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Abstract This paper presents a study of the combined influence of battery models and sizing strategy for hybrid and battery-based electric vehicles. In particular, the aim is to find the number of battery (and supercapacitor) cells to propel a light vehicle to run two different standard driving cycles. Three equivalent circuit models are considered to simulate the battery electrical performance: linear static, non-linear static and non-linear with first-order dynamics. When dimensioning a battery-based vehicle, less complex models may lead to a solution with more battery cells and higher costs. Despite the same tendency, when a hybrid vehicle is taken into account, the influence of the battery models is dependent on the sizing strategy. In this work, two sizing strategies are evaluated: dynamic programming and filter-based. For the latter, the complexity of the battery model has a clear influence on the result of the sizing problem. On the other hand, a modest influence is observed when a dynamic programming strategy is followed.

Abstract Nitrous oxide (N2O) is an important long-lived greenhouse gas and precursor of stratospheric ozone-depleting mono-nitrogen oxides. The atmospheric concentration of N2O is persistently increasing; however, large uncertainties are associated with the distinct source strengths. Here we investigate for the first time N2O emission from terrestrial vegetation in response to natural solar ultra violet radiation. We conducted field site measurements to investigate N2O atmosphere exchange from grass vegetation exposed to solar irradiance with and without UV-screening. Further laboratory tests were conducted with a range of species to study the controls and possible loci of UV-induced N2O emission from plants. Plants released N2O in response to natural sunlight at rates of c. 20–50 nmol m−2 h−1, mostly due to the UV component. The emission response to UV-A is of the same magnitude as that to UV-B. Therefore, UV-A is more important than UV-B given the natural UV-spectrum at Earth's surface. Plants also emitted N2O in darkness, although at reduced rates. The emission rate is temperature dependent with a rather high activation energy indicative for an abiotic process. The prevailing zone for the N2O formation appears to be at the very surface of leaves. However, only c. 26% of the UV-induced N2O appears to originate from plant-N. Further, the process is dependent on atmospheric oxygen concentration. Our work demonstrates that ecosystem emission of the important greenhouse gas, N2O, may be up to c. 30% higher than hitherto assumed.

Decentralized economic operation schemes have several advantages when compared with the traditional centralized management system for microgrids. Specifically, decentralized schemes are more flexible, less computationally intensive, and easier to implement without relying on communication infrastructure. Economic operation of existing decentralized schemes is also usually achieved by either tuning the droop characteristics of distributed generators (DGs) or prioritizing their dispatch order. For the latter, an earlier scheme has tried to prioritize the DG dispatch based on their no-load generation costs. Although the prioritization works well with some generation costs saved, its reliance on only the DG no-load generation costs does not allow it to operate well under certain conditions. This paper thus presents a more comprehensive economic dispatch scheme, which considers the DG generation costs, their power ratings, and other necessary constraints, before deciding the DG dispatch priorities and droop characteristics. The proposed scheme also allows online power reserve to be set and regulated within the microgrid. This, together with the generation cost saved, has been verified experimentally under different reserve, load, and DG conditions.

The production of glucoamylase (GAM) by Aspergillus niger B1, a genetic transformant containing an additional 20 copies of the homologous glucoamylase gene (glaA) was studied in nutrient (maltodextrin)-limited chemostat and nutrient-excess pH auxostat cultures. In these culture systems the specific production rate of GAM increased with dilution rate and reached a maximum (up to 15.0 mg GAM [g biomass]-1 h-1) when A. niger B1 was grown at its maximum specific growth rate in pH auxostat culture, indicating that GAM is a growth-associated product. The appearance of spontaneous morphological mutants was observed in all continuous flow cultures grown at pH 5.4, with a light brown mutant always displacing the parental strain. However, no morphological mutants were observed in cultures grown at pH 4.0. Further, when A. niger B1 was grown in pH auxostat culture, the specific production rate of GAM was 31% higher at pH 4.0 than at pH 5.4. Southern blot analyses showed that some morphological mutants (including the light brown mutant) isolated from a pH auxostat culture had lost copies of the glaA genes.

In renewable energy systems, fluctuating outputs from energy sources and variable power demand may deteriorate the voltage quality. In this paper, a model predictive control strategy without using any proportional–integral–derivative (PID) regulators is proposed. The proposed strategy consists of a model predictive current and power (MPCP) control scheme and a model predictive voltage and power (MPVP) control method. By controlling the bidirectional dc–dc converter of the battery energy storage system based on the MPCP algorithm, the fluctuating output from the renewable energy sources can be smoothed while stable dc-bus voltage can be maintained. Meanwhile, the ac/dc interlinking converter is controlled by using the MPVP scheme to ensure stable ac voltage supply and proper power flow between the microgrid and the utility grid. Then, a system-level energy management scheme is developed to ensure stable operation under different operation modes by considering fluctuating power generation, variable power demand, battery state of charge, and electricity price. Compared with the traditional cascade control, the proposed method is simpler and shows better performance, which is validated in simulation based on a 3.5-MW PV-wind-battery system with real-world solar and wind profiles.

Abstract This work presents the control development of a single stage grid-connected photovoltaic (PV) system. The topology used by the PV system consists of two PV panels, a single-phase half-bridge inverter using two DC link capacitors, and an LCL filter in the grid side. The aim is to design a nonlinear controller to ensure simultaneously the following three objectives: (i) PV panels to provide their maximum power, (ii) balanced power exchange by regulating the DC link voltage, and (iii) power factor correction, i.e. grid current in phase with grid voltage. In order to fulfill these objectives, a multi-loop controller is designed by using back-stepping and Lyapunov approaches for the power factor correction objective and a filtered PI regulator to ensure the power balance between the grid and the PV panels. The controller performances are formally analyzed using an average large signal model. The performance of the proposed controller is shown by means of simulation results in MATLAB/SimPowerSystems environment.

Abstract Fuel cell-gas turbine hybrid system is a potential field of investigation. This study establishes a modeling and optimization framework for a novel hybrid system consisting of a solid oxide fuel cell, a gas turbine and a supercritical carbon dioxide Brayton cycle. Based on the proposed thermodynamical model, a parametric analysis is investigated to determine the impacts of several key parameters on the system exergoeconomic performance. Meanwhile, bi-objective optimization is conducted for maximizing the exergy efficiency and minimizing the levelized cost of electricity via the Epsilon-constraint approach. The Linear Programming Techniques for Multidimensional Analysis of Preference decision-making approach is further employed to select the Pareto optimum solution from Pareto frontiers. The results show that several extreme values for the exergy efficiency and the levelized cost of electricity exist in a series of sensitivity curves, respectively. The Pareto frontiers indicates that with the increase of the exergy efficiency, the levelized cost of electricity shows a moderately increasing trend at first and increases rapidly afterward. Overall, at the Pareto optimum solution, the combined system can achieve an optimal exergy efficiency and levelized cost of electricity by 68% and 0.0575 $ kWh−1, respectively.

Abstract In this paper, a bi-level Stackelberg-based model between an electricity retailer and consumers is presented, in which the upper-level consists of a price-maker retailer (PMR) modeled as the leader who seeks to maximize its own profit by adopting optimal pricing strategies for a pool-based electricity market. At the same time, PMR reduces its risks by encouraging consumers to actively participate in demand response (DR) programs. The lower-level of the model consists of 4 followers, three of them represent customer groups with distinct reactions to DR programs, and their objective function is defined as minimizing the cost of purchased electricity while preserving the welfare level. The fourth follower is the electricity pool, which is responsible for implementation of market mechanism and determination of market clearing price (MCP) with the aim of increasing the consumers’ welfare. In the proposed framework, the reaction of consumers to prices and DR programs are also organized and studied in form of several scenarios. The outputs of the proposed model will be enhancing the retailer’s profit and determination of the effect of DR programs on power consumption during peak hours as well as consumers’ welfare.