+Δt-QR-Tin,t)e-Δt/RC (1)
Tin,t+Δt＝Tout,t+Δt-(Tout,t+Δt-Tin,t)e-Δt/RC (2)
wherein, Tin,t: indoor temperature (. degree. C.) at time T, Tin,t+Δt: indoor temperature (. degree. C.) at time T + Δ T, Tout,t+Δt: outdoor temperature (deg.C) at time t + Deltat, R: equivalent thermal resistance, C: equivalent heat capacity, Q: equivalent thermal efficiency, Δ t: and (5) simulating step length.
Preferably, the controllable air-conditioning load optimization regulation model in step (1) is represented by the following formula:
Figure BDA0000988316140000021
Figure BDA0000988316140000022
wherein, Tin,t: indoor temperature value, T, at each momentbestIndicating the optimum indoor temperature of the set user, eTOU,tRepresents the time-of-use electricity price at peak valley of electricity purchase of the user, P'load,tAnd the air conditioner load power value at each moment after the optimization regulation is shown.
Preferably, the constraint conditions of the air conditioning load optimization regulation and control model are respectively represented by the following formulas:
the peak time period is represented by the following formula: pdown≤P′load,t≤Pload,t (5)
The off-peak period is represented by the following formula: pload,t≤P′load,t≤Pup (6)
Wherein, Pup、PdownAre respectively the upper and lower limits of air conditioner load power, P'load,tRepresents the air conditioner load power value P at each moment after optimized regulationload,tAnd the air conditioner load power value at each moment before the optimal regulation is shown.
Preferably, a multi-objective optimization algorithm based on a particle swarm algorithm is adopted to solve the air conditioner load optimization regulation model;
the multi-objective optimization algorithm comprises the following steps:
1) initializing a population;
2) calculating a fitness value;
3) updating the optimal particle;
4) screening a non-inferior solution set;
5) particle velocity and position updates;
6) judging whether the termination condition is met, if so, entering a step 7), and otherwise, returning to the step 2);
7) and obtaining an optimal air conditioner load power curve.
Preferably, the particle velocity V and the position update X in step 5) are respectively expressed by the following formulas:
Figure BDA0000988316140000031
Xk+1＝Xk+Vk+1 (8)
in the formula, V: particle velocity, X: particle position, ω: inertial weight, r1、r2Is distributed in [0,1 ]]Random number of interval, k: the number of current iterations is then determined,
Figure BDA0000988316140000032
the position of the individual optimum particles is,
Figure BDA0000988316140000033
global optimal particle position, c1And c2Is a constant.
Preferably, the step (3) of charging and discharging the energy storage system comprises: when the electricity price is low, the stored energy purchases more electricity from the power grid, the stored energy is charged, when the electricity price is high, the stored energy supplies power to the load, the stored energy is discharged, if the photovoltaic system supplies power to the load, the stored energy is charged, and when the photovoltaic system is not enough to supply power to the load, the stored energy supplies power to the load;
from the overall system power balance the following equation is obtained:
Pgrid,t+Ppv,t+Pess,t＝Pcon-load,t+P′load,t (9)
in the formula, Pgrid,t: purchasing power from the power grid at each moment by the users in the commercial park; ppv,t: the output power of each month of the photovoltaic power generation system in the commercial park is reduced; pess,t: charging and discharging power of the energy storage system at each moment; during discharge of stored energy, Pess,tPositive, during charging of stored energy, Pess,tIs negative; p'load,t: optimizing the air conditioner load power value at each moment after regulation and control; pcon-load,t: and (4) normal loading.
Preferably, the energy storage system cost for establishing the source-storage-load optimization configuration model of the commercial park in the step (4) is calculated according to the following formula:
Cess＝Cess,ini+Cess,O&M (10)
Figure BDA0000988316140000034
Figure BDA0000988316140000041
Cess，O&M＝Cba，O&M+Cinv，O&M
＝kba×(Qba×eba×R)+kinv×(Pinv×einv) (12)
in the formula, Cess: cost of investing in energy storage systems in commercial parks: cess,ini: initial investment costs for the commercial park; cba,O&MThe operation and maintenance cost of the energy storage battery; cess,O&M: representing a commercial parkZone operational maintenance costs; cinv,O&M: the operation and maintenance cost of the converter; cba,ini: initial investment cost of the energy storage battery; cinv,ini: initial investment cost of the converter; qbaRated capacity of the energy storage battery; e.g. of the typebaThe unit price of the energy storage battery; r is the replacement frequency of the energy storage battery within the system operation life; pinvRated power of the energy storage converter; e.g. of the typeinvThe energy storage converter unit price; r is0: the current sticking rate; m: the operating life of the system; k is a radical ofba、kinvAnnual operation maintenance coefficients of the energy storage system and the energy storage converter are respectively set;
cost of purchasing electricity from large power gridgridCalculated using the formula:
Figure BDA0000988316140000043
Pgrid,t＝Pcon-load,t+P′load,t-Pess,t-Ppv,t (14)
wherein, Pgrid,t: power, P, purchased from the large grid by the user at each momentess,tCharging and discharging power of energy storage system at each moment, P'load,t: optimizing the air conditioner load power value at each moment after regulation and control, eTOU,tRepresents the peak-valley time-of-use electricity price of the userpv,tRepresenting the annual output power, P, of a photovoltaic power generation system in a commercial parkcon-load,t: normal load curve, Ppv,t: output curve of each month in a year of photovoltaic power generation in commercial park, Pcon-load,t: and (4) normal loading.
Preferably, the constraint condition for establishing the source-storage-load optimization configuration model of the business park in the step (4) comprises: the method comprises the following steps of energy storage battery charging and discharging power constraint, energy storage battery charging and discharging conservation constraint and power balance constraint;
the charge and discharge power constraint of the energy storage battery is represented by the following formula:
p is not less than 0 during energy storage and dischargeess,t≤ηoutPinv (15)
During charging of stored energy, -Pinv≤Pess,t≤0 (16)
The charge-discharge conservation constraint of the energy storage battery is represented by the following formula:
Figure BDA0000988316140000044
the power balance constraint is represented by:
Pgrid,t+Ppv,t+Pess,t＝Pcon-load,t+P′load,t (19)
in the formula, Pinv: rated power of the energy storage converter; etaout: discharge coefficient of energy storage battery, tdis: discharge period of energy storage battery, tcha: charging period of the energy storage battery, Pess,t: charge and discharge power, eta, of the energy storage system at each momentin: coefficient of charge, P 'of energy storage battery'load,t: optimizing and controlling the cold and heat load power values P of the air conditioner at all timesgrid,t: power, P, purchased from the large grid by the user at each momentpv,t: annual output power P of photovoltaic power generation system in commercial parkcon-load,t: and (4) normal loading.
Preferably, the step (5) adopts a multi-objective optimization algorithm based on a genetic algorithm to calculate the charge-discharge power P of the energy storage battery at each momentess,t；
The multi-objective optimization algorithm based on the genetic algorithm comprises the following steps: calling an MATLAB function gamultiobj to solve;
the algorithm adopted by the gamultiobj function is a multi-objective optimization algorithm improved based on NSGA-II.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a commercial park source-storage-charge optimal configuration method, which takes a controllable air conditioner as virtual energy storage, optimally regulates and controls the cold and heat loads of the air conditioner to obtain an optimal cold and heat load curve of the air conditioner under the peak-valley time-of-use electricity price, and performs the commercial park source-storage-charge optimal configuration by taking the minimized energy storage cost and the electricity purchasing cost of a user from a large power grid as targets.
Drawings
FIG. 1 is a schematic flow diagram of a method for optimal source-storage-load configuration of a commercial park according to the present invention;
FIG. 2 is a schematic diagram of a source-store-load configuration of a commercial park in accordance with the present invention;
Detailed Description
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
As shown in fig. 1, a method for configuring a source-storage-load optimization of a business park includes the following steps:
101. and (3) classifying the load of the commercial park:
and dividing the load of the commercial park into a normal load and a controllable load according to whether the load is controllable or not. The conventional load refers to the load with a relatively regular and stable load curve in one year such as illumination; the controllable load refers to the load which shows seasonal regularity within one year, such as air conditioning load, and the air conditioning load can participate in demand side response through optimized regulation. The air conditioning load is divided into a cold load and a heat load, wherein the cold load refers to a summer air conditioning refrigeration load, and the heat load refers to a winter air conditioning heating load.
102. Acquiring required load data and peak-valley time-of-use electricity price data:
obtaining a normal load Pcon-load,t(ii) a Acquiring cold and heat load P of air conditionerc-load,t、Ph-load,t(ii) a Obtaining the time-of-use electricity price e of the peak valley of the electricity purchase of the userTOU,tThe peak-valley time-of-use electricity price is divided into a plurality of time periods of peak, valley, average and the like in 24 hours a day;
103. establishing a dynamic process model of the single air conditioning equipment:
let us take the air conditioner cooling load as an example, and set the upper and lower limits T of the air conditioner cooling temperatureup、TdownSetting the optimum indoor temperature T for the userbest。
When the air conditioner is in an on state:
Tin,t+Δt＝Tout,t+Δt–QR-(Tout,t+Δt-QR-Tin,t)e-Δt/RC
when the air conditioner is in an off state:
Tin,t+Δt＝Tout,t+Δt-(Tout,t+Δt-Tin,t)e-Δt/RC
wherein, Tin,t: indoor temperature (. degree. C.) at time T, Tin,t+Δt: indoor temperature (. degree. C.) at time T + Δ T, Tout,t+Δt: outdoor temperature (deg.C) at time t + Deltat, R: equivalent thermal resistance, C: equivalent heat capacity, Q: equivalent thermal efficiency, Δ t: and (5) simulating step length.
104. Establishing a controllable air conditioner cold and heat load optimization regulation model:
the control strategy of the controllable air conditioner load optimization control model comprises the following steps: in the peak electricity price stage, the cooling or heating load of the air conditioner is reduced as much as possible, namely, a part of the air conditioner is shut down; in the off-hour electricity price stage, the cooling or heating load of the air conditioner is increased as much as possible, namely, a part of the air conditioner is started;
the objective function of the controllable air conditioner load optimization regulation model is represented by the following formula:
Figure BDA0000988316140000061
Figure BDA0000988316140000062
wherein, Tin,t: indoor temperature value, T, at each momentbestIndicating the optimum indoor temperature of the set user, eTOU,tRepresents the time-of-use electricity price at peak valley of electricity purchase of the user, P'load,tThe value of the air conditioner cold load power at each moment after the optimization regulation and control is represented;
constraint conditions of the air conditioner load optimization regulation model are as follows: in the peak time electricity price stage, the optimized regulated load power is not more than the power in the period before regulation; in the off-peak electricity price stage, the optimized and regulated load power is not less than the power in the period before regulation; represented by the formula:
peak time electricity price stage: pdown≤P′load,t≤Pload,t
The electricity price at valley time stage: pload,t≤P′load,t≤Pup
In the formula, Pup、PdownRespectively an upper limit and a lower limit of air conditioner cooling load power, P'load,tRepresents the air conditioner load power value P at each moment after optimized regulationload,tAnd the air conditioner load power value at each moment before the optimal regulation is shown.
105. Solving the model by adopting a particle swarm multi-objective optimization algorithm:
106. the multi-objective optimization algorithm comprises the following steps:
1) initializing a population, and randomly initializing the position x and the speed v of particles;
2) calculating the fitness, namely calculating two fitness values of each individual according to the target function;
3) updating the optimal particle;
4) updating a non-inferior solution set;
5) particle velocity and position updates are performed using the following equations:
Figure BDA0000988316140000071
Xk+1＝Xk+Vk+1
wherein V is the particle velocity; x is the particle position; omega is the inertial weight; r is1、r2Is distributed in [0,1 ]]A random number of intervals; k is the current iteration number;
Figure BDA0000988316140000072
optimal particle locations for the individual;
Figure BDA0000988316140000073
is the global optimal particle position; c. C1And c2Is a constant;
6) judging whether the termination condition is met, if so, entering a step 7), and otherwise, returning to the step 2);
7) and obtaining the optimized cold and hot load power curve of the air conditioner.
107. Acquiring output power of each month of the photovoltaic power generation system;
108. determining a charge and discharge strategy of the energy storage system:
when the electricity price is low, the stored energy purchases more electricity from the power grid, and the stored energy is charged; when the electricity price is high, the stored energy supplies power to the load, and the stored energy is discharged;
if the residual electric quantity exists after the photovoltaic system supplies power to the load, charging the stored energy; when the photovoltaic system is not enough to supply power to the load, the energy storage supplies power to the load;
from the overall system power balance the following equation is obtained:
Pgrid,t+Ppv,t+Pess,t＝Pcon-load,t+P′load,t (9)
in the formula, Pgrid,t: purchasing power from the power grid at each moment by the users in the commercial park; ppv,t: the output power of each month of the photovoltaic power generation system in the commercial park is reduced; pess,t: charging and discharging power of the energy storage system at each moment; during discharge of stored energy, Pess,tPositive, during charging of stored energy, Pess,tIs negative; p'load,t: optimizing and controlling the air conditioner load power value P at each momentcon-load,t: and (4) normal loading.
109. Establishing a source-storage-load optimization configuration model of a commercial park;
energy storage system cost for the commercial park source-storage-load optimization configuration model:
Cess＝Cess,ini+Cess,O&M
Figure BDA0000988316140000081
Cess,O&M＝Cba,O&M+Cinv,O&M
＝kba×(Qba×eba×R)+kinv×(Pinv×einv)
in the formula, Cess: cost of investing in energy storage systems in commercial parks: cess,ini: initial investment costs for the commercial park; cba,O&MThe operation and maintenance cost of the energy storage battery; cess,O&M: represents the operation and maintenance cost of the commercial park; cinv,O&M: the operation and maintenance cost of the converter; cba,ini: initial investment cost of the energy storage battery; cinv,ini: initial investment cost of the converter; qbaRated capacity of the energy storage battery; e.g. of the typebaThe unit price of the energy storage battery; r is the replacement frequency of the energy storage battery within the system operation life; pinvRated power of the energy storage converter; e.g. of the typeinvThe energy storage converter unit price; r is0: the current sticking rate; m: the operating life of the system; k is a radical ofba、kinvAnnual operation maintenance coefficients of the energy storage system and the energy storage converter are respectively set;
cost of purchasing electricity from large power gridgridCalculated using the formula:
Figure BDA0000988316140000082
Pgrid,t＝Pcon-load,t+P′load,t-Pess,t-Ppv,t
wherein, Pgrid,t: power, P, purchased from the large grid by the user at each momentess,tCharging and discharging power of energy storage system at each moment, P'load,t: optimizing the air conditioner load power value at each moment after regulation and control, eTOU,tRepresents the peak-valley time-of-use electricity price of the userpv,tRepresenting the annual monthly output power of the photovoltaic power generation system of the commercial park.
110. And performing model solution by adopting a multi-objective optimization algorithm based on a genetic algorithm, specifically calling an MATLAB function gamtobj to perform solution, wherein the algorithm adopted by the gamtobj function is an improved multi-objective optimization algorithm based on NSGA-II, solving the optimal energy storage charge and discharge power at each moment, and completing energy storage capacity configuration.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (7)

1.一种商业园区源-储-荷优化配置方法，其特征在于，所述方法包括如下步骤：1. A source-storage-load optimal configuration method in a business park, characterized in that the method comprises the steps: (1)、建立可控空调负荷优化调控模型；(1) Establish a controllable air conditioning load optimization control model; (2)、获取商业园区光伏发电系统的各月出力功率；(2) Obtain the monthly output power of the photovoltaic power generation system in the business park; (3)、确定储能系统充放电策略；(3) Determine the charging and discharging strategy of the energy storage system; (4)、建立商业园区源-储-荷优化配置模型；(4) Establish the optimal allocation model of source-storage-load in business parks; (5)、求得满足需要的储能电池各时刻充放电功率值；(5) Obtain the charging and discharging power value of the energy storage battery at each moment that meets the needs; 所述步骤(1)可控空调负荷优化调控模型的建立包括：设定空调的温度，并建立单台空调设备动态过程简化模型；The establishment of the controllable air conditioner load optimization regulation model in the step (1) includes: setting the temperature of the air conditioner, and establishing a simplified model of the dynamic process of a single air conditioner; 空调处于开启和关断状态时的温度，分别用下式表示：The temperature when the air conditioner is on and off is expressed by the following equations: Tin，t+Δt＝Tout，t+Δt-QR-(Tout，t+Δt-QR-Tin，t)e-Δt/RC (1)T in, t+Δt =T out, t+Δt -QR-(T out, t+Δt -QR-T in,t )e -Δt/RC (1) Tin，t+Δt＝Tout，t+Δt-(Tout，t+Δt-Tin，t)e-Δt/RC (2)T in, t+Δt =T out, t+Δt −(T out, t+Δt −T in, t )e −Δt/RC (2) 其中，Tin，t：t时刻室内温度(℃)，Tin，t+Δt：t+Δt时刻室内温度(℃)，Tout，t+Δt：t+Δt时刻室外温度(℃)，R：等值热阻，C：等值热容，Q：等值热效率，Δt：仿真步长；Wherein, T in, t : indoor temperature at time t (°C), T in, t+Δt : indoor temperature at time t+Δt (°C), T out, t+Δt : outdoor temperature at time t+Δt (°C), R : equivalent thermal resistance, C: equivalent thermal capacity, Q: equivalent thermal efficiency, Δt: simulation step size; 按下式计算所述步骤(4)建立商业园区源-储-荷优化配置模型的储能系统成本：Calculate the energy storage system cost of establishing the source-storage-load optimal configuration model of the business park in step (4) as follows: Cess＝Cess，ini+Cess，O&M (10)C ess = C ess, ini + C ess, O&M (10)
Figure FDA0003438290930000011
Figure FDA0003438290930000011
 Cess，O&M＝Cba，O&M+Cinv，O&M Cess, O&M = Cba, O&M +C inv, O&M ＝kba×(Qba×eba×R)+kinv×(Pinv×einv) (12)=k ba ×(Q ba ×e ba ×R)+k inv ×(P inv ×e inv ) (12) 式中，Cess：商业园区投资储能系统的成本：Cess，ini：商业园区的初始投资成本；Cba，O&M：储能电池的运行维护成本；Cess，O&M：表示商业园区运行维护成本；Cinv，O&M：变流器的运行维护成本；Cba，ini：储能电池的初始投资成本；Cinv，ini：变流器的初始投资成本；Qba：储能电池额定容量；eba：储能电池单价；R：储能电池在系统运行年限内的更换次数；Pinv:储能变流器额定功率；einv:储能变流器单价；r0：贴现率；m：系统运行年限；kba、kinv分别为储能系统和储能变流器的年运行维护系数；In the formula, Cess : the cost of investing in the energy storage system in the business park: Cess, ini : the initial investment cost of the business park; Cba, O&M : the operation and maintenance cost of the energy storage battery; Cess, O&M : the operation and maintenance of the business park Cost; C inv, O&M : operation and maintenance cost of converter; C ba, ini : initial investment cost of energy storage battery; C inv, ini : initial investment cost of converter; Q ba : rated capacity of energy storage battery; e ba : unit price of energy storage battery; R: number of replacements of energy storage battery within the operating life of the system; P inv : rated power of energy storage converter; e inv : unit price of energy storage converter; r 0 : discount rate; m : system operating years; k ba and k inv are the annual operation and maintenance coefficients of the energy storage system and energy storage converter, respectively; 从大电网购电的费用Igrid用下式计算：The cost of purchasing electricity from the large grid, I grid , is calculated by the following formula:
Figure FDA0003438290930000021
Figure FDA0003438290930000021
 Pgrid,t＝Pcon-load,t+P′load,t-Pess,t-Ppv,t (14)P grid,t =P con-load,t +P' load,t -P ess,t -P pv,t (14) 其中，Pgrid,t：各时刻用户从大电网购电的功率，Pcon-load,t：常规负荷；Pess,t:储能系统各时刻充放电功率，P′load,t：优化调控后各时刻的空调负荷功率值，eTOU,t表示用户购电峰谷分时电价，Ppv，t表示商业园区光伏发电系统一年中各月出力功率；Among them, P grid,t : the power purchased by the user from the large power grid at each moment, P con-load,t : the conventional load; P ess,t : the charging and discharging power of the energy storage system at each moment, P′ load,t : the optimal regulation The air-conditioning load power value at each subsequent moment, e TOU,t represents the peak and valley time-of-use electricity price of the user’s purchase of electricity, P pv, t represents the monthly output power of the photovoltaic power generation system in the business park in a year; 所述步骤(5)采用基于遗传算法的多目标优化算法计算储能电池各时刻充放电功率Pess，t；The step (5) adopts the multi-objective optimization algorithm based on the genetic algorithm to calculate the charge and discharge power P ess,t of the energy storage battery at each moment; 所述基于遗传算法的多目标优化算法包括：调用MATLAB函数gamultiobj进行求解；The genetic algorithm-based multi-objective optimization algorithm includes: calling the MATLAB function gamultiobj to solve; 所述gamultiobj函数所采用的算法为基于NSGA-Ⅱ改进的一种多目标优化算法。The algorithm adopted by the gamultiobj function is an improved multi-objective optimization algorithm based on NSGA-II. 2.如权利要求1所述的优化配置方法，其特征在于，所述步骤(1)可控空调负荷优化调控模型用下式表示：2. The optimal configuration method according to claim 1, wherein the step (1) controllable air conditioning load optimization regulation model is represented by the following formula:
Figure FDA0003438290930000022
Figure FDA0003438290930000022
Figure FDA0003438290930000023
Figure FDA0003438290930000023
 其中，Tin,t：各时刻室内温度值，Tbest表示设定用户最适宜室内温度，eTOU,t表示用户购电峰谷分时电价，P′load，t表示优化调控后各时刻空调负荷功率值。Among them, T in,t : the indoor temperature value at each time, T best represents the optimal indoor temperature for the user, e TOU, t represents the time-of-use price of electricity purchased by the user at peak and valley, P' load, t represents the air conditioner at each time after the optimal regulation load power value. 3.如权利要求2所述的优化配置方法，其特征在于，所述空调负荷优化调控模型的约束条件分别用下式表示：3. The optimal configuration method according to claim 2, wherein the constraints of the air-conditioning load optimization control model are respectively represented by the following formulas: 峰时阶段用下式表示：Pdown≤P′load，t≤Pload，t (5)The peak time phase is expressed by the following formula: P down ≤ P' load, t ≤ P load, t (5) 谷时阶段用下式表示：Pload，t≤P′load，t≤Pup (6)The valley time stage is expressed by the following formula: P load, t ≤ P' load, t ≤ P up (6) 其中，Pup、Pdown分别为空调负荷功率的上下限，P′load，t表示优化调控后各时刻空调负荷功率值，Pload,t表示优化调控前各时刻空调负荷功率值。Among them, P up and P down are the upper and lower limits of the air-conditioning load power, respectively, P' load,t represents the air-conditioning load power value at each moment after the optimal regulation, and P load,t represents the air-conditioning load power value at each moment before the optimal regulation. 4.如权利要求2所述的优化配置方法，其特征在于，采用基于粒子群算法的多目标优化算法求解空调负荷优化调控模型；4. The optimal configuration method according to claim 2, wherein a multi-objective optimization algorithm based on particle swarm algorithm is adopted to solve the air-conditioning load optimization control model; 所述多目标优化算法包括如下步骤：The multi-objective optimization algorithm includes the following steps: 1)种群初始化；1) Population initialization; 2)计算适应度值；2) Calculate the fitness value; 3)粒子最优更新；3) Optimal update of particles; 4)筛选非劣解集；4) Screen the non-inferior solution set; 5)粒子速度和位置更新；5) Particle velocity and position update; 6)判读是否满足终止条件，若满足进入步骤7)，否则返回步骤2)；6) Judging whether the termination condition is met, if so, enter step 7), otherwise return to step 2); 7)得到最优空调负荷功率曲线。7) Obtain the optimal air-conditioning load power curve. 5.如权利要求4所述的优化配置方法，其特征在于，所述步骤5)粒子速度V和位置更新X分别用下式表示：5. The method for optimizing configuration as claimed in claim 4, wherein the step 5) particle velocity V and position update X are respectively represented by the following formula:
Figure FDA0003438290930000031
Figure FDA0003438290930000031
 Xk+1＝Xk+Vk+1 (8)X k+1 =X k +V k+1 (8) 式中，V：粒子速度，X：粒子位置，ω：惯性权重，r1、r2为分布于[0,1]区间的随机数，k：当前迭代次数，
Figure FDA0003438290930000032
个体最优粒子位置，
Figure FDA0003438290930000033
全局最优粒子位置，c1和c2为常数。In the formula, V: particle velocity, X: particle position, ω: inertia weight, r 1 and r 2 are random numbers distributed in the [0,1] interval, k: the current iteration number,
Figure FDA0003438290930000032
individual optimal particle position,
Figure FDA0003438290930000033
Global optimal particle position, c 1 and c 2 are constants. 6.如权利要求1所述的优化配置方法，其特征在于，所述步骤(3)储能系统充放电策略包括：在电价低时，储能从电网多购电，储能充电，电价高时，储能向负荷供电，储能放电，光伏系统向负荷供电后若有剩余电量，对储能进行充电，光伏系统不足以向负荷供电时，储能向负荷供电；6 . The optimal configuration method according to claim 1 , wherein the charging and discharging strategy of the energy storage system in step (3) comprises: when the electricity price is low, the energy storage purchases more electricity from the power grid, the energy storage is charged, and the electricity price is high. 7 . When the energy storage is used to supply power to the load, the energy storage discharges. If the photovoltaic system supplies power to the load, if there is remaining power, the energy storage is charged. When the photovoltaic system is insufficient to supply power to the load, the energy storage supplies power to the load; 由整体系统功率平衡得到下式：The following formula is obtained from the overall system power balance: Pgrid，t+Ppv，t+Pess，t＝Pcom-load，t+P′load，t (9)P grid, t + P pv, t + P ess, t = P com-load, t + P' load, t (9) 式中，Pgrid，t：商业园区用户各时刻从电网购电的功率；Ppv，t：商业园区光伏发电系统一年中各月出力功率；Pess，t：储能系统各时刻充放电功率；储能放电时，Pess，t为正，储能充电时，Pess，t为负；Pcon-load,t：常规负荷；P′load，t：优化调控后各时刻的空调负荷功率值。In the formula, P grid, t : the power purchased by the users of the business park from the power grid at each moment; P pv, t : the output power of the photovoltaic power generation system in the business park in each month of the year; P ess, t : the charging and discharging of the energy storage system at each moment Power; P ess, t is positive when the energy storage is discharging, and P ess, t is negative when the energy storage is charging; P con-load, t : regular load; P′ load, t : air conditioning load at each moment after optimal regulation power value. 7.如权利要求1所述的优化配置方法，其特征在于，所述步骤(4)中建立商业园区源-储-荷优化配置模型的约束条件包括：储能电池充放电功率约束、储能电池充放电守恒约束和功率平衡约束；7. The optimal configuration method according to claim 1, wherein the constraints for establishing the source-storage-load optimal configuration model in the business park in the step (4) include: energy storage battery charge and discharge power constraints, energy storage Battery charge and discharge conservation constraints and power balance constraints; 所述储能电池充放电功率约束用下式表示：The charging and discharging power constraint of the energy storage battery is expressed by the following formula: 储能放电时，0≤Pess，t≤ηoutPinv (15)When the stored energy is discharged, 0≤P ess, t ≤η out P inv (15) 储能充电时，-Pinv≤Pess，t≤0 (16)When the energy storage is charged, -P inv ≤P ess, t ≤0 (16) 所述储能电池充放电守恒约束用下式表示：The charge-discharge conservation constraint of the energy storage battery is expressed by the following formula:
Figure FDA0003438290930000041
Figure FDA0003438290930000041
 所述功率平衡约束用下式表示：The power balance constraint is expressed as: Pgrid,t+Ppv,t+Pess,t＝Pcon-load,t+P′load,t (19)P grid,t +P pv,t +P ess,t =P con-load,t +P' load,t (19) 式中，Pinv：储能变流器额定功率；ηout：储能电池放电系数，tdis：储能电池放电时段，tcha：储能电池充电时段，Pess,t：储能系统各时刻充放电功率，ηin：储能电池充电系数，P′load,t：优化调控后各时刻的空调冷、热负荷功率值，Pgrid,t：各时刻用户从大电网购电的功率，Ppv，t：商业园区光伏发电系统一年中各月出力功率，Pcon-load,t：常规负荷。In the formula, P inv : the rated power of the energy storage converter; η out : the discharge coefficient of the energy storage battery, t dis : the discharge period of the energy storage battery, t cha : the charging period of the energy storage battery, P ess,t : the energy storage system Time charging and discharging power, η in : energy storage battery charging coefficient, P' load,t : air conditioning cooling and heating load power value at each moment after optimal regulation, P grid,t : power purchased by users from the large grid at each moment, P pv, t : monthly output power of the photovoltaic power generation system in the business park in a year, P con-load,t : regular load.
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