A large industrial base water-light storage collaborative energy supply optimization control method and system for green electricity direct connection scene

Abstract

The application discloses a large industrial base water-light-storage collaborative energy supply optimization control method and system for a green electricity direct connection scene, and belongs to the technical field of new energy collaborative energy supply and intelligent energy management. The method is based on collected load operation data, constructs a unified operation state sequence, and predicts new energy output, enterprise load and energy storage state. On the basis of the prediction results, a multi-objective optimization scheduling model is established with system operation cost, carbon emission, output fluctuation and new energy consumption rate as optimization targets, and the NSGA-II non-dominated sorting genetic algorithm is used to solve the optimal scheduling scheme, realizing collaborative optimization control of photovoltaic, hydroelectric, energy storage and power grid. At the same time, a multi-time scale scheduling mechanism combining day-ahead scheduling, intra-day rolling optimization and real-time control is adopted to realize dynamic optimization of the energy supply process under the green electricity direct connection scene. The application can improve the new energy consumption rate and green electricity supply ratio, reduce the system operation cost and carbon emission, and improve the energy supply stability and operation efficiency.

Description

Technical Field

[0001] This invention relates to the field of new energy collaborative energy supply and intelligent energy management technology, specifically to an optimized control method and system for collaborative energy supply of water, solar and energy storage in large industrial bases for green electricity direct connection scenarios. Background Technology

[0002] Against the backdrop of the national "dual carbon" goals and the construction of a new power system, direct green power connection is gradually becoming an important energy supply method for large industrial bases to improve the utilization rate of new energy sources and reduce carbon emissions. By coordinating photovoltaic, hydropower, and energy storage, the capacity for green power absorption can be improved, dependence on the public power grid can be reduced, and the reliability and economic efficiency of power supply can be enhanced.

[0003] However, photovoltaic power generation is characterized by randomness and volatility, hydropower and energy storage systems are subject to operational constraints, and the coupling relationships between multiple energy sources are complex. Furthermore, green electricity experiences transmission losses during transmission, and when renewable energy supply is insufficient, electricity must be purchased from the public grid. Time-of-use pricing further increases the difficulty of system optimization and scheduling. Existing collaborative scheduling methods mostly employ fixed control strategies or single-objective optimization approaches, failing to fully consider green electricity transmission losses, grid purchase costs, and green electricity priority supply mechanisms. They lack collaborative scheduling methods that combine prediction, optimization, and rolling control, making it difficult to balance system operating costs, carbon emissions, renewable energy absorption rates, and power supply reliability. Therefore, it is necessary to propose a collaborative optimization scheduling and control method for large-scale industrial bases using hydropower, photovoltaic power, and energy storage, oriented towards direct green electricity connection scenarios. Summary of the Invention

[0004] To address the problems of low green electricity utilization, insufficient consideration of green electricity transmission losses, inadequate multi-energy coordinated dispatch capabilities, and the difficulty in balancing system operating costs, carbon emissions, and power supply reliability in existing green electricity direct connection scenarios for large industrial bases, this invention provides an optimized control method for the coordinated power supply of water, photovoltaic, and energy storage systems in large industrial bases under green electricity direct connection scenarios. This invention centers on green electricity direct connection power supply, constructing direct green electricity supply relationships between the new energy side, energy storage side, grid side, and industrial base load side, and unifying photovoltaic systems, hydropower systems, and energy storage systems into a coordinated optimized dispatch framework. Furthermore, it establishes a green electricity transmission loss model based on the operating status of green electricity transmission lines, and addresses the issues related to green electricity... The system assesses the effective supply capacity; it uses historical operating data, real-time operating data, and meteorological forecasts to predict the supply capacity of new energy sources, the load demand of industrial bases, and the operating status of energy storage during future scheduling cycles; it further establishes a multi-objective optimization scheduling model that takes into account the overall system operating cost, carbon emissions, power supply reliability, new energy absorption rate, and green electricity transmission loss, and comprehensively considers the grid purchase cost under time-of-use pricing; it generates the optimal scheduling scheme by combining day-ahead scheduling, intraday rolling optimization, and real-time control through a multi-timescale scheduling mechanism; and finally, it continuously optimizes and updates the scheduling results through real-time operating status feedback to achieve priority utilization of green electricity, coordinated regulation of multiple energy sources, and dynamic matching of industrial base loads.

[0005] Through the above technical solutions, this invention constructs a closed-loop control system of "green electricity direct connection - state prediction - green electricity transmission modeling - collaborative optimization - rolling scheduling - real-time feedback". On the basis of ensuring safe and reliable energy supply to large industrial bases, it improves the effective utilization rate of green electricity and the capacity for new energy consumption, reduces green electricity transmission losses, grid purchase costs and system carbon emissions, and achieves synergistic optimization of system economy, low carbon emissions and power supply reliability.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an optimized control method for coordinated hydro-solar-storage energy supply in large industrial bases for green electricity direct connection scenarios, specifically including the following steps: S1. Based on photovoltaic systems, hydropower systems, energy storage systems, grid side and industrial base load side, collect operation datasets, perform preprocessing operations on the operation datasets, and form a system operation status sequence within a unified scheduling cycle; The dataset includes: photovoltaic system output power, solar irradiance, module temperature, hydropower unit operating status, reservoir adjustable capacity, energy storage system state of charge, energy storage charging and discharging power, industrial base load power, grid interaction power, and green power transmission line operating status data. The green power transmission line operation status data includes transmission distance, line impedance, line current, and line voltage; Preprocessing operations include outlier removal, missing value compensation, time scale unification, and normalization of the running dataset.

[0007] S2. Based on the system operation state sequence, establish the green electricity direct connection energy supply relationship between the new energy side, energy storage side, grid side and industrial base load side, construct a green electricity transmission loss model, and determine the green electricity priority energy supply path and the source-load-storage-grid collaborative operation mode. Establish a direct green power supply relationship between photovoltaic, hydropower, energy storage, power grid and industrial base loads. The energy storage system will only use surplus renewable energy for charging and will not accept charging from the public power grid. S3. Based on historical operating status, real-time operating status and meteorological forecast information, predict the photovoltaic output, hydropower adjustable capacity, energy storage operating status, grid interaction power and industrial base load demand in the next scheduling cycle to obtain the future operating status sequence. S4. Construct a collaborative optimization scheduling model based on the future operating state sequence, with the optimization objectives being the lowest overall system operating cost, the lowest system carbon emissions, the highest new energy absorption rate, and the highest power supply reliability, while comprehensively considering green electricity transmission losses, grid purchase costs, and system operating constraints. S5. The NSGA-II multi-objective optimization algorithm is used to solve the cooperative optimization scheduling model to obtain the Pareto optimal solution set. Based on the Pareto optimal solution set, a comprehensive evaluation strategy is adopted to determine the optimal scheduling scheme and generate photovoltaic utilization scheme, hydropower unit regulation scheme, energy storage charging and discharging scheme, grid power purchase scheme and industrial base load response scheme. S6. Perform collaborative control according to the optimal scheduling scheme and obtain the system operating status in real time. When the deviation between the actual operating status and the predicted operating status exceeds the preset threshold, or when the green electricity transmission loss and industrial base load change exceed the preset range, repeat steps S3 to S5 to achieve rolling updates of collaborative optimization scheduling.

[0008] Optionally, step S2 includes: Energy supply pathways should be established in the following order: priority of direct power supply from photovoltaic power, compensation and regulation from hydropower, dynamic balancing from energy storage, and backup support from the public power grid. This will establish green electricity supply relationships. Establish a green electricity transmission loss model: The actual green electricity power obtained by the industrial base meets the following requirements: The proportion of green electricity supply is met: in, For the total green electricity power, Indicates photovoltaic output power. Indicates the output power of hydroelectric power. Indicates the energy storage discharge power. Indicates green electricity transmission loss. The transmission loss coefficient per unit line length. For the length of green electricity transmission lines, The actual green electricity generated by the industrial base Indicates the load power of the industrial base. This indicates the proportion of green electricity supply.

[0009] Optionally, step S3 includes: Establish a state mapping relationship based on the running state to obtain the future running state sequence: in, Indicates predicted power. Represents the state mapping function, Indicates the current running status. Indicates meteorological parameters, Indicates the load status of the industrial base. This indicates the state of charge of the energy storage.

[0010] Optionally, the electro-carbon synergistic optimization objective in step S4 satisfies: in, To achieve the goal of synergistic optimization of electricity and carbon, This represents the overall operating cost of the system, including the cost of purchasing electricity from the power grid, the operating cost of hydropower, the operating and maintenance cost of energy storage, the cost of green electricity transmission losses, and the cost of penalties for curtailment. Indicates the system's carbon emissions; Indicates the power supply deviation; Indicates the renewable energy consumption rate; The target weight coefficient.

[0011] The system operating cost satisfies: in, This indicates the cost of purchasing electricity from the power grid. This indicates the operating cost of hydropower. This indicates the cost of energy storage operation and maintenance. This indicates the cost of green electricity transmission losses. This represents the penalty cost for curtailing renewable energy. Among these, the grid purchase cost satisfies: in, This indicates the time-of-use electricity price for the corresponding period. Indicates the scheduling time interval. Indicates the scheduling period. for The index.

[0012] Green electricity transmission loss costs are met: in, This indicates the value of a unit of green electricity.

[0013] The carbon emissions of the system meet the following requirements: in, Indicates the power purchased from the public power grid. This represents the carbon emission factor of the power grid during the corresponding time period. Indicates the scheduling period.

[0014] The system operation constraints include: power balance constraints, hydropower operation constraints, energy storage operation constraints, grid interaction constraints, green electricity transmission loss constraints, green electricity supply ratio constraints, and supply-demand deviation constraints.

[0015] The power balance constraint relationship satisfies: in, Indicates the power exchange between the power grids. Indicates the energy storage charging power; The operating constraints of the hydropower unit satisfy: as well as: in, This indicates the maximum output power of the hydroelectric generator unit. This indicates the minimum output power of the hydroelectric generator unit. Indicates the maximum climbing rate. This indicates the maximum gradient rate.

[0016] Energy storage operation constraints include state of charge constraints and state of charge / discharge constraints, among which: And satisfy: in, In the state of energy storage charge, The state of charge of the energy storage at the next moment. For energy storage, the minimum permissible state of charge, This represents the highest permissible state of charge for energy storage. Indicates the energy storage charging power. Indicates the energy storage discharge power. Indicates the rated capacity of energy storage. and These represent the energy storage charging and discharging efficiency, respectively.

[0017] And the supply-demand deviation constraint is satisfied: in, Indicates the system power supply. This indicates the power demand of the industrial base. This indicates the allowable deviation threshold.

[0018] Green electricity transmission loss constraints are satisfied: in, This indicates the maximum green electricity transmission loss.

[0019] Optionally, in step S5, solving the cooperative optimization scheduling model using the NSGA-II multi-objective optimization algorithm includes: The objective function is constructed based on the overall operating cost of the system, the carbon emissions of the system, the renewable energy absorption rate and the power supply reliability, and the population is initialized by combining the green electricity transmission loss model and the power grid purchase cost model. Pareto rank classification of the population was performed using fast non-dominated sorting. The population diversity of the Pareto solution set is maintained based on the crowding distance assessment. A new generation of population is generated using selection, crossover, and mutation operations, and fitness is evaluated based on the objective function values ​​of each individual. Repeat the iteration until the termination condition is met, and output the Pareto optimal solution set; Based on comprehensive evaluation indicators, the final scheduling scheme with the lowest overall operating cost, the highest green energy utilization rate, and the satisfaction of system operation constraints is selected from the Pareto optimal solution set.

[0020] Optionally, in step S6, the collaborative control adopts a multi-timescale scheduling mechanism that combines day-ahead scheduling, intraday rolling scheduling, and real-time scheduling.

[0021] Optionally, step S6 employs a multi-timescale rolling optimization scheduling mechanism, including a day-ahead scheduling layer, an intraday rolling scheduling layer, and a real-time control layer. The rolling optimization trigger condition satisfies: in, Indicates real-time operating power; Indicates predicted operating power; This indicates a preset deviation threshold. When the above conditions are met, or the green electricity transmission loss deviates from the predicted value to a preset range, or the industrial base load change exceeds a preset ratio, steps S3 to S5 are executed again to re-predict the future operating status and update the collaborative optimization scheduling scheme.

[0022] The day-ahead dispatch layer generates day-ahead optimized dispatching schemes based on predicted operating status, time-of-use electricity price information, green electricity transmission capacity, and industrial base load forecast results. The intraday rolling dispatch layer makes rolling revisions to the day-ahead dispatch plan based on changes in new energy output, industrial base load, and green electricity transmission losses. The real-time control layer dynamically coordinates the control of hydropower units, energy storage systems, grid interaction power, and industrial base load based on real-time operating status, and corrects the green electricity supply path in real time.

[0023] Secondly, this invention provides a large-scale industrial base hydro-solar-storage collaborative optimization scheduling and control system for green power direct connection scenarios, comprising: The data acquisition module is used to collect load operation data from photovoltaic, hydropower, energy storage, power grid and industrial base, as well as the operating status data of green power transmission lines; The data processing module is used to complete the preprocessing of runtime data, the conversion to a unified time scale, and the construction of runtime state sequences; The operation status prediction module is used to predict photovoltaic output, hydropower regulation capacity, energy storage operation status, grid interaction power and industrial base load demand; The green electricity direct connection control module is used to establish a green electricity direct connection energy supply relationship between the new energy side, energy storage side, grid side and industrial base load side, and to calculate green electricity transmission loss and effective green electricity supply ratio. The collaborative optimization scheduling module is used to establish a multi-objective optimization scheduling model that includes the overall system operating cost, system carbon emissions, new energy absorption rate, power supply reliability and green electricity transmission loss, and uses the NSGA-II multi-objective optimization algorithm to generate scheduling schemes. The collaborative optimization scheduling module includes: The forecasting submodule is used to forecast photovoltaic output, hydropower regulation capacity, energy storage status, grid interaction power, and industrial base load demand. The green energy supply modeling submodule is used to establish a green energy supply relationship model and a green energy transmission loss model. The multi-objective modeling submodule is used to establish a multi-objective optimization model for comprehensive operating costs, system carbon emissions, renewable energy absorption rate, and power supply reliability. The NSGA-II solver submodule is used to solve for the Pareto optimal solution set. The Pareto decision submodule is used to select the final scheduling scheme based on comprehensive evaluation indicators; The real-time feedback module is used to monitor system operation deviations, green electricity transmission loss deviations, and industrial base load changes, and trigger rolling optimization. The execution control module is used to control the operation of photovoltaic systems, hydropower units, energy storage systems, grid interaction, and industrial base loads based on scheduling results.

[0024] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and when the processor runs the computer program, the electronic device performs the above-described method.

[0025] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0026] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention establishes a direct green electricity supply relationship between the new energy side and the load side of the industrial base, thereby enabling the priority use of new energy and improving the green electricity absorption capacity; (2) This invention integrates photovoltaic power generation, hydropower regulation and energy storage dynamic response capabilities to achieve multi-energy coordinated power supply, thereby improving system operation stability and power supply reliability; (3) This invention achieves comprehensive optimization of operating costs, carbon emissions and new energy consumption rate by constructing an electric carbon synergy optimization model, thereby improving the overall operating efficiency of the system; (4) This invention improves the system’s adaptability to new energy fluctuations and load changes by adopting a multi-timescale scheduling mechanism that combines day-ahead planning, intraday rolling optimization and real-time control. (5) This invention achieves dynamic closed-loop control of the energy supply process through real-time feedback and rolling optimization update mechanism, thereby improving the system's supply and demand matching accuracy and control effect. Attached Figure Description

[0027] Figure 1 A flowchart of a large-scale industrial base hydro-solar-storage collaborative optimization scheduling and control method for green electricity direct connection scenarios is provided in an embodiment of the present invention. Figure 2 A schematic diagram of a large-scale industrial base water-solar-storage collaborative optimization scheduling and control system for green electricity direct connection scenarios is provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of a hydro-solar-storage collaborative scheduling framework based on prediction and NSGA-II multi-objective optimization, provided as an embodiment of the present invention. Detailed Implementation

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0029] like Figure 1 As shown in the figure, this embodiment provides a method for coordinated optimization scheduling and control of water, solar and energy storage in large industrial bases for green power direct connection scenarios.

[0030] This embodiment takes a large-scale industrial base green electricity direct connection energy supply system as an example. The system includes a photovoltaic power generation unit, a hydropower regulation unit, an energy storage unit, a grid interaction unit, an industrial base load unit, and an energy management platform (such as...). Figure 2 (As shown). The energy management platform connects to each energy unit through a communication network to realize operation data acquisition, operation status prediction, collaborative optimization scheduling, and control execution.

[0031] During system operation, the data acquisition module first obtains operational data from the photovoltaic system, hydropower system, energy storage system, and the industrial base load side. Specifically, the photovoltaic system acquires information such as irradiance, ambient temperature, module temperature, and real-time output power; the hydropower system acquires unit output, water level, and adjustable capacity information; the energy storage system acquires state of charge (SOC), charge / discharge power, and state of health (SOH); and the industrial base load side acquires total load power and the operating status of key equipment. The energy management platform performs time synchronization and standardization processing on all types of data and constructs a unified operational status sequence.

[0032] After obtaining the operational status sequence, a direct green electricity supply relationship is established between the new energy source side and the industrial base side. The system operates according to the principle of prioritizing green electricity supply, giving priority to using photovoltaic power generation to meet the load demand of enterprises. When the photovoltaic output exceeds the load demand of the industrial base, the excess electricity is used to charge the energy storage system; when the photovoltaic output is insufficient, hydropower units provide power compensation; when the new energy output still cannot meet the load demand of the industrial base, the energy storage system enters a discharge state to compensate for the power gap; when the total output of the new energy source and the energy storage system is still less than the load demand of the industrial base, the public power grid provides auxiliary power.

[0033] Green electricity supply relationships: Green electricity transmission loss model: The actual green electricity power obtained by the industrial base meets the following requirements: The proportion of green electricity supply is met: in, Indicates photovoltaic output power. Indicates the output power of hydroelectric power. Indicates the energy storage discharge power. This indicates the load power of the industrial base.

[0034] Subsequently, the energy management platform predicts the operating status for future scheduling cycles based on historical operating data, real-time operating data, and meteorological information. The prediction inputs include historical photovoltaic output, meteorological conditions, enterprise load status, and energy storage charge status. The prediction results include photovoltaic power generation capacity, hydropower regulation capacity, adjustable energy storage capacity, and enterprise load demand for the future scheduling cycle. The prediction process satisfies the following: in, Indicates predicted power. Indicates weather conditions. Indicates the load status of the industrial base. This indicates the state of charge of the energy storage.

[0035] The model aims to minimize overall system operating cost, carbon emissions, system output fluctuations, and the renewable energy integration rate. The overall system operating cost includes the cost of purchasing electricity from the public grid, hydropower operation costs, energy storage operation and maintenance costs, green electricity transmission loss costs, and renewable energy curtailment penalty costs. The cost of purchasing electricity from the public grid is calculated based on time-of-use pricing and purchased power output, while the cost of green electricity transmission loss is calculated based on the power loss during green electricity transmission. System carbon emissions are calculated based on purchased power output and the corresponding carbon emission factor. System output fluctuations are measured by the change in total output between adjacent time points. The renewable energy integration rate characterizes the level of green electricity utilization.

[0036] This embodiment employs the NSGA-II non-dominated sorting genetic algorithm to solve the aforementioned multi-objective optimization model. First, an initial scheduling population is generated based on the prediction results, with each individual corresponding to a set of hydropower output schemes, energy storage charging / discharging schemes, and grid interaction schemes. Then, the fitness of each individual is calculated according to the objective function, and non-dominated sorting and congestion calculations are performed to form a Pareto optimal solution set. The population is iteratively updated through selection, crossover, and mutation operations until a preset termination condition is reached. Finally, the scheduling scheme that satisfies the green energy priority principle and has the best overall benefits is selected from the Pareto optimal solution set as the control output.

[0037] During the solution process, the model satisfies the power balance constraint: Simultaneously meet energy storage operation constraints: In addition, the power output constraints and ramping constraints of hydropower units are taken into account to ensure that the dispatching scheme meets the actual engineering operation requirements.

[0038] After obtaining the optimal scheduling scheme, the energy management platform generates hydropower unit regulation instructions, energy storage charging and discharging control instructions, and grid interaction control instructions, and sends them to the field execution terminal through the control network to achieve coordinated energy supply control.

[0039] Furthermore, such as Figure 3 As shown, this embodiment employs a multi-timescale rolling optimization mechanism. The day-ahead layer generates an initial energy supply plan based on the forecast results for the next 24 hours; the intraday layer modifies the day-ahead plan according to a rolling time window; and the real-time control layer dynamically adjusts the plan based on the actual operating status. When the deviation between the real-time operating status and the predicted status exceeds a preset threshold, the system re-executes the prediction and optimization process, continuously updating the scheduling scheme, thereby forming a closed-loop control mechanism of "status perception—operation prediction—optimized scheduling—control execution—feedback correction," realizing the coordinated energy supply operation of the hydro-photovoltaic-storage system in the green electricity direct connection scenario.

[0040] To verify the effectiveness of the proposed method for coordinated optimization scheduling and control of water, solar and energy storage in large industrial bases, a carbon / carbon composite material production enterprise was used as a typical application scenario for large industrial bases.

[0041] This carbon / carbon composite material enterprise features continuous high-temperature production, all-weather continuous operation, and low load fluctuations but high power supply reliability requirements. It is equipped with a photovoltaic power generation system, a hydropower regulation system, an energy storage system, and a green electricity direct connection power supply line, and uses the public power grid as a backup power source.

[0042] Because the direct green electricity connection lines to enterprises have a certain transmission distance, the energy management platform calculates green electricity transmission losses in real time and determines the energy storage charging and discharging strategy and the public grid power purchase strategy based on the actual green electricity power obtained by the industrial base. When the green electricity transmission loss increases, resulting in insufficient actual green electricity supply, the system prioritizes the use of the energy storage system for compensation; if energy storage is insufficient, it then selects to purchase electricity from the public grid based on the real-time time-of-use electricity price.

[0043] First, the energy management platform collects photovoltaic power output information, hydropower operation status information, energy storage status information, and enterprise load information, forming a unified operation status sequence. Then, the operation status prediction module is used to predict the photovoltaic power generation capacity, enterprise load demand, and adjustable energy storage capacity in the future scheduling cycle, obtaining the future operation status sequence.

[0044] After obtaining the prediction results, the NSGA-II multi-objective optimization scheduling model described in this invention is used for solution. The model takes minimizing system operating cost, carbon emissions, system output fluctuation, and renewable energy absorption rate as optimization objectives, and comprehensively considers power balance constraints, hydropower output constraints, energy storage operation constraints, and green electricity supply ratio constraints. After non-dominated sorting, congestion calculation, crossover mutation, and iterative optimization processes, the optimal scheduling scheme that satisfies the constraints is obtained.

[0045] During the scheduling process, when the photovoltaic output exceeds the enterprise's load demand, the system prioritizes using photovoltaic power to meet the enterprise's energy needs, and uses the excess power for energy storage charging, while reducing the output of hydropower units; when the photovoltaic output decreases, the energy storage system first releases stored energy for rapid compensation; when the energy storage regulation capacity is insufficient, the hydropower units further increase their output to meet the enterprise's load demand; when the total output of new energy sources and energy storage is still lower than the enterprise's load demand, the grid auxiliary power supply mechanism is activated to ensure the continuous operation of enterprise production.

[0046] Furthermore, the system employs a multi-timescale scheduling mechanism combining day-ahead scheduling, intraday rolling optimization, and real-time control. Day-ahead scheduling generates an energy supply plan based on future forecasts; intraday rolling optimization corrects the scheduling results based on the latest operating status; and the real-time control layer dynamically adjusts the control strategy based on actual operating deviations. When the deviation between the predicted state and the actual operating state exceeds a preset threshold, the system re-executes the forecasting and optimization process to achieve rolling updates.

[0047] After adopting the large-scale industrial base hydro-photovoltaic-storage synergistic optimization scheduling and control method proposed in this invention, the system effectively mitigates the fluctuations in new energy output under the synergistic regulation of photovoltaic, hydropower, and energy storage, significantly reduces power curtailment, makes the energy storage charging and discharging process more rational, and fully utilizes the hydropower regulation capacity. In this embodiment, the system's daily peak-valley output difference decreased from 334.95 MW to 299.06 MW, a reduction of approximately 10.72%, and the output fluctuation improvement rate during working hours reached 49.6%, significantly improving the system's power supply stability. Simultaneously, the photovoltaic absorption rate increased from 92.5% to 98.5%. Furthermore, this invention further considers the transmission loss of green electricity direct connection lines and the cost of purchasing electricity from the public grid. By optimizing the green electricity transmission path and grid interaction strategy, it improves the actual utilization rate of green electricity and reduces the demand for public grid electricity and overall operating costs. While ensuring reliable power supply for continuous production of carbon / carbon composite material enterprises, it achieves a synergistic improvement in system power supply stability, economy, and low carbon emissions, verifying the good engineering application value of this invention in green electricity direct connection scenarios.

[0048] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Equivalent substitutions, parameter adjustments, or functional extensions made by those skilled in the art based on the technical concept of the present invention should all fall within the scope of protection of the present invention.

Claims

1. A method for optimized control of hydro-solar-storage synergistic energy supply in large industrial bases for green electricity direct connection scenarios, characterized in that, Includes the following steps: S1. Based on photovoltaic systems, hydropower systems, energy storage systems, grid side and industrial base load side, collect operation datasets, perform preprocessing operations on the operation datasets, and form a system operation status sequence within a unified scheduling cycle; S2. Based on the system operation state sequence, establish the green electricity direct connection energy supply relationship between the new energy side, energy storage side, grid side and industrial base load side, construct a green electricity transmission loss model, and determine the green electricity priority energy supply path and the source-load-storage-grid collaborative operation mode. S3. Based on historical operating status, real-time operating status and meteorological forecast information, predict the photovoltaic output, hydropower adjustable capacity, energy storage operating status, grid interaction power and industrial base load demand in the next scheduling cycle to obtain the future operating status sequence. S4. Establish a water-solar-storage collaborative optimization scheduling model based on the future operating state sequence; The collaborative optimization scheduling model aims to achieve the following optimization objectives: the lowest overall system operating cost, the lowest system carbon emissions, the highest new energy absorption rate, and the highest power supply reliability. It is constrained by power balance constraints, hydropower operation constraints, energy storage operation constraints, grid interaction constraints, green electricity transmission loss constraints, and green electricity supply ratio constraints. S5. The NSGA-II multi-objective optimization algorithm is used to solve the cooperative optimization scheduling model to obtain the Pareto optimal solution set. Based on the Pareto optimal solution set, a comprehensive evaluation strategy is adopted to determine the optimal scheduling scheme. The optimal scheduling scheme includes: photovoltaic utilization scheme, hydropower unit regulation scheme, energy storage charging and discharging scheme, grid power purchase scheme, and industrial base load response scheme; S6. Perform collaborative control according to the optimal scheduling scheme and collect the system operation status in real time. When the deviation between the real-time operation status and the predicted operation status exceeds the preset threshold, trigger the rolling optimization mechanism and re-execute S3 to S5 to realize the dynamic collaborative optimization scheduling of the water-photovoltaic-storage system in the green electricity direct connection scenario.

2. The optimized control method for coordinated hydro-solar-storage energy supply in large industrial bases for green electricity direct connection scenarios, as described in claim 1, is characterized in that... In S1, the operational dataset includes: photovoltaic system output power, solar irradiance, module temperature, hydropower unit operating status, reservoir adjustable capacity, energy storage system state of charge, energy storage charging and discharging power, industrial base load power, grid interaction power, and green power transmission line operating status data. The green power transmission line operation status data includes transmission distance, line impedance, line current, and line voltage; The preprocessing operations include: outlier removal, missing value compensation, time scale unification, and normalization of the running dataset.

3. The optimized control method for coordinated hydro-solar-storage energy supply in large industrial bases for green electricity direct connection scenarios, as described in claim 1, is characterized in that... In S2, the expression for the direct green electricity supply relationship is as follows: in, For the total green electricity power, Indicates photovoltaic output power. Indicates the output power of hydroelectric power. Indicates the energy storage discharge power; Establish a green electricity transmission loss model: in, Indicates green electricity transmission loss. The transmission loss coefficient per unit line length. The length of green electricity transmission lines; The actual green electricity generated by the industrial base satisfies: The proportion of green electricity supply is met: in, The actual green electricity generated by the industrial base Indicates the load power of the industrial base. This indicates the proportion of green electricity supply.

4. The optimized control method for coordinated hydro-solar-storage energy supply in large industrial bases for green electricity direct connection scenarios, as described in claim 1, is characterized in that... In step S3, the expression for obtaining the future running state sequence is as follows: in, Indicates predicted power. Indicates the current operating power. Indicates weather conditions. Indicates the load status. Indicates the state of charge of the energy storage. This represents the state mapping function.

5. The optimized control method for coordinated hydro-solar-storage energy supply in large industrial bases for green electricity direct connection scenarios, as described in claim 1, is characterized in that... In step S4, the expression for the optimization objective is as follows: in, To achieve the goal of synergistic optimization of electricity and carbon, This represents the overall operating cost of the system. This indicates the system's carbon emissions. Indicates the energy supply deviation. Indicates the renewable energy consumption rate. These represent the corresponding target weight coefficients; The carbon emissions of the system meet the following requirements: in, Indicates the scheduling period. for index, Indicates the power purchased from the public power grid. This indicates the carbon emission factor of the power grid during the corresponding time period.

6. The optimized control method for coordinated hydro-solar-storage energy supply in large industrial bases for green electricity direct connection scenarios, as described in claim 5, is characterized in that... The overall operating cost of the system meets the following requirements: in, This indicates the cost of purchasing electricity from the power grid. This indicates the operating cost of hydropower. This indicates the operation and maintenance costs of energy storage. Indicates the cost of green electricity transmission losses. This indicates the penalty cost for abandoning renewable energy. The electricity purchase cost from the power grid satisfies the following expression: in, This indicates the time-of-use electricity price for the corresponding period. Indicates the scheduling time interval. Indicates the scheduling period. for index, Indicates the power purchased from the public power grid; Green electricity transmission loss costs are met: in, Indicates the value of a unit of green electricity. This indicates the transmission loss of green electricity.

7. The optimized control method for coordinated hydro-solar-storage energy supply in large industrial bases for green electricity direct connection scenarios, as described in claim 1, is characterized in that... In S4, the power balance constraint satisfies: in, Indicates the output power of hydroelectric power. Indicates photovoltaic output power. Indicates the energy storage discharge power. Indicates the power exchange between the power grids. Indicates the load power of the industrial base. Indicates the energy storage charging power; The operating constraints of the hydropower unit are satisfied as follows: as well as: in, This indicates the maximum output power of the hydroelectric generator unit. This indicates the minimum output power of the hydroelectric generator unit. Indicates the maximum climbing rate. Indicates the maximum descent rate. This indicates the hydropower output power at the previous moment; Energy storage operation constraints include state of charge constraints and state of charge / discharge constraints, among which: And satisfy: in, In the state of energy storage charge, The state of charge of the energy storage at the next moment. For energy storage, the minimum permissible state of charge, This represents the highest permissible state of charge for energy storage. Indicates the energy storage charging power. Indicates the energy storage discharge power. Indicates the rated capacity of energy storage. and These represent the energy storage charging and discharging efficiency, respectively. And the supply-demand deviation constraint is satisfied: in, Indicates the system power supply. This indicates the power demand of the industrial base. Indicates the allowable deviation threshold; Green electricity transmission loss constraints are satisfied: in, This indicates the maximum green electricity transmission loss.

8. The optimized control method for coordinated hydro-solar-storage energy supply in large industrial bases for green electricity direct connection scenarios, as described in claim 1, is characterized in that... In step S5, the NSGA-II multi-objective optimization algorithm is used to solve the cooperative optimization scheduling model, including: The objective function is constructed based on the overall operating cost of the system, the carbon emissions of the system, the renewable energy absorption rate and the power supply reliability, and the population is initialized by combining the green electricity transmission loss model and the power grid purchase cost model. Pareto rank classification of the population was performed using fast non-dominated sorting. The population diversity of the Pareto solution set is maintained based on the crowding distance assessment. A new generation of population is generated using selection, crossover, and mutation operations, and fitness is evaluated based on the objective function values ​​of each individual. Repeat the iteration until the termination condition is met, and output the Pareto optimal solution set; Based on comprehensive evaluation indicators, the final scheduling scheme with the lowest overall operating cost, the highest green energy utilization rate, and the satisfaction of system operation constraints is selected from the Pareto optimal solution set.

9. The optimized control method for coordinated hydro-solar-storage energy supply in large industrial bases for green electricity direct connection scenarios, as described in claim 1, is characterized in that... In S6, the collaborative control adopts a multi-time-scale rolling optimization scheduling mechanism, including a day-ahead scheduling layer, an intraday rolling scheduling layer, and a real-time control layer; Among them, the day-ahead dispatch layer generates a day-ahead optimized dispatch scheme based on the predicted operating status, time-of-use electricity price information, green electricity transmission capacity and industrial base load forecast results; The intraday rolling dispatch layer makes rolling revisions to the day-ahead dispatch plan based on changes in new energy output, industrial base load, and green electricity transmission losses. The real-time control layer dynamically coordinates the control of hydropower units, energy storage systems, grid interaction power and industrial base load based on real-time operating status, and corrects the green electricity supply path in real time. The rolling optimization trigger condition satisfies: in, Indicates real-time operating power. Indicates the predicted operating power. Indicates the preset deviation threshold; When the above conditions are met, or when the green electricity transmission loss deviates from the predicted value to the preset range, or when the load change of the industrial base exceeds the preset ratio, S3 to S5 are executed again to re-predict the future operating status and update the collaborative optimization scheduling scheme.

10. A large-scale industrial base hydro-solar-storage collaborative optimization scheduling and control system for green power direct connection scenarios, used to execute the method described in any one of claims 1 to 9, characterized in that, include: The data acquisition module is used to collect load operation data from photovoltaic, hydropower, energy storage, power grid and industrial base, as well as the operating status data of green power transmission lines; The data processing module is used to complete the preprocessing of runtime data, the conversion to a unified time scale, and the construction of runtime state sequences; The operation status prediction module is used to predict photovoltaic output, hydropower regulation capacity, energy storage operation status, grid interaction power and industrial base load demand; The green electricity direct connection control module is used to establish a green electricity direct connection energy supply relationship between the new energy side, energy storage side, grid side and industrial base load side, and to calculate green electricity transmission loss and effective green electricity supply ratio. The collaborative optimization scheduling module is used to establish a multi-objective optimization scheduling model that includes the overall system operating cost, system carbon emissions, new energy absorption rate, power supply reliability and green electricity transmission loss, and uses the NSGA-II multi-objective optimization algorithm to generate scheduling schemes. The collaborative optimization scheduling module includes: The forecasting submodule is used to forecast photovoltaic output, hydropower regulation capacity, energy storage status, grid interaction power, and industrial base load demand. The green energy supply modeling submodule is used to establish a green energy supply relationship model and a green energy transmission loss model. The multi-objective modeling submodule is used to establish a multi-objective optimization model for comprehensive operating costs, system carbon emissions, renewable energy absorption rate, and power supply reliability. The NSGA-II solver submodule is used to solve for the Pareto optimal solution set. The Pareto decision submodule is used to select the final scheduling scheme based on comprehensive evaluation indicators; The real-time feedback module is used to monitor system operation deviations, green electricity transmission loss deviations, and industrial base load changes, and trigger rolling optimization. The execution control module is used to control the operation of photovoltaic systems, hydropower units, energy storage systems, grid interaction, and industrial base loads based on scheduling results.

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