Abstract: This study aims to optimize the capacity configuration and coordinated control of energy storage photovoltaic power plants. By analyzing the structural design of photovoltaic power plants, the capacity configuration and charging and discharging strategies of energy storage units, as well as the design of power grids and load units, a reliability model for photovoltaic storage power plants is constructed. By analyzing and comparing the performance parameters of different types of energy storage batteries, a scientifically reasonable capacity optimization configuration scheme has been proposed. In addition, the study also explored the coordinated control strategies of multiple hybrid energy storage systems, designed corresponding event triggering functions and adjustment mechanisms to achieve better energy management and improve the stability of system operation. The research results indicate that lead-acid power density batteries exhibit excellent performance under current technological conditions, making them an ideal choice for optimizing the capacity configuration of photovoltaic energy storage stations. Meanwhile, the optimized control strategy significantly improves the overall performance of energy storage photovoltaic power plants, providing important reference for the design and operation of similar systems in the future.

Keywords: photovoltaic power station; Battery capacity optimization; coordinated control

Introduction

Energy storage photovoltaic power stations, as an effective means to solve the intermittency and instability of renewable energy, are of great significance in improving energy utilization efficiency, optimizing power grid operation, and promoting green energy transformation. With the development of photovoltaic technology and the reduction of costs, the proportion of photovoltaic power generation in the global energy structure continues to rise. However, the volatility and unpredictability of photovoltaic power generation pose challenges to the stable operation of the power grid. Therefore, how to effectively configure the capacity of energy storage batteries and coordinate the control of energy storage systems and photovoltaic power generation has become a hot research topic. Domestic and foreign scholars have conducted extensive research on energy storage photovoltaic power plants, involving optimization of battery capacity configuration, energy management strategies, economic analysis, and stability evaluation. Existing research has to some extent improved the efficiency and economy of energy storage systems, but there are still some shortcomings. Firstly, most studies focus on system performance analysis under specific conditions, lacking in-depth exploration of adaptability in different environments and operating modes. Secondly, research on the coordinated control of energy storage and photovoltaic systems is not sufficient, especially in terms of the synergistic mechanism between multiple hybrid energy storage systems. The existing optimization models and control strategies often overlook the technical limitations and cost factors in practical applications, making it difficult to apply theoretical results to practice. In response to the shortcomings of existing research, this paper proposes a new research approach for optimizing the capacity configuration and coordinated control of energy storage photovoltaic power plants. Firstly, taking into account the uncertainty of photovoltaic power generation and the dynamic changes in load demand, establish a more universal reliability model for photovoltaic energy storage stations. On this basis, key indicators such as power penetration rate and capacity penetration rate are introduced to analyze their impact on the stability of photovoltaic power stations. Furthermore, by comparing the performance parameters of different types of energy storage batteries in detail, a capacity optimization configuration scheme is proposed. At the same time, design coordinated control strategies for multiple hybrid energy storage systems, achieve energy management and improve system stability through event triggered functions and regulation mechanisms.

Structural Design of Energy Storage Photovoltaic Power Station

In the design of photovoltaic power generation units, they are mainly composed of photovoltaic cell modules in series and parallel. In order to optimize the photovoltaic control strategy, a thorough analysis of the characteristics of photovoltaic cells is conducted. This involves considering the performance of photovoltaic cells under different environmental conditions. Through a deep understanding of these characteristics, the output of photovoltaic power generation units can be more predicted and adjusted to match the demand of energy storage units and the load situation of the power grid. The key to the design of energy storage units lies in their capacity configuration and charging and discharging strategies. Given the intermittency and uncertainty of photovoltaic power generation, energy storage units need to have sufficient capacity to store excess energy and release energy to meet load demand when photovoltaic power generation is insufficient.

For the design of the power grid and load units, it is necessary to consider the interaction with the external power grid and the dynamic changes in internal loads. This means that the system needs to be able to flexibly adjust its output according to the demand of the power grid and the actual situation of the load, while ensuring the stability of the power grid and the quality of power supply for the load.

Optimization of Capacity Configuration for 2 Photovoltaic Storage Stations

2.1 Establishment of Reliability Model for Photovoltaic Storage Power Station

Through in-depth research and data analysis, it has been found that the impact of photovoltaic penetration rate on the stability of the power system cannot be ignored. When the installed capacity of photovoltaics exceeds a certain threshold, regional distribution networks may face the challenge of being unable to absorb the electricity generated, leading to an exacerbation of the phenomenon of photovoltaic power being transmitted back to the grid. Especially in the case of high penetration photovoltaic power stations being integrated into the grid, this phenomenon is more significant. Once the net load exceeds the system output range, local power grid units may be forced to stop operating, posing a threat to the stability of the power system.

To address this challenge, this study proposes using photovoltaic penetration rate as a key indicator for evaluating the energy storage configuration of photovoltaic power plants. By comprehensively considering the installed capacity of photovoltaics, the absorption capacity of regional distribution networks, and the stability requirements of the power system, the energy storage configuration of photovoltaic power plants can be reasonably planned to ensure the stable operation of the power system under high penetration rates. At the same time, this also provides a scientific basis for the construction and operation of photovoltaic power plants, which helps to achieve a win-win situation of economic benefits and power system stability.

In the process of constructing the integrated model of photovoltaic and energy storage, this study considers the photovoltaic system and energy storage device as an integrated unit. Special attention was paid to two key indicators during model construction: power penetration rate (PP) and capacity penetration rate (Cp).

The power penetration rate is defined as the ratio between the electricity generated by a distributed photovoltaic power generation system at a specific moment and the total load at that moment. If the ratio exceeds 100%, it indicates that the photovoltaic power generation exceeds the immediate load demand at that moment, and the excess electricity will be transmitted back to the grid.

Capacity penetration rate is the ratio of the cumulative annual photovoltaic power generation to the annual load in a distribution network within a region. This indicator reflects the potential saturation level of photovoltaic system installation, that is, the ratio of photovoltaic power generation capacity to regional electricity load.

In the process of conducting photovoltaic penetration rate analysis and energy storage configuration research, this work is based on a series of assumptions. Firstly, it is assumed that there is no loss of photovoltaic energy during transmission. This assumption aims to simplify the calculation process and achieve 100% energy transfer efficiency. Secondly, the study overlooked the differences between different photovoltaic inverter manufacturers and the impact of environmental changes on the output of photovoltaic systems.

On a time scale, the transient issue of photovoltaic output is not involved, but rather evaluated in hours as the unit of measurement for photovoltaic output. In addition, the distribution network model is simplified as a single load node, without considering the interaction between feeders. This simplification can effectively reduce the complexity of the model, making analysis and computation more efficient, while ensuring that research results have standardized language expression.

2.2 Capacity optimization of photovoltaic storage power station system

Through in-depth research on the operating power of the photovoltaic power storage system, it was found that the system can meet the demand of an average load of 198.54 kWh per hour. This article requires the configuration of 837 kW of photovoltaic installed capacity and 2998 kW · h of energy storage installed capacity. Under this configuration, the average hourly power generation of the photovoltaic system is 198.78 kWh, which is sufficient to meet the load demand of the distribution network.

In practical operation, due to quarterly changes, there are differences between photovoltaic power generation and load electricity, so it is necessary to conduct photovoltaic penetration rate analysis to optimize the configuration of photovoltaic storage power stations. According to the annual penetration rate curve, under clear weather conditions, photovoltaics not only meet the electricity demand of the distribution network, but also transfer energy back to the grid. In addition, the photovoltaic penetration rate in all four seasons is generally slightly higher than the photovoltaic penetration rate calculated under the typical curve, indicating that the performance of photovoltaic power plants in actual operation is better than expected. However, it also highlights the dependence on energy storage systems and the need to adjust energy management strategies in different seasons.

By analyzing and comparing the performance parameters of these different types of energy storage batteries, we can better understand their performance in different application scenarios and make scientifically reasonable capacity optimization configurations based on this. Taking into account factors such as energy density, cycle life, cost, and environmental impact of various batteries, research has found that lead-acid power density batteries exhibit performance under current technological conditions. This type of battery not only has advantages in power output, but also stands out in terms of economy and maturity, making it an ideal choice for optimizing the capacity configuration of photovoltaic storage power plants.

Coordination and control of photovoltaic and energy storage systems

In the research on optimizing the capacity configuration and coordinated control of energy storage photovoltaic power plants, the coordinated control strategy of multiple hybrid energy storage systems is one of the core contents. Due to the capacity of a single energy storage unit and the limitations of power electronic converters, a single group of hybrid energy storage often cannot meet the large energy regulation requirements. Therefore, more energy management can be achieved through the joint participation and coordinated control of multiple mixed energy storage systems.

To achieve this goal, this article first designs a multi hybrid energy storage coordination event triggering function. The main purpose of this function is to allocate the charging and discharging power of different hybrid energy storage units reasonably to ensure that the overall performance of the system reaches the state. The droop control principle was adopted in the design process, through which the output voltage reference values and output currents of multiple hybrid energy storage systems can be calculated. This article introduces the concept of an average voltage observer, which can monitor the voltage changes of each energy storage unit in real time and adjust the output voltage reference value based on this information. In addition, this article also adopts a proportional current regulation mechanism. This mechanism can adjust the output voltage reference value based on the actual current changes, making the output voltage closer to the rated value.

In this complex operating mode, multiple sets of hybrid energy storage systems will be in constant dynamic changes, which requires the upper level controller to make periodic adjustments to ensure the coordination and stability of the entire system. In order to reduce the communication burden of the upper level controller and improve the operational efficiency of the system, a corresponding mixed energy storage regulation trigger event function has been established. The design of this function aims to reduce unnecessary adjustment operations by intelligently judging the system status and triggering adjustment instructions only when necessary. This not only optimizes the use of communication resources, but also improves the overall response speed and reliability of the system.

Compared to traditional control methods, this strategy has demonstrated significant advantages in multiple aspects. Firstly, it effectively reduces power fluctuations in the system during dynamic and steady-state conditions, improving power quality. Secondly, as this strategy can optimize the working state of the energy storage unit, it reduces the triggering frequency of coordinated control of multiple sets of mixed energy storage, thereby extending the service life of the equipment and reducing maintenance costs.

In addition, by reducing unnecessary frequent charge and discharge cycles, this strategy also improves the overall efficiency and reliability of the system. Ultimately, these improvements make the operation of the entire system more stable, better able to cope with various complex working conditions and load demands, and provide users with better power services.

4 Ankerui Smart Energy Management Platform

4.1 Ankerui Smart Energy Management Platform

AcrelEMS Smart Energy Management Platform is an energy efficiency management platform designed for enterprise microgrids. It provides real-time monitoring, intelligent prediction, dynamic allocation, strategy optimization, diagnostic alarms, and the ability to schedule orderly interaction between source and load, as well as energy panoramic analysis of distributed power sources, municipal power sources, energy storage systems, charging facilities, and various AC/DC loads in enterprise microgrids. It meets the needs of digital energy efficiency management, intelligent safety analysis, dynamic adjustment and control, and visualized panoramic analysis of enterprise microgrids. It achieves flexible interaction and economic operation between light storage and charging resources under different strategies, reduces energy costs for users, and improves the operational efficiency of microgrids. The AcrelEMS smart energy management platform can accept scheduling instructions and demand responses from virtual power plants, and is an enterprise level subsystem of the virtual power plant platform.

Figure 1: AcrelEMS Smart Energy Management Platform Main Interface

4.2 Platform Structure

The system covers all aspects of the enterprise microgrid, including source grid load storage charging. It collects data from measurement and control devices, photovoltaics, energy storage, charging piles, and conventional loads through an intelligent gateway. Based on load changes and grid scheduling, it optimizes control to promote the consumption of new energy while reducing the maximum demand on the grid, ensuring safe operation.

Figure 2 Structure of AcrelEMS Smart Energy Management Platform

4.3 Platform Functions

4.3.1. Digital Display of Energy

By displaying real-time data on mains electricity, photovoltaic, wind power, energy storage, charging piles, and other loads on a large screen, one can quickly understand the energy operation situation.

4.3.2. Optimization Control

Intuitively display energy production and flow, including mains electricity, photovoltaics, energy storage charging and consumption processes, improve new energy consumption through optimized control of energy storage and controllable loads, peak shaving and valley filling, smooth system output, and display a comparison of energy curves before and after optimization.

4.3.3. Intelligent prediction

Based on meteorological data and historical data, predict the power and load of photovoltaic and wind power generation, compare and analyze them with actual power, and optimize scheduling through energy storage systems and load control to reduce demand and electricity costs.

4.3.4. Energy consumption analysis

Collect the consumption of various energy media such as electricity, water, natural gas, and cold/heat in enterprises, compare them on a month on month basis, display energy flow, benchmark energy consumption, and convert them into standard coal or carbon emissions.

4.3.5. Orderly Charging

The system supports the connection of AC and DC charging stations, and controls them in conjunction with the enterprise load and transformer capacity, as well as the transformer load rate, to guide users to charge in an orderly manner and ensure the safe operation of the enterprise microgrid.

4.3.6. Operation and Maintenance Inspection

The system supports task management, inspection/defect/alarm/repair records, and notification work order management, and tracks the trajectory of operation and maintenance personnel through Beidou positioning to achieve closed-loop management of the operation and maintenance process.

4.4 Equipment selection

In addition to the smart energy management platform, it also has on-site sensors, intelligent gateways and other equipment, forming a complete "cloud edge end" energy digitalization system, including high and low voltage distribution comprehensive protection and monitoring products, power quality online monitoring devices, power quality governance, lighting control, charging piles, electrical fire protection solutions, etc. It can provide one-stop service capabilities for enterprise level energy management systems of virtual power plants.

The Ankerui system solution also includes power operation and maintenance cloud platform, energy comprehensive billing management platform, environmental protection electricity supervision cloud platform, charging pile operation management cloud platform, intelligent fire protection cloud platform, power monitoring system, microgrid energy management system, intelligent lighting control system, power quality governance system, electrical fire protection system, isolated power source insulation monitoring system and other system solutions, covering all aspects of enterprise microgrid, creating an accurate perception, edge intelligence, and intelligent operation of enterprise microgrid smart energy management system.

5 Conclusion

After in-depth research and analysis, this study has achieved significant results in optimizing the configuration and coordinated control of battery capacity in energy storage photovoltaic power plants. By constructing a reliability model for photovoltaic storage power stations that considers the uncertainty of photovoltaic power generation and dynamic changes in load demand, and introducing key indicators such as power penetration rate and capacity penetration rate, the stability of photovoltaic storage power stations was effectively evaluated. At the same time, a detailed comparison of performance parameters for different types of energy storage batteries was conducted, and a scientifically reasonable capacity optimization configuration scheme was proposed. In addition, a coordinated control strategy for multiple hybrid energy storage systems was designed, which achieved energy management and system stability improvement through event triggered functions and regulation mechanisms. These research results not only provide practical guidance for the design and operation of energy storage photovoltaic power plants, but also lay a solid foundation for the widespread application and sustainable development of renewable energy in the energy system. Based on the actual situation, more technical limitations and cost factors need to be considered in the future, and how to balance economy and technology to achieve configuration still needs further research.

References:

[1] He Qi, Xiang Jiaqiang, Guo Zhenxing, Zhou Feng, Wei Jijun Research on Capacity Optimization Configuration and Coordinated Control of Energy Storage Photovoltaic Power Station Cells

[2] Wang Youxin Analysis of Photovoltaic Power Generation System and Power Station Electrical Design

[3] Ankerui Enterprise Microgrid Design and Application Manual. 2022.05 Edition