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IJGI
Special Issues
Geospatial Electrification and Energy Access Planning
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Geospatial Electrification and Energy Access Planning

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 18858

Special Issue Editor

Dr. Dimitrios Mentis

E-Mail Website
Guest Editor
Senior Energy Geographer, Energy Program, World Resources Institute, Washington, DC 20002, USA
Interests: spatial analysis; GIS; energy access; energy systems analysis; electrification modeling

Special Issue Information

Dear Colleagues,

Modern energy services are proven to be a key enabler for socio-economic development and can upgrade the level and quality of health care, education, gender equality, indoor environments, and several daily activities. Nonetheless, life without power is a reality for about 790 million people. To effectively achieve universal energy access (SDG 7), policymakers, business leaders, and civil society need to access better data and effective planning tools that capture key attributes of the populations and institutions they are trying to reach. The importance of energy planning cannot be emphasized enough. Planning is requisite to match supply with the growing demand in the most cost-effective way and vital if we are to add fluctuating, decentralized, and cost-effective renewable energy production into the energy mix.

We need to better understand the needs of customers, whether households or institutions, to design viable electrification strategies. Additionally, these may vary from one location to another depending on several socio-economic characteristics, energy resource availability, and proximity to power infrastructure. However, such geospatial data have often been scarce, fragmented, or inconsistent, deteriorating their use for strategic planning in developing countries.

Papers in this Special issue might consider questions such as what is the likely cost-optimal electrification mix to reach universal energy access for different geographies; what are the spatially explicit associated investment and capacity needs; how do social and productive loads influence the electrification mix; how can we take into account geospatial aspects of demand, such as affordability and willingness to pay for energy services; is there a spatial correlation between energy access and development outcomes; what are the data guidelines and/or standards that geospatial electrification and energy access planning tools should be considering; and what is or can be the role of spatially explicit machine learning in electrification and energy access planning?

Dr. Dimitrios Mentis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electrification
  • energy access
  • SDG 7
  • productive uses of electricity
  • GIS
  • spatial analysis
  • energy planning
  • energy systems

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Published Papers (5 papers)

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Research

17 pages, 2751 KiB  
Article
Geo-Enabled Sustainable Municipal Energy Planning for Comprehensive Accessibility: A Case in the New Federal Context of Nepal
by Hari Krishna Dhonju, Bikash Uprety and Wen Xiao
ISPRS Int. J. Geo-Inf. 2022, 11(5), 304; https://doi.org/10.3390/ijgi11050304 - 10 May 2022
Cited by 3 | Viewed by 3134
Abstract
Energy is a fundamental need of modern society and a basis for economic and social development, and one of the major Sustainable Development Goals (SDG), particularly SDG7. However, the UN’s SDG Report 2021 betrays millions of people living without electricity and one-third of [...] Read more.
Energy is a fundamental need of modern society and a basis for economic and social development, and one of the major Sustainable Development Goals (SDG), particularly SDG7. However, the UN’s SDG Report 2021 betrays millions of people living without electricity and one-third of the world’s population deprived of using modern energy cooking services (MECS) through access to electricity. Achieving the SDG7 requires standard approaches and tools that effectively address the geographical, infrastructural, and socioeconomic characteristics of a (rural) municipality of Nepal. Furthermore, Nepal’s Constitution 2015 incorporated a federal system under the purview of a municipality as the local government that has been given the mandate to ensure electricity access and clean energy. To address this, a methodology is developed for local government planning in Nepal in order to identify the optimal mix of electrification options by conducting a detailed geospatial analysis of renewable energy (RE) technologies by exploring accessibility and availability ranging from grid extensions to mini-grid and off-grid solutions, based on (a) life cycle cost and (b) levelized cost of energy. During energy assessment, geospatial and socio-economic data are coupled with household and community level data collected from a mobile survey app, and are exploited to garner energy status-quo and enable local governments to assess the existing situation of energy access/availability and planning. In summary, this paper presents a geo-enabled municipal energy planning method and a comprehensive toolkit to facilitate sustainable energy access to local people. Full article
(This article belongs to the Special Issue Geospatial Electrification and Energy Access Planning)
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25 pages, 11366 KiB  
Article
Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning
by Simiao Ren, Jordan Malof, Rob Fetter, Robert Beach, Jay Rineer and Kyle Bradbury
ISPRS Int. J. Geo-Inf. 2022, 11(4), 222; https://doi.org/10.3390/ijgi11040222 - 24 Mar 2022
Cited by 10 | Viewed by 4584
Abstract
Solar home systems (SHS), a cost-effective solution for rural communities far from the grid in developing countries, are small solar panels and associated equipment that provides power to a single household. A crucial resource for targeting further investment of public and private resources, [...] Read more.
Solar home systems (SHS), a cost-effective solution for rural communities far from the grid in developing countries, are small solar panels and associated equipment that provides power to a single household. A crucial resource for targeting further investment of public and private resources, as well as tracking the progress of universal electrification goals, is shared access to high-quality data on individual SHS installations including information such as location and power capacity. Though recent studies utilizing satellite imagery and machine learning to detect solar panels have emerged, they struggle to accurately locate many SHS due to limited image resolution (some small solar panels only occupy several pixels in satellite imagery). In this work, we explore the viability and cost-performance tradeoff of using automatic SHS detection on unmanned aerial vehicle (UAV) imagery as an alternative to satellite imagery. More specifically, we explore three questions: (i) what is the detection performance of SHS using drone imagery; (ii) how expensive is the drone data collection, compared to satellite imagery; and (iii) how well does drone-based SHS detection perform in real-world scenarios? To examine these questions, we collect and publicly-release a dataset of high-resolution drone imagery encompassing SHS imaged under a variety of real-world conditions and use this dataset and a dataset of imagery from Rwanda to evaluate the capabilities of deep learning models to recognize SHS, including those that are too small to be reliably recognized in satellite imagery. The results suggest that UAV imagery may be a viable alternative to identify very small SHS from perspectives of both detection accuracy and financial costs of data collection. UAV-based data collection may be a practical option for supporting electricity access planning strategies for achieving sustainable development goals and for monitoring the progress towards those goals. Full article
(This article belongs to the Special Issue Geospatial Electrification and Energy Access Planning)
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22 pages, 6539 KiB  
Article
SmartEle: Smart Electricity Dashboard for Detecting Consumption Patterns: A Case Study at a University Campus
by Changfeng Jing, Shasha Guo, Hongyang Zhang, Xinxin Lv and Dongliang Wang
ISPRS Int. J. Geo-Inf. 2022, 11(3), 194; https://doi.org/10.3390/ijgi11030194 - 12 Mar 2022
Cited by 3 | Viewed by 4135
Abstract
To achieve Sustainable Development Goal 7 (SDG7), it is essential to detect the spatiotemporal patterns of electricity consumption, particularly the spatiotemporal heterogeneity of consumers. This is also crucial for rational energy planning and management. However, studies investigating heterogeneous users are lacking. Moreover, existing [...] Read more.
To achieve Sustainable Development Goal 7 (SDG7), it is essential to detect the spatiotemporal patterns of electricity consumption, particularly the spatiotemporal heterogeneity of consumers. This is also crucial for rational energy planning and management. However, studies investigating heterogeneous users are lacking. Moreover, existing works focuses on mathematic models to identify and predict electricity consumption. Additionally, owing to the complex non-linear interrelationships, interactive visualizations are more effective in detecting patterns. Therefore, by combining geospatial dashboard knowledge and interactive visualization technology, a Smart Electricity dashboard (SmartEle) was designed and developed to interactively visualize big electrical data and interrelated factors. A university campus as the study area. The SmartEle system addressed three challenges. First, it permitted user group-oriented monitoring of electricity consumption patterns, which has seldom been considered in existing studies. Second, a visualization-driven data mining model was proposed, and an interactive visualization dashboard was designed to facilitate the perception of electricity usage patterns at different granularities and from different perspectives. Finally, to deal with the non-linear features of electricity consumption, the ATT-LSTM machine learning model to support multivariate collaborative predicting was proposed to improve the accuracy of short-term electricity consumption predictions. The results demonstrated that the SmartEle system is usable for electricity planning and management. Full article
(This article belongs to the Special Issue Geospatial Electrification and Energy Access Planning)
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25 pages, 85347 KiB  
Article
A GIS-Based Approach to Estimate Electricity Requirements for Small-Scale Groundwater Irrigation
by Anna Nilsson, Dimitrios Mentis, Alexandros Korkovelos and Joel Otwani
ISPRS Int. J. Geo-Inf. 2021, 10(11), 780; https://doi.org/10.3390/ijgi10110780 - 15 Nov 2021
Cited by 6 | Viewed by 2915
Abstract
Access to modern energy services is a precondition to improving livelihoods and building resilience against climate change. Still, electricity reaches only about half of the population in Sub-Saharan Africa (SSA), while about 40% live under the poverty line. Heavily reliant on the agriculture [...] Read more.
Access to modern energy services is a precondition to improving livelihoods and building resilience against climate change. Still, electricity reaches only about half of the population in Sub-Saharan Africa (SSA), while about 40% live under the poverty line. Heavily reliant on the agriculture sector and increasingly affected by prolonged droughts, small-scale irrigation could be instrumental for development and climate change adaptation in SSA countries. A bottom-up understanding of the demand for irrigation and associated energy services is essential for designing viable energy supply options in an effective manner. Using Uganda as a case study, the study introduces a GIS-based methodology for the estimation of groundwater irrigation requirements through which energy demand is derived. Results are generated for two scenarios: (a) a reference scenario and (b) a drought scenario. The most critical need is observed in the northern and southern regions of the country. The total annual irrigation demand is estimated to be ca. 90 thousand m3, with the highest demand observed in the months of December through February, with an average irrigation demand of 445 mm per month. The highest energy demand is observed in the northern part of the study area in January, reaching 48 kWh/ha. The average energy demand increases by 67% in the drought scenario. The study contributes to current gaps in the existing literature by providing a replicable methodological framework and data aimed at facilitating energy system planning through the consideration of location-specific characteristics at the nexus of energy–water–agriculture. Full article
(This article belongs to the Special Issue Geospatial Electrification and Energy Access Planning)
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20 pages, 3540 KiB  
Article
Electrification Planning for Healthcare Facilities in Low-Income Countries, Application of a Portfolio-Level, Multi Criteria Decision-Making Approach
by Mohammad H. Pakravan and Andrea C. Johnson
ISPRS Int. J. Geo-Inf. 2021, 10(11), 750; https://doi.org/10.3390/ijgi10110750 - 8 Nov 2021
Cited by 1 | Viewed by 2645
Abstract
This study presents a multi-platform analysis for accelerating the deployment of distributed renewable energy (DRE) systems for the electrification of healthcare facilities (HCFs) in low-income regions. While existing tools capture national and regional scale planning for DRE deployment in HCFs, there are limited [...] Read more.
This study presents a multi-platform analysis for accelerating the deployment of distributed renewable energy (DRE) systems for the electrification of healthcare facilities (HCFs) in low-income regions. While existing tools capture national and regional scale planning for DRE deployment in HCFs, there are limited tools for facility level energy needs and no existing data-driven approach for systematic decision-making and resource allocation across a portfolio of HCFs. We address this gap by utilizing decentralized data collection, and multi-criteria decision-making to evaluate each HCF against a set of weighted decision criteria. We applied the approach presented in this research in a case study across 56 HCF in Uganda. Results present current and future energy needs for each individual clinic and the prioritization of HCFs for allocation of resources for DRE deployment. Additionally, results provide insight for best practices for reliability of services that are specific to each HCF. For example, failures in the existing solar photovoltaic (PV) systems are approximately up to 60% due to a lack of proper operation and management (O&M) strategy, and 40% is attributable to improper system design and installation. Thus, this study enables decision-makers to better understand the electrification needs of different HCFs, prioritize DRE deployment, financial investments, cost-effective procurement, and long-term O&M. Full article
(This article belongs to the Special Issue Geospatial Electrification and Energy Access Planning)
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