A method and system for operating cloud energy storage and sustainable distributed energy system

Abstract

The present invention discloses an operating method and system for a cloud energy storage and sustainable distributed energy system, relating to the field of energy scheduling technology. The method includes: collaboratively managing cloud energy storage and sustainable distributed energy systems for electrical energy storage through multi-stage dynamic configuration and dual-scenario collaborative scheduling based on charging power demand; establishing an objective function based on charging power demand, taking the maximization of the comprehensive net present value of investment profit as the goal, and adding flexibility to the objective function. An optimization scheduling model for measuring economic and environmental benefits is constructed using design constraints, energy balance constraints, optimal scheduling constraints for charging piles, and scheduling constraints for battery energy storage systems (BESS) and transformers as constraints; and optimizing the management of cloud energy storage and sustainable distributed energy systems through collaborative management and optimization of the scheduling model, and adding an aggregator for energy coordinated scheduling. The present invention can improve the stability of power supply.

Description

Technical Field

[0001] The present invention relates to the field of energy scheduling technology, and in particular to an operation method and system for a cloud energy storage and sustainable distributed energy system. Background Art

[0002] Photovoltaics (PV) power generation, as a clean and renewable energy source, has experienced rapid growth in recent years. However, the intermittent and volatile nature of PV power generation poses significant challenges to power grids. Furthermore, the increasing popularity of electric vehicles (EVs) has placed higher demands on charging infrastructure. Traditional EV charging stations primarily rely on the grid for power, leading to significant increases in grid load, especially during peak charging periods. This can cause power shortages and grid instability. To address these issues, EV charging stations integrating PV and energy storage systems have emerged. These charging stations not only reduce reliance on the grid but also increase the utilization of renewable energy.

[0003] In the existing technology, traditional electric vehicle charging stations mainly rely on power grid for power supply. Electric vehicle charging stations are usually directly connected to the grid and adjust the grid voltage to a voltage and current level suitable for electric vehicle charging through a transformer.

[0004] However, the above technology will cause a sharp increase in grid load during charging peak periods, leading to power shortages and unstable power supply. Summary of the Invention

[0005] The embodiments of the present invention provide a method and system for operating a cloud energy storage and sustainable distributed energy system, which can solve the problem of power grid instability caused by power shortage in the prior art.

[0006] An embodiment of the present invention provides an operating method for a cloud energy storage and sustainable distributed energy system, comprising the following steps: integrating a photovoltaic system and a battery energy storage system (BESS) into an electric vehicle charging station as a sustainable distributed energy system; obtaining the charging power demand of the electric vehicle charging station by simulating the charging behavior of an electric vehicle at the charging station; collaboratively managing the cloud energy storage and the sustainable distributed energy system for electric energy storage based on the charging power demand through multi-stage dynamic configuration and dual-scenario coordinated scheduling; the multi-stage dynamic configuration represents adjusting storage capacity and power service contracts during different power consumption phases, and the dual-scenario coordinated scheduling represents managing the energy balance between various system components and system loads of the cloud energy storage and the sustainable distributed energy system under different scenarios; establishing an objective function based on the charging power demand with maximizing the comprehensive net present value (NPV) that measures investment profits as the goal, adding flexibility to the objective function, and constructing an optimization scheduling model for measuring economic and environmental benefits based on design constraints, energy balance constraints, optimal scheduling constraints for charging stations, and scheduling constraints for the battery energy storage system (BESS) and transformers as constraints; and optimizing the cloud energy storage and sustainable distributed energy system through collaborative management and optimization of the scheduling model and adding an aggregator for energy coordinated scheduling to the cloud energy storage and sustainable distributed energy system.

[0007] Furthermore, the charging power demand of the electric vehicle charging pile is obtained by simulating the charging behavior of the electric vehicle at the charging pile. The specific steps include: setting the electric vehicle arrival time interval to obey the exponential distribution, and setting the charging demand and the additional stay time to obey the normal distribution; using the Monte Carlo simulation method to simulate the electric vehicle information arriving at the charging station throughout the year; according to the results obtained by the simulation, the electric vehicles are assigned to the corresponding charging piles so that each charging pile can only charge one electric vehicle; and obtaining the charging power demand of the electric vehicle charging pile.

[0008] Furthermore, the cloud energy storage and sustainable distributed energy system for electric energy storage are collaboratively managed through multi-stage dynamic configuration and dual-scenario collaborative scheduling, and the specific steps include: constructing a multi-stage dynamic configuration, including: the cloud energy storage and the sustainable distributed energy system sign a service contract with the cloud energy storage CES; the service contract includes storage power and capacity subscription, and the service request of the distributed energy system DES to the cloud energy storage CES is within the capacity and power range subscribed by the distributed energy system DES; determining whether the distributed energy system DES needs to change the storage service contract or is in a contract adjustment transition period; obtaining the storage service price based on the service contract and the storage service contract, and adjusting the energy storage capacity and price based on the actual operating conditions; constructing dual-scenario collaborative scheduling, including: formulating active maintenance and scheduling strategies for the cloud energy storage CES and the distributed energy system DES according to different operating scenarios; conducting a year-round operation evaluation of the cloud energy storage CES and the sustainable distributed energy system DES; and collaboratively managing the cloud energy storage and the sustainable distributed energy system through the constructed multi-stage dynamic configuration and dual-scenario collaborative scheduling.

[0009] Furthermore, the optimization scheduling model is solved by a multi-objective non-dominant classification genetic algorithm NSGA-II.

[0010] Furthermore, the construction of an optimization scheduling model for measuring economic and environmental benefits includes the following specific steps:

[0011] Taking the maximization of the comprehensive net present value of cloud energy storage and sustainable distributed energy systems during construction and operation as the objective function, the comprehensive net present value NPV is formulated as follows:

[0012]

[0013] Among them, AOP represents the annual operating profit of cloud energy storage and sustainable distributed energy system; CRF l,i represents the capital recovery coefficient, which is a function of the discount rate i and the system life cycle l; and TCI represents the total construction investment of cloud energy storage and sustainable distributed energy systems, including the economic cost and equivalent carbon emission cost during the construction investment process;

[0014] By adjusting the form and parameters of the objective function, the flexibility of the established comprehensive net present value maximization objective is increased to adapt to different constraints and different scenarios;

[0015] The flexibility FI is formulated as follows:

[0016]

[0017] in, is the total amount of electricity purchased from the power grid, is the total amount of electricity sold by the power grid, The energy value of the battery energy storage system BESS without energy storage, The energy value of cloud energy storage CES is not stored;

[0018] The equipment design parameters are used as design constraints, the charge-discharge balance in each time period is used as the energy balance constraint, the ability of the charging pile to meet charging demand is used as the optimal scheduling constraint for the charging pile, and ensuring stable charge and discharge is used as the scheduling constraint for the battery energy storage system (BESS) and transformer.

[0019] Construct an optimization scheduling model based on the objective function and constraints.

[0020] Furthermore, the aggregator adopts a charge and discharge power scheduling algorithm.

[0021] Furthermore, the design constraints specifically include: the design capacity of all equipment in the electric vehicle charging station is limited to a fixed range due to the limitations of system scale and occupied physical space, which serves as a design constraint.

[0022] Furthermore, the energy balance constraint specifically includes: within any time period, the input power of the bus should be equal to the output power. As the energy balance constraint, the input power includes power purchased from the main grid, generated by photovoltaics and discharged by the battery energy storage system BESS. The output power includes power sold to the main grid and power charging electric vehicles and the battery energy storage system BESS. The bus is an electric bus, which represents the flow and distribution point of electric energy between different parts.

[0023] Furthermore, the optimal scheduling constraint of the charging pile includes the following specific steps: obtaining the charging power demand of the electric vehicle charging pile based on the simulated charging behavior of the electric vehicle at the charging pile, and obtaining the arrival and departure time of the electric vehicle; optimizing the output power of the charging pile at a certain moment through the synchronization model as the optimal scheduling constraint of the charging pile.

[0024] Furthermore, the photovoltaic system needs to obtain real-time output power, and the steps for obtaining the real-time output power include: collecting weather-related data and collecting the temperature of the photovoltaic cell; training a radial basis function-based neural network model RBFNN through weather-related data to predict the temperature of the photovoltaic cell and obtain a prediction model; inputting future weather-related data into the prediction model to obtain the predicted photovoltaic cell temperature; using the predicted photovoltaic cell temperature and solar irradiance, inputting a circuit parameter model to obtain circuit parameters, and solving a single diode model based on the circuit parameters to obtain the output power of the photovoltaic system.

[0025] An embodiment of the present invention provides an operating system for a cloud energy storage and sustainable distributed energy system, comprising: a system building module for integrating a photovoltaic system and a battery energy storage system (BESS) into an electric vehicle charging station as a sustainable distributed energy system; obtaining the charging power demand of an electric vehicle charging pile by simulating the charging behavior of an electric vehicle at a charging pile; a collaborative management building module for collaboratively managing the cloud energy storage and the sustainable distributed energy system for electric energy storage through multi-stage dynamic configuration and dual-scenario collaborative scheduling according to the charging power demand; the multi-stage dynamic configuration means adjusting the storage capacity and the power service contract at different power consumption stages, and the dual-scenario collaborative scheduling means managing the cloud energy storage and the sustainable distributed energy system under different scenarios. Energy balance between various system components and system loads of the sustainable distributed energy system; a scheduling model construction module, which is used to establish an objective function based on the charging power demand, with the goal of maximizing the comprehensive net present value of measuring investment profits, and add flexibility to the objective function, using design constraints, energy balance constraints, charging pile optimal scheduling constraints, and battery energy storage system BESS and transformer scheduling constraints as constraints to construct an optimization scheduling model for measuring economic and environmental benefits; a system management module, which is used to optimize the management of cloud energy storage and sustainable distributed energy systems through collaborative management and optimization of scheduling models, and adding an aggregator for energy coordinated scheduling to cloud energy storage and sustainable distributed energy systems.

[0026] The embodiments of the present invention provide a method and system for operating a cloud energy storage and sustainable distributed energy system. Compared with the prior art, the advantages thereof are as follows:

[0027] Based on the charging power demand, cloud energy storage and sustainable distributed energy systems used for electrical energy storage are collaboratively managed through multi-stage dynamic configuration and dual-scenario collaborative scheduling. Based on the charging power demand, an objective function is established with the goal of maximizing the comprehensive net present value of investment profits, and flexibility is added to the objective function. With design constraints, energy balance constraints, optimal scheduling constraints for charging piles, and scheduling constraints for battery energy storage systems (BESS) and transformers as constraints, an optimization scheduling model for measuring economic and environmental benefits is constructed.

[0028] Among them, the collaborative management and optimized scheduling models of multi-stage dynamic configuration and dual-scenario collaborative scheduling are both designed to coordinate and schedule cloud energy storage and sustainable distributed energy systems during the charging process of electric vehicles, so as to balance power and ensure stability in power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Schematic diagram of a comprehensive electric vehicle charging station integrating a photovoltaic system and a battery energy storage system provided by an embodiment of the present invention;

[0030] Figure 2Schematic diagram of the cloud storage and distributed energy system provided by an embodiment of the present invention;

[0031] Figure 3 A flowchart for implementing a method for synchronous capacity configuration and scheduling optimization of an integrated electric vehicle charging station integrating a photovoltaic system and a battery energy storage system provided by an embodiment of the present invention;

[0032] Figure 4 This is a flowchart of the overall implementation of the cloud storage and distributed energy system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth numerous specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0034] See also Figures 1 to 4 , an embodiment of the present invention provides an operation method of a cloud energy storage and sustainable distributed energy system, comprising the following steps:

[0035] Step 1: Integrate the photovoltaic system and battery energy storage system (BESS) into the electric vehicle charging station as a sustainable distributed energy system; obtain the charging power demand of the electric vehicle charging station by simulating the charging behavior of electric vehicles at the charging station.

[0036] Step 2: Based on charging power demand, cloud energy storage and sustainable distributed energy systems are collaboratively managed through multi-stage dynamic configuration and dual-scenario coordinated scheduling. Multi-stage dynamic configuration adjusts storage capacity and power service contracts during different power consumption phases, while dual-scenario coordinated scheduling manages the energy balance between various system components and system loads of cloud energy storage and sustainable distributed energy systems under different scenarios.

[0037] Step 3: Based on the charging power demand, an objective function is established with the goal of maximizing the comprehensive net present value of investment profits. Flexibility is added to the objective function. Using design constraints, energy balance constraints, optimal scheduling constraints for charging piles, and scheduling constraints for the battery energy storage system (BESS) and transformers as constraints, an optimal scheduling model is constructed to measure economic and environmental benefits.

[0038] Step 4: Optimize the management of cloud energy storage and sustainable distributed energy systems through collaborative management and optimized scheduling models, and add an aggregator for energy coordinated scheduling to cloud energy storage and sustainable distributed energy systems.

[0039] 1. Hybrid modeling of photovoltaic systems.

[0040] Photovoltaic output power is a nonlinear function of solar irradiance and battery temperature. Battery temperature is affected by weather conditions, including solar irradiance (G), ambient temperature (Tamb) and wind speed (vwind). Linear models cannot accurately capture the power characteristics of photovoltaics, resulting in unsatisfactory capacity configuration and scheduling of integrated charging stations. Therefore, a hybrid modeling approach is adopted to accurately predict photovoltaic cell temperature and output power using a radial basis function-based neural network RBFNN (Radial Basis Function Neural Network). First, based on weather condition parameters such as solar irradiance (G), ambient temperature (Tamb) and wind speed (vwind), the photovoltaic cell temperature is calculated using the RBFNN in formula (1) below and the empirical equation in formula (2). Then, using solar irradiance and the calculated battery temperature, I is calculated according to formula (3) PV , I S ,a,R S and R P Finally, by solving the single diode R in equation (4), P The photovoltaic output power P is obtained from the model.

[0041]

[0042] Among them, x T,m and x T,m y0 represents the output layer of the RBFNN for the battery temperature model and output power prediction, which represents the thermal characteristics of photovoltaics. y1 and y2 are the output layers of the RBFNN for the output power prediction model, which affect the circuit parameters in the single diode RP model. T , σ T 、 They represent the number of hidden neurons, kernel width, hidden neuron center, output layer and constant bias of RBFNN in the battery temperature model. PV , I S ,a,R S and R P are the circuit parameters of the model, representing the photocurrent, reverse saturation current, series resistance, shunt resistance and ideal factor of the diode respectively; I PV ,STC,I S , STC, a STC 、RS,STC 、R P,STC represents the circuit parameters under standard test conditions. μ is the electrical parameter. y1 and y2 represent the output layer neurons of RBFNN. P,m and y n,m They are the input layer and the nth output layer of the RBF (Radial basis function network) neural network for the mth set of historical data. n,P ,σ n,P , and are the parameters of RBFNN in the output power prediction model. I and V are the photovoltaic output current and voltage respectively. B is the Boltzmann constant, and q is the absolute value of the electron's charge.

[0043] The method has good generalization across different photovoltaic types and a wide range of weather conditions.

[0044] 2. Random simulation method for electric vehicle charging behavior.

[0045] The key prerequisite for achieving optimal scheduling of charging piles is to build a correct modeling method for electric vehicle charging demand. For an integrated charging station, the most important parameters describing the charging behavior of electric vehicles include the arrival time t of each electric vehicle. a , departure time t l and charging power demand P EV However, predicting these parameters can be challenging due to strong uncertainties in charging habits across regions. In previous studies, these parameters were always simulated using probability distribution functions, such as normal, uniform, and exponential distribution functions, which were evaluated from available historical data. This paper constructs a stochastic simulation method for EV charging behavior, which is described in detail as follows:

[0046] ① First, make some reasonable assumptions about the significant parameter distribution function. For example, suppose the EV arrival time interval Δt EV obeys the exponential distribution, and P EV and the additional residence time Δt after the EV completes the charging process extra Obeys normal distribution. The specific formula is as follows:

[0047]

[0048] Among them, λ h μ represents the expected EV arrival time interval at hour h of the day. EV and σ EV P EV The mean and standard deviation of P EV,min and P EV,max PEV The minimum and maximum bounds of μ. extra and σ extra are Δt extra The mean and standard deviation of Δt extra,min and Δt extra,max are Δt extra The minimum and maximum bounds of .

[0049] ②Then, a random simulation method based on Monte Carlo simulation is used to generate the information of electric vehicles arriving at the integrated charging station throughout the year. a By Δt EV Distribution simulation, leaving time t l Calculated based on the charging time and the additional residence time. Iterate the above simulation until the current time t a More than 8760h. The mathematical equation is as follows:

[0050]

[0051] Among them, n and N a Respectively represent the serial number and total number of electric vehicles arriving at the integrated charging station. CP,max Maximum indicates the maximum output power of the charging pile.

[0052] ③Finally, the electric vehicles are assigned to appropriate charging piles according to the time intervals occupied by different electric vehicles. A charging pile can only charge electric vehicles at any given time. Therefore, we use a binary variable z c,t To describe the occupied state of pile c at time t, it can be described as:

[0053]

[0054] in, are the arrival time and departure time of the c-pile EV respectively.

[0055] Using the above simulation method, we can obtain the total number of electric vehicles N that can be served by the comprehensive charging station throughout the year. s , as well as entry and exit times, charging power requirements and designated charging stations for each electric vehicle.

[0056] 3. Collaborative management strategy of cloud energy storage and sustainable distributed energy systems.

[0057] A distributed energy system consisting of photovoltaic (PV) and energy storage, combined with cloud energy storage, is an independent energy storage service provider that profits from leasing storage services. A CES can simultaneously serve a large-scale grid or a large number of distributed energy systems (DESs) and communicate with CES users in real time through information technology. When a DES charges or discharges energy, it sends or withdraws energy from the cloud storage within the CES to the grid. The CES provider then charges or discharges its storage facilities to compensate the DES load on the grid. Compared to traditional DESs that interact directly with the grid, the interaction between the DES and CES is self-balancing and self-compensating, with less impact on the grid. It is important to note that CESs are grid-dependent. When the grid line between the CES and DES is congested due to grid capacity constraints, CES services may sometimes be unavailable. To avoid grid imbalance, the CES may be offline. This work assumes that the DES selects a downtime CES provider for reliable storage services. Furthermore, since the DES itself provides energy, it does not need to pay real-time grid prices. Connecting a DES to a CES has two advantages. First, DES storage costs will be lower because storage services (from CES) are inexpensive, and CES storage facilities / pools can be built cost-effectively. Second, storage services (from CES) serve as virtual energy storage for DES and can be dynamically adjusted by changing service agreements with CES. DES-CES incorporates multiple mature and available technologies, such as photovoltaics (PV), wind turbines (WT), gas turbines (GT), ground source heat pumps (GSHP), absorption heat pumps (AHP), gas boilers (GB), thermal energy storage (TES), and CES storage services. It blends renewable geothermal, solar, and wind energy for combined cooling, heating, and power generation. DES-CES is powered by heat from GT and GB, as well as electricity from GT, PV, and WT. Both GB and GT consume natural gas. GSHP consumes electricity to collect stable geothermal energy to provide heating or cooling, while AHP converts heat into cooling. Finally, TES and energy storage services (from CES) can consume excess heat and electricity, respectively.

[0058] With the introduction of CES, DES can dynamically adjust the configuration of its energy storage system. A multi-stage dual-scenario collaborative management strategy was developed to explore these advantages. In this work, the starting power factor is 20%. PV and WT are combined with user demand to form a net load. The scenarios derived from the DES operating domain and net load distribution are defined as follows:

[0059] The operational scenario is defined as the normal scenario: a scenario that can accommodate a diverse net load profile with the help of auxiliary equipment. The robust scenario is defined as a scenario that exceeds the capacity of energy conversion flexibility. CES, TES, GB, GSHP, and the grid must work to shift the net load to the normal scenario.

[0060] The energy storage system has a higher priority and dominates energy management. Sufficient TES and CES discharge enables the capacity of the energy storage system to meet larger net loads in robust scenarios. Sufficient CES charging capacity can absorb excess renewable energy to cope with smaller net loads under strong load conditions. Under normal circumstances, sufficient TES charging capacity can absorb excess energy. However, the charging and discharging capabilities of TES and CES depend on their state of charge SOC (State of Charge). Since TES and CES are not energy generation units, the present invention introduces an active energy reserve mechanism for active maintenance in normal scenarios, so it has stronger regulation capabilities in robust scenarios. Therefore, the robust and normal scenarios are connected to form a dual-scenario collaboration.

[0061] The collaborative management strategy primarily consists of multi-stage dynamic configuration and dual-scenario collaborative scheduling. The former involves adjusting storage capacity and power contracts at different stages. The latter manages the energy balance between various components and loads in normal and robust scenarios. The specific steps are as follows:

[0062] ① Sign a service contract with CES. The contract includes storage power and capacity subscription. DES's service requests to CES should be within the capacity and power range of its subscription.

[0063] ② Check whether DES needs to change the storage service contract or is in the transition period of contract adjustment. If so, set the delay to 24 in the first case, or subtract 1 from the delay in the second case, and then go to ③. If not, transfer the excess power (S p ) is set to 0, and then go to ④. The contract transition period is the buffer time (24h) allowed by BESS when BESS changes the storage contract.

[0064] ③ Calculate the price. When DES switches the storage service subscription at hour t, the remaining energy in CES may exceed the subscription capacity in the new contract phase. This remaining energy is calculated and allocated to S p.

[0065] ④Determine the adjustment range of CES.

[0066] ⑤ Determine the operating scenario. If it is normal, go to ⑥. Otherwise, go to ⑦.

[0067] ⑥ Actively maintain the CES and DES. However, the behavior of the TES and CES must first be determined. When the remaining energy in the DES in the CES falls below (exceeds) the optimal state of charge, the CES should charge (discharge) to bring the SOC closer to the optimal state of charge. When the remaining thermal energy in the TES exceeds the optimal state of charge, the TES releases the stored thermal energy to bring the SOC closer to the optimal state of charge. Proceed to ⑧.

[0068] ⑦ CES and TES can operate at full capacity. The CES and TES will shift the net load to the normal scenario at full capacity. Active maintenance under normal conditions prevents grid purchases and sales and GB operation. This avoids underutilized fossil fuels and high costs. The discharge power from the CES can drive the GSHP to meet the thermal load.

[0069] ⑧ Check if all data has been evaluated. If not, return to ②. Otherwise, the annual operation results will be obtained.

[0070] 4. Synchronize capacity configuration and scheduling optimization model.

[0071] 4.1 Objective function

[0072] The objective function of the optimization model is to maximize the comprehensive net present value during the construction and operation process, while taking into account both economic and environmental benefits. Its mathematical equation is described as:

[0073]

[0074] Among them, AOP and TCI represent the annual operating profit and total construction investment of the system respectively. l,i represents the capital recovery factor, which is a function of the discount rate i and the system life cycle l. The mathematical equation is shown as:

[0075]

[0076] TCI includes the economic cost and equivalent carbon emission cost during the construction investment process, as shown below:

[0077]

[0078] Among them C I and E Iare the economic cost and equivalent carbon emission cost during the construction investment process. r is the transaction price per carbon. q represents the equipment type, including photovoltaic, BESS and transformer. W q is the capacity of device q, and are the basic constant cost and capacity unit cost of equipment q during construction investment. q Is a binary variable indicating whether device q is constructed. CP and are the number of charging piles and investment costs respectively. and are the basic constant of capacity and unit equivalent carbon emission during the construction process of equipment q, respectively.

[0079] The annual operating profit AOP is described as:

[0080] AOP=RC P -C D -C M -rE O (11)

[0081] Where R represents the annual revenue of the integrated charging station, which includes the benefits of selling electricity to the grid and charging electric vehicles. The mathematical equation is described as:

[0082]

[0083] Where Δt is the time step of the scheduling process. sell and β load These are the prices for selling electricity to the grid and for charging electric vehicles. and are the power sold to the grid and the power used to charge electric vehicles at time t. Note that if the generated power is only self-produced and the remaining power is prohibited from being connected to the main grid, then β sell is zero, Indicates the wasted power of the system.

[0084] C P Represents the annual cost of purchasing electricity, which includes the base rate and time-of-use charges. The mathematical equation is:

[0085]

[0086] Among them, α buy This is the basic price for electricity purchased from the main grid each month. and They are the time-of-use electricity price and the power of the purchased electricity.

[0087] C Drepresents the annual cost of equipment degradation. In this study, the PV system is assumed to degrade linearly with operating time, while the aging of the BESS is assumed to depend on the cycle life. Therefore, C D It can be calculated as:

[0088]

[0089] in, is the degradation cost of BESS per kWh capacity. and are the discharge power and charging power of BESS at time t respectively. cycle is the total number of charge and discharge cycles of the BESS during its entire life cycle. PV is the annual degradation cost per kilowatt of capacity of the PV system.

[0090] C M Represents the annual equipment maintenance cost, which can be described as:

[0091]

[0092] in, Annual maintenance cost for each charging station. is the annual maintenance cost of equipment q during the power generation process.

[0093] E O It represents the annual equivalent carbon emissions during operation, including the carbon emissions from purchased electricity and recycled power generation equipment. The mathematical equation is shown as:

[0094]

[0095] Among them, μ buy 、 and Represents the unit equivalent carbon emissions of operating BESS and photovoltaic respectively when purchasing electricity from the main grid.

[0096] When cloud energy storage and sustainable distributed energy systems are collaboratively managed, flexibility FI is added to the objective function:

[0097]

[0098] in, and They are respectively the sum of electricity purchased from (sold to) the power grid. and = is the corresponding value without energy storage (BESS and CES storage services). Energy storage, which is actively charged and maintained by GT under normal circumstances, can effectively cope with variable renewable energy and load. This reduces grid buying and selling and fuel consumption. FI is meaningful because the former reduces the independence of BESS and increases energy bills, while the latter does not fully utilize fossil fuels. When the energy storage system with cloud storage can completely avoid both components, FI will reach 100%. By adding a flexibility model to the objective function, energy distribution of the battery energy storage system (BESS) and cloud storage CES can be optimized when cloud storage is managed in coordination with sustainable distributed energy systems.

[0099] 4.2 Constraints.

[0100] The capacity allocation and scheduling of integrated PV and energy storage electric vehicle charging stations are subject to five constraints: design constraints, energy balance constraints, and scheduling constraints. These constraints are described in detail below.

[0101] (1) Design constraints.

[0102] The design capacity of all equipment in a comprehensive electric vehicle charging station is limited to a specific range due to the limitations of system scale and occupied physical space. The mathematical equation is:

[0103]

[0104] in, and are the minimum and maximum capacities of device q, respectively.

[0105] (2) Energy balance constraint.

[0106] At any time period t, the bus's input power must equal its output power. Input power includes power purchased from the main grid, generated by PV, and discharged by the BESS. Output power includes power sold to the main grid and power used to charge EVs and the BESS. Therefore, the bus's balancing constraint can be described as:

[0107]

[0108] Among them, η dch and η ch are the discharge efficiency and charging efficiency of BESS, respectively.

[0109] The energy storage St of BESS at time t is equal to the energy storage S at the previous time t-1 t-1 The sum of the net charging power Δt during this period is shown in formula (20).

[0110]

[0111] (3) Constraints for optimal scheduling of charging piles.

[0112] The traditional scheduling method prioritizes meeting the charging needs of electric vehicles in a timely manner. Once the electric vehicle arrives, the charging pile is operated at maximum power. This method ignores the differences in PV capacity and time-of-use electricity prices in different time periods, which may lead to unsatisfactory comprehensive charging station scheduling results. The present invention develops an optimal scheduling method for charging piles and combines it with a parallel capacity configuration and scheduling optimization model. The method first assigns all electric vehicles to suitable charging piles based on arrival and departure times. The arrival and departure times of the charging piles are obtained by a random simulation method. Then the output power of the charging pile c at time t is optimized by a synchronous model. To meet the power requirements of all electric vehicles. The mathematical equation for the optimal scheduling constraint of charging piles is:

[0113]

[0114]

[0115] in, Represents the power demand of the t-th charging pile at the moment. represents the power demand of the vth electric vehicle at the cth charging station. CP,min and P CP,max They are the minimum and maximum output power of the charging pile respectively.

[0116] (4) Dispatch constraints of BESS and transformers.

[0117] To improve the security and stability of the main grid, the power transactions between the integrated charging station and the main grid should be constrained as follows:

[0118]

[0119] Among them, P buy,min and P buy,max are the minimum and maximum power that can be purchased from the mains. sell,min and P sell ,max are the minimum and maximum power that can be supplied to the main grid, respectively. and is a binary variable that determines whether electricity is purchased or sold at time t. Assume that the purchase and sale of electricity from the grid cannot occur at the same time t.

[0120]

[0121] Note that P buy,max Equal to the active power of the transformer, which is the capacity W T and transformer efficiency η T function.

[0122] P buy,max =η T W T (27)

[0123] The constraints in equation (24) are nonlinear because and P buy,max are all optimization variables. Therefore, the optimization model is a mixed integer nonlinear optimization programming (MINLP) problem with high computational difficulty. Therefore, the large M linearization method is used to linearize the nonlinear constraints and equation (24) to equivalently convert it into:

[0124]

[0125] The discharge and charge power of BESS are affected by both capacity and c-rate, as shown in Equations (29) and (30):

[0126]

[0127] in, and is a binary variable that determines whether the BESS is in the discharge or charging state at time t. dch,min and γ dch,max are the minimum and maximum discharge c rates, respectively. γ ch,min and γ ch,max are the minimum and maximum charge c-rates, respectively. For BESS, discharge and charge can be performed simultaneously, which is described as:

[0128]

[0129] Formulas (29) and (30) are nonlinear constraints and are linearized using the large M method. The equivalent equations are:

[0130]

[0131]

[0132] In addition, the state of energy (SOE) is introduced to represent the current energy storage state of BESS, and its calculation formula is:

[0133] SOE t =S t / W BESS (34)

[0134] To prevent BESS from overcharge and overdischarge, S t Stay within the proper range:

[0135]

[0136] Among them, SOEmin and SOEmax are the minimum and maximum SOE of BESS, respectively.

[0137] 4.3 Optimization algorithm.

[0138] In terms of algorithms, the optimization model proposed in the patent is solved by the well-known multi-objective non-dominant classification genetic algorithm-II (NSGA-II). The specific optimization steps are as follows:

[0139] Step 1: Randomly initialize the population. Randomly initialize several sets of decision variables based on the number of individuals in the population. Then, set the evolution time to 1.

[0140] Step 2: Fitness value evaluation: Import the decision variables, simulate the model for annual operation, and then analyze the results through equations.

[0141] Step 3: Population update. Based on the individual performance obtained in step 2, the individuals are sorted by non-dominant factors. Then, dominant individuals are selected for crossover and mutation operations to update the population.

[0142] Step 4: Determine the termination condition. When the population's evolution time reaches its maximum value, output the optimal solution and exit. Otherwise, increase the evolution time by 1 and return to step 2.

[0143] 5. Bidirectional charging station.

[0144] In addition to the EV charging stations, photovoltaic (PV) and battery energy storage systems (BESS), an aggregator is added to the system, which also determines energy management decisions. The proposed charging and discharging power scheduling algorithm is executed in the aggregator. It is assumed that charging stations can automatically detect the EV's arrival time, initial state of charge (SOC), and battery capacity via a unified communication protocol. Before charging, EV users provide their departure time and final desired SOC through a user interface.

[0145] The EV charging behavior is completed in Section 2, fully capturing the behavior of the EV fleet. PV forecasting is completed in Section 1. Load and PV data are collected and fitted to a normal probability distribution function. The obtained normal probability distribution function is then used to generate sample load data for further use.

[0146] The aggregator coordinates dispatch operations. EVs and BESSs are first charged from the grid. Furthermore, the grid provides energy for system construction loads, primarily served by BESSs and PV. During periods of high electricity prices, power requests from the grid lead the aggregator to develop a strategy to maximize profits from transactions with the grid. This trading strategy stipulates that PV and BESSs are the first suppliers to sell electricity to the grid. If grid demand exceeds the power these two systems can provide, EVs are used to mitigate the shortfall while ensuring they receive the required charge status on time.

[0147] This paper proposes a synchronous capacity configuration and scheduling optimization model for electric vehicle charging stations that integrate photovoltaic and energy storage systems. To establish the system model, a radial basis function neural network model is first used, combining historical weather data with real-time monitoring data, to accurately predict photovoltaic cell temperature and output power. This approach effectively addresses the inability of traditional linear models to accurately capture the power characteristics of photovoltaic systems, providing more accurate power generation forecasts. Secondly, to optimize charging station scheduling, a non-stochastic simulation method is used to establish an accurate electric vehicle (EV) charging demand model. Finally, a multi-objective optimization algorithm that comprehensively considers photovoltaic power generation capacity, the charge and discharge status of the energy storage system, time-segmented electricity prices, and electric vehicle charging demand is used to develop an optimal charging scheduling plan to maximize photovoltaic power generation and reduce peak charging load. Cloud energy storage technology is also introduced, integrating a hybrid renewable energy distributed energy system (DES) with cloud energy storage (shared energy storage provider CES) to construct a DES storage system using a subscription model. Using this subscription model, the DES-CES can dynamically adjust storage capacity and power consumption by changing the storage service contract. A multi-stage, dual-scenario collaborative management strategy based on active energy reserves is then designed to schedule the DES-CES.

[0148] The present invention also has the following beneficial effects:

[0149] 1. Improved renewable energy utilization: By integrating photovoltaic power generation and battery energy storage systems into electric vehicle charging stations, this invention maximizes the use of solar energy. The photovoltaic system generates electricity during the day, and excess electricity can be stored in batteries for use at night or on cloudy days, reducing reliance on traditional fossil fuels.

[0150] 2. Reduced carbon emissions: The use of clean energy and energy storage technologies effectively reduces carbon emissions generated during electric vehicle charging. Photovoltaic systems do not produce greenhouse gases when generating electricity. By increasing the utilization of clean energy, the carbon footprint of the power system can be significantly reduced.

[0151] 3. Enhanced grid stability: This invention improves grid stability and power supply reliability through the coordinated operation of the energy storage system. The energy storage system can release energy during peak load periods, alleviating grid pressure and reducing grid fluctuations.

[0152] 4. Optimize energy management: The proposed multi-objective optimization model can comprehensively consider multiple factors such as photovoltaic power generation capacity, energy storage system status, electric vehicle charging needs, etc., to achieve efficient energy management and optimal allocation.

[0153] 5. Significant economic benefits: By optimizing charging and discharging strategies, the cost of charging electric vehicles can be reduced, improving the economic benefits of charging stations. Utilizing time-segmented electricity pricing strategies, charging during low electricity prices and discharging during peak electricity prices further reduces overall operating costs.

[0154] 6. Promote the popularization of electric vehicles: It improves the efficiency and reliability of electric vehicle charging infrastructure, solves the concerns of electric vehicle users about long charging time and high charging costs, and thus promotes the popularization and application of electric vehicles.

[0155] 7. Promote the transformation of the energy system: Integrating photovoltaic and energy storage technologies into electric vehicle charging stations has promoted the transformation of the energy system towards a clean, efficient and sustainable direction, and helped to achieve energy structure adjustment and energy revolution.

[0156] 8. Environmental Protection and Sustainable Development: By reducing the use of fossil energy and lowering carbon emissions, this invention has important implications for environmental protection and sustainable development. It can not only alleviate climate change issues, but also improve air quality and protect the ecological environment.

[0157] 9. Improve energy utilization efficiency: The application of multi-objective optimization algorithms and real-time scheduling strategies has improved energy utilization efficiency, reduced energy waste, and maximized the utilization of energy resources.

[0158] 10. Innovative charging station capacity configuration and scheduling optimization model: A hybrid modeling method for photovoltaic systems based on radial basis function neural networks is proposed to improve the accuracy of photovoltaic power generation predictions. An electric vehicle charging demand model is constructed through a non-random simulation method, which optimizes the scheduling strategy of charging piles and improves charging efficiency. Multi-objective optimization algorithm: This invention introduces a multi-objective optimization algorithm, which comprehensively considers photovoltaic power generation, energy storage system status, electric vehicle charging demand and time-segmented electricity prices to achieve the optimal charging scheduling plan and maximize the economic and environmental benefits of the system. Comprehensive energy management strategy: A comprehensive energy management strategy is proposed, including real-time monitoring, data transmission, prediction models and scheduling algorithms, to ensure efficient operation and continuous optimization of the system under various operating conditions.

[0159] This invention proposes a synchronous capacity configuration and scheduling optimization method for integrated electric vehicle charging stations based on photovoltaic and battery energy storage systems. This method, combined with a multi-objective optimization and collaborative management strategy for sustainable distributed energy systems using cloud energy storage, improves the energy efficiency and reliability of electric vehicle charging stations by utilizing photovoltaic power generation and energy storage technologies. Through a multi-objective optimization algorithm, system operating costs and carbon emissions are reduced, achieving a win-win situation for both economic and environmental benefits. This patent not only promotes the popularization and application of electric vehicles but also provides strong support for the clean, efficient, and sustainable development of energy systems, with significant implications for environmental protection and the energy revolution.

[0160] An embodiment of the present invention provides an operating system for a cloud energy storage and sustainable distributed energy system, comprising: a system construction module for integrating a photovoltaic system and a battery energy storage system (BESS) into an electric vehicle charging station as a sustainable distributed energy system; obtaining the charging power demand of the electric vehicle charging station by simulating the charging behavior of electric vehicles at the charging station; a collaborative management construction module for collaboratively managing the cloud energy storage and sustainable distributed energy system for energy storage based on the charging power demand through multi-stage dynamic configuration and dual-scenario collaborative scheduling; multi-stage dynamic configuration means adjusting storage capacity and power service contracts during different power consumption phases; and dual-scenario collaborative scheduling means managing the energy balance between various system components and system loads of the cloud energy storage and sustainable distributed energy system under different scenarios; and a scheduling model construction module for establishing an objective function based on the charging power demand, with the goal of maximizing the comprehensive net present value (NPV) that measures investment returns. This module also adds flexibility to the objective function, using design constraints, energy balance constraints, optimal charging station scheduling constraints, and scheduling constraints for the battery energy storage system (BESS) and transformer as constraints to construct an optimized scheduling model that measures economic and environmental benefits. The system management module is used to optimize the management of cloud energy storage and sustainable distributed energy systems through collaborative management and optimization of scheduling models, and to add an aggregator for energy coordinated scheduling to cloud energy storage and sustainable distributed energy systems.

[0161] A specific embodiment is as follows:

[0162] 1. Synchronous capacity configuration and scheduling optimization of integrated electric vehicle charging stations based on photovoltaic and battery energy storage systems.

[0163] ① Photovoltaic system hybrid modeling.

[0164] Step 1: Collect and prepare data. Collect local weather data such as solar irradiance, ambient temperature, and wind speed. The data can come from weather stations, satellite data, or local sensor networks.

[0165] Step 2: Build and train an RBF neural network model. Use the collected historical weather data to train a radial basis function neural network (RBFNN) model. The goal is to predict the temperature of the photovoltaic cells based on the input weather data.

[0166] Step 3: Predicting PV cell temperature: Input real-time solar irradiance, ambient temperature, and wind speed data into the trained RBFNN model to predict PV cell temperature.

[0167] Step 4: Calculate the photovoltaic output power parameters. Use the predicted photovoltaic cell temperature and solar irradiance to calculate the key parameters of photovoltaic output power.

[0168] Step 5: Solve the single diode model. Use the parameters calculated in the previous step to solve the single diode model and obtain the real-time output power of the photovoltaic system.

[0169] ② Random simulation method of electric vehicle charging behavior.

[0170] Step 1: Define the distribution of EV arrivals and charging requirements. Assume that the EV arrival time follows an exponential distribution. Assume that charging requirements and additional dwell time follow a normal distribution.

[0171] Step 2: Monte Carlo simulation method. This method generates information about electric vehicles arriving at charging stations throughout the year, including each vehicle's arrival time, departure time, and charging requirements.

[0172] Step 3: Assign charging piles. Based on the data generated by the simulation, electric vehicles are assigned to appropriate charging piles. Ensure that each charging pile can only charge one electric vehicle at any time.

[0173] ③Synchronous capacity configuration and scheduling optimization model.

[0174] Step 1: Define the optimization objective function, the goal is to maximize the comprehensive net present value (NPV), taking into account both economic and environmental benefits.

[0175] Step 2: Calculate the system's annual operating profit and total construction investment, including the economic and carbon emission costs of the PV system, battery energy storage system, transformer, and other equipment.

[0176] Step 3: Calculate the annual revenue of the integrated charging station, including the revenue from selling electricity to the grid and charging electric vehicles.

[0177] Step 4: Optimize the configuration and scheduling strategy. Using an optimization algorithm, synchronized capacity configuration and scheduling are optimized for the PV system, BESS, and charging station. This ensures that demand is met while minimizing system operating costs and carbon emissions. An aggregator is also incorporated, which also determines energy management decisions. The proposed charging and discharging power scheduling algorithm is executed in the aggregator. It is assumed that charging stations can automatically detect the EV's arrival time, initial state of charge (SOC), and battery capacity via a unified communication protocol. Before charging, EV users provide their departure time and desired final SOC through a user interface. The aggregator coordinates scheduling operations. EVs and BESSs are first charged from the grid. Furthermore, the grid provides energy for system construction loads, primarily served by the BESS and PV. During periods of high electricity prices, power requests from the grid lead the aggregator to develop a strategy to maximize trading profits with the grid. This trading strategy stipulates that PV and BESSs are the first suppliers to sell power to the grid. If grid demand exceeds the power they can provide, EVs are used to mitigate the shortfall while ensuring they achieve the required state of charge on time.

[0178] 2. Multi-objective optimization collaborative management strategy for sustainable distributed energy systems considering cloud energy storage.

[0179] ① Collaborative management of cloud energy storage and distributed energy systems.

[0180] Step 1: Identify energy storage service needs and providers: Identify distributed energy systems (DES) that require energy storage services and cloud energy storage service providers (CES) that provide energy storage services.

[0181] Step 2: Establish a real-time communication mechanism: The DES communicates with the CES in real time through information technology (such as the Internet of Things platform). When the DES needs energy storage services, it sends a storage or discharge request to the CES.

[0182] Step 3: Providing Energy Storage Services: CES provides energy storage services according to the contract, responding to DES needs and dynamically adjusting energy storage capacity and power output to compensate for grid load.

[0183] ② Multi-stage dual-scenario collaborative management strategy.

[0184] Step 1: Sign an energy storage service contract. DES and CES sign a service contract to define the storage power and capacity subscription scope.

[0185] Step 2: Regularly check and adjust. Regularly check and adjust storage service contracts, and dynamically adjust storage capacity based on demand changes.

[0186] Step 3: Calculate and adjust prices. Calculate storage service prices at each stage to ensure economic rationality. Adjust storage capacity and prices based on actual operating conditions to ensure optimal performance.

[0187] Step 4: Develop operation and scheduling strategies. Develop proactive maintenance and energy scheduling strategies for CES and DES based on different operation scenarios (normal and robust). Ensure system stability and efficiency under various load conditions.

[0188] Step 5: Full-year Operation Evaluation. Evaluate full-year operational data and optimize storage service contracts and scheduling strategies to ensure efficient operation and continuous optimization of the system under various operating conditions.

[0189] Through these detailed steps and methods, integrating photovoltaic systems, battery energy storage systems and cloud energy storage technologies can significantly improve the efficiency, stability and sustainability of electric vehicle charging stations and distributed energy systems.

[0190] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for operating a cloud energy storage and sustainable distributed energy system, characterized in that: The following steps are involved: Integrate photovoltaic systems and battery energy storage systems (BESS) into electric vehicle charging stations as sustainable distributed energy systems; obtain the charging power demand of electric vehicle charging stations by simulating the charging behavior of electric vehicles at charging stations; Based on charging power demand, cloud energy storage and sustainable distributed energy systems are collaboratively managed through multi-stage dynamic configuration and dual-scenario coordinated scheduling. The multi-stage dynamic configuration adjusts storage capacity and power service contracts during different power consumption phases, while the dual-scenario coordinated scheduling manages the energy balance between various system components and system loads of the cloud energy storage and sustainable distributed energy systems under different scenarios. Based on charging power demand, an objective function is established with the goal of maximizing the comprehensive net present value of investment profits. This objective function is given flexibility, and an optimal scheduling model is constructed to measure economic and environmental benefits, using design constraints, energy balance constraints, optimal scheduling constraints for charging piles, and scheduling constraints for battery energy storage systems (BESS) and transformers as constraints. Optimize the management of cloud energy storage and sustainable distributed energy systems through collaborative management and optimized scheduling models, and add an aggregator for energy coordinated scheduling for cloud energy storage and sustainable distributed energy systems; The specific steps of obtaining the charging power demand of the electric vehicle charging pile by simulating the charging behavior of the electric vehicle at the charging pile include: The time between arrivals of electric vehicles is set to follow an exponential distribution, and the charging demand and the additional stay time are set to follow a normal distribution; Monte Carlo simulation method is used to simulate the electric vehicle information arriving at charging stations throughout the year; According to the simulation results, electric vehicles are assigned to corresponding charging piles so that each charging pile can only charge one electric vehicle; Obtain charging power requirements for electric vehicle charging piles; The cloud energy storage for electric energy storage and the sustainable distributed energy system are collaboratively managed through multi-stage dynamic configuration and dual-scenario collaborative scheduling. The specific steps include: Constructing a multi-stage dynamic configuration, including: cloud energy storage and sustainable distributed energy systems (DESs) signing a service contract with the cloud energy storage CES; the service contract includes storage power and capacity subscriptions, and the DESs' service requests to the cloud energy storage CESs are within the capacity and power ranges subscribed to by the DESs; determining whether the DESs need to change their storage service contracts or are in a contract adjustment transition period; obtaining storage service prices based on the service and storage service contracts, and adjusting the storage capacity and prices based on actual operating conditions; Establish dual-scenario coordinated scheduling, including: formulating proactive maintenance and scheduling strategies for cloud energy storage (CES) and distributed energy systems (DES) based on different operating scenarios; conducting year-round operational evaluations of cloud energy storage (CES) and sustainable distributed energy systems (DES); Through the construction of multi-stage dynamic configuration and dual-scenario coordinated scheduling, cloud energy storage and sustainable distributed energy systems are managed collaboratively; The construction of the optimization scheduling model for measuring economic and environmental benefits includes the following specific steps: Taking the maximization of the comprehensive net present value of cloud energy storage and sustainable distributed energy systems during construction and operation as the objective function, the comprehensive net present value NPV is formulated as follows: Among them, AOP represents the annual operating profit of cloud energy storage and sustainable distributed energy system; CRF l,i represents the capital recovery coefficient, which is a function of the discount rate i and the system life cycle l; and TCI represents the total construction investment of cloud energy storage and sustainable distributed energy systems, including the economic cost and equivalent carbon emission cost during the construction investment process; By adjusting the form and parameters of the objective function, the flexibility of the established comprehensive net present value maximization objective is increased to adapt to different constraints and different scenarios; The flexibility FI is formulated as follows: in, is the total amount of electricity purchased from the power grid, is the total amount of electricity sold by the power grid, The energy value of the battery energy storage system BESS without energy storage, The energy value of cloud energy storage CES is not stored; The equipment design parameters are used as design constraints, the charge-discharge balance in each time period is used as the energy balance constraint, the ability of the charging pile to meet charging demand is used as the optimal scheduling constraint for the charging pile, and ensuring stable charge and discharge is used as the scheduling constraint for the battery energy storage system (BESS) and transformer. Construct an optimization scheduling model based on the objective function and constraints.

2. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, wherein: The optimization scheduling model is solved by a multi-objective non-dominant classification genetic algorithm NSGA-II.

3. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, wherein: The aggregator adopts a charge and discharge power scheduling algorithm.

4. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, wherein: The design constraints specifically include: The design capacity of all equipment in an EV charging station is limited to a fixed range as a design constraint due to the limitations of the system scale and the occupied physical space.

5. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, wherein: The energy balance constraints specifically include: In any time period, the input power of the bus should be equal to the output power as an energy balance constraint. The input power includes power purchased from the main grid, generated by photovoltaics, and discharged by the battery energy storage system (BESS). The output power includes power sold to the main grid and power used to charge electric vehicles and the battery energy storage system (BESS). The bus is an electric bus, representing the flow and distribution point of electric energy between different parts.

6. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, wherein: The optimal scheduling constraint of the charging pile includes the following specific steps: The charging power demand of the electric vehicle charging pile is obtained based on the simulated charging behavior of the electric vehicle at the charging pile, and the arrival and departure time of the electric vehicle is obtained; The output power of the charging pile at a certain moment is optimized through the synchronization model as the optimal scheduling constraint of the charging pile.

7. The method for operating a cloud energy storage and sustainable distributed energy system according to claim 1, wherein: The photovoltaic system needs to obtain real-time output power, and the steps of obtaining the real-time output power include: Collect weather-related data and the temperature of photovoltaic cells; The temperature of photovoltaic cells is predicted by training a radial basis function neural network model RBFNN using weather-related data to obtain a prediction model. Input future weather-related data into the prediction model to obtain the predicted photovoltaic cell temperature; The predicted photovoltaic cell temperature and solar irradiance are used to input the circuit parameter model to obtain the circuit parameters. The single diode model is solved according to the circuit parameters to obtain the output power of the photovoltaic system.

8. An operating system for cloud energy storage and sustainable distributed energy system, characterized in that: include: System building module for integrating photovoltaic systems and battery energy storage systems (BESS) into electric vehicle charging stations as a sustainable distributed energy system; obtaining the charging power demand of electric vehicle charging stations by simulating the charging behavior of electric vehicles at charging stations; A collaborative management building block for collaboratively managing cloud energy storage and sustainable distributed energy systems based on charging power demand through multi-stage dynamic configuration and dual-scenario collaborative scheduling. The multi-stage dynamic configuration adjusts storage capacity and power service contracts during different power consumption phases, while the dual-scenario collaborative scheduling manages the energy balance between various system components and system loads of the cloud energy storage and sustainable distributed energy systems under different scenarios. A scheduling model construction module is used to establish an objective function based on charging power demand, maximizing the comprehensive net present value of investment profits. This module also adds flexibility to the objective function and uses design constraints, energy balance constraints, optimal scheduling constraints for charging piles, and scheduling constraints for battery energy storage systems (BESS) and transformers as constraints to construct an optimized scheduling model that measures economic and environmental benefits. The system management module is used to optimize the management of cloud energy storage and sustainable distributed energy systems through collaborative management and optimization of scheduling models, and to add an aggregator for energy coordination and scheduling for cloud energy storage and sustainable distributed energy systems; The specific steps of obtaining the charging power demand of the electric vehicle charging pile by simulating the charging behavior of the electric vehicle at the charging pile include: The time between arrivals of electric vehicles is set to follow an exponential distribution, and the charging demand and the additional stay time are set to follow a normal distribution; Monte Carlo simulation method is used to simulate the electric vehicle information arriving at charging stations throughout the year; According to the simulation results, electric vehicles are assigned to corresponding charging piles so that each charging pile can only charge one electric vehicle; Obtain charging power requirements for electric vehicle charging piles; The cloud energy storage for electric energy storage and the sustainable distributed energy system are collaboratively managed through multi-stage dynamic configuration and dual-scenario collaborative scheduling. The specific steps include: Constructing a multi-stage dynamic configuration, including: cloud energy storage and sustainable distributed energy systems (DESs) signing a service contract with the cloud energy storage CES; the service contract includes storage power and capacity subscriptions, and the DESs' service requests to the cloud energy storage CESs are within the capacity and power ranges subscribed to by the DESs; determining whether the DESs need to change their storage service contracts or are in a contract adjustment transition period; obtaining storage service prices based on the service and storage service contracts, and adjusting the storage capacity and prices based on actual operating conditions; Establish dual-scenario coordinated scheduling, including: formulating proactive maintenance and scheduling strategies for cloud energy storage (CES) and distributed energy systems (DES) based on different operating scenarios; conducting year-round operational evaluations of cloud energy storage (CES) and sustainable distributed energy systems (DES); Through the construction of multi-stage dynamic configuration and dual-scenario coordinated scheduling, cloud energy storage and sustainable distributed energy systems are managed collaboratively; The construction of the optimization scheduling model for measuring economic and environmental benefits includes the following specific steps: Taking the maximization of the comprehensive net present value of cloud energy storage and sustainable distributed energy systems during construction and operation as the objective function, the comprehensive net present value NPV is formulated as follows: Among them, AOP represents the annual operating profit of cloud energy storage and sustainable distributed energy system; CRF l,i represents the capital recovery coefficient, which is a function of the discount rate i and the system life cycle l; and TCI represents the total construction investment of cloud energy storage and sustainable distributed energy systems, including the economic cost and equivalent carbon emission cost during the construction investment process; By adjusting the form and parameters of the objective function, the flexibility of the established comprehensive net present value maximization objective is increased to adapt to different constraints and different scenarios; The flexibility FI is formulated as follows: in, is the total amount of electricity purchased from the power grid, is the total amount of electricity sold by the power grid, The energy value of the battery energy storage system BESS without energy storage, The energy value of cloud energy storage CES is not stored; The equipment design parameters are used as design constraints, the charge-discharge balance in each time period is used as the energy balance constraint, the ability of the charging pile to meet charging demand is used as the optimal scheduling constraint for the charging pile, and ensuring stable charge and discharge is used as the scheduling constraint for the battery energy storage system (BESS) and transformer. Construct an optimization scheduling model based on the objective function and constraints.

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