• Energy Research

A sustainable circular economy involves designing and promoting products with the least environmental impact. This research presents an experimental performance investigation of direct contact membrane distillation with feed approaching supersaturation salinity, which can be useful for the sustainable management of reverse osmosis reject water. Traditionally, reject water from the reverse osmosis systems is discharged in the sea or in the source water body. The reinjection of high salinity reject water into the sea has the potential to put the local sea environment at risk. This paper presents a design of a solar membrane distillation system that can achieve close to zero liquid discharge. The theoretical and experimental analysis on the performance of the lab scale close to zero liquid discharge system that produces supersaturated brine is studied. The lab-based experiments were conducted at boundary conditions, which were close to the real-world conditions where feed water temperatures ranged between 40 °C and 85 °C and the permeate water temperatures ranged between 5 °C and 20 °C. The feed water was supplied at salinity between 70,000 ppm to 110,000 ppm, similar to reject from reverse osmosis. The experimental results show that the maximum flux of 17.03 kg/m2·h was achieved at a feed temperature of 80 °C, a feed salinity of 10,000 ppm, a permeate temperature of 5 °C and at constant feed and a permeate flow rate of 4 L/min. Whereas for the same conditions, the theoretical mass flux was 18.23 kg/m2·h. Crystal formation was observed in the feed tank as the feed water volume reduced and the salinity increased, reaching close to 308,000 ppm TDS. At this condition, the mass flux approached close to zero due to crystallisation on the membrane surface. This study provides advice on the practical limitations for the use of membrane distillation to achieve close to zero liquid discharge.

Abstract Vehicle-to-grid (V2G) describes a system in which plug-in electric vehicles (PEV), which includes all electric vehicles and plug-in hybrid electric vehicles, utilize power by plugging into an electric power source and stored in rechargeable battery packs. PEVs significantly increase the load on the grid, much more than you would see in a typical household. The objective of this paper is to demonstrate the use of intelligent solutions for monitoring and controlling the electrical grid when connected to and recharging PEV batteries. In order to achieve this aim, the study examines the distribution of electricity in the power grid of a large-scale city so that PEVs can tap into the system using smart grid electricity. The electricity grid for the large-scale city is modelled, and it can be shown that the vehicle electrification can play a major role in helping to stabilize voltage and load. This developed grid model includes 33 buses, 10 generators, 3 reactors, 6 capacitors, and 33 consumer centers. In addition, the grid model proposes 10 parking servicing 150,000 vehicles per day. The smart grid model uses intelligent controllers. Two intelligent controllers including (i) fuzzy load controllers and (ii) fuzzy voltage controllers have been used in this study to optimize the grid stability of load and voltage. The results show that the smart grid model can respond to any load disturbance in less time, with increased efficiency and improved reliability compared to the traditional grid. In conclusion it is emphasized that smart grid electricity should contribute to PEVs accessing renewable energy. Although the V2G will play a major role in the future portfolio of vehicle technologies, but does not make much sense if the carbon content of the electricity generated by the grid will not be reduced. Thus, the recourse to renewable energy and other alternatives is crucial. The energy is stored in electrochemical power sources (such as battery, fuel cells, supercapacitors, photoelectrochemical) when generated and then delivered to the grid during peak demand times.

Whilst air conditioning systems increase thermal comfortableness in vehicles, they also raise the energy consumption of vehicles. Achieving thermal comfort in an energy-efficient way is a difficult task requiring good coordination between engine and the air conditioning system. This paper presents a coordinated energy management system to reduce the energy consumption of the vehicle air conditioning system while maintaining the thermal comfortableness. The system coordinates and manages the operation of evaporator, blower, and fresh air and recirculation gates to provide the desired comfort temperature and indoor air quality, under the various ambient and vehicle conditions, the energy consumption can then be optimized. Three simulations of the developed coordinated energy management system are performed to demonstrate its energy saving capacity.

Many regions around the world have limited access to clean water and power. Low-grade thermal energy in the form of industrial waste heat or non-concentrating solar thermal energy is an underutilized resource and can be used for water desalination and power generation. This paper experimentally and theoretically examines a thermoelectric-based simultaneous power generation and desalination system that can utilize low-grade thermal energy. The paper presents concept design and the theoretical analysis of the proposed system followed by experimental analysis and comparison with the theoretical estimations. Experiments were carried out at three heat loads 50, 100 and 150 W to achieve varying temperature gradients across thermoelectric generators. During the experiments, thermoelectric generators were maintained at a hot to cold side temperature difference between 20 to 60 °C. The experiments showed that the power generation flux and freshwater mass flux increased with the increase in the thermal energy source temperature. The power flux varied between 12 to 117 W/m2 of thermoelectric generator area, while freshwater mass flux varied between 4.8 to 23.7 kg/m2⋅h. The specific thermal energy consumption varied between 3.6 to 5.7 MJ/kg of freshwater; this is comparable to the single-stage conventional distillation system.

Autonomous vehicles are aimed to reduce accidents and traffic congestion. Since hybrid electric vehicles offer feasible solutions to reduce energy consumption and emission to the environment, it is expected that autonomous vehicles will be powered through a hybrid electric system compared to other alternatives. In this paper, a hybrid electric autonomous vehicle is studied under significant amount of uncertainty and ambiguity in the road environment and driver behavior. A Type 1 fuzzy logic controller is constructed here to address the uncertainties of driving conditions. The design involves building an intelligent energy management system for the hybrid electric autonomous vehicle. We have also examined the potentials of the Interval Type 2 fuzzy logic control, especially for energy consumption management. Two simulations are implemented, to demonstrate that the intelligent system, proposed trough Type 1 and Interval Type 2 fuzzy logic control, decreases the fuel usage of the vehicle from 6.74 to 6.58 L/100km, respectively. It is also demonstrated that the Interval Type 2 fuzzy logic controller saves more battery life compared to the Type-1 when the vehicle works under uncertain and ambiguous road conditions. Finally, Interval Type-2 fuzzy logic controller facilitates a reduction of carbon footprint in the autonomous vehicle as desired by the automotive industry stakeholders.

Abstract This paper proposes a day-ahead self-healing scheduling approach in isolated networked microgrid (NMG) systems. The proposed approach is based on a two-level flexible energy management system (EMS). The upper-level EMS is responsible for optimal scheduling of the normal-operated MGs, while the lower-level help the MGs for operating on-fault in self-healing and islanded modes. When a fault occurs in an MG, it divides the on-fault MG into two zones. The grid-connected zone receives power support from the other normal-operated MGs. Furthermore, a secure optimization problem is presented to operate the separated zone in an islanded operation. The proposed approach is implemented on a test system with five NMGs. To cope with the operational uncertainties, a stochastic programming approach is applied. The simulation is run over a 24-hour scheduling time horizon. The results of the simulations in the normal, self-healing, and islanded operation modes for normal-operated and on-faulted MGs in cases without and with DRP are presented. The results reveal the effectiveness of the proposed stochastic EMS model in enhancing the performance of the network and decreasing the operational costs of the MGs.

Efficient energy management in hybrid vehicles is the key for reducing fuel consumption and emissions. To capitalize on the benefits of using PHEVs (Plug-in Hybrid Electric Vehicles), an intelligent energy management system is developed and evaluated in this paper. Models of vehicle engine, air conditioning, powertrain, and hybrid electric drive system are first developed. The effect of road parameters such as bend direction and road slope angle as well as environmental factors such as wind (direction and speed) and thermal conditions are also modeled. Due to the nonlinear and complex nature of the interactions between PHEV–Environment–Driver components, a soft computing based intelligent management system is developed using three fuzzy logic controllers. The crucial fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system with genetic algorithm optimization. For adaptive learning, a number of datasets were created for different road conditions and a hybrid learning algorithm based on the least squared error estimate using the gradient descent method was proposed. The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation. It is shown that the proposed intelligent energy management system is improving the performance of other existing systems.

Abstract Air conditioning systems (A/C) significantly increase the energy consumption of a vehicle and negatively influence its performance. A/C can be considered the main auxiliary load on a vehicle engine when it is operating. Thus, there are significant savings to be made by operating an A/C system smartly, both in terms of running costs and the effect on the environment. This paper presents an intelligent energy management system that is able to reduce the energy consumption of a vehicle with an air conditioning system and improve its efficiency by using the look-ahead system uses information from various information systems to make intelligent decisions. The new energy management system features: a prediction of road power demand by using look-ahead control of vehicle systems, an intelligent control strategy to manage the operation of the A/C, the blower, and the gates, to provide the optimum comfort temperature with the consideration of the in cabin air quality while minimizing energy consumption. Two simulations are performed by using the developed fuzzy air conditioning enhanced look-ahead System and ordinary fuzzy air conditioning and then the results are compared together with the results from Coordinated Energy Management System (CEMS). The results of fuzzy air conditioning enhanced with look-ahead system demonstrate it is capable of saving 12% and 3% more energy comparing with CEMS and ordinary fuzzy air conditioning system respectively.