Mobile Energy Storage Units Research Articles

Multi-Scale Modeling and Simulation of All-Solid-State Sodium-Ion Batteries with Polymer-Ceramic Hybrid Electrolytes

Lithium-ion batteries (LIBs) have been widely considered as the most promising power source for mobile devices. However, LIBs cannot meet the increasing global demand for electrochemical energy storage in the foreseeable future. Sodium ion-batteries (SIBs) are based on more ecofriendly and earth-abandoned materials. Commonly researched SIBs are based on liquid electrolytes. They pose leakage and thermal runaway risks. Solid polymer electrolytes solve these problems but need to operate at temperatures between 60-80°C to compensate insufficient ionic conductivity of polymer electrolytes at ambient temperatures.We present a polymer-ceramic hybrid electrolyte which overcomes the necessity to operate at high temperatures. It combines the advantages of high ion-conducting solid ceramic particles and flexible polymers. Sodium metal serves as the anode and PEO with inorganic fillers is used as cathode electrolyte. Due to their sustainability and high efficiency —comparable to LIBs— sodium-ion batteries are a promising candidate for small scale stationary energy storage applications or mobile energy storage applications with less constrains regarding energy density (e.g. trains, ships, submarines etc.). This contribution highlights a modeling approach for simulations of a single battery cell to develop an optimal cell design of the aforementioned battery system. A newly developed multiscale modeling method for hybrid polymer-ceramic sodium ion batteries reflects the different length scales of the active particle level and the electrode level. We precisely describe the spatially resolved mass and charge transport as well as electrochemical reactions in the active particle and the surrounding electrolyte employing a finite volume method [1]. Both domains are resolved in 3D and the model framework was validated against experimental data. The model output is incorporated in the connected pseudo-two-dimensional battery model for fast simulations of the overall battery cell considering mass and charge transport. The results support the importance of particle (active particle and ceramic filler particle) properties and configurations as well as electrolyte composition. Based on this, we exploit an evolutionary algorithm to suggest optimized battery designs for different use cases. In Addition, we present the evaluation of electrolyte properties with solid-state NMR spectroscopy and their model implementation [2]. μCT imaging experimentally investigates the cathode half-cell morphology. The derived virtual reconstructions enable realistic microstructure simulations of the battery cells. In this presentation, we will suggest two cell designs: The high-energy cell optimizes material efficiency and energy density. The high-power cell opts for high power densities (fast charging).In summary, we present a novel all-solid-state polymer-ceramic hybrid sodium-ion battery type for small stationary or large mobile energy storage units. Our contribution outlines a simulation approach that sheds light on macroscopic and microscopic processes inside the battery-cell using a multiscale modeling method. As a result, a high-energy and a high-power design is derived. Gerbig, M. Holzapfel and H. Nirschl, J. Electrochem. Soc., 170(4), 40517 (2023).Moradipour, A. Markert, T. Rudszuck, N. Röttgen, G. Dück, M. Finsterbusch, F. Gerbig, H. Nirschl and G. Guthausen, J Energ Power Technol, 05(04), 1–21 (2023).

Collaborative Operation Optimization Scheduling Strategy of Electric Vehicle and Steel Plant Considering V2G

With the shortage of fossil fuels and the increasingly serious problem of environmental pollution, low-carbon industrial production technology has become an effective way to reduce industrial carbon emissions. Electrified steel plants based on electronic arc furnaces (EAF) can reduce most carbon emissions compared with traditional steel production methods, but the production steps have fixed electricity consumption behavior, and impact loads are easily generated in the production process, which has an impact on the stability of the power system. EV has the characteristics of a mobile energy storage unit. When a large number of EVs are connected to the power grid, they can be regarded as distributed energy storage units with scheduling flexibility. Through the orderly scheduling of EVs, the spatial–temporal transfer of EV charging and discharging load can be realized. Therefore, the EV situated in the steel plant’s distribution network node has the capacity to be utilized by providing peak shaving and valley filling services for the steel production load. This study proposes an operation optimization scheduling method for EVs and steel plants. Taking the lowest overall operating cost as the objective, an optimal scheduling model considering EVs operation, steel plant, and distributed generator is established. Based on the IEEE-33 node distribution network model considering distributed generators, the proposed model is simulated and analyzed, and the effectiveness of the EV steel plant operation optimization scheduling strategy is investigated.

Optimizing Distribution System Resilience in Extreme Weather Using Prosumer-Centric Microgrids with Integrated Distributed Energy Resources and Battery Electric Vehicles

Electric power networks face vulnerabilities from various hazards, including extreme weather and natural disasters, resulting in prolonged outages and service disruptions. This paper proposes prosumer-centric networked electrical microgrids as a solution. EMGs integrate DERs, like SPV panels, WTs, BESSs, and BEVs, to form autonomous microgrids capable of operating independently during grid disruptions. The SMA was used to identify the appropriate allocation of DERs and BEVs to improve the resilience of the system. Prosumers, acting as both producers and consumers, play a crucial role by generating and sharing electricity within the microgrid. BEVs act as mobile energy storage units during emergencies. Load management and demand response strategies prioritize the energy needs for essential facilities, ensuring uninterrupted operation during adverse weather. Robust communication and control systems improve the emergency coordination and response. The resilience analysis focused on two case studies: moderate and severe damage, both under varying weather conditions. Simulations and experiments assessed the microgrid performance with different levels of DERs and demand. By testing on the IEEE 69-bus RDS, evaluated the EMGs’ strengths and limitations, demonstrating their potential to enhance distribution grid resilience against natural disasters.

Coordinated integration of wind energy in microgrids: A dual strategy approach leveraging dynamic thermal line rating and electric vehicle scheduling

As the world shifts towards sustainable energy, integrating wind energy as a key renewable resource is crucial, despite the challenge of its intermittent output. This paper proposes an integrated approach to enhance wind energy utilization within microgrids, introducing a novel synergy of Dynamic Thermal Line Rating (DTLR) and Electric Vehicle (EV) scheduling. This synergy dynamically adjusts power transmission capacities and harnesses EVs as mobile energy storage units. Notably, case study simulations reveal that compared to other approaches, our proposed solution reduces total operational costs by 24.6% (STLR only) and 11.6% (STLR+EV), and increases wind power absorption by 41.7% (STLR only) and 12.1% (STLR+EV). This marks a significant stride towards sustainable microgrid operation amidst renewable energy integration challenges.

V2G optimized power control strategy based on time-of-use electricity price and comprehensive load cost

Electric vehicles (EVs), as a nascent industry, have seen significant advancements in technology and policy, allowing for a clearer development trend and promising future prospects. The integration of EVs with the energy sector is becoming a key focus, with vehicle-to-grid (V2G) technology and efficient collaboration serving as major areas for technical breakthroughs. This paper begins by analyzing the impact of disordered charging of EVs on the power grid. Given that EVs can function as mobile energy storage units, they have the potential to provide flexible support for the secure operation of the power grid. Building upon this, the paper proposes an optimized V2G power control strategy that is based on time-of-use electricity pricing and comprehensive load cost optimization. The principle behind this proposed control strategy is elaborated upon in the subsequent sections. To validate the effectiveness of the proposed V2G optimized power control strategy, the paper provides simulation results. These results serve to demonstrate the practical feasibility and benefits of the proposed strategy in leveraging the capabilities of EVs and promoting grid stability and efficiency.

Centralized MPC for Home Energy Management With EV as Mobile Energy Storage Unit

Electric vehicle (EV) is commonly considered as an electric load in a residential energy network. However, the large capacity EV battery can be used as electric storage when the EV is plugged in at home. While model predictive control (MPC) offers an efficient and reliable control mechanism for a home energy management system (HEMS), the uncertainty related to the availability of an EV at home poses an interesting challenge for the MPC optimization strategy. In this article, the thermal comfort and energy management performance of a centralized MPC-based HEMS is presented for such a scenario where an EV is used as a mobile energy storage unit in a home energy network. The MPC-based HEMS simultaneously controls the zone-based heating system consisting of a heat pump and baseboard units along with the energy flow among the different components of the home energy network. This network comprises the home baseload, charging and discharging of the home-battery and the EV battery, in-house solar energy generation and storage. The MPC-based HEMS optimizes the buying/selling of energy from/to the grid based on time of use electricity rates. A Monte-Carlo based uncertainty analysis is presented here to estimate the robustness of the MPC against the randomness in the EV arrival and departure schedule. Finally, the responsiveness of the centralized MPC is showcased using a vehicle to home communication scenario where the owner notifies the home network regarding their arrival within a short interval, along with the preferred indoor temperature upon arrival.

Spatial–Temporal Scheduling of Electric Bus Fleet in Power-Transportation Coupled Network

With large passenger and battery capacity, electric buses can play dual roles – commuting tools in transportation networks and mobile energy storage units in power networks. To promote the beneficial synergy between power-transportation coupled networks, it is necessary to properly optimize the operation of electric buses in both networks. This paper proposes a spatial-temporal scheduling framework for the electric bus fleet in a power-transportation coupled network. By scheduling the plug-in locations and charging/discharging profiles of the electric buses, the model not only minimizes the total operational costs of the fleet (i.e., the electricity consumption and battery degradation costs), but also maximizes the revenue by providing flexibility to the power network. The coupled power and transportation constraints are explicitly described in the model. To address the uncertainty from fleet’s energy consumption, the chance-constrained programming is adopted that can be reformulated into second-order cones and efficiently solved by off-the-shelf solvers. Numerical experiments are conducted to validate the superiority of the proposed method based on the IEEE 33-bus distribution network and the Sioux Falls transportation network.

A load frequency coordinated control strategy for multimicrogrids with V2G based on improved MA-DDPG

The capacity of a single microgrid is limited, so the multimicrogrid system (MMS) has been widely used to improve the reliability and stability. And the mobile energy storage unit electric vehicles (EVs) with vehicle-to-grid (V2G) capability can improve the flexibility of the microgrid frequency control. Considering the complex structure of the MMS, a load frequency control (LFC) strategy for multimicrogrids with V2G based on improved multiagent deep deterministic policy gradient (MA-DDPG) is proposed in this paper. Firstly, a multimicrogrid LFC model including distributed power generation, micro gas turbine (MT), EVs which consider the random behavior of EV users and the mobility of EVs as dynamic limitations is constructed, the contact relationship between the submicrogrid controllers is added. Secondly, the MA-DDPG algorithm is improved to significantly reduce transmission cost and computational complexity. Additionally, the state, action space and reward function of the multiagent controller is designed. Finally, the simulation results show that, compared with PID, Fuzzy and traditional MA-DDPG control, the improved MA-DDPG can ensure the overall stability of MMS through cooperative control in the face of various complex operating scenarios. And under the premise of ensuring the control effect, the loss caused by transmission is significantly reduced.

An overview of the operation architectures and energy management system for multiple microgrid clusters

The emerging novel energy infrastructures, such as energy communities, smart building-based microgrids, electric vehicles enabled mobile energy storage units raise the requirements for a more interconnective and interoperable energy system. It leads to a transition from simple and isolated microgrids to relatively large-scale and complex interconnected microgrid systems named multi-microgrid clusters. In order to efficiently, optimally, and flexibly control multi-microgrid clusters, cross-disciplinary technologies such as power electronics, control theory, optimization algorithms, information and communication technologies, cyber-physical, and big-data analysis are needed. This paper introduces an overview of the relevant aspects for multi-microgrids, including the outstanding features, architectures, typical applications, existing control mechanisms, as well as the challenges.

A Mobile Energy Storage Unit Serving Multiple EV Charging Stations

Due to the rapid increase in electric vehicles (EVs) globally, new technologies have emerged in recent years to meet the excess demand imposed on the power systems by EV charging. Among these technologies, a mobile energy storage system (MESS), which is a transportable storage system that provides various utility services, was used in this study to support several charging stations, in addition to supplying power to the grid during overload and on-peak hours. Thus, this paper proposes a new day-ahead optimal operation of a single MESS unit that serves several charging stations that share the same geographical area. The operational problem is formulated as a mixed-integer non-linear programming (MINLP), where the objective is to minimize the total operating cost of the parking lots (PLs). Two different case studies are simulated to highlight the effectiveness of the proposed system compared to the current approach.

Investigation of the Physico-Chemical Properties of the Products Obtained after Mixed Organic-Inorganic Leaching of Spent Li-Ion Batteries

Lithium-ion batteries are currently one of the most important mobile energy storage units for portable electronics such as laptops, tablets, smartphones, etc. Their widespread application leads to the generation of large amounts of waste, so their recycling plays an important role in environmental policy. In this work, the process of leaching with sulfuric acid for the recovery of metals from spent Li-ion batteries in the presence of glutaric acid and hydrogen peroxide as reducing agents is presented. Experimental results indicate that glutaric-acid application improves the leaching performance compared to the use of just hydrogen peroxide under the same conditions. Obtained samples of leaching residues after mixed inorganic-organic leaching were characterized with Scanning Electron Microscopy, Fourier Transform Infrared Spectroscopy, and X-ray diffraction.

Vehicle-to-Grid and vehicle-to-load strategies and demand response program with bender decomposition approach in electrical vehicle-based microgrid for profit profile improvement

Electric Vehicle (EV)-based microgrid (MG) has an opportunity of using of mobile energy storage units where EV can help the MG in supporting load demands and maintaining the quality of voltage and power profile. However, in EV traffic time, MG suffers from high demands of EVs and loads. EV power scheduling can overcome the challenges of profit profile, demand support and cost profile in the MG. Two strategies as power flow from EV to the main grid (V2G) and from EV to load (V2L) can be used to reduce the cost and prevent buying energy from main grid. In high-demand times, the MG should buy energy from main grid which consequently increases the cost profile. To overcome the high-demand supporting problem, demand response program is also employed to reduce the loads in on-peak time durations or shift them to off-peak times. Applying the EV scheduling and demand response program helps MG to reduce the cost and improve the profit profile. Bender decomposition and Lagrange method provide a robust approach in this paper for optimization of MG in terms of EV scheduling and demand response program to obtain desirable cost and profit profile. As shown in simulation results, using the proposed approach, the cost profile is reduced by 14.67% compared to applying no optimized EV scheduling and no demand response program in the MG.

Hierarchical Distributed Control Strategy for Electric Vehicle Mobile Energy Storage Clusters

The stability problem of the power system becomes increasingly important for the penetration of renewable energy resources (RESs). The inclusion of electric vehicles (EVs) in a power system can not only promote the consumption of RESs, but also provide energy for the power grid if necessary. As a mobile energy storage unit (MESU), EVs should pay more attention to the service life of their batteries during operation. A hierarchical distributed control strategy was proposed in this paper for mobile energy storage clusters (MESCs) considering the life loss of each EV’s battery. This strategy was divided into a two-layer control structure. Firstly, numerous EVs were divided into different clusters according to their regional relationships. The lower layer adopted a distributed collaborative control approach for allocating energy among EVs in the cluster. Under this condition, an aggregate EVs response model was established and the characteristic of the MESC was analyzed. Secondly, the upper layer applied the multi-agent consensus algorithm to achieve the optimal allocation among different clusters. Therefore, the control strategy realized the two-way communication of energy between EVs and the power grid, and ensured the optimal economical dispatch for the mobile energy storage system (MESS). Finally, the simulation of testing examples verified the effectiveness of the proposed strategy.

Load Frequency Control in Isolated Micro-Grids with Electrical Vehicles Based on Multivariable Generalized Predictive Theory

In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G) technique, electric vehicles (EVs) can act as mobile energy storage units, which could be a solution for load frequency control (LFC) in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC) theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG) are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances.

Study on protection Scheme for Micro-grid with Mobile Energy Storage Units

A method of dividing complex micro-grid into minor micro-grids and finally to the minimal micro-grid which consists of one AC bus or one DC bus is presented according to classification principles. The protection scheme of minimal micro-grid is discussed and then applied to the whole micro-grid. By analyzing parallel operation mode and islanding operation mode of micro-grid, the protection scheme of each mode is given respectively.