Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Promoting Social Behaviour in Reducing Peak Electricity Consumption Using Multi-Agent Systems (2211.10198v2)

Published 18 Nov 2022 in cs.MA

Abstract: As we transition to renewable energy sources, addressing their inflexibility during peak demand becomes crucial. It is therefore important to reduce the peak load placed on our energy system. For households, this entails spreading high-power appliance usage like dishwashers and washing machines throughout the day. Traditional approaches to spreading out usage have relied on differential pricing set by a centralised utility company, but this has been ineffective. Our previous research investigated a decentralised mechanism where agents receive an initial allocation of time-slots to use their appliances, which they can exchange with others. This was found to be an effective approach to reducing the peak load when we introduced social capital, the tracking of favours, to incentivise agents to accept exchanges that do not immediately benefit them. This system encouraged self-interested agents to learn socially beneficial behaviour to earn social capital that they could later use to improve their own performance. In this paper we expand this work by implementing real world household appliance usage data to ensure that our mechanism could adapt to the challenging demand needs of real households. We also demonstrate how smaller and more diverse populations can optimise more effectively than larger community energy systems.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (46)
  1. Cooperative electricity consumption shifting. Sustainable Energy, Grids and Networks, 9:38–58.
  2. Distributed energy generation and sustainable development. Renewable and sustainable energy reviews, 10(6):539–558.
  3. Trusting intelligent machines: Deepening trust within socio-technical systems. IEEE Technology and Society Magazine, 37(4):76–83.
  4. Socially-sensitive systems design: Exploring social potential. IEEE Technology and Society Magazine, 36(3):72–80.
  5. Culture and the evolutionary process. University of Chicago press.
  6. A mechanism to promote social behaviour in household load balancing. In ALIFE 2020: The 2020 Conference on Artificial Life, pages 95–103.
  7. Mo money, mo problems: When and why financial incentives backfire. Journal of Management Information and Decision Sciences, 22(3):191–206.
  8. Economic models for resource management and scheduling in grid computing. Concurrency and computation: practice and experience, 14(13-15):1507–1542.
  9. Dynamic, self-organized clusters as a means to supply and demand matching in large-scale energy systems. In 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), pages 568–573. IEEE.
  10. An approach to quantify the technical benefits of distributed generation. IEEE Transactions on energy conversion, 19(4):764–773.
  11. Demand management for peak-to-average ratio minimization via intraday block pricing. IEEE Transactions on Smart Grid, 14(5):3584–3599.
  12. Decentralized optimization approaches for using the load flexibility of electric heating devices. Energy, 193:116651.
  13. A literature review on dynamic pricing of electricity. Journal of the Operational Research Society, 68(10):1131–1145.
  14. The current state of research on energy communities. e & i Elektrotechnik und Informationstechnik, 138(8):515–524.
  15. The concept of energy justice across the disciplines. Energy Policy, 105:658–667.
  16. Procedural justice in negotiation: Procedural fairness, outcome acceptance, and integrative potential. Law & Social Inquiry, 33(2):473–500.
  17. The evolution of coercive institutional punishment. Dynamic Games and Applications, 2:97–109.
  18. Energy justice: A conceptual review. Energy Research & Social Science, 11:174–182.
  19. Social justice, fairness and exclusion in the south korean electricity sector. Energy Research & Social Science, 51:55–66.
  20. Two-level agent-based scheduling for a cluster of heating systems. Sustainable cities and society, 30:273–281.
  21. Optimization strategy based on deep reinforcement learning for home energy management. CSEE Journal of Power and Energy Systems, 6(3):572–582.
  22. Neumann, M. (2020). Indirect reciprocity with contagious reputation in large-scale small-world networks. Journal of Artificial Societies and Social Simulation, 23(4).
  23. Evolution of indirect reciprocity by image scoring. Nature, 393(6685):573–577.
  24. Further analysis of the household electricity survey - Early findings: Demand side management. Cambridge Architectural Research Limited, Loughborough University and Element Energy, London, United Kingdom.
  25. Pasimeni, F. (2019). Community-based adoption and diffusion of micro-grids: analysis of the italian case with agent-based model. Journal of Artificial Societies and Social Simulation, 22(1).
  26. Distributed generation: definition, benefits and issues. Energy policy, 33(6):787–798.
  27. Self organising flexible demand for smart grid. In 2013 IEEE 7th International Conference on Self-Adaptation and Self-Organizing Systems Workshops, pages 21–22. IEEE.
  28. differsith social capital in multi-agent systems. In International Conference on Principles and Practice of Multi-Agent Systems, pages 18–33. Springer.
  29. Multi-dimensional fairness for auction-based resource allocation. Knowledge-Based Systems, 73:134–148.
  30. Multi-agent deep reinforcement learning for zero energy communities. In 2019 IEEE PES innovative smart grid technologies Europe (ISGT-Europe), pages 1–5. IEEE.
  31. When do financial incentives reduce intrinsic motivation? comparing behaviors studied in psychological and economic literatures. Health Psychology, 32(9):950.
  32. Putnam, R. D. (1994). Social capital and public affairs. Bulletin of the American Academy of Arts and Sciences, pages 5–19.
  33. Rescher, N. (1966). Distributive Justice: A Constructive Critique of the Utilitarian Theory of Distribution. The Bobbs-Merrill Company, Inc., Indianapolis.
  34. Social diversity promotes the emergence of cooperation in public goods games. Nature, 454(7201):213–216.
  35. Hybrid algorithm for cloud-fog system based load balancing in smart grids. Bulletin of Electrical Engineering and Informatics, 11(1):477–487.
  36. Regulation (eu) 2021/1119 of the european parliament and of the council.
  37. TOU rates and vulnerable households: Electricity consumption behavior in a Canadian case study.
  38. Skidmore, C. (2019). The climate change act 2008 (2050 target amendment) order 2019.
  39. Peer-to-peer energy trading: A review of the literature. Applied Energy, 283:116268.
  40. Energy justice: Conceptual insights and practical applications. Applied Energy, 142:435–444.
  41. Dynamic pricing and balancing mechanism for a microgrid electricity market. In Intelligent Systems’ 2014, pages 793–806. Springer.
  42. The effectiveness of incentives for residential energy conservation. Evaluation Review, 10(2):147–176.
  43. Community renewable energy: What should it mean? Energy policy, 36(2):497–500.
  44. Auction approaches for resource allocation in wireless systems: A survey. IEEE Communications surveys & tutorials, 15(3):1020–1041.
  45. ides: Incentive-driven distributed energy sharing in sustainable microgrids. In International Green Computing Conference, pages 1–10. IEEE.
  46. Household Electricity Survey: A study of domestic electrical product usage. Intertek Testing & Certification Ltd, pages 213–214.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now