Virtual power plant collaborative optimization operation method based on distributed energy

Abstract

The invention discloses a virtual power plant collaborative optimization operation method based on distributed energy, and the method achieves the real-time monitoring of the operation state of a distributed energy resource through the precise modeling of the distributed energy resource, and predicts the change of the power load demand. A multi-objective optimization model and an advanced optimization algorithm, such as a genetic algorithm or a particle swarm algorithm, are utilized, so that the virtual power plant can dynamically adjust energy supply and load demands, and economic benefit maximization, energy efficiency optimization and carbon emission minimization are realized. The method also considers the energy efficiency and economic benefits of the virtual power plant, and improves the comprehensive benefits of the system through comprehensive analysis and optimization. Through effective interaction with the power grid, the virtual power plant can provide peak regulation, frequency modulation and standby services, a demand response mechanism is implemented, and operation of the power grid is further optimized. The method has the beneficial effects of improving the energy utilization efficiency, enhancing the stability of a power grid, optimizing economic benefits, promoting environmental protection, improving the flexibility and adaptability of the system and supporting policies and market mechanisms.

Description

Technical Field

[0001] The present invention relates to the field of electric power, and in particular to a method for collaborative optimization operation of a virtual power plant based on distributed energy. Background Art

[0002] With the continuous growth of global energy demand and the increasingly serious environmental pollution problem, the traditional centralized energy supply mode is facing unprecedented challenges. Especially in the power industry, with the rapid development of renewable energy and the continuous advancement of distributed power generation technology, the operation and management of power systems are undergoing profound changes. In this context, virtual power plants, as a new type of power system operation and management mode, came into being.

[0003] A virtual power plant is a system that aggregates distributed energy resources (such as photovoltaics, wind power, energy storage, and electric vehicles) and centrally dispatches and manages them through advanced information and communication technologies and optimization algorithms. Compared with traditional centralized power plants, virtual power plants have higher flexibility and adaptability, can better cope with the intermittent and volatile nature of renewable energy, and improve energy utilization efficiency and the economic benefits of the system.

[0004] However, the implementation and development of virtual power plants face a series of technical and management challenges. First, the diversity and complexity of distributed energy resources require the establishment of precise mathematical models to describe their dynamic characteristics in order to achieve accurate prediction and scheduling. Second, the uncertainty and volatility of power load demand require virtual power plants to have the ability to monitor and adjust dynamically in real time. In addition, the economic benefits and environmental impact of virtual power plants are also key factors in their sustainable development.

[0005] In the existing technology, the modeling and prediction methods of distributed energy resources are not perfect enough to meet the needs of accurate scheduling of virtual power plants. At the same time, the optimization scheduling algorithm and collaborative operation mechanism of virtual power plants are not mature enough, making it difficult to achieve multi-objective optimization and real-time adjustment. In addition, the energy efficiency and economic benefit analysis methods of virtual power plants are not systematic and comprehensive enough, making it difficult to provide strong support for the decision-making of virtual power plants. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a method for collaborative optimization operation of a virtual power plant based on distributed energy.

[0007] The present invention is achieved through the following technical solutions:

[0008] A method for collaborative optimization operation of a virtual power plant based on distributed energy sources of the present invention comprises the following steps:

[0009] Model and classify distributed energy resources, and establish corresponding mathematical models according to different energy types, such as photovoltaics, wind power, energy storage and electric vehicles. These models need to take into account the intermittent and volatile characteristics of energy supply to ensure that the dynamic changes of distributed energy can be accurately described. At the same time, load demand forecasting is also required. Through historical data, meteorological information, user behavior and other factors, time series forecasting models or machine learning algorithms are applied to accurately predict changes in power load demand and ensure reasonable estimates of future load demand.

[0010] Design the overall architecture of the virtual power plant, and form an energy resource pool within the virtual power plant by aggregating multiple distributed energy sources. This process is achieved through real-time monitoring and scheduling on the cloud platform. Within the virtual power plant, a dynamic balance mechanism between energy supply and load demand is constructed to ensure the stability between power supply and consumption and achieve the optimal configuration of distributed energy. In addition, a reasonable energy distribution model is designed to ensure coordinated operation among various distributed energy sources while ensuring that the load demand on the user side is met.

[0011] Construct a collaborative optimization dispatching mechanism for virtual power plants. In this link, a multi-objective optimization model is established, including multiple optimization objectives such as maximizing economic benefits, optimizing energy efficiency, and minimizing carbon emissions. In terms of optimization algorithms, advanced algorithms such as genetic algorithms, particle swarm algorithms, or deep reinforcement learning are introduced to collaboratively optimize energy distribution and load dispatching. In actual operation, virtual power plants need to be dynamically adjusted according to the real-time status of distributed energy and changes in load demand. Combined with electricity market price information, power distribution and energy storage strategies are optimized in real time to ensure that virtual power plants can efficiently and flexibly respond to fluctuations in energy demand.

[0012] In the process of coordinated optimization operation, it is necessary to conduct a comprehensive analysis of the energy efficiency and economic benefits of the virtual power plant. Therefore, in terms of energy efficiency, an energy efficiency analysis model is constructed to evaluate indicators such as energy utilization and system loss in the operation of the virtual power plant. In terms of economic benefits, the overall economic benefits of the virtual power plant are evaluated by combining factors such as electricity market prices, government subsidy policies, and carbon emission rights trading to ensure that the virtual power plant can not only improve energy utilization, but also achieve good economic returns.

[0013] Through the coordinated work of the above steps, the virtual power plant can achieve optimized operation based on distributed energy, ensuring a balance between energy supply and load demand, while improving the energy efficiency and economic benefits of the system.

[0014] Furthermore, the modeling and classification of the distributed energy resources, respectively establishing corresponding mathematical models according to different energy types, and considering the intermittent and fluctuating nature of energy supply, the specific steps are as follows:

[0015] Energy classification and identification, classify distributed energy according to its different sources. Typical distributed energy includes photovoltaic power generation, wind power generation, energy storage systems (such as battery energy storage), electric vehicles (as mobile energy storage devices), etc. Each energy source has different characteristics, so these different types of energy need to be independently identified and modeled.

[0016] Photovoltaic energy modeling: For photovoltaic power generation, a mathematical model with light intensity, temperature, solar radiation, etc. as input variables is established. The output power of the photovoltaic power generation system is intermittent and is mainly affected by sunlight conditions. Therefore, its modeling method is based on physical characteristics or uses empirical models, as follows:

[0017] Output power model: P PV (t) = A × G (t) × η PV (T)

[0018] Where: A is the area of ​​the photovoltaic panel, G(t) is the solar radiation intensity, and ηPV(T) is the conversion efficiency based on temperature. In addition, factors such as the inclination angle of the photovoltaic panel and the shadow effect need to be considered to improve the accuracy of the model.

[0019] Wind energy modeling: Wind power generation is mainly affected by wind speed and wind direction. A common modeling method is to establish a wind turbine output power model through a power curve. The output power of a wind turbine can usually be expressed by the following formula:

[0020] Output power model: P wind (t) = 0.5 × ρ × A rotor ×V 3 (t)×η wind

[0021] Where ρ is the air density, A rotor is the swept area of ​​the wind wheel, V(t) is the wind speed, η wind is the efficiency of the wind turbine. It is necessary to consider the volatility of wind speed and the impact of geographical location on wind speed, and the wind speed model can be further modified through meteorological data.

[0022] Energy storage system modeling: When modeling an energy storage system (such as battery energy storage), the battery's charge and discharge characteristics, battery capacity, charge and discharge efficiency, and battery state of charge are considered. The battery energy storage system can be modeled using the following formula:

[0023] Charge and discharge power model:

[0024] Among them, η charge is the charging efficiency, P in (t) is the input power, P out (t) is the output power, η dischargeis the discharge efficiency. At the same time, the battery state of charge SoC(t) is a key parameter in energy storage system modeling and can be expressed by the following formula:

[0025]

[0026] Among them, C battery is the capacity of the battery.

[0027] Electric vehicle modeling: As a mobile energy storage device, the modeling process of electric vehicles is similar to that of static energy storage systems, but the mobility of the vehicle and the constraints of charging time need to be considered. The following model can be constructed based on factors such as vehicle charging status, battery capacity, and driving time:

[0028] Electric vehicle charging model: P EV (T) = η EV ×P charge (t)

[0029] Among them, η EV is the charging efficiency, P charge (t) is the charging power. The mobility path and availability of electric vehicles need to be considered, and the energy storage capacity should be dynamically adjusted according to their charging and discharging status.

[0030] Consider intermittency and volatility: Due to the intermittency and volatility of renewable energy such as photovoltaic and wind power, time series models, meteorological models, etc. need to be considered when modeling energy to simulate long-term and short-term power generation fluctuations. This can be done by introducing random variables or scenario analysis (such as Monte Carlo simulation) to reflect the uncertainty of energy supply.

[0031] Comprehensive model building: After completing the independent modeling of different energy types, the model of the entire distributed energy system is comprehensively constructed, integrating the above subsystems into an overall model, and coordinating the virtual power plant through dynamic optimization methods. This process is achieved by establishing the overall state equation of the system and adjusting it according to the real-time energy supply situation.

[0032] Through the above steps, distributed energy resources can be accurately modeled, providing basic data support for the optimal scheduling of virtual power plants and ensuring the reliability and stability of energy supply.

[0033] Furthermore, the load demand forecasting includes the following steps:

[0034] 1. Collect data sets for load demand forecasting, including historical power load data, meteorological information (such as temperature, humidity, wind speed, solar radiation, etc.), user behavior data (such as power consumption patterns in different time periods, seasonal changes, etc.), and price information in the power market. It is necessary to ensure the integrity and accuracy of the data and eliminate missing data or outliers.

[0035] 2. The collected data may contain noise or incomplete data, and data preprocessing is required. Specifically, it includes: Missing value filling: fill missing data through interpolation, mean filling or nearest neighbor filling. Removal of outliers: Identify and remove possible abnormal data points, which can be achieved through statistical methods such as box plots or standard deviation screening. Data normalization: In order to ensure the accuracy of model predictions, the data needs to be normalized or standardized, especially when using machine learning algorithms such as neural networks, to avoid the difference in data magnitude in different dimensions affecting the prediction effect.

[0036] 3. Feature engineering, in order to improve the prediction accuracy, feature engineering is carried out to optimize the model input by extracting key time-related features, meteorological-related features, and user behavior-related features.

[0037] For example: Time features: extract time dimension features such as date, time, week, holidays, etc. to help the model identify periodic fluctuations. Meteorological features: extract meteorological factors such as temperature, humidity, wind speed, solar radiation, etc. These features can affect the fluctuation of power load, especially the use of air conditioning and heating equipment. User behavior features: extract behavioral features such as peak hours of electricity consumption and users' daily electricity consumption patterns to help the model understand the patterns of users' electricity demand.

[0038] 4. Select appropriate time series forecasting models or machine learning algorithms based on data characteristics and forecasting requirements. The models include:

[0039] Time series models: such as ARIMA (autoregressive integrated moving average model), SARIMA (seasonal ARIMA), exponential smoothing model (ETS), etc. These models are suitable for power load data with obvious time correlation and can handle trend and seasonal changes.

[0040] Machine learning algorithms: such as decision trees, random forests, support vector machines (SVM), XGBoost, etc. These models are good at handling complex nonlinear relationships.

[0041] Deep learning models: such as LSTM (Long Short-Term Memory Network), GRU (Gated Recurrent Unit), etc. These neural network models are suitable for processing long-term dependent load demand forecasts, especially in the presence of complex nonlinearities and long-term trends.

[0042] 5. Input the preprocessed data set into the selected model and train the model using the training data. The specific steps are as follows:

[0043] Training set and test set division: Divide the dataset into training set and test set, generally with 70%-80% as training set and 20%-30% as test set, to evaluate the generalization ability of the model.

[0044] Model training: According to the training set, adjust the parameters of the model so that the model can learn the relationship between power load and input features.

[0045] Model optimization: Tune the model through hyperparameter optimization techniques (such as grid search and random search) to ensure the optimal accuracy of the prediction results.

[0046] 6. Model verification and evaluation: Use the test set to verify and evaluate the trained model. The evaluation indicators include:

[0047] Mean Squared Error (MSE): measures the squared difference between the model's predictions and the true values.

[0048] Mean Absolute Error (MAE): measures the absolute difference between the model's predictions and the true values.

[0049] Coefficient of determination (R 2 ): Evaluate how well the model fits the data.

[0050] MAPE (mean absolute percentage error): measures the percentage of prediction error relative to the true value. Through these evaluation indicators, the prediction performance of the model is verified, and the structure and parameters of the model are adjusted to improve accuracy.

[0051] 7. Prediction and dynamic adjustment. After training is completed, use the trained model to predict future power load demand. Since power load demand will change over time, the model needs to have the ability to adjust dynamically. When new data comes in, retrain or adjust the model so that it can accurately predict the latest changes in power demand. You can set up regular model training (such as daily, weekly) or real-time dynamic adjustment mechanism to ensure the timeliness and accuracy of the prediction.

[0052] 8. Multi-scenario simulation of load demand: Based on different meteorological conditions, user behavior patterns and market prices, multiple load demand scenarios are simulated to analyze the load demand fluctuations under different circumstances. Through this scenario simulation, multiple alternatives can be provided for the coordinated dispatch of virtual power plants to improve the robustness and flexibility of overall operation.

[0053] 9. Feedback and application of results: provide the forecast results to the dispatching system of the virtual power plant as the key input for collaborative optimization operation. Load forecast results are used to guide the optimal configuration of distributed energy resources, ensure the balance between energy supply and demand, and provide decision-making basis for demand response and market transactions.

[0054] Through the above steps, power load forecasting based on historical data, meteorological information and user behavior can provide accurate demand estimates for the scheduling and optimization of virtual power plants, thereby improving operational efficiency and the economic benefits of the system.

[0055] Furthermore, in the process of forming an energy resource pool in a virtual power plant by aggregating multiple distributed energy sources, the specific steps are as follows:

[0056] 1. Design the overall architecture of the virtual power plant, determine the overall architecture of the virtual power plant, including the type of distributed energy resources, user-side load requirements, communication networks, dispatching systems and cloud platforms. The core part of the architecture is the distributed energy aggregation platform, which can unify the management and dispatch of multiple geographically dispersed energy resources to build a virtual energy resource pool.

[0057] The resource pool includes wind power, solar power, energy storage devices, gas turbines and other energy sources to ensure flexibility and diversity. By aggregating distributed energy, a virtual overall power generation capacity is formed.

[0058] Dispatching system and cloud platform: Build a dispatching system based on the cloud platform to monitor the operating status of the virtual power plant in real time, including the power generation of each distributed energy source, the power of the energy storage device, the user's load demand, etc., to ensure the intelligent dispatching and rapid response of the system.

[0059] 2. Real-time monitoring and data collection of distributed energy. Each distributed energy is connected to the cloud platform through sensors and smart terminals to collect real-time data of each energy system, including power generation, charging and discharging status of energy storage devices, etc. These data are uploaded to the cloud platform through Internet of Things (IoT) devices and communication networks. Specifically include:

[0060] Data collection equipment installation: Install intelligent data collection equipment at each distributed energy point to ensure real-time acquisition of energy production and usage data.

[0061] Communication and data transmission: Data is transmitted to the cloud platform in real time via 5G or dedicated communication networks to ensure low-latency and high-reliability data transmission.

[0062] Data storage and processing: Data is stored, analyzed and processed through the cloud platform to ensure that the virtual power plant can understand the operating status of each distributed energy source in real time.

[0063] 3. The dynamic balance mechanism between energy supply and load demand is established to ensure the dynamic balance between energy supply within the virtual power plant and load demand on the user side. This mechanism can automatically adjust the allocation of energy resources according to the volatility of energy supply and changes in load demand to ensure the balance between supply and demand. Specifically, it includes:

[0064] Real-time monitoring of load demand: Utilize the load demand forecasting model designed in advance to monitor the load changes on the user side in real time, including instantaneous load demand and demand trends in the short term.

[0065] Dynamic adjustment of energy supply: According to the power generation and volatility of different distributed energy sources, the output of each energy source is dynamically adjusted to ensure that the energy supply can keep up with the changes in demand. For example, when the wind power output fluctuates greatly, the energy storage system can be used to adjust the energy and supplement the shortage.

[0066] Load demand response: When electricity demand reaches its peak, the demand response mechanism is triggered to adjust non-critical loads (such as delaying the startup of certain electrical equipment) to avoid insufficient power supply.

[0067] 4. Design of energy distribution model: Design a reasonable energy distribution model to ensure the optimal configuration of distributed energy within the virtual power plant. The model should be reasonably scheduled according to the real-time load demand and the power generation capacity of each distributed energy source to ensure the coordinated operation of different energy sources and maximize system efficiency. Specifically include:

[0068] Determination of allocation rules: Design allocation rules based on fluctuations in energy supply and demand. The rules can consider giving priority to the consumption of green energy (such as wind power and photovoltaics) and calling on energy storage or fossil energy generation when supply is insufficient.

[0069] Application of optimization algorithms: Introduce optimization techniques such as linear programming, dynamic programming or genetic algorithms to design an energy distribution model that can meet the user's load requirements while minimizing operating costs and maximizing energy utilization.

[0070] Load priority setting: Set load priorities based on the importance and urgency of load demand, such as ensuring stable power supply for critical loads (such as medical institutions and important infrastructure) and optimizing the scheduling of controllable loads (such as industrial electricity).

[0071] 5. Collaborative operation within the virtual power plant. Collaborative operation within the virtual power plant requires ensuring the coordination between various distributed energy sources and the dynamic satisfaction of load demand. The system performs intelligent scheduling and adjustment based on the predicted load demand and real-time energy supply status to ensure the reasonable allocation of energy among different distributed resources and the stable satisfaction of load.

[0072] Coordinated dispatch of energy sources: Coordinated dispatch of the output of each energy source based on its characteristics, especially when dealing with short-term power fluctuations, through compensation through energy storage systems or fast-response distributed power generation equipment to ensure system stability.

[0073] Accurate response to load demand: Through load demand forecasting, the output of various energy sources can be dispatched in advance to ensure the matching of energy supply and demand. When load forecasting is relatively accurate, energy distribution can be optimized in advance, the use of energy storage and fossil fuels can be reduced, and the overall economic benefits can be improved.

[0074] Linkage between market response and electricity price mechanism: Virtual power plants can also adjust their operating strategies according to the dynamic electricity prices in the electricity market, such as storing energy when electricity prices are low and outputting more electricity when electricity prices are high, to obtain higher economic benefits.

[0075] 6. Satisfaction of user-side demand: When designing the energy allocation strategy of the virtual power plant, ensure that the load demand on the user side is met. This includes flexibly dispatching distributed energy according to the demand patterns of different users to ensure their safe and stable electricity use. In addition, demand-side management technology can be combined to guide users to use electricity reasonably and balance supply and demand.

[0076] Load pattern analysis: Analyze the load pattern of users based on their electricity usage habits to ensure that the power needs of key users are met first during peak hours.

[0077] Demand response guidance: Through economic incentives or automated control methods, guide users to participate in demand response, reduce peak power load, and achieve supply and demand balance.

[0078] Through the above steps, the virtual power plant can achieve aggregation, optimized scheduling, and intelligent management of distributed energy, ensuring the balance and coordinated operation between energy supply and load demand, while maximizing the economic benefits and flexibility of the system.

[0079] Furthermore, the collaborative optimization scheduling mechanism for building a virtual power plant has the following specific steps:

[0080] 1. Establish a multi-objective optimization model and define the multi-objective optimization model of the virtual power plant. The model needs to take into account multiple optimization objectives to ensure that the virtual power plant achieves the optimal balance in terms of economy, energy efficiency and environmental benefits. The optimization objectives include:

[0081] Maximizing economic benefits: By optimizing the energy distribution strategy, the economic benefits can be maximized. Specific factors to be considered include electricity market prices, electricity sales revenue, operating costs of distributed energy, and the cost of using energy storage devices.

[0082] Energy efficiency optimization: In the process of energy distribution, we will try our best to improve energy utilization efficiency, reduce energy loss, give priority to the use of renewable energy, and avoid energy waste. This goal takes into account the supply fluctuations of distributed energy and the charging and discharging efficiency of energy storage devices.

[0083] Minimization of carbon emissions: In order to meet the requirements of minimizing carbon emissions, priority should be given to the use of low-carbon or carbon-free energy (such as wind power and photovoltaics) and to reducing dependence on fossil energy. The carbon emissions per unit of energy should be calculated and used as one of the optimization parameters.

[0084] 2. Select optimization algorithms. In order to solve the above multi-objective optimization problems, advanced optimization algorithms are introduced to coordinate the optimization of energy distribution and load scheduling. The optimization algorithms include:

[0085] Genetic algorithm: Genetic algorithm is based on the principles of natural selection and genetics. It generates different solution sets through iteration and gradually optimizes the adaptability of the solution. This algorithm can be used to find the global optimal solution and deal with nonlinear complex problems. It is suitable for solving multi-objective optimization problems of virtual power plants.

[0086] Particle Swarm Optimization: Particle Swarm Optimization simulates group intelligent behavior and seeks the optimal solution through information exchange between particles and individual learning ability. It can converge quickly and is suitable for complex problems of optimizing energy consumption scheduling and energy allocation.

[0087] Deep reinforcement learning: Deep reinforcement learning is an optimization algorithm based on a reward mechanism. It continuously adjusts strategies to maximize rewards by interacting with the environment. In the virtual power plant scenario, deep reinforcement learning can be used to dynamically adjust energy distribution and load scheduling strategies, adapt to environmental changes, and gradually improve the flexibility and efficiency of system operation.

[0088] 3. Collaborative optimization of energy distribution and load scheduling: Based on the optimization algorithm, the energy distribution and load scheduling of distributed energy are collaboratively optimized. The optimization contents are as follows:

[0089] Energy allocation: According to the real-time power supply of various distributed energy sources, combined with the goals in the optimization model, energy is allocated reasonably. Renewable energy (such as wind energy and solar energy) is allocated first to meet the current load demand, and energy storage is reasonably scheduled according to the charging status of the energy storage device.

[0090] Load dispatching: Load dispatching dynamically adjusts the output of each distributed energy source according to the real-time needs of the user side. At the same time, combined with load forecasting and demand response mechanisms, energy storage equipment is used to supplement power during peak load periods, or part of the load is delayed during non-critical periods to ensure the smooth operation of the power system.

[0091] 4. Dynamic adjustment and real-time optimization: In the actual operation of the virtual power plant, the power supply status and load demand of distributed energy are uncertain, so the system needs to be dynamically adjusted and optimized in real time. Specifically:

[0092] Real-time status monitoring: Through the cloud platform, the power generation of each distributed energy source within the virtual power plant, the charging and discharging status of the energy storage device, and the user-side load demand are monitored in real time to ensure that the system responds promptly to sudden load changes and energy fluctuations.

[0093] Dynamically adjust energy supply: Based on the intermittent and volatile nature of distributed energy generation and combined with load forecasting results, the energy supply is adjusted dynamically in real time. For example, when the output of wind power or photovoltaic power generation increases, the system prioritizes the allocation of this green energy to reduce carbon emissions; when the power supply is insufficient, the system prioritizes the use of energy storage devices or fast-response conventional energy generation.

[0094] 5. Combined with the price information of the electricity market, in order to improve the economic benefits of the virtual power plant, it is also necessary to combine the real-time price information of the electricity market to adjust the power distribution and energy storage strategy. Specifically, it includes:

[0095] Electricity price forecasting and decision-making: Predict price fluctuations in the electricity market through historical data and market conditions, and update electricity price expectations in real time. According to changes in electricity prices, virtual power plants can choose to store energy when electricity prices are low and output electricity when electricity prices are high, maximizing economic benefits.

[0096] Energy storage strategy optimization: Combine market electricity prices and future load forecasts to optimize the charging and discharging strategies of energy storage equipment. Avoid large-scale power output when electricity prices are low, and maximize the economic efficiency of energy storage devices.

[0097] 6. Efficient and flexible operation of virtual power plants. On the basis of completing the above-mentioned coordinated optimization and dispatching, virtual power plants can respond to various energy demands and market fluctuations with high efficiency and flexibility, ensuring system stability and economy. The system continuously optimizes energy distribution and load dispatching strategies to improve overall energy efficiency and reduce carbon emissions, while adapting to price fluctuations in the electricity market and dynamic changes in user needs.

[0098] Optimization result verification: The effect of the optimization algorithm is verified through simulation operation and historical data to ensure the feasibility and practicality of the model. According to the operation results, the parameters and algorithms are dynamically adjusted to continuously improve the dispatch performance of the virtual power plant.

[0099] Through these specific steps, the collaborative optimization scheduling mechanism of the virtual power plant can maximize the multi-objective balance of economic benefits, energy efficiency and environmental benefits, and can flexibly respond to changes in energy supply and demand and fluctuations in the electricity market.

[0100] Furthermore, the energy efficiency and economic benefits of the virtual power plant are comprehensively analyzed, including:

[0101] 1. Build an energy efficiency analysis model to evaluate the energy efficiency and system loss of the virtual power plant during operation. This includes the following steps:

[0102] Collect energy data: Through the real-time monitoring system, the power generation data of various distributed energy sources in the virtual power plant are collected, including the charging and discharging status of wind energy, solar energy, and energy storage devices, as well as the power generation efficiency of conventional energy.

[0103] Energy efficiency index definition: Based on the collected data, define the key indicators for energy efficiency evaluation. The indicators include energy utilization rate, system loss rate, energy storage efficiency, power generation equipment utilization rate, etc.

[0104] Among them: Energy utilization rate refers to the proportion of energy that is effectively used in the total power generation of the virtual power plant, reflecting the overall utilization of energy. System loss rate refers to the loss of the system during energy conversion and transmission, and calculates the energy loss in each link of the virtual power plant. Energy storage efficiency refers to the evaluation of the charging and discharging loss of the energy storage device, and determines the efficiency of the energy storage system in the entire scheduling process.

[0105] Energy efficiency evaluation formula: Using the above indicators, an energy efficiency evaluation formula is established to comprehensively calculate the energy utilization efficiency of the virtual power plant in different time periods, identify links with low energy efficiency and make optimization adjustments.

[0106] The energy efficiency evaluation formula is used to quantify the energy utilization efficiency of the virtual power plant (VPP) and evaluate its energy consumption, conversion efficiency and system loss during operation. In order to specify this formula, it is necessary to combine the characteristics of various distributed energy sources in the virtual power plant, including power generation, energy storage and power consumption. The derivation steps are as follows:

[0107] Energy utilization rate is a measure of the proportion of the total power generation of the virtual power plant that is effectively used. The total energy utilization rate can be expressed by the following formula:

[0108]

[0109] Where: E used is the effective electric energy actually used by the load (unit: kWh), E generated The total power generated by various distributed energy sources in the virtual power plant (unit: kWh);

[0110] The system loss rate reflects the energy loss caused by transmission, conversion and other links in the process from power generation to load use of the virtual power plant. The system loss rate can be calculated using the following formula:

[0111]

[0112] Where: E loss Including energy losses in cable transmission, transformers, inverters, etc. (unit: kWh);

[0113] Energy storage efficiency is used to evaluate the energy loss of the energy storage system during the charging and discharging process. The energy storage efficiency formula is:

[0114]

[0115] Where: E discharged is the amount of electricity released by the energy storage device (unit: kWh), E charged The amount of electricity charged into the energy storage device (unit: kWh);

[0116] For a variety of distributed energy sources (such as wind energy, solar energy, etc.), the utilization formula of each energy source can be expressed as:

[0117]

[0118] Among them, E used,i is the amount of electricity effectively used by the i-th energy source (unit: kWh), E generated,i is the total amount of electricity generated by the i-th energy source (unit: kWh);

[0119] When comprehensively evaluating energy efficiency, the comprehensive energy efficiency can be comprehensively reflected by total energy utilization rate, energy storage efficiency and system loss rate. The overall energy efficiency comprehensive evaluation formula can be expressed as:

[0120]

[0121] Where: E used,i is the effective amount of electricity used by various energy sources, E generated is the total power generation of various distributed energy sources, η storage is the efficiency of the energy storage system, L sys is the system loss rate, and n is the number of distributed energy types.

[0122] 2. Analyze the economic benefits of virtual power plants, and further evaluate the overall economic benefits of virtual power plants based on energy efficiency analysis. Specifically include:

[0123] Power market price analysis: Analyze the trend of power market prices through historical data, and estimate the power sales revenue of virtual power plants by combining future market price forecast models. During periods of high power prices, power is exported and sold to the market first to maximize revenue; during periods of low power prices, excess power is stored first and sold when power prices rise.

[0124] Policy subsidies and incentive mechanisms: Evaluate the current national and local government subsidy policies and incentive mechanisms for distributed energy and virtual power plants. Collect relevant policy documents on renewable energy generation subsidies, energy storage subsidies, etc., and calculate the subsidy benefits that virtual power plants can obtain. Evaluate the potential impact of future policy changes on the economic benefits of virtual power plants, and adjust the overall operation strategy accordingly to maximize subsidy benefits.

[0125] Carbon emission rights trading: Analyze the potential benefits of virtual power plants in carbon emissions, especially in countries or regions with carbon trading markets, where low-carbon or carbon-free energy generation can bring benefits from carbon emission rights trading. Calculate carbon emission reductions based on the power generation structure of virtual power plants and evaluate their value in the carbon emission rights trading market. Develop trading strategies to sell the saved carbon emission quotas to other high-emission companies to obtain additional income.

[0126] 3. Joint optimization analysis of energy efficiency and economic benefits. Combining energy efficiency and economic benefit analysis, virtual power plants can optimize operation strategies and achieve the optimal balance of dual goals. Specifically, they include:

[0127] Energy efficiency and economic benefits trade-off: In some cases, energy efficiency and economic benefits may conflict (such as using energy storage systems to improve energy efficiency but increase costs). Through comprehensive analysis, evaluate how to strike a balance between improving energy efficiency and maximizing economic benefits. When the electricity market price is low, more attention may be paid to improving energy efficiency, while when the electricity price is high, the virtual power plant should pay more attention to economic benefits and give priority to outputting electricity. Establish a weighted evaluation model for energy efficiency and economic benefits, and dynamically adjust the strategy according to different market conditions.

[0128] Operation strategy optimization: Based on the analysis results, the operation strategy of the virtual power plant is optimized. For example, when the electricity market price is predicted to rise, the system will prioritize storing electricity for sale; when the electricity price drops, it will prioritize the use of energy storage devices or cheap power sources to reduce the use of high-cost energy.

[0129] When the price of carbon emission rights rises, virtual power plants can give priority to clean energy generation to obtain additional benefits from the carbon trading market.

[0130] 4. Data feedback and adjustment: Through the real-time monitoring system, the energy efficiency and economic performance of the virtual power plant are continuously tracked, and the operation strategy is regularly analyzed and adjusted. Specifically, it includes:

[0131] Data feedback mechanism: Establish a complete data feedback mechanism to monitor the various operating indicators of the virtual power plant in real time through the cloud platform, and make adjustments based on the operating results.

[0132] Optimization iteration: Based on the actual feedback data, continuously optimize the energy efficiency analysis model and economic benefit model to gradually improve the comprehensive benefits of the virtual power plant.

[0133] Through the above steps, a comprehensive analysis of the energy efficiency and economic benefits of the virtual power plant can be conducted to ensure that it operates efficiently and with low consumption while achieving considerable economic returns, ultimately improving the overall operational efficiency of the virtual power plant.

[0134] Furthermore, virtual power plants also need to maintain effective interaction with the power grid and provide a variety of grid auxiliary services. By providing peak load regulation, frequency regulation and backup services, virtual power plants can help the power grid improve stability and security. At the same time, virtual power plants can implement demand response mechanisms to encourage users to adjust their electricity consumption patterns according to power market price signals or grid demand, thereby achieving coordinated optimization between virtual power plants and the grid and improving the overall efficiency of the energy system.

[0135] Furthermore, system security and stability are important guarantees for the operation of virtual power plants. Fault detection and recovery mechanisms are introduced, and fault detection algorithms are used to monitor the operating status of distributed energy and load equipment, so that faults can be discovered and handled in a timely manner to ensure the stable operation of virtual power plants. In addition, advanced network security technologies need to be adopted to ensure the security of the communication network of virtual power plants, prevent malicious attacks and data leakage, and protect the overall operation security of the system.

[0136] Furthermore, the interaction between the virtual power plant and the power grid includes the following steps:

[0137] The virtual power plant and the power grid achieve two-way real-time communication through a data exchange platform. The platform collects operating status data from the power grid, including grid frequency, voltage, supply and demand balance, etc., and obtains power market price signals to ensure that the virtual power plant can understand the demand changes of the power grid in real time. Through the internal monitoring system, the virtual power plant collects and analyzes key information such as the operating status, power generation, and charging and discharging status of various distributed energy sources, and provides accurate energy supply forecasts for the power grid.

[0138] Virtual power plants support the stable operation of the power grid through peak load regulation services. Specifically, when the power grid experiences a peak in electricity consumption, virtual power plants dispatch distributed energy and energy storage systems to quickly increase power generation or release stored electricity to meet the grid's additional demand and avoid grid overload. Conversely, when electricity consumption is low, virtual power plants reduce energy output according to grid demand and even charge energy storage systems to prepare for future peak electricity consumption. The entire peak load regulation process is monitored and dispatched in real time through the cloud platform to ensure a smooth transition of the grid load curve.

[0139] Virtual power plants provide frequency regulation services to maintain the frequency stability of the power grid. When the grid frequency deviates from the normal range, virtual power plants can participate in frequency regulation by quickly adjusting the output power of distributed energy. The specific operation steps include: when the grid frequency is too high, the virtual power plant reduces power generation to reduce the power supply burden on the grid; when the frequency is too low, the virtual power plant quickly increases power generation or calls on the energy storage system to discharge to help restore the stability of the grid frequency.

[0140] Virtual power plants also provide backup services to enhance the safety and reliability of the power grid. When an emergency occurs in the power grid (such as a generator failure or other emergency) resulting in insufficient power supply, virtual power plants can quickly start backup distributed energy resources or energy storage systems to fill the power gap of the power grid and ensure continuous power supply to the grid. Virtual power plants pre-set a certain proportion of backup capacity and monitor the status of the power grid in real time so that they can respond quickly when the grid needs it.

[0141] The demand response mechanism is another key part of the interaction between virtual power plants and the power grid. Through dynamic monitoring of price signals in the electricity market, virtual power plants send price signals to users, encouraging them to reduce electricity consumption when electricity prices are high or postpone electricity consumption to low-price periods. This process is achieved through smart terminal devices or mobile applications, through which users can receive price signals and automatically adjust the operating status of home appliances. In addition, when the grid load is too high, virtual power plants can send demand response requests to users to encourage users to reduce peak loads and fill valleys to avoid the risk of grid overload. The real-time linkage between the user response mechanism and the grid demand ensures that the virtual power plant can further optimize the operation of the grid through flexible adjustments on the user side.

[0142] Finally, by providing the above-mentioned peak load regulation, frequency regulation and standby services as well as demand response mechanisms, the virtual power plant and the power grid achieve coordinated optimization. The virtual power plant continuously adjusts the power generation and energy storage strategy of distributed energy according to the demand signals, real-time market prices and load status of the power grid, ensuring effective interaction between the virtual power plant and the power grid, and improving the overall stability and economic benefits of the energy system.

[0143] The beneficial effects of the present invention are:

[0144] 1. Improved energy efficiency

[0145] By accurately modeling distributed energy resources and monitoring their operating status in real time, the present invention can accurately predict and respond to changes in power load demand. This refined management method improves energy utilization efficiency and reduces energy waste, especially in the use of renewable energy. By optimizing configuration and scheduling, it maximizes the use of clean energy such as wind energy and solar energy and reduces dependence on fossil energy.

[0146] 2. Enhanced grid stability

[0147] Virtual power plants effectively balance the supply and demand relationship and reduce the load fluctuation of the power grid by real-time monitoring and dynamic adjustment of energy supply and load demand. In addition, by providing peak load regulation, frequency regulation and backup services, virtual power plants enhance the stability and reliability of the power grid, especially in the face of emergencies or extreme weather conditions, and can quickly respond to changes in power grid demand and ensure the continuity and security of power supply.

[0148] 3. Optimized economic benefits

[0149] The present invention achieves the maximization of economic benefits through multi-objective optimization models and advanced optimization algorithms. Virtual power plants can not only dynamically adjust power distribution and energy storage strategies according to power market price signals to obtain higher economic benefits, but also obtain additional economic benefits by participating in mechanisms such as carbon emission rights trading. At the same time, by optimizing operation strategies, virtual power plants can also reduce operating costs and improve overall economic returns.

[0150] 4. Promoted environmental protection

[0151] By giving priority to the use of clean energy such as wind and solar energy and optimizing the charging and discharging strategy of the energy storage system, the present invention significantly reduces carbon emissions. In addition, by reducing the use of fossil energy and improving energy efficiency, virtual power plants help reduce greenhouse gas emissions, combat global climate change, and promote environmental protection and sustainable development.

[0152] 5. Improved system flexibility and adaptability

[0153] The virtual power plant of the present invention can flexibly respond to changes in energy supply and demand and fluctuations in the electricity market. Through real-time monitoring and dynamic adjustment, the virtual power plant can quickly respond to changes in grid demand and market price fluctuations, and achieve optimal configuration and scheduling of distributed energy resources. This flexibility and adaptability enables the virtual power plant to maintain efficient operation under various conditions, improving the robustness of the system.

[0154] 6. Supported policies and market mechanisms

[0155] The virtual power plant of the present invention can be combined with existing policies and market mechanisms, such as government subsidies and carbon emission rights trading, to provide strong support for the development of distributed energy. By optimizing operation strategies and economic benefit analysis, the virtual power plant can maximize the benefits of policies and market mechanisms, creating favorable conditions for the promotion and application of distributed energy.

[0156] In summary, the present invention not only realizes the optimal configuration and scheduling of distributed energy resources at the technical level, but also produces significant beneficial effects at the economic, environmental and social levels. It has broad application prospects and important practical value. DETAILED DESCRIPTION

[0157] The present invention will be further described below in conjunction with specific embodiments:

[0158] Embodiment: A collaborative optimization operation method of a virtual power plant comprises the following steps:

[0159] 1. Modeling and classification of distributed energy resources

[0160] 1.1 Photovoltaic Energy Modeling

[0161] Take a specific photovoltaic power generation system as an example. The system consists of 1,000 photovoltaic panels, and the area of ​​each photovoltaic panel is 1.6 square meters. The following output power model is established:

[0162] P PV (t) = A × G (t) × η PV (T)

[0163] Where: A is the area of ​​the photovoltaic panel, G(t) is the solar radiation intensity, ηPV(T) is the conversion efficiency based on temperature, which can be obtained by fitting experimental data;

[0164] 1.2 Wind energy modeling

[0165] For the wind power generation system, we adopt the following output power model:

[0166] P wind (t) = 0.5 × ρ × A rotor ×V 3 (t)×η wind

[0167] Where ρ is the air density, A rotor is the swept area of ​​the wind wheel, V(t) is the wind speed, η wind is the efficiency of the wind turbine, which can be determined from the power curve of wind speed.

[0168] 1.3 Energy Storage System Modeling

[0169] For the energy storage system, we establish the following charging and discharging power model:

[0170]

[0171] Among them, η charge is the charging efficiency, P in (t) is the input power, P out (t) is the output power, η discharge is the discharge efficiency; at the same time, the battery state of charge SoC(t) is a key parameter in energy storage system modeling and can be expressed by the following formula:

[0172]

[0173] Among them, C battery is the capacity of the battery;

[0174] 1.4 Electric vehicle modeling

[0175] For electric vehicles, we establish the following charging model:

[0176] P EV (t) = η EV ×P charge (t)

[0177] Among them, η EV is the charging efficiency, P charge (t) is the charging power.

[0178] 2. Load demand forecast

[0179] 2.1 Data Collection

[0180] Collect historical power load data, meteorological information, user behavior data and power market price information from the past year. Data sources include smart meters, meteorological stations, user power consumption surveys and power market trading platforms.

[0181] 2.2 Data Preprocessing

[0182] The collected data are preprocessed, including missing value filling, outlier removal, and data normalization. For example, interpolation is used to fill missing power load data, box plot method is used to remove outliers, and Z-score method is used for data normalization.

[0183] 2.3 Feature Engineering

[0184] Extract key features, including time features (such as hours, weeks, months), meteorological features (such as temperature, humidity, wind speed, solar radiation) and user behavior features (such as peak electricity consumption hours, seasonal changes).

[0185] 2.4 Model Selection and Training

[0186] Select a suitable forecasting model, such as the ARIMA model, and perform model training and optimization. Divide the data set into a training set (70%) and a test set (30%), use the training set to train the model, and use the test set to evaluate the model's forecasting performance.

[0187] 3. Overall architecture design of virtual power plant

[0188] 3.1 Architecture Design

[0189] Design the overall architecture of the virtual power plant, including the types of distributed energy resources, user-side load requirements, communication networks, dispatching systems, and cloud platforms. The core part of the architecture is the distributed energy aggregation platform, which can unify the management and dispatch of multiple geographically dispersed energy resources to build a virtual energy resource pool.

[0190] 3.2 Real-time monitoring and data collection

[0191] Through sensors and smart terminals connected to the cloud platform, real-time data of each energy system is collected, including power generation, charging and discharging status of energy storage devices, etc. These data are uploaded to the cloud platform through Internet of Things (IoT) devices and communication networks.

[0192] 3.3 Dynamic balance mechanism

[0193] Establish an automated balancing mechanism to automatically adjust the allocation of energy resources according to the volatility of energy supply and changes in load demand to ensure a balance between supply and demand. For example, when wind power output fluctuates greatly, the energy storage system can be used to adjust the energy and make up for the shortfall.

[0194] 4. Collaborative optimization of scheduling mechanism

[0195] 4.1 Multi-objective optimization model

[0196] A multi-objective optimization model is established, including objectives such as maximizing economic benefits, optimizing energy efficiency, and minimizing carbon emissions.

[0197] 4.2 Optimization Algorithm Selection

[0198] Select an optimization algorithm, such as a genetic algorithm, to coordinate the optimization of energy distribution and load scheduling. The genetic algorithm generates different solution sets through iteration and gradually optimizes the adaptability of the solution.

[0199] 4.3 Dynamic Adjustment and Real-time Optimization

[0200] In the actual operation of the virtual power plant, dynamic adjustments are made according to the real-time status of distributed energy and changes in load demand. Combined with electricity market price information, power distribution and energy storage strategies are optimized in real time.

[0201] 5. Energy efficiency and economic benefit analysis

[0202] 5.1 Energy efficiency analysis model

[0203] Build an energy efficiency analysis model to evaluate the energy utilization efficiency and system loss in the operation of virtual power plants. Define key indicators for energy efficiency evaluation, such as energy utilization rate, system loss rate, energy storage efficiency, etc.

[0204] 5.2 Economic Benefit Analysis

[0205] Analyze the economic benefits of virtual power plants, and evaluate the overall economic benefits of virtual power plants by combining factors such as electricity market prices, government subsidy policies, and carbon emission rights trading.

[0206] 6. Interaction with the power grid

[0207] 6.1 Peak Loading Service

[0208] Virtual power plants support the stable operation of the power grid through peak load regulation services. When the power grid experiences a peak in electricity demand, virtual power plants dispatch distributed energy and energy storage systems to quickly increase power generation or release stored energy to meet the additional demand of the power grid.

[0209] 6.2 Frequency Modulation Service

[0210] Virtual power plants provide frequency regulation services to maintain the frequency stability of the power grid. When the power grid frequency deviates from the normal range, virtual power plants can participate in frequency regulation by quickly adjusting the output power of distributed energy.

[0211] 6.3 Backup Services

[0212] Virtual power plants also provide backup services to enhance the safety and reliability of the power grid. When an emergency occurs in the power grid and causes a power shortage, virtual power plants can quickly start backup distributed energy resources or energy storage systems.

[0213] 6.4 Demand Response Mechanism

[0214] Demand response mechanism is another key part of the interaction between virtual power plants and the power grid. Through dynamic monitoring of power market price signals, virtual power plants send price signals to users, encouraging them to reduce electricity consumption when electricity prices are high or postpone electricity consumption to low-price periods.

[0215] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for collaborative optimization operation of a virtual power plant based on distributed energy, characterized in that: The following steps are involved: Modeling and classification of distributed energy resources, establishing corresponding mathematical models according to different energy types, and taking into account the intermittent and volatile nature of energy supply; at the same time, based on historical data, meteorological information and user behavior, applying time series prediction models or machine learning algorithms to forecast power load demand and estimate future load demand changes; Design the architecture of a virtual power plant, aggregate multiple distributed energy sources into a virtual power plant, conduct real-time monitoring and scheduling through the cloud platform, build a dynamic balance mechanism between energy supply and load demand, ensure the coordinated operation of power supply and consumption, and optimize the configuration of distributed energy; Construct a collaborative optimization dispatching mechanism for virtual power plants, establish a multi-objective optimization model that includes maximizing economic benefits, optimizing energy efficiency, and minimizing carbon emissions, use genetic algorithms or particle swarm algorithms or deep reinforcement learning algorithms to collaboratively optimize energy distribution and load dispatching, and make dynamic adjustments based on the real-time status of distributed energy, changes in load demand, and electricity market price information to optimize power distribution and energy storage strategies; Conduct energy efficiency and economic benefit analysis of virtual power plants, build energy efficiency analysis models, evaluate energy utilization and system losses in the operation of virtual power plants, conduct economic benefit evaluation based on electricity market prices, government subsidies and carbon emission rights trading, and ensure efficient operation and economic benefits of virtual power plants.

2. A method for collaborative optimization operation of a virtual power plant based on distributed energy as claimed in claim 1, characterized in that: The modeling and classification of the distributed energy resources, establishing corresponding mathematical models according to different energy types, and considering the intermittent and fluctuating nature of energy supply, include the following steps: Energy classification and identification, classify distributed energy according to its different sources; identify and model these different types of energy independently; different types of models are as follows: The output power model for photovoltaic energy modeling is: P PV (t)=A×G(t)×ηPV(t) Where: A is the area of ​​the photovoltaic panel, G(t) is the solar radiation intensity, ηPV(T) is the conversion efficiency based on temperature; The output power model for wind energy modeling is: P wind (t)=0.5×ρ×A roior ×V 3 (t)×η wind Where ρ is the air density, A rotor is the swept area of ​​the wind wheel, V(t) is the wind speed, η wind is the efficiency of the wind turbine; The charging and discharging power model of the energy storage system is: Among them, η charge is the charging efficiency, P in (t) is the input power, P out (t) is the output power, η discharge is the discharge efficiency; at the same time, the battery state of charge SoC(t) is a key parameter in energy storage system modeling and can be expressed by the following formula: Among them, C battery is the capacity of the battery; The electric vehicle charging model for electric vehicle modeling is: P EV (t)=η EV ×P charge (t) Among them, η EV is the charging efficiency, P charge (t) is the charging power; Reflect the uncertainty of energy supply by introducing random variables or scenario analysis; Comprehensive model construction: After completing the independent modeling of different energy types, a comprehensive model of the entire distributed energy system is constructed, integrating the above subsystems into an overall model, and coordinating the virtual power plant through dynamic optimization methods.

3. The method for collaborative optimization operation of a virtual power plant based on distributed energy as claimed in claim 1, characterized in that: The load demand forecasting comprises the following steps: Collecting data sets for load demand forecasting; Perform data preprocessing on the collected data, including missing value filling, outlier removal, and data normalization; Perform feature engineering to optimize model input by extracting key time-related features, weather-related features, and user behavior-related features; According to the data characteristics and prediction requirements, select a time series model or a machine learning algorithm or a deep learning model, input the preprocessed data set into the selected model, and train the model using the training data; After the training is completed, the trained model is used to predict future power load demand. At the same time, based on different meteorological conditions, user behavior patterns and market prices, multiple load demand scenarios are simulated to analyze the load demand fluctuations under different circumstances. The prediction results are provided to the dispatching system of the virtual power plant as a key input for collaborative optimization operation.

4. The method for collaborative optimization operation of a virtual power plant based on distributed energy according to claim 1, characterized in that: The process of designing the architecture of a virtual power plant, aggregating multiple distributed energy sources to form a virtual power plant, performing real-time monitoring and scheduling through a cloud platform, and building a dynamic balance mechanism between energy supply and load demand includes the following steps: Determine the overall architecture of the virtual power plant, including the type of distributed energy resources, user-side load requirements, communication networks, dispatching systems, and cloud platforms, and build a virtual energy resource pool; Build a cloud-based dispatching system to monitor the operating status of the virtual power plant in real time, including the power generation of each distributed energy source, the power of the energy storage device, and the load demand of users; Each distributed energy source is connected to the cloud platform through sensors and smart terminals to collect real-time data of each energy system, including power generation and the charging and discharging status of energy storage devices. The real-time data is uploaded to the cloud platform through Internet of Things (IoT) devices and communication networks. The dynamic balance mechanism between energy supply and load demand is established, which automatically adjusts the allocation of energy resources according to the volatility of energy supply and changes in load demand to ensure the balance between supply and demand; Use the load demand forecasting model designed in the early stage to monitor the load changes on the user side in real time, including instantaneous load demand and demand trends in the short term in the future; Dynamically adjust the output of each energy source based on the power generation and volatility of different distributed energy sources to ensure that energy supply can keep up with changes in demand; When electricity demand reaches its peak, the demand response mechanism is triggered to adjust non-critical loads to avoid power shortages; Design a reasonable energy allocation model to ensure the optimal configuration of distributed energy within the virtual power plant; Intelligent scheduling and adjustment are carried out according to the predicted load demand and real-time energy supply status to ensure the reasonable allocation of energy among different distributed resources and the stable satisfaction of load; Flexible dispatch of distributed energy sources based on the demand patterns of different users ensures safe and stable electricity use.

5. The method for collaborative optimization operation of a virtual power plant based on distributed energy according to claim 1, characterized in that: The collaborative optimization scheduling mechanism for building a virtual power plant includes the following steps: Establish a multi-objective optimization model and define the multi-objective optimization model of the virtual power plant; the optimization objectives include: maximizing economic benefits, optimizing energy efficiency, and minimizing carbon emissions; An optimization algorithm is introduced, and based on the optimization algorithm, the energy distribution and load scheduling of distributed energy are collaboratively optimized; the optimization algorithm includes genetic algorithm, particle swarm algorithm, and deep reinforcement learning; the content of the collaborative optimization includes: reasonably allocating energy according to the real-time power supply of various distributed energy sources and combining the goals in the optimization model; and load scheduling dynamically adjusts the output of each distributed energy source according to the real-time needs of the user side; In the actual operation of the virtual power plant, the power supply status and load demand of distributed energy are uncertain, so the system is dynamically adjusted and optimized in real time; including real-time monitoring of the power generation status of each distributed energy source in the virtual power plant, the charging and discharging status of the energy storage device, and the load demand on the user side through the cloud platform to ensure that the system responds promptly to sudden load changes and energy fluctuations; and according to the intermittent and volatile nature of distributed energy generation, combined with load forecasting results, real-time dynamic adjustment of energy supply; During the actual operation of the virtual power plant, the power distribution and energy storage strategies are adjusted in combination with the real-time price information of the power market. This includes predicting the price fluctuations of the power market through historical data and market conditions, and updating the power price expectations in real time. In addition, the charging and discharging strategies of the energy storage equipment are optimized in combination with the market power price and future load forecasts. The effectiveness of the optimization algorithm is verified through simulation operation and historical data to ensure the feasibility and practicality of the model.

6. The method for collaborative optimization operation of a virtual power plant based on distributed energy according to claim 1, characterized in that: The comprehensive analysis of the energy efficiency and economic benefits of the virtual power plant includes the following steps: Construct an energy efficiency analysis model, and collect power generation data of various distributed energy sources in the virtual power plant through a real-time monitoring system; define key indicators of energy efficiency evaluation based on the collected data, including energy utilization rate, system loss rate, energy storage efficiency, and power generation equipment utilization rate; establish an energy efficiency evaluation formula based on the key indicators, comprehensively calculate the energy utilization efficiency of the virtual power plant in different time periods, identify the links with low energy efficiency and make optimization adjustments; Based on the energy efficiency analysis, further evaluate the overall economic benefits of the virtual power plant; Combining energy efficiency and economic benefit analysis, virtual power plants can optimize operating strategies to achieve the best balance between dual goals; Through the real-time monitoring system, the energy efficiency and economic performance of the virtual power plant can be continuously tracked, and regular analysis and adjustment of operating strategies can be carried out.

7. A method for collaborative optimization operation of a virtual power plant based on distributed energy according to any one of claims 1 to 6, characterized in that: The virtual power plant interacts with the power grid to improve the stability and security of the power grid by providing power grid auxiliary services such as peak shaving, frequency regulation, and standby. It implements a demand response mechanism to encourage users to adjust their electricity usage patterns based on electricity market price signals or power grid demand, thereby achieving coordinated optimization of the virtual power plant and the power grid.

8. A method for collaborative optimization operation of a virtual power plant based on distributed energy according to any one of claims 1 to 6, characterized in that: When using the virtual power plant collaborative optimization operation method, in order to ensure the system safety and stability of the virtual power plant, a fault detection algorithm is used to monitor the operating status of distributed energy and load equipment, and faults are detected and handled in a timely manner; Use network security technology to ensure the security of the virtual power plant's communication network and prevent malicious attacks and data leaks.

9. The method for collaborative optimization operation of a virtual power plant based on distributed energy according to claim 6, characterized in that: The comprehensive energy efficiency evaluation formula is: Where: E used,i is the effective amount of electricity used by various energy sources, E generated is the total power generation of various distributed energy sources, η storage is the efficiency of the energy storage system, L sys is the system loss rate, and n is the number of distributed energy types.

10. The method for collaborative optimization operation of a virtual power plant based on distributed energy according to claim 7, characterized in that: The interaction between the virtual power plant and the power grid includes the following steps: Realize two-way real-time communication between the virtual power plant and the power grid through the data exchange platform, collect the operating status data of the power grid and the price signal of the power market, and provide energy supply forecasts based on the operating status of the distributed energy and energy storage system within the virtual power plant; When the power grid experiences a peak in electricity consumption, the virtual power plant dispatches distributed energy and energy storage systems to increase power output to meet the grid's additional demand; when electricity consumption is low, it reduces energy output or charges the energy storage system to achieve peak load management of the grid; When the grid frequency deviates from the normal range, the virtual power plant participates in frequency regulation by quickly adjusting the output power of distributed energy. When the grid frequency is too high, the power generation is reduced. When the frequency is too low, the power generation is increased or the energy storage system is called to discharge to maintain the stability of the grid frequency. Virtual power plants provide backup services. When an emergency occurs in the power grid and causes insufficient power supply, backup distributed energy or energy storage systems are quickly activated to make up for the power gap and ensure continuous power supply to the grid. Through the demand response mechanism, virtual power plants send price signals or demand response requests to users based on power market price signals or grid load status, encouraging users to reduce electricity consumption or adjust electricity consumption patterns when electricity prices are high or loads are too high, thereby optimizing the load status of the grid and avoiding grid overload; The virtual power plant dynamically adjusts the power generation and storage strategy of distributed energy according to grid demand signals, market prices and load conditions, ensuring effective coordinated interaction between the virtual power plant and the grid, and achieving improved stability of the energy system and economic benefits.

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