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The AC/DC microgrid-based charging station utilizes renewable resources, operating off-grid and on-grid based on charging demand, and trading electricity with the main grid when required. The diverse nature of hybrid AC/DC systems makes voltage stability a challenge in the present AC/DC microgrid management environment. Traditional algorithms have challenges in ensuring the appropriate cost of power purchased from Distributed Generators (DGs), start-up and shutdown, power switching, AC/DC demand balance, feeder thermal limit, bus voltage limit, the radiality of the microgrid system and connected Electric Vehicles (EVs) become more prevalent. In this study, a novel Optimized Feedforward Neural Network with Elephant Herding Intelligence (OFNN-EHI) has been proposed to handle AC/DC hybrid microgrids energy regulation issues and preserve power stability in hybrid AC/DC systems. The OFNN was used to identify the feeder line status and EHI is used to boost up the classification parameters. The simulation setup integrates an OFNN-EHI approach with an IEEE 33-bus AC/DC microgrid, allowing power exchange at bus 11. The ML model optimizes energy management, enhances stability and preserves power quality by dynamically controlling micro-turbines and wind turbines. According to research, the OFNN-EHI technique considerably improves energy management in hybrid AC/DC microgrids by maintaining constant voltage levels and power quality, outperforming standard algorithms in dealing with operational problems, and improving stability, power quality and cost efficiency.

In this paper, we performed a work combining numerical and theoretical studies to investigate the optimal energy-management of two cylinders under flow-induced motion using dynamic programming. First, we considered the energy generation of two freely oscillated cylinders with different configurations at Reynolds number of 200. Then, we constructed a theoretical model to maximize the energy-harvesting capacity of two cylinders using dynamic programming. This work can provide helpful suggestions for designing the energy-harvesting apparatus.

In this paper, a theoretical study is conducted to investigate the propulsive performance of two flapping foils with the inline arrangement. At first, we consider the propulsive performance of two flapping foils when they are relative static to each other, that is, the horizontal gap between the foils is fixed. Then, we provide a model to consider the dynamic stability of the two-foil system when the relative movement between them has occurred, that is, the horizontal gap between the two foils can be changed depending on the propulsive force acting on the foil. This work provides helpful suggestions for managing the trajectories and energy cost of multiple bio-inspired robotics.

Storage components are a major source of energy dissipation in modern superscalar microprocessors. As the instruction windows get larger with each generation of processors, register files and other structures that hold intermediate data become larger and dissipate more power. Therefore it is important to find new ways to reduce the energy dissipation of data holding components. Many values written to and read from these storage components are known to require fewer bits than is provided by the storage space. Upper order bits of these values are unnecessary and energy savings can be achieved by not writing and reading these bits. Floating value data has different characteristics and offer special energy savings opportunities. In this paper we analyze the bit-level statistics of data values and propose simple modifications to data holding components that save significant energy inside the processors. Our results show that by using simple modifications on the storage components it is possible to achieve 40% reduction in the integer register file energy dissipation and up to 60% reduction in the immediate field of the issue queue. We also show that energy dissipation can be reduced by half for some floating point benchmarks by identifying values that indicate a zero operand.

This paper describes an energy management system and an algorithm for an energy-aware operation. The system obtains energy from an energy harvester and manages the energy adaptively according to the monitored energy status. Based on a low-power microcontroller, the system controls the energy harvester so that it always harvests energy at maximum power and tracks it when the operating condition changes. It also controls the power consumption of all parts of the system so that they are adjusted dynamically for the management of harvested or stored energy. To manage the energy transfer to a battery, a DC–DC converter, called the energy management IC, is optimized for the operating voltage control of the harvester. In an experiment using an energy harvester and a battery modified for the system, the energy management IC fabricated in a 0.18 μm process maximizes the energy transfer power with a simple, low-power algorithm. The proposed system is verified to be more efficient for low-energy harvesting by the adaptive energy and power management.

Recently, the use of distributed power generation systems (DPGSs) based on renewable energy resources is increasingly being pursued as a supplement and a reliable alternative to the large traditional energy sources. For it, power-electronic interface technologies and control have also emerged as the most important key elements in the area of energy management and integrating DPGSs. The specification of a power-electronic interface is subject to several requirements that are related not only to the DPGS itself but also to its interactions with the power system especially where the utility grid is subject to events that can potentially lead to large-scale disturbances or even to its collapse if it operates near its capacity without fault margin. This study deals, first, with an optimized energy management strategy and, second, with a newly-conceived control strategy called symmetrical components control algorithm (SCCA) that was proposed for four-leg three-phase grid-connected voltage source inverter (VSI) used for DPGSs with wind–solar–battery sources. A mechanism of negative and zero sequences injection based on the control of ($d,q$) current coordinates has been introduced. The performance of entire control system, to enhance the unbalanced fault ride-through capability of DPGSs, has been evaluated by time domain simulations with MATLAB/Simulink. Advantages of the combined active–reactive control ensuring both current and voltage controls have been achieved compared to the majority of already published strategies. The distinct features of the proposed SCCA strategy prove that it allows to meet the requirements for grid interconnection and the new stricter standards with respect to power quality, safe running, and islanding protection.

Currently, the parallel hybrid electric vehicle (PHEV) is the most common type of architecture on the hybrid vehicle market. Therefore, a PHEV can be a solution to reduce emission and fuel consumption. The main challenge in the development of HEVs is the power management between the components that ensure vehicle movement. Energy management is now highly necessary by applying a control strategy (CS) in the vehicle’s traction chain, which directly affects the PHEV emission and fuel economy. The CSs have different performances, namely the control of the different power sources operation mode and the control of the battery state of charge. For this purpose, we propose a fuzzy logic CS to optimize emissions (FLCS-em) for PHEV. To assess this approach, we compare it with the most commonly used and recent EMS, in particular the strategy to optimize fuel use (FLCS-f), the efficiency optimization strategy (FLCS-eff) and the electric assist CS (EACS), in urban and highway driving cycles. The results show that the elaborate FLCS-em, characterized by a limited number of rulers, provide significant advantage than CSs mentioned in terms of the efficiency of PHEV performance and emissions and fuel consumption minimization.

An Internet of Things (IoT) device that can automatically measure water consumption can help prevent excessive water usage or leaks. However, automating too many residences or condominiums with multiple IoT devices can lead to extra energy consumption and more network congestion. We propose controlling the energy consumption of an IoT water consumption management system by dynamically controlling its duty cycle. By analyzing the energy consumption of the developed prototype and its duty cycle variation, we calculated how much energy could be saved by controlling the antenna and the water flow sensor used in the IoT device. While controlling the antenna offered some energy savings, having some way to cut down on the water flow sensor’s consumption can have a dramatic impact on the overall IoT energy consumption or its battery longevity. Our results showed that we could get up to 69% extra energy savings compared to just putting the antenna in sleep mode. There is an observable trade-off in saving so much energy, as we can also see that water reading error rates go up alongside the extra energy savings.

In this paper, a dynamic parameters lightning search algorithm (DP-LSA) is proposed for optimal operation management of electric vehicle (EV) parking lots (PLs) and distributed generations (DGs) in a microgrid (MG). The MG comprises distributed generation sources such as diesel generators and fuel cells. The batteries of electric vehicles parked in the PL are also used for energy storage. A proposed objective function combines operating, maintenance, and replacement costs, considering inflation, motivation, and annual growth. Simulations are done in two scenarios, and the proposed algorithm results are compared with particle swarm optimization (PSO) algorithm results. Simulation results prove the better performance of the proposed LSA algorithm than the PSO algorithm in reducing the microgrid operation cost.

For the month of September 2021, APBN looks at what we can do as consumers to contribute to our global endeavour towards a greener planet. In Features, we have an article contribution by Monique Suryokusumo from Monde Nissin Corporation to share with us the current innovations in the alternative protein arena and the key factors that will integrate it into the mainstream. In Spotlights, we spoke to professors at the Singapore Institute of Technology to learn more about how the institute and its collaborators will push for a more sustainable Singapore with their key initiatives. Then, we cover the key highlights from the Future Food Asia 2021 conference, where we hear about the new developments in the agritech food space and how we may utilise technology to build a more balanced food system. Moving away from sustainability, we learn more about maternal mental health from the Third Temasek Shophouse Conversations, and we also hear from Dr. Sanjay Prabhu, who will enlighten us about a rare condition plaguing children with COVID-19 in India

A Smart Hybrid Home (SHH) system studies power concerns for individual clients with an off-grid electricity connection are studied in a Smart Hybrid Home (SHH) system. The creation of autonomous hybrid electricity systems has been regarded as a way to improve energy independence. The primary objective is to ensure efficient management of energy. Detailed modeling is proposed in which the solar energy component is regarded as the significant source to achieve this aim. A multi-agent energy management system (MA-EMS) is presented in this article. It also includes the storing devices for power (Fuel Cell/Capacitors) for safe power delivery. The technical performance of the decentralised solution is in line with the existing central solution, which offers improved financially and operationally for the implementation and operation of the autonomous method. Furthermore, an IT-management system is created and described to guarantee that the system functions correctly utilizing a multi-agent structure. The proposed electricity management system is meant to recognize multi-agent jobs and comprehend probable situations based on energy attributes and requirements. The findings showed that the method suggested meets the aims of an operational database taken from different climatic services using the Matlab/Simulink Application set for the Smart Electricity Management Method. By implementing the proposed technique the power consumption is increased to 410kw, and it improves the efficiency rate to 88.4%.

The production and consumption of crude oil became a major issue with the sharp increase in crude oil prices that took place during the last few months. We investigate the relationship between crude oil consumption and the GDP of the top crude oil consuming countries. The amount of GDP produced per barrel of crude oil varies significantly between different countries; the ratio is in the range of 2% to 10% of the GDP when the price of a barrel of crude oil is $100. The paper attempts to explain the high variability with the aim of learning from high GDP producers as to how they are able to generate a larger GDP per barrel of crude oil consumption. The paper also identifies a hysteresis effect in crude consumption reduction and illustrates how understanding it can lead to better production and conservation policies.

This paper analyzes the structure of energy consumption in Iran. The demand side is divided into five major categories. Each category is further classified into subcategories and modelling is accomplished for the time horizon of 2012 to 2041. In order to determine final results, six technical and economic scenarios have been considered, entailing economic development scenarios and governmental energy management programs. Results show that final energy demand in Iran in all scenarios has an ever growing trend. It is expected that the total emission on demand side reaches to the range of 572 to 856 million tons in 2041. Results also indicate that there is a high potential for carbon reduction in energy demand side up to 5.68% annually.

Harvesting environmental energy and converting it into electrical energy are of important significance for self-powered and low-powered wireless sensor nodes (WSNs), but a single energy source tends to have some shortcomings. So it is much necessary to study hybrid energy harvesting technologies. Vibrations and temperature differences are two common environmental energies, but they have different electrical characteristics of energy conversion. Therefore, how to achieve the maximum conversion efficiency simultaneously is a key problem in engineering applications. This paper presents a hybrid energy harvesting architecture for vibrations and temperature differences. An efficient energy conversion and management circuit is designed in detail, mainly including energy conversion circuit and energy management circuit. In the end simulations and experiments are carried out and the results show that the proposed circuit can harvest vibration and temperature difference energy efficiently simultaneously, and meet the power consumption requirement of a WSN.

Energy management of the geostationary orbit (GEO) satellite is essential for its function, especially for GEO satellite energy systems with installed lithium ion batteries. This paper emphasizes the energy management and usage of lithium ion batteries according to research conducted on the hardware of the GEO satellite energy system and proposes an energy management system based on lithium ion batteries. This system provides effective management of the lithium ion batteries in orbit and guarantees the stabilization of the satellite energy system.

Three kinds of rule-based energy management control strategies for a 12 meter-long extended range electric bus were designed and compared. Based on Simulink/Stateflow hybrid modeling method, the models of the full vehicle model and the control strategy were built, and the simulation was conducted. Aiming to solve the problem of frequent engine start-stop of extended-range electric vehicle, a new constraint to limit the engine start-stop interval was proposed in the energy management control strategy, and the interval was optimized with an improved non-dominated sorting genetic algorithm. The simulation results under typical city driving cycles illustrate that, the control strategy proposed can well meet the vehicular performance requirements, and the engine start-stop interval constraint could effectively limit the minimum duration of engine start-stop interval, improve the service life of battery, and achieve less energy consumption and better emission performance.