Energy management method and system for reducing demand cost of operational centralized charging and battery swap facility

Abstract

The invention discloses a demand cost-saving energy management method and system for an operational centralized charging facility, and the method comprises the steps: collecting various data in the charging facility, including the load data, operation data and electricity price information of a charging pile and a charging station, and the charging demand and use habit data of an electric vehicle user; carrying out analysis processing based on various data, wherein the analysis processing comprises equivalent energy storage characteristic analysis, vehicle network interaction potential evaluation, load prediction, demand calculation and cost benefit analysis of a charging pile and battery swap station aggregation cluster; making an optimization decision strategy based on an analysis processing result, wherein the optimization decision strategy comprises determining an optimal scheduling strategy and configuration parameters of a charging pile and battery swap station aggregation cluster and a vehicle network interactive scheduling scheme; and performing charging power control, vehicle charging guidance and energy interaction control with a power grid on the charging pile and the battery swap station based on the optimization decision strategy. According to the utility model, the economic benefit maximization and the efficient utilization of energy sources of the operational centralized battery charging and replacing facility can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management for charging and swapping facilities, and particularly to an energy management method and system for demand charge savings of commercial centralized charging and swapping facilities. Background Art

[0002] With the rapid development of the electric vehicle market, the number of public charging stations is increasing day by day. The electricity cost of public charging stations occupies an important part of their operating costs. Under the two-part tariff rule, demand charge and energy charge are the main components of the electricity cost. How to reduce the demand charge and energy charge has become a problem that must be faced in the future operation and management of commercial centralized charging and swapping facilities.

[0003] Traditional energy storage devices have certain applications in public charging stations, but there are problems such as high cost, inflexible and inefficient configuration. At the same time, the aggregation clusters of charging piles and swapping stations in public charging stations have potential energy storage functions that have not been fully explored, and the role of vehicle-to-grid interactive scheduling in demand charge savings has not been effectively utilized. Existing technologies lack comprehensive consideration and effective methods in these aspects and cannot meet the needs of efficient operation and demand charge savings of public charging stations.

[0004] For example, the patent application CN117578522A discloses an energy storage system and method for an electric vehicle fast charging station based on source-network-load-storage collaborative services. It includes a data acquisition module, a data storage and analysis module, and a collaborative optimization scheduling module. The data acquisition module is connected to the distribution network system, the energy storage electric vehicle charging system, and the fast charging station facility configuration; the data acquisition module is connected to the data storage and analysis module; the data storage and analysis module is connected to the collaborative optimization scheduling module, the collaborative optimization scheduling module is connected to the data transmission module, and the data transmission module is connected to the distribution network system, the energy storage electric vehicle charging system, and the fast charging station facility configuration to realize data interaction and communication between the system modules; the collaborative optimization scheduling module includes an optimization configuration and operation strategy, and a microgrid collaborative energy storage scheduling. The optimization configuration and operation strategy are connected to the fast charging station setting configuration. However, this patent application does not fully consider the equivalent energy storage characteristics of the aggregation cluster, does not fully explore the energy storage potential, and it is difficult to achieve efficient energy utilization; and it does not optimize the configuration for different operation modes and is difficult to adapt to different operation modes.

[0005] For another example, the invention patent CN105389627B discloses an optimization system for evaluating the benefits of electric vehicle charging and swapping facilities, including: a logic server for loading the charging and swapping facilities and classifying each charging and swapping facility into an electric vehicle swapping network, an electric vehicle charging station, or an electric vehicle AC charging pile; a data acquisition device connected to the logic server for collecting electric vehicle swapping network data, electric vehicle charging station data, and electric vehicle AC charging pile data according to the classification results; a calculation server connected to the data acquisition device for calculating the input-output ratios of the electric vehicle swapping network, the electric vehicle charging station, and the electric vehicle AC charging pile based on the data collected by the data acquisition device; and a service server connected to the calculation server for evaluating the benefits of the electric vehicle swapping network, the electric vehicle charging station, and the electric vehicle AC charging pile according to the input-output ratios and optimizing the configuration of the charging and swapping facilities according to the evaluation results. However, this patent does not consider the influence of multiple factors (especially different electricity price structures), cannot perform refined management, and has high operating costs. Summary of the Invention

[0006] To solve the above problems, the present invention proposes an energy management method and system for demand charge savings of operating centralized charging and swapping facilities, which can make full use of the energy storage characteristics of the aggregated clusters of charging piles and swapping stations and vehicle-grid interactive scheduling to achieve the maximization of the economic benefits of operating centralized charging and swapping facilities and the efficient utilization of energy.

[0007] The technical solution adopted by the present invention is as follows:

[0008] An energy management method for demand charge savings of operating centralized charging and swapping facilities, including:

[0009] Collecting various types of data within the charging and swapping facilities, including load data, operation data, and electricity price information of charging piles and swapping stations, as well as charging demands and usage habit data of electric vehicle users;

[0010] Performing analysis and processing based on the various types of data, including analysis of the equivalent energy storage characteristics of the aggregated clusters of charging piles and swapping stations, evaluation of vehicle-grid interactive potential, load forecasting, demand calculation, and cost-benefit analysis;

[0011] Formulating an optimization decision-making strategy based on the analysis and processing results, including determining the optimal scheduling strategy and configuration parameters of the aggregated clusters of charging piles and swapping stations based on the analysis results of equivalent energy storage characteristics and cost-benefit analysis; and determining a vehicle-grid interactive scheduling plan based on the evaluation results of vehicle-grid interactive potential, load forecasting, and demand calculation in combination with the health status of vehicle batteries;

[0012] Performing charging power control, vehicle charging guidance, and energy interaction control with the power grid on the charging piles and swapping stations based on the optimization decision-making strategy.

[0013] Further, the analysis of the equivalent energy storage characteristics of the charging pile and battery swapping station aggregation cluster includes:

[0014] Statistical analysis of the idle time and charging power of charging piles in different time periods, and calculation of the available charging power and power regulation range;

[0015] Analysis of the changes in the battery storage power, replacement frequency, and remaining battery power distribution of the battery swapping station at different times, and evaluation of the available discharge power and power regulation range;

[0016] Comprehensively considering the synergistic effect of charging piles and battery swapping stations, determining the equivalent energy storage capacity and power regulation ability of the aggregation cluster.

[0017] Further, the evaluation of the vehicle-grid interaction potential includes: based on the charging demands and usage habits of electric vehicle users, as well as the real-time data of charging piles and battery swapping stations, analyzing the power range that can be adjusted by the interaction between vehicles and the grid and the impact on the grid load.

[0018] Further, based on the results of the equivalent energy storage characteristics analysis and cost-benefit analysis, determining the optimal scheduling strategy and configuration parameters of the charging pile and battery swapping station aggregation cluster, including:

[0019] For charging piles and battery swapping stations with a single entity operation mode, aiming at maximizing the net income of the entire life cycle of the charging pile and battery swapping station aggregation cluster, attributing the income and cost items to the entire life cycle, and establishing an equivalent self-investment configuration model; the income and cost items include demand charge savings income, electricity charge income, initial investment cost, and operation and maintenance cost;

[0020] Decoupling the operation constraint conditions, transforming the equivalent self-investment configuration model into a mixed-integer linear programming problem, and solving it with an optimization solver to obtain the optimal scheduling strategy and configuration parameters of the charging pile and battery swapping station aggregation cluster; the operation constraint conditions include power balance constraint, charging and discharging constraint, power state constraint, and maximum demand constraint.

[0021] Further, based on the results of the equivalent energy storage characteristics analysis and cost-benefit analysis, determining the optimal scheduling strategy and configuration parameters of the charging pile and battery swapping station aggregation cluster, including:

[0022] For charging piles and battery swapping stations with a multi-entity cooperative operation mode, based on the income of each entity in the entire life cycle of the charging pile and battery swapping station aggregation cluster, considering that the shared cost is jointly borne by the participating cooperative entities, establishing an equivalent shared energy storage configuration model based on the generalized Nash bargaining theory;

[0023] The equivalent shared energy storage configuration model is decomposed into a total net revenue maximization problem and a benefit allocation problem, and then the optimal scheduling strategy and configuration parameters of the charging pile and swapping station aggregation cluster are obtained through solution; the total net revenue maximization problem regards the subject set as a whole, considers the total revenue and total cost, and ignores the sharing cost sharing and does not participate in the bargaining problem among the subjects in the cooperation; after the optimal scheduling strategy and configuration parameters of the charging pile and swapping station aggregation cluster and the daily scheduling cycle power interaction optimization results between each subject and the cluster are obtained through the solution of the total net revenue maximization problem, according to the bargaining power of each subject, a benefit allocation sub-problem model is constructed, and the problem is further transformed into a logarithmic form through the natural logarithm for solution.

[0024] An energy management system for demand charge savings of operating centralized charging and swapping facilities, comprising:

[0025] A data acquisition module, configured to acquire various types of data in the charging and swapping facilities, including the load data, operation data, and electricity price information of the charging piles and swapping stations, as well as the charging demands and usage habit data of electric vehicle users;

[0026] An analysis and processing module, configured to perform analysis and processing based on the various types of data, including the equivalent energy storage characteristics analysis of the charging pile and swapping station aggregation cluster, the evaluation of vehicle-grid interaction potential, load forecasting, demand calculation, and cost-benefit analysis;

[0027] An optimization decision-making module, configured to formulate an optimization decision-making strategy based on the analysis and processing results, including determining the optimal scheduling strategy and configuration parameters of the charging pile and swapping station aggregation cluster based on the equivalent energy storage characteristics analysis and cost-benefit analysis results; and determining the vehicle-grid interaction scheduling plan in combination with the vehicle battery health status based on the vehicle-grid interaction potential evaluation, load forecasting, and demand calculation results;

[0028] A control execution module, configured to perform charging power control, vehicle charging guidance, and energy interaction control with the power grid on the charging piles and swapping stations based on the optimization decision-making strategy.

[0029] Further, the equivalent energy storage characteristics analysis of the charging pile and swapping station aggregation cluster includes:

[0030] Statistically analyze the idle time and charging power of the charging piles in different time periods, and calculate the available charging power and power adjustment range;

[0031] Analyze the changes in the battery storage power, replacement frequency, and remaining battery power distribution of the swapping station batteries at different times, and evaluate the available discharge power and power adjustment range;

[0032] Comprehensively consider the synergistic effect of the charging piles and swapping stations, and determine the equivalent energy storage capacity and power adjustment ability of the aggregation cluster.

[0033] Further, the vehicle-grid interaction potential assessment includes: analyzing the adjustable power and energy range of vehicle-grid interaction and its impact on the grid load based on the charging demands and usage habits of electric vehicle users and the real-time data of charging piles and battery swapping stations.

[0034] Further, based on the results of equivalent energy storage characteristic analysis and cost-benefit analysis, determining the optimal scheduling strategy and configuration parameters of the charging pile and battery swapping station aggregation cluster, including:

[0035] For charging piles and battery swapping stations with a single-operator operation mode, aiming at maximizing the net income of the whole life cycle of the charging pile and battery swapping station aggregation cluster, attributing the income and cost items to the whole life cycle, and establishing an equivalent self-investment configuration model; the income and cost items include demand charge savings, electricity charge income, initial investment cost, and operation and maintenance cost;

[0036] Decoupling the operation constraint conditions, transforming the equivalent self-investment configuration model into a mixed-integer linear programming problem, and solving with the help of an optimization solver to obtain the optimal scheduling strategy and configuration parameters of the charging pile and battery swapping station aggregation cluster; the operation constraint conditions include power balance constraint, charging and discharging constraint, power state constraint, and maximum demand constraint.

[0037] Further, based on the results of equivalent energy storage characteristic analysis and cost-benefit analysis, determining the optimal scheduling strategy and configuration parameters of the charging pile and battery swapping station aggregation cluster, including:

[0038] For charging piles and battery swapping stations with a multi-operator cooperation operation mode, based on the income of each operator in the whole life cycle of the charging pile and battery swapping station aggregation cluster, considering that the shared cost is jointly borne by the participating cooperation operators, and establishing an equivalent shared energy storage configuration model based on the generalized Nash bargaining theory;

[0039] Decomposing the equivalent shared energy storage configuration model into a total net income maximization problem and an interest distribution problem, and then solving to obtain the optimal scheduling strategy and configuration parameters of the charging pile and battery swapping station aggregation cluster; the total net income maximization problem regards the operator set as a whole, considers the total income and total cost, and ignores the shared cost sharing and the bargaining problems among non-participating operators; after the optimal scheduling strategy and configuration parameters of the charging pile and battery swapping station aggregation cluster and the optimal results of daily scheduling cycle power interaction between each operator and the cluster are obtained in the total net income maximization problem, according to the bargaining power of each operator, constructing an interest distribution sub-problem model, and further transforming the problem into a logarithmic form through the natural logarithm for solution.

[0040] The beneficial effects of the present invention are as follows:

[0041] 1. Fully exploit the energy storage potential to achieve efficient energy utilization.

[0042] When the present invention collects data, it comprehensively collects the detailed operation data of charging piles and battery swapping stations, including charging status, battery information, replacement frequency, etc., as well as the charging behavior data of electric vehicle users; when analyzing and processing, it uses these operation data to analyze the equivalent energy storage characteristics, calculates the available charging power, power regulation range, etc., determines the equivalent energy storage capacity and power regulation ability of the aggregation cluster, and provides a basis for subsequent optimization configuration; when making an optimization decision, it formulates a scheduling strategy according to the equivalent energy storage characteristics, such as adjusting the charging power limit and vehicle guidance strategy at an appropriate time, and gives full play to the energy storage function of the aggregation cluster.

[0043] Through in-depth analysis of the equivalent energy storage characteristics of the aggregation cluster of charging piles and battery swapping stations, the present invention incorporates it as an adjustable energy storage resource into the energy management system, improves the energy utilization efficiency of the entire charging and swapping facility system, realizes refined energy management, and can solve the shortcomings of patent application CN117578522A and others that do not fully consider the equivalent energy storage characteristics of the aggregation cluster.

[0044] 2. Dynamic and refined energy management to effectively reduce operating costs.

[0045] The present invention collects various types of data in the charging and swapping facilities in real time, including real-time load data, electricity price information, user charging demand and habit data, etc., to provide comprehensive data support for subsequent analysis; when analyzing and processing, it conducts load forecasting, demand calculation, vehicle-grid interaction potential assessment, cost-benefit analysis, etc., and judges whether it is necessary to adjust the scheduling strategy and design a vehicle-grid interaction scheduling plan according to the analysis results. For example, according to the load forecast and time-of-use electricity price, it encourages charging to increase power consumption during low electricity price periods and controls charging or guides discharging during high electricity price periods; when making an optimization decision, it formulates an optimization decision according to the analysis results, including the scheduling strategy of the aggregation cluster of charging piles and battery swapping stations and the vehicle-grid interaction scheduling plan, such as formulating a vehicle charging guidance and discharging control strategy considering the grid load and the health status of vehicle batteries; when controlling and executing, it converts the decision into actual control instructions to precisely control the charging piles, battery swapping stations and vehicle-grid interaction, and realizes dynamic energy management.

[0046] The present invention comprehensively considers various factors such as real-time load changes, time-of-use electricity prices, vehicle-grid interaction, etc., realizes dynamic and refined energy management of the charging and swapping facilities, effectively reduces demand-side electricity charges and energy consumption charges, thereby reducing operating costs, and can solve the shortcoming of the inability to conduct refined management in patent CN105389627B.

[0047] 3. Flexibly adapt to different operation modes and optimize resource allocation.

[0048] For single-entity operation, the present invention establishes an equivalent self-investment configuration model, calculates demand-saving benefits, electricity consumption and electricity fee benefits, initial investment costs and operation and maintenance costs, etc., sets constraints such as power balance, charging and discharging, power status and maximum demand, optimizes the configuration with the goal of maximizing the net benefit over the entire life cycle, and uses the Big-M method to process nonlinear constraints and solve them to obtain the optimal strategy and parameters.

[0049] For cooperative operation of multiple entities, the present invention establishes an equivalent shared energy storage configuration model. Based on the generalized Nash bargaining theory of cooperative games, the benefits and costs of each cooperative entity are calculated, and shared cluster operation constraints are set. The model is decomposed into two sub-problems: maximizing total net benefits and allocating benefits. The benefits are allocated by calculating the bargaining power through appropriate methods to achieve optimal allocation of resources.

[0050] The present invention establishes equivalent energy storage optimization configuration models for two modes: single-entity operation and multiple-entity cooperative operation, respectively, to make resource allocation more reasonable and flexible, and improve the adaptability and economy of the entire charging and swapping facility system in different operating scenarios.

[0051] 4. Improve the overall stability and reliability of the system.

[0052] The present invention takes into account the impact of various factors on the grid load when performing load forecasting and vehicle-grid interaction potential assessment. The formulated dispatching strategy and vehicle-grid interaction plan can be dynamically adjusted according to the grid load conditions, avoiding grid instability caused by a large number of electric vehicles charging or discharging at the same time.

[0053] The present invention controls the charging power of charging piles and battery swap stations and the energy scheduling of vehicle-grid interaction, which can ensure that the power balance and power state are within a reasonable range and ensure the stable operation of the system. For example, by setting power balance constraints, the load power of the charging station is always in a controllable state.

[0054] Through reasonable scheduling strategies and optimized configurations, the present invention makes the energy interaction between the charging and swapping facilities and the power grid more stable, reduces the impact of load fluctuations on the power grid, and at the same time ensures the stable operation of the charging and swapping facilities themselves, thereby improving the overall stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is one of the flow charts of an energy management method for demand-saving costs of a commercial centralized charging and swapping facility according to Example 1 of the present invention.

[0056] Figure 2 This is the second flow chart of an energy management method for demand-saving costs of a commercial centralized charging and swapping facility according to Example 1 of the present invention. DETAILED DESCRIPTION

[0057] To have a clearer understanding of the technical features, objectives, and effects of the present invention, the specific implementation manners of the present invention will now be described. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without making creative efforts fall within the scope of protection of the present invention.

[0058] Embodiment 1

[0059] As Figure 1 and Figure 2 shown, this embodiment provides an energy management method for demand charge savings of operating centralized charging and swapping facilities, including:

[0060] Data collection: Collect various types of data in the charging and swapping facilities, including load data, operation data, and electricity price information of charging piles and swapping stations, as well as charging demands and usage habit data of electric vehicle users;

[0061] Analysis and processing: Analyze and process based on various types of data, including analysis of the equivalent energy storage characteristics of the aggregated clusters of charging piles and swapping stations, evaluation of the vehicle-grid interaction potential, load forecasting, demand calculation, and cost-benefit analysis;

[0062] Optimization decision-making: Develop an optimization decision-making strategy based on the analysis and processing results, including determining the optimal scheduling strategy and configuration parameters of the aggregated clusters of charging piles and swapping stations based on the analysis results of the equivalent energy storage characteristics and cost-benefit analysis; and determining the vehicle-grid interaction scheduling plan in combination with the vehicle battery health status based on the evaluation results of the vehicle-grid interaction potential, load forecasting, and demand calculation;

[0063] Control execution: Perform charging power control, vehicle charging guidance, and energy interaction control with the power grid on the charging piles and swapping stations based on the optimization decision-making strategy.

[0064] When collecting data, the specific types of data collected in the charging and swapping facilities include:

[0065] Load data of charging piles and swapping stations, such as total power, real-time power of each charging pile, etc.;

[0066] Operation data of charging piles and swapping stations, such as charging status of charging piles, connected vehicle battery information, charging power change, battery inventory information, battery replacement frequency, remaining battery power of swapping stations, etc.;

[0067] Electricity price information, including time-of-use electricity price and demand charge electricity price;

[0068] Charging demands and usage habit data of electric vehicle users, such as vehicle type, charging time preference, average charging duration, etc.

[0069] During the analysis and processing, the collected data is comprehensively analyzed and processed, specifically including:

[0070] Analysis of the equivalent energy storage characteristics of the charging pile and battery swapping station aggregation cluster. By analyzing its operation data and user behavior data, determine the adjustable power range and evaluate its potential as an energy storage resource;

[0071] Assessment of the vehicle-grid interaction potential. Based on the user's charging needs, usage habits, and real-time data of the charging piles and battery swapping stations, analyze the adjustable power range of the vehicle-grid interaction and its impact on the grid load;

[0072] Load forecasting. Using historical load data and real-time data, adopt appropriate forecasting algorithms to predict the future load changes of the charging station;

[0073] Demand calculation. Based on the load data and demand tariff rules, calculate the demand electricity cost of the charging station;

[0074] Cost-benefit analysis. Considering factors such as demand electricity cost, energy electricity cost, energy storage investment cost, and operation and maintenance cost, analyze the cost-benefit under different strategies.

[0075] During the optimization decision-making, according to the results of the analysis and processing module, formulate optimization decisions, including:

[0076] Formulation of the scheduling strategy for the charging pile and battery swapping station aggregation cluster. Based on the equivalent energy storage characteristics and operation cost-benefit, determine the charging power limit and vehicle guidance strategy at different time periods to maximize the utilization of the energy storage function;

[0077] Design of the vehicle-grid interaction scheduling plan. Considering time-of-use electricity price, grid load, and vehicle battery health status, formulate vehicle charging guidance strategy and discharge control strategy to achieve efficient scheduling of the vehicle-grid interaction.

[0078] During the control execution, convert the strategies formulated in the optimization decision-making into actual control instructions to control each device of the charging station, specifically including:

[0079] Control the charging power of the charging piles and battery swapping stations. Adjust the charging power of the charging piles according to the scheduling strategy to guide vehicles to charge reasonably, and at the same time manage the battery replacement and charging operations of the battery swapping stations;

[0080] Realize the energy scheduling of the vehicle-grid interaction. According to the vehicle-grid interaction scheduling plan, control the energy interaction between the vehicle and the grid, including guiding the vehicle to charge or discharge at the appropriate time.

[0081] Preferably, the analysis of the equivalent energy storage characteristics of the charging pile and battery swapping station aggregation cluster includes:

[0082] Statistically analyze the idle time and charging power of charging piles within different time periods, and calculate the available charging power and power regulation range.

[0083] Analyze the changes in the stored battery power, replacement frequency, and remaining battery power distribution of batteries in the battery swapping station at different times, and evaluate the available discharge power and power regulation range.

[0084] Comprehensively consider the synergistic effect of charging piles and battery swapping stations, and determine the equivalent energy storage capacity and power regulation ability of the aggregated cluster.

[0085] Preferably, based on the results of equivalent energy storage characteristic analysis and cost-benefit analysis, determine the optimal scheduling strategy and configuration parameters of the aggregated cluster of charging piles and battery swapping stations, including: according to the operation mode of the station (single entity operation or multi-entity cooperative operation), call the corresponding equivalent energy storage configuration model (equivalent self-investment configuration model or equivalent shared energy storage configuration model) for optimization calculation to obtain the optimal scheduling strategy and configuration parameters of the aggregated cluster of charging piles and battery swapping stations.

[0086] More preferably, for charging piles and battery swapping stations with a single entity operation mode, with the goal of maximizing the net income of the entire life cycle of the aggregated cluster of charging piles and battery swapping stations, attribute the income and cost items to the entire life cycle, and establish an equivalent self-investment configuration model; use methods such as the Big-M method to decouple the operation constraint conditions, transform the equivalent self-investment configuration model into a mixed integer linear programming problem, and solve it with the help of an optimization solver to obtain the optimal scheduling strategy and configuration parameters of the aggregated cluster of charging piles and battery swapping stations, such as the charging power limit in different time periods, vehicle guidance strategy, etc.

[0087] Among them, the income and cost items specifically include:

[0088] Demand charge savings income: Based on real-time load data and demand tariffs, by establishing a load regulation model, considering the impact of the aggregated cluster of charging piles and battery swapping stations on the load, calculate the demand charge savings income obtained by reducing the maximum demand.

[0089] Electricity charge income: Combine time-of-use tariffs to analyze the impact of the charging and discharging behaviors of charging piles and battery swapping stations at different times on the electricity charge. Encourage vehicle charging during low-tariff periods to increase power consumption; reasonably control vehicle charging or guide vehicle discharging during high-tariff periods to reduce power purchase, thereby calculating the electricity charge income.

[0090] Initial investment cost: Consider the construction cost, equipment purchase cost, and operation and management cost of charging piles and battery swapping stations as the initial investment cost of equivalent energy storage.

[0091] Operation and maintenance costs: including equipment maintenance costs, personnel management costs, and site rental costs of charging piles and battery swapping stations. The annual operation and maintenance costs are calculated according to the actual operation situation.

[0092] In addition, the operation constraint conditions include:

[0093] Power balance constraint: At any moment, ensure that the power in different forms of the load at the charging station is equal to the power purchased from the grid plus the equivalent discharge power of the aggregated cluster of charging piles and battery swapping stations minus the equivalent charging power. The equivalent discharge and charging powers are dynamically calculated according to the actual operation status of the charging piles and battery swapping stations and the charging and discharging behaviors of the vehicles.

[0094] Charging and discharging constraints: The charging and discharging powers of the charging piles and battery swapping stations need to meet the rated power limits of their own equipment and the overall power capacity limits of the charging station. At the same time, the charging demands of the vehicles and battery safety should be considered to avoid overcharging and over-discharging.

[0095] Electric energy state constraint: Set the upper and lower limit constraints on the equivalent electric energy storage state of the aggregated cluster of charging piles and battery swapping stations. The lower limit is determined by counting the idle time of the charging piles and the remaining battery power of the battery swapping stations, avoiding energy waste and equipment damage caused by overcharging, and ensuring sufficient discharge power when needed.

[0096] Maximum demand constraint: Regard the reduction rate of the maximum demand of the synthetic load at the charging station after regulating the aggregated cluster of charging piles and battery swapping stations as an optimization variable, and ensure that the synthetic load meets the maximum demand limit conditions.

[0097] More preferably, for the charging piles and battery swapping stations in the multi-subject cooperative operation mode, based on the revenues of each subject during the whole life cycle of the aggregated cluster of charging piles and battery swapping stations, considering that the sharing costs are jointly borne by the participating cooperative subjects, an equivalent shared energy storage configuration model based on the generalized Nash bargaining theory is established; the equivalent shared energy storage configuration model is decomposed into a total net revenue maximization problem and an interest distribution problem, and then the optimal scheduling strategy and configuration parameters of the aggregated cluster of charging piles and battery swapping stations are obtained by solving; the total net revenue maximization problem regards the subject set as a whole to consider the total revenue and total cost, ignoring the sharing cost sharing and not participating in the bargaining problem among the subjects; after the optimal scheduling strategy and configuration parameters of the aggregated cluster of charging piles and battery swapping stations and the optimal results of the daily scheduling cycle power interaction between each subject and the cluster are obtained by solving the total net revenue maximization problem, according to the bargaining power of each subject, an interest distribution sub-problem model is constructed, and the problem is further transformed into a logarithmic form by the natural logarithm for solution.

[0098] Among them, the calculation of revenues and effects includes:

[0099] Demand charge savings: Each cooperation entity participates in regulating the charging pile and swapping station aggregation cluster, modifies the original electricity consumption curve, realizes demand reduction, and calculates the demand charge savings obtained by each entity.

[0100] Electricity quantity and electricity charge income: Each cooperation entity obtains electricity quantity and electricity charge income on a daily time scale under time-of-use electricity prices. By participating in the operation and management of charging piles and swapping stations, it reasonably guides vehicle charging and discharging.

[0101] Initial investment cost: Calculate the initial configuration cost of the charging pile and swapping station aggregation cluster jointly invested by the cooperation entities, including the parts of construction cost, equipment purchase cost, and operation and management cost allocated according to the cooperation ratio.

[0102] Operation and maintenance cost: Calculate the annual operation and maintenance cost of the charging pile and swapping station aggregation cluster jointly borne by the cooperation entities, including the parts of equipment maintenance cost, personnel management cost, and site rental area allocated according to the cooperation ratio.

[0103] Optimization configuration model establishment: Based on the income of each cooperation entity in the whole life cycle of the charging pile and swapping station aggregation cluster, considering that the sharing cost is jointly borne by the participating users, an equivalent shared energy storage optimization configuration model based on the generalized Nash bargaining theory is established. Calculate the bargaining power of each user through a suitable method (adjusted by referring to the non-linear energy path of traditional energy storage) to reflect the contribution degree of each entity in the cooperation.

[0104] In this embodiment, the equivalent shared energy storage configuration model is based on the generalized Nash bargaining theory of cooperative game. Shared cluster operation constraints: Similar to the equivalent self-investment configuration model, power balance constraints, charging and discharging constraints, energy state constraints, and maximum demand constraints are set, but for the interaction between multiple cooperation entities and the charging pile and swapping station aggregation cluster.

[0105] Embodiment 2

[0106] This embodiment provides an energy management system for demand charge savings of operating centralized charging and swapping facilities, including:

[0107] Data acquisition module, configured to acquire various types of data in the charging and swapping facilities, including load data, operation data, and electricity price information of charging piles and swapping stations, as well as charging demand and usage habit data of electric vehicle users;

[0108] Analysis and processing module, configured to perform analysis and processing based on various types of data, including equivalent energy storage characteristic analysis of the charging pile and swapping station aggregation cluster, vehicle-grid interaction potential assessment, load forecasting, demand calculation, and cost-benefit analysis;

[0109] The optimization decision-making module is configured to formulate an optimization decision-making strategy based on the analysis and processing results, including determining the optimal scheduling strategy and configuration parameters of the charging pile and swapping station aggregation cluster based on the equivalent energy storage characteristic analysis and cost-benefit analysis results; and determining the vehicle-grid interaction scheduling plan based on the vehicle-grid interaction potential assessment, load forecasting, and demand calculation results, in combination with the vehicle battery health status.

[0110] The control execution module is configured to perform charging power control, vehicle charging guidance, and energy interaction control with the power grid on the charging piles and swapping stations based on the optimization decision-making strategy.

[0111] Preferably, the equivalent energy storage characteristic analysis of the charging pile and swapping station aggregation cluster includes:

[0112] Statistically analyze the idle time and charging power of the charging piles within different time periods, and calculate the available charging power and power regulation range.

[0113] Analyze the battery storage power changes, replacement frequencies, and remaining battery power distributions of the swapping station batteries at different times, and evaluate the available discharge power and power regulation range.

[0114] Comprehensively consider the synergistic effect of the charging piles and swapping stations to determine the equivalent energy storage capacity and power regulation ability of the aggregation cluster.

[0115] Preferably, the vehicle-grid interaction potential assessment includes: based on the charging demands and usage habits of electric vehicle users and the real-time data of the charging piles and swapping stations, analyze the power and energy ranges that can be regulated by the interaction between the vehicles and the power grid, and the impact on the power grid load.

[0116] Preferably, based on the equivalent energy storage characteristic analysis and cost-benefit analysis results, determining the optimal scheduling strategy and configuration parameters of the charging pile and swapping station aggregation cluster includes:

[0117] For the charging piles and swapping stations with a single entity operation mode, with the goal of maximizing the net income of the entire life cycle of the charging pile and swapping station aggregation cluster, attribute the income and cost items to the entire life cycle, and establish an equivalent self-investment configuration model; the income and cost items include demand charge savings income, electricity charge income, initial investment cost, and operation and maintenance cost.

[0118] Decouple the operation constraint conditions, transform the equivalent self-investment configuration model into a mixed-integer linear programming problem, and use an optimization solver to solve for the optimal scheduling strategy and configuration parameters of the charging pile and swapping station aggregation cluster; the operation constraint conditions include power balance constraint, charging and discharging constraint, power energy state constraint, and maximum demand constraint.

[0119] Preferably, based on the results of equivalent energy storage characteristic analysis and cost-benefit analysis, determine the optimal scheduling strategy and configuration parameters of the charging pile and battery swapping station aggregation cluster, including:

[0120] For the charging piles and battery swapping stations in the multi-subject cooperative operation mode, based on the benefits of each subject during the whole life cycle of the charging pile and battery swapping station aggregation cluster, considering that the sharing cost is jointly borne by the participating cooperative subjects, establish an equivalent shared energy storage configuration model based on the generalized Nash bargaining theory;

[0121] Decompose the equivalent shared energy storage configuration model into a total net benefit maximization problem and an interest distribution problem, and then solve to obtain the optimal scheduling strategy and configuration parameters of the charging pile and battery swapping station aggregation cluster; the total net benefit maximization problem regards the subject set as a whole to consider the total benefit and total cost, ignoring the sharing cost sharing and not participating in the mutual bargaining problem among the subjects; after the optimal scheduling strategy and configuration parameters of the charging pile and battery swapping station aggregation cluster and the optimal results of the daily scheduling cycle power interaction between each subject and the cluster are obtained by solving the total net benefit maximization problem, according to the bargaining power of each subject, construct an interest distribution sub-problem model, and further transform the problem into a logarithmic form through the natural logarithm for solution.

[0122] Embodiment 3

[0123] This embodiment is based on Embodiment 1:

[0124] This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the energy management method for demand-side cost saving of the operating centralized charging and swapping facilities in Embodiment 1. Among them, the computer program can be in the form of source code, object code, executable file or some intermediate form, etc.

[0125] Embodiment 4

[0126] This embodiment is based on Embodiment 1:

[0127] This embodiment provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the energy management method for demand-side cost savings of the operating centralized charging and swapping facilities in Embodiment 1. The computer program can be in the form of source code, object code, executable file, or some intermediate form, etc. The storage medium includes any entity or device capable of carrying the computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the storage medium does not include electrical carrier signals and telecommunication signals.

[0128] It should be noted that for the foregoing method embodiments, for the sake of simplicity of description, they are expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

Claims

1. An energy management method for demand saving of commercial centralized charging and swapping facilities, characterized in that: include: Collect various data from charging and swapping facilities, including load data, operation data and electricity price information of charging piles and swapping stations, as well as charging needs and usage habits of electric vehicle users; Analyze and process the above-mentioned data, including equivalent energy storage characteristics analysis of charging pile and battery swap station aggregation cluster, vehicle-grid interaction potential assessment, load forecasting, demand calculation and cost-benefit analysis; Formulate an optimization decision-making strategy based on the analysis and processing results, including determining the optimal scheduling strategy and configuration parameters of the charging pile and battery swap station aggregation cluster based on the equivalent energy storage characteristics analysis and cost-benefit analysis results; and determine the vehicle-grid interaction scheduling plan based on the vehicle-grid interaction potential assessment, load forecasting and demand calculation results, combined with the vehicle battery health status; Based on the optimization decision strategy, charging power control, vehicle charging guidance and energy interaction control with the power grid are performed on the charging piles and battery swap stations.

2. The energy management method for demand saving of commercial centralized charging and swapping facilities according to claim 1 is characterized in that: The equivalent energy storage characteristics analysis of the charging pile and battery swap station aggregation cluster includes: Count the idle time and charging power of the charging piles in different time periods, and calculate the available charging power and power adjustment range; Analyze the battery storage capacity changes, replacement frequency, and remaining battery capacity distribution of the battery in the battery swap station at different times, and evaluate the discharge capacity and power adjustment range that can be provided; Taking into account the synergy of charging piles and battery swap stations, the equivalent energy storage capacity and power regulation capability of the aggregated cluster are determined.

3. The energy management method for demand saving of commercial centralized charging and swapping facilities according to claim 1 is characterized in that: The vehicle-grid interaction potential assessment includes: based on the charging needs and usage habits of electric vehicle users and the real-time data of charging piles and battery swap stations, analyzing the amount of electricity that can be adjusted by the interaction between vehicles and the grid, the power range, and the degree of impact on the grid load.

4. The energy management method for demand saving of commercial centralized charging and swapping facilities according to claim 1 is characterized in that: The optimal dispatching strategy and configuration parameters of the charging pile and battery swap station aggregation cluster are determined based on the equivalent energy storage characteristics analysis and cost-benefit analysis results, including: For charging piles and battery swap stations with a single-entity operation model, the goal is to maximize the net income of the charging pile and battery swap station aggregation cluster over the entire life cycle, and the income and cost items are calculated over the entire life cycle to establish an equivalent self-investment configuration model; the income and cost items include demand saving income, electricity and electricity fee income, initial investment cost and operation and maintenance cost; The operating constraints are decoupled, and the equivalent self-investment configuration model is transformed into a mixed integer linear programming problem. The optimal scheduling strategy and configuration parameters of the charging pile and battery swap station aggregation cluster are obtained with the help of an optimization solver; the operating constraints include power balance constraints, charging and discharging constraints, power state constraints, and maximum demand constraints.

5. The energy management method for demand saving of commercial centralized charging and swapping facilities according to claim 1 is characterized in that: The optimal dispatching strategy and configuration parameters of the charging pile and battery swap station aggregation cluster are determined based on the equivalent energy storage characteristics analysis and cost-benefit analysis results, including: For charging piles and battery swap stations in a cooperative operation model with multiple entities, an equivalent shared energy storage configuration model based on the generalized Nash bargaining theory is established based on the income of each entity over the entire life cycle of the charging pile and battery swap station aggregation cluster, and considering that the shared costs are shared by the participating cooperative entities; The equivalent shared energy storage configuration model is decomposed into the total net benefit maximization problem and the benefit distribution problem, and then the optimal scheduling strategy and configuration parameters of the charging pile and battery swap station aggregation cluster are solved; the total net benefit maximization problem considers the subject set as a whole to consider the total benefit and total cost, and ignores the bargaining problem between the subjects who do not participate in the shared cost sharing; after the total net benefit maximization problem is solved to obtain the optimal scheduling strategy and configuration parameters of the charging pile and battery swap station aggregation cluster and the daily scheduling cycle power interaction optimization results between each subject and the cluster, the benefit distribution sub-problem model is constructed according to the bargaining power of each subject, and the problem is further converted into logarithmic form through the natural logarithm for solution.

6. An energy management system for demand-saving of commercial centralized charging and swapping facilities, characterized in that: include: The data collection module is configured to collect various data in the charging and swapping facilities, including load data, operation data and electricity price information of charging piles and swapping stations, as well as charging demand and usage habit data of electric vehicle users; An analysis and processing module is configured to perform analysis and processing based on the various types of data, including equivalent energy storage characteristics analysis of charging piles and battery swap station aggregation clusters, vehicle-grid interaction potential assessment, load forecasting, demand calculation, and cost-benefit analysis; An optimization decision module is configured to formulate an optimization decision strategy based on the analysis and processing results, including determining the optimal scheduling strategy and configuration parameters of the charging pile and battery swap station aggregation cluster based on the equivalent energy storage characteristics analysis and cost-benefit analysis results; and determining the vehicle-grid interaction scheduling plan based on the vehicle-grid interaction potential assessment, load forecasting and demand calculation results, combined with the vehicle battery health status; The control execution module is configured to perform charging power control, vehicle charging guidance, and energy interaction control with the power grid on the charging piles and battery swap stations based on the optimization decision strategy.

7. The energy management system for demand-saving of commercial centralized charging and swapping facilities according to claim 6 is characterized in that: The equivalent energy storage characteristics analysis of the charging pile and battery swap station aggregation cluster includes: Count the idle time and charging power of the charging piles in different time periods, and calculate the available charging power and power adjustment range; Analyze the battery storage capacity changes, replacement frequency, and remaining battery capacity distribution of the battery in the battery swap station at different times, and evaluate the discharge capacity and power adjustment range that can be provided; Taking into account the synergy of charging piles and battery swap stations, the equivalent energy storage capacity and power regulation capability of the aggregated cluster are determined.

8. The energy management system for demand-saving of commercial centralized charging and swapping facilities according to claim 6 is characterized in that: The vehicle-grid interaction potential assessment includes: based on the charging needs and usage habits of electric vehicle users and the real-time data of charging piles and battery swap stations, analyzing the amount of electricity that can be adjusted by the interaction between vehicles and the grid, the power range, and the degree of impact on the grid load.

9. The energy management system for demand-saving of commercial centralized charging and swapping facilities according to claim 6 is characterized in that: The optimal dispatching strategy and configuration parameters of the charging pile and battery swap station aggregation cluster are determined based on the equivalent energy storage characteristics analysis and cost-benefit analysis results, including: For charging piles and battery swap stations with a single-entity operation model, the goal is to maximize the net income of the charging pile and battery swap station aggregation cluster over the entire life cycle, and the income and cost items are calculated over the entire life cycle to establish an equivalent self-investment configuration model; the income and cost items include demand saving income, electricity and electricity fee income, initial investment cost and operation and maintenance cost; The operating constraints are decoupled, and the equivalent self-investment configuration model is transformed into a mixed integer linear programming problem. The optimal scheduling strategy and configuration parameters of the charging pile and battery swap station aggregation cluster are obtained with the help of an optimization solver; the operating constraints include power balance constraints, charging and discharging constraints, power state constraints, and maximum demand constraints.

10. The energy management system for demand-saving of commercial centralized charging and swapping facilities according to claim 6 is characterized in that: The optimal dispatching strategy and configuration parameters of the charging pile and battery swap station aggregation cluster are determined based on the equivalent energy storage characteristics analysis and cost-benefit analysis results, including: For charging piles and battery swap stations in a cooperative operation model with multiple entities, an equivalent shared energy storage configuration model based on the generalized Nash bargaining theory is established based on the income of each entity over the entire life cycle of the charging pile and battery swap station aggregation cluster, and considering that the shared costs are shared by the participating cooperative entities; The equivalent shared energy storage configuration model is decomposed into the total net benefit maximization problem and the benefit distribution problem, and then the optimal scheduling strategy and configuration parameters of the charging pile and battery swap station aggregation cluster are solved; the total net benefit maximization problem considers the subject set as a whole to consider the total benefit and total cost, and ignores the bargaining problem between the subjects who do not participate in the shared cost sharing; after the total net benefit maximization problem is solved to obtain the optimal scheduling strategy and configuration parameters of the charging pile and battery swap station aggregation cluster and the daily scheduling cycle power interaction optimization results between each subject and the cluster, the benefit distribution sub-problem model is constructed according to the bargaining power of each subject, and the problem is further converted into logarithmic form through the natural logarithm for solution.

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