The evolving global landscape for electrical distribution and use created a need area for energy storage systems (ESS), making them among the fastest growing electrical power system products. A key element in any energy storage system is the capability to monitor, control, and optimize performance of an individual or multiple battery modules in an energy storage system and the ability to control the disconnection of the module(s) from the system in the event of abnormal conditions. This management scheme is known as “battery management system (BMS)”, which is one of the essential units in electrical equipment. BMS reacts with external events, as well with as an internal event. It is used to improve the battery performance with proper safety measures within a system. Therefore, a safe BMS is the prerequisite for operating an electrical system. This report analyzes the details of BMS for electric transportation and large-scale (stationary) energy storage. The analysis includes different aspects of BMS covering testing, component, functionalities, topology, operation, architecture, and BMS safety aspects. Additionally, current related standards and codes related to BMS are also reviewed. The report investigates BMS safety aspects, battery technology, regulation needs, and offer recommendations. It further studies current gaps in respect to the safety requirements and performance requirements of BMS by focusing mainly on the electric transportation and stationary application. The report further provides a framework for developing a new standard on BMS, especially on BMS safety and operational risk. In conclusion, four main areas of (1) BMS construction, (2) Operation Parameters, (3) BMS Integration, and (4) Installation for improvement of BMS safety and performance are identified, and detailed recommendations were provided for each area. It is recommended that a technical review of the BMS be performed for transportation electrification and large-scale (stationary) applications. A comprehensive evaluation of the components, architectures, and safety risks applicable to BMS operation is also presented.

To minimize the anticipated shocks to economic, environmental, and social systems for developing and least developed countries, the reduction of Greenhouse Gas (GHG) emissions is mandatory to a large extend. The nuclear-renewable integrated system is proficient in optimal energy distribution to multiple production schemes to reduce GHG emissions and maximize profit. This paper addresses the hybridization of the micronuclear reactor and Renewable Energy Sources (RESs) Energy Sources (RESs) to develop a flexible, cost-effective, sustainable, and resilient off-grid Hybrid Energy System (HES). The paper presents three types of hybridization methods, termed "Direct Coupling," "Single Resource and Multiple products-based Coupling," and "Multiple Resources and Multiple products-based Coupling." The hybridization techniques are used to plan and identify the most efficient Nuclear-Renewable Micro-Hybrid Energy System (N-R MHES). The sizing, performance, and characterization of N-R MHES solely depend on the RES and load characteristics’ availability. Based on proposed hybridization techniques, mathematical modeling of N-R MHES's economy is carried out in the MATLAB environment. An artificial intelligence optimization algorithm, namely Particle Swarm Optimization (PSO), is used to minimize the Net Present Cost (NPC) and achieve the optimal system configurations of different N-R MHESs. The simulation results determine that "Multiple Resources and Multiple Products-based N-R MHES" provides around 1.8 times and 1.3 times lower NPC than "Single Resource and Multiple products-based Coupling" and "Multiple Resources and Multiple products-based Coupling," respectively, with an acceptable margin of reliability. A sensitivity analysis has also been conducted in this paper to strengthen the findings of the study.

Renewable energy sources (RESs) play an indispensable role in sustainable advancement by reducing greenhouse gas (GHG) emissions. Nevertheless, due to the shortcomings of RESs, an energy mix with RESs is required to support the baseload and to avoid the effects of RES variability. Fossil fuel-based thermal generators (FFTGs), like diesel generators, have been used with RESs to support the baseload. However, using FFTGs with RESs is not a good option to reduce GHG emissions. Hence, the small-scale nuclear power plant (NPPs), such as the micro-modular reactor (MMR), have become a modern alternative to FFTGs. In this paper, the authors have investigated five different hybrid energy systems (HES) with combined heat and power (CHP), named ‘conventional small-scale fossil fuel-based thermal energy system,’ ‘small-scale stand-alone RESs-based energy system,’ ‘conventional small-scale fossil fuel-based thermal and RESs-based HES,’ ‘small-scale stand-alone nuclear energy system,’ and ‘nuclear-renewable micro hybrid energy system (N-R MHES),’ respectively, in terms of net present cost (NPC), cost of energy (COE), and GHG emissions. A sensitivity analysis was also conducted to identify the impact of the different variables on the systems. The results reveal that the N-R MHES could be the most suitable scheme for decarbonization and sustainable energy solutions.

This paper investigates the applications of Proportional-Integrator-Derivative (PID) and Fractional Order PID (FOPID) controllers in Nuclear-Renewable Hybrid Energy Systems (N-R HESs). The N-R HES is a recent technology in the area of decarbonized energy systems. N-R HESs are expected to contribute immensely to providing carbon-free and sustainable energy infrastructure in the upcoming days. It is also anticipated that system resiliency will be the primary concern when nuclear reactors are incorporated with intermittent renewable energy resources. Therefore, in this research, the authors intend to evaluate the compatibility of two classical controllers, PID and FOPID, to ensure the stability of N-R HESs. The N-R HES of this paper consists of different energy sources, such as solar, wind, nuclear, fuel cell systems, Battery Energy Storage Systems (BESS), and Flywheel Energy Storage Systems (FESS). To encounter system performance requirements, the PID and FOPID controller parameters are adjusted using a metaheuristic algorithm, namely Artificial-Bee-Colony (ABC) optimization algorithm. Metaheuristic optimization algorithms always do not guarantee global maxima/minima. Hence, another metaheuristic optimization algorithm, Teaching-Learning-based Optimization (TLBO), is used to validate the results. The results clearly show that the optimal PID and FOPID controllers can handle the system frequency and maintain the stability of the studied N-R HES.