Energy Informatics: Tackling energy poverty in vulnerable communities
The UN Sustainable Development Goal Affordable and Clean Energy (SDG7) aims to ensure access to affordable, reliable, sustainable, and modern energy for all. Lack of access to energy hinders economic and human development, and drastic inequalities in energy access endure.
Energy informatics is the foundation for developing and testing energy solutions to energy poverty that many vulnerable communities are facing. In this guest post, the editors-in-chief of 50業子s journal explain the challenges of energy poverty, and present some considerations for the development and testing of energy ecosystem solutions within the field of energy informatics.
Access to electricity is essential for economic development and the alleviation of poverty, yet many challenges impede the fulfillment of SDG7s main goal of providing universal access to modern energy services. While nearly 9 out of 10 people in the world currently do have access to electricity, global disparities in electricity access are enormous and enduring. Some 770 million people live without access to electricity, mostly in Africa and Asia.
Rural populations are unsurprisingly the most affected: 17.3% of the worlds rural population has no access to electricity, compared to less than 3% of urban population still lacking access to electricity. Without electricity, rural populations become vulnerable communities, and experience more barriers to social, economic, political, and environmental resources.
The application of energy informatics enables the creation of an affordable, sustainable, and resilient energy ecosystem, which can support the SDGs promise to leave no one behind. However, the predicament of reaching those hardest to reach is manifested in the multitude of challenges of energy poverty. We can divide these into five dimensions:
1. Climate, environment, and energy resource utilization.
- The consequences of climate change, such as recurring droughts, increased average temperatures, decreasing rainfall, and changing rainfall variability, significantly impact livelihood opportunities due to poverty and dependency on forest resources.
- Due to the lack of electricity access, energy use is based on traditional biomass, which results in Carbon emissions and deforestation.
- While many countries suffering from energy poverty have huge renewable energy resources, they have only been able to utilize an insignificant amount of their energy resource potential.
2. Societal culture and demographic environment.
Inequity is a critical societal and cultural challenge in vulnerable communities. For instance, women are not allowed to participate in the decision-making process actively and have limited access to and control over resources, particularly in terms of productive resources. On the other hand, women are the main household energy users, e.g. for cooking and water.
3. Infrastructure and technological skills and capacity.
Besides the areas with no electricity access, the actual electricity connectivity of households is very low in the areas with grid networks, especially in rural areas. Furthermore, the grid networks lack maintenance and large power plant projects are usually delayed due to financial and political issues. In many countries, the technical capacity is restricted due to limited education opportunities, particularly for the youth population.
4. Economy and finance.
Significant investments are needed to improve the energy poverty situation, yet one of the critical challenges facing the countries most affected by energy poverty is the absence of a self-sufficient energy business model. Besides low electricity tariffs and the limited capacity of households to pay, there is also a lack of private sector engagement due to unclear policy and few incentives.
5. Policies and regulation.
The political situation in many countries affected by energy poverty is usually unstable due to challenges such as conflicts, migration, and corruption. Subsequently, the institutional structure of the energy sector changes frequently, and energy policy is not up to date. Political and regulatory challenges also hinder the implementation of policy, where it is available. Confronted with such multifaceted challenges, standalone solutions or traditional approaches such as static planning are inefficient and ineffective in combating energy poverty, particularly for vulnerable communities. Ecosystem driven energy solutions are required. In the following, we touch upon some aspects of energy informatics specifically recommended to be considered for the energy ecosystem solution development and testing.
Energy community virtual living lab.
The development of solutions that fulfill local needs require case studies, yet these are difficult to conduct. Accessibility is one of the main challenges for solution development and testing. Vulnerable communities are usually located in remote rural areas and are difficult to reach. Security and safety risks, such as wars, global pandemics, or natural disasters, further complicate and prevent access to these communities. Virtual living labs of energy communities, that allow for investigating what-if and future scenarios and conducting various testing, are recommended as a way to overcome the challenges of conducting case studies. Related technologies include digital twin, big data, and AI (Artificial Intelligent) prediction models. Data and information are of course still required to develop virtual living labs.
Energy ecosystem synthetic data generation.
Besides the data accessibility challenge, energy ecosystem solutions and implementation require multiple data sources that can portray the energy community with the five dimensions discussed above (Climate, environment, and energy resource utilization; Societal culture and demographic environment; Infrastructure and technological skills and capacity; Economy and finance; Policies and regulation). Therefore, synthetic data is needed. Related methods include ecosystem analysis and modeling, multi-agent based simulations, and others.
Digital twin based solution-in-the-loop testing.
Many energy solutions and services have been developed to serve different purposes, and have been implemented within different contexts and conditions. These contexts and conditions vary between locations and the implementation of solutions and services must reflect these differences. Solution development, implementation, and maintenance are costly, especially for vulnerable communities, and testing before the development and implementation is therefore essential. Testing in a virtual environment can resolve the challenge of accessibility to targeted communities discussed earlier. In-the-loop testings such as hardware-in-the-loop, software-in-the-loop, or human-in-the-loop testing are unable to fully capture the solution testing. Digital twin based solution-in-the-loop testing is more suitable, and the solution here refers to products, services, strategies, and business models. Related methods include digital twin, business modeling, multi-criteria assessment, and multi-objective optimization.
AI prediction for ecosystem stakeholders behaviors and decision-making.
Many prediction methods and models have focused on energy production, especially renewable energy resources, energy consumption from the demand side or the grid side, etc. However, AI prediction for ecosystem stakeholders behaviors and decision-making is rarely discussed. Multiple stakeholders relate to the energy ecosystem solutions development and implementation:, energy system operators, local communities, households, governmental authorities, technology providers, and others. The implementation feasibility strongly depends on the ecosystem stakeholders co-adoption. To investigate the emergent behaviors and the consequences when implementing a solution, ecosystem stakeholders behaviors and decision-making need to be decoded and captured. Related methods include transfer learning, reinforcement learning, and explainable AI.
The last decades have seen an increase in global electricity access rate, and the number of people without electricity shrank. However, there is still a long way to go until we can secure universal access to clean and affordable energy. Energy informatics supports the attainment of this urgent goal, especially for the vulnerable communities who need it the most.
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About the authors
Zheng Grace Ma, Editor-in-Chief, Energy Informatics, is an associate Professor at the SDU Center for Energy Informatics, Mc-Kinney Moller Institute, University of Southern Denmark. She is the research lead of SDU strategic research areas of digital twins for industrial processes, value chains, and business ecosystems. She develops a multi-dimensional, multi-criteria evaluation and forecasting method for business ecosystem impact analysis and long-term prediction. Her research combines multiple methods (agent-based simulation, discrete event simulation, big data analytics, and scientific qualitative and quantitative methods) to investigate future scenarios and evaluate solutions (algorithm, software, technologies, and services) and business models in targeted ecosystems.
Bo N淡rregaard J淡rgensen, Editor-in-Chief, Energy Informatics, is the founder and head of the Center for Energy Informatics at the University of Southern Denmark, an interdisciplinary research center focusing on digital solutions for facilitating the transition toward a sustainable energy system. The centers research is conducted in close collaboration with industrial partners, public bodies, and government agencies. Dr. J淡rgensen research focuses on digital solutions for integration of the demand-side with the supply-side in the energy sector, from the technology and business perspectives. He holds a Ph.D. in Computer Science from the University of Southern Denmark, a M.Sc. and a B.Sc. in Computer System Engineering from Odense University, Denmark.
