Multi-energy system cooperative operation optimization method based on improved particle swarm optimization and related device

Abstract

The invention relates to the field of power systems and automation thereof, in particular to a multi-energy system collaborative operation optimization method based on improved particle swarm optimization and a related device. Modeling according to the operation characteristics of the multiple energy systems to obtain a multi-energy system model; optimizing the multi-energy system model based on particle swarm optimization, and calculating and generating an energy scheduling result by taking system operation cost, energy utilization efficiency and pollutant emission as optimization targets; and distributing output priorities of different energies according to an energy scheduling result, and generating an energy storage system operation strategy based on the output priorities. According to the invention, the comprehensive power system model of photovoltaic, wind power, hydroelectric, thermal power and energy storage systems is constructed, and according to the operation characteristics of each energy device, the physical characteristics and dynamic interaction relationships of different energy sources can be accurately simulated. According to the model, equipment constraint conditions and real-time operation parameters are fully considered, high-precision basic data are provided for optimal scheduling, and the practicability and reliability of the model are remarkably enhanced.

Description

Technical Field

[0001] The present invention relates to the field of power systems and automation thereof, and in particular to a multi-energy system coordinated operation optimization method based on improved particle swarm optimization and related devices. Background Art

[0002] As the global energy structure transitions toward cleaner, lower-carbon energy, renewable energy, represented by photovoltaics and wind power, is being integrated into the power system on a large scale. However, the complexity of multi-energy systems and the strong volatility of renewable energy pose severe challenges to the planning and operation of traditional power systems. Currently, the research and application of multi-energy collaborative optimization technologies still face the following prominent issues: Renewable energy sources such as photovoltaics and wind power are significantly affected by weather conditions, and their output is intermittent and uncertain. Existing scheduling strategies mostly adopt a fixed model of "prioritizing the absorption of new energy", but lack a dynamic coordination mechanism with energy storage systems and demand response.

[0003] Existing optimization models are mostly dominated by a single objective, such as minimizing operating costs or controlling carbon emissions, which makes it difficult to achieve multi-objective balance. In addition, the efficiency of carbon emission and cost collaborative optimization of traditional single-objective optimization schemes can deviate from the optimal value by up to 15%-30%.

[0004] The response characteristics of different energy sources in a multi-energy system vary significantly. Lack of dynamic coordination in dispatch strategies can easily lead to frequency and voltage instability. Existing technologies often simplify grid stability constraints to static limits, without deep integration with real-time dispatch. This results in frequency deviations exceeding ±0.5Hz in actual operation, threatening system safety.

[0005] The demand response mechanism is rigid. Existing demand response strategies mostly rely on fixed time-of-use electricity prices or agreement binding, and lack dynamic matching with renewable energy power generation forecasts. Summary of the Invention

[0006] In response to the problems mentioned in the prior art, the present invention proposes a multi-energy system collaborative operation optimization method and related devices based on improved particle swarm optimization. Through an improved swarm intelligence optimization algorithm and dynamic scheduling strategy, it solves problems such as insufficient new energy consumption, high operating costs, and poor grid stability, and achieves efficient energy utilization and stable system operation.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: The present invention provides a multi-energy system coordinated operation optimization method based on improved particle swarm optimization, comprising the following steps: Modeling based on the operating characteristics of multiple energy systems to obtain a multi-energy system model; The multi-energy system model is optimized based on particle swarm optimization, and the energy scheduling results are calculated based on the system operation cost, energy utilization efficiency and pollutant emissions as optimization targets; According to the energy scheduling results, the output priorities of different energy sources are assigned, and the energy storage system operation strategy is generated based on the output priorities.

[0008] As a further improvement of the present invention, the energy system includes a combination of at least two of a photovoltaic power generation system, a wind power generation system, a hydropower generation system, a thermal power generation system and an energy storage system.

[0009] As a further improvement of the present invention, the multi-energy system model includes the power output relationship of each energy device, operation constraints, and the charging and discharging efficiency and capacity limitations of the energy storage system.

[0010] As a further improvement of the present invention, the optimization of the multi-energy system model based on particle swarm optimization includes optimizing the balance between global search and local convergence capabilities according to the number of iterations and particle fitness values through a dynamic inertia weight adjustment mechanism.

[0011] As a further improvement of the present invention, the optimization goal also includes limiting the output fluctuation range of the thermal power unit and the charge and discharge rate of the energy storage system to ensure the stability of the system frequency and voltage.

[0012] As a further improvement of the present invention, allocating output priorities of different energy sources according to energy scheduling results includes giving priority to calling thermal power generation systems during peak load periods and dynamically adjusting hydropower generation plans to match real-time load demands.

[0013] As a further improvement of the present invention, during periods of excess power generation, the energy storage system is controlled to store excess electrical energy, and the charging and discharging strategy is optimized based on energy storage capacity limitations and charging and discharging efficiency.

[0014] A multi-energy system collaborative operation optimization system based on improved particle swarm optimization, comprising: Establish a module for modeling the operating characteristics of multiple energy storage systems to obtain a multi-energy system model; The optimization module is used to optimize the multi-energy system model based on particle swarm optimization, and calculate and generate energy scheduling results with system operating costs, energy utilization efficiency and pollutant emissions as optimization targets; A generation module is used to assign output priorities to different energy sources based on energy scheduling results and generate an energy storage system operation strategy based on the output priorities; A multi-energy system collaborative operation optimization device based on improved particle swarm optimization includes a processor and a memory, wherein when the processor executes a computer program stored in the memory, it implements the multi-energy system collaborative operation optimization method based on improved particle swarm optimization as described above.

[0015] A computer-readable storage medium is used to store a computer program, wherein when the computer program is executed by a processor, it implements the above-mentioned multi-energy system collaborative operation optimization method based on improved particle swarm optimization.

[0016] Compared with the prior art, the present invention has achieved the following technical effects: This invention constructs a comprehensive power system model of photovoltaic, wind power, hydropower, thermal power, and energy storage systems, and accurately simulates the physical characteristics and dynamic interactions of different energy sources based on the operating characteristics of each energy device. The model fully considers equipment constraints and real-time operating parameters, providing high-precision basic data for optimized scheduling, significantly enhancing the model's practicality and reliability. By integrating the operating characteristics of multiple energy sources, the model can dynamically reflect the complementarity and substitutability of different energy sources in different scenarios. The flexible charging and discharging characteristics of the energy storage system can smooth out fluctuations in new energy sources, thereby improving the adaptability and flexibility of multi-energy collaborative operation.

[0017] The present invention uses operating costs, energy efficiency and pollutant emissions as multi-objective functions, breaking through the limitations of traditional single-objective optimization. Through dynamic weight adjustment and constraint fusion, it can achieve scientific trade-offs between different objectives, ensuring the coordinated improvement of economy, environmental protection and efficient energy utilization. This application gives priority to the rapid response capabilities of thermal power and energy storage during peak load periods, and dynamically adjusts the scheduling strategy in combination with new energy output forecasts, effectively alleviating the supply and demand imbalance caused by new energy fluctuations. During periods of excess new energy, excess electricity is actively absorbed through energy storage or demand response mechanisms, significantly reducing the risk of wind and solar power abandonment and improving the utilization rate of new energy.

[0018] The improved algorithm uses a dynamic inertia weight adjustment mechanism to balance global search and local convergence, ensuring rapid generation of optimal scheduling solutions in high-dimensional, multi-constrained scenarios. Furthermore, it dynamically adjusts hydropower generation plans and energy storage charging and discharging strategies based on real-time load characteristics and equipment operating status, improving the system's responsiveness to unexpected conditions and its ability to resist interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a multi-energy system collaborative operation optimization method based on improved particle swarm optimization of the present invention; Figure 2 Schematic diagram of the multi-energy system collaborative operation optimization system based on improved particle swarm optimization of the present invention; Figure 3 The figure is a structural diagram of a computer device according to the present invention. DETAILED DESCRIPTION

[0020] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0021] like Figure 1 As shown, the present invention proposes a multi-energy system collaborative operation optimization method based on improved particle swarm optimization, comprising the following steps: Based on the operating characteristics of various energy storage systems, a multi-energy system model is obtained; The multi-energy system model is optimized based on particle swarm optimization, and the energy scheduling results are calculated based on the system operation cost, energy utilization efficiency and pollutant emissions as optimization targets; According to the energy scheduling results, the output priorities of different energy sources are assigned, and the energy storage system operation strategy is generated based on the output priorities.

[0022] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments: Step 1: Build a multi-energy system model Photovoltaic power generation system modeling: The core of PV power generation system modeling is to accurately reflect the characteristics of its output power that vary with environmental conditions. This requires inputs such as light intensity, ambient temperature, and PV module parameters (such as conversion efficiency and temperature coefficient). By establishing a nonlinear output model, the actual power generation capacity under different light and temperature combinations can be simulated. During the modeling process, model parameters must be regularly calibrated, and real-time meteorological data must be used to correct for prediction errors to ensure that the model is consistent with actual operating conditions.

[0023] Wind power system modeling: A wind turbine's power curve describes the relationship between wind speed and output power. Implementation requires constructing a segmented power curve model based on historical wind speed data and turbine design parameters (such as cut-in wind speed, rated wind speed, and cut-out wind speed). Furthermore, the impact of turbulence intensity on power fluctuations must be considered. Smoothing algorithms can be used to reduce short-term fluctuations in the model output and improve scheduling stability.

[0024] Hydropower System Modeling: Hydropower modeling requires integrating the relationship between reservoir water level, turbine flow, and generated power. During implementation, a turbine flow-to-power conversion model must be established based on the reservoir scheduling plan and hydrological data (such as inflow and reservoir capacity curves). For example, during the flood season, when reservoir water levels are high, each unit of flow can drive the turbine to produce greater power. During the dry season, however, baseload power supply must be prioritized, limiting generated power to avoid wasting water resources. Furthermore, the start and stop time constraints of hydropower units must be considered to ensure the feasibility of scheduling instructions.

[0025] Thermal Power Generation System Modeling: Modeling a thermal power unit requires specifying the unit's minimum stable output (i.e., the minimum power required to maintain safe operation), maximum output limit, and ramp rate (i.e., the ability to increase or decrease power per unit time). Furthermore, economic and environmental parameters such as fuel cost and emission coefficients must be integrated to provide a cost calculation basis for the optimization algorithm.

[0026] Energy storage system modeling: Energy storage system modeling must include charge and discharge efficiency, capacity limitations, and cycle life degradation characteristics. During implementation, the energy storage system's state of charge (SOC) must be monitored in real time to dynamically adjust the charge and discharge strategies. For example, during charging, the actual stored energy must be multiplied by the charge efficiency (e.g., 90%), while during discharging, the discharge efficiency (e.g., 85%) must be considered. Furthermore, safe upper and lower SOC limits (e.g., 20%-90%) must be set to prevent damage to the equipment from overcharging or over-discharging. Furthermore, a capacity degradation model must be established to estimate the remaining life based on the number of charge and discharge cycles to provide a reference for long-term scheduling.

[0027] Step 2: Particle Swarm Optimization The core goals of the optimization algorithm include minimizing operating costs, maximizing energy efficiency, and minimizing pollutant emissions. During implementation, each goal needs to be quantified into a calculable indicator: Operating costs: covering fuel costs, equipment maintenance costs and energy storage loss costs; Energy efficiency: measured by indicators such as the penetration rate of new energy and the number of hours of thermal power utilization; Pollutant emissions: converted emissions of carbon dioxide, sulphide, etc.

[0028] Constraints include power balance (total power generation equals total load demand), upper and lower limits on equipment output, and energy storage SOC limits. These constraints must be converted into boundary conditions in the algorithm to ensure that the solution meets actual operational requirements.

[0029] This embodiment is implemented by improving the particle swarm optimization (PSO) algorithm. The traditional PSO algorithm uses particle swarms to iteratively search for the optimal solution, but it is prone to falling into local optimality and has a slow convergence speed. Improvement measures include: Dynamic inertia weight adjustment: Set a higher inertia weight at the beginning of the iteration to expand the search range; gradually reduce the weight as the number of iterations increases to focus on local fine search.

[0030] Multi-objective fitness evaluation: Calculate multiple objective function values for each particle, screen the Pareto optimal solution set through non-dominated sorting, and then select the final solution based on decision preferences (such as cost priority or environmental protection priority).

[0031] Constraint handling mechanism: A penalty function is imposed on particles that violate the constraints. For example, if the energy storage output exceeds the capacity limit, its fitness score is reduced to guide the population to converge to the feasible region.

[0032] After the algorithm generates the optimization plan, it needs to be dynamically adjusted according to the actual working conditions: During peak load periods, thermal power and energy storage systems are prioritized for rapid response. Thermal power units ramp up at their maximum output, while energy storage systems discharge as needed to fill power gaps. For example, if a regional load suddenly surges by 200MW, thermal power will provide 150MW and energy storage will provide 50MW. Non-essential hydropower peak-shaving plans will be suspended.

[0033] During periods of excess renewable energy: During periods of abundant sunlight or high wind speeds, if the output of photovoltaic and wind power exceeds the load demand, the energy storage system is activated to store excess energy. If the energy storage capacity is full, demand response agreements (such as time-of-use electricity price discounts) are used to guide high-energy-consuming enterprises such as electrolytic aluminum and data centers to increase their electricity load, achieving "source-load" synergy.

[0034] Hydropower scheduling optimization: Dynamically adjust hydropower output based on reservoir water levels and water inflow forecasts. For example, during low-load periods, hydropower generation can be reduced to store water for backup; during peak load periods, hydropower generation can be increased to replace some thermal power to reduce emissions.

[0035] During the actual operation of the model, a real-time data collection and feedback mechanism needs to be established: Data collection: Real-time operating data of each energy device is obtained through the SCADA system; Model calibration: The model parameters are corrected with the latest data at regular intervals to reduce prediction errors; When major operating condition changes are detected (such as a sudden drop in wind power due to extreme weather), the algorithm is triggered to recalculate and generate an emergency dispatch plan.

[0036] Step 3: Implementation requires the deployment of an integrated energy management system (EMS), which includes data acquisition equipment (smart meters, sensors), communication networks and control terminals at the hardware level; and integration of model databases, optimization algorithm modules and human-computer interaction interfaces at the software level.

[0037] Operation monitoring and exception handling: During system operation, key indicators need to be continuously monitored.

[0038] New energy consumption rate: ensure that the wind and solar power curtailment rate is below the set threshold; Grid frequency and voltage: Through the coordinated regulation of energy storage and thermal power, the frequency deviation is controlled within ±0.2Hz; Equipment health status: Early warning of risks such as thermal power unit overload and energy storage capacity degradation. If an anomaly occurs (such as an energy storage system failure), immediate switching to a backup dispatch plan is implemented, such as activating a backup thermal power unit or requesting inter-regional power support.

[0039] By regularly analyzing historical operating data, we optimize model parameters and algorithm strategies. For example, we can adjust sunlight and wind speed forecasting models based on seasonal changes, or update cost calculation rules based on fuel price fluctuations. We also regularly maintain the energy storage system and calibrate the charge and discharge efficiency model to extend the equipment's lifespan.

[0040] This invention reduces thermal power generation fuel consumption and operation and maintenance costs through multi-objective optimization. Flexible deployment of energy storage systems reduces reliance on expensive peak-shaving resources. The optimization algorithm prioritizes renewable energy and hydropower, reducing thermal power output and significantly reducing carbon and pollutant emissions. The energy storage system works in conjunction with demand response to minimize wind and solar curtailment and increase renewable energy penetration.

[0041] Based on the same inventive concept, an embodiment of the present invention also provides a multi-energy system collaborative operation optimization system based on improved particle swarm optimization. Since the principle of solving the problem by the multi-energy system collaborative operation optimization system based on improved particle swarm optimization is similar to the aforementioned multi-energy system collaborative operation optimization method based on improved particle swarm optimization, the implementation of the multi-energy system collaborative operation optimization system based on improved particle swarm optimization can refer to the implementation of the multi-energy system collaborative operation optimization method based on improved particle swarm optimization, and the repeated parts will not be repeated.

[0042] In specific implementation, the multi-energy system collaborative operation optimization system based on improved particle swarm optimization provided by the embodiment of the present invention specifically includes: Establish a module for modeling the operating characteristics of multiple energy storage systems to obtain a multi-energy system model; The optimization module is used to optimize the multi-energy system model based on particle swarm optimization, and calculate and generate energy scheduling results with system operating costs, energy utilization efficiency and pollutant emissions as optimization targets; The generation module is used to assign output priorities of different energy sources according to the energy scheduling results and generate an energy storage system operation strategy based on the output priorities.

[0043] Accordingly, an embodiment of the present invention also provides a multi-energy system collaborative operation optimization device based on improved particle swarm optimization, comprising a processor and a memory, wherein when the processor executes the computer program stored in the memory, it implements the multi-energy system collaborative operation optimization method based on improved particle swarm optimization as provided in an embodiment of the present invention.

[0044] For more specific details about the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be described again here.

[0045] Accordingly, an embodiment of the present invention further provides a computer-readable storage medium for storing a computer program, wherein, when the computer program is executed by a processor, it implements the multi-energy system collaborative operation optimization method based on improved particle swarm optimization as provided in an embodiment of the present invention.

[0046] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar portions of the various embodiments will be sufficient. The systems, devices, and storage media disclosed in the embodiments are described briefly because they correspond to the methods disclosed in the embodiments. For relevant details, refer to the method descriptions.

[0047] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0048] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0049] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0050] The above is a detailed introduction to the multi-energy system collaborative operation optimization method, system, device and storage medium based on improved particle swarm optimization provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A multi-energy system collaborative operation optimization method based on improved particle swarm optimization, characterized in that: The following steps are involved: Modeling based on the operating characteristics of multiple energy systems to obtain a multi-energy system model; The multi-energy system model is optimized based on particle swarm optimization, and the energy scheduling results are calculated based on the system operation cost, energy utilization efficiency and pollutant emissions as optimization targets; According to the energy scheduling results, the output priorities of different energy sources are assigned, and the energy storage system operation strategy is generated based on the output priorities.

2. The multi-energy system collaborative operation optimization method based on improved particle swarm optimization according to claim 1, characterized in that: The energy system includes at least two combinations of a photovoltaic power generation system, a wind power generation system, a hydropower generation system, a thermal power generation system and an energy storage system.

3. The method for optimizing the coordinated operation of a multi-energy system based on improved particle swarm optimization according to claim 2, characterized in that: The multi-energy system model includes the power output relationship of each energy device, operating constraints, and the charging and discharging efficiency and capacity limitations of the energy storage system.

4. The method for optimizing the coordinated operation of a multi-energy system based on improved particle swarm optimization according to claim 1, characterized in that: The optimization of the multi-energy system model based on particle swarm optimization includes optimizing the balance between global search and local convergence ability according to the number of iterations and particle fitness values through a dynamic inertia weight adjustment mechanism.

5. The method for optimizing the coordinated operation of a multi-energy system based on improved particle swarm optimization according to claim 1, characterized in that: Optimization objectives also include limiting the output fluctuation range of thermal power units and the charging and discharging rate of the energy storage system to ensure the stability of system frequency and voltage.

6. The method for optimizing the coordinated operation of a multi-energy system based on improved particle swarm optimization according to claim 1, characterized in that: The output priorities of different energy sources are allocated based on the energy scheduling results, including giving priority to thermal power generation systems during peak load periods and dynamically adjusting hydropower generation plans to match real-time load demands.

7. The method for optimizing the coordinated operation of a multi-energy system based on improved particle swarm optimization according to claim 6, characterized in that: During periods of excess power generation, the energy storage system is controlled to store excess electricity, and the charging and discharging strategy is optimized based on energy storage capacity limitations and charging and discharging efficiency.

8. A multi-energy system collaborative operation optimization system based on improved particle swarm optimization, characterized by: include: Establish a module for modeling the operating characteristics of multiple energy storage systems to obtain a multi-energy system model; The optimization module is used to optimize the multi-energy system model based on particle swarm optimization, and calculate and generate energy scheduling results with system operating costs, energy utilization efficiency and pollutant emissions as optimization targets; The generation module is used to assign output priorities of different energy sources according to the energy scheduling results and generate an energy storage system operation strategy based on the output priorities.

9. A multi-energy system collaborative operation optimization device based on improved particle swarm optimization, characterized in that: The method comprises a processor and a memory, wherein when the processor executes the computer program stored in the memory, the method for optimizing the coordinated operation of a multi-energy system based on improved particle swarm optimization as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that Used to store a computer program, wherein when the computer program is executed by a processor, it implements the multi-energy system collaborative operation optimization method based on improved particle swarm optimization according to any one of claims 1 to 7.

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