Micro-grid demand response identification and optimal scheduling method and system considering satisfaction

Abstract

According to the micro-grid demand response identification and optimal scheduling method and system considering satisfaction, the machine learning classifier based on the random forest model is utilized to construct the integrated classification identification center, the user time-of-use electricity price response behavior is quantified through the demand elastic coefficient matrix, the demand response of the micro-grid is accurately identified, and the user experience is improved. The economy of multi-microgrid cooperative scheduling is improved, and the function of balancing the user satisfaction and the economy of the microgrids by coordinating power balance and minimizing the operation cost is achieved. According to the method, the data exception problem is solved through the master-slave management framework and the machine learning classifier, the interference of data exception on user behavior feature extraction is effectively processed, the accuracy of a scheduling strategy is improved, and collaborative optimization of multiple microgrids is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of demand response identification, and particularly relates to a micro-grid demand response identification and optimal scheduling method and system considering satisfaction. BACKGROUND

[0002] In the process of deepening the development of smart grids, micro-grids (MG) as the core carrier of integrating distributed energy and user-side resources, efficient operation is of great importance to improve the flexibility and supply-demand balance capability of the power grid. However, the current micro-grid faces significant theoretical challenges in customer-side demand response (DR) management, multi-micro-grid collaborative scheduling and user experience optimization.

[0003] Firstly, micro-grid operation relies on massive user power consumption data, but factors such as communication failure, equipment error or network security threats often lead to data loss, label error or time sequence disorder. Traditional identification methods based on rule matching or single algorithm are difficult to effectively handle the interference of data anomalies on user behavior feature extraction, leading to ambiguous DR participation state judgment, and further affecting the accuracy of scheduling strategies.

[0004] Secondly, the game equilibrium problem of multi-micro-grid collaborative scheduling restricts the overall efficiency of the system. Existing collaborative mechanisms mostly adopt centralized optimization framework, taking the global stability of the distribution network as the goal, and requiring micro-grids to sacrifice individual economic benefits to maintain system balance. This asymmetric collaborative mode ignores the decision-making autonomy of micro-grids as independent market subjects, leading to insufficient endogenous motivation for their participation in collaboration, especially in renewable energy fluctuation scenarios, which easily causes conflicts of interest and power interaction imbalance among micro-grids. The lack of quantitative evaluation of user satisfaction and integration of scheduling objectives is another theoretical gap. Traditional micro-grid scheduling takes economy or reliability as a single optimization target, lacking quantitative consideration of users' subjective experience in participating in DR. The evaluation of user satisfaction has not formed a theoretical system deeply coupled with the physical model of micro-grids, leading to the inability of scheduling strategies to respond to the differentiated needs of user groups. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a micro-grid demand response identification and optimal scheduling method and system considering satisfaction, which balances user satisfaction and the economy of micro-grids by coordinating power balance and minimizing operating costs.

[0006] The technical scheme adopted by the present application to solve the above technical problem is: a micro-grid demand response identification and optimal scheduling method considering satisfaction, comprising the following steps: S1: initialization, inputting information of the micro-grid; S2: constructing master-slave management architecture and economic optimization model of the micro-grid according to the input information, respectively used for coordinating power balance and minimizing operating costs; S3: Construct an integrated classification recognition center for master-slave management architecture call by using a machine learning classifier based on a random forest model, quantify user time-of-use electricity price response behavior through a demand elasticity coefficient matrix, and identify customer-side demand response participation labels; S4: Introduce a power purchase cost evaluation model and a power supply power evaluation model through customer-side demand response participation labels, dynamically adjust the scheduling strategy based on user actual cost and power deviation, quantify satisfaction, and balance economic efficiency and power supply reliability.

[0007] According to the above scheme, in step S1, the information of the microgrid includes microgrid operation data, customer-side demand response data, machine learning input features, and interactive collaborative scheduling data.

[0008] According to the above scheme, in step S2, the master-slave management architecture of the microgrid includes a master microgrid, a slave microgrid, and an upper grid; The master microgrid is a power output party, including a distributed power source and an electric / thermal energy storage system; the master microgrid is used to generate a low-price power sales plan through optimal scheduling; The slave microgrid is a power input party, used to adjust the power purchase strategy according to the power sales plan, and realize economic collaboration of the multi-microgrid and the upper grid; The upper grid is a system-level energy hub, used to provide a peak-valley time-of-use electricity price signal, guide the power consumption behavior of the microgrid, and collaboratively schedule the power balance and stable operation of the master microgrid and the slave microgrid.

[0009] Further, in step S2, the collaborative scheduling of the master microgrid and the slave microgrid follows a closed-loop process of plan generation, cross-grid transaction, and grid collaboration, and the specific steps are: S21: The master microgrid generates a power sales plan containing multiple time periods based on the analysis of the next day's renewable energy prediction, load curve, and energy storage state, and shares it in real time to the slave microgrid through the master-slave management platform; S22: The slave microgrid matches the power sales plan of the master microgrid according to the demand response label of the customers, and preferentially purchases low-price power for adjustable loads; for customers who do not participate in demand response, the energy storage system or the upper grid is used to ensure power supply stability; S23: If the master microgrid is insufficient in power supply, the slave microgrid purchases power from the upper grid to make up for the gap, and the upper grid dynamically adjusts the electricity price according to the system load state; the master microgrid, the slave microgrid, and the upper grid regularly synchronize power data to correct prediction errors and adjust the transaction plan of subsequent time periods in real time, ensuring power balance and economic optimization among microgrids.

[0010] According to the above scheme, in the step S2, the economic optimization model is used to optimize the internal resources of the micro-grid by mathematical modeling, and the comprehensive cost of the micro-grid is minimized under the conditions of satisfying the power balance constraint, the energy storage state of charge constraint and the device operation constraint.

[0011] Further, in the step S2, the comprehensive cost is the sum of the total fuel cost, the power interaction cost, the system maintenance cost and the controllable unit start-stop cost, minus the thermal energy sale income. The total fuel cost includes the gas turbine operation cost and the fuel cell operation cost; the gas turbine operation cost is the unit cost of natural gas divided by the unit low price, multiplied by the total heat consumed by the gas turbine; the fuel cell operation cost is the unit cost of natural gas divided by the unit low price, multiplied by the total heat consumed by the fuel cell; The power interaction cost is the average of the purchase price and the sale price multiplied by the interaction power between the micro-grid and the upper grid, and half of the difference between the purchase price and the sale price multiplied by the absolute value of the interaction power between the micro-grid and the upper grid, and then summed up; The system maintenance cost is the product of the maintenance cost of each customer of each unit in the micro-grid and the power, and then summed up; The controllable unit start-stop cost is the sum of the start cost when the adjacent two start-stop states of the controllable unit are inconsistent; The thermal energy sale income is the product of the unit price of thermal energy sales and the thermal load power.

[0012] Further, in the step S2, The power balance constraint includes: The sum of the power of each customer of each unit in the micro-grid minus the charging power of the energy storage system, plus the discharging power of the energy storage system, the interaction power between the micro-grid and the upper grid, is equal to the sum of the load power of the micro-grid and the electric power of the electric boiler; The heat generated by the gas turbine plus the heat generated by the electric boiler minus the absorption power of the thermal energy storage system plus the release power of the thermal energy storage system is equal to the thermal load power; The heat generated by the gas turbine is equal to the part of the power consumption of the gas turbine other than power generation and heat loss, divided by the power generation efficiency; The energy storage state of charge constraint includes: The product of the maximum charging rate and the capacity of the energy storage system is used as the lower limit of the power range of the energy storage system, and the product of the maximum discharging rate and the capacity of the energy storage system is used as the upper limit of the power range of the energy storage system; the product of the maximum charging rate and the capacity of the thermal energy storage system is used as the lower limit of the power range of the thermal energy storage system, and the product of the maximum discharging rate and the capacity of the thermal energy storage system is used as the upper limit of the power range of the thermal energy storage system. The upper and lower limits of the state of charge are respectively multiplied by the capacity of the energy storage system to determine the upper and lower limits of the capacity range of the energy storage system. The initial capacity and final capacity of the energy storage system are equal. The capacity of the electrical energy storage system is the remaining portion of the capacity of the electrical energy storage system after self-discharge at the previous moment, plus the product of the charging power and charging efficiency of the electrical energy storage system per unit time, minus the quotient of the discharging power and discharging efficiency; the capacity of the thermal energy storage system at the current moment is the remaining portion of the capacity of the thermal energy storage system after heat loss at the previous moment, plus the product of the heat absorption power and heat absorption efficiency of the thermal energy storage system per unit time, minus the quotient of the heat release power and heat release efficiency. Equipment operating constraints include: Define the upper and lower power limits for a certain controllable generator unit; Let the upper and lower limits of the power difference between adjacent moments of a certain controllable unit be the rising ramp rate and the falling ramp rate.

[0013] According to the above scheme, the specific steps in step S3 are as follows: S31: Establish a data chain, including the acquisition layer and the preprocessing layer; The acquisition layer is used to collect basic data, price signals, and environmental data in real time; The basic data includes customer ID, time period, and real-time active power; the customer ID is a unique identifier for users, used to distinguish different electricity users; the time period is at fixed intervals to provide a time scale for capturing the temporal variation patterns of electricity consumption behavior; the real-time active power is used to reflect the customer's electricity consumption in each time period; Price signals include time-of-use pricing for the upper-level grid and demand response incentive pricing; the upper-level grid time-of-use pricing is used to define different pricing periods for peak, flat, and valley periods; the demand response incentive pricing is determined by the microgrid. Environmental data includes temperature and light intensity; temperature affects customers' electricity demand; light intensity determines the output of photovoltaic power generation. The preprocessing layer is used to repair missing values ​​in the data using linear interpolation and to identify outlier data using the interquartile range algorithm to filter out noise points in the data. S32: A random forest model is used to classify the state of demand response participation, with price response features as classification features and demand elasticity coefficient matrix used to describe demand response, thereby identifying customer-side demand response. The demand elasticity coefficient matrix is ​​obtained by statistical analysis of customer-side electricity consumption under historical time-of-use pricing; further, the power of each time period after the time-of-use pricing adjustment and the probability density function of the adjusted power of participating customers are obtained.

[0014] According to the above scheme, in step S4, the electricity purchase cost assessment model is: the proportion of the user's electricity cost for participating in demand response under the ideal situation to the user's initial satisfaction minus the difference between the electricity cost of a user participating in demand response under actual and ideal conditions. The power supply assessment model is: the percentage of a user's non-demand response power demand under ideal conditions, calculated by subtracting the difference between a user's initial satisfaction and the user's actual power demand under ideal conditions. Divide the percentage by the unit interval parameter to eliminate evaluation bias.

[0015] A microgrid demand response identification and optimization scheduling system that considers user satisfaction. The initialization submodule is used for initialization, inputting information about the microgrid; The modeling submodule is used to construct the microgrid's master-slave management architecture and economic optimization model based on the input information, which are used to coordinate power balance and minimize operating costs, respectively. The demand identification submodule is used to build an integrated classification and identification center for use by the master-slave management architecture by using a machine learning classifier based on the random forest model. It quantifies the user's time-of-use electricity price response behavior through the demand elasticity coefficient matrix and identifies the customer-side demand response participation tags. The evaluation submodule is used to introduce the electricity purchase cost evaluation model and the power supply evaluation model through customer-side demand response participation tags. Based on the actual user cost and power deviation, the scheduling strategy is dynamically adjusted to quantify satisfaction and balance economy and power supply reliability.

[0016] The beneficial effects of this invention are as follows: 1. The present invention provides a microgrid demand response identification and optimized scheduling method and system that considers user satisfaction. It utilizes a machine learning classifier based on a random forest model to construct an integrated classification and identification center, quantifies users' time-of-use electricity price response behavior through a demand elasticity coefficient matrix, accurately identifies the demand response of the microgrid, improves the economic efficiency of coordinated scheduling of multiple microgrids, and achieves the function of balancing user satisfaction and microgrid economic efficiency by coordinating power balance and minimizing operating costs.

[0017] 2. This invention solves the data anomaly problem through a master-slave management framework and a machine learning classifier, effectively handles the interference of data anomalies on user behavior feature extraction, improves the accuracy of scheduling strategies, and realizes collaborative optimization of multiple microgrids.

[0018] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of an embodiment of the present invention.

[0021] Figure 2 This is a customer electricity demand information diagram according to an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0023] Example 1 See Figure 1 The specific steps of the microgrid demand response identification and optimal scheduling method considering satisfaction are as follows: S1: Initialization, inputting microgrid information; S2: Construct the microgrid master-slave management architecture and economic optimization model based on the input information, which are used to coordinate power balance and minimize operating costs, respectively. S3: Utilize a machine learning classifier based on a random forest model to build an integrated classification and identification center for use by the master-slave management architecture. Quantify users' time-of-use electricity price response behavior through the demand elasticity coefficient matrix and identify customer-side demand response participation tags. S4: By introducing a power purchase cost assessment model and a power supply assessment model through customer-side demand response participation tags, the scheduling strategy is dynamically adjusted based on the user's actual cost and power deviation, quantifying satisfaction and balancing economy and power supply reliability.

[0024] Furthermore, in step S1, the microgrid information includes microgrid operation data, customer-side demand response data, machine learning input features, and interactive collaborative scheduling data.

[0025] In step S2, the master-slave management architecture of the microgrid includes the master microgrid, the slave microgrid, and the upper-level grid; The main microgrid is the power supplier, including distributed generation and electric / thermal energy storage systems; the main microgrid is used to generate low-cost electricity sales plans through optimal scheduling. Microgrids are the source of electricity, used to adjust power purchase strategies according to electricity sales plans, and to achieve economic synergy between multiple microgrids and the upper-level grid. The upper-level power grid is a system-level energy hub used to provide peak-valley time-of-use pricing signals, guide the electricity consumption behavior of microgrids, and coordinate dispatch to ensure power balance and stable operation of the main microgrid and slave microgrids.

[0026] Furthermore, in step S2, the coordinated scheduling of the master microgrid and the slave microgrid follows a closed-loop process of plan generation, cross-grid transactions, and grid coordination. The specific steps are as follows: S21: Based on the analysis of the next day's renewable energy forecast, load curve, and energy storage status, the master microgrid generates an electricity sales plan that includes multiple time periods and shares it with the slave microgrid in real time through the master-slave management platform; S22: Based on the customer's demand response tag, the microgrid matches the main microgrid's power sales plan and prioritizes the purchase of low-priced electricity for adjustable loads; for customers who do not participate in demand response, the power supply stability is ensured through energy storage systems or the upper-level grid. S23: If the main microgrid's power supply is insufficient, the secondary microgrid will purchase electricity from the upper-level grid to make up the gap. The upper-level grid will dynamically adjust the electricity price according to the system load status. The main microgrid, secondary microgrid, and upper-level grid will periodically synchronize power data, correct prediction errors, and adjust the trading plan for subsequent periods in real time to ensure power balance and optimal economy among the microgrids.

[0027] In step S2, the economic optimization model is used to optimally allocate resources within the microgrid through mathematical modeling, minimizing the overall cost of the microgrid while satisfying power balance constraints, energy storage state of charge constraints, and equipment operation constraints.

[0028] Furthermore, in step S2, the comprehensive cost is the sum of total fuel cost, power interaction cost, system maintenance cost, and controllable unit start-up and shutdown cost, minus the revenue from the sale of heat energy. Total fuel cost includes gas turbine operating cost and fuel cell operating cost; gas turbine operating cost is the unit cost of natural gas divided by the unit price, and then multiplied by the total heat consumed in the gas turbine; fuel cell operating cost is the unit cost of natural gas divided by the unit price, and then multiplied by the total heat consumed in the fuel cell. The power interaction cost is calculated by multiplying the average of the purchase price and the selling price of electricity by the interaction power between the microgrid and the upstream grid, and by multiplying half of the difference between the purchase price and the selling price by the absolute value of the interaction power between the microgrid and the upstream grid, and then summing them up. The system maintenance cost is calculated by separately calculating the maintenance cost and power of each customer in each unit of the microgrid, and then summing them up. The start-up and shutdown cost of a controllable unit is the sum of the start-up costs when the start-up and shutdown states of the controllable unit are inconsistent in two consecutive times. Revenue from the sale of heat energy is the product of the unit price of heat energy sold and the heat load power.

[0029] Furthermore, in step S2, Power balance constraints include: The sum of the power of each customer in each unit of the microgrid minus the charging power of the energy storage system, plus the discharging power of the energy storage system and the interaction power between the microgrid and the upper-level grid, equals the sum of the load power of the microgrid and the electric power of the electric boiler. The heat generated by the gas turbine plus the heat generated by the electric boiler, minus the power absorbed by the thermal energy storage system, plus the power released by the thermal energy storage system, equals the heat load power. The heat generated by a gas turbine is equal to the ratio of the gas turbine's power consumption excluding power generation and heat loss to its power generation efficiency. Energy storage state of charge constraints include: The product of the maximum charging rate and the capacity of the energy storage system is used as the lower limit of the power range of the energy storage system, and the product of the maximum discharging rate and the capacity of the energy storage system is used as the upper limit of the power range of the energy storage system; the product of the maximum charging rate and the capacity of the thermal energy storage system is used as the lower limit of the power range of the thermal energy storage system, and the product of the maximum discharging rate and the capacity of the thermal energy storage system is used as the upper limit of the power range of the thermal energy storage system. The upper and lower limits of the state of charge are respectively multiplied by the capacity of the energy storage system to determine the upper and lower limits of the capacity range of the energy storage system. The initial capacity and final capacity of the energy storage system are equal. The capacity of the electrical energy storage system is the remaining portion of the capacity of the electrical energy storage system after self-discharge at the previous moment, plus the product of the charging power and charging efficiency of the electrical energy storage system per unit time, minus the quotient of the discharging power and discharging efficiency; the capacity of the thermal energy storage system at the current moment is the remaining portion of the capacity of the thermal energy storage system after heat loss at the previous moment, plus the product of the heat absorption power and heat absorption efficiency of the thermal energy storage system per unit time, minus the quotient of the heat release power and heat release efficiency. Equipment operating constraints include: Define the upper and lower power limits for a certain controllable generator unit; Let the upper and lower limits of the power difference between adjacent moments of a certain controllable unit be the rising ramp rate and the falling ramp rate.

[0030] In step S3, the specific steps are as follows: S31: Establish a data chain, including the acquisition layer and the preprocessing layer; The acquisition layer is used to collect basic data, price signals, and environmental data in real time; The basic data includes customer ID, time period, and real-time active power; the customer ID is a unique identifier for users, used to distinguish different electricity users; the time period is at fixed intervals to provide a time scale for capturing the temporal variation patterns of electricity consumption behavior; the real-time active power is used to reflect the customer's electricity consumption in each time period; Price signals include time-of-use pricing for the upper-level grid and demand response incentive pricing; the upper-level grid time-of-use pricing is used to define different pricing periods for peak, flat, and valley periods; the demand response incentive pricing is determined by the microgrid. Environmental data includes temperature and light intensity; temperature affects customers' electricity demand; light intensity determines the output of photovoltaic power generation. The preprocessing layer is used to repair missing values ​​in the data using linear interpolation and to identify outlier data using the interquartile range algorithm to filter out noise points in the data. S32: A random forest model is used to classify the state of demand response participation, with price response features as classification features and demand elasticity coefficient matrix used to describe demand response, thereby identifying customer-side demand response. The demand elasticity coefficient matrix is ​​obtained by statistical analysis of customer-side electricity consumption under historical time-of-use pricing; further, the power of each time period after the time-of-use pricing adjustment and the probability density function of the adjusted power of participating customers are obtained.

[0031] In step S4, the electricity purchase cost assessment model is: the proportion of the electricity cost of a user participating in demand response under the ideal situation to the user's initial satisfaction minus the difference between the electricity cost of a user participating in demand response under actual and ideal conditions. The power supply assessment model is: the percentage of a user's non-demand response power demand under ideal conditions, calculated by subtracting the difference between a user's initial satisfaction and the user's actual power demand under ideal conditions. Divide the percentage by the unit interval parameter to eliminate evaluation bias.

[0032] This embodiment utilizes a machine learning classifier based on a random forest model to construct an integrated classification and identification center. By quantifying users' time-of-use electricity price response behavior through a demand elasticity coefficient matrix, it accurately identifies the demand response of microgrids, improves the economic efficiency of coordinated scheduling of multiple microgrids, and achieves the function of balancing user satisfaction and microgrid economics by coordinating power balance and minimizing operating costs.

[0033] Example 2 The steps in this embodiment are the same as in Embodiment 1, except that each step is applied to the actual power data of a specific power grid. Specifically, it includes the following steps: S1: Initialization, the input includes information such as microgrid operation data, customer-side demand response data, machine learning input features, and interactive collaborative scheduling data; S2: Construct a master-slave management architecture and economic optimization model including a master microgrid and slave microgrids based on the input information; the master microgrid is used to generate low-price electricity sales plans through optimal scheduling; the slave microgrids are used to adjust the electricity purchase strategy according to the plan to achieve economic synergy between multiple microgrids and the upper-level grid; the specific steps are as follows: S21: Construct a master-slave management framework; As the power supplier, the main microgrid possesses abundant renewable energy resources, equipped with distributed power sources such as wind and solar power, as well as electric / thermal energy storage systems. Through optimized scheduling, aiming to minimize unit operating costs, it generates electricity sales plans, intending to sell surplus renewable energy at prices lower than those of the upper-level grid, thereby maximizing energy utilization and revenue. Simultaneously, the energy storage system is used to smooth out fluctuations in renewable energy output and ensure power supply reliability.

[0034] Microgrids, acting as the electricity input source, primarily serve industrial and residential customers. They utilize an integrated classification and identification center to analyze customer electricity consumption behavior and identify customer demand response participation tags. Based on these tags, customers participating in demand response are given priority in purchasing low-cost electricity from the microgrid; for customers not participating in demand response, a stable power supply is supplemented through the upper-level grid.

[0035] The upper-level power grid, acting as a system-level energy hub, provides peak-valley time-of-use pricing signals to guide the electricity consumption behavior of microgrids. When power imbalances occur between the master and slave microgrids, the upper-level power grid provides regulation support to ensure the power balance and stable operation of the entire system.

[0036] The coordinated dispatch of master-slave microgrids follows a closed-loop process of "plan generation - cross-grid trading - grid coordination". First, the master microgrid generates a power sales plan covering multiple time periods based on analysis of next day's renewable energy forecasts, load curves, and energy storage status, and shares this plan with the slave microgrid in real time through the master-slave management platform. Next, the slave microgrid matches the master microgrid's sales plan with customer demand response tags, prioritizing the purchase of low-priced electricity for adjustable loads. For customers not participating in demand response, power supply stability is ensured through energy storage systems or the upper-level grid. Finally, if the master microgrid's power supply is insufficient, the slave microgrid needs to purchase electricity from the upper-level grid to make up the gap, and the upper-level grid dynamically adjusts electricity prices based on system load status. Simultaneously, all three parties periodically synchronize power data, correct forecast errors, and adjust trading plans for subsequent time periods in real time to ensure power balance and optimal economic efficiency among the microgrids.

[0037] S22: Establish an economic optimization model; The economic optimization model is a core component of the master-slave management approach, aiming to achieve optimal resource allocation within the microgrid through mathematical modeling. Its objective is to minimize the overall operating cost of the microgrid while satisfying power balance and equipment operating constraints. The mathematical expression for minimizing overall cost is as follows:

[0038] In the formula, Total fuel cost; For power interaction costs; For system maintenance costs; Costs for controllable unit start-up and shutdown; Revenue from the sale of heat energy; Total fuel cost per unit of time. The calculation formula is as follows:

[0039]

[0040]

[0041] In the formula, For gas turbine operating costs; For fuel cell operating costs; The unit cost of natural gas; Lowest unit price for natural gas; , These are the power and efficiency of the gas turbine, respectively. , These represent the power and efficiency of the fuel cell, respectively. Power exchange cost. The calculation formula is as follows:

[0042] In the formula, , These are the electricity prices for sale and purchase, respectively; This refers to the interaction power between the microgrid and the upstream grid. System maintenance costs. The calculation formula is as follows:

[0043] In the formula, , Units i Customers w Maintenance costs and power consumption. Start-up and shutdown costs of controllable units. The calculation formula is as follows:

[0044] In the formula, , It is a controllable unit j Start-up and shutdown status and initial startup cost. Revenue from heat energy sales. The calculation formula is as follows:

[0045] In the formula, , This refers to the unit price of heat energy sales and the heat load power.

[0046] Considering power balance constraints, energy storage state of charge constraints, and unit operation constraints, the power balance constraints are as follows:

[0047]

[0048]

[0049] In the formula, , These are the charging and discharging power of the energy storage system, respectively. This represents the load power of the microgrid. The electric power of the electric boiler; The heat generated by the gas turbine; For the heat generated by the electric boiler; , These are the absorbed and released power of the thermal energy storage system, respectively. , These represent the power generation efficiency and heat loss rate of the gas turbine, respectively. The energy storage state-of-charge constraints are as follows:

[0050]

[0051]

[0052]

[0053] In the formula, , These refer to the power of the electrical energy storage system and the thermal energy storage system, respectively. , These refer to the capacities of the electrical energy storage system and the thermal energy storage system, respectively. , These are the maximum charge and discharge rates of the energy storage system, respectively. , These are the maximum charge and discharge rates of the thermal energy storage system, respectively. , These are the upper and lower limits of the state of charge, respectively; and These are the initial capacity and final capacity of the energy storage system, respectively. , These are the charging and discharging power of the energy storage system, respectively. , These are the heat absorption and release power of the thermal energy storage system, respectively. , These are the self-discharge rate of the electrical energy storage system and the heat loss rate of the thermal energy storage system, respectively. , These are the charging and discharging efficiencies of the energy storage system, respectively. , These represent the heat absorption and release efficiencies of the thermal energy storage system, respectively. The unit's operating constraints are as follows:

[0054]

[0055] In the formula, , Controllable units i Upper and lower power limits; , These are the ascending and descending slope rates, respectively. Controllable unit i The power.

[0056] S3: An integrated classification and identification center is built using a machine learning classifier based on a random forest model. The user's time-of-use electricity price response behavior is quantified through the demand elasticity coefficient matrix to solve anomalies such as missing data and incorrect labels, and to achieve high-precision identification of customer-side demand response participation labels.

[0057] S31: Establish a data link; The data chain is divided into an acquisition layer and a preprocessing layer. The acquisition layer uses smart meters to collect the following three types of key data in real time: Basic data: Customer ID serves as a unique identifier for users, accurately distinguishing different electricity users; time periods are divided into 96 time periods with precise 15-minute intervals, providing a time scale for capturing the temporal variation patterns of electricity consumption behavior; real-time active power directly reflects the customer's electricity consumption in each time period, serving as the core basic data for analyzing electricity consumption patterns.

[0058] Price signals: The time-of-use pricing of the upper-level power grid clearly defines the three price periods: peak, flat, and valley. Peak-hour prices are higher to reflect the tight power supply and high costs, while valley-hour prices are lower to encourage users to use electricity during off-peak hours. This price difference guides users to adjust their electricity consumption behavior rationally. Demand response incentive pricing introduced from microgrids directly affects users' economic interests and becomes an important economic incentive for users to participate in demand response.

[0059] Environmental data: Temperature significantly impacts the electricity demand for residential air conditioning and heating equipment; high temperatures increase electricity consumption for air conditioning and low temperatures raise electricity consumption for heating equipment. Sunlight intensity directly determines the output of photovoltaic power generation; sufficient sunlight increases photovoltaic power output, and vice versa. These two types of environmental data are crucial for accurately predicting distributed energy output and changes in user-side electricity consumption response due to environmental factors.

[0060] Further repair and filtering are performed in the preprocessing layer. Missing values ​​are repaired using linear interpolation, which is based on the assumption of linear change in the data over time. When missing data is encountered, a linear function is constructed using known neighboring data points to extrapolate the missing values. Then, the interquartile range algorithm is used to identify outlier data, the data is sorted, and the first quartile is calculated. (Values ​​at the 25th percentile of the data) and the third quartile (Values ​​at 75% of the data), thus yielding the interquartile range. Set outlier boundaries as and Data points exceeding this boundary are identified as outliers and marked as "to be verified". These outliers can then be manually checked or further analyzed to filter out noise points in the data and improve data quality.

[0061] S32: Identify customer-side demand responses; A random forest model was chosen for demand response participation state classification, with price response features selected as classification features. The demand elasticity coefficient, which helps describe the demand response, is defined as:

[0062]

[0063]

[0064] In the formula, For customers m The demand elasticity coefficient; , They are time t Changes in power and time x Price changes; , The electricity consumption before and after the implementation of time-of-use pricing is respectively; , These represent the electricity prices before and after the implementation of time-of-use pricing. In the above formula, time... hour, As the self-elasticity coefficient, when the electricity price increases, the demand from end users decreases accordingly; when time... hour, These are the mutual elasticity coefficients. The demand elasticity coefficient matrix, composed of the self-elasticity coefficient and the mutual elasticity coefficient, is as follows:

[0065] Elasticity coefficient matrix H This can be calculated through statistical analysis of customer-side electricity consumption under historical time-of-use pricing. Given... H In this case, the power consumption for each time period after the time-of-use electricity price adjustment is:

[0066] probability density function It can be represented as:

[0067] In the formula, To assist customers in adjusting power; , These are the mean and standard deviation of the response, respectively.

[0068] S4: Introduce electricity purchase cost assessment and power supply assessment models, dynamically adjust dispatch strategies based on actual user costs and power deviations, quantify satisfaction and balance economic efficiency and power supply reliability.

[0069] By constructing a dual-indicator evaluation system, supplemented by a dynamic closed-loop mechanism, we can achieve accurate assessment of customer satisfaction and timely strategy adjustments, thereby ensuring a positive customer electricity experience and stable system operation. The electricity purchase cost assessment is as follows:

[0070] In the formula, For users under ideal circumstances mElectricity costs for participating in demand response; This refers to the electricity cost incurred by users participating in demand response under actual circumstances; Initial satisfaction level; The unit interval parameter is used to evaluate the deviation. The power supply is evaluated as follows:

[0071] In the formula, For customers who do not participate in demand response under ideal conditions m The electricity demand; For customers who do not participate in demand response under actual conditions m The electricity demand; Initial satisfaction level; The unit interval parameter for evaluating bias.

[0072] To dynamically track customer satisfaction, the system calculates electricity purchase cost and power supply assessment indicators every 15 minutes (corresponding to the time interval for smart meter data collection). When the indicator value reaches a preset threshold, customer satisfaction is considered low, triggering the corresponding strategy adjustment process. Low-cost response strategy: Optimizes customer electricity costs and experience by adjusting demand response time periods. High-cost response strategy: Activates the gas turbine backup capacity of the main microgrid for situations with significant power supply gaps and high requirements for power supply reliability.

[0073] Electricity consumption information from 50 customers of a certain power grid was collected, and their participation in demand response was recorded, such as... Figure 2 As shown in the diagram, customers 1-25 are labeled "1," indicating a need for response, while customers 26-50 are labeled "0," indicating no need for response. Assuming the test set represents 20% of the total, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Decision Tree (GBDT) are used to classify and predict customer need response participation types. The accuracy is evaluated using true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) from the confusion matrix. The recognition accuracy is described below:

[0074] The machine learning performance comparison and customer satisfaction evaluation of this embodiment are listed below: Table 1

[0075] Table 1 shows a performance comparison of different machine learning methods. As can be seen from Table 1, the F1 score of the Random Forest method is 0.85, higher than that of SVM (0.800). Furthermore, the PR score of the Random Forest method is 1.000, higher than that of SVM (0.800). In this process, the most critical goal is to obtain the highest possible recognition accuracy. Therefore, Random Forest was chosen as the recognition method.

[0076] Table 2

[0077] Table 2 shows customer satisfaction and information before and after machine learning identification and dynamic adjustment of demand response. As can be seen from Table 2, customer 25, who was willing to participate in demand response but was identified as a non-participant, experienced the largest decrease in satisfaction, with an electricity purchase cost assessment of only 1.147. When customer 48 was identified as a participating customer but did not participate in demand response, the power supply assessment was only 3.487.

[0078] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0079] Example 3 This embodiment is used to implement the principle of the above method embodiment to construct a microgrid demand response identification and optimization scheduling system that considers satisfaction, including an initialization submodule, a modeling submodule, a demand identification submodule and an evaluation submodule; The initialization submodule is used for initialization, inputting information about the microgrid; The modeling submodule is used to construct the microgrid's master-slave management architecture and economic optimization model based on the input information, which are used to coordinate power balance and minimize operating costs, respectively. The demand identification submodule is used to build an integrated classification and identification center for use by the master-slave management architecture by using a machine learning classifier based on the random forest model. It quantifies the user's time-of-use electricity price response behavior through the demand elasticity coefficient matrix and identifies the customer-side demand response participation tags. The evaluation submodule is used to introduce the electricity purchase cost evaluation model and the power supply evaluation model through customer-side demand response participation tags. Based on the actual user cost and power deviation, the scheduling strategy is dynamically adjusted to quantify satisfaction and balance economy and power supply reliability.

[0080] Each submodule is mainly used to implement the various steps of the method implementation, which will not be elaborated here.

[0081] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0082] This embodiment also includes a processor, a communication interface, a memory, and a communication bus; wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory stores a computer program, and when the program is executed by the processor, the processor performs the steps of a microgrid demand response identification and optimization scheduling method that considers satisfaction.

[0083] This embodiment also provides a computer-readable storage medium storing executable instructions that, when executed by a processor, enable the processor to implement a microgrid demand response identification and optimized scheduling method that takes satisfaction into account.

[0084] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0085] Furthermore, this application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] This application is described with reference to the flowchart of the method and computer program product according to Embodiment 1 and the block diagram of the device (system) according to Embodiment 3. It should be understood that each step or block in the flowchart or block diagram, as well as combinations of steps or blocks in the flowchart or block diagram, can be implemented by computer program instructions.

[0087] These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which are executable by the processor of the computer or other programmable data processing device, produce instructions for implementing the process. Figure 1 One or more processes or boxes Figure 1 A microgrid demand response identification and optimized scheduling system that considers satisfaction for functions specified in one or more boxes.

[0088] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes or boxes Figure 1 The steps of a microgrid demand response identification and optimized scheduling method that considers satisfaction are specified in one or more boxes.

[0090] The above embodiments are only used to illustrate the design concept and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications made based on the principles and design ideas disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A microgrid demand response identification and optimized scheduling method considering satisfaction, characterized in that: Includes the following steps: S1: Initialization, inputting microgrid information; S2: Construct the microgrid master-slave management architecture and economic optimization model based on the input information, which are used to coordinate power balance and minimize operating costs, respectively. S3: Utilize a machine learning classifier based on a random forest model to build an integrated classification and identification center for use by the master-slave management architecture. Quantify users' time-of-use electricity price response behavior through the demand elasticity coefficient matrix and identify customer-side demand response participation tags. S4: By introducing a power purchase cost assessment model and a power supply assessment model through customer-side demand response participation tags, the scheduling strategy is dynamically adjusted based on the user's actual cost and power deviation, quantifying satisfaction and balancing economy and power supply reliability.

2. The microgrid demand response identification and optimized scheduling method considering satisfaction as described in claim 1, characterized in that: In step S1, the microgrid information includes microgrid operation data, customer-side demand response data, machine learning input features, and interactive collaborative scheduling data.

3. The microgrid demand response identification and optimized scheduling method considering satisfaction as described in claim 1, characterized in that: In step S2, the microgrid master-slave management architecture includes a master microgrid, a slave microgrid, and an upper-level power grid. The main microgrid is the power supplier, including distributed generation and electric / thermal energy storage systems; the main microgrid is used to generate low-cost electricity sales plans through optimal scheduling. Microgrids are the source of electricity, used to adjust power purchase strategies according to electricity sales plans, and to achieve economic synergy between multiple microgrids and the upper-level grid. The upper-level power grid is a system-level energy hub used to provide peak-valley time-of-use pricing signals, guide the electricity consumption behavior of microgrids, and coordinate dispatch to ensure power balance and stable operation of the main microgrid and slave microgrids.

4. The microgrid demand response identification and optimized scheduling method considering satisfaction as described in claim 3, characterized in that: In step S2, the coordinated scheduling of the master microgrid and the slave microgrid follows a closed-loop process of plan generation, cross-grid transactions, and grid coordination. The specific steps are as follows: S21: Based on the analysis of the next day's renewable energy forecast, load curve, and energy storage status, the master microgrid generates an electricity sales plan that includes multiple time periods and shares it with the slave microgrid in real time through the master-slave management platform; S22: Based on the customer's demand response tag, the microgrid matches the main microgrid's power sales plan and prioritizes the purchase of low-priced electricity for adjustable loads; for customers who do not participate in demand response, the power supply stability is ensured through energy storage systems or the upper-level grid. S23: If the main microgrid's power supply is insufficient, the secondary microgrid will purchase electricity from the upper-level grid to make up the gap. The upper-level grid will dynamically adjust the electricity price according to the system load status. The main microgrid, secondary microgrid, and upper-level grid will periodically synchronize power data, correct prediction errors, and adjust the trading plan for subsequent periods in real time to ensure power balance and optimal economy among the microgrids.

5. The microgrid demand response identification and optimized scheduling method considering satisfaction as described in claim 1, characterized in that: In step S2, the economic optimization model is used to optimally allocate resources within the microgrid through mathematical modeling, minimizing the overall cost of the microgrid while satisfying power balance constraints, energy storage state of charge constraints, and equipment operation constraints.

6. The microgrid demand response identification and optimized scheduling method considering satisfaction as described in claim 5, characterized in that: In step S2, the comprehensive cost is the sum of total fuel cost, power interaction cost, system maintenance cost, and controllable unit start-up and shutdown cost, minus the revenue from the sale of thermal energy. Total fuel cost includes gas turbine operating cost and fuel cell operating cost; gas turbine operating cost is the unit cost of natural gas divided by the unit price, and then multiplied by the total heat consumed in the gas turbine; fuel cell operating cost is the unit cost of natural gas divided by the unit price, and then multiplied by the total heat consumed in the fuel cell. The power interaction cost is calculated by multiplying the average of the purchase price and the selling price of electricity by the interaction power between the microgrid and the upstream grid, and by multiplying half of the difference between the purchase price and the selling price by the absolute value of the interaction power between the microgrid and the upstream grid, and then summing them up. The system maintenance cost is calculated by separately calculating the maintenance cost and power of each customer in each unit of the microgrid, and then summing them up. The start-up and shutdown cost of a controllable unit is the sum of the start-up costs when the start-up and shutdown states of the controllable unit are inconsistent in two consecutive times. Revenue from the sale of heat energy is the product of the unit price of heat energy sold and the heat load power.

7. The microgrid demand response identification and optimized scheduling method considering satisfaction as described in claim 1, characterized in that: The specific steps in step S3 are as follows: S31: Establish a data chain, including the acquisition layer and the preprocessing layer; The acquisition layer is used to collect basic data, price signals, and environmental data in real time; The basic data includes customer ID, time period, and real-time active power; the customer ID is a unique identifier for users, used to distinguish different electricity users; the time period is at fixed intervals to provide a time scale for capturing the temporal variation patterns of electricity consumption behavior; the real-time active power is used to reflect the customer's electricity consumption in each time period; Price signals include time-of-use pricing for the upper-level grid and demand response incentive pricing; the upper-level grid time-of-use pricing is used to define different pricing periods for peak, flat, and valley periods; the demand response incentive pricing is determined by the microgrid. Environmental data includes temperature and light intensity; temperature affects customers' electricity demand; light intensity determines the output of photovoltaic power generation. The preprocessing layer is used to repair missing values ​​in the data using linear interpolation and to identify outlier data using the interquartile range algorithm to filter out noise points in the data. S32: A random forest model is used to classify the state of demand response participation, with price response features as classification features and demand elasticity coefficient matrix used to describe demand response, thereby identifying customer-side demand response. The demand elasticity coefficient matrix is ​​obtained by statistical analysis of customer-side electricity consumption under historical time-of-use pricing; further, the power of each time period after the time-of-use pricing adjustment and the probability density function of the adjusted power of participating customers are obtained.

8. The microgrid demand response identification and optimized scheduling method considering satisfaction as described in claim 1, characterized in that: In step S4, the electricity purchase cost assessment model is: the proportion of the electricity cost of a user participating in demand response under the ideal situation to the user's initial satisfaction minus the difference between the electricity cost of a user participating in demand response under actual and ideal conditions. The power supply assessment model is: the percentage of a user's non-demand response power demand under ideal conditions, calculated by subtracting the difference between a user's initial satisfaction and the user's actual power demand under ideal conditions. Divide the percentage by the unit interval parameter to eliminate evaluation bias.

9. A microgrid demand response identification and optimized scheduling system considering satisfaction, characterized in that: The initialization submodule is used for initialization, inputting information about the microgrid; The modeling submodule is used to construct the microgrid's master-slave management architecture and economic optimization model based on the input information, which are used to coordinate power balance and minimize operating costs, respectively. The demand identification submodule is used to build an integrated classification and identification center for use by the master-slave management architecture by using a machine learning classifier based on the random forest model. It quantifies the user's time-of-use electricity price response behavior through the demand elasticity coefficient matrix and identifies the customer-side demand response participation tags. The evaluation submodule is used to introduce the electricity purchase cost evaluation model and the power supply evaluation model through customer-side demand response participation tags. Based on the actual user cost and power deviation, the scheduling strategy is dynamically adjusted to quantify satisfaction and balance economy and power supply reliability.

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