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The rapid growth of rooftop photovoltaic (PV) arrays installed in residential houses leads to serious voltage quality problems in low voltage distribution networks (LVDNs). In this paper, a combined method using the battery energy management of plug-in electric vehicles (PEVs) and the active power curtailment (APC) of PV arrays is proposed to regulate voltage in LVDNs with high penetration level of PV resources. A distributed control strategy composed of two consensus algorithms is used to reach an effective utilization of limited storage capacity of PEV battery considering its power/capacity and state of charge (SoC). A consensus control algorithm is also developed to fairly share the required power curtailment among PVs during overvoltage periods. The main objective is to mitigate the voltage rise due to the reverse power flow and to compensate the voltage drop resulting from the peak load. Overall, the proposed algorithm contributes to a coordinated charging/discharging control of PEVs battery which provides a maximum utilization of available storage capacity throughout the network. In addition, the coordinated operation minimizes the required active power which is going to be curtailed from PV arrays. The effectiveness of the proposed control scheme is investigated on a typical three-phase four-wire LVDN in presence of PV resources and PEVs.

In this paper, we investigate how generators’ ownership of financial transmission rights (FTRs) may influence the effects of the transmission lines on competition. In order for concrete analysis, a simple symmetric market model is introduced and FTRs are modeled in two different forms: FTR options and FTR obligations. This paper shows that introducing FTRs in an appropriate manner may reduce the physical capacity needed for the full benefits of competition. Among the competitive effects of ownership of FTRs, we focus on the effects on two possible pure strategy equilibria: the unconstrained Cournot equilibrium and the passive/aggressive equilibrium. We also analyze an extension of the model: asymmetric markets. Finally, a numerical illustration of applying the analysis is presented.

The present study utilizes (a) recycled low density polyethylene oil extracted through catalytic pyrolysis and (b) 1-decanol and di-n-butyl ether, as a blend component with diesel to power a single...

Abstract Analytical solutions for superadiabatic filtration combustion of lean methane-air mixtures in a monolithic porous burner are sought. The one-dimensional, local volume-averaged equations of energy and species conservation that assume a non-thermal equilibrium (i.e., the two-medium treatment), are converted by a coordinate transformation using a combustion wave speed and solved to obtain close-form solutions. A parametric examination varying inlet gas velocity, fuel equivalence ratio, porosity, and thermal conductivity and diffusivity of the solid phase of the porous burner proves the validity of the analytical solutions which are in an excellent agreement with the numerical benchmark. The analytical solutions depict the key features of the filtration combustion such as non-thermal equilibrium between the solid and gas phases, superadiabatic flame temperature, and internal heat recirculation between solid and gas phases of the porous burner.

Abstract This study newly introduces a complementary cooperative algorithm considering generative adversarial network (GAN)-Conditional Latent Space (CLS) combined with bidirectional long short-term memory (BLSTM) for improved and efficient lithium-ion rechargeable battery state prediction. The GAN-CLS algorithm, which is an advanced method of GAN, can generate corresponding images from an input label description. Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture that can predict sequences more accurately than conventional RNNs. In terms of battery state prediction, the combination of two methods (GAN-CLS and LSTM) surely provides more improved and efficient rechargeable battery state prediction in contrast to conventional state predictors. The procedure of this study is as follows. First, we propose methods to enhance the data from battery charge/discharge by converting prepared data to images; then, the GAN-CLS method is used to generate corresponding battery data from previous images. Subsequently, the generated data is used to train the BLSTM model. Finally, the trained model is used to predict the battery state. By various experiments and verification, it is concluded that the proposed study can be a good solution for rechargeable battery state prediction (reduction of the time cost 50 times in modeling and 20 times in train/test, provision of a more accurate prediction mean square error (MSE) smaller than 0.0025 and the average MSE less than 0.0013).

Efficiency of energy resource use is a key factor for a sustainable energy future. By matching the exergy levels of supply and demand, Energy Quality Management (EQM) of building energy supply systems may achieve more efficient use of energy resources. Beyond this, environmental impact of energy supply systems is another essential issue. This work addresses the influence of EQM on CO 2 emissions in the operation optimization of a Distributed Energy System (DES). A multi-objective linear programming problem is formulated to reduce energy costs and increase the overall exergy efficiency. Total CO 2 emissions are evaluated for the optimized operation strategies of the DES. The operators of the DES can choose the operation strategy from the Pareto front, based on their priorities and also aware of effects on CO 2 emissions. Results demonstrate more efficient use of energy resources and reduction in CO 2 emissions through EQM, as compared with conventional energy supply systems.

Biomass is a promising alternative energy source for fossil fuel with the advantages of abundance, renewability, environmental friendliness, etc. This makes the development of biomass technology be of great potential and interesting. The experiments of biomass fast pyrolysis were performed in a microquartz reactor for rice husk (RH), corn stalk (CS) and birch wood (BW), and scanning electron microscope (SEM), energy dispersive spectrometer, and Raman microscope were then applied to analyze the collected chars. The average char yields of RH, CS, and BW pyrolyzed at 800 °C were 29.64%, 18.67%, and 8.64%, respectively. The morphological structures of RH and CS were mainly reserved in chars, while the raw surface textures of BW disappeared during the fast pyrolysis. The silicon concentrations in RH char and CS char were much higher than BW char, and the graphitization degree of CS char was the lowest among the three biomass chars.

Abstract Dynamic modeling of HVAC system is extremely important for control analysis toward building energy saving. In order to provide researchers a simulation platform to analyze different control strategies, this paper introduces a simulation platform with customized Simulink block library based on dynamic HVAC component model. As an initiating effort, the current simulation platform is composed of basic modular HVAC components, including conduit, damper/valve, fan/pump, flow merge, flow split, heating coil, cooling coil, and zone. These modules are developed into Simulink customized blocks. The simulation platform is capable to calculate the flow rates of fresh air, exhaust air, and return air based on system characteristic and fan curve with customizable basic/advanced control strategies. A case study is proposed using single-zone, constant volume system by comparing the uncontrolled, fixed temperature and damper position controlled, and schedule based reset controlled cases. The simulation result proves that schedule based temperature and damper position reset has a significant impact on energy saving for both heating and cooling seasons. This simulation platform can be especially useful for analyzing the dynamic performance of different HVAC control strategies.

Abstract In this paper, we investigate distributed thermal-electrochemical modeling of a Lithium-Ion battery cell to include the effect of temperature distribution across the thickness of the cell as a first step to study the module level temperature distribution at high charging rates. Most recent works have focused on lumped thermal models for a Li-Ion cell which ignore any temperature differential across cell thickness. However, even a small temperature differential across cell thickness at the cell level can contribute to significant temperature differential in the thickness direction of stacked-up Li-Ion cells at the module level. Such temperature differential can potentially impact the battery charging control system, especially at high charging rates. Here, the thermal-electrochemical partial differential and algebraic equations for a Li-ion cell are solved via a spatial finite difference method. Simulation results show that the temperature differentials over the cell thickness at the cell level are not insignificant, particularly at high charging rates.