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The aim of this study was to analyze whether, and if so, in what way risks would influence the design,costs and routing of CO2pipelines. This article assesses locational and societal risks of CO2pipelinetransport and analyses whether rerouting or implementing additional risk mitigation measures is themost cost-effective option. The models EFFECTS and RISKCURVES are used to estimate the dispersion andrisk, respectively. The pipeline routes are optimized by using the least cost path function in ArcGIS.This article evaluates three case studies in the Netherlands. The results show that pipelines transportingdense phase CO2(8–17 MPa) with a minimal amount of risk mitigation measures already meet the 10−6locational risk required in the Netherlands. 10−6locational risks of 135 m are calculated for intermediatepumping stations, handling 450 kg CO2/s (about 14 Mt CO2/year). In all the cases, pumping stations couldbe located along the pipeline route without any problem.For the cases studied transporting gaseous CO2(1.5–3 MPa) leads to larger 10−6locational risk distancesthan transporting dense phase CO2. This is caused by the large momentum behind a dense phase CO2release, leading to smaller but higher jet and to a higher mixing rate with the surrounding air than for agaseous CO2release.Based on our analysis, it can be concluded that dense phase CO2transport is safe if it is well organized.The risks are manageable and widely accepted under current legislation. In addition, risk mitigationmeasures, like marker tape and increased surveillance, are available which reduce the risk significantlyand increase the costs only slightly. Pipeline routing for gaseous CO2transport appears more challengingin densely populated areas, because larger safety zones are attached to it.

The aim of this study was to analyze whether, and if so, in what way risks would influence the design,costs and routing of CO2pipelines. This article assesses locational and societal risks of CO2pipelinetransport and analyses whether rerouting or implementing additional risk mitigation measures is themost cost-effective option. The models EFFECTS and RISKCURVES are used to estimate the dispersion andrisk, respectively. The pipeline routes are optimized by using the least cost path function in ArcGIS.This article evaluates three case studies in the Netherlands. The results show that pipelines transportingdense phase CO2(8–17 MPa) with a minimal amount of risk mitigation measures already meet the 10−6locational risk required in the Netherlands. 10−6locational risks of 135 m are calculated for intermediatepumping stations, handling 450 kg CO2/s (about 14 Mt CO2/year). In all the cases, pumping stations couldbe located along the pipeline route without any problem.For the cases studied transporting gaseous CO2(1.5–3 MPa) leads to larger 10−6locational risk distancesthan transporting dense phase CO2. This is caused by the large momentum behind a dense phase CO2release, leading to smaller but higher jet and to a higher mixing rate with the surrounding air than for agaseous CO2release.Based on our analysis, it can be concluded that dense phase CO2transport is safe if it is well organized.The risks are manageable and widely accepted under current legislation. In addition, risk mitigationmeasures, like marker tape and increased surveillance, are available which reduce the risk significantlyand increase the costs only slightly. Pipeline routing for gaseous CO2transport appears more challengingin densely populated areas, because larger safety zones are attached to it.

© 2022 IEEE.The stochastic charging behaviors of Electric Vehicle (EV) users illustrate the negative effects of bulk charging during peak hours on the grid. To overcome this problem, the bulk EV charging demand forecasting approach is investigated using historical EV charge demand dataset and EV driver mobility statictics in this paper. In this model, a Monte Carlo Simulation (MCS) is perfomed that considers the charging behavior of EV users for the generation of EV charging times. Moreover, the EV charging times are combined with the bulk EV demand hybrid forecasting model using decomposition and deep learning time series method. In first stage, the EV demand time series dataset are divided to improve the model performance by empirical mode decomposition (EMD). Then, all decomposed signals are forecasted separately using the Bayesian optimized Long Short-Term Memory LSTM network (BO-LSTM). Finally, to evaluate the model perfomance, the power system analysis using IEEE 33 busbar test system is performed in terms of distribution network power losses, busbar voltage drops and transformer loading conditions.

© 2022 IEEE.The stochastic charging behaviors of Electric Vehicle (EV) users illustrate the negative effects of bulk charging during peak hours on the grid. To overcome this problem, the bulk EV charging demand forecasting approach is investigated using historical EV charge demand dataset and EV driver mobility statictics in this paper. In this model, a Monte Carlo Simulation (MCS) is perfomed that considers the charging behavior of EV users for the generation of EV charging times. Moreover, the EV charging times are combined with the bulk EV demand hybrid forecasting model using decomposition and deep learning time series method. In first stage, the EV demand time series dataset are divided to improve the model performance by empirical mode decomposition (EMD). Then, all decomposed signals are forecasted separately using the Bayesian optimized Long Short-Term Memory LSTM network (BO-LSTM). Finally, to evaluate the model perfomance, the power system analysis using IEEE 33 busbar test system is performed in terms of distribution network power losses, busbar voltage drops and transformer loading conditions.

© 2022 IEEE.The uncoordinated charging of Electric Vehicles (EVs) into the grid increases the stochastic rebound peak on the grid. These charging demands can strain grid equipment at the street charging points in an area. In this study, a smart coordination approach is proposed for charging process management by considering the parking times of EVs. EV types with different characteristics are used in the smart coordination approach. This approach limits the charging powers to the minimum value between the charging point and the EV maximum power ratio. Also, the approach using quadratic programming (QP) for charge scheduling of 20 EV minimizes the cost of daily charging via the Generic Algebraic Modeling System (GAMS). The results show that EV charges occur within the maximum allowable grid limits, reducing the cost of charging. Additionally, the proposed smart coordination prevented the occurrence of daily on-grid rebound peaks at street charging points in the area.

© 2022 IEEE.The uncoordinated charging of Electric Vehicles (EVs) into the grid increases the stochastic rebound peak on the grid. These charging demands can strain grid equipment at the street charging points in an area. In this study, a smart coordination approach is proposed for charging process management by considering the parking times of EVs. EV types with different characteristics are used in the smart coordination approach. This approach limits the charging powers to the minimum value between the charging point and the EV maximum power ratio. Also, the approach using quadratic programming (QP) for charge scheduling of 20 EV minimizes the cost of daily charging via the Generic Algebraic Modeling System (GAMS). The results show that EV charges occur within the maximum allowable grid limits, reducing the cost of charging. Additionally, the proposed smart coordination prevented the occurrence of daily on-grid rebound peaks at street charging points in the area.

A mesocosm approach was used to investigate the effects of ocean acidification (OA) on a natural plankton community in coastal waters off Norway by manipulating CO2 partial pressure (pCO2). Eight enclosures were deployed in the Raunefjord near Bergen. Treatment levels were ambient (~320 µatm) and elevated pCO2 (~2000 µatm), each in 4 replicate enclosures. The experiment lasted for 53 d in May-June 2015. To assess impacts of OA on the plankton community, phytoplankton and protozooplankton biomass and total seston fatty acid content were analyzed. In both treatments, the plankton community was dominated by the dinoflagellate Ceratium longipes. In the elevated pCO2 treatment, however, biomass of this species as well as that of other dinoflagellates was strongly negatively affected. At the end of the experiment, total dinoflagellate biomass was 4-fold higher in the control group than under elevated pCO2 conditions. In a size comparison of C. longipes, cell size in the high pCO2 treatment was significantly larger. The ratio of polyunsaturated fatty acids to saturated fatty acids of seston decreased at high pCO2. In particular, the concentration of docosahexaenoic acid (C 22:6n3c), essential for development and reproduction of metazoans, was less than half at high pCO2 compared to ambient pCO2. Thus, elevated pCO2 led to a deterioration in the quality and quantity of food in a natural plankton community, with potential consequences for the transfer of matter and energy to higher trophic levels

A mesocosm approach was used to investigate the effects of ocean acidification (OA) on a natural plankton community in coastal waters off Norway by manipulating CO2 partial pressure (pCO2). Eight enclosures were deployed in the Raunefjord near Bergen. Treatment levels were ambient (~320 µatm) and elevated pCO2 (~2000 µatm), each in 4 replicate enclosures. The experiment lasted for 53 d in May-June 2015. To assess impacts of OA on the plankton community, phytoplankton and protozooplankton biomass and total seston fatty acid content were analyzed. In both treatments, the plankton community was dominated by the dinoflagellate Ceratium longipes. In the elevated pCO2 treatment, however, biomass of this species as well as that of other dinoflagellates was strongly negatively affected. At the end of the experiment, total dinoflagellate biomass was 4-fold higher in the control group than under elevated pCO2 conditions. In a size comparison of C. longipes, cell size in the high pCO2 treatment was significantly larger. The ratio of polyunsaturated fatty acids to saturated fatty acids of seston decreased at high pCO2. In particular, the concentration of docosahexaenoic acid (C 22:6n3c), essential for development and reproduction of metazoans, was less than half at high pCO2 compared to ambient pCO2. Thus, elevated pCO2 led to a deterioration in the quality and quantity of food in a natural plankton community, with potential consequences for the transfer of matter and energy to higher trophic levels