IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i16p4191-d398730.html
   My bibliography  Save this article

A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services

Author

Listed:
  • Sean Williams

    (School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK)

  • Michael Short

    (School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK)

  • Tracey Crosbie

    (School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK)

  • Maryam Shadman-Pajouh

    (Teesside University Business School, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK)

Abstract

This paper presents a decentralized informatics, optimization, and control framework to enable demand response (DR) in small or rural decentralized community power systems, including geographical islands. The framework consists of a simplified lumped model for electrical demand forecasting, a scheduling subsystem that optimizes the utility of energy storage assets, and an active/pro-active control subsystem. The active control strategy provides secondary DR services, through optimizing a multi-objective cost function formulated using a weight-based routing algorithm. In this context, the total weight of each edge between any two consecutive nodes is calculated as a function of thermal comfort, cost (tariff), and the rate at which electricity is consumed over a short future time horizon. The pro-active control strategy provides primary DR services. Furthermore, tertiary DR services can be processed to initiate a sequence of operations that enables the continuity of applied electrical services for the duration of the demand side event. Computer simulations and a case study using hardware-in-the-loop testing is used to evaluate the optimization and control module. The main conclusion drawn from this research shows the real-time operation of the proposed optimization and control scheme, operating on a prototype platform, underpinned by the effectiveness of the new methods and approach for tackling the optimization problem. This research recommends deployment of the optimization and control scheme, at scale, for decentralized community energy management. The paper concludes with a short discussion of business aspects and outlines areas for future work.

Suggested Citation

  • Sean Williams & Michael Short & Tracey Crosbie & Maryam Shadman-Pajouh, 2020. "A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services," Energies, MDPI, vol. 13(16), pages 1-30, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4191-:d:398730
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/16/4191/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/16/4191/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Borhanazad, Hanieh & Mekhilef, Saad & Gounder Ganapathy, Velappa & Modiri-Delshad, Mostafa & Mirtaheri, Ali, 2014. "Optimization of micro-grid system using MOPSO," Renewable Energy, Elsevier, vol. 71(C), pages 295-306.
    2. Price, James & Zeyringer, Marianne & Konadu, Dennis & Sobral Mourão, Zenaida & Moore, Andy & Sharp, Ed, 2018. "Low carbon electricity systems for Great Britain in 2050: An energy-land-water perspective," Applied Energy, Elsevier, vol. 228(C), pages 928-941.
    3. Patteeuw, Dieter & Bruninx, Kenneth & Arteconi, Alessia & Delarue, Erik & D’haeseleer, William & Helsen, Lieve, 2015. "Integrated modeling of active demand response with electric heating systems coupled to thermal energy storage systems," Applied Energy, Elsevier, vol. 151(C), pages 306-319.
    4. Radenković, Miloš & Bogdanović, Zorica & Despotović-Zrakić, Marijana & Labus, Aleksandra & Lazarević, Saša, 2020. "Assessing consumer readiness for participation in IoT-based demand response business models," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    5. Cai, Wei & Zhao, Jingyi & Zhu, Ming, 2020. "A real time methodology of cluster-system theory-based reliability estimation using k-means clustering," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    6. Sanna Uski & Erkka Rinne & Janne Sarsama, 2018. "Microgrid as a Cost-Effective Alternative to Rural Network Underground Cabling for Adequate Reliability," Energies, MDPI, vol. 11(8), pages 1-16, July.
    7. Tracey Crosbie & Michael Short & Muneeb Dawood & Richard Charlesworth, 2017. "Demand response in blocks of buildings: opportunities and requirements," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 4(3), pages 271-281, March.
    8. Di Silvestre, Maria Luisa & Favuzza, Salvatore & Riva Sanseverino, Eleonora & Zizzo, Gaetano, 2018. "How Decarbonization, Digitalization and Decentralization are changing key power infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 483-498.
    9. Ratnam, Kamala Sarojini & Palanisamy, K. & Yang, Guangya, 2020. "Future low-inertia power systems: Requirements, issues, and solutions - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    10. Richard Bellman, 1954. "On some applications of the theory of dynamic programming to logistics," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(2), pages 141-153, June.
    11. Bradley, Peter & Coke, Alexia & Leach, Matthew, 2016. "Financial incentive approaches for reducing peak electricity demand, experience from pilot trials with a UK energy provider," Energy Policy, Elsevier, vol. 98(C), pages 108-120.
    12. Neelawela, U.D. & Selvanathan, E.A. & Wagner, L.D., 2019. "Global measure of electricity security: A composite index approach," Energy Economics, Elsevier, vol. 81(C), pages 433-453.
    13. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    14. Eriksson, E.L.V. & Gray, E.MacA., 2019. "Optimization of renewable hybrid energy systems – A multi-objective approach," Renewable Energy, Elsevier, vol. 133(C), pages 971-999.
    15. Kosai, Shoki & Unesaki, Hironobu, 2020. "Short-term vs long-term reliance: Development of a novel approach for diversity of fuels for electricity in energy security," Applied Energy, Elsevier, vol. 262(C).
    16. Copiello, Sergio, 2017. "Building energy efficiency: A research branch made of paradoxes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1064-1076.
    17. Thellufsen, J.Z. & Lund, H. & Sorknæs, P. & Østergaard, P.A. & Chang, M. & Drysdale, D. & Nielsen, S. & Djørup, S.R. & Sperling, K., 2020. "Smart energy cities in a 100% renewable energy context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 129(C).
    18. Harris, Tyler M. & Devkota, Jay P. & Khanna, Vikas & Eranki, Pragnya L. & Landis, Amy E., 2018. "Logistic growth curve modeling of US energy production and consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 46-57.
    19. Wang, Yongli & Ma, Yuze & Song, Fuhao & Ma, Yang & Qi, Chengyuan & Huang, Feifei & Xing, Juntai & Zhang, Fuwei, 2020. "Economic and efficient multi-objective operation optimization of integrated energy system considering electro-thermal demand response," Energy, Elsevier, vol. 205(C).
    20. Michael Short & Sergio Rodriguez & Richard Charlesworth & Tracey Crosbie & Nashwan Dawood, 2019. "Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach," Energies, MDPI, vol. 12(22), pages 1-20, November.
    21. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    22. Zizzo, G. & Beccali, M. & Bonomolo, M. & Di Pietra, B. & Ippolito, M.G. & La Cascia, D. & Leone, G. & Lo Brano, V. & Monteleone, F., 2017. "A feasibility study of some DSM enabling solutions in small islands: The case of Lampedusa," Energy, Elsevier, vol. 140(P1), pages 1030-1046.
    23. Richard Bellman, 1954. "Some Applications of the Theory of Dynamic Programming---A Review," Operations Research, INFORMS, vol. 2(3), pages 275-288, August.
    24. Jaume Freire-Gonz lez & Ignasi Puig-Ventosa, 2015. "Energy Efficiency Policies and the Jevons Paradox," International Journal of Energy Economics and Policy, Econjournals, vol. 5(1), pages 69-79.
    25. Abdullah, M.A. & Agalgaonkar, A.P. & Muttaqi, K.M., 2014. "Assessment of energy supply and continuity of service in distribution network with renewable distributed generation," Applied Energy, Elsevier, vol. 113(C), pages 1015-1026.
    26. Loisel, Rodica & Lemiale, Lionel, 2018. "Comparative energy scenarios: Solving the capacity sizing problem on the French Atlantic Island of Yeu," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 54-67.
    27. Cabrera, Pedro & Lund, Henrik & Carta, José A., 2018. "Smart renewable energy penetration strategies on islands: The case of Gran Canaria," Energy, Elsevier, vol. 162(C), pages 421-443.
    28. Sylvia Breukers & Tracey Crosbie & Luc van Summeren, 2020. "Mind the gap when implementing technologies intended to reduce or shift energy consumption in blocks-of-buildings," Energy & Environment, , vol. 31(4), pages 613-633, June.
    29. Pfeifer, Antun & Dobravec, Viktorija & Pavlinek, Luka & Krajačić, Goran & Duić, Neven, 2018. "Integration of renewable energy and demand response technologies in interconnected energy systems," Energy, Elsevier, vol. 161(C), pages 447-455.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shuxin Liu & Jing Xu & Chaojian Xing & Yang Liu & Ersheng Tian & Jia Cui & Junzhu Wei, 2023. "Study on Dynamic Pricing Strategy for Industrial Power Users Considering Demand Response Differences in Master–Slave Game," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
    2. da Fonseca, André L.A. & Chvatal, Karin M.S. & Fernandes, Ricardo A.S., 2021. "Thermal comfort maintenance in demand response programs: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. Mimica, Marko & Krajačić, Goran, 2021. "The Smart Islands method for defining energy planning scenarios on islands," Energy, Elsevier, vol. 237(C).
    3. Xiaoyue Li & John M. Mulvey, 2023. "Optimal Portfolio Execution in a Regime-switching Market with Non-linear Impact Costs: Combining Dynamic Program and Neural Network," Papers 2306.08809, arXiv.org.
    4. Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
    5. Dawei Chen & Fangxu Mo & Ye Chen & Jun Zhang & Xinyu You, 2022. "Optimization of Ramp Locations along Freeways: A Dynamic Programming Approach," Sustainability, MDPI, vol. 14(15), pages 1-13, August.
    6. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    7. Wadi Khalid Anuar & Lai Soon Lee & Hsin-Vonn Seow & Stefan Pickl, 2021. "A Multi-Depot Vehicle Routing Problem with Stochastic Road Capacity and Reduced Two-Stage Stochastic Integer Linear Programming Models for Rollout Algorithm," Mathematics, MDPI, vol. 9(13), pages 1-44, July.
    8. Sorknæs, Peter & Thellufsen, Jakob Zinck & Knobloch, Kai & Engelbrecht, Kurt & Yuan, Meng, 2023. "Economic potentials of carnot batteries in 100% renewable energy systems," Energy, Elsevier, vol. 282(C).
    9. Matthias Breuer & David Windisch, 2019. "Investment Dynamics and Earnings‐Return Properties: A Structural Approach," Journal of Accounting Research, Wiley Blackwell, vol. 57(3), pages 639-674, June.
    10. Laha, Priyanka & Chakraborty, Basab, 2021. "Low carbon electricity system for India in 2030 based on multi-objective multi-criteria assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    11. Diefenbach, Heiko & Emde, Simon & Glock, Christoph H., 2020. "Loading tow trains ergonomically for just-in-time part supply," European Journal of Operational Research, Elsevier, vol. 284(1), pages 325-344.
    12. Michael J. Pennock & William B. Rouse & Diane L. Kollar, 2007. "Transforming the Acquisition Enterprise: A Framework for Analysis and a Case Study of Ship Acquisition," Systems Engineering, John Wiley & Sons, vol. 10(2), pages 99-117, June.
    13. Quetschlich, Mathias & Moetz, André & Otto, Boris, 2021. "Optimisation model for multi-item multi-echelon supply chains with nested multi-level products," European Journal of Operational Research, Elsevier, vol. 290(1), pages 144-158.
    14. Nielsen, Steffen & Østergaard, Poul Alberg & Sperling, Karl, 2023. "Renewable energy transition, transmission system impacts and regional development – a mismatch between national planning and local development," Energy, Elsevier, vol. 278(PA).
    15. Sasanka Adikari & Norou Diawara, 2024. "Utility in Time Description in Priority Best–Worst Discrete Choice Models: An Empirical Evaluation Using Flynn’s Data," Stats, MDPI, vol. 7(1), pages 1-18, February.
    16. Coit, David W. & Zio, Enrico, 2019. "The evolution of system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    17. Peng, Hujun & Li, Jianxiang & Löwenstein, Lars & Hameyer, Kay, 2020. "A scalable, causal, adaptive energy management strategy based on optimal control theory for a fuel cell hybrid railway vehicle," Applied Energy, Elsevier, vol. 267(C).
    18. Vincent Huang & James Unwin, 2019. "Markov Chain Models of Refugee Migration Data," Papers 1903.08255, arXiv.org.
    19. Esmaeili Aliabadi, Danial & Chan, Katrina, 2022. "The emerging threat of artificial intelligence on competition in liberalized electricity markets: A deep Q-network approach," Applied Energy, Elsevier, vol. 325(C).
    20. Alex Sharp & Ryan Browne, 2021. "Functional data clustering by projection into latent generalized hyperbolic subspaces," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 735-757, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4191-:d:398730. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.