Multi-modal optimization combined peak shaving method and system of molten salt heat storage and coal power unit

Abstract

The embodiment of the present disclosure provides a molten salt heat storage and coal power unit multimode optimization combined peak shaving method and system. The method comprises the following steps: collecting the power grid dispatching instruction, new energy and load prediction data and combined system operation state data in real time; identifying the feasible operation modes of the combined system in the current and future dispatching period, including pure condensing power generation, steam extraction heat storage, heat release power generation and cogeneration mode; for each mode, a multi-objective collaborative optimization model containing specific coupling constraints is established, and the total operation cost, peak shaving income, equipment life consumption and carbon dioxide emission are optimized simultaneously; a multi-objective optimization algorithm is used to solve each mode model in parallel, and based on a preset decision rule, a final dispatching scheme is selected from the global Pareto optimal solution set; the scheme is analyzed into control instructions and executed, and the system state is monitored to form a closed loop control. The multimode intelligent collaboration and multi-objective comprehensive optimization are realized, and the flexibility, economy and environmental protection of the combined system peak shaving are improved.

Description

Technical Field

[0001] The embodiments disclosed herein belong to the field of molten salt thermal energy storage and coal-fired power unit combined peak shaving technology, specifically involving a multimodal optimized combined peak shaving method and system for molten salt thermal energy storage and coal-fired power units. Background Technology

[0002] The proportion of intermittent renewable energy sources such as wind power and photovoltaic power in the power grid is growing rapidly. The randomness and volatility of their output seriously threaten the real-time balance and safe and stable operation of the power system. Therefore, the power grid’s demand for flexible and efficient peak-shaving resources has become extremely urgent.

[0003] Coal-fired power units, as the main peak-shaving power source in the current power system, face a fundamental bottleneck in their peak-shaving capacity. The minimum technical output of these units limits the scope for deep peak-shaving, the inertia of load fluctuations leads to insufficient response speed, and low-load operation results in increased coal consumption, a sharp drop in efficiency, and accelerated equipment wear, deteriorating both economic efficiency and environmental impact. The traditional peak-shaving model, relying solely on the flexibility of coal-fired power units, is no longer adequate to meet the real-time requirements of a high-proportion renewable energy grid. Molten salt thermal energy storage technology, due to its large scale, long lifespan, and good stability, is considered an effective way to improve the flexibility of traditional power sources. Coupled with coal-fired power units, it can form a combined "heat-power" system. During periods of low load and high renewable energy generation, surplus electricity or extracted steam can be used for thermal energy storage to help units exceed the lower limit of deep peak-shaving and promote renewable energy consumption; during peak load periods, heat can be released to generate electricity, enhancing the peak-shaving capacity of the combined system.

[0004] However, existing scheduling methods for coupled systems mostly rely on optimization of a single objective or empirical rules, making it difficult to simultaneously coordinate multiple objectives such as unit safety, economy, environmental protection, and grid peak-shaving needs. Furthermore, they lack accurate identification and dynamic optimization switching of various potential operating modes of the system. Summary of the Invention

[0005] The embodiments disclosed herein aim to at least solve one of the technical problems existing in the prior art, and provide a multimodal optimization joint peak shaving method and system for molten salt thermal storage and coal-fired power units.

[0006] One aspect of this disclosure provides a multi-modal optimized joint peak-shaving method for molten salt thermal storage and coal-fired power units, the method comprising: Real-time acquisition of power grid data and combined system data; wherein, the power grid data includes power grid dispatch instructions, ultra-short-term renewable energy power and load forecast data, and the combined system data includes the operating status data of the combined system of molten salt thermal storage and coal-fired power units; Based on the power grid data and the joint system data, identify the feasible operating modes of the joint system in the current and future scheduling cycles; For each of the aforementioned feasible operating modes, a multi-objective collaborative optimization model is established, wherein the optimization model includes specific coupling constraints between the molten salt thermal storage system and the coal-fired power unit under the corresponding operating mode; A multi-objective optimization algorithm is used to solve the optimization model under each operating mode in parallel, and the final scheduling scheme is selected from all Pareto optimal solution sets based on preset decision rules; The final scheduling scheme is parsed into specific control instructions and sent to the joint system for execution, while the status of the joint system is monitored to form a closed-loop control.

[0007] Furthermore, the feasible operating modes include at least: Pure condensation power generation mode: The molten salt thermal storage system does not operate, and the coal-fired power unit responds to dispatch commands independently; Steam extraction and thermal storage mode: Steam extraction from coal-fired power units heats molten salt, reducing the net on-grid power of the combined system and enabling deep peak shaving; Exothermic power generation mode: Molten salt thermal storage system releases thermal energy to generate electricity, increasing the total output of the combined system; Cogeneration mode: While meeting the electrical load, the thermal load is provided by the molten salt thermal storage system.

[0008] Furthermore, the multi-objective function of the multi-objective collaborative optimization model is shown in the following equation:

[0009]

[0010]

[0011]

[0012]

[0013] In the formula, Total operating cost, For total peak shaving revenue, This represents the total equipment lifespan loss. The total carbon dioxide emissions are represented by T, the total number of time periods in the scheduling cycle is T, and the time period number is t. For coal consumption costs, For operation and maintenance costs, For peak power, For peak-shaving electricity pricing, This is an indicator of equipment lifespan loss. This refers to carbon dioxide emissions.

[0014] Furthermore, the specific coupling constraint includes: Mode switching logic constraints are used to ensure the consistency of the operating state of each device; Upper and lower limits of coal-fired power unit output and extraction / reheat parameters; Constraints on the heat storage and release power and capacity limits of molten salt thermal energy storage systems.

[0015] Furthermore, the preset decision rule is the TOPSIS decision method based on the entropy weight method; or, The weighting strategy is manually set based on the real-time dispatch priority of the power grid.

[0016] Furthermore, the multi-objective optimization algorithm is the NSGA-II algorithm, the MOEA / D algorithm, or the multi-objective particle swarm optimization algorithm.

[0017] Furthermore, the monitoring of the joint system status to form closed-loop control includes: Closed-loop real-time control is implemented with a rolling optimization scheduling cycle of 5 to 15 minutes.

[0018] Another aspect of this disclosure provides a multi-modal optimized joint peak-shaving system for molten salt thermal storage and coal-fired power units, used to implement the multi-modal optimized joint peak-shaving method for molten salt thermal storage and coal-fired power units described above. The system includes: The data acquisition module is used to acquire grid data and combined system data in real time; wherein, the grid data includes grid dispatch instructions, ultra-short-term renewable energy power and load forecast data, and the combined system data includes the operating status data of the combined system of molten salt thermal storage and coal-fired power units; The mode identification module is used to identify the feasible operating modes of the joint system in the current and future scheduling cycles based on the power grid data and the joint system data; The multimodal optimization decision module is used to establish a multi-objective collaborative optimization model for each of the feasible operating modes, solve the optimization model under each operating mode, and select the final scheduling scheme based on preset decision rules. The instruction execution and feedback module is used to parse the final scheduling scheme into specific control instructions and send them to the joint system for execution, while monitoring the status of the joint system to form closed-loop control.

[0019] Another aspect of this disclosure provides an electronic device, comprising: At least one processor; and, A memory communicatively connected to the at least one processor is used to store one or more programs, which, when executed by the at least one processor, enable the at least one processor to implement the multimodal optimization joint peak-shaving method for molten salt thermal storage and coal-fired power units described above.

[0020] Another aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the multimodal optimization joint peak-shaving method for molten salt thermal storage and coal-fired power units described above.

[0021] This disclosure discloses a multi-modal optimization joint peak-shaving method and system for molten salt thermal storage and coal-fired power units. By intelligently identifying multiple operating modes and establishing dedicated optimization models for each mode, it solves the multi-state coordination problem of complex joint systems, significantly improving the accuracy and adaptability of peak-shaving strategies. Employing a multi-objective optimization algorithm, it simultaneously coordinates multiple objectives, overcoming the limitations of traditional single-objective optimization and ensuring that the scheduling scheme achieves optimal comprehensive benefits. Through a closed-loop decision-making and execution system, it responds to grid scheduling needs in real time, maximizing the peak-shaving potential of the joint system and providing a safe, economical, and flexible peak-shaving solution for high-proportion renewable energy grids. Attached Figure Description

[0022] Figure 1 This is a schematic flowchart illustrating a multimodal optimization joint peak-shaving method for molten salt thermal storage and coal-fired power units according to an embodiment of this disclosure. Figure 2 This is a schematic diagram of the structure of a multimodal optimized combined peak-shaving system for molten salt thermal storage and coal-fired power units according to another embodiment of this disclosure; Figure 3 This is a schematic diagram of the structure of an electronic device according to another embodiment of the present disclosure. Detailed Implementation

[0023] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this disclosure.

[0024] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0025] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0026] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Therefore, the first component discussed below may be referred to as the second component without departing from the teachings of this disclosure. As used in this disclosure, the term "and / or" includes all combinations of any and more of the associated listed items.

[0027] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing this disclosure, and therefore cannot be used to limit the scope of protection of this disclosure.

[0028] One embodiment of this disclosure provides a multimodal optimized joint peak-shaving method for molten salt thermal storage and coal-fired power units, the method comprising: Step S1: Real-time acquisition of power grid data and integrated system data.

[0029] Specifically, multi-source heterogeneous data is acquired and integrated in real time through sensors, Supervisory Control and Data Acquisition (SCADA) systems, Energy Management Systems (EMS), and data interfaces deployed at power dispatch centers and joint system sites.

[0030] The power grid data includes power grid dispatch instructions, ultra-short-term renewable energy power and load forecast data. Power grid dispatch instructions are real-time dispatch instructions received from the superior power dispatching agency, including but not limited to active power setpoints for the joint system, peak-shaving ancillary service invocation instructions, and frequency regulation requirements. Ultra-short-term renewable energy power and load forecast data are the renewable energy (wind power, photovoltaic, etc.) power generation forecast curves and the total system load forecast curves for the regional power grid within a future dispatching cycle (e.g., the next 15 minutes to several hours), obtained through the power grid dispatching system. These data are key inputs for assessing future peak-shaving demand and renewable energy absorption capacity.

[0031] The combined system data includes operational status data of the molten salt thermal energy storage and coal-fired power unit combined system. Specifically, the operational status data may cover real-time data from the coal-fired power unit side, real-time data from the molten salt thermal energy storage system side, and data from the coupling points of the combined system. The real-time data for the coal-fired power units includes, but is not limited to, the unit's current active power output, main steam pressure and temperature, reheat steam parameters, turbine extraction steam pressure and flow rate, boiler efficiency, coal consumption rate, environmental parameters (such as flue gas emission data), and the health status or lifespan loss indicators of key equipment (such as turbines and boilers). The real-time data for the molten salt thermal storage system includes, but is not limited to, the molten salt temperature, liquid level (or heat storage capacity), current heat storage / release power, heat exchanger inlet and outlet working fluid parameters (temperature, pressure, flow rate), electric heater operating status and power, and the operating status and safety parameters of each piece of equipment in the thermal storage system. The data at the coupling point of the combined system is the key node data connecting the coal-fired power units and the molten salt thermal storage system, such as the steam parameters (pressure, temperature, flow rate) on the extraction steam pipe used to heat the molten salt, or the working fluid parameters of the heat exchange / power generation system that uses the heat of the molten salt to generate steam.

[0032] The aforementioned data is collected at a high frequency of seconds or minutes and transmitted to the central processing unit via industrial communication networks (such as OPC UA, Modbus TCP / IP, etc.) to provide real-time and accurate data support for subsequent modal identification and optimization calculations.

[0033] Step S2: Based on the power grid data and the joint system data, identify the feasible operating modes of the joint system in the current and future scheduling cycles.

[0034] Specifically, based on the real-time and predicted data collected in step S1 above, the system dynamically determines and enumerates all technically feasible and scheduling-compliant potential operating states of the joint system in the next rolling optimization cycle, i.e., "operational modes". The identification process follows physical constraints, security constraints, and scheduling requirements, and its specific implementation logic is as follows: 1. A pre-defined modal knowledge base containing all possible operating modes is provided within the system. Each mode is defined by a specific set of device state logic, energy flow, and scheduling functions. For example: 1.1 Pure Condensation Power Generation Mode: Defined as all main equipment (such as molten salt pumps, heat exchangers, and electric heaters) of the molten salt thermal storage system are in a shutdown or hot standby state and do not participate in energy exchange; the coal-fired power unit operates independently, and its output responds to the grid dispatch instructions within its own technical output range.

[0035] 1.2 Steam extraction thermal storage mode: This is defined as a coal-fired power unit extracting a portion of steam from a designated steam extraction port (such as a medium-pressure cylinder or a low-pressure cylinder) to heat the low-temperature molten salt in the molten salt thermal storage system. At this time, the power generation of the unit is reduced due to steam extraction, and the net on-grid power of the combined system decreases, thereby achieving deep peak shaving or freeing up power generation space for new energy sources.

[0036] 1.3 Exothermic power generation mode: defined as the release of heat by high-temperature molten salt, which generates steam through a heat exchange system to drive auxiliary steam turbine generator sets to generate electricity, or is directly used to improve the main steam parameters; at this time, the total grid-connected power of the combined system is the sum of the power of coal-fired power units and the power of thermal storage power generation, which is used for peak load or to provide up-regulation backup.

[0037] 1.4 Cogeneration mode: Defined as the method of providing industrial steam or heating load to users through heat exchangers while meeting the electricity load demand, using the thermal energy stored in the molten salt thermal storage system.

[0038] 2. For each time period within the future scheduling cycle, the power grid dispatch instructions, ultra-short-term renewable energy and load forecast data, and real-time status of the joint system obtained in step S1 are used as inputs, and constraint matching verification is performed against each mode in the modal knowledge base. The constraints for verification mainly include: 2.1 Physical and Equipment Constraints: Verify whether the equipment required for this mode (such as specific extraction valves, molten salt pumps, heat exchangers) is in a usable state; whether the coal-fired power unit can safely provide the required extraction steam parameters under the current state (such as load rate, main steam pressure); whether the current heat storage capacity of the molten salt thermal storage system meets the heat release requirements, or whether the remaining thermal storage capacity meets the thermal storage requirements.

[0039] 2.2 Safe operation constraints: Verify whether this mode will cause any critical parameters (such as the minimum stable combustion load of the boiler, the minimum cooling steam flow of the steam turbine, the minimum safe temperature of the molten salt system, etc.) to exceed the limit.

[0040] 2.3 Dispatch Demand Constraints: Verify whether the function of this mode (such as deep peak shaving, peak load, and heating) matches the current and predicted grid peak shaving demand (reflected by the difference between load and new energy forecasts) or heat load demand. For example, during periods when new energy generation is predicted to be high and the grid needs deep peak shaving, the "steam extraction and thermal storage mode" is mainly selected; during periods when peak load is predicted and peak output is needed, the "heat release and power generation mode" is given priority.

[0041] 3. Through the rule matching and constraint verification described above, a set of feasible operating modes is output for each time period within the future scheduling cycle. This set may contain one or more modes. For example, when grid demand is stable and the thermal storage system is in a permissible state, the system may simultaneously be feasible for operating "pure condensing power generation," "extraction thermal storage," and "exothermic power generation." The output of this step provides a clear decision space for subsequent steps to perform refined modeling and optimization for each feasible mode.

[0042] Step S3: For each of the feasible operating modes, establish a multi-objective collaborative optimization model, wherein the optimization model includes specific coupling constraints between the molten salt thermal storage system and the coal-fired power unit under the corresponding operating mode.

[0043] Specifically, for each feasible operating mode identified in step S2 above, a dedicated mathematical optimization model considering multi-objective collaboration is constructed. This model aims to find the optimal collaborative operation strategy between the generating unit and the thermal storage system within its physical boundaries. The construction of each model includes the following key components: 1. Definition of decision variables: For a specific mode, define its core adjustable control variables. For example: 1.1 Under the extraction steam storage mode, the decision variables mainly include: the active power output setpoint of the coal-fired power unit, the extraction steam flow rate (or pressure, temperature) used to heat the molten salt, and the thermal storage power of the molten salt thermal storage system.

[0044] 1.2 Under the exothermic power generation mode, the decision variables mainly include: the active power output setpoint of the coal-fired power unit and the exothermic power of the molten salt thermal storage system (or the active power output of the auxiliary generator unit).

[0045] 1.3 In the combined heat and power (CHP) mode, the decision variables also need to include: the heat power supplied to the heating network.

[0046] 2. Multi-objective collaborative optimization objective function: Establish a multi-objective function with a unified form but different parameter and variable ranges for each mode to comprehensively balance economy, profitability, equipment lifespan, and environmental protection. The objective function can be specifically expressed as follows:

[0047]

[0048]

[0049]

[0050]

[0051] In the formula, The total operating cost within the scheduling cycle, including fuel (coal) cost and operation and maintenance cost, needs to be minimized; The total peak-shaving revenue within the scheduling cycle must be maximized; The total equipment life loss index within the scheduling cycle is calculated based on equipment operating status (such as load rate, number of start-stop cycles, and temperature change rate) and needs to be minimized. The total carbon dioxide emissions within the scheduling cycle are calculated from coal consumption and need to be minimized; T is the total number of time periods in the scheduling cycle; t is the time period number. For coal consumption costs; For operation and maintenance costs; For peak shaving power; For peak-shaving electricity pricing; This is an indicator of equipment lifespan loss. This refers to carbon dioxide emissions.

[0052] 3. Modality-specific coupling constraints: Constraints vary by mode, ensuring that the model accurately reflects the physical and operational limitations of the corresponding mode. 3.1 Mode Switching Logic Constraints: Ensure that the state variables (0 / 1 variables or continuous variables) of the relevant equipment conform to the consistency definition of the mode under the corresponding mode. For example, in the "pure condensing power generation mode", the thermal storage power is constrained to zero; in the "steam extraction thermal storage mode", the heat release power is constrained to zero, and the energy balance equation between the steam extraction flow rate and the thermal storage power is activated.

[0053] 3.2 Operating Constraints of Coal-fired Power Units: These include the upper and lower limits of the unit's active power output under the corresponding mode (which may differ from the pure condensing mode), the main steam pressure range, and specific parameter constraints related to that mode. For example, the upper and lower limits of extraction steam and reheat parameters. Under the "extraction steam storage mode," the extraction steam pressure must be strictly constrained within the allowable range, and a coupling relationship model between the extraction steam flow rate and the decrease in unit power generation must be established.

[0054] 3.3 Operational Constraints of Molten Salt Thermal Storage Systems: These include the upper and lower limits of the thermal storage or release power under the corresponding mode, the upper and lower limits of the molten salt storage tank capacity, and the energy conversion constraints related to that mode. For example, under the "thermal power generation mode," a conversion efficiency model between the thermal release power and the output of the auxiliary generator set needs to be established.

[0055] 3.4 Power Balance Constraints of the Combined System: Ensure that the total grid-connected power of the combined system meets the net load demand of the grid dispatch instructions or forecasts. For example, under the "steam extraction and thermal storage mode", the total grid-connected power = unit power generation - plant power consumption; under the "heat release and power generation mode", the total grid-connected power = unit power generation + thermal storage and power generation - plant power consumption.

[0056] 3.5 Dynamic process constraints: For rolling optimization, dynamic constraints such as unit ramp rate limit and thermal storage system power change rate limit may also need to be considered.

[0057] This step, by independently establishing the aforementioned multi-objective optimization model with specific coupling constraints for each feasible mode, can accurately characterize the technical details and optimization potential under different operating conditions, laying a precise mathematical model foundation for the parallel optimization solution in subsequent steps.

[0058] Step S4: Use a multi-objective optimization algorithm to solve the optimization model under each operating mode in parallel, and select the final scheduling scheme from all Pareto optimal solution sets based on preset decision rules.

[0059] Specifically, the multi-objective optimization problem established for each feasible mode in step S3 above, which may conflict with each other, is processed efficiently and in parallel, and a final execution scheme is intelligently selected from all possible optimal schemes.

[0060] For each feasible operating mode identified in step S2 above, its corresponding multi-objective collaborative optimization model (including the objective function and specific coupling constraints) is treated as an independent optimization problem. Multi-objective optimization algorithms are used to solve these problems in parallel. Possible algorithms include, but are not limited to: Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multiobjective Evolutionary Algorithm based on Decomposition (MOEA / D), or multi-objective particle swarm optimization. These algorithms can effectively handle complex optimization models with multiple conflicting objectives, nonlinearity, and potentially mixed integer variables.

[0061] The optimization process for each mode aims to find its Pareto optimal solution set (or non-dominated solution set). Each solution in this set represents a possible operating strategy (i.e., the values ​​of a set of decision variables) under that mode, and no objective can be further improved without compromising at least one other objective. Therefore, the Pareto optimal solution set depicts all possible optimal trade-offs under that mode.

[0062] The Pareto optimal solutions obtained from solving all feasible operating modes are aggregated to form a global Pareto front covering all possible operating modes. Since the solutions in the Pareto optimal solution set cannot be directly compared mathematically, a pre-defined decision rule is introduced to select a final solution from the global Pareto front as the execution instruction for the current scheduling cycle. The pre-defined decision rule can be flexibly configured according to actual operational needs, including but not limited to: 1. TOPSIS Decision Rule Based on Entropy Weight Method: First, calculate the entropy value of each objective (operating cost, peak-shaving benefit, lifetime loss, carbon emissions) in the aggregated solution set, and determine the objective weight of each objective based on this, reflecting the dispersion and importance of the objective data. Then, use the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to calculate the distance between each Pareto optimal solution and the ideal optimal and ideal worst solutions, and select the solution with the highest relative closeness as the final solution. This method can reduce the influence of subjective preferences and achieve multi-objective comprehensive optimization.

[0063] 2. Decision-making rules based on manually set weights: The system operator pre-sets the weight coefficients for each objective based on the current urgency of the power grid, policy guidance, or equipment health status. Then, each Pareto optimal solution is weighted and summed, and the solution with the best weighted comprehensive score is selected. For example, when the power grid is under extreme peak-shaving pressure, the peak-shaving revenue objective can be given a higher weight; when equipment is nearing its maintenance period, the equipment lifespan loss objective can be given a higher weight.

[0064] 3. Hierarchical priority rule: First, ensure that the power grid security constraints (such as peak shaving instructions) are met, and then select the solution with the lowest operating cost among the solutions that meet these constraints.

[0065] The final scheme selected through the above decision-making rules not only includes the optimal operating mode sequence of the joint system for each time period within a future scheduling cycle (e.g., [Time Period 1: Steam extraction and thermal storage, Time Period 2: Pure condensing power generation, ...]), but also includes the specific values ​​of all decision variables under that mode (such as unit output, steam extraction flow rate, and thermal storage / release power for each time period). The parallel solution and intelligent decision-making in this step can systematically handle multiple conflicting objectives while considering various complex operating modes, thereby automatically finding the joint system scheduling scheme with the best overall benefits or that best suits the current operating strategy within the technically feasible scope.

[0066] Step S5: Parse the final scheduling scheme into specific control instructions and send them to the joint system for execution, while monitoring the status of the joint system to form closed-loop control.

[0067] Specifically, the final scheduling scheme output in step S4 is analyzed. The scheduling scheme clarifies the target operating mode for each time period (e.g., 1 minute) within a future scheduling cycle (e.g., 15 minutes) and the key equipment settings under that mode. Based on the target operating mode, corresponding mode switching instructions are generated, including sending instructions to the Distributed Control System (DCS) or Programmable Logic Controller (PLC) to activate or deactivate corresponding valves, pumps, and other equipment, thereby switching the system's operating state. For example, when switching to the "steam extraction and thermal storage mode," specific steam extraction regulating valves and molten salt pumps need to be activated, while the relevant valves in the heat release circuit need to be closed.

[0068] Based on the equipment setpoints in the final scheduling plan, continuous control commands are generated. These commands will serve as setpoints (SPs) for each closed-loop control loop (such as proportional-integral-derivative controllers), specifically including: generator active power setpoints, boiler main steam pressure setpoints, and turbine regulating valve opening commands on the coal-fired power unit side; molten salt pump speed or flow setpoints, steam regulating valve opening commands, and electric heater power setpoints on the molten salt thermal storage system side; and steam regulating valve opening commands on the extraction steam pipeline on the coupling interface side, as well as relevant valve commands for the feedwater / steam heat exchange loop.

[0069] The generated discrete mode switching commands and continuous control setpoint commands are transmitted in real time to the DCS, PLC, or dedicated controllers deployed at the power plant site via industrial communication networks (such as OPC UA, Modbus TCP / IP, Profibus, etc.). Actuators (such as variable frequency molten salt pumps, electric regulating valves, and electric heat tracing devices) receive the commands and act accordingly, driving the physical equipment of the integrated system to operate according to a predetermined scheme. For example, in the "exothermic power generation mode," the controller will calculate and dynamically adjust the molten salt flow setpoint based on the turbine power deviation value to precisely control the total output of the integrated system.

[0070] While executing commands, new operational status data of the integrated system are collected in real time, including actual unit output, molten salt temperature and level, key valve opening, and equipment health status. This data is used for control effect evaluation, model parameter correction, and safety boundary monitoring. Control effect evaluation is performed by comparing actual operating parameters (such as unit power and molten salt heat storage) with target values ​​in the scheduling plan. Model parameter correction involves comparing actual operating data with the predictive model to correct and optimize certain parameters of the model online (such as equipment efficiency coefficient and heat loss coefficient), improving the prediction accuracy of subsequent optimization cycles. Safety boundary monitoring includes real-time monitoring of whether key parameters exceed limits (such as molten salt temperature exceeding limits or unit vibration exceeding standards). Once a protection threshold is triggered, a preset safety protection mechanism can be immediately activated. For example, when the molten salt temperature is detected to be below the freezing point, the electric heating device is automatically activated; when the temperature exceeds the upper limit, the system automatically switches to the emergency cooling circuit.

[0071] Rolling optimization is performed using fixed short cycles (5-15 minutes). At the beginning of each cycle, steps S1 to S5 are re-executed based on the latest grid commands, forecast data, and the actual system state feedback from the previous cycle. This rolling mechanism enables optimized scheduling to adapt to rapid changes in grid demand, uncertainties in renewable energy output, and the dynamic characteristics of the system itself. It achieves adaptive closed-loop real-time control, avoids the error accumulation that may result from open-loop control based on a fixed plan, and ensures that the joint system always operates on an optimal or near-optimal trajectory.

[0072] This disclosure discloses a multi-modal optimization joint peak-shaving method for molten salt thermal storage and coal-fired power units. By intelligently identifying multiple operating modes and establishing dedicated optimization models for each mode, it solves the multi-state coordination problem of complex joint systems, significantly improving the accuracy and adaptability of peak-shaving strategies. Employing a multi-objective optimization algorithm, it simultaneously coordinates multiple objectives, overcoming the limitations of traditional single-objective optimization and ensuring that the scheduling scheme achieves optimal comprehensive benefits. Through a closed-loop decision-making and execution system, it responds to grid scheduling needs in real time, maximizing the peak-shaving potential of the joint system and providing a safe, economical, and flexible peak-shaving solution for high-proportion renewable energy grids.

[0073] like Figure 2 As shown, another embodiment of this disclosure provides a multi-modal optimized joint peak-shaving system for molten salt thermal storage and coal-fired power units, used to implement the multi-modal optimized joint peak-shaving method for molten salt thermal storage and coal-fired power units described above. The system includes: The data acquisition module 210 is used to acquire power grid data and combined system data in real time; wherein, the power grid data includes power grid dispatch instructions, ultra-short-term renewable energy power and load forecast data, and the combined system data includes the operating status data of the combined system of molten salt thermal storage and coal-fired power units; The mode identification module 220 is used to identify the feasible operating modes of the joint system in the current and future scheduling cycles based on the power grid data and the joint system data. The multimodal optimization decision module 230 is used to establish a multi-objective collaborative optimization model for each of the feasible operating modes, solve the optimization model under each operating mode, and select the final scheduling scheme based on preset decision rules. The instruction execution and feedback module 240 is used to parse the final scheduling scheme into specific control instructions and send them to the joint system for execution, while monitoring the status of the joint system to form closed-loop control.

[0074] Specifically, the multimodal optimization joint peak-shaving system of molten salt thermal storage and coal-fired power unit in this embodiment is used to implement the multimodal optimization joint peak-shaving method of molten salt thermal storage and coal-fired power unit described in the above embodiments. The specific implementation process has been described in detail in the above embodiments and will not be repeated here.

[0075] This disclosure discloses a multi-modal optimized joint peak-shaving system for molten salt thermal storage and coal-fired power units. By intelligently identifying multiple operating modes and establishing dedicated optimization models for each mode, it solves the multi-state coordination problem of complex joint systems, significantly improving the accuracy and adaptability of peak-shaving strategies. Employing a multi-objective optimization algorithm, it simultaneously coordinates multiple objectives, overcoming the limitations of traditional single-objective optimization and ensuring that the scheduling scheme achieves optimal overall benefits. Through a closed-loop decision-making and execution system, it responds to grid scheduling needs in real time, maximizing the peak-shaving potential of the joint system and providing a safe, economical, and flexible peak-shaving solution for high-proportion renewable energy grids.

[0076] like Figure 3 As shown, another embodiment of this disclosure provides an electronic device, including: At least one processor 301; and a memory 302 communicatively connected to the at least one processor 301 for storing one or more programs that, when executed by the at least one processor 301, enable the at least one processor 301 to implement the multimodal optimization joint peak shaving method for molten salt thermal storage and coal-fired power units described above.

[0077] The memory 302 and processor 301 are connected via a bus, which can include any number of interconnecting buses and bridges. The bus connects various circuits of one or more processors 301 and memory 302 together. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 301 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 301.

[0078] Processor 301 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 302 can be used to store data used by processor 301 during operation.

[0079] Another embodiment of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the multimodal optimization joint peak-shaving method for molten salt thermal storage and coal-fired power units described above.

[0080] The computer-readable storage medium may be included in the systems or electronic devices disclosed herein, or it may exist independently.

[0081] Computer-readable storage media can be any tangible medium that contains or stores a program, and can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, optical fibers, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0082] Computer-readable storage media may also include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code, specific examples of which include, but are not limited to, electromagnetic signals, optical signals, or any suitable combination thereof.

[0083] It is understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of this disclosure, and this disclosure is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this disclosure, and these modifications and improvements are also considered to be within the scope of protection of this disclosure.

Claims

1. A multi-modal optimization joint peak-shaving method for molten salt thermal storage and coal-fired power units, characterized in that, The method includes: Real-time acquisition of power grid data and combined system data; wherein, the power grid data includes power grid dispatch instructions, ultra-short-term renewable energy power and load forecast data, and the combined system data includes the operating status data of the combined system of molten salt thermal storage and coal-fired power units; Based on the power grid data and the joint system data, identify the feasible operating modes of the joint system in the current and future scheduling cycles; For each of the aforementioned feasible operating modes, a multi-objective collaborative optimization model is established, wherein the optimization model includes specific coupling constraints between the molten salt thermal storage system and the coal-fired power unit under the corresponding operating mode; A multi-objective optimization algorithm is used to solve the optimization model under each operating mode in parallel, and the final scheduling scheme is selected from all Pareto optimal solution sets based on preset decision rules; The final scheduling scheme is parsed into specific control instructions and sent to the joint system for execution, while the status of the joint system is monitored to form a closed-loop control.

2. The multi-modal optimized joint peak-shaving method for molten salt thermal storage and coal-fired power units according to claim 1, characterized in that, The feasible operating modes include at least: Pure condensation power generation mode: The molten salt thermal storage system does not operate, and the coal-fired power unit responds to dispatch commands independently; Steam extraction and thermal storage mode: Steam extraction from coal-fired power units heats molten salt, reducing the net on-grid power of the combined system and enabling deep peak shaving; Exothermic power generation mode: Molten salt thermal energy storage system releases thermal energy to generate electricity, increasing the total output of the combined system; Cogeneration mode: While meeting the electrical load, the thermal load is provided by the molten salt thermal storage system.

3. The multi-modal optimized joint peak-shaving method for molten salt thermal storage and coal-fired power units according to claim 1, characterized in that, The multi-objective function of the multi-objective collaborative optimization model is shown in the following equation: In the formula, Total operating cost, For total peak shaving revenue, This represents the total equipment lifespan loss. The total carbon dioxide emissions are represented by T, the total number of time periods in the scheduling cycle is T, and the time period number is t. For coal consumption costs, For operation and maintenance costs, For peak power, For peak-shaving electricity pricing, This is an indicator of equipment lifespan loss. This refers to carbon dioxide emissions.

4. The multi-modal optimized joint peak-shaving method for molten salt thermal storage and coal-fired power units according to claim 1, characterized in that, The specific coupling constraints include: Mode switching logic constraints are used to ensure the consistency of the operating state of each device; Upper and lower limits of coal-fired power unit output and extraction / reheat parameters; Constraints on the heat storage and release power and capacity limits of molten salt thermal energy storage systems.

5. The multi-modal optimized joint peak-shaving method for molten salt thermal storage and coal-fired power units according to claim 1, characterized in that, The preset decision rule is the TOPSIS decision method based on the entropy weight method; or, The weighting strategy is manually set based on the real-time dispatch priority of the power grid.

6. The multi-modal optimized joint peak-shaving method for molten salt thermal storage and coal-fired power units according to claim 1, characterized in that, The multi-objective optimization algorithm mentioned is the NSGA-II algorithm, the MOEA / D algorithm, or the multi-objective particle swarm optimization algorithm.

7. The multi-modal optimized joint peak-shaving method for molten salt thermal storage and coal-fired power units according to any one of claims 1 to 6, characterized in that, The monitoring of the joint system status to form a closed-loop control includes: Closed-loop real-time control is implemented with a rolling optimization scheduling cycle of 5 to 15 minutes.

8. A multimodal optimized joint peak-shaving system for molten salt thermal storage and coal-fired power units, used to implement the multimodal optimized joint peak-shaving method for molten salt thermal storage and coal-fired power units as described in any one of claims 1 to 7, characterized in that, The system includes: The data acquisition module is used to acquire grid data and combined system data in real time; wherein, the grid data includes grid dispatch instructions, ultra-short-term renewable energy power and load forecast data, and the combined system data includes the operating status data of the combined system of molten salt thermal storage and coal-fired power units; The mode identification module is used to identify the feasible operating modes of the joint system in the current and future scheduling cycles based on the power grid data and the joint system data; The multimodal optimization decision module is used to establish a multi-objective collaborative optimization model for each of the feasible operating modes, solve the optimization model under each operating mode, and select the final scheduling scheme based on preset decision rules. The instruction execution and feedback module is used to parse the final scheduling scheme into specific control instructions and send them to the joint system for execution, while monitoring the status of the joint system to form closed-loop control.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor is used to store one or more programs, which, when executed by the at least one processor, enable the at least one processor to implement the multimodal optimization joint peak-shaving method for molten salt thermal storage and coal-fired power units as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the multimodal optimization joint peak shaving method of molten salt thermal storage and coal-fired power units as described in any one of claims 1 to 7.

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