Search

Virtual power plants (VPPs) are an effective platform for attracting private investment and customer engagement to speed up the integration of green renewable resources. In this paper, a robust bidding strategy to participate in both energy and ancillary service markets in the wholesale electricity market is proposed for a realistic VPP in Western Australia. The strategy is accurate and fast, so the VPP can bid in a very short time period. To engage customers in the demand management schemes of the VPP, the gamified approach is utilized to make the exercise enjoyable while not compromising their comfort levels. The modelling of revenue, expenses, and profit for the load-following ancillary service (LFAS) is provided, and the effective bidding strategy is developed. The simulation results show a significant improvement in the financial indicators of the VPP when participating in both the LFAS and energy markets. The payback period can be improved by 3 years to the payback period of 6 years and the internal rate of return (IRR) by 7.5% to the IRR of 18% by participating in both markets. The accuracy and speed of the proposed bidding strategy method is evident when compared with a mathematical method. [ABSTRACT FROM AUTHOR]

Penetration of the power grid by renewable energy sources, distributed storage, and distributed generators is becoming increasingly common. Increased utilization of these distributed energy resources (DERs) has given rise to additional protection coordination concerns, particularly where they are utilized in an unbalanced manner or where loading among phases is unbalanced. Digital relays such as the SEL-751 (produced by Schweitzer Engineering Laboratories, Pullman, WA, USA) series have the capability of being set on a per-phase basis. This capability is underutilized in common practice. Additionally, in optimization algorithms for determining relay settings, the time-overcurrent characteristics (TOCs) of relays are generally not treated as variables and are assigned before running the optimization algorithm. In this paper, TOC options themselves are treated as discrete variables to be considered in the optimization algorithm. A mixed integer nonlinear programming problem (MINLP) is set up where the goal is to minimize relay operating times. A genetic algorithm (GA) approach is implemented in MATLAB where two cases are considered. In the first case, the TOC and Time dial setting (TDS) of each relay is set on a three-phase basis. In the second case, per-phase settings are considered. Relay TDSs and TOCs are both considered as simultaneous discrete control variables. Despite the stochastic nature of using per-phase settings for unbalanced systems is found to generally allow for shorter operating times. However, for relatively balanced systems, it is best to use three-phase settings if computation time is of importance. [ABSTRACT FROM AUTHOR]

To integrate large-scale renewable energy into energy systems, an effective participation from private investors and active customer engagement are essential. Virtual power plants (VPPs) are a very promising approach. To realize this engagement, an efficient monitoring and control system needs to be implemented for the VPP to be flexible, scalable, secure, and cost-effective. In this paper, a realistic VPP in Western Australia is studied, comprising 67 dwellings, including a 810 kW rooftop solar photovoltaic (PV) system, a 700 kWh vanadium redox flow battery (VRFB), a heat pump hot water system (HWS), an electric vehicle (EV) charging station, and demand management mechanisms. The practical and detailed concept design of the monitoring and control system for EEBUS-enabled appliances, and also for the PV and VRFB system, with smart inverters, is proposed. In addition, a practical fog-based storage and computing system is developed to enable the VPP owner to manage the PV, VRFB, and EV charging station for maximizing the benefit to the customers and the VPP owner. Further, the proposed cloud-based applications enable customers to participate in gamified demand response programs for increasing the level of their engagement while satisfying their comfort level. All proposed systems and architecture in this paper have the capability of being implemented fully and relevant references for practical devices are given where necessary. [ABSTRACT FROM AUTHOR]

Achieving the renewable energy integration target will require the extensive engagement of consumers and the private sector in investment and operation of renewable-based energy systems. Virtual power plants are an efficient way to implement this engagement. In this paper, the detailed costs and benefits of implementing a realistic virtual power plant (VPP) in Western Australia, comprising 67 dwellings, are calculated. The VPP is designed to integrate and coordinate rooftop solar photovoltaic panels (PV), vanadium redox flow batteries (VRFB), heat pump hot water systems (HWSs), and demand management mechanisms. An 810-kW rooftop solar PV system is designed and located using the HelioScope software. The charging and the discharging of a 700-kWh VRFB are scheduled for everyday use over a year using an optimization algorithm, to maximize the benefit of it for the VPP owners and for the residents. The use of heat pump HWSs provides a unique opportunity for the residents to save energy and reduce the total cost of electricity along with demand management on some appliances. The cost-and-benefit analysis shows that the cost of energy will be reduced by 24% per dwelling in the context of the VPP. Moreover, the internal rate of return for the VPP owner is at least 11% with a payback period of about 8.5 years, which is a promising financial outcome. [ABSTRACT FROM AUTHOR]

A nuclear magnetic resonance (NMR) logging tool can provide important rock and fluid properties that are necessary for a reliable reservoir evaluation. Pore size distribution based on T2 relaxation time and resulting permeability are among those parameters that cannot be provided by conventional logging tools. For wells drilled before the 1990s and for many recent wells there is no NMR data available due to the tool availability and the logging cost, respectively. This study used a large database of combinable magnetic resonance (CMR) to assess the performance of several well-known machine learning (ML) methods to generate some of the NMR tool's outputs for clastic rocks using typical well-logs as inputs. NMR tool's outputs, such as clay bound water (CBW), irreducible pore fluid (known as bulk volume irreducible, BVI), producible fluid (known as the free fluid index, FFI), logarithmic mean of T2 relaxation time (T2LM), irreducible water saturation (Swirr), and permeability from Coates and SDR models were generated in this study. The well logs were collected from 14 wells of Western Australia (WA) within 3 offshore basins. About 80% of the data points were used for training and validation purposes and 20% of the whole data was kept as a blind set with no involvement in the training process to check the validity of the ML methods. The comparison of results shows that the Adaptive Boosting, known as AdaBoost model, has given the most impressive performance to predict CBW, FFI, permeability, T2LM, and SWirr for the blind set with R2 more than 0.9. The accuracy of the ML model for the blind dataset suggests that the approach can be used to generate NMR tool outputs with high accuracy. [ABSTRACT FROM AUTHOR]

The suitability of sewage-sludge derived washing agents (SS_WAs) (dissolved organic matter DOM; humic-like substances HLS; soluble humic substances SHS), was assessed for removing Cd from highly contaminated (300 mg/kg) sandy clay loam and clay. The soils were remediated via column flushing at two flow rates, 0.5 and 1.0 mL/min. The stability of the flow velocity (FV) depended on the type of SS_WA and decreased in the following order: DOM > HLS > SHS. Cd was most effectively removed during the first hours of flushing, and the process proceeded with a first-order kinetics. The overall process efficiency was higher at flow rate of 1.0 mL/min than at 0.5 mL/min and ranged from 65.7 (SHS) to 75.5% (DOM) for the sandy clay loam and from 64.7% (SHS) to 67.8% (DOM) for the clay. However, all SS_WAs at both flow rates removed the most mobile Cd fraction (F1) with an efficiency above 90%. Flushing improved soil characteristics in terms of the content of organic matter, humic substances and nutrients. Among all SS_WAs, DOM was the most suitable for remediation of highly Cd-contaminated soils due to high efficiency of Cd removal, the high stability of its FV during flushing and the simple manner of DOM recovery from sewage sludge. [ABSTRACT FROM AUTHOR]

Precise estimation of total organic carbon (TOC) is extremely important for the successful characterization of an unconventional shale reservoir. Indirect traditional continuous TOC prediction methods from well-logs fail to provide accurate TOC in complex and heterogeneous shale reservoirs. A workflow is proposed to predict a continuous TOC profile from well-logs through various ensemble learning regression models in the Goldwyer shale formation of the Canning Basin, WA. A total of 283 TOC data points from ten wells is available from the Rock-Eval analysis of the core specimen where each sample point contains three to five petrophysical logs. The core TOC varies largely, ranging from 0.16 wt % to 4.47 wt % with an average of 1.20 wt %. In addition to the conventional MLR method, four supervised machine learning methods, i.e., ANN, RF, SVM, and GB are trained, validated, and tested for continuous TOC prediction using the ensemble learning approach. To ensure robust TOC prediction, an aggregated model predictor is designed by combining the four ensemble-based models. The model achieved estimation accuracy with R2 value of 87%. Careful data preparation and feature selection, reconstruction of corrupted or missing logs, and the ensemble learning implementation and optimization have improved TOC prediction accuracy significantly compared to a single model approach. [ABSTRACT FROM AUTHOR]

Polyoxymethylene dimethyl ethers (OMEn) are frequently discussed as alternative diesel fuels, with various synthesis routes considered. OME3–5 syntheses demand significant amounts of thermal energy due to the complex separation processes that they entail. Therefore, innovative process designs are needed. An important tool for the development of new processes is process simulation software. To ensure sound process simulations, reliable physico-chemical models and component property data are necessary. Herein we present the implementation of a state-of-the-art thermodynamic model to describe the component systems of formaldehyde-water and formaldehyde-methanol using Microsoft® Excel (2010, Microsoft Corp, Redmond, WA, USA) and Aspen Plus®, (V8.8, Aspen Tech, Bedford, MA, USA) determine the deviation between the calculated results and experimental literature data, and minimize the deviation by means of parameter fitting. To improve the accuracy of the estimation of the missing property data of hemiformals and methylene glycols formed from formaldehyde using group contribution methods, the normal boiling points were estimated based on molecular analogies. The boiling points of OME6-10 are determined through parameter regression in accordance with the vapor pressure equation. As an application example, an optimization of the product separation of the state-of-the-art formaldehyde synthesis is presented that helps decrease the losses of methanol and formaldehyde in flue gas and wastewater. [ABSTRACT FROM AUTHOR]

Porosity and pore size distribution (PSD) are essential petrophysical parameters controlling permeability and storage capacity in shale gas reservoirs. Various techniques to assess pore structure have been introduced; nevertheless, discrepancies and inconsistencies exist between each of them. This study compares the porosity and PSD in two different shale formations, i.e., the clay-rich Permian Carynginia Formation in the Perth Basin, Western Australia, and the clay-poor Monterey Formation in San Joaquin Basin, USA. Porosity and PSD have been interpreted based on nuclear magnetic resonance (NMR), low-pressure N2 gas adsorption (LP-N2-GA), mercury intrusion capillary pressure (MICP) and helium expansion porosimetry. The results highlight NMR with the advantage of detecting the full-scaled size of pores that are not accessible by MICP, and the ineffective/closed pores occupied by clay bound water (CBW) that are not approachable by other penetration techniques (e.g., helium expansion, low-pressure gas adsorption and MICP). The NMR porosity is largely discrepant with the helium porosity and the MICP porosity in clay-rich Carynginia shales, but a high consistency is displayed in clay-poor Monterey shales, implying the impact of clay contents on the distinction of shale pore structure interpretations between different measurements. Further, the CBW, which is calculated by subtracting the measured effective porosity from total porosity, presents a good linear correlation with the clay content (R2 = 0.76), implying that our correlated equation is adaptable to estimate the CBW in shale formations with the dominant clay type of illite. [ABSTRACT FROM AUTHOR]