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Distributed Multi-objective Optimization in Cyber-Physical Energy Systems (2403.04627v1)

Published 7 Mar 2024 in cs.MA

Abstract: Managing complex Cyber-Physical Energy Systems (CPES) requires solving various optimization problems with multiple objectives and constraints. As distributed control architectures are becoming more popular in CPES for certain tasks due to their flexibility, robustness, and privacy protection, multi-objective optimization must also be distributed. For this purpose, we present MO-COHDA, a fully distributed, agent-based algorithm, for solving multi-objective optimization problems of CPES. MO-COHDA allows an easy and flexible adaptation to different use cases and integration of custom functionality. To evaluate the effectiveness of MO-COHDA, we compare it to a central NSGA-2 algorithm using multi-objective benchmark functions from the ZDT problem suite. The results show that MO-COHDA can approximate the reference front of the benchmark problems well and is suitable for solving multi-objective optimization problems. In addition, an example use case of scheduling a group of generation units while optimizing three different objectives was evaluated to show how MO-COHDA can be easily applied to real-world optimization problems in CPES.

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References (44)
  1. A multi-objective optimization model for the operation of decentralized multi-energy systems. In Journal of Physics: Conference Series, Vol. 1343. IOP Publishing, 012104. https://doi.org/10.1088/1742-6596/1343/1/012104
  2. Resilience-driven restoration model for interdependent infrastructure networks. Reliability Engineering & System Safety 185 (2019), 12–23.
  3. O Apata and DTO Oyedokun. 2020. An overview of control techniques for wind turbine systems. Scientific African 10 (2020), e00566.
  4. Convergence of set-based multi-objective optimization, indicators and deteriorative cycles. Theoretical Computer Science 456 (2012), 2–17.
  5. SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research 181, 3 (2007), 1653–1669.
  6. Julian Blank and Kalyanmoy Deb. 2020. Pymoo: Multi-objective optimization in python. Ieee access 8 (2020), 89497–89509.
  7. Joerg Bremer and Sebastian Lehnhoff. 2017. An agent-based approach to decentralized global optimization-adapting cohda to coordinate descent. In International Conference on Agents and Artificial Intelligence, Vol. 2. SCITEPRESS, 129–136.
  8. Jörg Bremer and Sebastian Lehnhoff. 2018. Hybridizing s-metric selection and support vector decoder for constrained multi-objective energy management. In International Conference on Hybrid Intelligent Systems. Springer, 249–259.
  9. Jörg Bremer and Sebastian Lehnhoff. 2019. Towards fully decentralized multi-objective energy scheduling. In 2019 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, 193–201.
  10. Distributed control strategies for microgrids: An overview. IEEE Access 8 (2020), 193412–193448.
  11. The hypervolume indicator: Computational problems and algorithms. ACM Computing Surveys (CSUR) 54, 6 (2021), 1–42.
  12. Taxonomy for evaluation of distributed control strategies for distributed energy resources. IEEE Transactions on Smart Grid 9, 5 (2017), 5185–5195.
  13. Distributed hybrid constraint handling in large scale virtual power plants. In IEEE PES ISGT Europe 2013. IEEE, 1–5. https://doi.org/10.1109/ISGTEurope.2013.6695312
  14. Christian Hinrichs and Michael Sonnenschein. 2017. A distributed combinatorial optimisation heuristic for the scheduling of energy resources represented by self-interested agents. Int. J. Bio Inspired Comput. 10, 2 (2017), 69–78.
  15. Self-organizing demand response with comfort-constrained heat pumps. In EnviroInfo and ICT for Sustainability 2015. Atlantis Press, 353–360. https://doi.org/10.2991/ict4s-env-15.2015.40
  16. Stefanie Holly and Astrid Nieße. 2021. Dynamic communication topologies for distributed heuristics in energy system optimization algorithms. In 2021 16th Conference on Computer Science and Intelligence Systems (FedCSIS). IEEE, 191–200.
  17. Hamid Karimi and Shahram Jadid. 2020. Optimal energy management for multi-microgrid considering demand response programs: A stochastic multi-objective framework. Energy 195 (2020), 116992.
  18. Rahmat Khezri and Amin Mahmoudi. 2020. Review on the state-of-the-art multi-objective optimisation of hybrid standalone/grid-connected energy systems. IET Generation, Transmission & Distribution 14, 20 (2020), 4285–4300.
  19. Multi-objective optimal power flow problems based on slime mould algorithm. Sustainability 13, 13 (2021), 7448.
  20. Multi-objective optimization using genetic algorithms: A tutorial. Reliability engineering & system safety 91, 9 (2006), 992–1007.
  21. A distributed electricity trading system in active distribution networks based on multi-agent coalition and blockchain. IEEE Transactions on Power Systems 34, 5 (2018), 4097–4108.
  22. Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm. Applied Energy 269 (2020), 115058.
  23. Multi-Objective Optimisation of Power-to-Mobility in Decentralised Multi-Energy Systems. Energy 205 (2020), 117792. https://doi.org/10.1016/j.energy.2020.117792
  24. A novel hybrid self-adaptive heuristic algorithm to handle single-and multi-objective optimal power flow problems. International Journal of Electrical Power & Energy Systems 125 (2021), 106492.
  25. Astrid Nieße and Martin Tröschel. 2016. Controlled self-organization in smart grids. In 2016 IEEE International Symposium on Systems Engineering (ISSE). IEEE, 1–6. https://doi.org/10.1109/SysEng.2016.7753189
  26. Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method. Applied Soft Computing 77 (2019), 761–779.
  27. Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm. Renewable energy 121 (2018), 400–411.
  28. Multi-objective optimization of combined cooling, heating and power system integrated with solar and geothermal energies. Energy conversion and management 197 (2019), 111866.
  29. A multi-objective operation strategy optimization for ice storage systems based on decentralized control structure. Building Services Engineering Research and Technology 42, 1 (2021), 62–81. https://doi.org/10.1177/0143624420966259
  30. Fang Ruiming. 2019. Multi-objective optimized operation of integrated energy system with hydrogen storage. International Journal of Hydrogen Energy 44, 56 (2019), 29409–29417.
  31. Surender Reddy Salkuti. 2019. Optimal power flow using multi-objective glowworm swarm optimization algorithm in a wind energy integrated power system. International Journal of Green Energy 16, 15 (2019), 1547–1561.
  32. mango: A Modular Python-Based Agent Simulation Framework. arXiv:2311.17688 [cs.MA]
  33. A Multi-Criteria Metaheuristic Algorithm for Distributed Optimization of Electric Energy Storage. ACM SIGENERGY Energy Informatics Review 2, 4 (2023), 44–59.
  34. Coordination of different DGs, BESS and demand response for multi-objective optimization of distribution network with special reference to Indian power sector. International Journal of Electrical Power & Energy Systems 121 (2020), 106074.
  35. Why your power system restoration does not work and what the ICT system can do about it. In Proceedings of the twelfth ACM international conference on future energy systems. Virtual Event, Italy, 269–273.
  36. Steven Strogatz. 2001. Strogatz, S.H.: Exploring Complex Networks. Nature 410, 268. Nature 410 (04 2001), 268–76. https://doi.org/10.1038/35065725
  37. A multi-objective energy optimization in smart grid with high penetration of renewable energy sources. Applied Energy 299 (2021), 117104.
  38. Multi-objective optimal design of hybrid renewable energy system under multiple scenarios. Renewable Energy 151 (2020), 226–237.
  39. Decentralized and Multi-Objective Coordinated Optimization of Hybrid AC/DC Flexible Distribution Networks. Frontiers in Energy Research (2021), 646. https://doi.org/10.3389/fenrg.2021.762423
  40. Study on operation optimization of decentralized integrated energy system in northern rural areas based on multi-objective. Energy Reports 8 (2022), 3063–3084. https://doi.org/10.1016/j.egyr.2022.01.246
  41. A novel hybrid system based on multi-objective optimization for wind speed forecasting. Renewable energy 146 (2020), 149–165.
  42. A multiagent system based optimal load restoration strategy in distribution systems. International Journal of Electrical Power & Energy Systems 124 (2021), 106314.
  43. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary computation 8, 2 (2000), 173–195.
  44. Eckart Zitzler and Simon Künzli. 2004. Indicator-based selection in multiobjective search. In International conference on parallel problem solving from nature. Springer, 832–842.

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