CN112949976A - Optimal scheduling method for microgrid energy in commercial park - Google Patents

Optimal scheduling method for microgrid energy in commercial park Download PDF

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CN112949976A
CN112949976A CN202110047392.7A CN202110047392A CN112949976A CN 112949976 A CN112949976 A CN 112949976A CN 202110047392 A CN202110047392 A CN 202110047392A CN 112949976 A CN112949976 A CN 112949976A
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高强
周晋杭
郭俊辉
张晶
李建飞
藏玉清
陈迪雨
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Taizhou Hongyuan Electric Power Design Institute Co ltd
Taizhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention discloses a method for optimizing and scheduling energy of a microgrid in a commercial park, which comprises the following steps: step S1: constructing a micro-grid of a commercial park comprising electric automobiles; step S2: constructing a random network-in and network-out scene model of the electric automobile; step S3: constructing a battery loss model of the electric automobile; step S4: constructing a commercial park microgrid system scheduling model based on MPC; step S5: and solving the scheduling model of the microgrid system of the commercial park. The method considers the condition that the electric automobile is randomly accessed into the micro-grid of the commercial park, adopts the random model prediction control method aiming at the random access of the electric automobile into the micro-grid, realizes the power balance of energy flow, and has good economy.

Description

Optimal scheduling method for microgrid energy in commercial park
Technical Field
The invention belongs to the technical field of power dispatching engineering, and particularly relates to a micro-grid dispatching method.
Background
Clean and low carbon become a development trend of global energy transformation, an energy revolution characterized by deep integration of new energy and information technology is pushing the human society to enter a brand new energy system, at present, a power system still has a series of problems of how to consume the new energy, uncertain disturbance, source and load imbalance and the like, and as of 2018, the installed capacity of solar energy and wind energy in China reaches 3.6 hundred million KW and accounts for 19% of the installed capacity, but the generated energy only accounts for 7.8% of the total amount, and the problems of wind abandoning and light abandoning are still serious. The proposal of the concept of Energy internet becomes a key for a good solution and Energy transformation of the above problems, and microgrid is regarded as an "organic cell" constructed by future Energy Internet (EIS) as an advanced stage of distributed power generation under the concept architecture of "cell-organization" Energy internet.
The microgrid can be an independent controllable system which only contains electric energy and can realize local energy supply and demand balance, or can be a multi-energy microgrid which contains various energy sources such as cold/heat/electricity/gas and the like, and a plurality of microgrids can form an active power distribution network with complete functions, so that building the microgrid is a preferable and precedent scheme for building the EIS. Different from the traditional power grid, the optimized scheduling of the microgrid is influenced by uncertain factors such as a distributed power supply and an energy storage system.
At present, a scheduling strategy of a microgrid in a commercial park environment is lacked, and particularly, with the fact that electric vehicles are randomly accessed to the microgrid in the commercial park, the situation is more and more common, and a general microgrid scheduling strategy is difficult to adapt to the scheduling requirement of the microgrid in the commercial park.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for optimizing and scheduling the energy of the microgrid in the commercial park, which considers the condition that the electric automobile is randomly accessed into the microgrid in the commercial park and improves the scientificity and economy of scheduling.
In order to solve the technical problems, the invention adopts the following technical scheme: a method for optimizing and scheduling micro-grid energy in a commercial park comprises the following steps:
step S1: constructing a commercial park microgrid comprising electric automobiles: the micro-grid of the commercial park is connected with an external power grid and comprises a combined heat and power unit, wherein the combined heat and power unit comprises a back-pressure combined heat and power unit CHP _ I and an extraction combined heat and power unit CHP _ II;
step S2: constructing a random network-in and network-out scene model of the electric automobile: the micro-grid time t of the electric automobile connected to or leaving the commercial park accords with normal distribution, and the expression is as follows:
Figure RE-GDA0002993549310000021
wherein f represents the microgrid time distribution condition of the electric automobile accessing or leaving the commercial park, and mu and sigma represent the average time and variance of accessing or leaving respectively;
step S3: constructing a battery loss model of the electric automobile: the electric automobile battery loss model expression is as follows:
Figure RE-GDA0002993549310000022
wherein the content of the first and second substances,
Figure RE-GDA0002993549310000023
which represents a cost of the loss of the battery,
Figure RE-GDA0002993549310000024
which represents the total investment cost of the battery,
Figure RE-GDA0002993549310000025
represents the battery cost loss factor, where t represents the current time;
step S4: constructing a commercial park microgrid system scheduling model based on MPC:
under the condition of satisfying the balance of the heat and electricity supply and demand of the system, the optimal economic target of the system optimization is ensured, the target function represents the minimum cost of the system operation,
Figure RE-GDA0002993549310000026
in the formula: p is a radical ofsIndicating the probability that the scene s corresponds to after the scene cut,
Figure RE-GDA0002993549310000027
representing the interaction power of the microgrid and the external power grid in a scene s, cGrid(t + i | t) represents the purchase price of electricity, cGasIs the price of natural gas, V (t + i | t) represents the purchase amount of natural gas,
Figure RE-GDA0002993549310000028
representing the battery loss cost, and T representing the prediction time domain length;
step S5: solving the scheduling model of the microgrid system of the commercial park:
solving the scheduling model of the microgrid system of the commercial park comprises the following steps:
when t is 0, acquiring a cut electric automobile random network access scene through Latin hypercube sampling and scene cutting technology, and initializing parameters;
solving an objective function through CPLEX by using a random MPC optimization model in a prediction time domain T according to constraint conditions;
predicting the forward rolling optimization of time domain, moving from the current time t to the t +1 time, and executing the first timeThe scale value acts on the whole system to obtain a scheduling result PCHP_I(t+1|t)、PCHP_II(t +1| t) and a power predicted value of the electric automobile cluster are used as initial state values of the system at the next moment;
updating the initial parameters and repeating the steps.
Preferably, the probability density function expression of the last return time of the electric vehicle is as follows:
Figure RE-GDA0002993549310000031
wherein f isa(t) time of arrival, μa＝17.6，σa＝3.4。
Preferably, the probability density function expression at the initial trip time of the electric vehicle is as follows:
Figure RE-GDA0002993549310000032
wherein f iss(t) represents the trip start time, μs＝9.24，σs＝3.16。
Preferably, the expression of the total investment cost of the battery is as follows:
Figure RE-GDA0002993549310000033
wherein the content of the first and second substances,
Figure RE-GDA0002993549310000034
representing the total investment cost, DAN, of the batterys(t + i) represents the number of electric vehicles in scene s, PEV-maxRepresenting maximum power of charge and discharge, CapEVRepresents the battery capacity of the electric vehicle, CpRepresents the unit power cost of the battery of the electric automobile, CcRepresents the cost per unit capacity of the battery of the electric automobile, CpAnd CcRepresenting the investment cost factor, and t and i represent time instants.
Preferably, the constraint condition includes:
considering mutual exclusion constraint condition when charging the battery pack, introduce
Figure RE-GDA0002993549310000041
And
Figure RE-GDA0002993549310000042
the 0-1 flag variables of (1) respectively represent the charge and discharge states of the battery at a certain time,
Figure RE-GDA0002993549310000043
Figure RE-GDA0002993549310000044
Figure RE-GDA0002993549310000045
the charge and discharge power of the battery is expressed as:
Figure RE-GDA0002993549310000046
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0002993549310000047
indicating that the discharge is being made at that time,
Figure RE-GDA0002993549310000048
indicating that the charge is being made at that time.
Preferably, the constraint condition further includes:
SOC value at t + i moment SOC under scene ss(t + i | t) is calculated by the following formula:
Figure RE-GDA0002993549310000049
the battery SOC constraint for time t + i is expressed as:
SOCmin≤SOCs(t+i|t)≤SOCmax,i＝0,…,T-1。
preferably, the constraint further includes a battery loss cost:
Figure RE-GDA00029935493100000410
the expression of the battery cost loss coefficient is as follows:
Figure RE-GDA00029935493100000411
wherein the content of the first and second substances,
Figure RE-GDA0002993549310000051
the cell cost loss factor is expressed as a factor,
Figure RE-GDA0002993549310000052
which is indicative of the state of charge of the battery,
Figure RE-GDA0002993549310000053
indicating the discharge state of the battery, PEV-maxRepresents the maximum power of the battery, delta t represents the time difference of charging and discharging, AtotalRepresenting the desired throughput of the battery.
Preferably, the constraint condition further comprises electric power balance in the commercial park microgrid system scheduling model,
the electric power balance condition expression in the commercial park microgrid system scheduling model is as follows:
Figure RE-GDA0002993549310000054
wherein the content of the first and second substances,
Figure RE-GDA0002993549310000055
represents electric vehicle power, P, under scene sCHP_I(t + I | t) represents the CHP _ I type unit power supply, PCHP_II(t + i | t) represents the CHP _ II type unit power supply,
Figure RE-GDA0002993549310000056
expressing the interaction power between the microgrid and the external power grid under the scene s, Pload(t + i | t) represents the total power demand of the consumer electrical load.
Preferably, the constraint condition further includes a power purchase constraint from an external power grid, and the power purchase constraint from the external power grid satisfies the following formula:
Figure RE-GDA0002993549310000057
in the formula:
Figure RE-GDA0002993549310000058
the power supply device represents the maximum power for purchasing power from an external power grid, only considers the power purchasing situation and does not consider the power selling situation.
Preferably, the constraint condition further comprises supply and demand constraints of heat energy,
the supply and demand constraint formula expression of the heat energy is as follows:
ηh1Hload(t+i|t)≤HCHP_I(t+i|t)+HCHP_II(t+i|t)≤ηh2Hload(t+i|t),i＝1,…,T，
wherein eta ish1And ηh2Reliable lower and upper limit fluctuation coefficients, H, representing thermal load demandload(t + i | t) represents the thermal load demand, HCHP_I(t + I | t) represents the heat output value of the CHP _ I type unit, HCHP_II(t + i | t) represents the heat capacity output value of the CHP _ II type unit.
According to the technical scheme, the situation that the electric vehicle is randomly accessed into the micro-grid of the commercial park is considered, the random model predictive control method is adopted for the random access of the electric vehicle into the micro-grid, the power balance of energy flow is achieved, and the method has good economy.
The following detailed description of the present invention will be provided in conjunction with the accompanying drawings.
Drawings
The invention is further described with reference to the accompanying drawings and the detailed description below:
fig. 1 is a schematic composition diagram of a microgrid of a commercial park in a method for optimizing and scheduling energy of the microgrid of the commercial park, provided by the invention;
fig. 2 is a schematic flow chart of a method for optimizing and scheduling energy of a microgrid in a commercial park according to the present invention;
fig. 3 is a flow chart of a solution to a business park microgrid system scheduling model.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the application provides a method for optimizing and scheduling energy of a microgrid in a commercial park, which comprises the following steps:
constructing a micro-grid of a commercial park comprising electric automobiles;
constructing a random network-in and network-out scene model of the electric automobile;
constructing a battery loss model of the electric automobile;
constructing a commercial park microgrid system scheduling model based on MPC;
and solving the scheduling model of the microgrid system of the commercial park.
As shown in fig. 1, in the embodiment of the present application, a micro grid in a commercial park includes a Combined Heat and Power (CHP) unit, which can supply Power and meet the Heat supply requirement of the system, and has a high operation efficiency, in general, the CHP unit operates in a "fixed Heat and Power" mode, which reduces the flexibility of the system, and for reasons of incoordination between the Heat load and the Power load requirement, needs to perform adaptive deep peak regulation on the CHP unit to improve the flexibility of the system and the energy distribution absorption capability, in order to simplify the model, generally adopts a mode of configuring a Heat storage device to perform translation of the Heat load, weakens the Heat load relationship between users, breaks the "fixed Heat and Power" mode, and implements Heat and Power decoupling, and other modes of configuring Heat pumps, electric boilers and other Heat and Power conversion devices to perform decoupling.
In the embodiment of the application, probability density functions of the last return time and the initial trip time of the electric vehicle are shown as formula (4.1) and formula (4.2).
Figure RE-GDA0002993549310000071
In the formula (f)a(t) time of arrival, μa＝17.6，σa＝3.4。
Figure RE-GDA0002993549310000072
In the formula (f)s(t) represents the trip start time, μs＝9.24，σs＝3.16。
In the embodiment of the present application, the CHP unit may be selected from the following forms:
(1) back pressure type combined heat and power unit output model
The combined heat and power unit mainly comprises a back pressure type, a steam extraction back pressure type, an adjustable steam extraction condensing type and the like, for the back pressure type CHP unit, the working principle is that the back pressure steam exhaust of a steam turbine exchanges heat with heat supply network water through a water-steam converter, heat supply to a heat load is realized by a heat supply network, and the output of the unit type is completely determined by heat output, so that the thermoelectric ratio of the back pressure type CHP unit is fixed, the regulating capacity is not available, the combined heat and power unit has the advantages of higher efficiency and low operation cost, and a mathematical model is shown in a formula (4.3).
HCHP_I(t)＝cCHP_IPCHP_I(t) (4.3)
In the formula, HCHP_I(t) and PCHP_I(t) represents heating power and generating power, respectively, cCHP_IIndicating the thermoelectric ratio coefficient.
Therefore, the power generation power of the back-pressure cogeneration unit CHP _ I type unit satisfies the constraint as shown in the following formula:
Figure RE-GDA0002993549310000081
(2) output model of steam extraction type cogeneration unit
The extraction formula combined heat and power unit during operation extracts a part of steam as the heat source and supplies heat outward from the intermediate pressure jar of steam turbine to the low-pressure jar, can adjust the thermoelectric ratio through the size of adjusting the steam extraction volume under the prerequisite that satisfies the system operation, so extraction formula CHP unit can work in a flexible way and can exert oneself the territory within range, and its electrothermal property satisfies:
Figure RE-GDA0002993549310000082
Figure RE-GDA0002993549310000083
in the formula PCHP_II(t) denotes the CHP _ II type unit power generation power at time t, HCHP_II(t) represents the heating power of the unit, cCHP_II1And cCHP_II2And the coefficient respectively represents the feasible region constraint coefficient and respectively corresponds to the slope values of the upper limit and the lower limit of the electric power output in the working characteristic diagram.
In the operation process of the CHP _ II unit, when other energy supplies are sufficient, the steam extraction unit works in the minimum condensing working condition state, the power supply output is determined by the heat load at the moment, the heat load is not flexible, and when the heat load is higher, in order to meet the requirements of energy supply balance and turbine cooling, the adjustment range of the power generation output of the CHP _ II unit is continuously reduced along with the increase of the steam extraction amount.
In the embodiment of the present application, the micro grid of the commercial park includes a gas boiler, which provides heat energy through the combustion of natural gas, in this case, the CHP _ I type and CHP _ II type units are jointly scheduled, and the heat production is respectively expressed as the following formulas (4.7) and (4.8):
FCHP_I(t)＝aCHP_IPCHP_I(t) (4.7)
FCHP_II(t)＝aCHP_IIHCHP_II(t)+bCHP_IIPCHP_II(t)+gCHP_II (4.8)
in the formula, FCHP_I(t) represents the thermal power produced by the CHP _ I type unit consuming natural gas, FCHP_II(t) represents the thermal power produced by the consumption of natural gas by the CHP _ II type unit, aCHP_I、aCHP_II、bCHP_II、gCHP_IIThe coefficient constant is determined by the working characteristics of the cogeneration unit.
The gas boiler consumes natural gas volume V (t) (m3) and generates heat power (F)CHP_I(t)+FCHP_II(t)) (kW) is expressed as follows:
V(t)＝(FCHP_I(t)+FCHP_II(t))/η (4.9)
wherein eta is the low calorific value (kWh/m3) of the natural gas, and conversion efficiency of different units is considered in actual operation.
The number of the electric automobiles which can be accessed in the microgrid is large, the access time is different, and if the working time and the SOC of each electric automobile are used as parameters to be input into the calculation model, the calculation complexity of the model will increase exponentially. Therefore, simplified grouping scheduling is used in the modeling process to reduce the complexity of the model through a strategy, and the practicability of the model in the scheduling process is improved.
In the embodiment of the application, the time t when the electric automobile is connected to or leaves the microgrid of the commercial park conforms to the normal distribution as shown in the formula (4.10):
Figure RE-GDA0002993549310000091
wherein f represents the micro-grid time distribution condition of the electric automobile entering or leaving the commercial park, and mu and sigma represent the average moment and variance of entering or leaving respectively. Mu in the present applicationha＝19，σha1 is the arrival time t of the electric automobilehaMean and variance of; mu.soa＝9，σoa1 is the electric vehicle arrival time toaMean and variance of.
In the embodiment of the application, a battery loss model in the electric automobile analyzes the attenuation characteristic of the stored energy of the battery by adopting a comprehensive kilowatt-hour throughput method, and does not consider the complex process of local circulation, incomplete or complete charge and discharge in the charge and discharge process. Kilowatt-hour throughput data for the entire battery may be obtained from the manufacturer for the entire full life cycle of the battery.
In the embodiment of the present application, in the process of the objective function optimization solution, the decay of the battery life is taken into consideration as a condition of the cost limit, and the total investment cost of the battery is shown in the formula (4.22).
Figure RE-GDA0002993549310000092
In the formula (I), the compound is shown in the specification,
Figure RE-GDA0002993549310000093
representing the total investment cost, DAN, of the battery in the corresponding scenarios(t + i) represents the number of electric vehicles in scene s, PEV-maxRepresenting maximum power of charge and discharge, CapEVRepresents the battery capacity of the electric vehicle, CpRepresents the unit power cost of the battery of the electric automobile, CcThe unit capacity cost and the investment cost coefficient C of the battery of the electric automobile are shownpAnd CcAnd the current time can be obtained by inquiring the specific battery model, wherein t represents the current time.
In the embodiment of the application, mutual exclusion constraint condition during battery pack charging is considered, and introduction is carried out
Figure RE-GDA0002993549310000101
And
Figure RE-GDA0002993549310000102
the 0-1 flag variables of (1) respectively represent the charge and discharge states of the battery at a certain time.
Figure RE-GDA0002993549310000103
Figure RE-GDA0002993549310000104
Figure RE-GDA0002993549310000105
In the embodiment of the present application, the charge and discharge power of the battery can be expressed as:
Figure RE-GDA0002993549310000106
in the formula
Figure RE-GDA0002993549310000107
Indicating that the discharge is being made at that time,
Figure RE-GDA0002993549310000108
indicating that the charge is being made at that time.
In the embodiment of the application, the value SOC of the SOC at the t + i moment under the scene ss(t + i | t) can be calculated by the formula (4.27):
Figure RE-GDA0002993549310000109
the battery SOC constraint for time t + i may be expressed as:
SOCmin≤SOCs(t+i|t)≤SOCmax,i＝0,…,T-1 (4.28)
in the embodiment of the present application, the battery cost loss coefficient may be expressed as:
Figure RE-GDA00029935493100001010
thus, the battery loss cost can be found from equations 4.22 and 4.29 as:
Figure RE-GDA00029935493100001011
in the embodiment of the application, in the process of power supply and demand, the cluster effect of the charging and discharging behaviors of the electric vehicle and the output conditions of different types of CHP units are considered, when the power supply is insufficient, power needs to be purchased from an external large power grid to meet the electric energy demand of a user, and the electric power balance condition of the micro-grid system is as shown in formula (4.32):
Figure RE-GDA0002993549310000111
in the formula:
Figure RE-GDA0002993549310000112
represents electric vehicle power, P, under scene sCHP_I(t + I | t) represents the CHP _ I type unit power supply, PCHP_II(t + i | t) represents the CHP _ II type unit power supply,
Figure RE-GDA0002993549310000113
expressing the interaction power between the microgrid and the external power grid under the scene s, Pload(t + i | t) represents the total power demand of the consumer electrical load.
The power purchase constraint from the external power grid satisfies the formula (4.33):
Figure RE-GDA0002993549310000114
in the formula:
Figure RE-GDA0002993549310000115
the maximum power of purchasing electricity from the external power grid is shown, in the case of the power supply, only the electricity purchasing situation is considered, and the electricity selling situation is not considered.
In the embodiment of the present application, the thermal energy supply does not necessarily satisfy the complete balance of the actual thermal energy demand, therefore, the thermal energy supply and demand can be satisfied within an allowable variable range in general design, and the supply and demand constraint of the thermal energy satisfies the formula (4.34):
ηh1Hload(t+i|t)≤HCHP_I(t+i|t)+HCHP_II(t+i|t)≤ηh2Hload(t+i|t),i＝1,…,T (4.34)
in the formula: etah1And ηh2Reliable lower and upper limit fluctuation coefficients, H, representing thermal load demandload(t + i | t) represents the thermal load demand, HCHP_I(t + I | t) represents the heat output value of the CHP _ I type unit, HCHP_II(t + i | t) represents the heat capacity output value of the CHP _ II type unit.
As shown in fig. 2 and fig. 3, in the embodiment of the present application, solving the scheduling model of the microgrid system of the commercial park includes the steps of:
step 1: when t is 0, acquiring a reduced scene through Latin hypercube sampling and scene reduction technology, namely the power condition of the electric vehicle capable of accessing the network, and initializing parameters;
step 2: solving an objective function (formula 4.31) by using a random MPC optimization model according to constraint conditions (formula 4.23-4.30, 4.32-4.34) in a prediction time domain T, and solving by CPLEX;
a micro-grid system optimization scheduling model based on MPC is established, and the purpose is to ensure that the economic target of system optimization is optimal under the condition of satisfying the balance of the system heat and electricity supply and demand. The objective function (4.31) represents the minimum cost of system operation, including the cost of interaction with the external grid, the cost of gas turbine units, and the cost of battery consumption.
Figure RE-GDA0002993549310000121
In the formula: p is a radical ofsIndicating the probability that the scene s corresponds to after the scene cut,
Figure RE-GDA0002993549310000122
representing the interaction power of the microgrid and the external power grid in a scene s, cGrid(t + i | t) represents the purchase price of electricity, cGasIs the price of natural gas, V (t + i | t) represents the purchase amount of natural gas,
Figure RE-GDA0002993549310000123
represents the battery loss cost and T represents the prediction time domain length (the prediction time domain length can be determined as required).
And step 3: predicting time domain forward rolling optimization, moving from the current time t to the time t +1, and executing the first time value to act on the whole system, namely the obtained scheduling result PCHP_I(t+1|t)、PCHP_II(t +1| t) and a power predicted value of the electric automobile cluster are used as initial state values of the system at the next moment;
and 4, step 4: updating the initial parameters, returning to the step 2, and repeating the steps.
And solving the output of the CHP unit by the scheduling model, including the output power generation and the heat energy supply of the unit, and optimally scheduling the microgrid energy of the commercial park according to the solving result of the scheduling model.
In the embodiment of the application, a solution is provided for randomly accessing the CHP type microgrid of the electric vehicle, a random model predictive control method is adopted for solving, continuous optimization scheduling is carried out according to charging and discharging behaviors of the electric vehicle on duty and off duty of a 24-hour user, a scene of random access of the electric vehicle is analyzed, simulation analysis is carried out by taking office buildings (on duty time) and residential communities (off duty time) as examples, the influence of the random charging and discharging of the electric vehicle on the optimization scheduling of the microgrid is researched, the microgrid comprises a scheduling network system integrating an electric power network, a heating power network and an information network, a control center carries out unified centralized scheduling, and the connection decision of information flow and the power balance of energy flow are realized.
The system adopts a random Model Predictive control (SMPC) method to solve, a schedulable scene Model and a battery loss cost Model of electric vehicle random access are established, the randomness of the electric vehicle is generated by analyzing the time from the user to the charging point, the scene of the user electric vehicle random access is generated by using LHS sampling and scene reduction methods, the number of the EV randomly accessing and charging at each time is respectively counted aiming at different situations from the user to the user, and for the situation of random access of a large number of electric vehicles, an integrated controller is arranged in an office area and a residential area, receives a unified scheduling instruction, and can monitor centralized charging and discharging power in real time; aiming at the fact that business electricity consumption and resident electricity consumption are different in Time of Use (TOU) strategy respectively adopted by office buildings and resident houses, when battery constraint is carried out, allowance of a battery SOC at the working Time and the working Time is considered, the lowest requirement of traveling is met, under the condition that economy is guaranteed to be the lowest in different types of microgrid systems, the power requirement of each energy source device is met, a system model is finally converted into a mixed integer linear programming problem, and MATLAB/CPLEX software is used for solving.
The model selects an office building type microgrid of a typical working day, the simulation takes 24 hours a day as the duration, the sampling time interval is 1 hour, the working time is 9:00 a.m., the arrival time in the working time accords with the normal distribution, the working time is 6:00 a.m., and the departure time of the electric vehicle also accords with the normal distribution. The number of the electric automobiles arriving at each moment in the working time is random, the number of the electric automobiles leaving at each moment in the working time is also random, the number scene of the electric automobiles available at each moment point is generated by Latin hypercube sampling and scene reduction, and the heat energy supply and demand balance is completely provided by the CHP unit. The capacity of each electric vehicle is set to be 48kWh, the total number of available electric vehicles is 100, and the upper and lower limits of the SOC of the cluster type electric vehicle are set to be 30% and 100%. The office building adopts commercial electricity price which is divided into four time periods of time-of-use electricity price of peak, valley, flat and peak, as shown in table 4.1.
TABLE 4.1 commercial timesharing electricity and gas prices Table
Figure RE-GDA0002993549310000131
The method generates 2000 scenes that electric vehicles randomly reach a charging point through LHS method sampling, each scene represents the number of the electric vehicles which have arrived at the moment, and 10 representative scenes { DAN ] are obtained through scene reduction technologys(1),…,DANs(T) }, s is 1, …,10, and the sum of the probabilities of the 10 cut scenes is 1. And generating 10 electric vehicle random scenes of the office building type microgrid after reduction, wherein each scene comprises the electric vehicle holding capacity of 24 time points and represents the number of electric vehicles which can be accessed into the microgrid at the time. Most electric vehicles arrive at the work place at 9:00 a.m. and leave the work place at 6:00 a.m.
According to the method for optimizing and scheduling the energy of the micro-grid in the commercial park, the condition that the electric automobile is randomly accessed into the micro-grid in the commercial park is considered, the randomness and the battery loss cost of the electric automobile are researched, different operation scenes are analyzed, the effect of a stochastic model prediction control method in micro-grid scheduling is researched, and the method has good economy.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that the invention is not limited thereto, and may be embodied in other forms without departing from the spirit or essential characteristics thereof. Any modification which does not depart from the functional and structural principles of the present invention is intended to be included within the scope of the claims.

Claims (10)

1. A method for optimizing and scheduling energy of a microgrid in a commercial park is characterized by comprising the following steps:
step S1: constructing a commercial park microgrid comprising electric automobiles: the micro-grid of the commercial park is connected with an external power grid and comprises a combined heat and power unit, wherein the combined heat and power unit comprises a back-pressure combined heat and power unit CHP _ I and an extraction combined heat and power unit CHP _ II;
step S2: constructing a random network-in and network-out scene model of the electric automobile: the micro-grid time t of the electric automobile connected to or leaving the commercial park accords with normal distribution, and the expression is as follows:
Figure FDA0002897844940000011
wherein f represents the microgrid time distribution condition of the electric automobile accessing or leaving the commercial park, and mu and sigma represent the average time and variance of accessing or leaving respectively;
step S3: constructing a battery loss model of the electric automobile: the electric automobile battery loss model expression is as follows:
Figure FDA0002897844940000012
wherein the content of the first and second substances,
Figure FDA0002897844940000013
which represents a cost of the loss of the battery,
Figure FDA0002897844940000014
which represents the total investment cost of the battery,
Figure FDA0002897844940000015
represents the battery cost loss factor, where t represents the current time;
step S4: constructing a commercial park microgrid system scheduling model based on MPC:
under the condition of satisfying the balance of the heat and electricity supply and demand of the system, the optimal economic target of the system optimization is ensured, the target function represents the minimum cost of the system operation,
Figure FDA0002897844940000016
in the formula: p is a radical ofsIndicating the probability that the scene s corresponds to after the scene cut,
Figure FDA0002897844940000017
representing the interaction power of the microgrid and the external power grid in a scene s, cGrid(t + i | t) represents the purchase price of electricity, cGasIs the price of natural gas, V (t + i | t) represents the purchase amount of natural gas,
Figure FDA0002897844940000018
representing the battery loss cost, and T representing the prediction time domain length;
step S5: solving the scheduling model of the microgrid system of the commercial park:
solving the scheduling model of the microgrid system of the commercial park comprises the following steps:
when t is 0, acquiring a cut electric automobile random network access scene through Latin hypercube sampling and scene cutting technology, and initializing parameters;
solving an objective function through CPLEX by using a random MPC optimization model in a prediction time domain T according to constraint conditions;
predicting forward rolling optimization of time domain, moving from the current time t to the time t +1, executing the first time value to act on the whole system, and obtaining a scheduling result PCHP_I(t+1|t)、PCHP_II(t +1| t) and a power predicted value of the electric automobile cluster are used as initial state values of the system at the next moment;
updating the initial parameters and repeating the steps.
2. The optimal scheduling method for microgrid energy in a commercial park according to claim 1, characterized in that: the probability density function expression of the last return time of the electric automobile is as follows:
Figure FDA0002897844940000021
wherein f isa(t) time of arrival, μa＝17.6，σa＝3.4。
3. The optimal scheduling method for microgrid energy in a commercial park according to claim 2, characterized in that: the probability density function expression of the electric automobile at the initial trip time is as follows:
Figure FDA0002897844940000022
wherein f iss(t) represents the trip start time, μs＝9.24，σs＝3.16。
4. The optimal scheduling method for microgrid energy in a commercial park according to claim 1, characterized in that: the expression of the total investment cost of the battery is as follows:
Figure FDA0002897844940000031
wherein the content of the first and second substances,
Figure FDA0002897844940000032
representing the total investment cost, DAN, of the batterys(t + i) represents the number of electric vehicles in scene s, PEV-maxRepresenting maximum power of charge and discharge, CapEVRepresents the battery capacity of the electric vehicle, CpRepresents the unit power cost of the battery of the electric automobile, CcRepresents the cost per unit capacity of the battery of the electric automobile, CpAnd CcRepresenting the investment cost factor, and t and i represent time instants.
5. The optimal scheduling method for microgrid energy in a commercial park according to claim 4, characterized in that: the constraint conditions include:
considering mutual exclusion constraint condition when charging the battery pack, introduce
Figure FDA0002897844940000033
And
Figure FDA0002897844940000034
the 0-1 flag variables of (1) respectively represent the charge and discharge states of the battery at a certain time,
Figure FDA0002897844940000035
Figure FDA0002897844940000036
Figure FDA0002897844940000037
the charge and discharge power of the battery is expressed as:
Figure FDA0002897844940000038
in the formula (I), the compound is shown in the specification,
Figure FDA0002897844940000039
indicating that the discharge is being made at that time,
Figure FDA00028978449400000310
indicating that the charge is being made at that time.
6. The optimal scheduling method for microgrid energy in a commercial park according to claim 5, characterized in that: the constraint further comprises:
SOC value at t + i moment SOC under scene ss(t + i | t) is calculated by the following formula:
Figure FDA00028978449400000311
the battery SOC constraint for time t + i is expressed as:
SOCmin≤SOCs(t+i|t)≤SOCmax,i＝0,…,T-1。
7. the optimal scheduling method for microgrid energy in a commercial park according to claim 6, characterized in that:
the constraints also include a battery loss cost:
Figure FDA0002897844940000041
the expression of the battery cost loss coefficient is as follows:
Figure FDA0002897844940000042
wherein the content of the first and second substances,
Figure FDA0002897844940000043
the cell cost loss factor is expressed as a factor,
Figure FDA0002897844940000044
which is indicative of the state of charge of the battery,
Figure FDA0002897844940000045
indicating the discharge state of the battery, PEV-maxRepresents the maximum power of the battery, delta t represents the time difference of charging and discharging, AtotalRepresenting the desired throughput of the battery.
8. The optimal scheduling method for microgrid energy in a commercial park according to claim 7, characterized in that:
the constraints further include electric power balance in the commercial park microgrid system scheduling model,
the electric power balance condition expression in the commercial park microgrid system scheduling model is as follows:
Figure FDA0002897844940000046
wherein the content of the first and second substances,
Figure FDA0002897844940000047
represents electric vehicle power, P, under scene sCHP_I(t + I | t) represents the CHP _ I type unit power supply, PCHP_II(t + i | t) represents the CHP _ II type unit power supply,
Figure FDA0002897844940000048
expressing the interaction power between the microgrid and the external power grid under the scene s, Pload(t + i | t) represents the total power demand of the consumer electrical load.
9. The optimal scheduling method for microgrid energy in a commercial park according to claim 8, characterized in that: the constraint conditions further comprise an external power grid electricity purchasing constraint which satisfies the following formula:
Figure FDA0002897844940000049
in the formula:
Figure FDA00028978449400000410
the power supply device represents the maximum power for purchasing power from an external power grid, only considers the power purchasing situation and does not consider the power selling situation.
10. The optimal scheduling method for microgrid energy in a commercial park according to claim 9, characterized in that: the constraints also include supply and demand constraints for thermal energy,
the supply and demand constraint formula expression of the heat energy is as follows:
ηh1Hload(t+i|t)≤HCHP_I(t+i|t)+HCHP_II(t+i|t)≤ηh2Hload(t+i|t),i＝1,…,T，
wherein eta ish1And ηh2Reliable lower and upper limit fluctuation coefficients, H, representing thermal load demandload(t + i | t) represents the thermal load demand, HCHP_I(t + I | t) represents the heat output value of the CHP _ I type unit, HCHP_II(t + i | t) represents the heat capacity output value of the CHP _ II type unit.
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