Micro-grid power utilization scheduling method

Abstract

The invention provides a micro-grid power consumption scheduling method, which comprises the following steps: generating a power supply and consumption electronic contract through user interaction, establishing a micro-grid power consumption optimization model combining distributed energy output prediction and load demand, solving to obtain an optimal power consumption scheme, programming contract terms and the scheme into a block chain intelligent contract code, and sending the block chain intelligent contract code to the micro-grid power consumption optimization model. And the APP is responsible for sending execution parameters to the intelligent contract and receiving and processing an execution result so as to realize automatic power utilization scheduling and user notification. According to the invention, efficient, economic and automatic scheduling of micro-grid power utilization is realized through the mobile APP and the block chain smart contract, and the energy management efficiency and the user satisfaction are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid power dispatching, and in particular to a microgrid power dispatching method. Background Art

[0002] With the growth of global energy demand and the increasing attention paid to environmental protection, microgrids, as a smart grid technology that integrates renewable energy and distributed energy, have become an important part of modern power systems. Microgrids can effectively integrate unstable renewable energy such as solar energy and wind energy, as well as distributed power generation units such as fuel cells and micro turbines, to improve energy efficiency and power supply reliability. However, the characteristics of multi-source and multi-load (i.e., multiple energy supplies and multiple load demands) in microgrids make their power dispatching face many challenges:

[0003] Uncertainty in energy supply: The output of renewable energy sources such as wind and solar energy is greatly affected by weather conditions and is difficult to accurately predict.

[0004] Volatility of load demand: User electricity consumption behavior is random, especially non-sensitive loads, which brings challenges to system scheduling.

[0005] Economic efficiency of system operation: How to reasonably arrange the operation of distributed energy and energy storage units to reduce operating costs and improve economic benefits.

[0006] Safety of power system: ensure the continuity and stability of power supply and prevent system failures caused by improper scheduling. Summary of the invention

[0007] The purpose of the present invention is to propose a microgrid power dispatching method to solve the above problems.

[0008] To achieve the above object, the present invention provides a microgrid power dispatching method, which is implemented based on a mobile operation APP and includes:

[0009] The APP interacts with the user and generates an electronic power supply and use contract based on the user's input information;

[0010] The APP establishes a microgrid power optimization model based on the power consumption plan in the power supply and consumption electronic contract and in combination with the distributed energy output forecast and load demand forecast within the microgrid;

[0011] The APP solves the microgrid power optimization model to obtain the optimal power consumption plan;

[0012] The APP programs the terms in the electricity supply and use electronic contract and the optimal electricity use plan into smart contract codes that can be automatically executed on the blockchain;

[0013] The APP obtains the parameters required for the execution of the smart contract code, and sends the parameters to the smart contract code, so that the smart contract code automatically executes the code logic according to the parameters;

[0014] The APP receives the execution result of the smart contract code and processes the execution result, including displaying the execution result or notifying the user according to the execution result.

[0015] Preferably, the APP interacts with the user and generates an electronic electricity supply and use contract based on the user's input information, including:

[0016] Receive user instructions, start and enter the power supply and use contract signing module;

[0017] Prompt the user to enter query conditions, the query conditions including power supply unit, work order number, user number and user name;

[0018] According to the query conditions entered by the user, a request is sent to the server to perform the query operation;

[0019] Receive the query result from the server and display the work order number, user name, user number and electricity address to the user;

[0020] Receive the user's selection and confirm the contract information selected by the user;

[0021] Provide a customer signature button or signature box on the interface for users to sign electronically;

[0022] Capture the user's signature and automatically attach the signature information to the electricity user's signature position in the contract;

[0023] Obtain the electricity user, certificate type and electricity address, and fill them in the contract form;

[0024] Apply electronic signature to the service provider's seal location in the contract form;

[0025] Provide contract preview function to allow users to check the accuracy of contract content;

[0026] Receive the user's submission instructions and confirm the user's approval of the contract content;

[0027] Send the final confirmed contract information to the server to complete the contract signing process;

[0028] The signed contract will be electronically archived and a notification of successful signing will be sent to the user.

[0029] Preferably, the APP establishes a microgrid power consumption optimization model based on the power consumption plan in the power supply and consumption electronic contract and in combination with the distributed energy output forecast and load demand forecast in the microgrid, including:

[0030] Obtaining historical output data of distributed energy and weather data within a preset future time, and predicting the output capacity of distributed energy in the microgrid within a preset future time based on the historical output data of distributed energy and weather data within a preset future time;

[0031] Obtain historical load data of users, and predict user load demand within a preset time in the future based on the historical load data;

[0032] Obtain the output plan of distributed energy and the power distribution in each time period as decision variables;

[0033] The microgrid power optimization model is constructed according to the predicted distributed energy output capacity and user load demand and the decision variables, the optimization goal is to comprehensively consider minimizing the total daily electricity bill, minimizing user inappropriateness and balancing the peak and valley of power consumption, and the constraints are power supply and demand balance, distributed energy output limit and load demand limit;

[0034] Preferably, the total daily electricity bill is the sum of the products of the electricity price and the electricity allocation in each time period; the user imbalance is a quantitative indicator calculated from load fluctuations or power outages; and the peak-valley balance of electricity consumption is the difference in electricity allocation during peak and valley periods.

[0035] Preferably, the power supply and demand balance is that at any given time point, the total power supply in the microgrid is equal to the total power demand; the distributed energy output limit is that the actual output of the distributed energy is less than the upper limit of the predicted output of the distributed energy; and the load demand limit is the upper limit of the user's load demand.

[0036] The present invention also provides a microgrid power dispatching system, which is applied to a mobile operation APP and includes:

[0037] The contract signing module is used to interact with the user and generate an electronic power supply and use contract based on the user's input information;

[0038] An optimization model building module is used to build a microgrid power optimization model based on the power consumption plan in the power supply and consumption electronic contract and in combination with the distributed energy output forecast and load demand forecast in the microgrid;

[0039] A model optimization solution module is used to solve the microgrid power optimization model to obtain an optimal power consumption plan;

[0040] A smart contract generation module, used to program the terms in the power supply and use electronic contract and the optimal power use plan into a smart contract code that can be automatically executed on the blockchain;

[0041] A parameter acquisition module, used to acquire the parameters required for the execution of the smart contract code, and send the parameters to the smart contract code, so that the smart contract code automatically executes the code logic according to the parameters;

[0042] A feedback receiving module is used to receive the execution result of the smart contract code and process the execution result, including displaying the execution result or notifying the user according to the execution result.

[0043] Preferably, the contract signing module includes:

[0044] A starting unit, used to receive user instructions, start and enter the power supply and use contract signing module;

[0045] A query condition prompting unit, used to prompt the user to input query conditions, wherein the query conditions include the power supply unit, work order number, user number and user name;

[0046] A query request unit, used to send a request to the server and perform a query operation according to the query conditions input by the user;

[0047] A query result receiving unit, used to receive the query result of the server and display the work order number, user name, user number and electricity address to the user;

[0048] A contract information selection unit, used to receive a user's selection and confirm the contract information selected by the user;

[0049] The signature guidance unit is used to provide a customer signature button or signature box on the APP interface for users to sign electronically;

[0050] A signature capture unit is used to capture the user's signature and automatically attach the signature information to the electricity user's signature position in the contract;

[0051] The contract filling unit is used to obtain the electricity user, certificate type and electricity address, and fill them into the contract form;

[0052] The contract stamping unit is used to apply an electronic signature to the service provider's stamping location in the contract form;

[0053] The contract preview unit is used to provide a contract preview function to allow users to check the accuracy of the contract content;

[0054] A confirmation unit, used to receive the user's submission instruction and confirm the user's approval of the contract content;

[0055] The contract submission unit is used to send the final confirmed contract information to the server to complete the contract signing process;

[0056] The contract archiving unit is used to electronically archive the signed contract and send a notification of successful signing to the user.

[0057] Preferably, the optimization model building module is used to:

[0058] Obtaining historical output data of distributed energy and weather data within a preset future time, and predicting the output capacity of distributed energy in the microgrid within a preset future time based on the historical output data of distributed energy and weather data within a preset future time;

[0059] Obtain historical load data of users, and predict user load demand within a preset time in the future based on the historical load data;

[0060] Obtain the output plan of distributed energy and the power distribution in each time period as decision variables;

[0061] The microgrid power optimization model is constructed according to the predicted distributed energy output capacity and user load demand and the decision variables, the optimization goal is to comprehensively consider minimizing the total daily electricity bill, minimizing user inappropriateness and balancing the peak and valley of power consumption, and the constraints are power supply and demand balance, distributed energy output limit and load demand limit;

[0062] Preferably, the daily total electricity fee is the sum of the products of the electricity price and the electricity distribution in each time period; the user unsuitability is a quantitative indicator calculated by load fluctuation or power outage times; the peak-valley balance of power consumption is the difference in electricity distribution during peak and valley periods;

[0063] The power supply and demand balance means that at any given time point, the total power supply in the microgrid is equal to the total power demand; the distributed energy output limit means that the actual output of distributed energy is less than the upper limit of the predicted output of distributed energy; the load demand limit means the upper limit of the user's load demand.

[0064] The present invention also provides a computer program product, comprising computer program instructions, wherein the computer program instructions instruct a computer device to execute operations corresponding to the above method.

[0065] The present invention has the following beneficial effects:

[0066] By combining distributed energy output forecasts and load demand forecasts, an electricity optimization model is established to achieve more accurate electricity scheduling; by solving the electricity optimization model, the optimal electricity plan is obtained, the operating cost of the microgrid is reduced, and energy utilization efficiency is improved; using mobile operation APP and blockchain technology, the intelligent management of electronic power supply and consumption contracts is realized, and the automation and transparency of microgrid electricity scheduling is improved; through the interaction between APP and users, user needs are responded to in a timely manner, the enthusiasm of users to participate in power system scheduling is increased, and demand-side management is promoted; through the automatic execution of smart contract code, the effective implementation of electricity scheduling strategies is ensured, human operational errors are reduced, and the stable operation of the microgrid system is guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0068] Figure 1 It is a framework structure diagram of a microgrid power dispatching method in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The detailed description of the accompanying drawings is intended as an illustration of the current embodiments of the present invention and is not intended to represent the only form in which the present invention can be implemented. It should be understood that the same or equivalent functions can be accomplished by different embodiments that are intended to be included in the spirit and scope of the present invention.

[0070] See also Figure 1 The embodiment of the present invention provides a microgrid power dispatching method, which is implemented based on a mobile operation APP and includes:

[0071] Step S10, the APP interacts with the user and generates an electronic power supply and use contract based on the user's input information;

[0072] Step S20, the APP establishes a microgrid power optimization model based on the power consumption plan in the power supply and consumption electronic contract and in combination with the distributed energy output forecast and load demand forecast in the microgrid;

[0073] Step S30, the APP solves the microgrid power optimization model to obtain an optimal power consumption plan;

[0074] Specifically, in step S30, a genetic algorithm or a particle swarm algorithm is used to solve the microgrid power optimization model to obtain an optimal power consumption plan. The following is a description of these two algorithms:

[0075] Genetic Algorithm (GA) is a search heuristic algorithm that simulates the principles of natural selection and genetics. In microgrid power dispatching, genetic algorithms can be used to find the optimal power distribution solution. The algorithm generates a series of solutions (called "individuals" or "chromosomes") by simulating the inheritance and mutation mechanisms in the biological evolution process, and gradually improves these solutions through selection, crossover (hybridization) and mutation operations until the optimal or near-optimal solution is found.

[0076] Particle Swarm Optimization (PSO) is an optimization technology based on swarm intelligence that simulates the social behavior of bird flocks or fish schools. In microgrid power dispatching, PSO can be used to optimize power distribution to minimize costs or maximize efficiency. In the algorithm, each "particle" represents a candidate solution in the problem space. The particle adjusts its flight trajectory by tracking its own historical best position and the best position of the entire group to find the optimal solution.

[0077] Specifically in step S30, the solution process is as follows:

[0078] APP initializes a group of candidate solutions (chromosome groups in genetic algorithms and particle groups in particle swarm algorithms);

[0079] Through an iterative process, these candidate solutions are evaluated based on the optimization objectives (e.g., minimizing the total electricity cost, minimizing user inappropriateness, balancing the peaks and valleys of power consumption) and constraints (e.g., balancing power supply and demand, output limitation, and load demand limitation);

[0080] In each iteration, the algorithm selects, crosses, mutates (genetic algorithm), or updates the velocity and position of particles (particle swarm algorithm) based on the performance (fitness) of the candidate solutions;

[0081] This process is repeated until the termination condition is met (such as reaching a predetermined number of iterations or the quality of the solution reaches a certain threshold);

[0082] Get the best electricity usage plan;

[0083] Ultimately, the algorithm outputs the optimal or nearly optimal power consumption plan, which will guide the microgrid on how to allocate power resources in different time periods to achieve the goals of lowest cost, highest efficiency, and maximum user satisfaction.

[0084] Step S40, the APP programs the terms in the electricity supply and use electronic contract and the optimal electricity use plan into a smart contract code that can be automatically executed on the blockchain;

[0085] Specifically, an electronic electricity supply and use contract refers to a contract signed electronically that stipulates the rights and obligations of the power supplier and the electricity user. It contains key information such as electricity use plans, price terms, service quality requirements, and liability for breach of contract.

[0086] The optimal power consumption plan is the result obtained by solving the microgrid power consumption optimization model. It details the optimal power distribution method for each distributed energy unit and load in the microgrid in different time periods.

[0087] A smart contract is a self-executing contract whose terms are written in code and stored on the blockchain. The execution of a smart contract does not require the involvement of an intermediary. Once the preset conditions are met, the contract will be automatically executed.

[0088] In step S40, the APP converts the terms and optimal electricity consumption plan in the electricity supply and consumption electronic contract into smart contract code, which means that each term in the contract and each decision point in the electricity consumption plan are translated into a programming language that the blockchain can understand (such as Solidity language for Ethereum blockchain). Once the smart contract code is deployed on the blockchain, it becomes tamper-proof and can be automatically executed without the intervention of a third party.

[0089] For example, smart contracts can automatically send start or stop signals to distributed energy units at specific times based on electricity usage plans, or adjust electricity purchasing strategies based on real-time prices in the electricity market.

[0090] The specific steps are as follows:

[0091] Define the contract structure: The APP first defines the structure of the smart contract, including variables (such as electricity price, allocation amount, timestamp, etc.) and functions (such as executing electricity allocation, recording transactions, etc.);

[0092] Write logic code: Then, the APP writes the logic of the electronic contract terms and the optimal electricity consumption plan into code, including condition judgment, operation execution, etc.

[0093] Deploy the contract: After the smart contract code is written, the APP deploys it to the blockchain network;

[0094] Trigger execution: After the smart contract is deployed, it can be triggered by external events (such as reaching a specific point in time, receiving a specific signal, etc.) to automatically execute the code in the contract.

[0095] In this way, the microgrid electricity dispatch process is not only automated, but also increases transparency and security, because all transactions on the blockchain are public and cannot be tampered with.

[0096] Step S50, the APP obtains the parameters required for the execution of the smart contract code, and sends the parameters to the smart contract code, so that the smart contract code automatically executes the code logic according to the parameters;

[0097] Specifically, the smart contract code may require some external data as input parameters when executing. These parameters may include but are not limited to the user's electricity demand, real-time output data of distributed energy, market electricity prices, timestamps, etc. The APP needs to obtain these parameters from external data sources (such as sensors, databases, API interfaces, etc.), which can be the monitoring system in the microgrid, the data service of the power market, or the information directly entered by the user through the APP. Once the APP obtains all the necessary parameters, it needs to pass these parameters to the smart contract on the blockchain. This can be achieved by sending a transaction, which contains the function of calling the smart contract and the required parameters. In blockchain technology, a transaction is a data packet containing instructions and data, which is sent to nodes in the network and verified and executed by validators in the network. When the smart contract receives the transaction, it will automatically execute according to the parameters contained in the transaction and the pre-defined logic in the contract.

[0098] For example, if the function of a smart contract is to adjust power distribution, the parameters passed in may include power demand during a specific time period and the available output of distributed energy resources. The smart contract will execute the corresponding logic based on these parameters, such as adjusting the power distribution plan.

[0099] The specific steps are as follows:

[0100] Parameter collection: The APP collects the parameters required for smart contract execution from different data sources through built-in interfaces or external APIs;

[0101] Construct a transaction: The app creates a transaction, encodes the collected parameters into the data part of the transaction, and specifies the smart contract function to be called;

[0102] Send transaction: The APP sends the constructed transaction to the blockchain network; this usually requires the use of a digital signature to prove the identity of the transaction initiator;

[0103] Execution logic: Nodes in the blockchain network verify transactions and execute smart contract code; smart contracts execute predetermined logic based on the passed parameters, such as updating status, recording events, or triggering other contracts.

[0104] Through this process, smart contracts can automatically execute microgrid power dispatch strategies based on real-time data without human intervention, ensuring the operating efficiency and response speed of the microgrid.

[0105] Step S60, the APP receives the execution result of the smart contract code and processes the execution result, including displaying the execution result or notifying the user according to the execution result;

[0106] Specifically, when the smart contract code is executed on the blockchain, it will generate an execution result. This result may be the status of the contract execution (success or failure), the data generated after the contract execution (such as the adjusted power distribution plan, transaction records, etc.), or any event triggered during the contract execution. The APP receives these execution results by monitoring the blockchain network or directly querying the status of the smart contract. Once the APP receives the execution results, it needs to process them.

[0107] The following methods may be used to deal with this problem:

[0108] Parsing results: The APP parses the data returned by the smart contract into a readable format for users or other systems to understand;

[0109] Verify results: The APP verifies whether the execution results meet expectations, such as checking whether the power distribution plan meets the optimization objectives and constraints;

[0110] Logging: The APP may record execution results and related data in a local or centralized logging system to facilitate future auditing and troubleshooting;

[0111] Display execution results: The APP displays the processed execution results to the user; this may be achieved through the user interface (UI) of the mobile APP, such as displaying the new power distribution plan, electricity fee calculation, energy usage, etc.; the display method can be in the form of charts, lists, notifications, etc., in order to enable users to intuitively understand the latest status of microgrid power dispatch;

[0112] Notify users: Depending on the execution results, the APP may send notifications to users; these notifications may be about changes in power distribution, electricity fee adjustments, service status updates, etc. Notifications can be sent via push messages, emails, text messages, etc. to ensure that users can obtain important information in a timely manner.

[0113] Through step S60, the APP ensures that users can keep abreast of the latest developments in microgrid power dispatching and can make corresponding adjustments or decisions based on this information, which improves the interactivity of the system and user participation, while also enhancing users' trust in the transparency of microgrid system operation.

[0114] Furthermore, in some embodiments, the step S10 includes:

[0115] (1) Receive user instructions, start and enter the power supply and use contract signing module;

[0116] (2) prompting the user to enter query conditions, wherein the query conditions include the power supply unit, work order number, user number and user name;

[0117] (3) Send a request to the server to perform a query operation based on the query conditions entered by the user;

[0118] (4) receiving the query result from the server and displaying the work order number, user name, user number and electricity address to the user;

[0119] (5) Receive the user's selection and confirm the contract information selected by the user;

[0120] (6) Provide a customer signature button or signature box on the interface for users to electronically sign;

[0121] (7) Capture the user's signature and automatically attach the signature information to the electricity user's signature position in the contract;

[0122] (8) Obtain the electricity user, certificate type and electricity address, and fill them into the contract form;

[0123] (9) Apply electronic signature to the service provider's seal location on the contract form;

[0124] (10) Provide a contract preview function to allow users to check the accuracy of the contract content;

[0125] (11) Receive the user's submission instruction and confirm the user's approval of the contract content;

[0126] (12) Send the final confirmed contract information to the server to complete the contract signing process;

[0127] The signed contract will be electronically archived and a notification of successful signing will be sent to the user.

[0128] In summary, this embodiment realizes a convenient and efficient electronic power supply contract signing process through the APP, reduces the use of paper documents, and improves the efficiency and quality of contract management.

[0129] Furthermore, in some embodiments, the step S20 includes:

[0130] Step S201, obtaining historical output data of distributed energy and weather data within a preset future time, and predicting the output capacity of distributed energy in the microgrid within a preset future time based on the historical output data of distributed energy and weather data within a preset future time;

[0131] Specifically, the historical output data of distributed energy includes the actual power generation records of various renewable energy sources (such as solar photovoltaic panels, wind turbines, etc.) and possible other distributed energy sources (such as fuel cells, micro turbines, etc.) over a period of time. These data usually exist in the form of time series, recording the output at each time interval (such as every hour or every day).

[0132] Weather data for a preset future time refers to the weather forecast for a period of time in the future (such as a day, a week or a month), which may include temperature, humidity, wind speed, light intensity, etc. These data are crucial for predicting the output of renewable energy because they directly affect the generation of energy. For example, the intensity of sunlight determines the efficiency of photovoltaic power generation, and the wind speed determines the output of wind power generation.

[0133] Based on the above data, statistical methods or machine learning models are used to predict the output capacity of distributed energy within a preset time in the future. Statistical methods may include time series analysis, regression analysis, etc., while machine learning models may include random forests, support vector machines, neural networks, etc.

[0134] The prediction process is as follows:

[0135] Data preprocessing: cleaning and normalizing data, handling missing values ​​and outliers;

[0136] Feature engineering: Select and construct features that are helpful for predicting output, such as the time window of historical output data, weather-related features, etc.

[0137] Model training: Use historical data to train the forecasting model and adjust the model parameters to minimize the forecast error;

[0138] Prediction: Use the trained model to predict the output in future time periods.

[0139] In this way, microgrid operators can obtain estimates of the output capacity of distributed energy resources in the future, which is crucial for formulating effective power dispatch strategies and optimizing microgrid operations.

[0140] Step S202, obtaining historical load data of the user, and predicting the user load demand within a preset time in the future according to the historical load data;

[0141] Specifically, the user's historical load data refers to the record of the user's electricity consumption in the past period of time. These data usually exist in the form of time series, recording the electricity consumption at each time interval (such as hourly, daily or monthly). The data source may include records from smart meters, user electricity consumption record systems or other energy management systems.

[0142] Before making a forecast, historical load data needs to be preprocessed, including cleaning data (removing erroneous records), filling missing values, normalizing data, etc., to ensure data quality and consistency.

[0143] Extract useful features from historical load data, which may include timestamps, seasonal factors, calendar events (such as holidays), weather conditions (such as the impact of temperature on air conditioning load), etc. The goal of feature engineering is to identify and construct key variables that can explain load changes.

[0144] Use statistical methods or machine learning models to predict user load demand within a preset time in the future. Statistical methods may include time series analysis (such as ARIMA model), exponential smoothing, etc. Machine learning models may include regression analysis (such as linear regression, ridge regression), decision trees, random forests, gradient boosting machines, etc.

[0145] The prediction process is as follows:

[0146] Model selection: Select an appropriate model based on data characteristics and prediction objectives;

[0147] Model training: Use historical load data to train the model and evaluate the performance of the model through cross-validation and other techniques;

[0148] Model optimization: adjusting model parameters to improve prediction accuracy;

[0149] Forecasting: Apply the trained model to predict future load demand.

[0150] Through this step, microgrid operators can obtain estimates of user load demand in the future, which is crucial for power dispatching and resource allocation. Accurate load forecasting can help microgrids manage energy supply and demand more effectively, optimize energy use, reduce operating costs, and improve system reliability and economy.

[0151] Step S203, obtaining the output plan of distributed energy and the power distribution in each time period as decision variables;

[0152] Specifically, distributed energy resources (DER) include solar photovoltaic panels, wind turbines, fuel cells, micro hydropower stations, etc., which can generate electricity in microgrids. Output plan refers to the distributed energy resources that need to be output. The output plan can be obtained directly from the distributed energy management system (DERMS) or energy management system (EMS) to obtain real-time data. Power distribution refers to how to allocate power resources in the microgrid in different time periods to meet load demand and optimize system operation. Decision variables refer to variables that need to be determined when performing power dispatch, which will affect the dispatch results.

[0153] Based on the obtained output plan and power distribution requirements as decision variables, a mathematical model or optimization problem is established to determine the best power distribution strategy. The model is a multi-objective optimization problem that aims to minimize costs, maximize benefits, and ensure the reliability of power supply.

[0154] Through step S203, the microgrid power dispatching system can comprehensively consider the output of distributed energy and user load requirements, and formulate a reasonable power distribution strategy, thereby improving the operating efficiency of the microgrid, reducing operating costs, and ensuring the stability and reliability of power supply.

[0155] Step S204, constructing the microgrid power optimization model according to the predicted distributed energy output capacity and user load demand and the decision variables, wherein the optimization goal is to comprehensively consider minimizing the total daily electricity bill, minimizing user inappropriateness and balancing power peaks and valleys, and the constraints are power supply and demand balance, distributed energy output limit and load demand limit.

[0156] Specifically, step S204 is the core part of constructing the optimization model in the microgrid power dispatching method, which defines the optimization target and the constraints that need to be met.

[0157] Minimize the total daily electricity bill: This goal aims to reduce the electricity operating cost of the microgrid, which involves the electricity purchase cost, the operation and maintenance cost of distributed energy, and possible electricity sales revenue. The optimization model will consider the changes in electricity prices in different time periods (such as peak electricity prices and valley electricity prices), as well as the generation cost of distributed energy, to find the lowest cost electricity distribution plan.

[0158] Minimize user discomfort: User discomfort refers to the degree of reduction in user comfort due to power dispatch, which may include the impact of power supply interruption, voltage fluctuation, frequency change, etc. on users. The optimization model will minimize the occurrence of these situations, or at least control them within the acceptable range for users.

[0159] Balancing power peaks and valleys: This goal is to reduce the peak-to-valley difference of the power system, that is, the difference in power demand during peak hours and off-peak hours. Balancing peak-to-valley power consumption can reduce the operating pressure of the power system, improve the stability and economy of the power grid, and also help reduce electricity costs.

[0160] Constraints:

[0161] Power supply and demand balance: At any given point in time, the total power supply in the microgrid must be equal to the total power demand. This constraint ensures the stable operation of the power system and avoids power shortages or surpluses.

[0162] Distributed energy output limit: The actual output of distributed energy cannot exceed the upper limit of its predicted output. This constraint takes into account the technical limitations and actual operating conditions of distributed energy.

[0163] Load demand limit: The user's load demand cannot exceed the preset upper limit. This constraint ensures that the user's demand does not exceed the power supply capacity that the microgrid can provide.

[0164] The optimization model is usually a mathematical model that expresses the optimization objective as a mathematical expression and converts the constraints into mathematical inequalities or equations. The model can be solved using linear programming, nonlinear programming, integer programming, or mixed integer programming. By solving this optimization model, an optimal power distribution plan can be obtained, which achieves the optimization objective as much as possible while satisfying all constraints.

[0165] The optimization model can be expressed as the following function:

[0166] Z=min(C+U+ΔP)

[0167] Among them, C is the normalized value that minimizes the total daily electricity bill, U is the normalized value that minimizes user inappropriateness, and ΔP is the normalized value that balances the peak and valley of electricity consumption.

[0168] The relationship between C, U and ΔP and the decision variables (output plan P_{der,t} and power distribution P_{grid,t}) is explained as follows:

[0169] (1) C (minimize the normalized value of the total daily electricity cost):

[0170] C represents the total electricity cost incurred by the microgrid due to power consumption in one day.

[0171] Its relationship with the output plan P_{der,t} is that when the generation of distributed energy increases, the electricity purchased from the grid P_{grid,t} will decrease, thereby reducing the electricity bill.

[0172] The electricity cost C will increase with the increase of grid power purchase P_{grid,t}, because grid power usually needs to be paid for, while the generation cost of distributed energy may be lower.

[0173] The normalized value C is obtained by comparing the actual electricity cost with a preset maximum possible electricity cost C_{max}, which ensures that the various objectives in the optimization problem are comparable.

[0174] (2) U (minimize the normalized value of user discomfort):

[0175] U represents the degree of reduced user satisfaction caused by insufficient or unstable power supply.

[0176] Its relationship with the output plan P_{der,t} and power distribution P_{grid,t} is that if the distributed energy generation is insufficient or the grid power distribution is unreasonable, the user's power demand may not be met, thereby increasing user discomfort.

[0177] User mismatch U will increase as the gap between electricity supply and demand increases.

[0178] The normalized value U is obtained by comparing the actual user imbalance with a preset maximum possible user imbalance U_{max}.

[0179] (3) ΔP (normalized value of peak and valley balance power):

[0180] ΔP represents the imbalance between electricity supply during peak hours and off-peak hours.

[0181] Its relationship with the output plan P_{der,t} and power distribution P_{grid,t} is that if the distributed energy generates insufficient power during peak hours or generates excess power during valley hours, the peak-valley difference will increase.

[0182] The goal of balancing the peaks and valleys in electricity consumption is to reduce this imbalance to reduce the pressure on the grid and improve the stability of the power system.

[0183] The normalized value ΔP is obtained by comparing the actual peak-to-valley difference with a preset maximum possible peak-to-valley difference \Delta P_{max}.

[0184] In general, the goal of the optimization model is to minimize the sum of these three normalized values ​​in order to find the best output plan P_{der,t} and power distribution P_{grid,t}, so that the operation of the microgrid is both economical and reliable while meeting the power needs of users.

[0185] The result of step S204 is an optimized power dispatching strategy, which guides the microgrid on how to allocate power resources in different time periods to achieve a balance between cost efficiency, user satisfaction and system reliability.

[0186] Furthermore, in some embodiments, the total daily electricity bill is the sum of the products of the electricity price and the electricity allocation in each time period; the user unsuitability is a quantitative indicator calculated by load fluctuation or power outage times; and the peak-valley balance of electricity consumption is the difference in electricity allocation between peak and valley periods.

[0187] Furthermore, in some embodiments, the power supply and demand balance is that at any given time point, the total power supply in the microgrid is equal to the total power demand; the distributed energy output limit is that the actual output of the distributed energy is less than the upper limit of the predicted output of the distributed energy; and the load demand limit is the upper limit of the user's load demand.

[0188] Another embodiment of the present invention further provides a microgrid power dispatching system, which is applied to a mobile operation APP and includes:

[0189] The contract signing module is used to interact with the user and generate an electronic power supply and use contract based on the user's input information;

[0190] An optimization model building module is used to build a microgrid power optimization model based on the power consumption plan in the power supply and consumption electronic contract and in combination with the distributed energy output forecast and load demand forecast in the microgrid;

[0191] A model optimization solution module is used to solve the microgrid power optimization model to obtain an optimal power consumption plan;

[0192] A smart contract generation module, used to program the terms in the power supply and use electronic contract and the optimal power use plan into a smart contract code that can be automatically executed on the blockchain;

[0193] A parameter acquisition module, used to acquire the parameters required for the execution of the smart contract code, and send the parameters to the smart contract code, so that the smart contract code automatically executes the code logic according to the parameters;

[0194] A feedback receiving module is used to receive the execution result of the smart contract code and process the execution result, including displaying the execution result or notifying the user according to the execution result.

[0195] Furthermore, in some embodiments, the contract signing module includes:

[0196] A starting unit, used to receive user instructions, start and enter the power supply and use contract signing module;

[0197] A query condition prompting unit, used to prompt the user to input query conditions, wherein the query conditions include the power supply unit, work order number, user number and user name;

[0198] A query request unit, used to send a request to the server and perform a query operation according to the query conditions input by the user;

[0199] A query result receiving unit, used to receive the query result of the server and display the work order number, user name, user number and electricity address to the user;

[0200] A contract information selection unit, used to receive a user's selection and confirm the contract information selected by the user;

[0201] The signature guidance unit is used to provide a customer signature button or signature box on the APP interface for users to sign electronically;

[0202] A signature capture unit is used to capture the user's signature and automatically attach the signature information to the electricity user's signature position in the contract;

[0203] The contract filling unit is used to obtain the electricity user, certificate type and electricity address, and fill them into the contract form;

[0204] The contract stamping unit is used to apply an electronic signature to the service provider's stamping location in the contract form;

[0205] The contract preview unit is used to provide a contract preview function to allow users to check the accuracy of the contract content;

[0206] A confirmation unit, used to receive the user's submission instruction and confirm the user's approval of the contract content;

[0207] The contract submission unit is used to send the final confirmed contract information to the server to complete the contract signing process;

[0208] The contract archiving unit is used to electronically archive the signed contract and send a notification of successful signing to the user.

[0209] Furthermore, in some embodiments, the optimization model building module is used to:

[0210] Obtaining historical output data of distributed energy and weather data within a preset future time, and predicting the output capacity of distributed energy in the microgrid within a preset future time based on the historical output data of distributed energy and weather data within a preset future time;

[0211] Obtain historical load data of users, and predict user load demand within a preset time in the future based on the historical load data;

[0212] Obtain the output plan of distributed energy and the power distribution in each time period as decision variables;

[0213] The microgrid power optimization model is constructed according to the predicted distributed energy output capacity and user load demand and the decision variables, the optimization goal is to comprehensively consider minimizing the total daily electricity bill, minimizing user inappropriateness and balancing the peak and valley of power consumption, and the constraints are power supply and demand balance, distributed energy output limit and load demand limit;

[0214] Further, in some embodiments, the total daily electricity fee is the sum of the product of the electricity price and the electricity distribution amount in each time period; the user unsuitability is a quantitative indicator calculated by load fluctuation or power outage times; the peak-valley balance of power consumption is the difference in electricity distribution amount in peak-valley time periods;

[0215] The power supply and demand balance means that at any given time point, the total power supply in the microgrid is equal to the total power demand; the distributed energy output limit means that the actual output of distributed energy is less than the upper limit of the predicted output of distributed energy; the load demand limit means the upper limit of the user's load demand.

[0216] The present invention also provides a computer program product, comprising computer program instructions, wherein the computer program instructions instruct a computer device to execute operations corresponding to the above method.

[0217] Specifically, the computer program product includes a series of computer program instructions, which are codes written in a computer program. They define how to perform specific operations. These program instructions are designed to be loaded onto a computer device and instruct the device to perform specific operations. In this embodiment, these instructions are used to execute the method of the above embodiment. In this way, the computer program product provides a complete software solution that can run on various computer devices to implement the method of the above embodiment.

[0218] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A microgrid power dispatching method, characterized in that: The method is implemented based on a mobile operation APP and includes: The APP interacts with the user and generates an electronic power supply and use contract based on the user's input information; The APP establishes a microgrid power optimization model based on the power consumption plan in the power supply and consumption electronic contract and in combination with the distributed energy output forecast and load demand forecast within the microgrid; The APP solves the microgrid power optimization model to obtain the optimal power consumption plan; The APP programs the terms in the electricity supply and use electronic contract and the optimal electricity use plan into smart contract codes that can be automatically executed on the blockchain; The APP obtains the parameters required for the execution of the smart contract code, and sends the parameters to the smart contract code, so that the smart contract code automatically executes the code logic according to the parameters; The APP receives the execution result of the smart contract code and processes the execution result, including displaying the execution result or notifying the user according to the execution result.

2. The method according to claim 1, characterized in that The APP interacts with the user and generates an electronic power supply and use contract based on the user's input information, including: Receive user instructions, start and enter the power supply and use contract signing module; Prompt the user to enter query conditions, the query conditions including power supply unit, work order number, user number and user name; According to the query conditions entered by the user, a request is sent to the server to perform the query operation; Receive the query result from the server and display the work order number, user name, user number and electricity address to the user; Receive the user's selection and confirm the contract information selected by the user; Provide a customer signature button or signature box on the interface for users to sign electronically; Capture the user's signature and automatically attach the signature information to the electricity user's signature position in the contract; Obtain the electricity user, certificate type and electricity address, and fill them in the contract form; Apply electronic signature to the service provider's seal location in the contract form; Provide contract preview function to allow users to check the accuracy of contract content; Receive the user's submission instructions and confirm the user's approval of the contract content; Send the final confirmed contract information to the server to complete the contract signing process; The signed contract will be electronically archived and a notification of successful signing will be sent to the user.

3. The method according to claim 1, characterized in that The APP establishes a microgrid power optimization model based on the power consumption plan in the power supply and consumption electronic contract and in combination with the distributed energy output forecast and load demand forecast in the microgrid, including: Obtaining historical output data of distributed energy and weather data within a preset future time, and predicting the output capacity of distributed energy in the microgrid within a preset future time based on the historical output data of distributed energy and weather data within a preset future time; Obtain historical load data of users, and predict user load demand within a preset time in the future based on the historical load data; Obtain the output plan of distributed energy and the power distribution in each time period as decision variables; The microgrid power optimization model is constructed based on the predicted distributed energy output capacity and user load demand as well as the decision variables. The optimization goal is to comprehensively consider minimizing the total daily electricity bill, minimizing user inappropriateness and balancing power peaks and valleys. The constraints are power supply and demand balance, distributed energy output limit and load demand limit.

4. The method according to claim 3, characterized in that The total daily electricity bill is the sum of the products of the electricity price and the electricity allocation in each time period; the user imbalance is a quantitative indicator calculated by load fluctuation or power outage times; and the peak-valley balance of electricity consumption is the difference in electricity allocation during peak and valley periods.

5. The method according to claim 3, characterized in that: The power supply and demand balance means that at any given time point, the total power supply in the microgrid is equal to the total power demand; the distributed energy output limit means that the actual output of distributed energy is less than the upper limit of the predicted output of distributed energy; the load demand limit means the upper limit of the user's load demand.

6. A microgrid power dispatching system, characterized in that: The system is applied to a mobile operation APP, including: The contract signing module is used to interact with the user and generate an electronic power supply and use contract based on the user's input information; An optimization model building module is used to build a microgrid power optimization model based on the power consumption plan in the power supply and consumption electronic contract and in combination with the distributed energy output forecast and load demand forecast in the microgrid; A model optimization solution module is used to solve the microgrid power optimization model to obtain an optimal power consumption plan; A smart contract generation module, used to program the terms in the power supply and use electronic contract and the optimal power use plan into a smart contract code that can be automatically executed on the blockchain; A parameter acquisition module, used to acquire the parameters required for the execution of the smart contract code, and send the parameters to the smart contract code, so that the smart contract code automatically executes the code logic according to the parameters; A feedback receiving module is used to receive the execution result of the smart contract code and process the execution result, including displaying the execution result or notifying the user according to the execution result.

7. The system according to claim 6, characterized in that The contract signing module includes: A starting unit, used to receive user instructions, start and enter the power supply and use contract signing module; A query condition prompting unit, used to prompt the user to input query conditions, wherein the query conditions include the power supply unit, work order number, user number and user name; A query request unit, used to send a request to the server and perform a query operation according to the query conditions input by the user; A query result receiving unit, used to receive the query result of the server and display the work order number, user name, user number and electricity address to the user; A contract information selection unit, used to receive a user's selection and confirm the contract information selected by the user; The signature guidance unit is used to provide a customer signature button or signature box on the APP interface for users to sign electronically; A signature capture unit is used to capture the user's signature and automatically attach the signature information to the electricity user's signature position in the contract; The contract filling unit is used to obtain the electricity user, certificate type and electricity address, and fill them into the contract form; The contract stamping unit is used to apply an electronic signature to the service provider's stamping location in the contract form; The contract preview unit is used to provide a contract preview function to allow users to check the accuracy of the contract content; A confirmation unit, used to receive the user's submission instruction and confirm the user's approval of the contract content; The contract submission unit is used to send the final confirmed contract information to the server to complete the contract signing process; The contract archiving unit is used to electronically archive the signed contract and send a notification of successful signing to the user.

8. The system according to claim 6, characterized in that The optimization model building module is used to: Obtaining historical output data of distributed energy and weather data within a preset future time, and predicting the output capacity of distributed energy in the microgrid within a preset future time based on the historical output data of distributed energy and weather data within a preset future time; Obtain historical load data of users, and predict user load demand within a preset time in the future based on the historical load data; Obtain the output plan of distributed energy and the power distribution in each time period as decision variables; The microgrid power optimization model is constructed based on the predicted distributed energy output capacity and user load demand as well as the decision variables. The optimization goal is to comprehensively consider minimizing the total daily electricity bill, minimizing user inappropriateness and balancing power peaks and valleys. The constraints are power supply and demand balance, distributed energy output limit and load demand limit.

9. The system according to claim 8, characterized in that The total daily electricity fee is the product of the electricity price and the electricity distribution amount in each period; the user unsuitability is a quantitative indicator calculated by load fluctuation or power outage times; the peak-valley balance of power consumption is the difference in electricity distribution amount during peak and valley periods; The power supply and demand balance means that at any given time point, the total power supply in the microgrid is equal to the total power demand; the distributed energy output limit means that the actual output of distributed energy is less than the upper limit of the predicted output of distributed energy; the load demand limit means the upper limit of the user's load demand.

10. A computer program product, characterized in that The method comprises computer program instructions, wherein the computer program instructions instruct a computer device to execute operations corresponding to the method according to any one of claims 1 to 5.

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