scholarly journals Peak load minimization of an e-bus depot: impacts of user-set conditions in optimization algorithms

2021 ◽  
Vol 4 (S3) ◽  
Author(s):  
Enrico Toniato ◽  
Prakhar Mehta ◽  
Stevan Marinkovic ◽  
Verena Tiefenbeck
Keyword(s):  
Optimization Algorithm ◽  
Peak Load ◽  
Mixed Integer ◽  
Computational Time ◽  
Transport Sector ◽  
Fleet Management ◽  
Peak Reduction ◽  
Management Options ◽  
Power Load ◽  
The Impact

AbstractThe transport sector is responsible for 25% of global CO2 emissions. To reduce emissions in the EU, a shift from the currently 745,000 operating public buses to electric buses (EBs) is expected in the coming years. Large-scale deployments of EBs and the electrification of bus depots will have a considerable impact on the local electric grid, potentially creating network congestion problems and spikes in the local energy load. In this work, we implement an exact, offline, modular multi-variable mixed-integer linear optimization algorithm to minimize the daily power load profile peak and optimally plan an electric bus depot. The algorithm accepts a bus depot schedule as input, and depending on the user input on optimization conditions, accounts for varying time granularity, preemption of the charging phase, vehicle-to-grid (V2G) charging capabilities and varying fleet size. The primary objective of this work is the analysis of the impact of each of these input conditions on the resulting minimized peak load. The results show that our optimization algorithm can reduce peak load by 83% on average. Time granularity and V2G have the greatest impact on peak reduction, whereas preemption and fleet splitting have the greatest impact on the computational time but an insignificant impact on peak reduction. The results bear relevance for mobility planners to account for innovative fleet management options. Depot infrastructure costs can be minimized by optimally sizing the infrastructure needs, by relying on split-fleet management or V2G options.

scholarly journals Stochastic Optimization for Network-Constrained Power System Scheduling Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
D. F. Teshome ◽  
P. F. Correia ◽  
K. L. Lian
Keyword(s):  
Power System ◽  
Load Shedding ◽  
Mixed Integer ◽  
Piecewise Linear Approximation ◽  
Computational Time ◽  
Forecast Errors ◽  
Scheduling Problem ◽  
Generation System ◽  
Network Constraints ◽  
The Impact

The stochastic nature of demand and wind generation has a considerable effect on solving the scheduling problem of a modern power system. Network constraints such as power flow equations and transmission capacities also need to be considered for a comprehensive approach to model renewable energy integration and analyze generation system flexibility. Firstly, this paper accounts for the stochastic inputs in such a way that the uncertainties are modeled as normally distributed forecast errors. The forecast errors are then superimposed on the outputs of load and wind forecasting tools. Secondly, it efficiently models the network constraints and tests an iterative algorithm and a piecewise linear approximation for representing transmission losses in mixed integer linear programming (MILP). It also integrates load shedding according to priority factors set by the system operator. Moreover, the different interactions among stochastic programming, network constraints, and prioritized load shedding are thoroughly investigated in the paper. The stochastic model is tested on a power system adopted from Jeju Island, South Korea. Results demonstrate the impact of wind speed variability and network constraints on the flexibility of the generation system. Further analysis shows the effect of loss modeling approaches on total cost, accuracy, computational time, and memory requirement.

Energy Impacts of Nonlinear Behavior of Phase Change Materials When Applied to Opaque Building Envelopes

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Paulo Cesar Tabares-Velasco
Keyword(s):  
Phase Change ◽  
Energy Use ◽  
Phase Change Materials ◽  
Energy Savings ◽  
Peak Load ◽  
Building Energy ◽  
Energy Simulation ◽  
Building Energy Simulation ◽  
Peak Reduction ◽  
The Impact

Research on phase change materials (PCM) as a potential technology to reduce peak loads and heating, ventilation and air conditioning (HVAC) energy use in buildings has been conducted for several decades, resulting in a great deal of literature on PCM properties, temperature, and peak reduction potential. However, there are few building energy simulation programs that include PCM modeling features, and very few of these have been validated. Additionally, there is no previous research that indicates the level of accuracy when modeling PCMs from a building energy simulation perspective. This study analyzes the effects a nonlinear enthalpy profile has on thermal performance and expected energy benefits for PCM-enhanced insulation. The impact of accurately modeling realistic, nonlinear enthalpy profiles for PCMs versus simpler profiles is analyzed based on peak load reduction and energy savings using the conduction finite difference (CondFD) algorithm in EnergyPlus. The PCM and CondFD models used in this study have been previously validated after intensive verification and validation done at the National Renewable Energy Laboratory. Overall, the results of this study show annual energy savings are not very sensitive to the linearization of enthalpy curve. However, hourly analysis shows that if simpler linear profiles are used, users should try to specify a melting range covering roughly 80% of the latent heat; otherwise, hourly results can differ by up to 20%.

scholarly journals A COVID-19 Public Transport Frequency Setting Model That Includes Short-Turning Options

2021 ◽  
Vol 1 (1) ◽  
pp. 3-20
Author(s):  
Yoran de Weert ◽  
Konstantinos Gkiotsalitis
Keyword(s):  
Public Transport ◽  
Mixed Integer ◽  
Transport Sector ◽  
Quadratic Program ◽  
High Demand ◽  
Social Distancing ◽  
The Public ◽  
Passenger Demand ◽  
Transport Services ◽  
The Impact

The COVID-19 pandemic has had an enormous impact on the public transport sector. After the start of the pandemic, passenger demand dropped significantly for public transport services. In addition, social distancing measures have resulted in introducing pandemic-imposed capacity limitations to public transport vehicles. Consequently, public transport operators should adjust their planning to minimize the impact of the COVID-19 pandemic. This study introduces a mixed-integer quadratic program that sets the optimal frequencies of public transport lines and sublines in order to conform with the pandemic-imposed capacity. The focus is on cases where the public transport demand is high, but the crowding levels inside public transport vehicles should remain below the pandemic-imposed capacities. Of particular interest are public transport lines with skewed demand profiles that can benefit from the introduction of short-turning sublines that serve the high-demand line segments. The frequency setting model is tested on a network containing two high-demand bus lines in the Twente region in the Netherlands, and it demonstrates that the revenue losses due to social distancing can be reduced when implementing short-turning service patterns.

Energy Impacts of Nonlinear Behavior of PCM When Applied Into Building Envelope

2012 ◽  
Author(s):  
Paulo Cesar Tabares-Velasco
Keyword(s):  
Energy Use ◽  
Phase Change Materials ◽  
Energy Savings ◽  
Peak Load ◽  
Building Energy ◽  
Building Envelope ◽  
Energy Simulation ◽  
Building Energy Simulation ◽  
Peak Reduction ◽  
The Impact

Research on phase change materials (PCM) as a potential technology to reduce peak loads and HVAC energy use in buildings has been conducted for several decades, resulting in a great deal of literature on PCM properties, temperature, and peak reduction potential. However, there are few building energy simulation programs that include PCM modeling features, and very few of these have been validated. Additionally, there is no previous research that indicates the level of accuracy when modeling PCMs from a building energy simulation perspective. This study analyzes the effects a nonlinear enthalpy profile has on thermal performance and expected energy benefits for PCM-enhanced insulation. The impact of accurately modeling realistic, nonlinear enthalpy profiles for PCMs versus simpler profiles is analyzed based on peak load reduction and energy savings using the Conduction Finite Difference (CondFD) algorithm in EnergyPlus. The PCM and CondFD models used in this study have been previously validated after intensive verification and validation done at the National Renewable Energy Laboratory. Overall, the results of this study show annual energy savings are not very sensitive to the linearization of enthalpy curve. However, hourly analysis shows that if simpler linear profiles are used, users should try to specify a melting range covering roughly 80% of the latent heat, otherwise, hourly results can differ by up to 20%.

Maternal-Fetal Management in Trombophilia Related and Placenta-Mediated Pregnancy Complications

2018 ◽  
Vol 69 (9) ◽  
pp. 2396-2401
Author(s):  
Costin Berceanu ◽  
Elena Loredana Ciurea ◽  
Monica Mihaela Cirstoiu ◽  
Sabina Berceanu ◽  
Anca Maria Ofiteru ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Pregnancy Complications ◽  
Case Control Study ◽  
Case Control ◽  
Placental Insufficiency ◽  
Management Options ◽  
Specific Therapy ◽  
Pregnancy Morbidity ◽  
The Impact ◽  
Control Study

It is widely accepted that thrombophilia in pregnancy greatly increases the risk of venous thromboembolism. Pregnancy complications arise, at least partly, from placental insufficiency. Any change in the functioning of the gestational transient biological system, such as inherited or acquired thrombophilia, might lead to placental insufficiency. In this research we included 64 pregnant women with trombophilia and 70 cases non-trombophilic pregnant women, with or without PMPC, over a two-year period. The purpose of this multicenter case-control study is to analyze the maternal-fetal management options in obstetric thrombophilia, the impact of this pathology on the placental structure and possible correlations with placenta-mediated pregnancy complications. Maternal-fetal management in obstetric thrombophilia means preconceptional or early diagnosis, prevention of pregnancy morbidity, specific therapy as quickly as possible and fetal systematic surveilance to identify the possible occurrence of placenta-mediated pregnancy complications.

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 723-728 ◽  
Author(s):  
Chenhao Niu ◽  
Xiaomin Xu ◽  
Yan Lu ◽  
Mian Xing
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Load Forecasting ◽  
Optimal Solution ◽  
Electric Power System ◽  
Local Optimum ◽  
Good Prediction ◽  
Ant Colony Optimization Algorithm ◽  
The Neural Network ◽  
Power Load

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

scholarly journals Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1250
Author(s):  
Daniel Medina ◽  
Haoqing Li ◽  
Jordi Vilà-Valls ◽  
Pau Closas
Keyword(s):  
Intelligent Transportation Systems ◽  
Robust Statistics ◽  
Real Data ◽  
Transportation Systems ◽  
Global Navigation Satellite Systems ◽  
Mixed Integer ◽  
Robust Filtering ◽  
Precise Positioning ◽  
The Impact ◽  
Filtering Techniques

Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness.

scholarly journals Prognostic implications of troponin T variations in inherited cardiomyopathies using systems biology

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Rameen Shakur ◽  
Juan Pablo Ochoa ◽  
Alan J. Robinson ◽  
Abhishek Niroula ◽  
Aneesh Chandran ◽  
...  
Keyword(s):  
Heart Failure ◽  
Systems Biology ◽  
Sudden Cardiac Death ◽  
Risk Stratification ◽  
Clinical Data ◽  
Cardiac Death ◽  
Troponin T ◽  
Management Options ◽  
Familial Cardiomyopathy ◽  
The Impact

AbstractThe cardiac troponin T variations have often been used as an example of the application of clinical genotyping for prognostication and risk stratification measures for the management of patients with a family history of sudden cardiac death or familial cardiomyopathy. Given the disparity in patient outcomes and therapy options, we investigated the impact of variations on the intermolecular interactions across the thin filament complex as an example of an unbiased systems biology method to better define clinical prognosis to aid future management options. We present a novel unbiased dynamic model to define and analyse the functional, structural and physico-chemical consequences of genetic variations among the troponins. This was subsequently integrated with clinical data from accessible global multi-centre systematic reviews of familial cardiomyopathy cases from 106 articles of the literature: 136 disease-causing variations pertaining to 981 global clinical cases. Troponin T variations showed distinct pathogenic hotspots for dilated and hypertrophic cardiomyopathies; considering the causes of cardiovascular death separately, there was a worse survival in terms of sudden cardiac death for patients with a variation at regions 90–129 and 130–179 when compared to amino acids 1–89 and 200–288. Our data support variations among 90–130 as being a hotspot for sudden cardiac death and the region 131–179 for heart failure death/transplantation outcomes wherein the most common phenotype was dilated cardiomyopathy. Survival analysis into regions of high risk (regions 90–129 and 130–180) and low risk (regions 1–89 and 200–288) was significant for sudden cardiac death (p = 0.011) and for heart failure death/transplant (p = 0.028). Our integrative genomic, structural, model from genotype to clinical data integration has implications for enhancing clinical genomics methodologies to improve risk stratification.

scholarly journals The Impact of Energy Storage along with the Allocation of RES on the Reduction of Energy Costs Using MILP

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3783
Author(s):  
Mateusz Andrychowicz
Keyword(s):  
Energy Storage ◽  
Mixed Integer Linear Programming ◽  
Distribution Systems ◽  
Operating Costs ◽  
Mixed Integer ◽  
Energy Costs ◽  
Power Exchange ◽  
Local Initiatives ◽  
One Year ◽  
The Impact

The paper shows a method of optimizing local initiatives in the energy sector, such as energy cooperatives and energy clusters. The aim of optimization is to determine the structure of generation sources and energy storage in order to minimize energy costs. The analysis is carried out for the time horizon of one year, with an hourly increment, taking into account various RES (wind turbines (WT), photovoltaic installations (PV), and biogas power plant (BG)) and loads (residential, commercial, and industrial). Generation sources and loads are characterized by generation/demand profiles in order to take into account their variability. The optimization was carried out taking into account the technical aspects of the operation of distribution systems, such as power flows and losses, voltage levels in nodes, and power exchange with the transmission system, and economic aspects, such as capital and fixed and variable operating costs. The method was calculated by sixteen simulation scenarios using Mixed-Integer Linear Programming (MILP).

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