Energy-saving power systems and control methods at construction sites

Abstract

This invention discloses an energy-saving power system and its control method for construction sites, belonging to the field of power management technology. The system includes: an energy monitoring module, a load forecasting module, a multi-objective optimization module, a dynamic scheduling module, a carbon efficiency assessment module, an execution feedback module, and a distributed power supply module. This invention collects equipment vibration and speed data via an industrial bus to construct equipment operating condition vectors, and integrates these vectors with temperature and humidity correction functions, thereby achieving dynamic and accurate prediction of construction loads. This solves the problem of large matching errors in existing construction site load forecasting due to the lack of consideration for environmental parameters and the dynamic characteristics of work processes.

Description

Technical Field

[0001] This invention relates to the field of electricity management technology, and in particular to energy-saving electricity systems and control methods for construction sites. Background Technology

[0002] In the field of energy management at construction sites, load forecasting is a core component for achieving energy-saving scheduling. Traditional techniques typically employ historical data statistics or static time-series models for load forecasting. These methods primarily build predictive models based on historical energy consumption data from equipment operation, failing to incorporate environmental parameters and the dynamic characteristics of construction procedures into the modeling system. For example, in high-temperature environments during summer, the energy consumption of cooling systems in construction machinery increases significantly. Traditional models, lacking temperature and humidity correction mechanisms, often result in large discrepancies between predicted loads and actual demand.

[0003] Another limitation of existing technologies lies in their insufficient adaptability to dynamic changes in construction processes. Building construction has typical phased characteristics, with significant differences in equipment configuration and operation modes across different processes. However, traditional forecasting models often use fixed parameters for modeling, making it impossible to dynamically adjust load forecasting parameters as processes switch. This leads to a sharp increase in forecasting errors during process transitions, which in turn causes a mismatch between distributed power generation and grid power purchases, resulting in energy waste or a decline in power supply reliability. Summary of the Invention

[0004] This application provides an energy-saving power system and control method for construction sites, which solves the problem of large matching errors in the load prediction of construction sites in the prior art due to the lack of consideration of environmental parameters and dynamic characteristics of the process. By collecting data such as equipment vibration and speed through an industrial bus to construct equipment operating condition vectors, and integrating the equipment operating condition state vectors with temperature and humidity correction functions, dynamic and accurate prediction of construction load is achieved.

[0005] This application provides an energy-saving power system for construction sites. The system is used to implement a control method for energy-saving power systems for construction sites, including: an energy monitoring module, a load forecasting module, a multi-objective optimization module, a dynamic scheduling module, a carbon efficiency assessment module, an execution feedback module, and a distributed power supply module.

[0006] The energy monitoring module is used to collect real-time data on photovoltaic power generation, electricity purchased from the grid, energy consumption of electrical equipment, and carbon emission intensity of the grid at the construction site.

[0007] The load prediction module is used to obtain the operating condition vector, construction equipment temperature and ambient humidity, and analyze the total load demand of the site.

[0008] The steps for obtaining the total site load demand include:

[0009] Data from vibration sensors and motor tachometers of construction equipment are collected via an industrial bus interface to construct the working state vector of construction equipment j at time t.

[0010] Obtain process codes, construction equipment temperatures, and ambient humidity from the BIM database, and calculate future load forecasts using an energy consumption mapping function;

[0011] The energy consumption mapping function is:

[0012] ;

[0013] in, Let j be the predicted load value for construction equipment j over a future period. Let be the working state vector of construction equipment j at time t. For construction equipment j Energy consumption mapping coefficient vector, For the temperature sensitivity coefficient of the equipment, The environmental humidity sensitivity coefficient For equipment temperature, This is the equipment temperature reference value. For ambient humidity, This is the baseline value for ambient humidity.

[0014] Finally, the future load forecasts of each device are aggregated into the total site load demand via industrial Ethernet. ;

[0015] The multi-objective optimization module is used to acquire real-time energy data, and based on the total load demand of the site and the real-time energy data, with the objectives of minimizing total electricity cost and carbon emissions, it generates a distributed power output plan and a grid purchase strategy.

[0016] The dynamic scheduling module is used to decompose controllable equipment-level control commands based on the distributed power output plan and the total load demand of the site.

[0017] The carbon efficiency assessment module is used to obtain real-time carbon efficiency indicators based on the distributed power generation plan and the grid power purchase strategy.

[0018] The execution feedback module is used to compare the instruction execution deviation with the change in carbon efficiency, and trigger adaptive correction of the optimization model parameters.

[0019] The distributed power module is used to execute controllable device-level control commands at the current moment and to provide feedback on the actual power output.

[0020] This application provides a control method for an energy-saving power system at a construction site, applied to the energy-saving power system at the construction site, including the following steps:

[0021] Real-time data collection of photovoltaic power generation, electricity purchased from the grid, energy consumption of electrical equipment, and carbon emission intensity of the grid within the construction site;

[0022] Obtain the operating condition vector, construction equipment temperature, and ambient humidity to analyze the total site load demand;

[0023] The steps for obtaining the total site load demand include:

[0024] Data from vibration sensors and motor tachometers of construction equipment are collected via an industrial bus interface to construct the working state vector of construction equipment j at time t.

[0025] Obtain process codes, construction equipment temperatures, and ambient humidity from the BIM database, and calculate future load forecasts using an energy consumption mapping function;

[0026] The energy consumption mapping function is:

[0027] ;

[0028] in, Let j be the predicted load value for construction equipment j over a future period. Let be the working state vector of construction equipment j at time t. For construction equipment j Energy consumption mapping coefficient vector, For the temperature sensitivity coefficient of the equipment, The environmental humidity sensitivity coefficient For equipment temperature, This is the equipment temperature reference value. For ambient humidity, This is the baseline value for ambient humidity.

[0029] Finally, the future load forecasts of each device are aggregated into the total site load demand via industrial Ethernet. ;

[0030] Acquire real-time energy data, and based on the total load demand of the site and real-time energy data, generate distributed power output plans and grid purchase strategies with the goals of minimizing total electricity costs and carbon emissions.

[0031] Based on the distributed power output plan and the total load demand of the site, controllable equipment-level control commands are decomposed;

[0032] Real-time carbon efficiency indicators are obtained based on distributed power generation output plans and grid power purchase strategies.

[0033] By comparing the command execution deviation with the change in carbon efficiency, the optimization model parameters are adaptively corrected.

[0034] Execute control commands at the controllable device level at the current moment and provide feedback on the actual output.

[0035] Furthermore, the method for obtaining the working state vector of the construction equipment j at time t is as follows:

[0036] ;

[0037] in, Let be the working state vector of construction equipment j at time t. Let be the working state vector of construction equipment j at time t-1. This refers to the quantized control command for construction equipment j at time t. for State transition matrix, for Control input matrix, quantization control commands Impact on the state For construction equipment to obey A distributed Gaussian noise vector.

[0038] Furthermore, the steps for generating distributed power generation plans and obtaining grid power purchase strategies include:

[0039] Establish an objective function that aims to minimize both total electricity cost and carbon emissions:

[0040] ;

[0041] Where F is the objective function value, The price is the real-time electricity price, t is the time index, and T is the total duration. This is the dynamic power grid carbon emission intensity factor. Purchase electricity for the power grid As a weighted average of economic costs, For carbon emission weighting, and ;

[0042] Set constraints for the objective function:

[0043] ;

[0044] Where m is the index number of the distributed power source, and M is the total number of distributed power sources in the construction site. Let m be the actual output power of the m-th distributed power source at time t. Let t be the total load demand of the site. This refers to the dynamic elasticity margin coefficient.

[0045] Finally, the Pareto optimal solution set is obtained using the particle swarm optimization algorithm to generate a distributed power generation plan. and power grid purchase strategy .

[0046] Furthermore, the dynamic elasticity margin coefficient is calculated as follows:

[0047] The acceleration of load change is obtained based on the total load demand of the site at time t.

[0048] Obtain the control command execution deviation over the past 24 hours and calculate its variance;

[0049] The dynamic elasticity margin coefficient is obtained through the formula for the dynamic elasticity margin coefficient.

[0050] ;

[0051] in, The load change sensitivity coefficient, To control the deviation sensitivity coefficient, For a moment Deviation in the execution of control commands The integral time variable has a range of values. .

[0052] Furthermore, the step of decomposing controllable device-level control commands includes:

[0053] Obtain the partial derivatives of the distributed power generation output plan with respect to the control command and the partial derivatives of the total site load demand with respect to the control command, and construct a weighted Euclidean norm.

[0054] in, This is the weight matrix. It is a diagonal weight matrix. The carbon efficiency sensitivity weight for controllable device k;

[0055] Get smoothing items ;

[0056] The optimal control command solution function is obtained based on the weighted Euclidean norm and the smoothing term:

[0057] ;

[0058] in, Let be the optimal control command for controllable device k at time t. The output plan of the distributed power source at time t. Let t be the total load demand of the site. Let be the control command for controllable device k at time t. To control the smoothing coefficient of the instructions, This is the control command value from the previous time t-1.

[0059] Furthermore, the formula for obtaining the carbon efficiency sensitivity weight of the controllable device k is as follows:

[0060] ;

[0061] in, This is the dynamic power grid carbon emission intensity factor. Let M be the distributed power output plan for the m-th distributed power source at time t, where M is the total number of distributed power sources in the construction site. For the power grid's power purchase strategy, The output plan of the distributed power source at time t. Let t be the control command for the controllable device k.

[0062] Furthermore, the formula for obtaining the real-time carbon efficiency index is as follows:

[0063] ;

[0064] in For a moment The optimal carbon efficiency index reflects the carbon emission efficiency of distributed power supply. For a moment No. Distributed power generation output plan for a distributed power source. This represents the total number of distributed power sources. For a moment Dynamic power grid carbon emission intensity factor For a moment The power grid's power purchase strategy.

[0065] Furthermore, the step of obtaining the dynamic power grid carbon emission intensity factor includes:

[0066] Real-time current at grid nodes is collected by current transformers, and line resistance and grid topology distance are read from non-volatile memory. The dynamic grid carbon emission intensity factor is calculated by 24-hour sliding window integration. The calculation formula for the dynamic grid carbon emission intensity factor is as follows:

[0067] ;

[0068] in, For integration time variable, For a moment The marginal carbon emission intensity of the power grid For a moment The actual amount of electricity purchased from the power grid.

[0069] Furthermore, the execution of the controllable device-level control command at the current moment includes the following steps:

[0070] The line impedance is obtained by using an impedance measuring device, and the real-time line loss rate is calculated. :

[0071] ;

[0072] in, For a moment The voltage and current phase difference;

[0073] based on Set upper and lower limits for output;

[0074] in, Minimum output limit for distributed power sources. For a moment The optimal photovoltaic power output plan For a moment The optimal diesel generator output plan, This is the maximum output limit for distributed power sources. For a moment The load forecast value;

[0075] Finally, the controllable device-level control command is executed through the solid-state relay at the current moment, and the trip protection is triggered when the upper limit is exceeded.

[0076] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0077] 1. By collecting data such as equipment vibration and rotation speed through industrial bus, an equipment operating condition vector is constructed. The equipment operating condition vector is then integrated with the temperature and humidity correction function, thereby realizing dynamic and accurate prediction of construction load. This effectively solves the problem of large matching error in the load prediction of construction sites in the existing technology due to the failure to consider environmental parameters and dynamic characteristics of the process.

[0078] 2. By calculating the dynamic carbon emission intensity factor through a 24-hour sliding window integral, the spatiotemporal changes of the grid's carbon emission intensity are tracked in real time, thereby realizing the dynamic quantification of the grid's electricity purchase carbon emissions. This provides a real-time carbon efficiency benchmark for multi-objective optimization and effectively solves the problem that existing carbon emission indicators cannot reflect the real-time carbon intensity of the grid's electricity.

[0079] 3. By combining multi-objective Pareto optimization with particle swarm optimization, the power cost and carbon emissions are optimized simultaneously and a solution set is generated, thereby achieving synergistic optimization of the "carbon-electricity" cost of the construction site. This provides an economic and low-carbon trade-off solution for different construction stages, effectively solving the problem that single-objective optimization in existing technologies cannot take into account both economic benefits and environmental protection requirements. Attached Figure Description

[0080] Figure 1 This is a structural diagram of an energy-saving power system for construction sites provided in an embodiment of this application. Detailed Implementation

[0081] This application provides an energy-saving power system and control method for construction sites, which solves the problem of large matching errors in the load prediction of construction sites in the prior art due to the lack of consideration of environmental parameters and dynamic characteristics of the process. By collecting data such as equipment vibration and speed through an industrial bus to construct equipment operating condition vectors, and integrating the equipment operating condition state vectors with temperature and humidity correction functions, dynamic and accurate prediction of construction load is achieved.

[0082] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0083] This application provides a control method for an energy-saving power system at a construction site, applied to the energy-saving power system at the construction site, including the following steps:

[0084] Real-time data collection of photovoltaic power generation, electricity purchased from the grid, energy consumption of electrical equipment, and carbon emission intensity of the grid within the construction site;

[0085] Obtain the operating condition vector, construction equipment temperature, and ambient humidity to analyze the total site load demand;

[0086] The steps for obtaining the total site load demand include:

[0087] Data from vibration sensors and motor tachometers of construction equipment are collected via an industrial bus interface to construct the working state vector of construction equipment j at time t.

[0088] Obtain process codes, construction equipment temperatures, and ambient humidity from the BIM database, and calculate future load forecasts using an energy consumption mapping function;

[0089] The energy consumption mapping function is:

[0090] ;

[0091] in, Let j be the predicted load value for construction equipment j over a future period. Let be the working state vector of construction equipment j at time t. For construction equipment j Energy consumption mapping coefficient vector, For the temperature sensitivity coefficient of the equipment, The environmental humidity sensitivity coefficient For equipment temperature, This is the equipment temperature reference value. For ambient humidity, This is the baseline value for ambient humidity.

[0092] Finally, the future load forecasts of each device are aggregated into the total site load demand via industrial Ethernet. ;

[0093] Acquire real-time energy data, and based on the total load demand of the site and real-time energy data, generate distributed power output plans and grid purchase strategies with the goals of minimizing total electricity costs and carbon emissions.

[0094] Based on the distributed power output plan and the total load demand of the site, controllable equipment-level control commands are decomposed;

[0095] Real-time carbon efficiency indicators are obtained based on distributed power generation output plans and grid power purchase strategies.

[0096] By comparing the command execution deviation with the change in carbon efficiency, the optimization model parameters are adaptively corrected.

[0097] Execute control commands at the controllable device level at the current moment and provide feedback on the actual output.

[0098] like Figure 1 As shown in the embodiment of this application, an energy-saving power system for construction sites is provided. This system is used to implement a control method for energy-saving power systems for construction sites, including an energy monitoring module, a load forecasting module, a multi-objective optimization module, a dynamic scheduling module, a carbon efficiency assessment module, an execution feedback module, and a distributed power supply module.

[0099] The energy monitoring module is used to collect real-time photovoltaic power generation data within the construction site. Electricity purchased from the power grid Energy consumption of electrical equipment and the carbon emission intensity of the power grid ;

[0100] The load prediction module is used to acquire operating condition vectors, construction equipment temperature, and ambient humidity, and analyze the total site load demand. ;

[0101] The steps for obtaining the total site load demand include:

[0102] Data from vibration sensors and motor tachometers of construction equipment are collected via an industrial bus interface to construct the working state vector of construction equipment j at time t.

[0103] ;

[0104] in, Let be the working state vector of construction equipment j at time t. Let be the working state vector of construction equipment j at time t-1. This refers to the quantized control command for construction equipment j at time t. for The state transition matrix describes the natural evolution of equipment operating conditions. for Control input matrix, quantization control commands Impact on the state For construction equipment to obey Distributed Gaussian noise vector, It is A symmetric positive definite matrix whose diagonal elements The variance of the i-th component in the operating condition state vector is represented by its off-diagonal elements. Let represent the covariance between the i-th component and the j-th component in the operating condition state vector. Vector used to describe the operating status of construction equipment Random interference in;

[0105] Obtain process codes, construction equipment temperatures, and ambient humidity from the BIM database, and calculate future load forecasts using an energy consumption mapping function;

[0106] The energy consumption mapping function is:

[0107] ;

[0108] in, Let j be the predicted load value for construction equipment j over a future period. For construction equipment j Energy consumption mapping coefficient vector, For the temperature sensitivity coefficient of the equipment, The environmental humidity sensitivity coefficient For equipment temperature, This is the equipment temperature reference value. For ambient humidity, This is the baseline value for ambient humidity.

[0109] Finally, the future load forecasts of each device are aggregated into the total site load demand via industrial Ethernet. .

[0110] The multi-objective optimization module is used to acquire real-time energy data and optimize it according to the total site load demand. Based on real-time energy data, a distributed power generation plan is generated with the goal of minimizing total electricity costs and carbon emissions. and power grid purchase strategy ;

[0111] The generated distributed power output plan and power grid purchase strategy The steps to obtain it include:

[0112] Establish an objective function that aims to minimize both total electricity cost and carbon emissions:

[0113] ;

[0114] Where F is the objective function value, The real-time electricity price is (yuan / kWh), t is the time index, and T is the total duration. This is the dynamic power grid carbon emission intensity factor. Purchase electricity for the power grid Carbon emissions from purchasing electricity from the grid (kgCO2). As a weighted average of economic costs, For carbon emission weighting, and ;

[0115] Next, set the constraints for the objective function:

[0116] ;

[0117] Where m is the index number of the distributed power source, and M is the total number of distributed power sources in the construction site. Let m be the actual output power of the m-th distributed power source at time t. Let t be the total load demand of the site. This refers to the dynamic elasticity margin coefficient.

[0118] Finally, the Pareto optimal solution set is obtained using the particle swarm optimization algorithm to generate a distributed power generation plan. and power grid purchase strategy .

[0119] When using the particle swarm optimization algorithm to find the Pareto optimal solution set, the distributed power generation output plan is first... And power grid purchase strategy The position is encoded as the particle position, parameters such as particle swarm size are set, and the position and velocity are randomly initialized. A penalty function is used to handle the constraints to ensure... Next, the objective function value (total electricity cost and carbon emissions) for each particle is calculated, and the Pareto solution set is selected and stored in the archive based on the non-dominated relation. Then, the particle velocity and position are iteratively updated (velocity is guided by individual optimality and global optimality), and the updated values ​​are projected onto the feasible region, using crowding distance to maintain the diversity of the solution set. Finally, when the iteration ends or the solution set stabilizes, the non-dominated solutions in the archive are output, yielding solutions with different cost and carbon emission tradeoffs. and .

[0120] The dynamic elasticity margin coefficient is calculated as follows:

[0121] The acceleration of load change is obtained based on the total load demand of the site at time t. (kW / h²);

[0122] Obtain the control command execution deviation over the past 24 hours and calculate its variance. ;

[0123] The dynamic elasticity margin coefficient is obtained through the formula for the dynamic elasticity margin coefficient.

[0124] ;

[0125] in, The load change sensitivity coefficient, To control the deviation sensitivity coefficient, For a moment Deviation in the execution of control commands The integral time variable has a range of values. .

[0126] Load change sensitivity coefficient and control deviation sensitivity coefficient Training with historical construction data: Using historical construction data, such as load changes, control deviations, and actual power supply margin requirements during foundation construction and main structure construction, the optimal power supply for different construction stages is fitted using the least squares method or machine learning algorithms. and The values ​​are used to form a phased coefficient library for real-time access.

[0127] The dynamic scheduling module is used to decompose controllable equipment-level control commands based on the distributed power output plan and the total site load demand. ;

[0128] The step of decomposing controllable device-level control commands includes:

[0129] Obtain the partial derivative of the distributed power generation output plan with respect to the control command. and the partial derivative of the total site load demand with respect to the control command The weighted Euclidean norm is constructed to characterize the degree of influence of control commands on power output and load matching (kW / control unit). ;

[0130] in, This is the weight matrix. It is a diagonal weight matrix. Let diag(⋅) be the carbon efficiency sensitivity weight of the controllable device k, and let diag(⋅) represent the construction of a diagonal matrix with the vector elements as diagonal elements.

[0131] Get smoothing items Prevent frequent start-ups and shutdowns of controllable equipment;

[0132] The optimal control command solution function is obtained based on the weighted Euclidean norm and the smoothing term:

[0133] ;

[0134] in, Let be the optimal control command for controllable device k at time t. The output plan of the distributed power source at time t. Let t be the total load demand of the site. Let be the control command for controllable device k at time t. To control the smoothing coefficient of the instructions, This is the control command value from the previous time t-1.

[0135] The carbon efficiency sensitivity weight of the controllable device k The formula for obtaining it is:

[0136] ;

[0137] in, This is the dynamic power grid carbon emission intensity factor. Let m be the optimal output plan for the m-th distributed power source at time t. Power purchase strategy for the power grid.

[0138] This indicates the sensitivity of control commands to carbon efficiency indicators, with the denominator being the square of the denominator term of the carbon efficiency indicator.

[0139] The carbon efficiency assessment module is used to obtain real-time carbon efficiency indicators based on the optimal power output plan and the power grid purchase strategy.

[0140] ;

[0141] in For a moment The optimal carbon efficiency index reflects the carbon emission efficiency of distributed power supply. For a moment No. Optimal output plan (kW) for a distributed power source. This represents the total number of distributed power sources. For a moment The dynamic grid carbon emission intensity factor (kgCO2 / kWh). For a moment The grid purchase strategy (kW);

[0142] The dynamic power grid carbon emission intensity factor The acquisition steps include:

[0143] Real-time current at grid nodes is collected by current transformers, and line resistance and grid topology distance are read from non-volatile memory. The dynamic grid carbon emission intensity factor is calculated by 24-hour sliding window integration. The calculation formula for the dynamic grid carbon emission intensity factor is as follows:

[0144] ;

[0145] in, For integration time variable ( ), For a moment The marginal carbon emission intensity of the power grid (kgCO2 / kWh). For a moment Actual grid-purchased electricity (kW).

[0146] The numerator represents the total carbon emissions from grid-purchased electricity in the past 24 hours (kgCO2), the denominator represents the total grid-purchased electricity during the same period (kWh), and the dynamic grid carbon emission intensity factor is also included. The rolling average carbon emission intensity (kgCO2 / kWh) reflects the power generation of the power grid.

[0147] The execution feedback module is used to compare instruction execution deviations. Changes in carbon efficiency This triggers adaptive correction of the optimization model parameters;

[0148] The distributed power module is used to execute controllable device-level control commands at the current moment. And provide feedback on actual output. .

[0149] The execution of the controllable device-level control command at the current moment includes the following steps:

[0150] The line impedance is obtained by using an impedance measuring device, and the real-time line loss rate is calculated. :

[0151]

[0152] in, For a moment The voltage and current phase difference (in radians). Real-time line loss rate;

[0153] Then based on Set upper and lower limits for output, where, Minimum output limit (kW) for distributed power sources. For a moment The optimal photovoltaic power output plan (kW). For a moment The optimal diesel generator output plan (kW). Maximum output limit (kW) for distributed power sources. For a moment Forecasted load (kW);

[0154] in The maximum available power (kW) taking line losses into account.

[0155] Finally, the controllable device-level control command is executed through the solid-state relay at the current moment, and the trip protection is triggered when the upper limit is exceeded.

[0156] In summary, the embodiments of this application construct equipment condition vectors by collecting equipment vibration, rotation speed and other data through industrial bus, and integrate the equipment condition state vectors with temperature and humidity correction functions, thereby realizing dynamic and accurate prediction of construction load.

[0157] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0158] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0159] 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 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0160] 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 and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0161] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0162] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A system for energy-efficient electricity use at a construction site, characterized in that The energy monitoring module, the load prediction module, the multi-objective optimization module, the dynamic scheduling module, the carbon efficiency evaluation module, the execution feedback module, and the distributed power supply module are included. The energy monitoring module is configured to collect photovoltaic power generation, grid power purchase, energy consumption of electrical equipment, and carbon emission intensity of the grid in real time. The load prediction module is configured to obtain a working condition state vector, a construction equipment temperature, and an environmental humidity, and analyze a total load demand of the site. The total load demand of the site is obtained by: The working condition state vector of the construction equipment j at time t is constructed by collecting vibration sensor and motor speed meter data of the construction equipment through an industrial bus interface. The working procedure code, the construction equipment temperature, and the environmental humidity are obtained from the BIM database, and the load prediction value of the future period is calculated through the energy consumption mapping function. The energy consumption mapping function is: ； wherein, is the future time period load forecast value of the construction equipment j, is the working condition state vector of the construction equipment j at time t, is the future time period load forecast value of the construction equipment j, is the energy consumption mapping coefficient vector, is the equipment temperature sensitive coefficient, is the environmental humidity sensitive coefficient, is the equipment temperature, is the equipment temperature reference value, is the environmental humidity, is the environmental humidity reference value; Finally, the load prediction values of each device in the future time period are collected as the total load demand of the site through industrial Ethernet ; The multi-objective optimization module is configured to obtain real-time energy data, generate a distributed power supply output plan and a grid power purchase strategy according to the total load demand of the site and the real-time energy data, and take the minimum total power consumption and the minimum carbon emission as the target. The dynamic scheduling module is configured to decompose controllable device-level control instructions according to the distributed power supply output plan and the total load demand of the site. The carbon efficiency evaluation module is configured to obtain real-time carbon efficiency indicators according to the distributed power supply output plan and the grid power purchase strategy. The execution feedback module is configured to compare the instruction execution deviation and the carbon efficiency change amount, and trigger adaptive correction of the optimization model parameters. The distributed power supply module is configured to execute the controllable device-level control instructions at the current time and feedback the actual output.

2. A control method for an energy-saving power supply system for a construction site, characterized by, The building construction site energy-saving power system of claim 1 comprises the following steps: Real-time collection of photovoltaic power generation, grid power purchase, energy consumption of electrical equipment, and carbon emission intensity of the grid in the construction site is performed. Obtaining a working condition state vector, a construction equipment temperature, and an environmental humidity, and analyzing a total load demand of the site. The total load demand of the site is obtained by: The working condition state vector of the construction equipment j at time t is constructed by collecting vibration sensor and motor speed meter data of the construction equipment through an industrial bus interface. The working procedure code, the construction equipment temperature, and the environmental humidity are obtained from the BIM database, and the load prediction value of the future period is calculated through the energy consumption mapping function. The energy consumption mapping function is: ； wherein, is the future time period load forecast value of the construction equipment j, is the working condition state vector of the construction equipment j at time t, is the future time period load forecast value of the construction equipment j, is the energy consumption mapping coefficient vector, is the equipment temperature sensitive coefficient, is the environmental humidity sensitive coefficient, is the equipment temperature, is the equipment temperature reference value, is the environmental humidity, is the environmental humidity reference value; Finally, the load prediction values of each device in the future time period are aggregated into the total load demand of the site through industrial Ethernet ; Obtaining real-time energy data, generating a distributed power supply output plan and a grid power purchase strategy according to the total load demand of the site and the real-time energy data, and taking the minimum total power consumption and the minimum carbon emission as the target. Decomposing controllable device-level control instructions according to the distributed power supply output plan and the total load demand of the site. Obtaining real-time carbon efficiency indicators according to the distributed power supply output plan and the grid power purchase strategy. Comparing the instruction execution deviation and the carbon efficiency change amount, and triggering adaptive correction of the optimization model parameters. Executing the controllable device-level control instructions at the current time and feeding back the actual output.

3. The control method of the construction site energy-saving power supply system according to claim 2, characterized by, The working condition state vector of the construction equipment j at time t is obtained by: ； wherein, is the working condition state vector of the construction equipment j at time instant t, is the working condition state vector of the construction equipment j at time instant t-1, is the quantized control command of the construction equipment j at time instant t, is the quantized control command of the construction equipment j at time instant t-1, is the state transition matrix, is the state transition matrix, is the control input matrix, the quantized control command has an influence on the state, is the Gaussian noise vector distributed according to is the Gaussian noise vector distributed according to 4. The control method for the power saving system for construction sites as set forth in claim 2, characterized by, The distributed power supply output plan and the grid power purchase strategy are generated by: A target function taking the minimum total power consumption and the minimum carbon emission as the target is established. ； wherein F is the objective function value, is the real-time electricity price, t is the time index, and T is the total duration, is the dynamic grid carbon intensity factor, is the grid electricity purchase amount, is the economic cost weight, is the carbon emission weight, and ; The constraint conditions of the target function are set. ； wherein m is the index number of the distributed power supply, M is the total number of distributed power supplies in the construction site, is the actual output power of the mth distributed power supply at time t, is the total load demand of the site at time t, is the dynamic elastic margin coefficient; Finally, the particle swarm optimization algorithm is used to solve the Pareto optimal solution set and generate the distributed generation output plan and the grid power purchase strategy .

5. The control method of the construction site energy-saving power feeding system according to claim 4, characterized by, The dynamic elastic margin coefficient is calculated as: According to the total load demand of the site at time t, a load change acceleration is obtained; A past 24-hour control instruction execution deviation is obtained, and a variance thereof is obtained; A dynamic elastic margin coefficient is obtained through a dynamic elastic margin coefficient formula ； wherein, is a load change sensitivity coefficient, is a control deviation sensitivity coefficient, is a time variable, is a control command execution deviation, is an integral time variable, and has a value range of .

6. The control method for the energy-saving power supply system for a construction site according to Claim 2, wherein The step of decomposing the controllable device-level control instruction comprises: A partial derivative of a distributed power output plan to the control instruction and a partial derivative of the total load demand of the site to the control instruction are obtained, and a weighted Euclidean norm is constructed; wherein, is a weight matrix, is a diagonal weight matrix, is the carbon efficiency sensitivity weight of controllable device k; acquiring a smoothing term ; An optimal control instruction solving function is obtained according to the weighted Euclidean norm and a smoothing term: ； wherein, is the optimal control instruction of controllable device k at time t, is the distributed power output plan at time t, is the total load demand of the site at time t, is the control instruction of controllable device k at time t, is the control instruction smoothing coefficient, is the control instruction value of the previous time t-1.

7. The control method of the construction site energy-saving power feeding system according to claim 6, characterized by, The acquisition formula of the carbon efficiency sensitivity weight of the controllable device k is: ； wherein, is a dynamic grid carbon intensity factor, is the distributed power output plan of the mth distributed power at time t, and M is the total number of distributed powers in the construction site, is the grid power purchase strategy, is the distributed power output plan at time t, is the control instruction of the controllable device k at time t.

8. The control method of the construction site energy-saving power feeding system according to claim 2, characterized by, The acquisition formula of the real-time carbon efficiency index is: ； in For a moment The optimal carbon efficiency index reflects the carbon emission efficiency of distributed power supply. For a moment No. Distributed power generation output plan for a distributed power source. This represents the total number of distributed power sources. For a moment Dynamic power grid carbon emission intensity factor For a moment The power grid's power purchase strategy.

9. The control method for the energy-saving power feeding system for a construction site according to Claim 8, wherein The step of obtaining the dynamic power grid carbon emission intensity factor comprises: Real-time current of a power grid node is collected through a current transformer, line resistance and power grid topology distance are read from a nonvolatile memory, and the dynamic power grid carbon emission intensity factor is calculated through 24-hour sliding window integral calculation, and a calculation formula of the dynamic power grid carbon emission intensity factor is: ； wherein is the integral time variable, is the time of the marginal carbon intensity of the grid, is the actual grid electricity purchase at the time of the marginal carbon intensity of the grid.

10. The control method for the energy-saving power feeding system for a construction site according to Claim 2, wherein The step of executing the controllable device-level control instruction at the current time comprises the following steps: Obtain line impedance through impedance measuring device, calculate real-time line loss rate : ； wherein is the voltage current phase difference at time t; based on setting upper and lower output limits; wherein, Pmin is the minimum power limit of the distributed power source, t is the time, PoptPV is the optimal photovoltaic power plan at time t, t is the time, PoptDG is the optimal diesel generator power plan at time t, Pmax is the maximum power limit of the distributed power source, t is the time, Pload is the load forecast value at time t. Finally, the controllable device-level control instruction at the current time is executed through a solid-state relay, and a trip protection is triggered when the upper limit is exceeded.

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