Three-layer control method and system for charging station providing frequency control service

Abstract

The invention provides a charging station three-layer control method and system for providing frequency control service, and the method comprises the steps: firstly constructing an economic prediction control layer model with the minimum economic expenditure as a target, and solving the optimal charging state set values of a battery energy storage system and an electric vehicle which adopts a plan for charging; after receiving the signal for providing the frequency control service from the economic predictive control layer model, the droop control layer calculates the power required when the charging station provides the frequency control service; and finally, constructing a predictive control layer model taking the minimum deviation between the charging and discharging state values of the battery energy storage system and the electric vehicle adopting planned charging and a set value as a target, and solving to obtain the charging and discharging state optimization values of the battery energy storage system and the electric vehicle adopting planned charging. The economical efficiency is improved while the operation reliability of the power grid is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging station control, and in particular relates to a three-layer control method and system for a charging station providing frequency control services. Background Art

[0002] In order to effectively reduce carbon emissions and further realize social electrification, it is necessary to vigorously promote the use of renewable energy. However, the increase in the use of renewable energy and the rapid development of social electrification have brought some challenges to the power system. Due to the reduction in the power generation and load capacity of synchronous motors, the system inertia decreases, which may lead to a decrease in system stability and reliability levels, and different frequency and voltage level requirements will also appear on the demand side. The transition from fuel vehicles to electric vehicles has an inevitable impact on the power system, so a charging station can be set up for electric vehicles to quickly charge when needed.

[0003] In order to meet the load requirements of future electric vehicles, distributed energy and energy storage systems can be integrated with charging to design high-performance power stations. However, the optimal design is usually based on complete load and power generation data, while real-time control strategies can only obtain limited information. In addition, the control strategies in existing studies have ignored the impact on the power grid and the profitability of charging operators. Therefore, ideally, the energy management system should be able to optimally control the power flow of charging stations while adapting to different grid conditions. The focus of the design should be on individual charging stations and their associated energy sources, so that both charging station operators and power systems benefit. Summary of the invention

[0004] The purpose of the present invention is to provide a three-layer control method and system for a charging station providing frequency control services in view of the above-mentioned problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention provides a three-layer control method for a charging station providing a frequency control service, comprising:

[0007] S1. Construct an economic forecasting control layer model with the goal of minimizing economic expenditure, solve the model, and obtain the optimal charging state setting values ​​of the battery energy storage system and the electric vehicle that adopts planned charging;

[0008] S2, after receiving the signal of providing frequency control service from the economic forecast control layer model, the droop control layer calculates the power required for the charging station to provide frequency control service;

[0009] S3. Based on the power required by the charging station to provide frequency control services, a predictive control layer model is constructed with the goal of minimizing the deviation between the charging and discharging state values ​​of the battery energy storage system and the electric vehicles that adopt planned charging and the set values ​​obtained in S1. The model is solved to obtain the optimized values ​​of the charging and discharging state of the battery energy storage system and the electric vehicles that adopt planned charging.

[0010] In S1, the objective function of the economic forecasting control layer model includes:

[0011]

[0012] In the above formula, N em is the number of samples in the economic forecast control layer, c nycb (k) is the energy cost in the kth sample, P g (k) is the active power provided by the charging station in the kth sample, Δt is the sampling time, c uc (k) is the compensation value for the charging station to deliver electricity to the electric vehicle that is not charged in the plan in the kth sample, P uc (k) is the power supply to the unplanned charging electric vehicle in the kth sample, κ f (k) is the decision variable for the charging station to provide frequency control service in the kth sample, c f (k) is the price of the frequency control service provided by the charging station in the kth sample, The maximum power that a charging station can allocate for frequency control;

[0013] In S3, the objective function of the prediction control layer model includes:

[0014]

[0015] In the above formula, N m is the number of samples in the prediction control layer, a1, a2, a3, a4, a5, and a6 are the priority parameters of each item in the objective function, and a4>a1>a3>a2>a5>a6. is the upper limit of the power of the charger in the charging station, are the optimal charging state setting values ​​of the battery energy storage system and the electric vehicle with planned charging in the kth sample, CDZT b (k), CDZT sc (k) are the charge and discharge status values ​​of the battery energy storage system and the electric vehicle with planned charging in the kth sample, γ sc (k) is the penalty variable of the kth sample, which is used to prevent the charging and discharging state values ​​of the electric vehicles planned to be charged from being lower than their set values. j (k) is the charging and discharging power of the local power station in the kth sample, Q g (k) are the reactive power generated by the charging station in the kth sample and its set value, respectively.

[0016] The constraints of the economic forecast control layer model include:

[0017] Power Constraints

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] ΔP g (k+1)=P g (k+1)-P g (k)

[0024]

[0025]

[0026] In the above formula, P b (k), P sc,j (k) are the charging and discharging power of the battery energy storage system and the charger j of the electric vehicle planned to be charged in the kth sample, respectively, n sc A collection of chargers for charging electric vehicles. The upper limit of active power provided to the charging station, ΔP g (k) is the change in active power provided by the charging station in the kth sample, P b are the upper and lower limits of the charging and discharging power of the battery energy storage system, respectively. P sc (k) Plan the upper and lower limits of the charging and discharging power of the electric vehicle charger, P sc (k) is the sum of the charging and discharging powers of the chargers of the electric vehicles planned to be charged in the kth sample, are the power generation set value of the local power station and the load demand set value of the unplanned charging electric vehicle in the kth sample respectively;

[0027] Battery state of charge constraints

[0028]

[0029]

[0030]

[0031]

[0032] In the above formula, E 0,b is the maximum capacity of the battery energy storage system, CDZT0(k) is the initial charge and discharge state of the kth sample, E 0,sc (k) is the maximum power capacity of the electric vehicle planned to be charged in the kth sample, ε f (k) is the auxiliary variable reflecting the charge and discharge state margin for providing frequency control service in the kth sample, E0 is the maximum power capacity of the charging station, ZGar b They are the upper and lower limits of the charge and discharge states respectively;

[0033] The constraints of the predictive control layer model include:

[0034] Power Constraints

[0035]

[0036]

[0037]

[0038]

[0039] ΔP g (k+1)=P g (k+1)-P g (k)

[0040] ΔQ g (k+1)=Q g (k+1)-Q g (k)

[0041]

[0042]

[0043] 0≤P uc (k)≤α⊙CDZT uc (k)+β

[0044]

[0045] In the above formula, n uc A collection of chargers for unplanned charging of electric vehicles, P uc,i(k) is the power supplied by charger i of the unplanned charging electric vehicle in the kth sample to the unplanned charging electric vehicle, P f The active power of the battery energy storage system when providing frequency control service for the charging station, Q b (k), Q uc,i (k), Q sc,j (k), Q j (k) are the reactive power of the battery energy storage system, the charger i of the unplanned charging electric vehicle, the charger j of the planned charging electric vehicle, and the local power station in the kth sample, respectively. is the upper limit of the charging and discharging power of unplanned charging electric vehicles, m1, m2, α, β are constants, CDZT uc (k) is the charging and discharging status of the non-planned charging electric vehicle in the kth sample, ΔQ g (k+1) is the reactive power change value provided by the charging station in the k+1th sample;

[0046] Battery charge and discharge state constraints

[0047]

[0048]

[0049]

[0050]

[0051] In the above formula, P uc (k) is the sum of the power supplied by the chargers of the non-planned charging electric vehicles in the kth sample to the non-planned charging electric vehicles, E 0,uc (k) is the maximum power capacity of the non-planned charging electric vehicle in the kth sample;

[0052] Voltage Constraint

[0053]

[0054]

[0055] In the above formula, U(k) is the voltage of the charging station in the kth sample, U They are the upper and lower limits of the charging station voltage respectively.

[0056] In S2, the power P required by the charging station to provide frequency control service f Calculated according to the following formula:

[0057] P f =K·κ f (f0-f)

[0058] In the above formula, K is the adjustment gain, κ f The decision variables for providing frequency control services for charging stations, f0 and f are the rated frequency and measured frequency respectively.

[0059] The economic forecasting control layer model and the forecasting control layer model are solved using solvers SDPT3 and lpSolve respectively.

[0060] In a second aspect, the present invention proposes a three-layer control system for a charging station providing frequency control services, comprising:

[0061] It includes economic forecasting control layer model building and solution module, droop control layer module, forecasting control layer model building and solution module;

[0062] The economic forecast control layer model construction and solution module is used to construct an economic forecast control layer model with the goal of minimizing economic expenditure, and solve the model to obtain the optimal charging state setting value of the battery energy storage system and the electric vehicle that adopts planned charging;

[0063] The droop control layer module is used to calculate the power required by the charging station to provide frequency control service after receiving the signal of providing frequency control service from the upper model;

[0064] The predictive control layer model construction and solution module is used to construct a predictive control layer model with the goal of minimizing the deviation between the charging and discharging state values ​​of the battery energy storage system and the electric vehicle that adopts planned charging and the optimal charging state setting value based on the power required when the charging station provides frequency control services, and solve the model to obtain the optimized values ​​of the charging and discharging state of the battery energy storage system and the electric vehicle that adopts planned charging.

[0065] The objective function of the economic forecasting control layer model includes:

[0066]

[0067] In the above formula, N em is the number of samples in the economic forecast control layer, c nycb (k) is the energy cost in the kth sample, P g (k) is the active power provided by the charging station in the kth sample, Δt is the sampling time, c uc (k) is the compensation value for the charging station to deliver electricity to the electric vehicle that is not charged in the plan in the kth sample, P uc (k) is the power supply to the unplanned charging electric vehicle in the kth sample, κ f (k) is the decision variable for the charging station to provide frequency control service in the kth sample, c f(k) is the price of the frequency control service provided by the charging station in the kth sample, The maximum power that a charging station can allocate for frequency control;

[0068] The objective function of the predictive control layer model includes:

[0069]

[0070] In the above formula, N m is the number of samples in the prediction control layer, a1, a2, a3, a4, a5, and a6 are the priority parameters of each item in the objective function, and a4>a1>a3>a2>a5>a6. is the upper limit of the power of the charger in the charging station, are the optimal charging state setting values ​​of the battery energy storage system and the electric vehicle with planned charging in the kth sample, CDZT b (k), CDZT sc (k) are the charge and discharge status values ​​of the battery energy storage system and the electric vehicle with planned charging in the kth sample, γ sc (k) is the penalty variable of the kth sample, which is used to prevent the charging and discharging state values ​​of the electric vehicles planned to be charged from being lower than their set values. j (k) is the charging and discharging power of the local power station in the kth sample, Q g (k) are the reactive power generated by the charging station in the kth sample and its set value, respectively.

[0071] The constraints of the economic forecast control layer model include:

[0072] Power Constraints

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] ΔP g (k+1)=P g (k+1)-P g (k)

[0079]

[0080]

[0081] In the above formula, P b (k), P sc,j (k) are the charging and discharging power of the battery energy storage system and the charger j of the electric vehicle planned to be charged in the kth sample, respectively, n sc A collection of chargers for charging electric vehicles. The upper limit of active power provided to the charging station, ΔP g (k) is the change in active power provided by the charging station in the kth sample, P b are the upper and lower limits of the charging and discharging power of the battery energy storage system, respectively. P sc (k) Plan the upper and lower limits of the charging and discharging power of the electric vehicle charger, P sc (k) is the sum of the charging and discharging powers of the chargers of the electric vehicles planned to be charged in the kth sample, are the power generation set value of the local power station and the load demand set value of the unplanned charging electric vehicle in the kth sample respectively;

[0082] Battery state of charge constraints

[0083]

[0084]

[0085]

[0086]

[0087] In the above formula, E 0,b is the maximum capacity of the battery energy storage system, CDZT0(k) is the initial charge and discharge state of the kth sample, E 0,sc (k) is the maximum power capacity of the electric vehicle planned to be charged in the kth sample, ε f (k) is the auxiliary variable reflecting the charge and discharge state margin for providing frequency control service in the kth sample, E0 is the maximum power capacity of the charging station, ZGar b They are the upper and lower limits of the charge and discharge states respectively;

[0088] The constraints of the predictive control layer model include:

[0089] Power Constraints

[0090]

[0091]

[0092]

[0093]

[0094] ΔP g (k+1)=P g (k+1)-P g (k)

[0095] ΔQ g (k+1)=Q g (k+1)-Q g (k)

[0096]

[0097]

[0098] 0≤P uc (k)≤α⊙CDZT uc (k)+β

[0099]

[0100] In the above formula, n uc A collection of chargers for unplanned charging of electric vehicles, P uc,i (k) is the power supplied by charger i of the unplanned charging electric vehicle in the kth sample to the unplanned charging electric vehicle, P f The active power of the battery energy storage system when providing frequency control service for the charging station, Q b (k), Q uc,i (k), Q sc,j (k), Q j (k) are the reactive power of the battery energy storage system, the charger i of the unplanned charging electric vehicle, the charger j of the planned charging electric vehicle, and the local power station in the kth sample, respectively. is the upper limit of the charging and discharging power of unplanned charging electric vehicles, m1, m2, α, β are constants, CDZT uc (k) is the charging and discharging status of the non-planned charging electric vehicle in the kth sample, ΔQ g (k+1) is the reactive power change value provided by the charging station in the k+1th sample;

[0101] Battery charge and discharge state constraints

[0102]

[0103]

[0104]

[0105]

[0106] In the above formula, P uc (k) is the sum of the power supplied by the chargers of the non-planned charging electric vehicles in the kth sample to the non-planned charging electric vehicles, E 0,uc (k) is the maximum power capacity of the non-planned charging electric vehicle in the kth sample;

[0107] Voltage Constraint

[0108]

[0109]

[0110] In the above formula, U(k) is the voltage of the charging station in the kth sample, U They are the upper and lower limits of the charging station voltage respectively.

[0111] The power P required when the charging station provides frequency control service f Calculated according to the following formula:

[0112] P f =K·κ f (f0-f)

[0113] In the above formula, K is the adjustment gain, κ f The decision variables for providing frequency control services for charging stations, f0 and f are the rated frequency and measured frequency respectively.

[0114] The economic forecast control layer model construction and solution module uses the solver SDPT3 to solve the economic forecast control layer model.

[0115] The predictive control layer model construction and solution module uses the solver lpSolve to solve the predictive control layer model.

[0116] Compared with the prior art, the present invention has the following beneficial effects:

[0117] The three-layer control method of a charging station providing frequency control service of the present invention first constructs an economic forecasting control layer model with the goal of minimizing economic expenditure, and solves to obtain the optimal charging state setting value of the battery energy storage system and the electric vehicle that adopts planned charging. Then, after receiving the signal of providing frequency control service from the economic forecasting control layer model, the drooping control layer calculates the power required when the charging station provides frequency control service. Finally, based on the power required when the charging station provides frequency control service, a forecasting control layer model with the goal of minimizing the deviation between the charging and discharging state value of the battery energy storage system and the electric vehicle that adopts planned charging and the setting value is constructed, and the charging and discharging state optimization value of the battery energy storage system and the electric vehicle that adopts planned charging is solved. The method proposes a three-layer control structure. On the one hand, it improves the economy while ensuring the reliability of power grid operation. On the other hand, the sampling time periods between the control layers of the charging station can be separated, and each control layer can be calculated separately according to its control target, which is highly flexible. BRIEF DESCRIPTION OF THE DRAWINGS

[0118] Figure 1 The flowchart of the method of the present invention is shown in FIG.

[0119] Figure 2 The structure diagram of the system of the present invention is shown in FIG. DETAILED DESCRIPTION

[0120] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0121] The present invention proposes a three-layer control method for a charging station providing frequency control services, which can achieve the following effects:

[0122] (1) The chargers and related energy in the charging station are controlled through a three-layer control structure, which maximizes economic benefits and improves economic efficiency while ensuring the reliability of power grid operation.

[0123] (2) The sampling time periods between the control layers of the charging station are separated, so that each control layer can calculate separately according to its control target.

[0124] (3) An energy management system framework is proposed, which provides frequency regulation control services by adjusting frequency-related loads in the droop control layer.

[0125] Embodiment 1:

[0126] This embodiment takes a typical 95-node system as the research object and implements a three-layer control method for a charging station providing frequency control services described in the present invention. Figure 1 As shown, the specific steps are as follows:

[0127] 1. Initialization, the rated voltage of the distribution network is 22kV, including 11 nodes, the charging station is installed on 10 nodes, and the power capacity involved in frequency regulation is set to 250kW. The charging station has 10 150kVA chargers for unplanned charging of electric vehicles and two 300kVA chargers for planned charging of electric vehicles. The simulation time is 24 hours, the sampling time of the economic forecast control layer (EMPC) model is every 15 minutes, the forecast control layer (MPC) is every minute, and the droop control layer (DP) is every second.

[0128] 2. Construct an economic forecasting control layer model with the goal of minimizing economic expenditure and taking into account the battery charging and discharging state constraints and power constraints.

[0129] The objective function of the economic forecasting control layer model is:

[0130]

[0131] In the above formula, N em is the number of samples in the economic forecast control layer. The samples are data sets consisting of parameters related to distribution networks and charging stations. nycb (k) is the energy cost in the kth sample, P g (k) is the active power provided by the charging station in the kth sample, Δt is the sampling time, c uc (k) is the compensation value for the charging station to deliver electricity to the electric vehicle that is not charged in the plan in the kth sample, P uc (k) is the power supply to the unplanned charging electric vehicle in the kth sample, κ f (k) is the decision variable for the charging station to provide frequency control service in the kth sample, c f (k) is the price of the frequency control service provided by the charging station in the kth sample, The maximum power that a charging station can allocate for frequency control;

[0132] The constraints include:

[0133] (1) Power Constraint

[0134] Active power balance constraints of charging stations

[0135]

[0136] In the above formula, P b (k), P sc,j (k) are the charging and discharging power of the battery energy storage system and the charger j of the electric vehicle planned to be charged in the kth sample, respectively, n sc a collection of chargers for planned electric vehicles;

[0137] In addition, when providing frequency control services, Pg (k) should only vary according to the frequency deviation. Therefore, when κ f When (k) = 1, the active power change value provided by the charging station is zero, and:

[0138]

[0139] In the above formula, The upper limit of active power provided to the charging station, ΔP g (k) is the change value of active power provided by the charging station in the kth sample;

[0140] Other power constraints

[0141]

[0142]

[0143]

[0144] ΔP g (k+1)=P g (k+1)-P g (k)

[0145]

[0146]

[0147] In the above formula, P b are the upper and lower limits of the charging and discharging power of the battery energy storage system, respectively. P sc (k) Plan the upper and lower limits of the charging and discharging power of the electric vehicle charger, P sc (k) is the sum of the charging and discharging powers of the chargers of the electric vehicles planned to be charged in the kth sample, They are respectively the power generation set value of the local power station in the kth sample and the load demand set value of the unplanned charging electric vehicle.

[0148] (2) Battery charging and discharging state constraints

[0149]

[0150]

[0151] In the above formula, E 0,b is the maximum capacity of the battery energy storage system, CDZT0(k) is the initial charge and discharge state of the kth sample, E 0,sc(k) is the maximum power capacity of the electric vehicle planned to be charged in the kth sample;

[0152] To ensure that the battery provides frequency control services, it must be able to provide or consume enough power and energy for the worst case scenario at its current state of charge, and the charge and discharge state of the battery energy storage system must be within the range of the frequency control service, so:

[0153]

[0154]

[0155] In the above formula, ε f (k) is an auxiliary variable that reflects the charge and discharge state margin for providing frequency control services in the kth sample. It is set to ensure that the battery energy storage system has sufficient power to provide frequency control services. E0 is the maximum power capacity of the charging station. ZGar b They are the upper and lower limits of the charge and discharge states respectively.

[0156] 3. Use the solver SDPT3 to solve the above economic forecast control layer model to obtain the optimal charging state setting value of the battery energy storage system and the electric vehicle with planned charging

[0157] 4. When the economic forecast control layer model determines that frequency control services should be provided, an activation signal is sent to the droop control layer. After receiving the activation signal, the droop control layer calculates the power P required for the charging station to provide frequency control services. f :

[0158] P f =K·κ f (f0-f)

[0159] In the above formula, K is the adjustment gain, κ f The decision variables for providing frequency control services for charging stations, f0 and f are the rated frequency and measured frequency respectively.

[0160] 5. Based on the power required by the charging station to provide frequency control services, a predictive control layer model is constructed with the goal of minimizing the deviation between the charge and discharge state values ​​of the battery energy storage system and the electric vehicles that are scheduled to be charged and the optimal charge state setting value, so as to provide the required energy for the charged electric vehicles and ensure that the voltage remains within the feasible range.

[0161] The objective function of the predictive control layer model is:

[0162]

[0163] In the above formula, N mis the number of samples in the prediction control layer, a1, a2, a3, a4, a5, and a6 are the priority parameters assigned to each item in the objective function. Considering that the highest priority is to ensure that the charging state of the planned charging electric vehicles is higher than its reference value, and at the same time to provide as much power as possible to the unplanned charging electric vehicles, the priority parameters are set to a4>a1>a3>a2>a5>a6. is the upper limit of the power of the charger in the charging station, are the optimal charging state setting values ​​of the battery energy storage system and the electric vehicle with planned charging in the kth sample, CDZT b (k), CDZT sc (k) are the charge and discharge status values ​​of the battery energy storage system and the electric vehicle with planned charging in the kth sample, γ sc (k) is the penalty variable of the kth sample, which is used to prevent the charging and discharging state value of the electric vehicle planned to be charged from being lower than its set value. The value is determined according to the gap between the state value and the reference value. j (k) is the charging and discharging power of the local power station in the kth sample, Q g (k) are the reactive power generated by the charging station in the kth sample and its set value, respectively.

[0164] The constraints include:

[0165] (1) Power Constraint

[0166] Active and reactive power constraints

[0167]

[0168]

[0169] In the above formula, n uc A collection of chargers for unplanned charging of electric vehicles, P uc,i (k) is the power supplied by charger i of the unplanned charging electric vehicle in the kth sample to the unplanned charging electric vehicle, P f The active power of the battery energy storage system when providing frequency control service for the charging station, Q b (k), Q uc,i (k), Q sc,j (k), Q j (k) are the reactive power of the battery energy storage system, the charger i of the unplanned charging electric vehicle, the charger j of the planned charging electric vehicle, and the local power station in the kth sample;

[0170] In order to maximize profitability, unscheduled charging EVs should charge as much as possible during the time they occupy the charger, so the most constraint is simplified to the following piecewise linear function based on their charging power curve:

[0171]

[0172] In the above formula, is the upper limit of the charging and discharging power of an unplanned charging electric vehicle, m1 and m2 are constants, which are determined according to the model of the electric vehicle and its current charging status;

[0173] Other power constraints

[0174]

[0175] ΔP g (k+1)=P g (k+1)-P g (k)

[0176] ΔQ g (k+1)=Q g (k+1)-Q g (k)

[0177]

[0178]

[0179] 0≤P uc (k)≤α⊙CDZT uc (k)+β

[0180]

[0181] In the above formula, α and β are constants, which are determined according to the model of the electric vehicle and its current charging state. uc (k) is the charging and discharging status of the non-planned charging electric vehicle in the kth sample, ΔQ g (k+1) is the reactive power change value provided by the charging station in the k+1th sample;

[0182] (2) Battery charging state constraints

[0183]

[0184]

[0185]

[0186]

[0187] In the above formula, Puc (k) is the sum of the power supplied by the chargers of the non-planned charging electric vehicles in the kth sample to the non-planned charging electric vehicles, E 0,uc (k) is the maximum power capacity of the non-planned charging electric vehicle in the kth sample;

[0188] (3) Voltage Constraint

[0189]

[0190]

[0191] In the above formula, U(k) is the voltage of the charging station in the kth sample, U They are the upper and lower limits of the charging station voltage respectively.

[0192] 6. Use the solver lpSolve to solve the above predictive control layer model to obtain the optimal value of the charging and discharging state of the battery energy storage system and the electric vehicle with planned charging.

[0193] In order to investigate the effectiveness of the method of the present invention, the scheme described in Example 1 is compared with the maximum capacity control scheme (Max.cn), the rule-based control scheme (RBC), and the substation three-layer control scheme that does not provide frequency control services. The supply cost and income of the energy source selected in each scheme, the frequency control service income, and the power factor cosφ representing the power grid performance are calculated. The results are shown in Table 1:

[0194] Table 1 Comparison results of various schemes

[0195]

[0196] It can be seen from Table 1 that the maximum capacity control scheme (Max.cn) obtains the most benefits from unplanned charging of electric vehicles, but the voltage drop it produces is too large and the grid performance is poor. The rule-based control scheme (RBC) can keep the voltage within the feasible range, but cannot maximize the profitability of the charging station. The three-layer control scheme proposed in this invention can keep the voltage within the feasible domain and improve the performance of the grid, while obtaining a profit value similar to that of the Max.cn control scheme. And if frequency control auxiliary services are further provided, the profit will be further increased.

[0197] Embodiment 2:

[0198] A three-tier control system for charging stations that provides frequency control services, such as Figure 2 As shown, it includes an economic forecasting control layer model building and solution module, a droop control layer module, and a forecasting control layer model building and solution module.

[0199] The economic forecast control layer model construction and solution module is used to construct an economic forecast control layer model with the goal of minimizing economic expenditure, and use the solver SDPT3 to solve the model to obtain the optimal charging state setting value of the battery energy storage system and the electric vehicle that adopts planned charging, wherein:

[0200] The objective function of the economic forecasting control layer model is:

[0201]

[0202] In the above formula, N em is the number of samples in the economic forecast control layer, c nycb (k) is the energy cost in the kth sample, P g (k) is the active power provided by the charging station in the kth sample, Δt is the sampling time, c uc (k) is the compensation value for the charging station to deliver electricity to the electric vehicle that is not charged in the plan in the kth sample, P uc (k) is the power supply to the unplanned charging electric vehicle in the kth sample, κ f (k) is the decision variable for the charging station to provide frequency control service in the kth sample, c f (k) is the price of the frequency control service provided by the charging station in the kth sample, The maximum power that a charging station can allocate for frequency control;

[0203] The constraints include:

[0204] Power Constraints

[0205]

[0206]

[0207]

[0208]

[0209]

[0210] ΔP g (k+1)=P g (k+1)-P g (k)

[0211]

[0212]

[0213] In the above formula, P b (k), P sc,j(k) are the charging and discharging power of the battery energy storage system and the charger j of the electric vehicle planned to be charged in the kth sample, respectively, n sc A collection of chargers for charging electric vehicles. The upper limit of active power provided to the charging station, ΔP g (k) is the change in active power provided by the charging station in the kth sample, P b are the upper and lower limits of the charging and discharging power of the battery energy storage system, respectively. P sc (k) Plan the upper and lower limits of the charging and discharging power of the electric vehicle charger, P sc (k) is the sum of the charging and discharging powers of the chargers of the electric vehicles planned to be charged in the kth sample, are the power generation set value of the local power station and the load demand set value of the unplanned charging electric vehicle in the kth sample respectively;

[0214] Battery state of charge constraints

[0215]

[0216]

[0217]

[0218]

[0219] In the above formula, E 0,b is the maximum capacity of the battery energy storage system, CDZT0(k) is the initial charge and discharge state of the kth sample, E 0,sc (k) is the maximum power capacity of the electric vehicle planned to be charged in the kth sample, ε f (k) is the auxiliary variable reflecting the charge and discharge state margin for providing frequency control service in the kth sample, E0 is the maximum power capacity of the charging station, ZGar b They are the upper and lower limits of the charge and discharge states respectively.

[0220] The droop control layer module is used to calculate the power P required for the charging station to provide frequency control service according to the following formula after receiving the signal of providing frequency control service from the upper model: f :

[0221] P f =K·κ f (f0-f)

[0222] In the above formula, K is the adjustment gain, κf The decision variables for providing frequency control services for charging stations, f0 and f are the rated frequency and measured frequency respectively.

[0223] The predictive control layer model construction and solution module is used to construct a predictive control layer model with the goal of minimizing the deviation between the charge and discharge state values ​​of the battery energy storage system and the electric vehicle that adopts planned charging and the optimal charge state setting value based on the power required when the charging station provides frequency control services, and use the solver lpSolve to solve the model to obtain the optimized value of the charge and discharge state of the battery energy storage system and the electric vehicle that adopts planned charging, wherein the objective function of the predictive control layer model is:

[0224]

[0225] In the above formula, N m is the number of samples in the prediction control layer, a1, a2, a3, a4, a5, and a6 are the priority parameters of each item in the objective function, and a4>a1>a3>a2>a5>a6. is the upper limit of the power of the charger in the charging station, are the optimal charging state setting values ​​of the battery energy storage system and the electric vehicle with planned charging in the kth sample, CDZT b (k), CDZT sc (k) are the charge and discharge status values ​​of the battery energy storage system and the electric vehicle with planned charging in the kth sample, γ sc (k) is the penalty variable of the kth sample, which is used to prevent the charging and discharging state values ​​of the electric vehicles planned to be charged from being lower than their set values. j (k) is the charging and discharging power of the local power station in the kth sample, Q g (k) are the reactive power generated by the charging station in the kth sample and its set value;

[0226] The constraints include:

[0227] Power Constraints

[0228]

[0229]

[0230]

[0231]

[0232] ΔP g (k+1)=P g (k+1)-P g (k)

[0233] ΔQ g (k+1)=Q g (k+1)-Q g (k)

[0234]

[0235]

[0236] 0≤P uc (k)≤α⊙CDZT uc (k)+β

[0237]

[0238] In the above formula, n uc A collection of chargers for unplanned charging of electric vehicles, P uc,i (k) is the power supplied by charger i of the unplanned charging electric vehicle in the kth sample to the unplanned charging electric vehicle, P f The active power of the battery energy storage system when providing frequency control service for the charging station, Q b (k), Q uc,i (k), Q sc,j (k), Q j (k) are the reactive power of the battery energy storage system, the charger i of the unplanned charging electric vehicle, the charger j of the planned charging electric vehicle, and the local power station in the kth sample, respectively. is the upper limit of the charging and discharging power of unplanned charging electric vehicles, m1, m2, α, β are constants, CDZT uc (k) is the charging and discharging status of the non-planned charging electric vehicle in the kth sample, ΔQ g (k+1) is the reactive power change value provided by the charging station in the k+1th sample;

[0239] Battery charge and discharge state constraints

[0240]

[0241]

[0242]

[0243]

[0244] In the above formula, P uc (k) is the sum of the power supplied by the chargers of the non-planned charging electric vehicles in the kth sample to the non-planned charging electric vehicles, E 0,uc (k) is the maximum power capacity of the non-planned charging electric vehicle in the kth sample;

[0245] Voltage Constraint

[0246]

[0247]

[0248] In the above formula, U(k) is the voltage of the charging station in the kth sample, U They are the upper and lower limits of the charging station voltage respectively.

Claims

1. A three-layer control method for a charging station providing frequency control services, characterized in that: The method comprises: S1. Construct an economic forecasting control layer model with the goal of minimizing economic expenditure, solve the model, and obtain the optimal charging state setting values ​​of the battery energy storage system and the electric vehicle that adopts planned charging; S2, after receiving the signal of providing frequency control service from the economic forecast control layer model, the droop control layer calculates the power required for the charging station to provide frequency control service; S3. Based on the power required by the charging station to provide frequency control services, a predictive control layer model is constructed with the goal of minimizing the deviation between the charging and discharging state values ​​of the battery energy storage system and the electric vehicles that adopt planned charging and the set values ​​obtained in S1. The model is solved to obtain the optimized values ​​of the charging and discharging state of the battery energy storage system and the electric vehicles that adopt planned charging.

2. A three-layer control method for a charging station providing frequency control services according to claim 1, characterized in that: In S1, the objective function of the economic forecasting control layer model includes: In the above formula, N em is the number of samples in the economic forecast control layer, c nycb (k) is the energy cost in the kth sample, P g (k) is the active power provided by the charging station in the kth sample, Δt is the sampling time, c uc (k) is the compensation value for the charging station to deliver electricity to the electric vehicle that is not charged in the plan in the kth sample, P uc (k) is the power supply to the unplanned charging electric vehicle in the kth sample, κ f (k) is the decision variable for the charging station to provide frequency control service in the kth sample, c f (k) is the price of the frequency control service provided by the charging station in the kth sample, The maximum power that a charging station can allocate for frequency control; In S3, the objective function of the prediction control layer model includes: In the above formula, N m is the number of samples in the prediction control layer, a1, a2, a3, a4, a5, and a6 are the priority parameters of each item in the objective function, and a4>a1>a3>a2>a5>a6. is the upper limit of the power of the charger in the charging station, are the optimal charging state setting values ​​of the battery energy storage system and the electric vehicle with planned charging in the kth sample, CDZT b (k), CDZT sc (k) are the charge and discharge status values ​​of the battery energy storage system and the electric vehicle with planned charging in the kth sample, γ sc (k) is the penalty variable of the kth sample, which is used to prevent the charging and discharging state values ​​of the electric vehicles planned to be charged from being lower than their set values. j (k) is the charging and discharging power of the local power station in the kth sample, Q g (k) are the reactive power generated by the charging station in the kth sample and its set value, respectively.

3. A three-layer control method for a charging station providing frequency control service according to claim 2, characterized in that: The constraints of the economic forecast control layer model include: Power Constraints ΔP g (k+1)=P g (k+1)-P g (k) In the above formula, P b (k), P sc,j (k) are the charging and discharging power of the battery energy storage system and the charger j of the electric vehicle planned to be charged in the kth sample, respectively, n sc A collection of chargers for charging electric vehicles. The upper limit of active power provided to the charging station, ΔP g (k) is the change in active power provided by the charging station in the kth sample, P b are the upper and lower limits of the charging and discharging power of the battery energy storage system, respectively. P sc (k) Plan the upper and lower limits of the charging and discharging power of the electric vehicle charger, P sc (k) is the sum of the charging and discharging powers of the chargers of the electric vehicles planned to be charged in the kth sample, are the power generation set value of the local power station and the load demand set value of the unplanned charging electric vehicle in the kth sample respectively; Battery state of charge constraints In the above formula, E 0,b is the maximum capacity of the battery energy storage system, CDZT0(k) is the initial charge and discharge state of the kth sample, E 0,sc (k) is the maximum power capacity of the electric vehicle planned to be charged in the kth sample, ε f (k) is the auxiliary variable reflecting the charge and discharge state margin for providing frequency control service in the kth sample, E0 is the maximum power capacity of the charging station, ZGar b They are the upper and lower limits of the charge and discharge states respectively; The constraints of the predictive control layer model include: Power Constraints ΔP g (k+1)=P g (k+1)-P g (k) ΔQ g (k+1)=Q g (k+1)-Q g (k) 0≤P uc (k)≤α⊙CDZT uc (k)+β In the above formula, n uc A collection of chargers for unplanned charging of electric vehicles, P uc,i (k) is the power supplied by charger i of the unplanned charging electric vehicle in the kth sample to the unplanned charging electric vehicle, P f The active power of the battery energy storage system when providing frequency control service for the charging station, Q b (k), Q uc,i (k), Q sc,j (k), Q j (k) are the reactive power of the battery energy storage system, the charger i of the unplanned charging electric vehicle, the charger j of the planned charging electric vehicle, and the local power station in the kth sample, respectively. uc (k) is the upper limit of the charging and discharging power of the non-planned charging electric vehicle, m1, m2, α, β are constants, CDZT uc (k) is the charging and discharging status of the non-planned charging electric vehicle in the kth sample, ΔQ g (k+1) is the reactive power change value provided by the charging station in the k+1th sample; Battery charge and discharge state constraints In the above formula, P uc (k) is the sum of the power supplied by the chargers of the non-planned charging electric vehicles in the kth sample to the non-planned charging electric vehicles, E 0,uc (k) is the maximum power capacity of the non-planned charging electric vehicle in the kth sample; Voltage Constraint U ≤U(k)≤U In the above formula, U(k) is the voltage of the charging station in the kth sample, U They are the upper and lower limits of the charging station voltage respectively.

4. A three-layer control method for a charging station providing frequency control services according to claim 1 or 2, characterized in that: In S2, the power P required by the charging station to provide frequency control service f Calculated according to the following formula: P f =K·κ f ·(f0-f) In the above formula, K is the adjustment gain, κ f The decision variables for providing frequency control services for charging stations, f0 and f are the rated frequency and measured frequency respectively.

5. A three-layer control method for a charging station providing frequency control service according to claim 2, characterized in that: The economic forecasting control layer model and the forecasting control layer model are solved using solvers SDPT3 and lpSolve respectively.

6. A three-layer control system for a charging station providing frequency control services, characterized in that: The system includes an economic forecast control layer model building and solution module, a droop control layer module, and a forecast control layer model building and solution module; The economic forecast control layer model construction and solution module is used to construct an economic forecast control layer model with the goal of minimizing economic expenditure, and solve the model to obtain the optimal charging state setting value of the battery energy storage system and the electric vehicle that adopts planned charging; The droop control layer module is used to calculate the power required by the charging station to provide frequency control service after receiving the signal of providing frequency control service from the upper model; The predictive control layer model construction and solution module is used to construct a predictive control layer model with the goal of minimizing the deviation between the charging and discharging state values ​​of the battery energy storage system and the electric vehicle that adopts planned charging and the optimal charging state setting value based on the power required when the charging station provides frequency control services, and solve the model to obtain the optimized values ​​of the charging and discharging state of the battery energy storage system and the electric vehicle that adopts planned charging.

7. A three-layer control system for a charging station providing frequency control services according to claim 6, characterized in that: The objective function of the economic forecasting control layer model includes: In the above formula, N em is the number of samples in the economic forecast control layer, c nycb (k) is the energy cost in the kth sample, P g (k) is the active power provided by the charging station in the kth sample, Δt is the sampling time, c uc (k) is the compensation value for the charging station to deliver electricity to the electric vehicle that is not charged in the plan in the kth sample, P uc (k) is the power supply to the unplanned charging electric vehicle in the kth sample, κ f (k) is the decision variable for the charging station to provide frequency control service in the kth sample, c f (k) is the price of the frequency control service provided by the charging station in the kth sample, The maximum power that a charging station can allocate for frequency control; The objective function of the predictive control layer model includes: In the above formula, N m is the number of samples in the prediction control layer, a1, a2, a3, a4, a5, and a6 are the priority parameters of each item in the objective function, and a4>a1>a3>a2>a5>a6. is the upper limit of the power of the charger in the charging station, are the optimal charging state setting values ​​of the battery energy storage system and the electric vehicle with planned charging in the kth sample, CDZT b (k), CDZT sc (k) are the charge and discharge status values ​​of the battery energy storage system and the electric vehicle with planned charging in the kth sample, γ sc (k) is the penalty variable of the kth sample, which is used to prevent the charging and discharging state values ​​of the electric vehicles planned to be charged from being lower than their set values. j (k) is the charging and discharging power of the local power station in the kth sample, Q g (k) are the reactive power generated by the charging station in the kth sample and its set value, respectively.

8. A three-layer control system for a charging station providing frequency control services according to claim 7, characterized in that: The constraints of the economic forecast control layer model include: Power Constraints ΔP g (k+1)=P g (k+1)-P g (k) In the above formula, P b (k), P sc,j (k) are the charging and discharging power of the battery energy storage system and the charger j of the electric vehicle planned to be charged in the kth sample, respectively, n sc A collection of chargers for charging electric vehicles. The upper limit of active power provided to the charging station, ΔP g (k) is the change in active power provided by the charging station in the kth sample, P b are the upper and lower limits of the charging and discharging power of the battery energy storage system, respectively. P sc (k) Plan the upper and lower limits of the charging and discharging power of the electric vehicle charger, P sc (k) is the sum of the charging and discharging powers of the chargers of the electric vehicles planned to be charged in the kth sample, are the power generation set value of the local power station and the load demand set value of the unplanned charging electric vehicle in the kth sample respectively; Battery state of charge constraints In the above formula, E 0,b is the maximum capacity of the battery energy storage system, CDZT0(k) is the initial charge and discharge state of the kth sample, E 0,sc (k) is the maximum power capacity of the electric vehicle planned to be charged in the kth sample, ε f (k) is the auxiliary variable reflecting the charge and discharge state margin for providing frequency control service in the kth sample, E0 is the maximum power capacity of the charging station, ZGar b They are the upper and lower limits of the charge and discharge states respectively; The constraints of the predictive control layer model include: Power Constraints ΔP g (k+1)=P g (k+1)-P g (k) ΔQ g (k+1)=Q g (k+1)-Q g (k) 0≤P uc (k)≤α⊙CDZT uc (k)+β In the above formula, n uc A collection of chargers for unplanned charging of electric vehicles, P uc,i (k) is the power supplied by charger i of the unplanned charging electric vehicle in the kth sample to the unplanned charging electric vehicle, P f The active power of the battery energy storage system when providing frequency control service for the charging station, Q b (k), Q uc,i (k), Q sc,j (k), Q j (k) are the reactive power of the battery energy storage system, the charger i of the unplanned charging electric vehicle, the charger j of the planned charging electric vehicle, and the local power station in the kth sample, respectively. is the upper limit of the charging and discharging power of unplanned charging electric vehicles, m1, m2, α, β are constants, CDZT uc (k) is the charging and discharging status of the non-planned charging electric vehicle in the kth sample, ΔQ g (k+1) is the reactive power change value provided by the charging station in the k+1th sample; Battery charge and discharge state constraints In the above formula, P uc (k) is the sum of the power supplied by the chargers of the non-planned charging electric vehicles in the kth sample to the non-planned charging electric vehicles, E 0,uc (k) is the maximum power capacity of the non-planned charging electric vehicle in the kth sample; Voltage Constraint In the above formula, U(k) is the voltage of the charging station in the kth sample, U They are the upper and lower limits of the charging station voltage respectively.

9. A three-layer control system for a charging station providing frequency control services according to claim 6 or 7, characterized in that: The power P required when the charging station provides frequency control service f Calculated according to the following formula: P f =K·κ f ·(f0-f) In the above formula, K is the adjustment gain, κ f The decision variables for providing frequency control services for charging stations, f0 and f are the rated frequency and measured frequency respectively.

10. A three-layer control system for charging stations providing frequency control services according to claim 7, characterized in that: The economic forecast control layer model construction and solution module uses the solver SDPT3 to solve the economic forecast control layer model. The predictive control layer model construction and solution module uses the solver lpSolve to solve the predictive control layer model.

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