Contemporary Energy Research: The Novel Domain of Energy Informatics
Looking at research that supports the Sustainable Development Goals (SDGs), we spoke to Chiara Bordin - who recently published a paper Educating the energy informatics specialist: opportunities and challenges in light of research and industrial trends in the open access journal SN Applied Sciences - about SDG 7 and the increasing importance of the novel domain of energy informatics.
Can you give us an overview of your research domain?
My research interests lie within the broad domain of 鈥渆nergy informatics鈥. Within such a domain, my main scientific discipline is 鈥渙perations research鈥, with a special focus on mathematical optimization applied to energy and power systems.
鈥淓nergy informatics鈥 is defined as an interdisciplinary domain that combines engineering, mathematics, computer science, energy economics, power systems, and energy systems.
The field of study referred to as "operations research" (often abbreviated as "OR") focuses on the creation and implementation of advanced statistical techniques with the goal of enhancing decision-making. Modeling, statistics, and optimization are used to find optimal or near-optimal solutions to complex decision-making problems. OR has strong ties to computer science, and often strong emphasis on practical applications. One relevant field of application is energy and power systems.
"Smart energy and power systems modeling" is a core subject that emerges as a specialized application of operations research, playing a key role within the energy informatics domain, as further elaborated in one of my articles [1]. It focuses on the optimal design, expansion, management, and operation of energy and power systems by including elements such as renewable sources, storage technologies, electric vehicles, smart grids, and smart buildings, as well as social aspects such as demand response.
That sounds exciting! Can you tell us more about your research interests and specialization within the energy informatics domain?
I work mainly within a few key research fields of specialization that are interconnected: predictive analytics, prescriptive analytics, and pedagogy.
鈥淧redictive analytics鈥 is defined as the process of using data to forecast future outcomes; 鈥減rescriptive analytics鈥 is defined as the process of using data to determine an optimal course of action; and 鈥減edagogy鈥 is defined as the method and practice of teaching, especially as an academic subject or theoretical concept.
When predictive and prescriptive analytics are implemented for energy and power system applications, they become relevant specializations within the energy informatics domain [1].
Pedagogical approaches and frameworks can be investigated to create and deliver new courses or study programs that cover predictive and prescriptive analytics applications for energy and power systems. In such a specialized context, pedagogy also becomes a relevant specialization within the energy informatics domain. The pedagogical aspects of energy informatics in general, and energy and power systems modeling in particular, are discussed in two articles that I published as part of my research activity [2, 3].
Among prescriptive analytics, predictive analytics, and pedagogy, is there one specialization in particular that you enjoy the most? And how did you end up working in these fields?
I have to say that all these specializations are extremely interesting to me, and I truly enjoy doing research in all these three fields.
However, prescriptive analytics applied to energy and power systems represents my very first research field of specialization, the one I have focused on since I started my PhD. As such, it is the one that I have consistently developed over time. Therefore, I have a more extensive publication record within the field of prescriptive analytics as compared to the other two fields. The scientific papers that I produced within this research field of specialization present and discuss novel mathematical optimization approaches to support decision making within a wide variety of short-term and long-term energy and power systems problems. I have applied prescriptive analytics in the form of mathematical optimization to both thermal energy systems and power systems. In the last few years I have been particularly interested in multi-horizon optimization for a wide variety of problems including battery degradation issues within charging sites expansion problem [4], reliability oriented network restructuring [5], and virtual power plants [6].
A few papers are also devoted to the discussion of research directions in energy systems modeling in general [7] and in emerging problems at the intersection of energy and informatics, such as the inclusion of data center flexibility within the power system [8, 9].
Predictive analytics is a field that I started investigating after my PhD, when I had opportunities to work within different research projects and collaborating with various researchers nationally and internationally. This mobility across countries and institutions, as well as increased interactions with industries, provided me with numerous chances to broaden my research scope and investigate opportunities for integrating predictive and prescriptive analytics in two main fields, hydropower and wind energy. My research within predictive analytics aims at enhancing the generation of datasets to be used as input within energy and power system optimization models. Examples of this type of research can be found in [10, 11, 12, 13].
In the last few years, since 2020 when I started working as a faculty member at UiT, The Arctic University of Norway, pedagogical research has become an integral part of my research effort, on top of being part of my academic duties. I published articles that discuss the subject of energy informatics and the role of mathematical optimization, the opportunities and challenges of interdisciplinary teaching and research, the pedagogical approaches to developing and delivering energy informatics courses at master's and PhD levels, recommendations to educate the future generation of energy informatics specialists, and the role of education for the development of the energy informatics field.
My pedagogical research articles take most of their content from the didactic experience within teaching and supervision that I gathered in the last few years as an associate professor at UiT [2, 3].
Why do you believe that pedagogy is important in your field of research?
The intrinsic interdisciplinarity of energy informatics is receiving attention in academia and industry as interdisciplinary approaches to energy and environmental issues become increasingly important. This tendency has generated novel concepts like the education-energy nexus to address the demand for energy-focused interdisciplinary education [14]. Higher education must develop the necessary skills and capabilities to attain long-term, sustainable goals. Educational institutions must rapidly develop skilled graduates to meet global demand for sustainable energy specialists. From this perspective, papers on sustainable energy engineering education should concentrate not only on course content but also provide information on the pedagogical methods employed to deliver that content.
Teaching and research can be strongly interconnected, especially in more advanced and specialized academic courses at the master鈥檚 and PhD level, such as the one that I teach at UiT, The Arctic University of Norway, titled 鈥 鈥 smart energy and power systems modeling鈥. This is because there is an active research community working on energy systems modeling, and there is a need to teach the fundamental background and tackle the interdisciplinary challenges to create the new generation of researchers.
In 2005, Mick Healey developed a teaching-research nexus paradigm to link varied activities along two dimensions: learning content (what students learn) and learning process (how students participate in learning). In this paradigm, students can be involved as participants or as an audience, and their learning can be focused on research findings or research procedures.
Based on these dimensions, Healey proposed four distinct approaches to establishing the teaching-research nexus: research-led, in which students learn about current research; research-tutored, in which students participate in research discussions; research-based, in which students conduct research and inquiry; and research-oriented, in which students learn about research methods and techniques. A thorough discussion on the teaching-research nexus aspects for the particular case of Energy Informatics and smart energy and power systems modeling can be found in my recent paper [3].
What are the challenges of your research, teaching, and supervision activities?
The main challenge is the intrinsic interdisciplinary aspects of my research fields, which require all the students to acquire new additional knowledge when they start, whether it is a master鈥檚 project, a thesis, or a PhD journey. Especially for younger students who have less experience, the interdisciplinary aspects can be intimidating and demotivating at the very beginning.
Indeed, when you start working in these fields, you very soon realize that there is a lot that you don鈥檛 know, on one side or the other. For example, a pure mathematician might feel strong on the mathematical aspects of optimization, but often lacks a good portion of knowledge in energy and power systems. Similarly, a power system specialist will have a deep understanding of power systems but may have very limited knowledge of the modeling and optimization aspects.
Some students take the challenge and work hard to acquire the skills they lack for research, eventually ending up successfully defending their PhD. Others may have a different reaction and struggle to accept that there is a knowledge gap to fill and hard work to do to reach the final research goal. These are the types of students who usually start a project and soon lose interest in it when the first challenges come up or the initial results are not as expected.
Students should be humble enough to accept and understand that 鈥渢here are many things they don鈥檛 know and many skills they lack鈥 at the beginning and work hard to acquire the necessary skills. In my experience, students who start their PhD journey with a humble attitude and willingness to acquire new skills, are more successful in the long term, and also develop a more productive relationship with their mentors and supervisors.
What is important when teaching and doing research within interdisciplinary subjects like energy informatics?
I believe that education should be driven by research and industrial needs. It is generally acknowledged that university teaching should be research-based; thus, focusing on teaching without providing the general current state of research in the topic would be inappropriate, especially within more specialized and advanced master鈥檚 level courses. The proposed learning objectives and outcomes should keep current research trends in mind. Similarly, the targeted knowledge and skills of an academic course or study program should not be detached from industrial innovation trends, since universities should educate not just those who will pursue a career in academic research, but also those who will work in the real world.
Your research has significant practical implications for addressing real-world problems. Do you have any insights to share about bridging the gap between industry and academia?
Throughout my activity as a faculty member at UiT, I could observe how collaboration across disciplines and with other parties (like industry) can be incredibly inspiring for students. This is because they experience that the issues they are resolving apply to real-world situations, and they can share themselves and their work outside of the confines of a closed course.
When possible and relevant, I enjoy organizing so-called guest lectures within my courses. These are seminars with key invited speakers (industrial and/or academic actors) aimed at communicating to the students the practical implications of the methodologies that they learn during the course. In 2022 I organized several guest lectures during my new course 鈥 鈥 Smart Energy and Power Systems Modelling鈥. Attendance at such guest lectures was very high, and the feedback received by the students has been extremely positive, especially regarding the more industrial-related seminars.
The industry is highly involved in my supervision activities at both the master's and PhD levels. Indeed, I am currently supervising an industrial PhD candidate on projects that are tightly connected to the local distribution system operator in Troms酶. I also had master鈥檚 students working on real-world problems for various industries, as well as several scientific papers that included industrial collaborations. The greatest advantage of industrial participation is the specialized knowledge of the problem at hand that helps shape the research questions and narrow the scope, as well as the data availability that is of paramount importance in my field for analytical purposes.
You have had a successful international career among well-recognized institutions. Do you have any tips for PhD students who are still at the beginning of their career and would like to find their path in life?
Be humble, work hard, deliver on the work you are assigned, even if you don鈥檛 like it much, and honor the commitments you make. Show respect for the time and competence of the people you meet along the way; treasure and value any feedback you receive from senior mentors and/or supervisors, whether positive or negative; don鈥檛 take negative feedback personally, but rather show maturity and work hard to improve yourself personally and professionally. Keep and nurture a good relationship with all the people you interact with, and strive to leave a good legacy in every working environment you find yourself in. Always make an effort to develop a positive and flourishing working relationship with all your mentors and supervisors, and strive to create a peaceful environment where people around you can do their work at their best and thrive in serenity.
Because the world is smaller than you think and you will always need a good reference or some kind of help from more senior colleagues and mentors in your journey, keep your words, don鈥檛 burn bridges, and finalize all the work you started, even if this means working harder than you initially thought or facing challenges that you did not expect. Cultivate values of respect, ethics, transparency, integrity, accountability, and trustworthiness. Try to have an accommodating nature and accept with serenity that you can't always have what you want and that when you want something, you must earn it by behaving appropriately and preparing a fertile ground with hard work and devotion. Remember, your actions speak more than thousands of words, and a peaceful, accommodating attitude can bring you very far.
Take a look at , by Chiara Bordin et al, published in .
We also invite you to visit the P站视频 SDG Programme hub, which includes hubs for all the SDGs, including SDG 7 Affordable and Clean Energy.
About the authors
Chiara Bordin is currently Associate Professor in Energy Informatics at UiT, the Arctic University of Norway. Before that, she was a research scientist at SINTEF Energy Research, in Norway, for four years; a postdoc at the Norwegian University of Science and Technology for two years; a visiting PhD student for a year at the Durham Energy Institute in the UK, where she stayed as a research associate for another year after her PhD; a PhD student at the University of Bologna, in Italy for three years; a junior researcher and assistant in Italian industries focused on energy-related activities.
Chiara Bordin鈥檚 PhD was focused on prescriptive analytics applications for energy and power systems. Throughout her career across different countries and institutions, Chiara has had the opportunity to further develop such a research area, as well as build on top of it additional specializations in predictive analytics and pedagogy.
References:
- Bordin, C., H氓kansson, A., & Mishra, S. (2020). 鈥淪mart Energy and power systems modelling: an IoT and Cyber-Physical Systems perspective, in the context of Energy Informatics.鈥 Procedia Computer Science, 176, 2254-2263.
- Bordin, C., Mishra, S., Safari, A. and Eliassen, F., 2021. 鈥淓ducating the energy informatics specialist: opportunities and challenges in light of research and industrial trends鈥. SN Applied Sciences, 3, pp.1-17.
- Bordin, C., Mishra, S. and Benth, F.E., 2023. 鈥淧edagogical Perspectives of Interdisciplinary Teaching and Research: An Energy System Modelling Outlook in Relation to Energy Informatics鈥. Energies, 16(15), p.5757.
- Bordin, C., & Tomasgard, A. (2019). 鈥淪MACS MODEL, a stochastic multihorizon approach for charging sites management, operations, design, and expansion under limited capacity conditions鈥. Journal of Energy Storage, 26, 100824.
- Bordin, C., Mishra, S., and Palu, I. (2021) "A multihorizon approach for the reliability oriented network restructuring problem, considering learning effects, construction time, and cables maintenance costs." Renewable Energy 168: 878-895
- Mishra, S., Bordin, C., Wu, Q., & Manninen, H. (2022). 鈥淩esilient expansion planning of virtual power plant with an integrated energy system considering reliability criteria of lines and towers.鈥 International Journal of Energy Research, 1:26
- Fodstad, M., del Granado, P. C., Hellemo, L., Knudsen, B. R., Pisciella, P., Silvast, A., Bordin, C., Schmidt. S., & Straus, J. (2022). 鈥淣ext frontiers in energy system modelling: A review on challenges and the state of the art.鈥 Renewable and Sustainable Energy Reviews, 160, 112:246.
- Ahwazi, A. Z., Bordin, C., Mishra, S., Horsch, A., & Ha, P. H. (2021). 鈥淪ustainable and decarbonized data-center facilities: A socio-techno-economic discussion.鈥 In 2021 IEEE PES Innovative Smart Grid Technologies-Asia (ISGT Asia) (pp. 1-5). IEEE.
- Ziagham Ahwazi, A., Bordin, C., Mishra, S., Ha, P. H., & Horsch, A. (2021). 鈥淰EDA-moVE DAta to balance the grid: research directions and recommendations for exploiting data centers flexibility within the power system鈥. In 2021 6th International Conference on Systems, Control and Communications (ICSCC) (pp. 13-18).
- Mishra, S., Bordin, C., Taharaguchi, K., & Purkayastha, A. (2022). 鈥淧redictive analytics beyond time series: Predicting series of events extracted from time series data.鈥 Wind Energy, 1:14.
- Mishra , S., Oren, E., Bordin, C., Wen, F., Palu, I., (2020). 鈥淔eatures extraction of wind ramp events from a virtual wind park鈥. Energy Reports 6, 237-249
- Bordin, C., Skjelbred, H. I., Kong, J., & Yang, Z. (2020). 鈥淢achine learning for hydropower scheduling: State of the art and future research directions鈥. Procedia Computer Science, 176, 1659-1668.
- Mishra, S; Bordin, C; Taharaguchi, K; Palu, I. (2020) "Comparison of deep learning models for multivariate prediction of time series wind power generation and temperature". Energy Reports 2020; Volum 6. ISSN 2352-4847.s 273 鈥 286
- Mishra, S.; Bordin, C. A health-energy nexus perspective for Virtual Power Plants: Power systems resiliency and pandemic uncertainty challenges. In Smart and Sustainable Technology for Resilient Cities and Communities; Springer: Singapore, 2022; pp. 267鈥284.
