Progress And Challenges Of New Energy Storage Technologies III: Energy Storage Integration Technology, Safety Technology, And System Planning And Scheduling

Energy storage integration technology is the key to realizing the practical application of energy storage technology. In recent years, high-voltage cascade technology, grid type energy storage technology, digitalization, and digital twin technology have been highly praised by the industry. High voltage cascade technology improves the voltage level of the energy storage system by cascading multiple energy storage units, achieving efficient energy conversion and transmission, and significantly enhancing the performance of the energy storage system. Grid based energy storage technology uses energy storage inverters to build voltage sources that support the power grid, enhance the stability of the energy storage system, and optimize the level of new energy consumption. Digital technology utilizes big data and artificial intelligence to achieve intelligent management and optimization of energy storage systems. Digital twin technology enhances the safety, diagnostic capabilities, and operational efficiency of energy storage systems by constructing virtual models. These technologies collectively drive the development of energy storage integration technology towards higher efficiency, intelligence, and safety.

Energy storage safety technology is an important foundation for ensuring the safe and stable operation of energy storage systems. With the widespread application of lithium-ion battery energy storage systems, their safety issues are becoming increasingly prominent. In order to ensure the safe and stable operation of the energy storage system, a series of safety protection measures must be implemented. Real time monitoring of gas and temperature parameters of various equipment in the energy storage system can reduce monitoring blind spots, detect faults and abnormal high temperatures in a timely manner, prevent thermal runaway and fires, and effectively improve the safety and reliability of the energy storage system. At the same time, the development of liquid cooling technology and direct cooling technology has effectively improved the cooling efficiency of energy storage equipment, thereby enhancing the overall safety of energy storage systems. In terms of energy storage fire protection, there are also some new trends, such as: on the one hand, from the perspective of handling fire accidents caused by energy storage systems, the current trend tends to prioritize controlling the fire rather than extinguishing it; On the other hand, due to the significant difference between the results of battery module combustion testing and actual safety issues, the work of conducting larger scale container fire extinguishing, explosion, and toxic substance testing should also be put forward.

The planning and scheduling of energy storage systems are prerequisites for ensuring the flexible, stable, and reliable operation of new power systems. Energy storage technology is not only used for power peak shaving and suppressing the fluctuation of transmission power in new energy power systems, but also can significantly improve power quality and system economy. The realization of these capabilities also depends on reasonable planning and scheduling. Energy storage systems can effectively reduce network losses, optimize resource allocation, and significantly improve the utilization rate of new energy in the power system. Diversified energy storage technologies, such as batteries, supercapacitors, etc., can provide more efficient energy management solutions for integrated energy systems through their complementary advantages. However, the planning and scheduling of energy storage systems still face many challenges, such as complex multi time scale energy storage system planning, integration and collaborative operation of multiple energy storage systems, etc. Therefore, it is necessary to continuously explore the boundaries of energy storage system planning and scheduling research, deeply integrate dynamic processes and their operation simulation, in order to improve the guiding role of planning and scheduling design in the actual design and operation of energy storage systems.

Based on the above three aspects, this article discusses high-voltage cascade technology, grid based energy storage technology, digitalization and digital twin technology, distributed temperature monitoring technology, etc.

 

1

Energy Storage Integration Technology

 

1.1 High Voltage Cascade Technology

 

High voltage cascade technology, as an efficient energy storage solution without parallel structure, can directly connect to the grid voltage of 6-35 kV through innovative power electronic equipment design, without the need for transformers, significantly reducing system losses, improving efficiency, and shortening the response time of energy storage systems. In addition, this technology can minimize or eliminate the parallel connection of battery clusters, making each battery cluster as independent and completely independent as possible; Reduce or eliminate circulation phenomena, improve battery consistency, and extend its cycle life. At present, multiple high-pressure Federal Reserve energy projects have been successfully put into operation, including:

1) Southern Power Grid Energy Storage Co., Ltd. (hereinafter referred to as "Southern Power Grid Energy Storage") has deployed the Baotang 300MW/600MWh energy storage project in Foshan City. The energy storage system of the project adopts five connection methods: string type, bipolar type, unipolar type, low-voltage cascade, and 100MW level high-voltage cascade direct hanging energy storage system. The topology diagram of the 100MW level high-voltage cascade direct hanging energy storage system is shown in Figure 1, where L is the inductor; Ia, ib, and ic are the alternating currents of phase a, phase b, and phase c, respectively; IGCT is an integrated gate commutated thyristor; USA, USB, and USC represent the voltage of phase A, phase B, and phase C on the grid side, respectively; UAO, UBO, and UCO are the output voltages of phase A, phase B, and phase C of the converter, respectively.

2) The 101MW/206MWh energy storage project of Huadian International Power Co., Ltd. Laicheng Power Plant adopts the "high-voltage cascade+centralized liquid cooling" technology.

3) The Southern Grid Energy Storage Hebei Baoding 10kV/6MW large capacity high-voltage cascade battery energy storage power station combines movable design. 4) The Suining electrochemical energy storage project in Shaoyang, Hunan has adopted a 35 kV high-voltage direct hanging energy storage system with full liquid cooling and heating management on a large scale for the first time. The high-voltage Federal Reserve energy system can effectively solve the stability problem of large-scale new energy stations connected to the grid and improve the grid's ability to accept new energy.

 

As a new technological route, the high-voltage cascade scheme also faces multiple technical challenges and needs further verification. The high-voltage cascade scheme has a voltage of 35kV per phase, and the electromagnetic environment is harsh, which puts higher requirements on the control of the battery management system (BMS); In a 35kV energy storage system, the DC side and AC side are located in the same position, which increases the difficulty of operation and maintenance and poses certain safety risks; Under high voltage levels and high energy density conditions, the thermal management and safety protection technologies of energy storage systems also need to be further considered and upgraded.

Currently, the penetration rate of high-voltage cascade schemes is still low, and more projects are needed to verify their reliability and stability. From the perspective of project cost, the investment cost of energy storage projects using high-voltage cascade schemes is gradually approaching that of traditional projects, indicating that this technology has great competitiveness and application potential in the future market.

 

1.2 Grid Based Energy Storage Technology

 

The core of grid based energy storage technology lies in its ability to establish a voltage source that supports stable operation of the power grid through energy storage inverters. It can quickly adjust frequency and voltage, increase inertia and short-circuit capacity support, suppress broadband oscillations, and thus enhance the stability of the power system. In 2023, Kehua ShuNeng Technology Co., Ltd. (hereinafter referred to as "Kehua ShuNeng") will carry out the application of grid type energy storage technology in the 100 megawatt shared energy storage project in Ningxia Hui Autonomous Region, helping weak grid areas achieve grid type power support. By adopting virtual synchronous generator technology, the behavior and performance of synchronous motors can be replicated to strengthen the power grid, achieve rapid frequency and voltage regulation, increase inertia and short-circuit capacity, suppress broadband oscillation and other effects. Enhance the anti-interference and proactive support capabilities of new energy generation and storage, actively innovate grid based energy storage technologies, and promote the transformation of new energy generation from grid connected to grid connected. In 2022, Kehua ShuNeng provided a "photovoltaic storage and diesel grid type microgrid" solution for the Iraqi Ministry of Oil and Power. It adopts a grid type energy storage combined with weak electricity grid characteristics of the "photovoltaic inverter+diesel generator" mode to construct a microgrid. The 50MW/100MWh Gaoqiao Energy Storage Project in Xingang District, Jingmen City, Hubei Province, adopts a grid type energy storage converter and achieves power self synchronization through a grid type control strategy, verifying the effectiveness of multi time scale power support applications in high proportion new energy integration into regional power grids.

 

1.3 Digitization And Digital Twin Technology

 

Digitization and digital twin technology are one of the key paths for energy storage technology to improve its energy efficiency and safety. The direction of digitalization development for energy storage batteries is shown in Figure 2.

 

The 350 MW Compressed Air Energy Storage Innovation Demonstration Project in Tai'an City, Shandong Province plans to use digital twin technology to achieve intelligent remote monitoring and diagnosis throughout the entire lifecycle of the project, identify and resolve potential risks in advance, and save user costs. The 70MW/140MWh Baohu Energy Storage Power Station project of Nanwang Energy Storage in Wuhua City, Guangdong Province, adopts two high-energy density 1500 V lithium iron phosphate energy storage systems, namely high-efficiency intelligent air cooling and submerged liquid cooling. It is equipped with an intelligent energy management and control system with second level data storage technology, and uses an intelligent energy storage digital operation and control platform to achieve remote intelligent operation and maintenance and operation assistance decision-making.

In the field of electrochemical energy storage, digital twin technology combines the mechanisms of battery safety failure and life decline, uses artificial intelligence to train high-precision digital twin models of batteries, achieves accurate risk warning and aging state prediction, ensures monitoring without blind spots, and comprehensively covers prediction needs. In addition, by collecting and integrating historical data, real-time data (including power generation and distribution, power market, network operation status, and power policies), as well as simulation data for power generation prediction, working condition identification, and fault diagnosis, standardizing heterogeneous data expression, unifying data conversion rules, establishing data fusion standards, and realizing comprehensive collection, storage, management, and sharing of multi-scale heterogeneous data for multiple operating entities, transforming multiple types of physical entities and their operating processes into data expression, Promote the iteration of multimodal models and optimization of application services. The intelligent control architecture of energy storage power stations is shown in Figure 3.

 

2

Energy Storage Safety Technology

 

The safety of lithium-ion battery energy storage systems is a key limiting factor in their development process. Thermal runaway of a battery refers to an unstoppable self heating chain reaction that occurs inside the battery, causing a rapid increase in battery temperature and ultimately leading to fire or even explosion. The main causes of battery thermal runaway include mechanical abuse, electrical abuse, and thermal abuse, as shown in Figure 4. In these three situations, the battery is highly prone to thermal runaway, causing safety accidents. Mechanical abuse refers to the occurrence of short circuits or leakage inside batteries after being impacted, squeezed, punctured, etc. The probability of mechanical abuse occurring in large-scale energy storage systems is relatively low; Electricity abuse refers to the occurrence of short circuits, overcharging, overdischarging, and other situations inside and outside the battery, resulting in a large amount of heat generation in the battery; Thermal abuse refers to the use of batteries at excessively high temperatures, which can cause short circuits and result in uncontrolled heat generation. Thermal abuse is the main factor causing safety accidents in large-scale energy storage systems, and its causes are complex, including mechanical and electrical abuse, as well as high external temperatures.

 

 

In the early stage of battery thermal runaway, the internal separator may be punctured due to lithium precipitation, or decomposed due to high temperature, resulting in a slight short circuit inside the battery. But battery thermal runaway is an "avalanche" process, and its initial characteristics are not obvious. If not suppressed in time, the internal structure of the battery will undergo more intense reactions, resulting in a decrease in battery capacity, an increase in temperature, and ultimately true thermal runaway. Temperature is the most fundamental characteristic parameter during battery operation. When a battery malfunctions, the temperature usually changes abnormally. Improving the accuracy of temperature detection and expanding the detection range can help detect small faults in the early stage, take corresponding measures in a timely manner, and avoid the occurrence of thermal runaway.

Fiber optic temperature measurement technology has broad application prospects in the field of energy storage. Compared to traditional thermocouple temperature measurement, optical fibers are not affected by electromagnetic interference. Hundreds or thousands of measuring points can be set on one optical fiber, and the layout of the battery pack can be simplified to achieve full coverage battery temperature measurement. The schematic diagram of optical fiber temperature measurement for the battery pack and battery compartment is shown in Figure 5. The accuracy of fiber optic temperature measurement is similar to that of thermocouple temperature measurement, and the response speed depends on the length of the fiber optic cable. The longer the fiber optic cable, the longer the response time. On the premise of ensuring the accuracy of measurement results, it is still possible to achieve a second level response. At present, most of the research on fiber optic temperature measurement is focused on single cells or small battery modules, lacking research at the level of energy storage systems.

 

 

In addition to preventing thermal runaway, maintaining stable and balanced battery temperature within the energy storage system is also crucial. Due to the dense distribution of batteries inside the energy storage system, the heat dissipation problem is particularly serious, and there are differences between battery cells, resulting in inconsistent temperatures, which poses a threat to the safe operation of the energy storage power station. Immersion liquid cooling technology provides a solution by immersing the battery in an insulating, high flash point, and high thermal conductivity immersion liquid inside the energy storage system, while the immersion liquid remains in a flowing state, timely removing the heat generated by the battery during charging and discharging, ensuring the stability of battery temperature and the consistency of temperature between individual cells, as shown in Figure 6.

 

 

Only by improving the safety performance of lithium-ion batteries can we promote the rapid development of the energy storage field. Compared to traditional lithium-ion batteries, solid-state batteries typically have higher energy density and improve safety by reducing the risk of liquid electrolyte leakage and flammability. However, solid-state battery technology still faces many challenges. Solid liquid hybrid batteries retain some electrolyte on the basis of existing liquid battery technology, and the mechanism of the battery is slightly different from traditional lithium-ion batteries. Although solid-liquid hybrid batteries can improve specific energy, they will reduce battery rate and affect charging and cycle life. Although all solid state batteries have extremely high energy density and safety performance, they are difficult to promote and apply on a large scale due to their high cost.

In addition, the gradual promotion and application of voiceprint recognition technology in the field of energy storage safety detection and diagnosis, and the standardization of voiceprint online monitoring devices in the energy storage industry have been gradually improved. However, there are still some issues, such as the inability to recognize features with the same frequency noise and the inability to recognize multiple features occurring simultaneously. It is necessary to use big data to drive the training of feature extraction models, extract and recognize the similarity of multiple features triggered simultaneously, and achieve the deep application of voiceprint recognition technology in the field of energy storage safety detection.

 

3

Planning And Scheduling Of Energy Storage Systems And Diversified Energy Storage Technologies

 

The planning and scheduling of energy storage systems are key factors in ensuring the flexible, stable, and reliable operation of new power systems. In the new power system, energy storage technology is mainly applied in power peak shaving, suppressing the fluctuation of transmission power in new energy power systems, improving power quality and system economy. The specific application scenarios are shown in Figure 7. By planning and scheduling the energy storage system reasonably, the system network loss can be effectively reduced, peak shaving and valley filling can be achieved, and the development and application of energy storage technology can be promoted.

 

 

Currently, research on energy storage system planning mainly focuses on three aspects: planning objectives, optimization algorithms, and application scenarios. The main objectives of energy storage system planning include system reliability, economy, and new energy utilization rate, as shown in Figure 8.

 

 

In terms of planning objectives, Cai Fulin et al. conducted collaborative planning research on centralized and distributed energy storage systems with the goal of enhancing the consumption capacity of new energy; Fang Ke and others aim to optimize cost-effectiveness, taking into account the technical and economic parameters of long-term energy storage, and carry out optimization planning for long-term energy storage towards low-carbon power systems; Meng Yuan et al. introduced "opportunity constraints" and "N-1 safety constraints" in the planning of wind solar energy storage joint projects to ensure the safety of project site selection and capacity determination.

As planning models become increasingly complex, researchers are constantly seeking more efficient optimization algorithms. Li et al. used an improved binary particle swarm optimization algorithm based on chaos optimization to iteratively implement the optimal joint planning for the dual layer programming model of distributed power generation and energy storage; Chen Qian et al. proposed a convex hull adaptive optimization algorithm to solve the problem of non convex thermal storage planning models caused by flow changes in regional heating network pipelines; Wang et al. used a genetic algorithm based on elite retention strategy to plan a comprehensive energy system considering the differentiated characteristics of hybrid energy storage.

The planning methods and application scenarios of energy storage systems are also closely related. Zhang et al. established a fuzzy response model for charging load and residential load of residential electric vehicle charging stations, achieving coordinated planning of photovoltaic power generation and energy storage; Bazdar et al. conducted planning and analysis on the adoption of emerging adiabatic air compression energy storage systems between urban buildings, and verified the feasibility of their actual construction; Wang et al. used industrial parks as an example to explore new models of leasing energy storage for further planning and research.

The optimization scheduling of diversified energy storage technologies is also a popular research direction in the field of integrated energy systems.

From the perspective of optimization objectives, Zhu Yongqiang et al. incorporated a multi-dimensional energy storage system consisting of batteries and supercapacitors into the optimization scheduling of microgrids, considering the economic feasibility of multi-dimensional energy storage operation, and proposed a method based on dynamic programming genetic algorithm for real-time scheduling. In terms of smoothing out the fluctuations of new energy grid connection, Liu Xiaoyan, based on existing research on diversified energy storage technologies, applied model predictive control and dynamic programming to address the problems of internal energy scheduling in diversified energy storage systems, limiting the range of power fluctuations in the grid connection of the power generation system. In terms of peak shaving and frequency regulation, Li Junhui et al. proposed an economic dispatch method for independent energy storage collaborative participation in peak shaving and frequency regulation tasks, with the goal of generating revenue from energy storage power stations. By comparing priority peak shaving or priority frequency regulation, the degree of improvement in revenue from collaborative dispatch was demonstrated.

In terms of wind power consumption, Hao et al. constructed a unified power flow model based on the heat flow method, including thermal storage units, and optimized the power generation and heating scheduling of various components including thermal storage units. In terms of system reliability, Wang et al. analyzed the inherent conflict between wind power curtailment, operating costs, and emission reduction issues, added system reliability related indicators, and used multi-objective particle swarm optimization algorithm to optimize energy storage scheduling.

Starting from the optimization strategy of multi temporal and spatial scales, Ma Zhenqi et al. proposed a stage optimization scheduling strategy of "day ahead+day ahead+real-time" for multi energy coupling systems, using hybrid energy storage to smooth fluctuations and improve their power response characteristics. On this basis, Li et al. considered the spatial scale characteristics and modeled the electric, thermal, and air conditioning energy storage system for different communities using multidimensional energy to achieve supply-demand balance, which improved the operational economy of the integrated energy system.

From the perspective of algorithm selection, Lu et al. achieved partition optimization and cost reduction of large-scale active distribution systems by reasonably scheduling the exchange power of energy storage systems based on the multi-agent deep reinforcement learning algorithm framework. Liang et al. used a dual delay deep deterministic policy gradient algorithm to train agents for energy optimization management. By scheduling batteries, hydrogen storage devices, and other devices in real-time, they maximized the goal of achieving a low-carbon economy.

In summary, the optimization objectives and algorithms for planning and scheduling energy storage systems are constantly improving, and the application scenarios are gradually becoming more diverse. Progress has been made in the application research of diversified energy storage technologies in multi energy coupled systems and collaborative operation at multiple spatiotemporal scales. However, at present, there is still room for development in the planning of large capacity energy storage systems in China, and the system integration and collaborative operation of diversified energy storage technologies face technical challenges. The market positioning and value evaluation of energy storage need to be further clarified. In addition, the large-scale configuration of energy storage systems needs to consider safety factors and environmental impacts. Therefore, in the face of challenges such as technological maturity, system integration, market mechanisms, and security environment, continuous technological innovation and policy support are needed to achieve sustainable development of energy storage technology and efficient operation of the power system through diversified technology layout, scale and layout optimization, multi time scale scheduling, technical and economic analysis, and formulation of policy market mechanisms.

Energy storage technology exhibits significant differences in application, as shown in Figure 9. With the increasing penetration rate of new energy, future energy storage systems will gradually show a trend of differentiation. Long term energy storage technology and grid supported energy storage technology will have greater development space, and energy storage technology will also be more applied in some special scenarios, such as large vehicles, space, oceans, etc. These demands and scenarios will provide broad space for the development of different energy storage technologies.

 

 

4

Summary And Outlook

 

This article analyzes the current progress and challenges faced by new energy storage technologies from three aspects: energy storage integration technology, energy storage safety technology, and energy storage system planning and scheduling. The new energy storage technology is developing towards high safety, low cost, large capacity, and high efficiency. In the future, more emphasis will be placed on digitization, intelligence, greening, and the combination of centralized and distributed technologies. This will play an important role in promoting the consumption of new energy, achieving energy system transformation, improving energy utilization efficiency, and reducing environmental pollution.

Looking ahead, with the further increase in the penetration rate of new energy, the application of energy storage systems will become more diversified, especially in long-term energy storage technology and grid supported energy storage technology, which will have greater development space. At the same time, energy storage technology will also demonstrate its unique application potential in special scenarios such as large vehicles, space, oceans, and other fields, providing broad space for the development of different energy storage technologies.

Source: Long term energy storage network