Advanced Optimization and Control Methods for Adaptive Microgrids
Lead Research Organisation:
Heriot-Watt University
Department Name: Sch of Engineering and Physical Science
Abstract
"This is a research project in electrical/electronic engineering. This project will explore the issue of enabling resilient energy communities or local microgrids, which increasingly rely on local renewable generation, with reduced dependency of a centralised (national or regional) power grid. Such settings are very frequent in ODA nations, such as India and Bhutan, but DNOs in developing countries (such as the UK) are increasingly interested in increasing local energy resiliency, especially given widespread embedded renewables, storage and new loads, such as EV charging.
This requires new control techniques, which enable assuring a stable power supply in decentralised grids with significant embedded generation, modeling user incentives and demand, as well as new platforms that enable peer-peer electricity exchanges. Relevant modeling techniques for this problem include multi-agent systems, distributed AI and machine learning, as well as technologies that empower users to take control of their own energy demand (such as blockchains). The pinch point analysis proposed by the candidate in a previous Elsevier "Energy" paper is also a good example of such a method.
The PhD student will be based within the Smart Systems Group (https://smartsystems.hw.ac.uk/). Work in this PhD project will benefit and complement research in two large EPSRC research projects the group runs: CESI (the UK National Centre for Energy Systems Integration) [EP/P001173/1], CEDRI (Community Energy Demand Reduction in India) [EP/R008655/1] and SCORRES (Smart Control of Rural Renewable Energy & Storage in India) [EP/P031145/1]. The experience of the candidate in the challenges of the power grid in Bhutan and India will bring considerable additional value to these research projects."
This requires new control techniques, which enable assuring a stable power supply in decentralised grids with significant embedded generation, modeling user incentives and demand, as well as new platforms that enable peer-peer electricity exchanges. Relevant modeling techniques for this problem include multi-agent systems, distributed AI and machine learning, as well as technologies that empower users to take control of their own energy demand (such as blockchains). The pinch point analysis proposed by the candidate in a previous Elsevier "Energy" paper is also a good example of such a method.
The PhD student will be based within the Smart Systems Group (https://smartsystems.hw.ac.uk/). Work in this PhD project will benefit and complement research in two large EPSRC research projects the group runs: CESI (the UK National Centre for Energy Systems Integration) [EP/P001173/1], CEDRI (Community Energy Demand Reduction in India) [EP/R008655/1] and SCORRES (Smart Control of Rural Renewable Energy & Storage in India) [EP/P031145/1]. The experience of the candidate in the challenges of the power grid in Bhutan and India will bring considerable additional value to these research projects."
Organisations
People |
ORCID iD |
| Valentin Robu (Primary Supervisor) | |
| Sonam Norbu (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509474/1 | 01/10/2016 | 30/09/2021 | |||
| 2123508 | Studentship | EP/N509474/1 | 01/10/2018 | 31/03/2022 | Sonam Norbu |
| EP/R513040/1 | 01/10/2018 | 30/09/2023 | |||
| 2123508 | Studentship | EP/R513040/1 | 01/10/2018 | 31/03/2022 | Sonam Norbu |
| Description | My PhD research is focused on exploring the issues of enabling resilient energy communities or local microgrids. During the first and second year of my PhD research, I have worked on modelling of single prosumer and energy community using real commercially-available dynamic tariffs from the UK market, as well as a whole year of high-granularity demand and renewable generation data. Specifically, the research work includes modelling of assets, control of assets incorporating real state-of-the-art battery control and degradation functions, and techno-economic approaches to assess the benefits of the renewable generator (solar PV and wind turbine) and battery storage assets adoption at the individual household scale to assets adoption on the community scale. Then, inspired by coalitional game theory methods and principles, I have focused on developing an algorithm to fairly redistribute among community members the benefits obtained from the community shared assets. In more detail, the key outcomes of the PhD research can be summarized as follows: (1) Modelling the single prosumer and energy community control algorithm for maximization of the self-consumption behind the meter for both fixed and dynamic time of use (ToU) tariffs. (2) Incorporating the latest heuristics of battery state of health into such control algorithm, both at individual prosumer and community level. The battery degradation model is integrated with renewable energy optimization by considering the battery depth of discharge in each control cycle. (3) Data-driven techno-economic analysis of energy model with individually-owned assets versus a model with centrally-shared community-owned assets. (4) Exploring a number of benefit redistribution schemes (four in total, based on different parameters). Some are intuitive and based on current practice, but we also proposed one based on the marginal contribution, a key coalitional game-theoretic principle. We show it achieves better performance than the others and it is also computationally tractable. The findings of the PhD research are disseminated through publication in the top-ranked Elsevier Applied Energy journal available as an open-source research paper (https://doi.org/10.1016/j.apenergy.2021.116575 ). Besides, my core PhD research I am also involved in other collaborative research with the team members of the Smart System Group (https://smartsystems.hw.ac.uk/ ), and the outcomes are highlighted through the publication in which I am involved as a co-author as reported in the publication outcome section. |
| Exploitation Route | The installation of assets such as renewable generator s (solar PV, wind turbine) and battery in the grid changes power flows and might create congestions or voltage excursions. Therefore, there is a need to include network constraints in the modelling of energy communities. I have enhanced the energy community model by incorporating the power flow (grid constraints) in the techno-economic analysis of individually-owned assets versus community-owned assets. Currently, I am in the process of documenting the key findings to be submitted to the reputed journal. Furthermore, for the remaining duration of my PhD study, I have identified to extend my study by considering the new revenue streams for batteries such as the provision of ancillary services, like frequency and demand response. Future work will also consider the exploration of emerging market structures of energy communities such as local and peer-to-peer energy markets. Consideration of flexibility services, and finally encoding redistribution schemes into smart contracts executed on blockchain systems are also identified as promising avenues for future research. |
| Sectors | Communities and Social Services/Policy,Creative Economy,Energy,Environment |
| URL | https://doi.org/10.1016/j.apenergy.2021.116575 |
| Description | The key findings from the PhD research, particularly the techno-economic analysis of individually-owned assets versus community-owned assets and the proposed redistribution mechanism addresses the following questions while implementing the energy community projects: (1) Planning stage: is it better to have a distributed individually-owned assets or centrally-shared community-owned assets? (2) Operation stage: How can we fairly redistribute the benefits obtained from centrally-shared community-owned assets? The proposed methods are all implemented and validated based on real data and case studies from the UK. More specifically, the setting is based on the Responsive Flexibility (ReFlex) smart energy demonstrator project (Innovate UK project code: 104780, https://www.reflexorkney.co.uk/about-reflex ) that aims to develop a large-scale demonstrator for community energy integration in Orkney, Scotland, UK. I have also presented the key findings and results of my PhD research with the industrial stakeholders of the ReFLEX project and Community-scale Energy Demand Reduction in India (CEDRI) project (EPSRC project code: EP/R008655/1, https://cedri.hw.ac.uk/project-team/ ) implementing real community energy schemes, and they found them very interesting and informative for their work. |
| First Year Of Impact | 2020 |
| Sector | Communities and Social Services/Policy,Creative Economy,Energy,Environment |