Multi-time-scale photovoltaic energy storage virtual power plant regulation and control optimization method and system

Abstract

The invention belongs to the technical field of power plant dispatching, and provides a multi-time-scale photovoltaic energy storage virtual power plant regulation and control optimization method and system, and the method comprises the steps: obtaining the real-time operation data of a photovoltaic power generation unit and an energy storage system in a virtual power plant; performing multi-time scale regulation and control on the photovoltaic energy storage virtual power plant based on the acquired real-time operation data to obtain a hierarchical regulation and control strategy of a photovoltaic power generation unit and an energy storage system; according to the obtained hierarchical regulation and control strategy, an inertia model of the photovoltaic power generation unit and the energy storage system is constructed; the output power of the photovoltaic power generation unit and the energy storage system is monitored in real time based on the constructed inertia model, and the inertia and the primary frequency modulation capability are evaluated by combining the real-time monitoring result of the output power; and performing hierarchical optimization of the photovoltaic energy storage virtual power plant according to the evaluation result of the inertia and the primary frequency modulation capability to realize multi-time scale photovoltaic virtual power plant regulation and control optimization.

Description

Technical Field

[0001] The present invention belongs to the technical field of power plant dispatching, and specifically relates to a multi-time-scale photovoltaic energy storage virtual power plant control optimization method and system. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the development of power technology, photovoltaic power generation and energy storage technology are increasingly used in power systems. However, there are many problems in the integration and application of photovoltaic energy storage systems and virtual power plants, which affect their stable operation and economic benefits in power systems.

[0004] At present, the independent operation mode of photovoltaic and energy storage systems is more common. There is a lack of effective methods to integrate multiple distributed energy sources into virtual power plants (VPPs), which makes it impossible to achieve optimal allocation and unified regulation of resources, resulting in low operating efficiency and poor dispatching flexibility of the power system. With the large-scale access of distributed photovoltaic power generation systems to distribution networks, especially in rural power grids, the power supply radius is long and the load is relatively stable. The randomness and intermittency of photovoltaic power generation have greatly increased the difficulty of voltage optimization control; frequent voltage fluctuations affect the stability of the power grid and the quality of power supply, which may damage user equipment and increase power loss.

[0005] Existing technologies lack effective means of regulating energy storage systems at multiple time scales, especially in dealing with fluctuations in photovoltaic power generation. They cannot fully exert the regulatory role of energy storage systems, affecting the stability and reliability of the power system. Lack of inertia and primary frequency regulation support capabilities Online assessment of the inertia and primary frequency regulation support capabilities of photovoltaic power generation units and energy storage systems; when the grid frequency fluctuates, there is a lack of effective inertia support and frequency regulation capabilities, affecting the stable operation of the power system. Without adding new reactive power sources to the existing distribution network, it is difficult to achieve the maximum absorption and economic optimization of distributed photovoltaics, and it is impossible to fully utilize electricity price information and load forecasts, resulting in high operating costs and low economic benefits for energy storage systems. Summary of the invention

[0006] To solve the above problems, the present invention proposes a multi-time-scale photovoltaic energy storage virtual power plant control optimization method and system, which integrates photovoltaic power generation and energy storage systems into a virtual power plant, and realizes the control of the virtual power plant by online evaluation of the inertia and primary frequency regulation support capability of the virtual power plant, thereby completing the optimal allocation of power resources and improving the stability and reliability of power system operation.

[0007] According to some embodiments, the first solution of the present invention provides a multi-time scale photovoltaic energy storage virtual power plant control optimization method, which adopts the following technical solutions:

[0008] A multi-time scale photovoltaic energy storage virtual power plant control optimization method, comprising:

[0009] Obtain real-time operating data of photovoltaic power generation units and energy storage systems in virtual power plants;

[0010] Based on the acquired real-time operation data, multi-time scale control of the photovoltaic energy storage virtual power plant is carried out to obtain a hierarchical control strategy for the photovoltaic power generation unit and the energy storage system;

[0011] The inertia model of the photovoltaic power generation unit and the energy storage system is constructed according to the obtained hierarchical control strategy;

[0012] Based on the constructed inertia model, the output power of the photovoltaic power generation unit and the energy storage system is monitored in real time, and the inertia and primary frequency regulation capabilities are evaluated based on the real-time monitoring results of the output power;

[0013] According to the evaluation results of inertia and primary frequency regulation capability, the photovoltaic energy storage virtual power plant is optimized in stages to achieve the optimization of photovoltaic virtual power plant regulation at multiple time scales.

[0014] As a further technical limitation, in the process of the multi-time scale control, the photovoltaic power generation unit and the energy storage system are hierarchically controlled according to the dynamic response time of the photovoltaic energy storage virtual power plant; the multi-time scale includes a short time scale, a medium time scale and a long time scale.

[0015] Furthermore, when the dynamic response time is at the second to minute level, which is a short time scale, the power generation of the photovoltaic power generation unit is monitored in real time, and the power of the energy storage system is adjusted based on the adaptive control strategy; when the dynamic response time is at the minute to hour level, which is a medium time scale, the output power of the photovoltaic power generation unit in the next few hours is predicted based on machine learning, and the charging and discharging plan of the energy storage system is adjusted according to the output power prediction value to realize the demand-side management of the virtual power plant; when the dynamic response time is at the hour to year level or even above the annual level, which is a long time scale, long-term demand forecasts are made for photovoltaic power generation units and energy storage systems, and resources are optimally allocated based on the forecast results.

[0016] As a further technical limitation, in the process of constructing the inertia models of the photovoltaic power generation unit and the energy storage system, the inertia model of the photovoltaic power generation unit and the inertia model of the energy storage system are constructed respectively. The constructed inertia model of the photovoltaic power generation unit is at least related to the light intensity, the ambient temperature and the time delay, and the constructed inertia model of the energy storage system is related to the internal resistance, open circuit voltage, charge and discharge state and charge state of the energy storage system.

[0017] As a further technical limitation, the output power of the photovoltaic power generation unit and the energy storage system is monitored in real time according to the constructed inertia model to obtain the frequency change of the simulated power grid, adjust the adjustment speed and amplitude of the output power of the photovoltaic power generation unit and the energy storage system, and obtain the frequency regulation response index used to evaluate the inertia and primary frequency regulation capability.

[0018] As a further technical limitation, the evaluation results at least include voltage exceeding a safe range, insufficient response speed, and substandard grid stability; when the voltage exceeds a safe range, the voltage is brought back to a safe range by adjusting the discharge amount of the energy storage system; when the response speed is insufficient, the reaction speed of the energy storage system is adjusted by an optimization algorithm; when grid stability is substandard, the capacity of the energy storage system is adjusted or the grid is re-dispatched according to the cause of grid instability.

[0019] According to some embodiments, the second solution of the present invention provides a multi-time scale photovoltaic energy storage virtual power plant control optimization system, which adopts the following technical solutions:

[0020] A multi-time scale photovoltaic energy storage virtual power plant control optimization system, comprising:

[0021] An acquisition module, which is configured to acquire real-time operating data of a photovoltaic power generation unit and an energy storage system in a virtual power plant;

[0022] A control module is configured to perform multi-time scale control of the photovoltaic energy storage virtual power plant based on the acquired real-time operation data to obtain a hierarchical control strategy for the photovoltaic power generation unit and the energy storage system;

[0023] A construction module configured to construct an inertia model of a photovoltaic power generation unit and an energy storage system according to the obtained hierarchical control strategy;

[0024] An evaluation module is configured to monitor the output power of the photovoltaic power generation unit and the energy storage system in real time based on the constructed inertia model, and evaluate the inertia and primary frequency regulation capabilities in combination with the real-time monitoring results of the output power;

[0025] The optimization module is configured to perform hierarchical optimization of the photovoltaic energy storage virtual power plant according to the evaluation results of inertia and primary frequency regulation capability, so as to realize the control optimization of the photovoltaic virtual power plant at multiple time scales.

[0026] According to some embodiments, a third solution of the present invention provides a computer-readable storage medium, which adopts the following technical solution:

[0027] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps in a multi-time-scale photovoltaic energy storage virtual power plant control optimization method as described in the first solution of the present invention.

[0028] According to some embodiments, a fourth solution of the present invention provides an electronic device, which adopts the following technical solution:

[0029] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in a multi-time-scale photovoltaic energy storage virtual power plant control optimization method as described in the first scheme of the present invention are implemented.

[0030] According to some embodiments, a fifth solution of the present invention provides a computer program product, which adopts the following technical solution:

[0031] A computer program product includes software code, wherein the program in the software code executes the steps in a multi-time scale photovoltaic energy storage virtual power plant control optimization method as described in the first embodiment of the present invention.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention optimizes the multi-time scale control of virtual power plants based on electricity prices, solves the problems existing in the integrated application of photovoltaic energy storage systems and virtual power plants in the prior art, adjusts the output of photovoltaic energy storage systems in real time through multi-time scale control, effectively smoothes the fluctuations of photovoltaic power generation, reduces the impact on the power grid, and significantly improves the stability and reliability of the power system.

[0034] The present invention adopts a hierarchical voltage optimization control strategy, which significantly improves the voltage control problem of the distribution network without adding new reactive power sources, ensures that the voltage of the distributed photovoltaic grid connection point is within a safe range, reduces equipment damage and power loss caused by overvoltage, and reduces the operation and maintenance costs of the distribution network;

[0035] The present invention evaluates the inertia and primary frequency regulation capability of the photovoltaic power generation unit and the energy storage system online, dynamically adjusts the output power of the energy storage system according to the evaluation results, provides effective inertia support, enhances the frequency regulation capability of the power system, and ensures the stability of the grid frequency.

[0036] The present invention combines electricity price information and load forecasting to optimize the charging and discharging plan of the energy storage system, while improving the distributed photovoltaic absorption capacity and maximizing the economic benefits. It reduces power loss and operating costs, improves the economic benefits of the photovoltaic energy storage system, provides a collaborative working strategy for each energy unit in the virtual power plant, enhances the flexibility and reliability of the power system, supports the efficient operation of the virtual power plant, and comprehensively improves the control capability of the photovoltaic energy storage system and the operating efficiency of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings in the specification that constitute a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments of this embodiment and their descriptions are used to explain this embodiment and do not constitute improper limitations on this embodiment.

[0038] Figure 1 It is a flow chart of the multi-time-scale photovoltaic energy storage virtual power plant control optimization method in the first embodiment of the present invention;

[0039] Figure 2 This is a structural block diagram of multiple distributed photovoltaics connected to a virtual power plant in Embodiment 1 of the present invention;

[0040] Figure 3 This is an architecture diagram of multiple distributed photovoltaics connected to a virtual power plant in Embodiment 1 of the present invention;

[0041] Figure 4 This is a structural block diagram of the multi-time scale photovoltaic energy storage virtual power plant control and optimization system in Example 1 of the present invention. DETAILED DESCRIPTION

[0042] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0043] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0044] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0045] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention and should not be understood as limitations on the present invention.

[0046] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0047] Embodiment 1

[0048] Embodiment 1 of the present invention introduces a multi-time scale photovoltaic energy storage virtual power plant control optimization method.

[0049] This embodiment achieves optimal allocation and efficient utilization of resources through unified regulation of virtual power plants, improves the stability and reliability of the power system in various operating scenarios, and ensures that the power system has sufficient response capabilities when facing frequency fluctuations. This embodiment comprehensively analyzes equipment parameters, equipment status, and photovoltaic grid-related operating status based on multi-source data, formulates operating adaptability evaluation indicators for supporting energy storage equipment, and implements hierarchical voltage optimization control to ensure that the voltage of distributed photovoltaic grid-connected points is within a safe range. Combined with electricity price information and load forecasts, the charging and discharging plan of the energy storage system is optimized to maximize economic benefits and reduce power losses while improving the distributed photovoltaic absorption capacity. Through the above technical solutions, the present invention can effectively solve the problems in the integration and application of photovoltaic energy storage systems and virtual power plants, realize efficient, safe, and stable operation of photovoltaic power generation systems, optimize economic benefits, and promote the development and application of new energy.

[0050] like Figure 1 A multi-time scale photovoltaic energy storage virtual power plant control optimization method is shown, comprising:

[0051] Obtain real-time operating data of photovoltaic power generation units and energy storage systems in virtual power plants;

[0052] Based on the acquired real-time operation data, multi-time scale control of the photovoltaic energy storage virtual power plant is carried out to obtain a hierarchical control strategy for the photovoltaic power generation unit and the energy storage system;

[0053] The inertia model of the photovoltaic power generation unit and the energy storage system is constructed according to the obtained hierarchical control strategy;

[0054] Based on the constructed inertia model, the output power of the photovoltaic power generation unit and the energy storage system is monitored in real time, and the inertia and primary frequency regulation capabilities are evaluated based on the real-time monitoring results of the output power;

[0055] According to the evaluation results of inertia and primary frequency regulation capability, the photovoltaic energy storage virtual power plant is optimized in stages to achieve the optimization of photovoltaic virtual power plant regulation at multiple time scales.

[0056] like Figure 2 and Figure 3As shown, this embodiment integrates the distributed photovoltaic power generation system and energy storage system into the virtual power plant to achieve unified regulation and optimal allocation of resources; uses advanced communication and control technologies to connect each distributed energy unit to the central control system of the virtual power plant, and monitors and regulates the operating status of each unit in real time; through the central control system of the virtual power plant, the photovoltaic power generation and energy storage system are uniformly dispatched to optimize resource utilization according to grid demand and electricity price information. The coordinated work of each energy unit in the virtual power plant is achieved, and the flexibility and response speed of the power system are improved.

[0057] As one or more implementation modes, this embodiment effectively regulates the energy storage system through a multi-time scale regulation method, combines load forecasting and electricity price information, optimizes the charge and discharge cycle of the energy storage system, and maximizes economic benefits; specifically,

[0058] (1) When the dynamic response time is in the range of seconds to minutes, which is a short time scale, the power generation of the photovoltaic power generation unit is monitored in real time, and the power of the energy storage system is adjusted based on the adaptive control strategy;

[0059] This embodiment uses sensors and intelligent metering technology to monitor photovoltaic power generation and grid voltage in real time, uses electrochemical energy storage (such as lithium-ion batteries) to quickly adjust power output in response to changes in seconds, and adopts PID controllers or more advanced adaptive control strategies to ensure that voltage and frequency are stable within a specified range.

[0060] (2) When the dynamic response time is in the medium time scale of minutes to hours, the output power of the photovoltaic power generation unit in the next few hours is predicted based on machine learning, and the charging and discharging plan of the energy storage system is adjusted according to the output power prediction value to realize the demand side management of the virtual power plant;

[0061] This embodiment uses a machine learning model based on weather forecasts and historical data to predict photovoltaic output in the next few hours; adjusts the charging and discharging plan of the energy storage system based on the prediction results to optimize energy utilization and grid demand; and collaborates with grid operators to achieve demand-side management to balance supply and demand.

[0062] (3) When the dynamic response time is on a long time scale ranging from hourly to annual level or even above annual level, long-term demand forecasts are made for photovoltaic power generation units and energy storage systems, and resources are optimally allocated based on the forecast results.

[0063] This embodiment conducts long-term planning and investment for facilities by studying market, technology and policy trends; formulates preventive maintenance plans based on equipment performance data and predicted demand changes; and optimizes resource allocation, including long-term energy purchase agreements and capital investment decisions.

[0064] This embodiment can effectively regulate and control photovoltaic power generation and power grid on different time scales through multi-time scale regulation; it not only improves energy utilization efficiency, but also enhances the stability and reliability of the power grid.

[0065] In this embodiment, the inertia model of the photovoltaic power generation unit and the energy storage system is constructed according to the obtained hierarchical control strategy, and the output power change is monitored in real time in combination with the constructed inertia model; the inertia and primary frequency regulation capability of the photovoltaic power generation and energy storage system are evaluated through the response data of the grid frequency fluctuation. When the grid frequency fluctuates, the output power of the energy storage system is quickly adjusted to provide the necessary inertia support. Combined with the power curve of photovoltaic power generation, the frequency regulation strategy of the energy storage system is optimized to ensure the stability of the grid frequency.

[0066] The inertia model constructed in this embodiment includes a photovoltaic power generation model and an energy storage system model, namely:

[0067] The photovoltaic power generation model includes the impact of environmental factors such as light intensity and temperature on the output power, which can be modeled through the performance curves of photovoltaic cells (for example, IV curve and PV curve); considering the response of the photovoltaic system to rapid environmental changes (such as the rapid movement of cloud shadows), time delays and filters can be introduced to simulate the dynamic response of photovoltaic power generation;

[0068] The battery model of the energy storage system uses an equivalent circuit model (such as the Thevenin model) to describe the charging and discharging behavior of the battery, taking into account the impact of the battery's internal resistance, open circuit voltage and state of charge (SOC) on the output performance; a power regulation based on a PID controller can be used to quickly respond to changes in grid demand.

[0069] This embodiment collects and analyzes multi-source data such as photovoltaic power generation, energy storage system, and distribution network operating status; establishes a data model to comprehensively evaluate equipment parameters, equipment status, and photovoltaic grid-related operating status; and ensures that the energy storage system can work effectively under various operating conditions by formulating operating adaptability evaluation indicators for supporting energy storage equipment. Based on the evaluation results, the operating strategy of the energy storage system is dynamically adjusted to improve system adaptability.

[0070] The evaluation indicators in this embodiment include at least:

[0071] (1) Equipment performance indicators

[0072] Capacity efficiency, which is the ratio of the maximum storage capacity of an energy storage device to its rated capacity, is used to evaluate the actual operating efficiency of the energy storage device;

[0073] Energy density, which is the maximum amount of energy that can be stored per unit weight or volume of an energy storage device, is one of the important indicators, especially in space-constrained application scenarios;

[0074] Power density, which is the output power per unit volume or weight of the energy storage system, is one of the key indicators that determines the rapid response capability of the energy storage system.

[0075] (2) Economic indicators

[0076] LCOE (standard cost of electricity), which is the average cost per unit of electricity of the energy storage system, including capital expenditure, operation and maintenance costs, etc.

[0077] The payback period, or the length of time it takes for a storage system to become profitable from commissioning, is an important metric for assessing investment attractiveness.

[0078] (3) Reliability and life indicators

[0079] Cycle life, which is the number of times a storage device can be charged and discharged before its performance drops to a specified threshold;

[0080] Stability refers to the degree to which the performance of energy storage equipment is maintained during long-term operation, including its performance under different load and environmental conditions.

[0081] (4) Safety indicators

[0082] Failure rate, which is the frequency of equipment failures during operation, is directly related to the risk of overall operations;

[0083] Emergency response time refers to the reaction speed and processing capacity of energy storage equipment in the event of a power grid failure or other emergency.

[0084] (5) Environmental adaptability index

[0085] Temperature adaptability, that is, the operating efficiency and safety performance of energy storage equipment under different ambient temperatures;

[0086] Environmental impact assessment, i.e. the impact of the energy storage system on the environment during its life cycle, including the manufacturing, use and recycling stages.

[0087] (6) Efficiency index

[0088] Charging efficiency, that is, the efficiency of energy conversion of energy storage devices during charging;

[0089] Discharge efficiency, that is, the energy conversion efficiency during the discharge process, usually determines the overall energy efficiency together with the charging efficiency.

[0090] (7) System integration and interoperability indicators

[0091] Integration with the photovoltaic system, that is, the synergistic efficiency of energy storage equipment and photovoltaic systems, including energy management and scheduling optimization;

[0092] Data communication and management capabilities, that is, whether the system can efficiently communicate with other devices and realize data exchange and control.

[0093] This embodiment can comprehensively evaluate the operational adaptability of the supporting energy storage equipment through the above comprehensive indicators, thereby ensuring the efficient, safe and reliable operation of the photovoltaic grid-connected system. The specific setting and weight distribution of these indicators should be adjusted according to actual application requirements and specific operating environment.

[0094] It should be noted that in the process of power grid evaluation in this embodiment, it is necessary to continuously evaluate key indicators such as the operating efficiency, voltage regulation performance, and response speed of the current energy storage system, including:

[0095] (1) Voltage level monitoring: Real-time monitoring of voltage values ​​at each level to ensure they are within the preset safety and operating range;

[0096] (2) Response efficiency: evaluates the speed and effectiveness of the energy storage system’s response to load changes and grid events;

[0097] (3) System stability: Analyze the contribution of the energy storage system to the stability of the entire distribution network, especially under high demand or low capacity conditions.

[0098] This embodiment defines the performance thresholds for triggering adjustment measures according to the design requirements of the energy storage system and the power grid, namely:

[0099] (1) Voltage deviation: Set the maximum acceptable voltage deviation. If it exceeds this value, adjustment is required.

[0100] (2) Response time: defines the maximum time limit for the energy storage system to respond to grid events.

[0101] In this embodiment, the energy storage system operation strategy is dynamically adjusted according to the evaluation results:

[0102] (1) If the voltage exceeds the safe range: increase or decrease the discharge of the energy storage system to help adjust the voltage back to the safe range. If the voltage is too high, the discharge rate can be increased to consume excess energy; if the voltage is too low, the discharge can be reduced or the charge can be increased to provide more energy support;

[0103] (2) If the response speed is insufficient: optimize the control algorithm to improve the response speed of the energy storage system; it may be necessary to update the software control strategy or upgrade the hardware when necessary;

[0104] (3) If the system stability does not meet the standard: Analyze the reasons for the instability, which may be insufficient energy storage capacity or unreasonable scheduling strategy; adjust the energy storage capacity or redesign the scheduling strategy according to the situation.

[0105] This embodiment adopts a hierarchical voltage optimization control strategy to ensure that the voltage at each level of the distribution network is within a safe range; prioritizes the scheduling of energy storage systems to reduce overvoltage problems caused by photovoltaic power generation; combines electricity price information and load forecasts to optimize the charging and discharging plans of the energy storage system to maximize economic benefits; and improves the absorption capacity of distributed photovoltaics and reduces power losses through the regulation of the energy storage system without adding new reactive power sources.

[0106] This embodiment performs voltage regulation at different levels and grades of the power system to improve the efficiency and reliability of the power system, while reducing energy consumption and losses, and realizing hierarchical and graded voltage optimization control; specifically:

[0107] (1) Hierarchical structure setting

[0108] The upper layer (central or regional control center), which is responsible for monitoring and managing the voltage stability and performance of the entire grid, uses advanced monitoring and prediction tools (such as advanced distribution management systems (ADMS) and centralized control systems) to perform large-scale voltage adjustments such as transformer tap switching, reactive power adjustment, etc.

[0109] The middle level (district or local control center) manages the voltage quality within a specific area (such as a city or a large industrial area) using a regional energy management system that integrates data from smart meters and local sensors to achieve regional voltage control, such as starting voltage regulators, capacitors, and local power generation facilities within the area.

[0110] The lower layer (end or user side) maintains voltage stability at the end user, especially the integration of distributed energy resources (such as photovoltaics, wind power, etc.), relying on terminal equipment such as smart inverters, home energy management systems, etc., to automatically adjust the operation of home or enterprise internal equipment, such as adjusting the output of the inverter, using battery energy storage, etc.

[0111] (2) Communication and coordination mechanism

[0112] Data exchange, that is, an efficient data exchange mechanism is required between each layer, including status information, control commands and early warning systems.

[0113] Coordinated operation means ensuring that there is no conflict between the control strategies at each layer and that they can support each other to achieve optimal voltage management for the entire network.

[0114] (3) Application of intelligent control strategies

[0115] Adaptive control means that the system can automatically adjust the control strategy according to real-time data to cope with changing network conditions and load demands.

[0116] Predictive control uses data analysis and machine learning to predict grid load and voltage changes and make adjustments in advance.

[0117] (4) Technical and equipment support

[0118] Equipment upgrade, that is, upgrading existing equipment and technology, introducing more efficient transformers, voltage regulators and capacity adjustment equipment.

[0119] New technology applications, that is, using the latest technologies such as smart grid technology, Internet of Things (IoT) devices and cloud computing to improve the accuracy and efficiency of control.

[0120] This embodiment can achieve accurate voltage control, optimize the operating efficiency of the power system, reduce energy consumption and operation and maintenance costs, and improve the overall stability and reliability of the power grid through the above-mentioned hierarchical and graded method. This method is particularly suitable for complex and large-scale modern power systems.

[0121] It should be noted that the process of hierarchical voltage control in this embodiment is as follows:

[0122] (1) Top-level control: macro-control at the central or regional control center to optimize the voltage levels of the main power sources and key connection points;

[0123] (2) Local control: In a sub-area or local control center, the output of local energy storage equipment is adjusted based on real-time data to ensure voltage stability;

[0124] (3) Terminal control: Use terminal intelligent devices (such as smart inverters) to make fine adjustments based on the specific needs of the home or community.

[0125] This embodiment optimizes the multi-time scale regulation of the virtual power plant based on electricity prices, solves the problems existing in the integrated application of photovoltaic energy storage systems and virtual power plants in the prior art, and adjusts the output of the photovoltaic energy storage system in real time through multi-time scale regulation, effectively smooths the fluctuation of photovoltaic power generation, reduces the impact on the power grid, and significantly improves the stability and reliability of the power system; adopts a hierarchical voltage optimization control strategy, and significantly improves the voltage control problem of the distribution network without adding new reactive power sources, ensuring that the voltage of the distributed photovoltaic grid-connected points is within a safe range, reducing equipment damage and power loss caused by overvoltage, and reducing the operation and maintenance costs of the distribution network; through online evaluation of the inertia and primary frequency regulation capability of the photovoltaic power generation unit and the energy storage system, the output power of the energy storage system is dynamically adjusted according to the evaluation results, providing effective inertia support, enhancing the frequency regulation capability of the power system, and ensuring the stability of the power grid frequency; combined with electricity price information and load forecasting, the charging and discharging plan of the energy storage system is optimized, while improving the distributed photovoltaic absorption capacity and maximizing economic benefits. Reduce power loss and operating costs, improve the economic benefits of photovoltaic energy storage systems, provide collaborative working strategies for various energy units in virtual power plants, enhance the flexibility and reliability of power systems, support the efficient operation of virtual power plants, and comprehensively improve the regulation and control capabilities of photovoltaic energy storage systems and the operating efficiency of power systems.

[0126] Embodiment 2

[0127] Embodiment 2 of the present invention introduces a multi-time-scale photovoltaic energy storage virtual power plant control and optimization system.

[0128] like Figure 4 A multi-time scale photovoltaic energy storage virtual power plant control optimization system is shown, comprising:

[0129] An acquisition module, which is configured to acquire real-time operating data of a photovoltaic power generation unit and an energy storage system in a virtual power plant;

[0130] A control module is configured to perform multi-time scale control of the photovoltaic energy storage virtual power plant based on the acquired real-time operation data to obtain a hierarchical control strategy for the photovoltaic power generation unit and the energy storage system;

[0131] A construction module configured to construct an inertia model of a photovoltaic power generation unit and an energy storage system according to the obtained hierarchical control strategy;

[0132] An evaluation module is configured to monitor the output power of the photovoltaic power generation unit and the energy storage system in real time based on the constructed inertia model, and evaluate the inertia and primary frequency regulation capabilities in combination with the real-time monitoring results of the output power;

[0133] The optimization module is configured to perform hierarchical optimization of the photovoltaic energy storage virtual power plant according to the evaluation results of inertia and primary frequency regulation capability, so as to realize the control optimization of the photovoltaic virtual power plant at multiple time scales.

[0134] The detailed steps are the same as those of the multi-time-scale photovoltaic energy storage virtual power plant control optimization method provided in Example 1, and will not be repeated here.

[0135] Embodiment 3

[0136] Embodiment 3 of the present invention provides a computer-readable storage medium.

[0137] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps in a multi-time-scale photovoltaic energy storage virtual power plant control optimization method as described in Embodiment 1 of the present invention.

[0138] The detailed steps are the same as those of the multi-time-scale photovoltaic energy storage virtual power plant control optimization method provided in Example 1, and will not be repeated here.

[0139] Embodiment 4

[0140] A fourth embodiment of the present invention provides an electronic device.

[0141] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in a multi-time-scale photovoltaic energy storage virtual power plant control optimization method as described in Embodiment 1 of the present invention are implemented.

[0142] The detailed steps are the same as those of the multi-time-scale photovoltaic energy storage virtual power plant control optimization method provided in Example 1, and will not be repeated here.

[0143] Embodiment 5

[0144] Embodiment 5 of the present invention provides a computer program product.

[0145] A computer program product includes software code, wherein the program in the software code executes the steps in the multi-time scale photovoltaic energy storage virtual power plant control optimization method as described in the first embodiment of the present invention.

[0146] The detailed steps are the same as those of the multi-time-scale photovoltaic energy storage virtual power plant control optimization method provided in Example 1, and will not be repeated here.

[0147] The above description is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. For those skilled in the art, the present embodiment may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. A multi-time scale photovoltaic energy storage virtual power plant control optimization method, characterized in that: include: Obtain real-time operating data of photovoltaic power generation units and energy storage systems in virtual power plants; Based on the real-time operation data obtained, multi-time scale control of the photovoltaic energy storage virtual power plant is carried out to obtain a hierarchical control strategy for the photovoltaic power generation unit and the energy storage system; The inertia model of the photovoltaic power generation unit and the energy storage system is constructed according to the obtained hierarchical control strategy; Based on the constructed inertia model, the output power of the photovoltaic power generation unit and the energy storage system is monitored in real time, and the inertia and primary frequency regulation capabilities are evaluated based on the real-time monitoring results of the output power; According to the evaluation results of inertia and primary frequency regulation capability, the photovoltaic energy storage virtual power plant is optimized in stages to achieve the optimization of photovoltaic virtual power plant regulation at multiple time scales.

2. A multi-time scale photovoltaic energy storage virtual power plant control optimization method as described in claim 1, characterized in that: In the process of multi-time scale regulation, the photovoltaic power generation unit and the energy storage system are hierarchically regulated according to the dynamic response time of the photovoltaic energy storage virtual power plant; the multi-time scale includes a short time scale, a medium time scale and a long time scale.

3. A multi-time scale photovoltaic energy storage virtual power plant control optimization method as described in claim 2, characterized in that: When the dynamic response time is at the second to minute level, which is a short time scale, the power generation of the photovoltaic power generation unit is monitored in real time, and the power of the energy storage system is adjusted based on the adaptive control strategy; when the dynamic response time is at the minute to hour level, which is a medium time scale, the output power of the photovoltaic power generation unit in the next few hours is predicted based on machine learning, and the charging and discharging plan of the energy storage system is adjusted according to the output power prediction value to realize the demand-side management of the virtual power plant; when the dynamic response time is at the hour to year level or even above the annual level, which is a long time scale, long-term demand forecasts are made for photovoltaic power generation units and energy storage systems, and resources are optimally allocated based on the forecast results.

4. A multi-time scale photovoltaic energy storage virtual power plant control optimization method as described in claim 1, characterized in that: In the process of constructing the inertia models of the photovoltaic power generation unit and the energy storage system, the inertia model of the photovoltaic power generation unit and the inertia model of the energy storage system are constructed respectively. The constructed inertia model of the photovoltaic power generation unit is at least related to the light intensity, the ambient temperature and the time delay, and the constructed inertia model of the energy storage system is related to the internal resistance, open circuit voltage, charging and discharging state and charge state of the energy storage system.

5. A multi-time scale photovoltaic energy storage virtual power plant control optimization method as described in claim 1, characterized in that: According to the constructed inertia model, the output power of the photovoltaic power generation unit and the energy storage system is monitored in real time to obtain the frequency change of the simulated power grid, adjust the adjustment speed and amplitude of the output power of the photovoltaic power generation unit and the energy storage system, and obtain the frequency regulation response index used to evaluate the inertia and primary frequency regulation capability.

6. A multi-time scale photovoltaic energy storage virtual power plant control optimization method as described in claim 1, characterized in that: The evaluation results at least include voltage exceeding the safety range, insufficient response speed and substandard grid stability; when the voltage exceeds the safety range, the voltage is brought back to the safety range by adjusting the discharge amount of the energy storage system; when the response speed is insufficient, the reaction speed of the energy storage system is adjusted through an optimization algorithm; when the grid stability is substandard, the capacity of the energy storage system is adjusted or the grid is re-dispatched according to the cause of the grid instability.

7. A multi-time scale photovoltaic energy storage virtual power plant control optimization system, characterized in that: include: An acquisition module, which is configured to acquire real-time operating data of a photovoltaic power generation unit and an energy storage system in a virtual power plant; A control module is configured to perform multi-time scale control of the photovoltaic energy storage virtual power plant based on the acquired real-time operation data to obtain a hierarchical control strategy for the photovoltaic power generation unit and the energy storage system; A construction module configured to construct an inertia model of a photovoltaic power generation unit and an energy storage system according to the obtained hierarchical control strategy; An evaluation module is configured to monitor the output power of the photovoltaic power generation unit and the energy storage system in real time based on the constructed inertia model, and evaluate the inertia and primary frequency regulation capabilities in combination with the real-time monitoring results of the output power; The optimization module is configured to perform hierarchical optimization of the photovoltaic energy storage virtual power plant according to the evaluation results of inertia and primary frequency regulation capability, so as to realize the control optimization of the photovoltaic virtual power plant at multiple time scales.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of a multi-time-scale photovoltaic energy storage virtual power plant control optimization method as described in any one of claims 1-6 are implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of a multi-time-scale photovoltaic energy storage virtual power plant control optimization method as described in any one of claims 1-6 are implemented.

10. A computer program product comprising software code, characterized in that The program in the software code executes the steps of a multi-time-scale photovoltaic energy storage virtual power plant control optimization method as described in any one of claims 1-6.

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