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The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

Abstract In this paper, a reintegration-based multi-objective intentional controlled islanding (ICI) model is proposed to enhance resiliency of electrical power systems under catastrophic events. This remedial measure plan relies on a mixed-integer linear programming model with two objective functions including reintegration risk and total load shedding value. While ensuring that each island includes only coherent generators, the proposed multi-objective model solves the controlled islanding problem using lexicographic optimization approach. To ease the islands’ reintegration, charging reactive power, reliability, capacity, and power flow disruption of transmission lines are considered in the model. After implementation of controlled islanding, each resulted island may face temporary active/reactive load-generation imbalance, which may put the islands at the risk of frequency instability, transient voltage instability or a combination of both. The proposed model reduces these risks by modeling energy storage systems (ESSs) and static VAR compensators (SVCs) as fast corrective control actions. In addition to modeling voltage dependent loads in the controlled islanding problem, a linear island frequency response (IFR) model is proposed for frequency stability assessment. The test results of the proposed ICI model on the IEEE 39-bus and IEEE 118-bus test systems demonstrate its performance.

This paper proposes a method to determine the location of sectionalizing switches in presence of stochastic renewable resources. Particle swarm optimization is used for placement of sectionalizing switches with considering an objective function. The proposed objective function is composed of two terms: minimizing of cost of sectionalizing switches and maximization of reliability benefit. Monte Carlo simulation is used to reliability assessment. The performance of the proposed approach is assessed by a real distribution network.

It has been known for a very long time that chemical energy may be converted into electrical energy by using biomass, considered a renewable energy source. In the study that is being presented here, an explanation and a presentation are offered on a one-of-a-kind hybrid system that generates dependable power and cooling by harnessing the chemical energy of biomass. An anaerobic digester takes in organic material and converts it into biomass by using the high-energy content of cow manure as fuel. The Rankin cycle is the primary engine that drives the system that produces energy, and its combustion-based byproducts are routed to an ammonia absorption refrigeration system in order to provide sufficient cooling for the process of pasteurizing and drying the milk. It is expected that solar panels might contribute to the production of sufficient amounts of power for necessary activities. The technical and financial facets of the system are both being investigated at the moment. In addition, the optimal working conditions are determined by employing a forward-thinking multi-objective optimization strategy. This method simultaneously raises the operational effectiveness to the greatest extent that is practically possible while simultaneously lowering both expenses and emissions. The findings indicate that under ideal conditions, the levelized cost of the product (LCOP), efficiency, and emission of the system are, respectively, 0.087 $/kWh, 38.2%, and 0.249 kg/kWh. The digester and the combustion chamber both have very high exergy destruction rates, with the digester having the highest rate and the combustion chamber having the second-highest rate among all of the system's components. This assertion is supported by every one of these components.

This paper deals with the optimization of the daily operation of Polerood hydropower station being constructed in the north of Iran. Dynamic Programming method (DP) is applied as the preferred optimization tool owing to the fact that it guarantees the optimal solution and is applicable to the present problem. Produced profit and peak-shaving are the two objectives considered separately in this study. The results show that the optimal water management of the case study through charging and discharging the reservoir at the appropriate times led to 4% increase in the produced profit. In another part of this study, the optimal performance strategies regarding to the two objectives (produced profit and peak-shaving) are compared. The observed similarity between the two performance strategies implies the substantial dependence of the electricity price and the network demand level. The paper ends with the profitability study of the project and the sensitivity analysis of the results to various economic parameters. Article History: Received December 15th 2017; Received in revised form April 18th 2018; Accepted September 16th 2018; Available onlineHow to Cite This Article: Feshalami, B.F. (2018) Optimal Operating Scenario for Polerood Hydropower Station to Maximize Peak Shaving and Produced Profit. International Journal of Renewable Energy Development, 7(3), 233-239.https://doi.org/10.14710/ijred.7.3.233-239

Abstract In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.

Abstract Clamping mechanisms have significant effect on the performance of polymer electrolyte membrane (PEM) fuel cells. In this paper, PEM fuel cell with new clamping mechanism is designed to study the contact pressure distribution over the active area of PEM fuel cell's membrane electrode assembly (MEA). The clamping pressure is pneumatically exerted on the PEM fuel cell assembly. A comparison between the conventional and new clamping mechanism is carried out with simulation, and the numerical results are validated against experimental investigation performed in the fuel cell technology research laboratory. The experimental results are gathered using embedded pressure measurement films in the designed single cell. The results achieved via finite element method are in good agreement with experimental results. It is concluded that the contact pressure distribution of MEA for the new clamping mechanism is more uniform than the conventional one.

In this article, a comparative study is carried out between two equations for the exergy efficiency of photovoltaic thermal (PV/T) air collectors; the first equation is based on net output exergy and the second equation is in terms of exergy losses. The exergy efficiency equation parametrically is dependent on thermal and electrical parameters of PV/T air collector; therefore, improved thermal and electrical models are used to calculate them. Developing an exergy balance for PV/T air collector system, the various exergy components in PV/T system are introduced and two equations for the exergy efficiency of PV/T air collector are derived. A computer simulation program is also developed which is based on the used improved thermal and electrical models. In order to validate the simulation results, a typical PV/T air collector has been built and some experiments have been carried out on it. The results of numerical simulation are in good agreement with the experimental results. Finally, parametric studies have been carried out and the effect of design and climatic parameters on two exergy efficiency equations has been investigated. It is observed that the improved exergy efficiency obtained in this paper is in good agreement with the one given by the previous literature and it is better because it shows the portion of each of exergy losses in the exergy efficiency equation, directly.

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.