Search

Hybrid Electric Vehicles (HEVs) are affected to a high extent by Intelligent Energy Management Systems (IEMS), especially during situations that are challenging and unpredictable including changes in traffic patterns, road gradients, and speed. These uncertainties are not easily solved using the existing energy management systems; therefore, this paper presents the design of an AFLC-IEMS employing Type 1 and Interval Type 2 Fuzzy Logic Controllers for energy distribution improvement. The AFLC-IEMS sustains the combustion of fuel and discharge of battery in a way that promotes efficiency in switching between the internal combustion engine and the electric motor. The simulation results with the one-way analysis of variance test confirm our finding that the proposed system is far superior to the traditional ones. The savings achieved by the AFLC-IEMS are a decrease in fuel consumption from 7.26 Liters/100 km down to 6.69 Liters/100 km, as well as an increase in the battery State of Charge (SoC) from 72.7% to 75.8%. The ANOVA analysis shows that the fuel consumption (p < 0.01), the motor torque (p < 0.01), as well as the SoC of the battery (p < 0.05) in the developed FLC are statistically superior to the Type 1 FLC and Type 2 FLC. These improvements are achieved by adapting the technology to the situation to adjust the control strategy; hence, the efficiency of the energy management system is optimized. Therefore, the AFLC-IEMS is more effective in improving the fuel economy and reducing emissions under various conditions.

This article presents the innovative integrated control strategies of the battery energy storage system (BESS) to support the system operation of an offshore island microgrid with high penetration of renewable energy. An intelligent energy management system (iEMS) was implemented to perform the supervisory control and data acquisition of diesel generators, distribution feeders, photovoltaic (PV) systems, and the BESS. An expert system was designed and embedded in the iEMS to derive the decision making for fast power discharging of BESS to improve the system transient stability for the severe contingency of large disturbances caused by the tripping of generators and PV farm. To mitigate the impact of PV generation fluctuation and to maintain the stable operation of diesel generators, the adaptive control of power charging and discharging of the BESS is applied to reduce the ramping rate of PV generation. To achieve the economic dispatch of the microgrid, the strategy of peak shaving and valley filling of the system net load curve by the BESS was derived considering the forecasting daily profiles of system loading and PV power generation. The computer simulation of actual diesel generator tripping and the field test of intentional PV system shutdown have been executed to demonstrate the effectiveness of the proposed control strategies of the BESS system to improve the system operation and transient stability of the study microgrid with high penetration of renewable energy. [ABSTRACT FROM AUTHOR]

Smart energy technologies, services, and business models are being developed to reduce energy consumption and emissions of CO2 and greenhouse gases and to build a sustainable environment. Renewable energy is being actively developed throughout the world, and many intelligent service models related to renewable energy are being proposed. One of the representative service models is the energy prosumer. Through energy trading, the demand for renewable energy and distributed power is efficiently managed, and insufficient energy is covered through energy transaction. Moreover, various incentives can be provided, such as reduced electricity bills. However, despite such a smart service, the energy prosumer model is difficult to expand into a practical business model for application in real life. This is because the production price of renewable energy is higher than that of the actual grid, and it is difficult to accurately set the selling price, restricting the formation of the actual market between sellers and consumers. To solve this problem, this paper proposes a small-scale energy transaction model between a seller and a buyer on a peer-to-peer (P2P) basis. This model employs a virtual prosumer management system that utilizes the existing grid and realizes the power system in real time without using an energy storage system (ESS). Thus, the profits of sellers and consumers of energy transactions are maximized with an improved return on investment (ROI), and an intelligent demand management system can be established.

Electric vehicles (EVs) are envisaged to be the future transportation medium, and demonstrate energy efficiency levels much higher than conventional gasoline or diesel-based vehicles. However, the sustainability of EVs is only justified if the electricity used to charge these EVs is availed from a sustainable source of energy and not from any fossil fuel or carbon generating source. In this paper, the challenges of the EV charging stations are discussed while highlighting the growing use of distributed generators in the modern electrical grid system. The benefits of the adoption of photovoltaic (PV) sources along with battery storage devices are studied. A multiport converter is proposed for integrating the PV, charging docks, and energy storage device (ESD) with the grid system. In order to control the bidirectional flow between the generating sources and the loads, an intelligent energy management system is proposed by adapting particle swarm optimization for efficient switching between the sources. The proposed system is simulated using MATLAB/Simulink environment, and the results depicted fast switching between the sources and less switching time without obstructing the fast charging to the EVs.

Abstract: In this paper, an IEMS (intelligent energy management system) is proposed for optimal operation of a CHP-based micro-grid over a 24-hour time interval. The IEMS aims to find the optimal set points of DERs (distributed energy resources) and thermal and electrical storage systems, in such a way that the total operation cost and the net emission are minimized, simultaneously. For optimal operation of the micro-grid, a smart ESS (energy storage system) is introduced which consists of electrical and thermal storage systems. A comprehensive mathematical model for the ESS and also the economic and environmental objective functions are formulated. To solve the problem, we propose an efficient MBFO (modified Bacterial Foraging Optimization) algorithm. An interactive fuzzy satisfying method is also used to simulate the trade-off between conflicting objectives (cost and emission). To validate the performance of the proposed algorithm, we compare the MBFO algorithm with some other previously proposed analytical methods. Finally, the proposed IEMS is applied to a micro-grid which consists of various DERs, CHP (combined heat and power) system, ESS and thermal and electrical loads. The results show that the proposed IEMS can smartly cover the total electrical and thermal load demands with respect to economic and environmental criteria. [Copyright &y& Elsevier]

Smart energy technologies, services, and business models are being developed to reduce energy consumption and emissions of CO2 and greenhouse gases and to build a sustainable environment. Renewable energy is being actively developed throughout the world, and many intelligent service models related to renewable energy are being proposed. One of the representative service models is the energy prosumer. Through energy trading, the demand for renewable energy and distributed power is efficiently managed, and insufficient energy is covered through energy transaction. Moreover, various incentives can be provided, such as reduced electricity bills. However, despite such a smart service, the energy prosumer model is difficult to expand into a practical business model for application in real life. This is because the production price of renewable energy is higher than that of the actual grid, and it is difficult to accurately set the selling price, restricting the formation of the actual market between sellers and consumers. To solve this problem, this paper proposes a small-scale energy transaction model between a seller and a buyer on a peer-to-peer (P2P) basis. This model employs a virtual prosumer management system that utilizes the existing grid and realizes the power system in real time without using an energy storage system (ESS). Thus, the profits of sellers and consumers of energy transactions are maximized with an improved return on investment (ROI), and an intelligent demand management system can be established. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are envisaged to be the future transportation medium, and demonstrate energy efficiency levels much higher than conventional gasoline or diesel-based vehicles. However, the sustainability of EVs is only justified if the electricity used to charge these EVs is availed from a sustainable source of energy and not from any fossil fuel or carbon generating source. In this paper, the challenges of the EV charging stations are discussed while highlighting the growing use of distributed generators in the modern electrical grid system. The benefits of the adoption of photovoltaic (PV) sources along with battery storage devices are studied. A multiport converter is proposed for integrating the PV, charging docks, and energy storage device (ESD) with the grid system. In order to control the bidirectional flow between the generating sources and the loads, an intelligent energy management system is proposed by adapting particle swarm optimization for efficient switching between the sources. The proposed system is simulated using MATLAB/Simulink environment, and the results depicted fast switching between the sources and less switching time without obstructing the fast charging to the EVs. [ABSTRACT FROM AUTHOR]