CN113799640A - Energy management method suitable for microgrid comprising electric vehicle charging pile - Google Patents

Energy management method suitable for microgrid comprising electric vehicle charging pile Download PDF

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CN113799640A
CN113799640A CN202110943610.5A CN202110943610A CN113799640A CN 113799640 A CN113799640 A CN 113799640A CN 202110943610 A CN202110943610 A CN 202110943610A CN 113799640 A CN113799640 A CN 113799640A
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charging
energy storage
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electric vehicle
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CN113799640B (en
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杨阳
计远帆
俞侃
李平
毛阗
江全元
耿光超
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Zhejiang University ZJU
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • 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]
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Abstract

The invention discloses an energy management method suitable for a microgrid comprising an electric vehicle charging pile. In the method, the electric automobile needs to reserve a charging pile before charging and submits charging demand information; on the day before the dispatching day, based on the load, the day-ahead prediction data of the renewable energy power generation and day-ahead electric vehicle charging reservation information, and with the aim of minimum operation cost and minimum maximum demand of the microgrid as targets, an electric vehicle ordered charging plan and an energy storage charging and discharging plan of each time period of the whole day are formulated; in each time interval of a scheduling day, based on load and real-time data generated by renewable energy, optimizing by taking the minimum deviation between the energy storage real-time charging and discharging power and the energy storage charging and discharging plan before the day as a target; for the case of electric vehicles, the day-ahead plan is updated in real time based on the latest information. The strategy is generally applicable to a micro-grid integrated with an electric automobile charging pile, and compared with the prior art, the strategy has the advantages of stability, high efficiency, economy, environmental protection, full utilization of the flexibility of the electric automobile, win-win effect and the like.

Description

Energy management method suitable for microgrid comprising electric vehicle charging pile
Technical Field
The invention belongs to the field of energy scheduling, simulation and analysis of a microgrid, and particularly relates to an energy management method suitable for the microgrid containing an electric vehicle charging pile.
Background
A microgrid is a system that integrates distributed energy, energy storage systems and flexible loads. In recent years, micro-grids have been widely used in industrial and commercial buildings with their characteristics contributing to improvement in reliability of local power supply, reduction in electricity cost, and promotion of consumption of renewable energy.
Under the trend of green and low carbon, the electric automobile becomes a novel vehicle which is greatly supported and developed by governments of various countries, and the holding amount of the electric automobile is rapidly increased. The new automobile sales volume of the new energy automobile in China is estimated to reach about 20% of the total automobile sales volume in 2025. The large-scale access of the electric automobile to the power grid can generate non-negligible influence on the planning and operation of the power distribution network and the micro-grid, such as increasing the burden of the power distribution system and bringing about large-scale capacity expansion requirements and the like. Meanwhile, the charging time and power of the electric automobile have certain flexibility, and the charging time and power are important flexibility resources in the micro-grid.
In order to more effectively and practically perform energy scheduling on the microgrid, the flexibility of electric vehicles, energy storage and distributed power supplies in the microgrid is fully utilized, the economy of microgrid scheduling is improved, and researchers deeply research on an optimal scheduling method of the microgrid. For example, patent document CN201110121088.9 discloses a microgrid economic operation optimization scheduling method based on multi-time scale coordination, which divides the scheduling of energy storage and controllable distributed power supplies in a microgrid into two time scales of day-ahead planning and real-time scheduling, and also sets up a coordination and coordination mechanism of the two time scales; patent document CN201710911697.1 regards a slow-charging electric vehicle as a translatable load, regards a fast-charging electric vehicle as a short-term power fluctuation, and uses an energy storage system to stabilize; patent document CN201910979289.9 makes a scheduling strategy for energy storage, electric vehicles, and public power grids in advance, and realizes accurate allocation of user demands; patent document CN202010563452.6 discloses a micro-grid energy management system based on a two-stage optimal charging strategy, which adopts the two-stage optimal charging strategy to achieve the minimum load curve "peak clipping and valley filling" and the minimum user charging cost. Therefore, in the current research, no energy management strategy can meet the actual conditions and requirements of various current micro-grids containing electric vehicle charging piles. The strategies and the methods cannot give full play to the flexibility of the electric automobile and the energy storage, and cannot give consideration to the economy, the rationality and the operability of the micro-grid dispatching.
Disclosure of Invention
The invention discloses an energy management method suitable for a microgrid comprising an electric vehicle charging pile. The strategy comprises an electric automobile charging reservation mechanism, a day-ahead plan, a real-time scheduling mechanism and a day-ahead plan updating mechanism. An electric automobile in the microgrid needs to make an appointment for a charging pile before charging and submits charging demand information; on the day before a dispatching day, modeling a day-ahead plan as a mixed integer linear programming problem by taking the minimum operation cost and the minimum maximum demand of a microgrid as targets based on day-ahead prediction data of load and renewable energy power generation and day-ahead electric vehicle charging reservation information, and making an electric vehicle ordered charging plan and an energy storage charging and discharging plan in each time period of the whole day; in each time interval of a scheduling day, modeling real-time scheduling as a secondary planning problem by taking the minimum deviation between the energy storage real-time charging and discharging power and a day-ahead energy storage charging and discharging plan based on the real-time data of load and renewable energy power generation as a target; in the case of an electric vehicle, the day-ahead plan is updated in real time based on the latest information. The strategy is generally applicable to micro grids of integrated electric vehicle charging piles in industrial and commercial parks, residential communities, electric vehicle charging stations and the like, and conventional loads, electric vehicle charging loads, renewable energy power generation equipment and energy storage equipment in any capacity proportion can be contained in the micro grids. Compared with the prior art, the invention has the advantages of stability, high efficiency, economy, environmental protection, full utilization of the flexibility of the electric automobile, win-win among multiple parties and the like.
The technical scheme adopted by the invention is as follows:
an energy management method suitable for a microgrid comprising an electric vehicle charging pile comprises the following steps:
s1, acquiring day-ahead electric vehicle charging reservation information, wherein the day-ahead electric vehicle charging reservation information is charging demand information submitted by an electric vehicle owner when the electric vehicle owner makes a reservation for an idle charging pile on a charging reservation platform one day before a scheduling day;
s2, dividing a scheduling day into a plurality of time intervals, taking the minimum running cost and the minimum maximum demand of the microgrid in the whole day as targets based on the day-ahead prediction of load and renewable energy power generation and day-ahead electric vehicle charging reservation information, considering the power balance constraint in the microgrid, the technical characteristic constraint of energy storage equipment, the transformer capacity constraint and the electric vehicle charging demand constraint, modeling a day-ahead plan as a mixed integer linear programming problem, solving the optimal demand limit value of the microgrid, and making an electric vehicle ordered charging plan and an energy storage charging and discharging plan in each time interval in the whole day;
s3, in each real-time scheduling time interval of the scheduling day, checking whether a new electric vehicle charging reservation exists or whether an electric vehicle has default behaviors, and immediately updating the day-ahead plan based on the latest information once the new electric vehicle charging reservation exists or the electric vehicle has the default behaviors;
and S4, charging the electric automobile according to an ordered charging plan in a day-ahead plan based on the load and the real-time data of renewable energy power generation in each real-time scheduling period of a scheduling day, considering the internal power balance constraint of the microgrid, the output constraint of energy storage equipment and the demand limit and modeling the real-time scheduling as a secondary planning problem by taking the minimum deviation between the energy storage real-time charging and discharging power and the day-ahead energy storage charging and discharging plan, and determining the energy storage real-time scheduling charging and discharging power.
Preferably, the charging demand information in S1 includes:
1) selecting a charging pile type and a charging pile number, wherein the charging pile type comprises a direct-current quick charging pile and an alternating-current slow charging pile;
2) a reserved charging period comprising a vehicle arrival time and a vehicle departure time;
3) rated capacity of the storage battery of the electric automobile;
4) an electric vehicle battery state of charge (SOC) estimate when the vehicle arrives;
5) desired electric vehicle battery state of charge when the vehicle is driven off.
Preferably, in S2, the method for modeling the day-ahead plan as the mixed integer linear programming problem is as follows:
s21, defining optimization variables according to two types of continuous variables and 0-1 variables as follows:
the continuous variables defined include: charging power of electric automobile numbered as i in t period
Figure BDA0003216074250000031
Charging power of energy storage device in t period
Figure BDA0003216074250000032
Discharge power of energy storage device in t period
Figure BDA0003216074250000033
State of charge (SOC) of energy storage equipment in t periodtAnd the power purchased by the micro-grid to the public power grid in the time period t
Figure BDA0003216074250000034
Power sold by micro-grid to public grid in t period
Figure BDA0003216074250000035
Demand limit value Dmax；
The defined variables 0-1 include: state of charge of energy storage device during time period t
Figure BDA0003216074250000036
Discharge state of energy storage device in t period
Figure BDA0003216074250000037
The micro-grid purchases electricity to the public power grid in the t period
Figure BDA0003216074250000038
Electricity selling state of micro-grid to public grid in t period
Figure BDA0003216074250000039
S22, comprehensively considering the minimum operation cost and the minimum maximum demand of the microgrid, and defining the target functions of the mixed integer linear programming problem as follows:
Figure BDA00032160742500000310
wherein
Figure BDA00032160742500000311
Price coefficients of electricity purchase and electricity sale of the micro-grid to the public power grid in the time period t are respectively obtained; c. CdFor conversion to a daily demand rate; c. CBEnergy storage wear cost coefficient; sigma is a penalty factor; thetaTA set of all time periods throughout the day in the day-ahead plan;
s23, setting the constraint conditions of the objective function of the mixed integer linear programming problem as follows:
1) and power balance constraint:
Figure BDA00032160742500000312
wherein
Figure BDA00032160742500000313
And
Figure BDA00032160742500000314
predicted values of load and photovoltaic power, theta, respectively, at time tEVA collection of electric vehicles is charged for a reservation.
2) Electric automobile electric quantity restraint that charges:
Figure BDA00032160742500000315
wherein
Figure BDA00032160742500000316
Respectively reserving an arrival time SOC and a driving-away time SOC for the electric automobile with the number i; eEViThe battery rated energy of the electric automobile with the number i is obtained;
3) electric vehicle charging power constraint:
Figure BDA00032160742500000317
wherein
Figure BDA00032160742500000318
The rated charging power of a charging pile j reserved for the electric automobile with the number i;
4) electric vehicle charging period constraint:
Figure BDA00032160742500000319
if it is
Figure BDA00032160742500000320
Wherein
Figure BDA00032160742500000321
The reserved arrival time interval and the reserved driving-away time interval are respectively the electric automobile with the number i;
5) and (3) transformer capacity constraint:
Figure BDA0003216074250000041
Figure BDA0003216074250000042
wherein S is the capacity of a transformer connecting the microgrid with a public power grid;
6) and (3) mutual exclusion constraint of the electricity purchasing and selling states of the microgrid:
Figure BDA0003216074250000043
7) demand limit constraints:
Figure BDA0003216074250000044
8) energy storage maximum charge and discharge power constraint:
Figure BDA0003216074250000045
Figure BDA0003216074250000046
wherein
Figure BDA0003216074250000047
Respectively storing energy maximum charging power and energy maximum discharging power;
9) mutual exclusion constraint of energy storage charging and discharging states:
Figure BDA0003216074250000048
10) the energy storage SOC defines constraints:
Figure BDA0003216074250000049
wherein EBRated capacity for energy storage; Δ T is the length of a planned time period in the day ahead; etaB-，ηB+Respectively representing energy storage charging efficiency and energy storage discharging efficiency;
11) energy storage SOC range constraint:
Figure BDA00032160742500000410
wherein
Figure BDA00032160742500000411
SOCRespectively an upper limit and a lower limit of the energy storage SOC.
Preferably, the electric vehicle default behavior in S3 includes:
1) no appointment was made 24:00 of the day before charging;
2) actively canceling the appointment of the previous day;
3) not reached before the scheduled start time of day;
4) driving away before the charging completion time planned in the day ahead;
5) the deviation of the actual SOC of the storage battery when the vehicle arrives and the estimated value when the vehicle is reserved is larger than a threshold value;
6) other behaviors may cause the actual charging process of the electric vehicle to be inconsistent with the schedule at the day-ahead.
Preferably, in S3, the specific method for updating the day-ahead plan based on the latest information is as follows: the day-ahead plan is assumed to divide the whole day into N time intervals, and the default behavior of the electric vehicle occurs in the mth time interval; and (3) continuing to use a model obtained by modeling of the original day-ahead plan, inputting latest reservation information of the electric automobile and the SOC of the energy storage equipment into the model, changing the day-ahead planning problem with the original time span of 1-N time periods into a day-in plan with m-N time periods, generating an energy dispatching plan from the current time to the day at 24:00, and updating the day-ahead planning.
Preferably, the specific method for modeling the real-time scheduling as the quadratic programming problem in S4 is as follows:
s41, defining the optimization variables as: output power P of energy storage system in current scheduling periodBAnd exchange power P of the micro-grid and the public power gridgrid(ii) a And the optimization variable is regulated to take a positive value when the micro-grid is supplied with power, and otherwise, the optimization variable takes a negative value;
s42, defining an objective function of the quadratic programming problem as the minimum deviation between the actual output power of the stored energy and the charge and discharge power of the stored energy in the plan before the day, wherein the objective function is described as follows:
Figure BDA0003216074250000051
wherein
Figure BDA0003216074250000052
The energy storage charging and discharging power of the corresponding time interval in the day-ahead plan;
s43, setting the constraint conditions of the objective function of the quadratic programming problem as follows:
1) and power balance constraint:
Figure BDA0003216074250000053
wherein P isLAnd PPVActual values of the current load and the photovoltaic power respectively; pEViThe actual value of the charging power of the electric automobile with the current serial number i is obtained;
2) energy storage maximum charge and discharge power constraint:
Figure BDA0003216074250000054
if the energy storage SOC is greater than or equal to the upper limit, the constraint is changed into:
Figure BDA0003216074250000055
if the energy storage SOC is less than or equal to the lower limit, the constraint becomes:
Figure BDA0003216074250000056
3) and (3) transformer capacity constraint:
-S≤Pgrid≤S
4) demand limit constraints:
Figure BDA0003216074250000057
wherein DmaxFor demand limits optimized in a day-ahead plan,
Figure BDA0003216074250000058
is the actual maximum demand of the microgrid.
The invention provides an energy management method suitable for a microgrid containing an electric vehicle charging pile, and compared with the prior art, the method provided by the invention mainly has the following advantages and improvements:
1. an electric vehicle charging reservation mechanism is considered in a micro-grid energy management strategy, charging demand information of the electric vehicle is effectively obtained, and the electric vehicle charging reservation mechanism is beneficial to making an ordered power utilization plan of the electric vehicle reasonably;
2. the ordered power utilization plan and the energy storage charging and discharging plan of each electric automobile are solved by the day-ahead plan, multi-target optimization (minimum operation cost and minimum demand of a microgrid) is realized from the global perspective, the energy storage charging and discharging power can be adjusted according to the real-time condition by real-time scheduling, and the flexibility of the electric automobiles and the energy storage is fully exerted;
3. the practical conditions of the micro-grid and the practical charging behaviors of the electric automobile are considered, the strategy has strong reality and operability, and can be conveniently applied to various micro-grids containing electric automobile charging piles.
Drawings
FIG. 1 is a flow chart of an energy management method for a microgrid including an electric vehicle charging pile;
FIG. 2 is a schematic diagram of a day-ahead plan update mechanism;
FIG. 3 is a schematic diagram of an exemplary embodiment of a microgrid configuration;
FIG. 4 is a microgrid day-ahead plan;
FIG. 5 is a day-ahead plan updated after an electric vehicle breach of default has occurred;
fig. 6 is a simulation result of the real-time scheduling of the microgrid all day.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. The technical features in the embodiments of the present invention can be combined correspondingly without mutual conflict.
As shown in fig. 1, in a preferred embodiment of the present invention, there is provided an energy management method for a microgrid including electric vehicle charging piles, comprising the steps of:
and S1, acquiring the day-ahead electric vehicle charging reservation information, wherein the day-ahead electric vehicle charging reservation information is the charging demand information submitted by an electric vehicle owner when the electric vehicle owner makes a reservation for the idle charging pile on the charging reservation platform one day before the scheduling day.
In S1 of the present embodiment, the charging demand information includes:
1) selecting a charging pile type and a charging pile number, wherein the charging pile type comprises a direct-current quick charging pile and an alternating-current slow charging pile;
2) a reserved charging period comprising a vehicle arrival time and a vehicle departure time;
3) rated capacity of the storage battery of the electric automobile;
4) an electric vehicle battery state of charge (SOC) estimate when the vehicle arrives;
5) desired electric vehicle battery state of charge when the vehicle is driven off.
S2, dividing a scheduling day into a plurality of time intervals, based on the day-ahead prediction of load and renewable energy power generation and day-ahead electric vehicle charging reservation information, aiming at the minimum operation cost and the minimum maximum demand of the microgrid in the whole day, considering the internal power balance constraint of the microgrid, the technical characteristic constraint of the energy storage equipment, the transformer capacity constraint and the electric vehicle charging demand constraint, modeling a day-ahead plan as a mixed integer linear programming problem, solving the optimal demand limit value of the microgrid, and making an electric vehicle ordered charging plan and an energy storage charging and discharging plan in each time interval in the whole day.
In S2 of the present embodiment, the method of modeling the day-ahead plan as the mixed integer linear programming problem is as follows:
s21, defining optimization variables according to two types of continuous variables and 0-1 variables as follows:
the continuous variables defined include: charging power of electric automobile numbered as i in t period
Figure BDA0003216074250000071
Charging power of energy storage device in t period
Figure BDA0003216074250000072
Discharge power of energy storage device in t period
Figure BDA0003216074250000073
State of charge (SOC) of energy storage equipment in t periodtAnd the power purchased by the micro-grid to the public power grid in the time period t
Figure BDA0003216074250000074
Power sold by micro-grid to public grid in t period
Figure BDA0003216074250000075
Demand limit value Dmax；
The defined variables 0-1 include: state of charge of energy storage device during time period t
Figure BDA0003216074250000076
Discharge state of energy storage device in t period
Figure BDA0003216074250000077
The micro-grid purchases electricity to the public power grid in the t period
Figure BDA0003216074250000078
Electricity selling state of micro-grid to public grid in t period
Figure BDA0003216074250000079
S22, comprehensively considering the minimum operation cost and the minimum maximum demand of the microgrid, and defining the target functions of the mixed integer linear programming problem as follows:
Figure BDA00032160742500000710
wherein
Figure BDA00032160742500000711
Price coefficients of electricity purchase and electricity sale of the micro-grid to the public power grid in the time period t are respectively obtained; c. CdFor conversion to a daily demand rate; c. CBEnergy storage wear cost coefficient; σ is a relatively small penalty factor, aiming to maintain the SOC of the energy storage device at a high level without affecting the economy; thetaTA set of all time periods throughout the day in the day-ahead plan;
s23, setting the constraint conditions of the objective function of the mixed integer linear programming problem as follows:
1) and power balance constraint:
Figure BDA00032160742500000712
wherein
Figure BDA00032160742500000713
And
Figure BDA00032160742500000714
predicted values of load and photovoltaic power, theta, respectively, at time tEVCharging a set of electric vehicles for a reservation;
the constraints of the present invention apply to any time period, as not specifically stated.
2) Electric automobile electric quantity restraint that charges:
Figure BDA00032160742500000715
wherein
Figure BDA00032160742500000716
Respectively reserving an arrival time SOC and a driving-away time SOC for the electric automobile with the number i; eEViThe battery rated energy of the electric automobile with the number i is obtained;
3) electric vehicle charging power constraint:
Figure BDA00032160742500000717
wherein
Figure BDA00032160742500000718
The rated charging power of a charging pile j reserved for the electric automobile with the number i;
4) electric vehicle charging period constraint:
Figure BDA0003216074250000081
if it is
Figure BDA0003216074250000082
Wherein
Figure BDA0003216074250000083
The reserved arrival time interval and the reserved driving-away time interval are respectively the electric automobile with the number i;
5) and (3) transformer capacity constraint:
Figure BDA0003216074250000084
Figure BDA0003216074250000085
wherein S is the capacity of a transformer connecting the microgrid with a public power grid;
6) and (3) mutual exclusion constraint of the electricity purchasing and selling states of the microgrid:
Figure BDA0003216074250000086
7) demand limit constraints:
Figure BDA0003216074250000087
8) energy storage maximum charge and discharge power constraint:
Figure BDA0003216074250000088
Figure BDA0003216074250000089
wherein
Figure BDA00032160742500000810
Respectively storing energy maximum charging power and energy maximum discharging power;
9) mutual exclusion constraint of energy storage charging and discharging states:
Figure BDA00032160742500000811
10) the energy storage SOC defines constraints:
Figure BDA00032160742500000812
wherein EBRated capacity for energy storage; Δ T is the length of a planned time period in the day ahead; etaB-，ηB+Respectively representing energy storage charging efficiency and energy storage discharging efficiency;
11) energy storage SOC range constraint:
Figure BDA00032160742500000813
wherein
Figure BDA00032160742500000814
SOCRespectively an upper limit and a lower limit of the energy storage SOC.
S3, in each real-time scheduling time interval of the scheduling day, whether a new electric vehicle charging reservation exists or whether an electric vehicle has default behaviors is checked, and once the new electric vehicle charging reservation exists or the situation that the electric vehicle has default behaviors occurs, the day-ahead plan is updated immediately based on the latest information.
In S3 of the present embodiment, the electric vehicle default behavior includes:
1) no appointment was made 24:00 of the day before charging;
2) actively canceling the appointment of the previous day;
3) not reached before the scheduled start time of day;
4) driving away before the charging completion time planned in the day ahead;
5) the deviation of the actual SOC of the storage battery when the vehicle arrives and the estimated value when the vehicle is reserved is larger than a threshold value;
6) other behaviors may cause the actual charging process of the electric vehicle to be inconsistent with the schedule at the day-ahead.
In addition, the update mechanism principle planned before the day based on the latest information update in this embodiment is shown in fig. 2, and the specific update method is as follows:
the day-ahead plan is assumed to divide the whole day into N time intervals, and the default behavior of the electric vehicle occurs in the mth time interval; and (3) continuing to use a model obtained by modeling of the original day-ahead plan, inputting latest reservation information of the electric automobile and the SOC of the energy storage equipment into the model, changing the day-ahead plan problem with the original time span of 1-N time periods into a day-in plan with m-N time periods, generating an energy scheduling plan from the current time to the day at 24:00, and updating the day-ahead plan.
And S4, charging the electric automobile according to an ordered charging plan in a day-ahead plan based on the load and the real-time data of renewable energy power generation in each real-time scheduling period of a scheduling day, considering the internal power balance constraint of the microgrid, the output constraint of energy storage equipment and the demand limit and modeling the real-time scheduling as a secondary planning problem by taking the minimum deviation between the energy storage real-time charging and discharging power and the day-ahead energy storage charging and discharging plan, and determining the energy storage real-time scheduling charging and discharging power.
In S4 of the present embodiment, a specific method for modeling real-time scheduling as a quadratic programming problem is as follows:
s41, defining the optimization variables as: output power P of energy storage system in current scheduling periodBAnd exchange power P of the micro-grid and the public power gridgrid(ii) a And the optimization variable is regulated to take a positive value when the micro-grid is supplied with power, and otherwise, the optimization variable takes a negative value;
s42, defining an objective function of the quadratic programming problem as the minimum deviation between the actual output power of the stored energy and the charge and discharge power of the stored energy in the plan before the day, wherein the objective function is described as follows:
Figure BDA0003216074250000091
wherein
Figure BDA0003216074250000092
The energy storage charging and discharging power of the corresponding time interval in the day-ahead plan;
s43, setting the constraint conditions of the objective function of the quadratic programming problem as follows:
1) and power balance constraint:
Figure BDA0003216074250000093
wherein P isLAnd PPVActual values of the current load and the photovoltaic power respectively; pEViThe actual value of the charging power of the electric automobile with the current serial number i is obtained;
2) energy storage maximum charge and discharge power constraint:
Figure BDA0003216074250000094
if the energy storage SOC is greater than or equal to the upper limit, the constraint is changed into:
Figure BDA0003216074250000095
if the energy storage SOC is less than or equal to the lower limit, the constraint becomes:
Figure BDA0003216074250000101
3) and (3) transformer capacity constraint:
-S≤Pgrid≤S
4) demand limit constraints:
Figure BDA0003216074250000102
wherein DmaxFor demand limits optimized in a day-ahead plan,
Figure BDA0003216074250000103
is the actual maximum demand of the microgrid. Considering that the above constraint may not be satisfied if the prediction error is large, the demand limit may be gradually increased until the optimization problem can be solved.
The energy management method for the microgrid including the electric vehicle charging pile is applied to an example for detailed description, and the frames of the steps are shown as S1-S4, which are not described again, and specific implementation details and technical effects of the method are mainly shown below.
Example (b):
considering the office park microgrid as shown in fig. 3, the base load in the park is primarily office building loads, with peak loads in summer of about 600kW occurring at 8:00-11: 00. According to the above-described embodiments, energy management is performed on a certain summer typical day of the microgrid, and the scheduling results are simulated throughout the day.
One day before the scheduling day, the energy management system executes a day-ahead plan (the time granularity is 15min) according to the load and photovoltaic prediction results and the electric vehicle charging reservation information, and optimizes the charging plan of each electric vehicle and charging and discharging of stored energy. The electric vehicle charging schedule information is shown in table 1, and the obtained day-ahead schedule is shown in fig. 4. The energy storage system is charged at the valley time period and discharged at the peak time period or the peak time period of the load, so that the effects of peak clipping, valley filling and price difference arbitrage are exerted. Each electric automobile has a definite charging plan, the charging time is staggered from the load peak, meanwhile, the charging can be completed before the leaving time, and the flexibility of the electric automobile is fully exerted. The power (namely the power of PCC) purchased from the micro-grid to the public power grid is limited below 400kW, so that the required electric charge of the micro-grid is reduced, and the capacity expansion requirement of the transformer can be delayed.
TABLE 1 electric vehicle charging appointment information day ahead
Figure BDA0003216074250000104
On the scheduling day, the real-time scheduling program is executed once per minute, and the energy storage charging and discharging power of the next minute is optimized. The electric vehicle follows an orderly charging schedule in a day-ahead schedule, and the energy storage system can increase the output power if necessary to compensate for load and photovoltaic prediction errors.
Assuming that all day-ahead charging reservations are complied with, only one electric vehicle (EV7) arrives at 14:00 for temporary demand without advance reservation, and it is desirable to charge an electric vehicle battery with a rated capacity of 80kWh from 15% to 90% before 16: 30. This default action triggers the day-ahead plan update mechanism, with day-ahead plans from 14:00 to 24:00 being updated. At 14:00, the EVs 1-5 have started charging or finished charging, the state is not affected, and the affected electric vehicles are only EVs 6 and 7. The updated day-ahead plan is shown in fig. 5.
After the real-time scheduling of the whole day is completed, the actual energy storage charge-discharge power and the PCC power are as shown in fig. 6. The real-time scheduling result better follows the day-ahead plan, the PCC power is more balanced, and the maximum demand of the micro-grid is greatly reduced. Even if one electric automobile is charged quickly under the condition of no reservation, the influence of the electric automobile is weakened by a day-ahead plan updating mechanism, and the maximum demand of the micro-grid is not increased obviously. The energy management strategy of the invention makes full use of the flexibility of the energy storage and electric vehicles. In addition, the strategy can reserve the energy of the stored energy to the most needed time, and the potential of the stored energy as a spare is excavated to a greater extent.
To demonstrate the superiority of the microgrid energy management strategy of the present invention, consider the following two strategies:
case1, disordered charging of the electric automobile; energy storage time sequence charging and discharging (charging in a valley period and discharging in a peak period with a larger load); case2 energy management strategy proposed by the present invention.
The simulation results corresponding to the two energy management strategies are shown in table 2.
For the micro-grid, stored energy can be used for arbitrage by utilizing the electrovalence difference, and the operation cost is reduced. The operating cost of Case2 is slightly higher compared to Case 1. This is because in Case1, stored energy is discharged only during the peak period of electricity prices, and arbitrage is performed to the maximum extent, while Case2 also needs to be taken into consideration to reduce the maximum demand. Compared with Case1, the maximum demand of Case2 is 100kW less, and the monthly demand charge can be reduced by 4200 yuan calculated according to the monthly demand rate of 42 yuan/kWh. In addition, the reduction of the maximum demand delays the capacity expansion demand of the transformer, and can help the micro-grid to save the capacity expansion cost of tens of thousands of yuan.
TABLE 2 comparison of two energy management strategies
Figure BDA0003216074250000111
The owner of the electric automobile not only enjoys convenient charging service, but also reduces the charging expense. Only the electricity charge during charging is considered, and all owners save 58 yuan in the day.
For a power distribution network, reduction of load peak-valley difference of a micro-grid is beneficial to reducing peak-load pressure of the micro-grid, economic and safe operation level of the power grid is improved, and power generation efficiency is improved.
In a word, the energy management strategy provided by the invention accords with the benefits of a micro-grid, an electric vehicle owner and a power distribution network, is a multi-win strategy and gives consideration to economic benefits, social benefits and ecological benefits.
The above-described embodiments are merely preferred embodiments of the present invention, which should not be construed as limiting the invention. Various changes and modifications may be made by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present invention. Therefore, the technical scheme obtained by adopting the mode of equivalent replacement or equivalent transformation is within the protection scope of the invention.

Claims (6)

1. The energy management method suitable for the microgrid comprising the electric vehicle charging pile is characterized by comprising the following steps of:
s1, acquiring day-ahead electric vehicle charging reservation information, wherein the day-ahead electric vehicle charging reservation information is charging demand information submitted by an electric vehicle owner when the electric vehicle owner makes a reservation for an idle charging pile on a charging reservation platform one day before a scheduling day;
s2, dividing a scheduling day into a plurality of time intervals, taking the minimum running cost and the minimum maximum demand of the microgrid in the whole day as targets based on the day-ahead prediction of load and renewable energy power generation and day-ahead electric vehicle charging reservation information, considering the power balance constraint in the microgrid, the technical characteristic constraint of energy storage equipment, the transformer capacity constraint and the electric vehicle charging demand constraint, modeling a day-ahead plan as a mixed integer linear programming problem, solving the optimal demand limit value of the microgrid, and making an electric vehicle ordered charging plan and an energy storage charging and discharging plan in each time interval in the whole day;
s3, in each real-time scheduling time interval of the scheduling day, checking whether a new electric vehicle charging reservation exists or whether an electric vehicle has default behaviors, and immediately updating the day-ahead plan based on the latest information once the new electric vehicle charging reservation exists or the electric vehicle has the default behaviors;
and S4, charging the electric automobile according to an ordered charging plan in a day-ahead plan based on the load and the real-time data of renewable energy power generation in each real-time scheduling period of a scheduling day, considering the minimum deviation between the energy storage real-time charging and discharging power and the day-ahead energy storage charging and discharging plan, and modeling the real-time scheduling as a secondary planning problem to determine the energy storage real-time scheduling charging and discharging power by taking the internal power balance constraint of the microgrid, the output constraint of the energy storage equipment and the demand limit into consideration.
2. The energy management method suitable for the microgrid containing an electric vehicle charging pile, as claimed in claim 1, characterized in that: the charging demand information in S1 includes:
1) selecting a charging pile type and a charging pile number, wherein the charging pile type comprises a direct-current quick charging pile and an alternating-current slow charging pile;
2) a reserved charging period comprising a vehicle arrival time and a vehicle departure time;
3) rated capacity of the storage battery of the electric automobile;
4) an electric vehicle battery state of charge (SOC) estimate when the vehicle arrives;
5) desired electric vehicle battery state of charge when the vehicle is driven off.
3. The energy management method suitable for the microgrid containing an electric vehicle charging pile, as claimed in claim 1, characterized in that: in S2, the method for modeling the day-ahead plan as the mixed integer linear programming problem is as follows:
s21, defining optimization variables according to two types of continuous variables and 0-1 variables as follows:
the continuous variables defined include: charging power of electric automobile numbered as i in t period
Figure FDA0003216074240000011
Charging power of energy storage device in t period
Figure FDA0003216074240000012
Discharge power of energy storage device in t period
Figure FDA0003216074240000013
State of charge (SOC) of energy storage equipment in t periodtAnd the power purchased by the micro-grid to the public power grid in the time period t
Figure FDA0003216074240000014
Power sold by micro-grid to public grid in t period
Figure FDA0003216074240000015
Demand limit value Dmax；
The defined variables 0-1 include: state of charge of energy storage device during time period t
Figure FDA0003216074240000016
Discharge state of energy storage device in t period
Figure FDA0003216074240000017
The micro-grid purchases electricity to the public power grid in the t period
Figure FDA0003216074240000021
Electricity selling state of micro-grid to public grid in t period
Figure FDA0003216074240000022
S22, comprehensively considering the minimum operation cost and the minimum maximum demand of the micro-grid, and defining an objective function of a mixed integer linear programming problem as follows:
Figure FDA0003216074240000023
wherein
Figure FDA0003216074240000024
Price coefficients of electricity purchase and electricity sale of the micro-grid to the public power grid in the time period t are respectively obtained; c. CdFor conversion to a daily demand rate; c. CBEnergy storage wear cost coefficient; sigma is a penalty factor; thetaTA set of all time periods throughout the day in the day-ahead plan;
s23, setting the constraint conditions of the objective function of the mixed integer linear programming problem as follows:
1) and power balance constraint:
Figure FDA0003216074240000025
wherein
Figure FDA0003216074240000026
And
Figure FDA0003216074240000027
predicted values of load and photovoltaic power, theta, respectively, at time tEVCharging a set of electric vehicles for a reservation;
2) electric automobile electric quantity restraint that charges:
Figure FDA0003216074240000028
wherein
Figure FDA0003216074240000029
Respectively reserving an arrival time SOC and a driving-away time SOC for the electric automobile with the number i; eEViThe battery rated energy of the electric automobile with the number i is obtained;
3) electric vehicle charging power constraint:
Figure FDA00032160742400000210
wherein
Figure FDA00032160742400000211
The rated charging power of a charging pile j reserved for the electric automobile with the number i;
4) electric vehicle charging period constraint:
Figure FDA00032160742400000212
if it is
Figure FDA00032160742400000213
Wherein
Figure FDA00032160742400000214
The reserved arrival time interval and the reserved driving-away time interval are respectively the electric automobile with the number i;
5) and (3) transformer capacity constraint:
Figure FDA00032160742400000215
Figure FDA00032160742400000216
wherein S is the capacity of a transformer connecting the microgrid with a public power grid;
6) and (3) mutual exclusion constraint of the electricity purchasing and selling states of the microgrid:
Figure FDA00032160742400000217
7) demand limit constraints:
Figure FDA00032160742400000218
8) energy storage maximum charge and discharge power constraint:
Figure FDA0003216074240000031
Figure FDA0003216074240000032
wherein
Figure FDA0003216074240000033
Respectively storing energy maximum charging power and energy maximum discharging power;
9) mutual exclusion constraint of energy storage charging and discharging states:
Figure FDA0003216074240000034
10) the energy storage SOC defines constraints:
Figure FDA0003216074240000035
wherein EBRated capacity for energy storage; Δ T is the length of a planned time period in the day ahead; etaB-，ηB+Respectively representing energy storage charging efficiency and energy storage discharging efficiency;
11) energy storage SOC range constraint:
Figure FDA0003216074240000036
wherein
Figure FDA0003216074240000037
SOCRespectively an upper limit and a lower limit of the energy storage SOC.
4. The energy management method suitable for the microgrid containing an electric vehicle charging pile, as claimed in claim 1, characterized in that: the electric vehicle default behavior in S3 includes:
1) no appointment was made 24:00 of the day before charging;
2) actively canceling the appointment of the previous day;
3) not reached before the scheduled start time of day;
4) driving away before the charging completion time planned in the day ahead;
5) the deviation of the actual SOC of the storage battery when the vehicle arrives and the estimated value when the vehicle is reserved is larger than a threshold value;
6) other behaviors may cause the actual charging process of the electric vehicle to be inconsistent with the schedule at the day-ahead.
5. The energy management method suitable for the electric vehicle pile-charging microgrid according to claim 1, characterized in that: the specific method for updating the day-ahead plan based on the latest information in S3 is as follows: the day-ahead plan is assumed to divide the whole day into N time intervals, and the default behavior of the electric vehicle occurs in the mth time interval; and (3) inputting the latest reservation information of the electric automobile and the SOC of the energy storage equipment into a model obtained by modeling by using the original day-ahead plan, changing the day-ahead plan problem with the original time span of 1-N time periods into a day-in plan with m-N time periods, generating an energy dispatching plan from the current time to the day at 24:00, and updating the day-ahead plan.
6. The energy management method suitable for the microgrid containing an electric vehicle charging pile, as claimed in claim 1, characterized in that: the specific method for modeling the real-time scheduling as the quadratic programming problem in S4 is as follows:
s41, defining the optimization variables as: output power P of energy storage system in current scheduling periodBAnd exchange power P of the micro-grid and the public power gridgrid(ii) a And the optimization variable is regulated to take a positive value when the micro-grid is supplied with power, and otherwise, the optimization variable takes a negative value;
s42, defining an objective function of the quadratic programming problem as the minimum deviation between the actual output power of the stored energy and the charge and discharge power of the stored energy in the plan before the day, wherein the objective function is described as follows:
Figure FDA0003216074240000041
wherein
Figure FDA0003216074240000042
The energy storage charging and discharging power of the corresponding time interval in the day-ahead plan;
s43, setting the constraint conditions of the objective function of the quadratic programming problem as follows:
1) and power balance constraint:
Figure FDA0003216074240000043
wherein P isLAnd PPVActual values of the current load and the photovoltaic power respectively; pEViThe actual value of the charging power of the electric automobile with the current serial number i is obtained;
2) energy storage maximum charge and discharge power constraint:
Figure FDA0003216074240000044
if the energy storage SOC is greater than or equal to the upper limit, the constraint is changed into:
Figure FDA0003216074240000045
if the energy storage SOC is less than or equal to the lower limit, the constraint becomes:
Figure FDA0003216074240000046
3) and (3) transformer capacity constraint:
-S≤Pgrid≤S
4) demand limit constraints:
Figure FDA0003216074240000047
wherein DmaxFor demand limits optimized in a day-ahead plan,
Figure FDA0003216074240000048
is the actual maximum demand of the microgrid.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114435165A (en) * 2022-01-25 2022-05-06 广东电网有限责任公司 Charging method and device of charging pile, electronic equipment and storage medium
CN115347588A (en) * 2022-10-19 2022-11-15 成都理工大学 Microgrid energy storage system
CN116757877A (en) * 2023-08-22 2023-09-15 国网山西省电力公司运城供电公司 Power grid line loss reduction optimization method and system for new energy access power distribution network

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011103741A (en) * 2009-11-11 2011-05-26 Mazda Motor Corp Charging facility management system in microgrid
WO2015049969A1 (en) * 2013-10-04 2015-04-09 Kabushiki Kaisha Toshiba Operation management device for electric vehicle, and operation planning method
CN109948816A (en) * 2018-09-20 2019-06-28 国网江苏省电力有限公司盐城供电分公司 A kind of microgrid dispatching method
CN110570007A (en) * 2019-06-17 2019-12-13 上海交通大学 Multi-time scale optimized scheduling method for electric vehicle
CN111740403A (en) * 2020-05-09 2020-10-02 国网浙江省电力有限公司台州供电公司 Master-slave game scheduling strategy for power grid operator and electric vehicle cluster
CN111740417A (en) * 2020-07-09 2020-10-02 上海电机学院 Joint microgrid operation method considering electric vehicle charging failure
CN112134300A (en) * 2020-10-09 2020-12-25 国网江苏省电力有限公司无锡供电分公司 Reservation-based rolling optimization operation method and system for electric vehicle light storage charging station
CN112202201A (en) * 2020-09-24 2021-01-08 上海电机学院 Joint microgrid operation strategy considering demand response and electric automobile
CN112488444A (en) * 2020-10-31 2021-03-12 海南电网有限责任公司 Electric vehicle fast-slow synchronous ordered charging scheduling method and electric quantity settlement method

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011103741A (en) * 2009-11-11 2011-05-26 Mazda Motor Corp Charging facility management system in microgrid
WO2015049969A1 (en) * 2013-10-04 2015-04-09 Kabushiki Kaisha Toshiba Operation management device for electric vehicle, and operation planning method
CN109948816A (en) * 2018-09-20 2019-06-28 国网江苏省电力有限公司盐城供电分公司 A kind of microgrid dispatching method
CN110570007A (en) * 2019-06-17 2019-12-13 上海交通大学 Multi-time scale optimized scheduling method for electric vehicle
CN111740403A (en) * 2020-05-09 2020-10-02 国网浙江省电力有限公司台州供电公司 Master-slave game scheduling strategy for power grid operator and electric vehicle cluster
CN111740417A (en) * 2020-07-09 2020-10-02 上海电机学院 Joint microgrid operation method considering electric vehicle charging failure
CN112202201A (en) * 2020-09-24 2021-01-08 上海电机学院 Joint microgrid operation strategy considering demand response and electric automobile
CN112134300A (en) * 2020-10-09 2020-12-25 国网江苏省电力有限公司无锡供电分公司 Reservation-based rolling optimization operation method and system for electric vehicle light storage charging station
CN112488444A (en) * 2020-10-31 2021-03-12 海南电网有限责任公司 Electric vehicle fast-slow synchronous ordered charging scheduling method and electric quantity settlement method

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114435165A (en) * 2022-01-25 2022-05-06 广东电网有限责任公司 Charging method and device of charging pile, electronic equipment and storage medium
CN114435165B (en) * 2022-01-25 2024-03-08 广东电网有限责任公司 Charging method and device of charging pile, electronic equipment and storage medium
CN115347588A (en) * 2022-10-19 2022-11-15 成都理工大学 Microgrid energy storage system
CN115347588B (en) * 2022-10-19 2022-12-20 成都理工大学 Microgrid energy storage system
CN116757877A (en) * 2023-08-22 2023-09-15 国网山西省电力公司运城供电公司 Power grid line loss reduction optimization method and system for new energy access power distribution network
CN116757877B (en) * 2023-08-22 2023-11-28 国网山西省电力公司运城供电公司 Power grid line loss reduction optimization method and system for new energy access power distribution network

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