A smart parking lot charging and discharging regulation method considering demand response

Abstract

The application provides a kind of smart parking lot charging and discharging regulation method considering demand response, comprising the following steps: obtaining external data;The obtained external data is transported to data receiving and processing module for processing, and then the processed data is integrated and sent to decision optimization module;Under the premise of processing data, construct the objective function of maximizing energy delivery and minimizing energy cost;Construct the constraint condition about maximizing energy delivery and minimizing energy cost;According to the objective function and the constraint condition, a smart parking lot charging and discharging decision optimization model considering demand response is constructed, and the decision optimization module optimizes the charging and discharging behavior of the smart parking lot according to the smart parking lot charging and discharging decision optimization model and the received external data.The application compared with prior art solves the difficulty in mobilizing the enthusiasm of electric vehicle owners to send power to the power grid, and the defect of reasonably regulating the charging and discharging behavior of the smart parking lot.

Description

Technical Field

[0001] This invention relates to the field of intelligent parking lot integrated control technology, specifically an intelligent parking lot charging and discharging control method that takes demand response into account. Background Technology

[0002] The power grid has undergone tremendous changes in recent decades, partly due to the exponential growth in customer expectations. On the other hand, operators need to run the grid under increasingly complex conditions created by renewable energy generation, energy storage systems, and plug-in electric vehicles. Therefore, utilities around the world are using information technology, communications, and sensors to better integrate operational tools, creating a more robust and interactive environment capable of comprehensively considering and handling generation / demand uncertainties. Among these tools, Respond to Demand (DR) is a fundamental component designed to involve end-users in shaping energy demand, enabling peak shaving, valley filling, load shifting, and flexible load shaping. In other words, disaster recovery modifies customer electricity consumption to reshape their normal consumption patterns, providing incentive payments to encourage reduced consumption during periods of high prices or when system reliability is at risk.

[0003] While storage system integration offers a major advantage for disaster recovery plans, users are still limited by installation costs. When used with appropriate charging solutions and communication infrastructure, PEVs (plug-in electric vehicles) can play a dual role in the smart grid, either becoming a dispatchable load while charging or serving as internet-connected storage in response to pricing commands, i.e., V2G (Vehicle-to-grid) technology.

[0004] Like other disaster recovery plans, the idea behind V2G is simply to benefit all parties and enable PEV owners to earn more income. However, under current market conditions, the traditional approach—where PEV drivers sign pre-defined contracts in exchange for annual cash rebates—is unlikely to attract drivers. Therefore, there is an urgent need for a solution that considers owner preferences and provides them with flexible charging and discharging options, allowing owners to immediately decide whether to discharge their batteries back to the grid. Summary of the Invention

[0005] The purpose of this invention is to comprehensively consider the preferences of car owners and provide them with flexible charging and discharging options, thereby motivating them to contribute power to the grid and optimizing the charging and discharging behavior of smart parking lots. This invention proposes a charging and discharging control method for smart parking lots that considers demand response to solve the above problems.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A method for regulating the charging and discharging of an intelligent parking lot that takes demand response into account includes the following steps:

[0008] Acquire external data, including battery status information, operator and data acquisition and monitoring control system pricing information, electric vehicle historical data, and the status of other parked electric vehicles;

[0009] The acquired external data is sent to the data receiving and processing module for processing, and then the processed data is integrated and sent to the decision optimization module.

[0010] Based on data processing, construct an objective function that maximizes transmitted energy and minimizes energy costs;

[0011] The constraints for maximizing transmitted energy and minimizing energy costs are constructed, including bus active and reactive power flow constraints, system feeder voltage and current constraints, electric vehicle battery remaining capacity constraints, energy transfer constraints, parking lot total power and load power constraints, battery remaining capacity update constraints, and charger charging and discharging constraints.

[0012] Based on the objective function and the constraints, a smart parking lot charging and discharging decision optimization model considering demand response is constructed. The decision optimization module optimizes the charging and discharging behavior of the smart parking lot based on the smart parking lot charging and discharging decision optimization model and the received external data.

[0013] Furthermore, the acquisition of external data includes the following steps:

[0014] First, a data receiving and processing module consisting of an owner interface module, a power grid interface module, and an electric vehicle prediction module is constructed.

[0015] The owner interface module enables communication with the electric vehicle, and the electric vehicle sends its battery status to the owner interface module.

[0016] The power grid interface module collects pricing information from operators and the SCADA (Supervisory Control and Data Acquisition) system.

[0017] The electric vehicle prediction module collects historical data on electric vehicles and the status of other parked electric vehicles.

[0018] Furthermore, the process of transmitting the acquired external data to the data receiving and processing module for processing includes the following steps:

[0019] Each parking lot is equipped with a set of chargers identified by a specific symbol. Each charger is connected to only one electric vehicle. The owner interface module enables communication and interaction between the owner and the electric vehicle. Based on the battery status sent by the electric vehicle and the user's charging needs, the owner interface module processes the data and sends a quote back to the electric vehicle, and receives a response from the electric vehicle.

[0020] The remaining charge of the EV battery after charging is:

[0021]

[0022] In the formula, τ is the time step, and SOC is... τ The remaining battery charge before charging, SOC τ+1 This represents the battery's charge level after charging. Let be the nominal function of battery charge as a function of time τ. T represents the charging power of the battery over time τ. S This refers to charging time;

[0023] The charging time t required for electric vehicles ch for:

[0024]

[0025] In the formula, SOC req For the required remaining battery power, SOC in The initial remaining battery power, Bat Cap For battery capacity, η BAT For battery charging efficiency, Chr Cap-rat The rated power of the charger facility;

[0026] The comfort factor, or CC factor, for electric vehicles is:

[0027]

[0028] In the formula, DChrg represents the discharge option, Chrg represents the charging option, FlxChrg represents the option to convert the charging process into a shorter charging interval, and t dep This refers to the time spent leaving the parking lot;

[0029] The grid interface module receives information about the grid status and the grid operator's requirements for energy pricing and ancillary services from the SCADA system. Based on this information, the grid interface module formulates an appropriate energy pricing scheme and sends it to the decision optimization module.

[0030] The electric vehicle prediction module predicts the number of vehicles arriving in the future and their corresponding charging / discharging options.

[0031] Furthermore, the electric vehicle prediction module predicts the future number of vehicles arriving and their corresponding charging / discharging options, specifically including:

[0032] Expected Number of Arriving Vehicles: Due to the charging needs of PEVs arriving at the parking lot later, the decision-making process is prone to significant changes. Therefore, historical data is needed to build a predictive model for PEV arrivals. In this model, historical data is used to construct and train an Artificial Neural Network (ANN) to predict the number of future arriving vehicles. The ANN structure includes a τ-dimensional input vector and an output, corresponding to the arrival of the first to the τ-th PEVs, and the next arrival (τ+1), respectively.

[0033]

[0034] In the formula, This is the number of arrivals for the (τ+1)th vehicle. Let f be the number of the first τ vehicles to arrive, and let f be the vehicle arrival prediction function.

[0035] Expected charging and discharging options: An ANN regression model provides each car owner with... and Let represent "charging", "charging at shorter intervals", and "discharging" respectively. Based on historical data, the system predicts the owner's next choice; therefore, its state transition matrix TM is:

[0036]

[0037] In the formula, P Chrg→Chrg Indicates that it remains in a "charging" state, P Flx→Chrg This indicates a change from "charging at shorter intervals" to "charging," P DChrg→Chrg P indicates a transition from "discharging" to "charging". DChrg→Flx This indicates a shift from "discharging" to "charging at shorter intervals";

[0038] The (τ+1)th charge / discharge state S τ+1 Represented as:

[0039] S τ+1 =S τ ×TM.

[0040] In the formula, S τ This represents the τth charge / discharge state.

[0041] Furthermore, constructing the objective function that maximizes transmitted energy and minimizes energy costs includes the following steps:

[0042] First, ensure "charging" (Chrg) class The state has sufficient energy reserves, so the objective function F1 is to maximize the increase of Chrg. class The energy level is determined to respond to the upcoming charging demand; therefore, the objective function F1 for this stage is described as follows:

[0043]

[0044] In the formula, X CH Let Γ be the charging decision variable, Γ be a 0 / 1 variable, and Γ be the system bus set. E is a time-weighted factor. del(j,τ) Energy delivered to the battery:

[0045]

[0046] In the formula, For the battery capacity of an electric vehicle connected to a charger, The battery level of the PEV after charging. This is the initial battery level;

[0047] Secondly, without considering charging costs, to maximize the FlxChrg delivered to the PEV class and DChrg class The energy level is such that the objective function F2 is described as follows:

[0048]

[0049] In the formula, For delivery to FlxChrg class Level of energy, For delivery to DChrg class Level of energy;

[0050] Finally, FlxChrg class and DChrg class The charging cost is minimized, while also considering the battery degradation cost per unit discharge; therefore, the objective function F3 is described as follows:

[0051]

[0052] In the formula, C E (τ) represents the depreciation cost C of the charger per unit of energy exchange. DCH (SOC k This represents the battery degradation cost per unit of discharge.

[0053] Furthermore, the construction of constraints regarding maximizing transmitted energy and minimizing energy costs includes the following steps:

[0054] Set active and reactive power flow constraints for the bus:

[0055]

[0056]

[0057] In the formula, Active power generation Reactive power generation The active load power, This refers to reactive load power. The active power required by the PEV, V i,τ V is the voltage on bus i. i',τ Y is the voltage on bus i'. i,i' Let θ be the admittance between bus i and i'. i,i' For the bus between i and i', δ i' Let δ be the voltage phase angle on bus i'. i The voltage phase angle on bus i;

[0058] Set system feeder voltage and current constraints:

[0059] V min ≤V i,τ ≤V max

[0060] I i,i',τ ≤I max

[0061] In the formula, V min V is the minimum bus voltage. max I is the maximum bus voltage. i,i',τ I is the unit current flowing through the line between bus i and i'. max This is the maximum current that the line can carry.

[0062] Set a constraint on the remaining capacity of electric vehicle batteries:

[0063]

[0064] In the formula, PL is the battery capacity that PEV owners expect to achieve. i Let i be the set of all chargers in the i-th parking lot;

[0065] Set energy transfer constraints:

[0066]

[0067] In the formula, Phase 1 Chrg glass Level of energy, Phase 2Chrg glass Level of energy;

[0068] Set battery remaining power update constraints:

[0069]

[0070] In the formula, Let τ+1 be the battery charge. Let τ be the battery charge at time τ. The charging decision variable is a 0 / 1 variable. For discharge decision variables, there are 0 / 1 variables. This refers to the charger's capacity.

[0071] Set total power and load power constraints for the parking lot:

[0072]

[0073]

[0074] In the formula, The total power of the parking lot This represents the maximum capacity of the parking lot.

[0075] Set the charger's charge and discharge constraints:

[0076]

[0077] In the formula, The charging decision variable is a 0 / 1 variable. For discharge decision variables, there are 0 / 1 variables.

[0078] Furthermore, based on the objective function and the constraints, a smart parking lot charging and discharging decision optimization model considering demand response is constructed. The decision optimization module optimizes the charging and discharging behavior of the smart parking lot based on the smart parking lot charging and discharging decision optimization model and the received external data, including the following steps:

[0079] Based on the relevant objective functions and constraints, a smart parking lot charging and discharging decision optimization model considering demand response is constructed. According to three different objective functions F1, F2, and F3, and the relevant constraints, the model is divided into three stages for step-by-step decision-making, ultimately achieving the goal of maximizing transmitted energy and minimizing energy costs. Specifically:

[0080] The first phase objective is to maximize delivery to Chrg. class The energy of the first stage is determined based on the objective function F1 and the processed external data, under the constraints of active and reactive power flow of the bus, system feeder voltage and current, electric vehicle battery remaining capacity, and battery remaining power update. The data after the decision is then sent to the second stage.

[0081] The second-stage objective is to maximize FlxChrg. class Level and DChrg classThe energy level is determined based on the objective function F2, external data, and data processed in the first stage. Under constraints of battery remaining power update, total parking lot power and load power, and total parking lot power and load power, a decision is made, and then the data after the decision is sent to the third stage.

[0082] The goal of the third stage is to minimize FlxChrg. class and DChrg class The cost is determined based on the objective function F3 and data from the second stage, under energy transfer constraints. The resulting charging and discharging scheme is then sent to each electric vehicle, and the database is updated simultaneously.

[0083] This invention provides a demand-response-based intelligent parking lot charging and discharging regulation method. Compared with existing technologies (traditional methods mainly guide PEV owners to supply power to the grid by pre-signing designated contracts with them), this invention comprehensively considers the preferences of PEV owners and introduces a driver comfort factor (CC factor) to motivate them to supply power to the grid, thus rationally regulating the charging and discharging behavior of the intelligent parking lot. In addition to introducing the CC factor, this invention also establishes a PEV prediction module to predict the upcoming number of PEVs and their corresponding charging and discharging options, providing owners with flexible choices. This method can optimize the charging and discharging behavior of intelligent parking lots, avoiding charging and discharging congestion caused by a large number of vehicles, thereby preventing damage to charging equipment or PEVs. Attached Figure Description

[0084] Figure 1 This is a flowchart of a smart parking lot charging and discharging control method that takes demand response into account, according to an embodiment of the present invention.

[0085] Figure 2 This is a diagram of the 38-channel bus test system in this embodiment;

[0086] Figure 3 This example compares the predicted arrival rate with the actual arrival rate. Detailed Implementation

[0087] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0088] like Figure 1As shown, this embodiment of the invention provides a smart parking lot charging and discharging control method that takes demand response into account, including the following steps:

[0089] The first step is data acquisition: acquiring external data, including battery status information, operator and data acquisition and monitoring control system pricing information, electric vehicle historical data, and the status of other parked electric vehicles, etc.

[0090] (1) First, a data receiving and processing module consisting of an owner interface module, a power grid interface module, and a pure electric vehicle (PEV) prediction module was constructed.

[0091] (2) The owner interface module mainly realizes communication with PEV, and mainly collects information on the battery status from PEV and the owner's charging needs.

[0092] (3) The power grid interface module mainly collects pricing information from operators and the Supervisory Control and Data Acquisition (SCADA) system;

[0093] (4) The PEV prediction module mainly collects historical data of PEVs and the status of other parked PEVs;

[0094] The second step is data processing: the external data obtained in the first step is sent to various modules for processing, and then the processed data is integrated and sent to the decision optimization module.

[0095] (1) Each parking lot is equipped with a set of chargers identified by specific symbols, with each charger connecting to only one PEV. This configuration allows the owner interface module to communicate with each PEV. The owner interface module processes information collected, including the remaining battery power of the PEV, the owner's desired battery power, the battery's charging power, charging efficiency, battery capacity, charger rated power, and the owner's charging / discharging options. It then calculates the remaining battery power after charging, the charging time, and the owner's comfort factor. The owner interface module then sends the processed data to the PEV owner and receives responses from the PEV. This information is also sent to the decision optimization module. The specific calculation formulas for data processing by the owner interface module are as follows:

[0096] (11) The remaining charge of the PEV battery after charging is:

[0097]

[0098] In the formula, τ is the time step, and SOC is... τ The remaining battery charge before charging, SOCτ+1 This represents the battery's charge level after charging. Let be the nominal function of battery charge as a function of time τ. T represents the charging power of the battery over time τ. S This refers to the charging time.

[0099] (12) Charging time t required for electric vehicles ch for:

[0100]

[0101] In the formula, SOC req For the required remaining battery power, SOC in The initial remaining battery power, Bat Cap For battery capacity, η BAT For battery charging efficiency, Chr Cap-rat This refers to the rated power of the charger facility.

[0102] (13) The comfort factor for PEV owners, i.e., the CC factor, is:

[0103]

[0104] In the formula, DChrg represents the discharge option, Chrg represents the charging option, FlxChrg represents the option to convert the charging process into a shorter charging interval, and t dep This refers to the time spent leaving the parking lot.

[0105] (2) The grid interface module receives information about the grid status from the SCADA system. In addition, it receives information from the grid operator regarding energy pricing and ancillary service requirements. Based on this information, the module formulates an appropriate energy pricing scheme, referencing historical cases, and sends it to the decision optimization module.

[0106] (3) The PEV prediction module includes two prediction tasks: the number of vehicles arriving in the future and their corresponding charging / discharging options.

[0107] (31) Expected Number of Arriving Vehicles: Due to the charging needs of PEVs arriving at the parking lot later, the decision-making results are prone to significant changes. Therefore, historical data is needed to build a predictive model for PEV arrivals. First, the number of PEV arrivals for each historical time period of the parking lot is collected, generating τ data points. Then, the historical data is used to train an artificial neural network (ANN) to generate a vehicle arrival prediction function. Finally, based on the first τ data points and the prediction function, the (τ+1)th number of vehicle arrivals is generated, and the data is sent to the decision optimization module. Therefore, the calculation of the (τ+1)th number of vehicle arrivals is as follows:

[0108]

[0109] In the formula, This is the number of arrivals for the (τ+1)th vehicle.

[0110] Let f be the number of the first τ vehicle arrivals, and let f be the vehicle arrival prediction function.

[0111] (32) Expected Charge / Discharge Options: The ANN regression model only processes the predicted number of PEVs arriving at the parking lot within the next decision interval. Nevertheless, it is important for the aggregator to predict the charge / discharge state of upcoming PEVs. The model provides each vehicle owner with... and These represent "charging", "charging at short intervals", and "discharging", respectively. First, based on historical data, the owner's next choice is predicted; therefore, the state transition matrix TM is:

[0112]

[0113] In the formula, P Chrg→Chrg Indicates that it remains in a "charging" state, P Flx→Chrg This indicates a change from "charging at shorter intervals" to "charging," P DChrg→Chrg P indicates a transition from "discharging" to "charging". DChrg→Flx This indicates a shift from "discharging" to "charging at shorter intervals".

[0114] Then, the PEV owner's τ+1th charge / discharge state S τ+1 It can be represented as:

[0115] S τ+1 =S τ ×TM.

[0116] In the formula, S τ This represents the τth charge / discharge state.

[0117] Finally, the predicted charge / discharge state of the PEV owner for the τ+1th time is sent to the decision optimization module.

[0118] The third step is to construct the objective function: given that the data has already been processed, construct the objective function that maximizes the transmitted energy (i.e., objective functions F1 and F2) and minimizes the energy cost (i.e., objective function F3).

[0119] (1) First, ensure "charging" (Chrg) class The state has sufficient energy reserves, so the objective function F1 is to maximize the increase of Chrg. class The energy level is determined to meet upcoming charging demands. Therefore, the objective function F1 for this stage can be described as:

[0120]

[0121] In the formula, X CH Let Γ be the charging decision variable, Γ be a 0 / 1 variable, and Γ be the system bus set. E is a time-weighted factor. del(j,τ) This refers to the energy delivered to the battery.

[0122]

[0123] In the formula, For the battery capacity of an electric vehicle connected to a charger, The battery level of the PEV after charging. This is the initial battery level.

[0124] (2) Secondly, without considering charging costs, in order to maximize the delivery to PEV at the "lower charging interval" level (FlxChrg) class ) and "Discharge" level (DChrg) class The energy of ), therefore the objective function F2 can be described as:

[0125]

[0126] In the formula, For delivery to FlxChrg class Level of energy, For delivery to DChrg class Level of energy.

[0127] (3) Finally, in this stage, FlxChrg class and DChrg class The charging cost is minimized, while also considering the battery degradation cost per unit discharge. Therefore, the objective function F3 can be described as:

[0128]

[0129] In the formula, C E (τ) represents the depreciation cost C of the charger per unit of energy exchange. DCH (SOC k This represents the battery degradation cost per unit of discharge.

[0130] The fourth step is to construct the constraints: construct constraints for maximizing the transmitted energy and minimizing the energy cost, including bus active and reactive power flow constraints, system feeder voltage and current constraints, electric vehicle battery remaining capacity constraints, energy transfer constraints, parking lot total power and load power constraints, battery remaining power update constraints, and charger charging and discharging constraints, etc.

[0131] (1) Set active and reactive power flow constraints for the bus:

[0132]

[0133]

[0134] In the formula, Active power generation Reactive power generation The active load power, This refers to reactive load power. The active power required by the PEV, V i,τ V is the voltage on bus i. i',τ Y is the voltage on bus i'. i,i' Let θ be the admittance between bus i and i'. i,i' For the bus between i and i', δ i' Let δ be the voltage phase angle on bus i'. i Let be the voltage phase angle on bus i.

[0135] (2) Set system feeder voltage and current constraints:

[0136] V min ≤V i,τ ≤V max

[0137] I i,i',τ ≤I max

[0138] In the formula, V min V is the minimum bus voltage. max I is the maximum bus voltage. i,i',τ I is the unit current flowing through the line between bus i and i'. max This is the maximum current that the line can carry.

[0139] (3) Set the remaining capacity constraint for electric vehicle batteries:

[0140]

[0141] In the formula, PL is the battery capacity that PEV owners expect to achieve. i Let be the set of all chargers in the i-th parking lot.

[0142] (4) Set energy transfer constraints:

[0143]

[0144] In the formula, Phase 1 Chrg glass Level of energy, Phase 2Chrg glass Level of energy.

[0145] (5) Set battery remaining power update constraints:

[0146]

[0147] In the formula, Let τ+1 be the battery charge. Let τ be the battery charge at time τ. The charging decision variable is a 0 / 1 variable. For discharge decision variables, there are 0 / 1 variables. This refers to the charger's capacity.

[0148] (6) Set constraints on the total power and load power of the parking lot:

[0149]

[0150]

[0151] In the formula, The total power of the parking lot This represents the maximum capacity of the parking lot.

[0152] (7) Set the charger's charge and discharge constraints:

[0153]

[0154] In the formula, The charging decision variable is a 0 / 1 variable. For discharge decision variables, there are 0 / 1 variables.

[0155] (8) Active power required by PEV

[0156]

[0157] In the formula, For charger capacity, The charging efficiency of the charger.

[0158] Fifth step, construction of the decision optimization model: Based on the objective function and constraints, construct a smart parking lot charging and discharging decision optimization model that considers demand response, and optimize the charging and discharging behavior of the smart parking lot using the data sent in step 12).

[0159] Based on the relevant objective functions and constraints, an intelligent parking lot charging and discharging decision optimization model considering demand response is constructed. According to three different objective functions F1, F2, and F3, and the relevant constraints, the model is divided into three stages for step-by-step decision-making, ultimately achieving the goal of maximizing delivered energy and minimizing energy costs.

[0160] (1) The first phase goal is to maximize delivery to Chrg class The energy level is determined. Based on the objective function F1 and the data processed in the second step, a decision is made under constraints of active and reactive power flow at the bus, system feeder voltage and current, remaining capacity of the electric vehicle battery, and battery remaining charge update. The data after the decision is then sent to the second stage.

[0161] (2) The goal of the second stage is to maximize FlxChrg class Level and DChrg class Level 1 energy. In this stage, without considering charging prices, services are provided to all PEVs until grid technology constraints and vehicle owner requirements are met. Therefore, this stage makes decisions based on the objective function F2 and the data processed from the second and first stages, under constraints of battery remaining power update, total parking lot power and load power, and total parking lot power and load power. The data after the decision is then sent to the third stage.

[0162] (3) The objective of the third stage is to minimize FlxChrg class and DChrg class The cost is considered. In this stage, the maximum delivered energy from all levels of both the first and second stages is maintained as a hard constraint, while also taking into account constraints such as the battery degradation cost per unit discharge. Therefore, this stage makes decisions based on the objective function F3 and data from the second stage, under the energy delivery constraint, and then sends the decided charge / discharge scheme to each PEV, while simultaneously updating the database.

[0163] This embodiment is implemented on a 38-busbar power distribution system, where the feeders are powered through a 12.66kV transformer in the main substation, and the total system peak load is 4.37MVa. The system structure is as follows. Figure 2 As shown, a smart parking lot with charging equipment is connected at bus 25 and 33 respectively, named Parking Lot 1 and Parking Lot 2. Different commercially available PEVs are selected, and their battery capacity data is used for simulation. The battery capacities of Chevrolet and [other vehicles] range from 17-85 kWh. It is assumed that the charger is a Class II AC charger with a rated power of 3.3 or 7 kW.

[0164] The system under study was modeled using the MATLAB software environment. To implement the different modules of the aggregator, a general algebraic modeling system was used in MATLAB, in which battery data, pricing, system data measurements, and future PEV predictions for PEVs were modeled in MATLAB. Therefore, decision optimization was performed in the general algebraic modeling system. Figure 3 The comparison of predicted and actual vehicle arrivals for two parking lots after considering demand response is shown, revealing little difference between the two. This indicates that the implementation of a smart parking lot charging and discharging control method that considers demand response can effectively predict vehicle arrivals and avoid unnecessary resource waste. Three case studies were conducted on this system to better evaluate the performance of the proposed method in reshaping demand. The first case study is the traditional First-Come, First-Served (FCFS) method for charging PEVs. The second case study is a charging-only scenario (i.e., two charging options). The third case study investigates the proposed charging and discharging solutions using the three charging options proposed in this invention. Table 1 shows a comparison of the effects after implementing the three cases.

[0165] Table 1

[0166]

[0167] It can be seen that after the implementation of the method of the present invention, the behavior of car owners can be predicted more accurately, providing them with more flexible charging and discharging options, which is conducive to the rational use of resources and avoids waste.

[0168] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for regulating charging and discharging of a smart parking lot considering demand response, characterized in that, Includes the following steps: Acquire external data, including battery status information, operator and data acquisition and monitoring control system pricing information, electric vehicle historical data, and the status of other parked electric vehicles; The acquired external data is sent to the data receiving and processing module for processing, and then the processed data is integrated and sent to the decision optimization module. Based on data processing, construct an objective function that maximizes transmitted energy and minimizes energy costs; The constraints for maximizing transmitted energy and minimizing energy costs are constructed, including bus active and reactive power flow constraints, system feeder voltage and current constraints, electric vehicle battery remaining capacity constraints, energy transfer constraints, parking lot total power and load power constraints, battery remaining capacity update constraints, and charger charging and discharging constraints. Based on the objective function and the constraints, a smart parking lot charging and discharging decision optimization model considering demand response is constructed. The decision optimization module optimizes the charging and discharging behavior of the smart parking lot based on the smart parking lot charging and discharging decision optimization model and the received external data. The process of transmitting the acquired external data to the data receiving and processing module for processing includes the following steps: Each parking lot is equipped with a set of chargers identified by a specific symbol. Each charger is connected to only one electric vehicle. The owner interface module enables communication and interaction between the owner and the electric vehicle. Based on the battery status sent by the electric vehicle and the user's charging needs, the owner interface module processes the data and sends a quote back to the electric vehicle, and receives a response from the electric vehicle. The remaining charge of the EV battery after charging is: ； In the formula, For time step, This represents the remaining battery power before charging. This represents the battery's charge level after charging. Battery charge over time The nominal function of change, For batteries over time The charging power, This refers to charging time; Charging time required for electric vehicles for: ； In the formula, For the required remaining battery power, This represents the initial remaining battery power. For battery capacity, Improve battery charging efficiency. The rated power of the charger facility; The comfort factor, or CC factor, for electric vehicles is: ； In the formula, To select the discharge option, To select charging options, To select a charging process that can be converted into shorter charging intervals, This refers to the time spent leaving the parking lot; The grid interface module receives information about the grid status and the grid operator's requirements for energy pricing and ancillary services from the SCADA system. Based on this information, the grid interface module formulates an appropriate energy pricing scheme and sends it to the decision optimization module. The electric vehicle prediction module predicts the number of vehicles arriving in the future and their corresponding charging / discharging options. The construction of the objective function that maximizes delivered energy and minimizes energy costs includes the following steps: First, ensure "charging" ( The state has sufficient energy reserves, so the objective function F1 is to maximize the energy reserve. The energy level is determined to respond to the upcoming charging demand; therefore, the objective function F1 for this stage is described as follows: ； In the formula, The charging decision variable is a 0 / 1 variable. For system bus collection, As a time-weighted factor, Energy delivered to the battery: ； In the formula, For the battery capacity of an electric vehicle connected to a charger, The battery level of the PEV after charging. This is the initial battery level; Secondly, without considering charging costs, in order to maximize the delivery to PEV... and The energy level is such that the objective function F2 is described as follows: ； In the formula, For delivery to Level of energy, For delivery to Level of energy; at last, and The charging cost is minimized, while also considering the battery degradation cost per unit discharge; therefore, the objective function F3 is described as follows: ； In the formula, Depreciation cost of charger per unit of energy exchange The cost of battery degradation per unit of discharge.

2. The intelligent parking lot charging and discharging control method considering demand response according to claim 1, characterized in that, The acquisition of external data includes the following steps: First, a data receiving and processing module consisting of an owner interface module, a power grid interface module, and an electric vehicle prediction module is constructed. The owner interface module enables communication with the electric vehicle, and the electric vehicle sends its battery status to the owner interface module. The power grid interface module collects pricing information from operators and the SCADA (Supervisory Control and Data Acquisition) system. The electric vehicle prediction module collects historical data on electric vehicles and the status of other parked electric vehicles.

3. The intelligent parking lot charging and discharging control method considering demand response according to claim 1, characterized in that, The electric vehicle prediction module predicts the future number of vehicles and their corresponding charging / discharging options, specifically including: Expected number of arriving vehicles: Due to the charging needs of PEVs arriving at the parking lot later, the decision-making results are prone to significant changes. Therefore, historical data is needed to build a predictive model for PEV arrivals. In this model, historical data is used to construct and train an artificial neural network (ANN) to predict the number of vehicles arriving in the future. The structure of the ANN includes a τ-dimensional input vector and an output, corresponding to the arrival of the first to the τ-th PEVs and the next arrival (τ+1), respectively. ； In the formula, For the output of the first Number of vehicles arriving, For the input before Number of vehicles arriving, For vehicle arrival prediction function; Expected charging and discharging options: An ANN regression model provides each car owner with... , and Let represent "charging", "charging at short intervals", and "discharging" respectively. Based on historical data, the system predicts the car owner's next choice; therefore, its state transition matrix TM is: ； In the formula, This indicates that the device is in a "charging" state. This indicates a change from "charging at shorter intervals" to "charging". This indicates a transition from "discharging" to "charging". This indicates a change from "discharging" to "charging at shorter intervals"; No. Sub-charge / discharge state Represented as: ； In the formula, For the first Secondary charge / discharge state.

4. The intelligent parking lot charging and discharging control method considering demand response according to claim 1, characterized in that, The process of constructing constraints regarding maximizing transmitted energy and minimizing energy costs includes the following steps: Set active and reactive power flow constraints for the bus: ； In the formula, Active power generation Reactive power generation For active load power, This refers to reactive load power. The active power required by PEV For bus The voltage on, For bus The voltage on, For bus and Admittance between For bus and Between For bus Voltage phase angle on, For bus Voltage phase angle; Set system feeder voltage and current constraints: ； In the formula, This is the minimum bus voltage. This is the maximum voltage of the bus. For flow through the bus and The unit current of the line between them This is the maximum current that the line can carry. Set a constraint on the remaining capacity of electric vehicle batteries: ； In the formula, This represents the battery capacity that PEV owners expect to achieve. For the first A collection of all chargers in a parking lot; Set energy transfer constraints: ； In the formula, Phase 1 Level of energy, Phase 2 Level of energy; Set battery remaining power update constraints: ； In the formula, for Always monitor the battery level. for Always monitor the battery level. The charging decision variable is a 0 / 1 variable. For discharge decision variables, there are 0 / 1 variables. This refers to the charger's capacity. Set total power and load power constraints for the parking lot: ； In the formula, The total power of the parking lot This represents the maximum capacity of the parking lot. Set the charger's charge and discharge constraints: ； In the formula, The charging decision variable is a 0 / 1 variable. For discharge decision variables, there are 0 / 1 variables.

5. The intelligent parking lot charging and discharging control method considering demand response according to claim 4, characterized in that, Based on the objective function and the constraints, a smart parking lot charging and discharging decision optimization model considering demand response is constructed. The decision optimization module optimizes the charging and discharging behavior of the smart parking lot based on the smart parking lot charging and discharging decision optimization model and received external data, including the following steps: Based on the relevant objective functions and constraints, a smart parking lot charging and discharging decision optimization model considering demand response is constructed. According to three different objective functions F1, F2, and F3, and the relevant constraints, the model is divided into three stages for step-by-step decision-making, ultimately achieving the goal of maximizing transmitted energy and minimizing energy costs. Specifically: The first phase goal is to maximize delivery to The energy of the first stage is determined based on the objective function F1 and the processed external data, under the constraints of active and reactive power flow of the bus, system feeder voltage and current, electric vehicle battery remaining capacity, and battery remaining power update. The data after the decision is then sent to the second stage. The second-stage goal is to maximize... Level and The energy level is determined based on the objective function F2, external data, and data processed in the first stage. Under constraints of battery remaining power update, total parking lot power and load power, and total parking lot power and load power, a decision is made, and then the data after the decision is sent to the third stage. The goal of the third phase is to minimize... and The cost is determined based on the objective function F3 and data from the second stage, and a decision is made under energy transfer constraints. The resulting charging and discharging scheme is then sent to each electric vehicle, and the database is updated simultaneously.

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