CN110751368A - Electric vehicle storage and charging station planning method considering flexibility of charging load - Google Patents
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Abstract
The invention provides an electric vehicle storage and charging station planning method considering flexibility of charging load, and belongs to the field of electric vehicle charging station planning and power grid energy storage planning. The method comprises the steps of firstly constructing a randomly planned output scene set of renewable energy sources and an electric vehicle charging energy boundary, then establishing an electric vehicle storage and charging station planning model which is composed of an objective function and constraint conditions and takes charging load flexibility into consideration, and solving the model by utilizing a Benders decomposition method to obtain an optimal planning result of the electric vehicle storage and charging station. According to the invention, the electric vehicle charging station and the energy storage are taken as a common planning main body for the first time, so that the problems of site selection and volume fixing of the electric vehicle charging station and the energy storage can be perfectly considered, the flexibility of the charging load of the electric vehicle can be considered in the planning, and a reasonable and effective storage and charging station planning scheme can be finally obtained through solving.
Description
Technical Field
The invention belongs to the field of electric vehicle charging station planning and power grid energy storage planning, and particularly relates to an electric vehicle charging station planning method considering flexibility of charging load.
Background
Electric vehicles have an unparalleled advantage in promoting global energy sustainability and mitigating global warming. In recent years, the world has witnessed dramatic developments in the electric vehicle industry. In 2017, the production and marketing scale of the global electric automobiles reaches 122.4 thousands, the same ratio is increased by 58%, and the production and marketing scale exceeds 200 thousands in 2018. With the large-scale access of electric vehicles to the power grid, the increased charging demand brings a severe test to the safety and reliability of the operation of the power distribution network, and the rational planning of the site selection layout and capacity of the electric vehicle charging station becomes a research hotspot. In addition, energy storage systems are beginning to play an increasingly critical role in power distribution systems, and are becoming a focus of another academic community. Energy storage if configured in an electric vehicle charging station, can provide more flexibility for the charging service that the charging station provides throughout the day. Therefore, the research on the common optimal configuration of the electric vehicle charging station and the energy storage in the power distribution network is of great significance.
Research on the co-planning of electric vehicle charging stations and energy storage is still in an early stage, but some research results have been published. Most of the related researches at present are optimized according to the capacity and the operation strategy of station-level energy storage in a charging station, and the problem of site selection of the energy storage in a power distribution network is not involved. In addition, when planning an electric vehicle charging station, most of research processes uncertainty of electric vehicle charging load through a method of a typical scene, and the uncertainty often does not fit with the charging requirement of the electric vehicle in reality. In summary, a planning technique that takes the electric vehicle charging station and the energy storage as a planning subject, considers the positions and the respective capacities of the two as variables and reasonably considers the flexibility of the electric vehicle charging load is still lacking.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a method for planning an electric vehicle storage and charging station by considering the flexibility of a charging load. The invention takes the electric vehicle charging station and the energy storage as a common planning main body for the first time, not only can the problems of site selection and volume fixing of the electric vehicle charging station and the energy storage be considered, but also the flexibility of the charging load of the electric vehicle can be considered in the planning, and finally, the reasonable and effective planning scheme of the storage and charging station can be obtained by solving.
The invention provides an electric vehicle storage and charging station planning method considering charging load flexibility, which is characterized by comprising the following steps of:
1) constructing a randomly planned output scene set of renewable energy sources and a charging energy boundary of the electric vehicle; the method comprises the following specific steps:
1-1) constructing a randomly planned output scene set of renewable energy sources;
clustering a sunlight-photovoltaic output curve, a sunlight-wind power output curve and a daily load curve of a region to be planned in the past whole year by using a K-means clustering algorithm respectively to obtain sunlight-photovoltaic output typical curves respectively corresponding to 4 weathers of sunny days, cloudy days and rainy days, sunlight-wind power output typical curves respectively corresponding to 4 seasons of spring, summer, autumn and winter and 2-class daily load typical curves of working days and non-working days, and combining the four weathers, the four seasons and the typical curves respectively corresponding to the working days and the non-working days into a 4 × 4 × 2-class typical scene as a randomly-planned output scene set of renewable energy sources;
1-2) sampling the charging behavior parameters of the electric vehicles by using a Monte Carlo sampling method to obtain the time of arrival and departure of all the electric vehicles at the charging stations, wherein the lower bound of the accumulated energy provided by a single charging station for the electric vehicles at any moment is the sum of the energy required by all the vehicles which have been charged at the charging station before the moment, and the upper bound of the provided energy is the sum of the energy required by all the vehicles which have arrived at the charging station before the moment;
2) establishing an electric vehicle storage and charging station planning model considering the flexibility of charging load, wherein the model consists of an objective function and constraint conditions; the method comprises the following specific steps:
2-1) determining an objective function of the model, wherein the expression is as follows:
wherein the equal annual investment costs of charging stations and energy storage
The calculation expression of (a) is:
in the formula, ΨevcsConfiguring a set of nodes to be selected, RR, of a storage and charging station in a regional power distribution network to be plannedevcsEqual annual value discount coefficient, RR, representing charging station investment costessAn equal-year-value discount coefficient representing the energy storage investment cost; x is the number ofiA 0-1 decision variable configured at a node i for the electric vehicle charging station,
represents the power of the charging station configured by the node i,
represents the power of the stored energy configured by node i,
an energy storage capacity configured for node i;
represents a fixed cost for node i to deploy the charging station,
represents a fixed cost for node i to configure the energy storage,
represents the unit power cost of the node i for configuring the charging station,represents the unit power cost of the node i configuration energy storage,
configuring the unit energy cost of energy storage for the node i;
annual operation and maintenance cost of charging station and energy storageThe calculation expression of (a) is:
in the formula (I), the compound is shown in the specification,annual operation and maintenance cost per unit power of a charging station allocated to the node i,
annual operation and maintenance cost of unit power of energy storage allocated to the node i;
cost for purchasing electricity from main network of distribution networkThe calculation expression of (a) is:
in the formula, omegasSet of output scenarios for renewable energy obtained in step 1), ξωWeight of scene omega in scene set, ξωThe number of days equal to the expected scene ω in the plan divided by the number of days of the year 365; p0,ω,tFor the average power of the distribution network purchasing power to the main network in the scene omega and at the moment t, delta t is the interval between adjacent moments,
unit cost for purchasing power for the distribution network at the moment t;
2-2) determining the constraint conditions of the model, specifically as follows:
and (3) restricting the investment quantity of the storage and charging station:
in the formula (I), the compound is shown in the specification, evcsnis the minimum value of the investment quantity of the storage and charging station,
respectively the maximum value of the investment quantity of the storage and charging station;
maximum configured power constraint for electric vehicle charging stations:
in the formula (I), the compound is shown in the specification,
configurable maximum power for all charging stations in the area to be planned;
and (3) constraint of the overall service capacity of the charging station:
in the formula (I), the compound is shown in the specification,
the sum of the rated charging power of all electric vehicles in the area to be planned, and gamma is the coincidence rate of the charging demand;
maximum configured power and capacity constraints of stored energy:
in the formula (I), the compound is shown in the specification,
the configurable maximum power of all energy storage in the area to be planned,
configurable maximum capacity for all stored energy in the area to be planned;
permeability constraint of the stored energy within the charging station:
wherein κ is the maximum permeability of the stored energy within the charging station;
and (3) operation restraint of a charging station:
in the formula (I), the compound is shown in the specification,
charging power η provided by charging stations allocated to node i at scene ω and time tevcsTo the charging efficiency;
and
respectively representing the lower bound and the upper bound of the accumulated charging energy obtained by the electric vehicle from the charging station allocated to the node i from the beginning to the moment t in the scene omega;
energy storage charge and discharge power constraint:
in the formula (I), the compound is shown in the specification,
and
respectively charging power and discharging power of the energy storage distributed by the node i in a scene omega and at a moment t;
energy coupling constraint among energy storage multi-time periods:
SOCi,ω,0＝SOCi,ω,24＝SOC0
in the formula, SOCi,ω,tIs the state of charge of the energy storage distributed by the node i in the scene omega at the moment t,
and
charging and discharging efficiencies of stored energy, SOC0An initial state of charge for energy storage during one day of operation;
and (4) energy storage energy upper and lower limit restraint:
in the formula (I), the compound is shown in the specification,SOCand
respectively the minimum value and the maximum value of the state of charge allowed by the stored energy in the running process;
and (3) alternating current power flow constraint:
in the formula, ΨbFor the set of all lines of the distribution network of the area to be planned, rijAnd xijRespectively the resistance and reactance of the branch (i, j);
and
respectively represents the active load and the reactive load of the node j under the scene omega and the moment t, Pij,ω,tAnd Qij,ω,tRespectively representing the active power and the reactive power flowing on the lower branch (i, j) at the moment t and the scene omegaij,ω,tIs the square of the branch (i, j) current amplitude at the scene omega, time t;
active power output, v, of distributed power supply allocated to node j under scene omega and time ti,ω,tThe square of the voltage amplitude of the node i under the scene omega and the moment t;
is the maximum value of the squared voltage magnitude at node i,maximum of the square of the branch (i, j) current amplitude;
3) solving the model established in the step 2) by using a Benders solving method to obtain an optimal planning result of the electric vehicle storage and charging station; the method comprises the following specific steps:
3-1) sorting the model established in the step 2) into a mixed integer second order cone programming model MISOCP, wherein the obtained form is as follows:
s.t.CIX≤0,
in the formula, X represents all decision variables related to the storage and charging station planning, including: storage and charging station position xiCharging station power
And the power of the stored energy
Capacity of
f represents the coefficient vector corresponding to the variable in X in the objective function; y isωRepresenting all operating variables, g, associated with scene omegaωRepresents YωCoefficient vectors corresponding to the variables in the target function; cIRepresenting a constraint matrix associated only with X, Aωj、bωj、
Andrespectively representing corresponding coefficients after the jth operation constraint of the model in the step 2) in the scene omega is written into a second-order cone constraint;
3-2) dividing the model in the step 3-1) into two layers, wherein the lower layer is a dual problem DSP, and the upper layer is a relaxation main problem RMP;
wherein, the dual sub-problem DSP of the lower layer corresponding to the scene omega is as follows:
in the formula, muωjAnd λωjIn order to introduce the dual variables, the variables,
representing the solution of the upper-layer relaxation main problem RMP before the iteration of the current round;
the main relaxation problem RMP of the upper layer is:
s.t.CIX≤0,
in the formula, rho is an introduced auxiliary variable, and iota is the iteration round of the current solving process;
3-3) iteratively solving the upper-layer relaxation main problem and all dual sub-problems of the lower layer by using a Benders solution method until the optimal value of the upper-layer optimization problem and the lower-layer optimization problem reaches the set threshold requirement, stopping iteration and obtaining variables
The optimal planning result of the electric vehicle storage and charging station is obtained, and the optimal planning result comprises the following steps: storage and charging station position xiOptimal value of (1), charging station power
Optimum value of (1), energy storage powerOptimum value of (2) and energy storage capacity
The optimum value of (c).
The invention has the characteristics and beneficial effects that:
according to the invention, the electric vehicle charging station and the energy storage are taken as a common planning main body for the first time, so that the problems of site selection and volume fixing of the electric vehicle charging station and the energy storage can be perfectly considered, the flexibility of the charging load of the electric vehicle can be considered in the planning, and a reasonable and effective storage and charging station planning scheme can be finally obtained through solving.
1) The invention utilizes the synergistic effect of the electric vehicle charging station and the stored energy in the configuration process, and the electric vehicle charging station and the stored energy are used as a whole to carry out location and volume fixing on the system level, so that the planning idea is novel and is in accordance with the reality;
2) the invention utilizes a random planning model to plan, considers the annual output variation of wind power, photovoltaic and load in the distribution network, and properly processes uncertainty;
3) the invention describes the uncertainty of the charging load of the electric automobile by constructing the boundary of the charging energy of the electric automobile, so that the solved planning scheme of the storage and charging station is closer to reality.
The method can be used in the field of combined planning of the electric vehicle charging station and the energy storage, and the obtained configuration result not only can realize location and volume fixing of the storage and the charging, but also can reduce the electricity purchasing cost of the distribution network to the maximum extent.
Drawings
FIG. 1 is an overall flow diagram of the method of the present invention.
Fig. 2 is a final result schematic diagram of an IEEE 33 node system network diagram containing wind power and photovoltaic and a storage and charging station planning according to an embodiment of the present invention.
Fig. 3 is a graph showing the output of a charging station installed in the distribution network node 21 according to the embodiment of the present invention.
Detailed Description
The invention provides a method for planning an electric vehicle storage and charging station by considering the flexibility of a charging load, which is further described in detail below by combining the accompanying drawings and specific embodiments. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The invention provides an electric vehicle storage and charging station planning method considering charging load flexibility, wherein the electric vehicle storage and charging station is a combination of an electric vehicle charging station and an energy storage system matched in the station, the overall flow is shown as a figure 1, and the method comprises the following steps:
1) constructing a randomly planned output scene set of renewable energy sources and a charging energy boundary of the electric vehicle; the method comprises the following specific steps:
1-1) constructing a randomly planned output scene set of renewable energy sources.
Clustering a sunlight-photovoltaic output curve, a sunlight-wind power output curve and a daily load curve of a region to be planned in the past whole year by using a K-means clustering algorithm respectively to obtain sunlight-photovoltaic output typical curves respectively corresponding to 4 weathers of sunny days, cloudy days and rainy days, sunlight-wind power output typical curves respectively corresponding to 4 seasons of spring, summer, autumn and winter and 2-class daily load typical curves of working days and non-working days, and combining the four weathers, the four seasons and the typical curves respectively corresponding to the working days and the non-working days into a 4 × 4 × 2-class typical scene as a randomly-planned output scene set of renewable energy sources;
1-2) assuming that the time of all electric vehicles arriving at a charging station and leaving the charging station meets a certain probability distribution (such as a normal distribution), sampling the charging behavior parameters of the electric vehicles by using a Monte Carlo sampling method to obtain the time of all electric vehicles arriving at the charging station and the time of leaving, wherein the lower bound of the accumulated energy provided by a single charging station for the electric vehicles at any moment is the sum of the energy required by all vehicles which have been charged at the charging station before the moment, and the upper bound of the provided energy is the sum of the energy required by all vehicles (including the vehicles which have left) which have arrived at the charging station before the moment; thus, the charging station can flexibly arrange the charging power provided for the electric vehicle at the moment as long as the accumulated provided energy is between the upper and lower bounds.
2) Establishing an electric vehicle storage and charging station planning model considering the flexibility of charging load, wherein the model consists of an objective function and constraint conditions; the method comprises the following specific steps:
2-1) determining an objective function of the model;
the investment entity of the electric vehicle charging station (i.e. the electric vehicle charging station and the energy storage are arranged together to form a charging station) is assumed to be a power distribution network operator (refer to the DSO of the california region of the united states). The objective function of the model is the sum of the investment cost of the electric vehicle charging station and the energy storage, the operation and maintenance cost of the electric vehicle charging station and the energy storage and the electricity purchasing cost of the distribution network slave main network:
wherein the equal annual investment costs of charging stations and energy storage
The calculation expression of (a) is:
in the formula, ΨevcsAnd configuring a set of nodes to be selected of the storage and charging stations in the distribution network of the area to be planned, wherein variables marked as evcs and ess are respectively related to the electric vehicle charging station and the energy storage. RRevcsEqual annual value discount coefficient, RR, representing charging station investment costessAn equal-year-value discount coefficient representing the energy storage investment cost; x is the number ofiA 0-1 decision variable configured at a node i for the electric vehicle charging station,
represents the power of the charging station configured by the node i,
represents the power of the stored energy configured by node i,
an energy storage capacity configured for node i;
represents a fixed cost for node i to deploy the charging station,
fixed form representing node i configuration storageThe utility model relates to a novel water-saving device,represents the unit power cost of the node i for configuring the charging station,
represents the unit power cost of the node i configuration energy storage,
configuring the unit energy cost of energy storage for the node i;
annual operation and maintenance cost of charging station and energy storage
The calculation expression of (a) is:
in the formula (I), the compound is shown in the specification,
annual operation and maintenance cost per unit power of a charging station allocated to the node i,
annual operation and maintenance cost of unit power of energy storage allocated to the node i;
cost for purchasing electricity from main network of distribution network
The calculation expression of (a) is:
in the formula, omegasSet of output scenarios for renewable energy obtained in step 1), ξωWeight of scene omega in scene set, ξωThe number of days equal to the expected scene ω in the plan divided by the number of days of the year 365; p0,ω,tFor distribution network in the omega and moment of scenet average power purchased from the main network, Δ t is the interval between adjacent time instants,
unit cost for purchasing power for the distribution network at the moment t;
2-2) determining the constraint conditions of the model, specifically as follows:
and (3) restricting the investment quantity of the storage and charging station:
in the formula (I), the compound is shown in the specification, evcsnis the minimum value of the investment quantity of the storage and charging station,
respectively the maximum value of the investment quantity of the storage and charging station;
maximum configured power constraint for electric vehicle charging stations:
in the formula (I), the compound is shown in the specification,
configurable maximum power for all charging stations in the area to be planned;
and (3) constraint of the overall service capacity of the charging station:
in the formula (I), the compound is shown in the specification,the sum of the rated charging power of all the electric automobiles in the area to be planned; since all the charging requirements are not concentrated at the same time, let γ be the coincidence rate of the charging requirements (value range 0-1). The larger the value of gamma is, the more conservative the planning is; (ii) a
Maximum configured power and capacity constraints of stored energy:
in the formula (I), the compound is shown in the specification,the configurable maximum power of all energy storage in the area to be planned,
configurable maximum capacity for all stored energy in the area to be planned;
permeability constraint of the stored energy within the charging station:
in the formula, kappa is the maximum permeability (value range of 0-1) of the stored energy in the charging station;
and (3) operation restraint of a charging station:
in the formula (I), the compound is shown in the specification,
charging power η provided by charging stations allocated to node i at scene ω and time tevcsTo the charging efficiency;
and
respectively representing the lower bound and the upper bound of the accumulated charging energy (from the beginning to the time t) obtained by the electric vehicle from the charging station allocated to the node i from the beginning to the time t in the scene omega;
energy storage charge and discharge power constraint:
in the formula (I), the compound is shown in the specification,
and
respectively charging power and discharging power of the energy storage distributed by the node i in a scene omega and at a moment t;
energy coupling constraint among energy storage multi-time periods:
SOCi,ω,0＝SOCi,ω,24＝SOC0
in the formula, SOCi,ω,tIs the state of charge of the energy storage distributed by the node i in the scene omega at the moment t,
and
charging and discharging efficiencies of stored energy, SOC0An initial state of charge for energy storage during one day of operation;
and (4) energy storage energy upper and lower limit restraint:
in the formula (I), the compound is shown in the specification,SOCand
respectively the minimum value and the maximum value of the state of charge allowed by the stored energy in the running process;
and (3) alternating current power flow constraint:
in the formula, ΨbFor the set of all lines of the distribution network of the area to be planned, rijAnd xijRespectively the resistance and reactance of the branch (i, j);
and
respectively represents the active load and the reactive load of the node j under the scene omega and the moment t, Pij,ω,tAnd Qij,ω,tRespectively representing the active power and the reactive power flowing on the lower branch (i, j) at the moment t and the scene omegaij,ω,tIs the square of the branch (i, j) current amplitude at the scene omega, time t;
is a node j place under the scene omega and the time tActive power output, v, of distributed power supplyi,ω,tThe square of the voltage amplitude of the node i under the scene omega and the moment t;
is the maximum value of the squared voltage magnitude at node i,
maximum of the square of the branch (i, j) current amplitude;
3) solving the model established in the step 2) by using a Benders solving method to obtain an optimal planning result of the electric vehicle storage and charging station; the method comprises the following specific steps:
3-1) sorting the model established in the step 2) into a standard mixed integer second order cone programming model MISOCP, wherein the obtained form is as follows:
s.t.CIX≤0,
in the formula, X represents all decision variables related to the storage and charging station planning, including: storage and charging station position xiCharging station power
And the power of the stored energy
Capacity of
f represents the coefficient vector corresponding to the variable in X in the objective function; y isωRepresenting all operating variables, g, associated with scene omegaωRepresents YωCoefficient vectors corresponding to the variables in the target function; cIRepresenting a constraint matrix associated only with X, Aωj、bωj、
And
representing coefficients corresponding to the model obtained in the step 2) after the jth operation constraint of the scene omega is written into a second-order cone (SOC) constraint respectively;
3-2) dividing the model in the step 3-1) into two layers by using an original MISOCP model and a dual theory, wherein the lower layer is a dual sub-problem DSP, and the upper layer is a relaxation main problem RMP;
wherein, the dual sub-problem DSP of the lower layer corresponding to the scene omega is as follows:
in the formula, muωjAnd λωjIn order to introduce the dual variables, the variables,
representing the solution of the upper-layer relaxation main problem RMP before the iteration of the current round;
the main relaxation problem RMP of the upper layer is:
s.t.CIX≤0,
in the formula, rho is an introduced auxiliary variable, and iota is the iteration round of the current solving process;
3-3) iteratively solving the upper-layer relaxation main problem and all dual sub-problems of the lower layer by using a Benders solution method until the optimal values of the upper-layer optimization problem and the lower-layer optimization problem reach the set threshold requirement, stopping iteration (the embodiment requires that the optimal values of the upper-layer optimization problem and the lower-layer optimization problem are within 0.5 percent of each other), and obtaining variables
The optimal planning result of the electric vehicle storage and charging station is obtained, and the optimal planning result comprises the following steps: storage and charging station position xiOptimal value of (1), charging station power
Optimum value of (1), energy storage power
Optimum value of (2) and energy storage capacity
The optimum value of (c).
The invention is further illustrated in detail below with reference to a specific example:
the power system of the area to be planned in the embodiment of the invention is a modified IEEE 33 node radial distribution network system, the voltage grade of the system is 12.66kV, 33 load nodes are contained, and the rated load is 3.715MW/2.3 MVar. As shown in fig. 2, the system has two photovoltaic units with power of 0.5MW at nodes 14 and 25, and one fan with power of 0.5MW at node 32. Suppose that there are a total of 300 electric vehicles in the area that need to be charged. Every automobile is charged once every two days, the rated charging power is 7kW, the battery capacity is 32kWh, and the charging is from the state of charge of 0.2 to 0.9.
The planning age is considered to be 15 years, and the number of the configurable storage and charging stations is 2-3. The maximum configuration power of each charging station is 1MW, the maximum energy storage power is 1MW, and the maximum capacity is 1 MWh. The charge state is limited to 0.1-0.95 during energy storage operation, and the initial charge state is assumed to be 0.2. The charge-discharge efficiency was set to 0.9 and the maximum permeability of stored energy was 50%. In addition, the time of 8-13 hours and the time of 17-21 hours of a day are set as high price time.
Under the parameter setting, the method of the invention is used for solving the model, and the storage and charging station planning result shown in fig. 2 can be obtained. As can be seen from the figure, the storage stations will be arranged at nodes 21, 24, 28. Taking the storage and charging station installed on the node 21 as an example, fig. 3 shows the output curve of the charging station installed on the node 21. As can be seen from fig. 3, under the assumption that the charging load of the charging station is flexibly adjustable, the charging station avoids charging at the peak time of the electricity price by reasonably arranging the charging power, thereby reducing the electricity consumption cost of the power grid.
Claims (1)
1. An electric vehicle storage and charging station planning method considering flexibility of charging load is characterized by comprising the following steps:
1) constructing a randomly planned output scene set of renewable energy sources and a charging energy boundary of the electric vehicle; the method comprises the following specific steps:
1-1) constructing a randomly planned output scene set of renewable energy sources;
clustering a sunlight-photovoltaic output curve, a sunlight-wind power output curve and a daily load curve of a region to be planned in the past whole year by using a K-means clustering algorithm respectively to obtain sunlight-photovoltaic output typical curves respectively corresponding to 4 weathers of sunny days, cloudy days and rainy days, sunlight-wind power output typical curves respectively corresponding to 4 seasons of spring, summer, autumn and winter and 2-class daily load typical curves of working days and non-working days, and combining the four weathers, the four seasons and the typical curves respectively corresponding to the working days and the non-working days into a 4 × 4 × 2-class typical scene as a randomly-planned output scene set of renewable energy sources;
1-2) sampling the charging behavior parameters of the electric vehicles by using a Monte Carlo sampling method to obtain the arrival time and departure time of all the electric vehicles at the charging stations, wherein the lower bound of the accumulated energy provided by a single charging station for the electric vehicles at any moment is the sum of the energy required by all the vehicles which have been completely charged at the charging station before the moment, and the upper bound of the provided energy is the sum of the energy required by all the vehicles which have arrived at the charging station before the moment;
2) establishing an electric vehicle storage and charging station planning model considering the flexibility of charging load, wherein the model consists of an objective function and constraint conditions; the method comprises the following specific steps:
2-1) determining an objective function of the model, wherein the expression is as follows:
wherein the equal annual investment costs of charging stations and energy storage
The calculation expression of (a) is:
in the formula, ΨevcsConfiguring a set of nodes to be selected, RR, of a storage and charging station in a regional power distribution network to be plannedevcsEqual annual value discount coefficient, RR, representing charging station investment costessAn equal-year-value discount coefficient representing the energy storage investment cost; x is the number ofiA 0-1 decision variable configured at a node i for the electric vehicle charging station,
represents the power of the charging station configured by the node i,
represents the power of the stored energy configured by node i,
an energy storage capacity configured for node i;
represents a fixed cost for node i to deploy the charging station,
representing a fixed cost of the node i configuration energy storage,
represents the unit power cost of the node i for configuring the charging station,represents the unit power cost of the node i configuration energy storage,
configuring the unit energy cost of energy storage for the node i;
annual operation and maintenance cost of charging station and energy storageThe calculation expression of (a) is:
in the formula (I), the compound is shown in the specification,
annual operation and maintenance cost per unit power of a charging station allocated to the node i,
annual operation and maintenance cost of unit power of energy storage allocated to the node i;
cost for purchasing electricity from main network of distribution network
The calculation expression of (a) is:
in the formula, omegasSet of output scenarios for renewable energy obtained in step 1), ξωIn scene convergence for scene omegaξ weight ofωThe number of days equal to the expected scene ω in the plan divided by the number of days of the year 365; p0,ω,tFor the average power of the distribution network purchasing power to the main network in the scene omega and at the moment t, delta t is the interval between adjacent moments,
unit cost for purchasing power for the distribution network at the moment t;
2-2) determining the constraint conditions of the model, specifically as follows:
and (3) restricting the investment quantity of the storage and charging station:
in the formula (I), the compound is shown in the specification, evcsnis the minimum value of the investment quantity of the storage and charging station,
respectively the maximum value of the investment quantity of the storage and charging station;
maximum configured power constraint for electric vehicle charging stations:
in the formula (I), the compound is shown in the specification,
configurable maximum power for all charging stations in the area to be planned;
and (3) constraint of the overall service capacity of the charging station:
in the formula (I), the compound is shown in the specification,
the sum of the rated charging power of all electric vehicles in the area to be planned, and gamma is the coincidence rate of the charging demand；
Maximum configured power and capacity constraints of stored energy:
in the formula (I), the compound is shown in the specification,
the configurable maximum power of all energy storage in the area to be planned,
configurable maximum capacity for all stored energy in an area to be planned;
permeability constraint of the stored energy within the charging station:
wherein κ is the maximum permeability of the stored energy within the charging station;
and (3) operation restraint of a charging station:
in the formula (I), the compound is shown in the specification,
charging power η provided by charging stations allocated to node i at scene ω and time tevcsTo the charging efficiency;
and
respectively representing the lower bound and the upper bound of the accumulated charging energy obtained by the electric vehicle from the charging station allocated to the node i from the beginning to the moment t in the scene omega;
energy storage charge and discharge power constraint:
in the formula (I), the compound is shown in the specification,and
respectively charging power and discharging power of the energy storage distributed by the node i in a scene omega and at a moment t;
energy coupling constraint among energy storage multi-time periods:
SOCi,ω,0＝SOCi,ω,24＝SOC0
in the formula, SOCi,ω,tIs the state of charge of the energy storage distributed by the node i in the scene omega at the moment t,
and
charging efficiency and discharging efficiency, SOC, respectively, of stored energy0An initial state of charge for energy storage during one day of operation;
and (4) energy storage energy upper and lower limit restraint:
in the formula (I), the compound is shown in the specification,SOCand
respectively the minimum value and the maximum value of the state of charge allowed by the stored energy in the running process;
and (3) alternating current power flow constraint:
in the formula, ΨbFor the set of all lines of the distribution network of the area to be planned, rijAnd xijRespectively the resistance and reactance of the branch (i, j);
and
respectively represents the active load and the reactive load of the node j under the scene omega and the moment t, Pij,ω,tAnd Qij,ω,tRespectively generation by generationTable active and reactive power, l, flowing on the lower branch (i, j) at time t, at scene ωij,ω,tIs the square of the current amplitude of the lower branch (i, j) at the scene omega and the time t;
active power output, v, of distributed power supply allocated to node j under scene omega and time ti,ω,tThe square of the voltage amplitude of the node i under the scene omega and the moment t;
is the maximum value of the squared voltage magnitude at node i,
is the maximum of the square of the current amplitude of branch (i, j);
3) solving the model established in the step 2) by using a Benders solving method to obtain an optimal planning result of the electric vehicle storage and charging station; the method comprises the following specific steps:
3-1) sorting the model established in the step 2) into a mixed integer second order cone programming model MISOCP, wherein the obtained form is as follows:
s.t.CIX≤0,
in the formula, X represents all decision variables related to the storage and charging station planning, including: storage and charging station position xiCharging station power
And the power of the stored energy
Capacity of
f represents the coefficient vector corresponding to the variable in X in the objective function; y isωRepresenting all operating variables, g, associated with scene omegaωRepresents YωCoefficient vectors corresponding to the variables in the target function; cIRepresenting a constraint matrix associated only with X, Aωj、bωj、
And
respectively representing corresponding coefficients after the jth operation constraint of the model in the step 2) in the scene omega is written into a second-order cone constraint;
3-2) dividing the model in the step 3-1) into two layers, wherein the lower layer is a dual problem DSP, and the upper layer is a relaxation main problem RMP;
wherein, the dual sub-problem DSP of the lower layer corresponding to the scene omega is as follows:
in the formula, muωjAnd λωjIn order to introduce the dual variables, the variables,representing the solution of the upper-layer relaxation main problem RMP before the iteration of the current round;
the main relaxation problem RMP of the upper layer is:
s.t.CIX≤0,
ι＝1,2,...
in the formula, rho is an introduced auxiliary variable, and iota is the iteration round of the current solving process;
3-3) iteratively solving the upper-layer relaxation main problem and all dual sub-problems of the lower layer by using a Benders solution method until the optimal value of the upper-layer optimization problem and the lower-layer optimization problem reaches the set threshold requirement, stopping iteration and obtaining variables
The optimal planning result of the electric vehicle storage and charging station is obtained, and the optimal planning result comprises the following steps: storage and charging station position xiOptimal value of (1), charging station powerOptimum value of (1), energy storage power
Optimum value of (2) and energy storage capacity
The optimum value of (c).
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