scholarly journals Energy System Models for City Climate Mitigation Plans—Challenges and Recommendations

2021 ◽  
pp. 15-28
Author(s):  
Burcu Unluturk ◽  
Anna Krook-Riekkola
Keyword(s):  
National Level ◽  
Model Development ◽  
System Optimization ◽  
Energy System ◽  
Civil Servants ◽  
Data Availability ◽  
Climate Mitigation ◽  
Scenario Development ◽  
Energy Transitions ◽  
Climate Action

AbstractMany cities around the world have adopted climate neutrality targets, and, to reduce their greenhouse gas emissions, they need climate action plans. Energy system optimization models (ESOMs) can be used as tools to support their energy transitions. ESOMs have been in use at the national level for several years and also have recently been used at the city level. Even though several researchers have focused on how city ESOMs can be developed, the literature lacks a discussion of the challenges that are faced in data collection during model development. In this paper, we share the challenges encountered in the model development, as well as in the scenario development and recommend practical solutions for overcoming these challenges. The following three challenges were identified and discussed in the model development process: (a) data availability and quality; (b) communication; and (c) knowledge and background of civil servants and researchers. The main challenges in the scenario development were: (a) parameter selection and (b) complexity. It was found that explanation of the terminology used in ESOMs, presentation of the model structure and preliminary base-year results were crucial actions for overcoming challenges during model development. During the scenario development, collaboration between modelers and civil servants when reviewing parameter combinations and working with preliminary scenario results were decisive strategies for improving the civil servants’ understanding of ESOMs. Complementarily, it was found that continuous communication between the researcher and the civil servant and good comprehension of the model on the municipality's side helped improve the usefulness of ESOMs in cities’ energy transitions.

scholarly journals Foregrounding the community: Geo-historical entanglements of community energy, environmental justice, and place in Taihsi Village, Taiwan

2021 ◽  
pp. 251484862110007
Author(s):  
Huei-Ling Lai
Keyword(s):  
Environmental Justice ◽  
National Level ◽  
Energy System ◽  
Green Energy ◽  
Level Energy ◽  
Disadvantaged Communities ◽  
Energy Transitions ◽  
Health Community ◽  
Community Initiative ◽  
Energy Justice

Studies on community energy have generated many useful insights concerning its potentials and challenges in facilitating energy transitions. However, this line of inquiry tends to overlook the crucial significance of site-specific contexts, concerns, and needs beyond the energy system and often generalizes these under a “civil society” umbrella. To study community energy on its own terms, this paper proposes a more grounded approach based on the relational place-making framework. It draws upon the case of the Taihsi Green Energy and Health Community Initiative in Taiwan to investigate how the emergence, development, and framing of this initiative are entangled with geo-historically produced concerns about the village’s socio-economic marginalization and suffering from petrochemical pollution. The findings suggest that community energy in this context was a proactive continuation of place-based activism for environmental justice; its value to this damaged community lied in its potential to create self-reliant socio-material relations alternative to those relied on the patronage of petrochemical interests. However, this justice-oriented aspiration tended to be discounted in national-level energy transitions agenda, revealing a tension between citizen-oriented and community-based energy projects. The paper argues that a relational place-based analysis is crucial in recognizing the grounded meanings and values of a community energy initiative, which can address the decontextualizing tendency in many community energy studies to better help policymakers and advocates enhance energy justice in disadvantaged communities.

Analysis of Land Use Land Cover in Annapurna Conservation Area in Gandaki Province, Nepal using Vegetation Index and Land Use Land Cover Datasets

2019 ◽  
Vol 2 (2) ◽  
pp. 87-99
Author(s):  
Shiva Pokhrel ◽  
Chungla Sherpa
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Index ◽  
National Level ◽  
Climate Mitigation ◽  
Land Use Land Cover ◽  
Conservation Area ◽  
Barren Land ◽  
Wide Range ◽  
Annapurna Conservation Area

Conservation areas are originally well-known for protecting landscape features and wildlife. They are playing key role in conserving and providing a wide range of ecosystem services, social, economic and cultural benefits as well as vital places for climate mitigation and adaptation. We have analyzed decadal changes in land cover and status of vegetation cover in the conservation area using both national level available data on land use land cover (LULC) changes (1990-2010) and normalized difference vegetation index (NDVI) (2010-2018) in Annapurna conservation area. LULC showed the barren land as the most dominant land cover types in all three different time series 1990, 2000 and 2010 with followed by snow cover, grassland, forest, agriculture and water body. The highest NDVI values were observed at Southern, Southwestern and Southeastern part of conservation area consisting of forest area, shrub land and grassland while toward low to negative in the upper middle to the Northern part of the conservation area.

scholarly journals Optimal Sizing of an Island Hybrid Microgrid Based on Improved Multi-Objective Grey Wolf Optimizer

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1581
Author(s):  
Wenqiang Zhu ◽  
Jiang Guo ◽  
Guo Zhao ◽  
Bing Zeng
Keyword(s):  
Power Supply ◽  
Tidal Current ◽  
System Optimization ◽  
Energy System ◽  
Energy Sources ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Hybrid Energy System ◽  
Hybrid Energy ◽  
Multi Objective

The hybrid renewable energy system is a promising and significant technology for clean and sustainable island power supply. Among the abundant ocean energy sources, tidal current energy appears to be very valuable due to its excellent predictability and stability, particularly compared with the intermittent wind and solar energy. In this paper, an island hybrid energy microgrid composed of photovoltaic, wind, tidal current, battery and diesel is constructed according to the actual energy sources. A sizing optimization method based on improved multi-objective grey wolf optimizer (IMOGWO) is presented to optimize the hybrid energy system. The proposed method is applied to determine the optimal system size, which is a multi-objective problem including the minimization of annualized cost of system (CACS) and deficiency of power supply probability (DPSP). MATLAB software is utilized to program and simulate the hybrid energy system. Optimization results confirm that IMOGWO is feasible to optimally size the system, and the energy management strategy effectively matches the requirements of system operation. Furthermore, comparison of hybrid systems with and without tidal current turbines is undertaken to confirm that the utilization of tidal current turbines can contribute to enhancing system reliability and reducing system investment, especially in areas with abundant tidal energy sources.

scholarly journals Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1044
Author(s):  
Yassine Bouabdallaoui ◽  
Zoubeir Lafhaj ◽  
Pascal Yim ◽  
Laure Ducoulombier ◽  
Belkacem Bennadji
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Predictive Maintenance ◽  
Data Availability ◽  
Machine Learning Techniques ◽  
Automation System ◽  
Building Maintenance ◽  
Collection Data ◽  
Iot Devices

The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.

scholarly journals An interrupted time series analysis of the lockdown policies in India: a national-level analysis of COVID-19 incidence

2021 ◽  
Author(s):  
Winter M Thayer ◽  
Md Zabir Hasan ◽  
Prithvi Sankhla ◽  
Shivam Gupta
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Incidence Rate ◽  
Case Reports ◽  
National Level ◽  
Model Development ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Additional Time ◽  
Series Analysis

Abstract India implemented a national mandatory lockdown policy (Lockdown 1.0) on 24 March 2020 in response to Coronavirus Disease 2019 (COVID-19). The policy was revised in three subsequent stages (Lockdown 2.0–4.0 between 15 April to 18 May 2020), and restrictions were lifted (Unlockdown 1.0) on 1 June 2020. This study evaluated the effect of lockdown policy on the COVID-19 incidence rate at the national level to inform policy response for this and future pandemics. We conducted an interrupted time series analysis with a segmented regression model using publicly available data on daily reported new COVID-19 cases between 2 March 2020 and 1 September 2020. National-level data from Google Community Mobility Reports during this timeframe were also used in model development and robustness checks. Results showed an 8% [95% confidence interval (CI) = 6–9%] reduction in the change in incidence rate per day after Lockdown 1.0 compared to prior to the Lockdown order, with an additional reduction of 3% (95% CI = 2–3%) after Lockdown 4.0, suggesting an 11% (95% CI = 9–12%) reduction in the change in COVID-19 incidence after Lockdown 4.0 compared to the period before Lockdown 1.0. Uptake of the lockdown policy is indicated by decreased mobility and attenuation of the increasing incidence of COVID-19. The increasing rate of incident case reports in India was attenuated after the lockdown policy was implemented compared to before, and this reduction was maintained after the restrictions were eased, suggesting that the policy helped to ‘flatten the curve’ and buy additional time for pandemic preparedness, response and recovery.

scholarly journals Pathways of China's PM2.5 air quality 2015–2060 in the context of carbon neutrality

2021 ◽  
Author(s):  
Jing Cheng ◽  
Dan Tong ◽  
Qiang Zhang ◽  
Yang Liu ◽  
Yu Lei ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Critical Role ◽  
Climate Mitigation ◽  
Air Pollution Control ◽  
Carbon Neutrality ◽  
Climate Action ◽  
Clean Air ◽  
Near Term ◽  
Who Guideline

ABSTRACT Clean air policies in China have substantially reduced PM2.5 air pollution in recent years, primarily by curbing end-of-pipe emissions. However, further reaching the WHO guideline may instead depend upon the air quality co-benefits of ambitious climate action. Here, we assess pathways of Chinese PM2.5 air quality from 2015 to 2060 under a combination of scenarios which link Global and China's climate mitigation pathways (i.e. global 2°C- and 1.5°C-pathways, NDC pledges, and carbon neutrality goals) to local clean air policies. We find that China can achieve both its near-term climate goals (peak emissions) and PM2.5 air quality annual standard (35 μg/m3) by 2030 by fulfilling its NDC pledges and continuing air pollution control policies. However, the benefits of end-of-pipe control reductions are mostly exhausted by 2030, and reducing PM2.5 exposure of the majority of the Chinese population to below 10 μg/m3 by 2060 will likely require more ambitious climate mitigation efforts such as China's carbon neutrality goals and global 1.5°C-pathways. Our results thus highlight that China's carbon neutrality goals will play a critical role in reducing air pollution exposure to the WHO guideline and protecting public health.

Hybrid energy system optimization with battery storage for remote area application considering loss of energy probability and economic analysis

Energy ◽  
2021 ◽  
pp. 122303
Author(s):  
Amirreza Naderipour ◽  
Amir Reza Ramtin ◽  
Aldrin Abdullah ◽  
Massoomeh Hedayati Marzbali ◽  
Saber Arabi Nowdeh ◽  
...  
Keyword(s):  
Economic Analysis ◽  
System Optimization ◽  
Energy System ◽  
Remote Area ◽  
Battery Storage ◽  
Hybrid Energy System ◽  
Hybrid Energy

Selected ‘Starter Kit’ energy system modelling data for Paraguay (#CCG)

2021 ◽  
Author(s):  
Carla Cannone ◽  
Lucy Allington ◽  
Ioannis Pappis ◽  
Karla Cervantes Barron ◽  
Will Usher ◽  
...  
Keyword(s):  
Model Development ◽  
Energy System ◽  
Simple Zero ◽  
System Model ◽  
System Modelling ◽  
Local Data ◽  
Energy System Model ◽  
Starting Point ◽  
Modelling Studies ◽  
Access To Data

Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to energy system modelling, causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for Paraguay, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

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