A systemic model predictive control based on adaptive power pinch analysis for load shifting and shedding in an isolated hybrid energy storage system

Abstract

This paper presents a novel systemic algorithm based on conservative power pinch analysis principles using a computationally efficient insight-based binary linear programming optimization technique in a model predictive framework for integrated load shifting and shedding in an isolated hybrid energy storage system. In a receding 24-hour predictive horizon, the energy demand and supply are integrated via an adaptive power grand composite curve tool to form a diagonal matrix of predicted hourly minimum and maximum energy constraints. The intgrated energy constraints must be satisfied recursively by the binary optimisation to ensure the energy storage’s state of charge only operates within 30% and 90%. Hence, the control command to shift or shed load is contingent on the energy storage state of the charge violating the operating constraints. The controllable load demand is shifted and/or shed to prevent any violations while ensuring energy supply to the most critical load without sacrificing the consumers' comfort. The proposed approach enhances efficient energy use from renewable energy supply as well as limits the use of the Hydrogen resources by a fuel cell to satisfy controllable load demands which can be shifted to periods in the day with excess renewable energy supply.

Keywords

• Demand side management
• Binary-linear programming optimization
• Demand side management
• Model predictive control
• Power pinch analysis

References

1. [1] Kantor, I, Rowlands, IH., Parker, P. Aggregated and disaggregated correlations of household electricity consumption with time-of-use shifting and conservation. Energy and Buildings 2017; 139: 326-339. DOI: 10.1016/j.enbuild.2016.12.054.
2. [2] Zohar, T, Parag, Y, Ayalon, O. Strategizing demand management from the middle out: Harnessing middle actors to reduce peak electricity consumption. Energy Research and Social Science 2020; 61: 101360. DOI: 10.1016/j.erss.2019.101360.
3. [3] Rogers, AP, Rasmussen BP. Opportunities for consumer-driven load shifting in commercial and industrial buildings. Sustainable Energy, Grids and Networks 2018; 16: 243-58. DOI: 10.1016/j.segan.2018.08.004.
4. [4] Santos-herrero, J M, Lopez-guede, JM, Flores I. A Short review on the use of renewable energies and model predictive control in buildings. Journal of Energy Systems 2017; 1(3): 112-119. DOI: 10.30521/jes.346653
5. [5] Yalcintas, M, Hagen, WT, Kaya, A. An analysis of load reduction and load shifting techniques in commercial and industrial buildings under dynamic electricity pricing schedules. Energy and buildings. 2015; 88: 15-24. DOI: 10.1016/j.enbuild.2014.11.069.
6. [6] Judge, MA, Manzoor, A, Maple C, Rodrigues, JJ, ul Islam S. Price-based demand response for household load management with interval uncertainty. Energy Reports 2021. DOI: 10.1016/j.egyr.2021.02.064.
7. [7] Fridgen, G, Keller, R, Thimmel, M, Wederhake, L. Shifting load through space–The economics of spatial demand-side management using distributed data centers. Energy Policy 2017; 109: 400-413. DOI: 10.1016/j.enpol.2017.07.018.
8. [8] Tang, Y, Zheng, G, Zhang, S. Optimal control approaches of pumping stations to achieve energy efficiency and load shifting. International Journal of Electrical Power and Energy Systems 2014; 55: 572-80. DOI: 10.1016/j.ijepes.2013.10.023.

9. [9] Favre, B, Peuportier, B. Application of dynamic programming to study load shifting in buildings. Energy and Buildings 2014; 82: 57-64. DOI: 10.1016/j.enbuild.2014.07.018.
10. [10] Shehadeh, SH, Moh'd, A, Aly, HH, El-Hawary, ME. An intelligent load management application for solar boiler system. Sustainable Energy Technologies and Assessments 2020; 38: 100644. DOI: 10.1016/j.seta.2020.100644.
11. [11] Ho, WS, Hashim, H, Lim, JS, Klemeš, JJ. Combined design and load shifting for distributed energy system. Clean Technologies and Environmental Policy 2013; 15: 433-44. DOI: 10.1007/s10098-013-0617-3.
12. [12] Langendahl, PA, Roby, H, Potter, S, Cook, M. Smoothing peaks and troughs: Intermediary practices to promote demand side response in smart grids. Energy Research and Social Science 2019; 58: 101277. DOI: 10.1016/j.erss.2019.101277.
13. [13] Goulden, M., Bedwell, B, Rennick-Egglestone, S, Rodden, T, Spence, A. Smart grids, smart users? The role of the user in demand-side management. Energy research and social science 2014; 2: 21-9. DOI: 10.1016/j.erss.2014.04.008.
14. [14] López, MA, De La Torre, S, Martín, S, Aguado, JA. Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support. International Journal of Electrical Power and Energy Systems 2015; 64: 689-98. DOI: 10.1016/j.ijepes.2014.07.065.
15. [15] Lokeshgupta, B, Sivasubramani, S. Multi-objective dynamic economic and emission dispatch with demand-side management. International Journal of Electrical Power and Energy Systems 2018; 97: 334-43. DOI: 10.1016/j.ijepes.2017.11.020.
16. [16] Ahmadi, SE, Rezaei, N, Khayyam, H. Energy management system of networked microgrids through optimal reliability-oriented day-ahead self-healing scheduling. Sustainable Energy, Grids and Networks 2020; 23: 100387. DOI: 10.1016/j.segan.2020.100387.
17. [17] Hakimi, SM, Hasankhani, A, Shafie-Khah M, Catalão JP. Demand response method for smart microgrids considering high renewable energies penetration. Sustainable Energy, Grids and Networks 2020; 21: 100325. DOI: 10.1016/j.segan.2020.100325.
18. [18] Roy, A, Auger, F, Dupriez-Robin, F, Bourguet, S, Tran, QT. A multi-level Demand-Side Management algorithm for off grid multi-source systems. Energy 2020; 191: 116536. DOI: 10.1016/j.energy.2019.116536.
19. [19] Haley, B, Gaede, J, Winfield, M, Love, P. From utility demand side management to low-carbon transitions: Opportunities and challenges for energy efficiency governance in a new era. Energy Research and Social Science 2020; 59: 101312. DOI: 10.1016/j.erss.2019.101312.
20. [20] Praveen, M, Rao, GS. Ensuring the reduction in peak load demands based on load shifting DSM strategy for smart grid applications. Procedia Computer Science 2020; 167: 2599-605. DOI: 10.1016/j.procs.2020.03.319.
21. [21] Verma, P, Patel, N, Nair, NK. Demand side management perspective on the interaction between a non-ideal grid and residential LED lamps. Sustainable Energy Technologies and Assessments 2017; 23: 93-103. DOI: 10.1016/j.seta.2017.08.002.
22. [22] Ebrahimi. J, Abedini, M, Rezaei, MM. Optimal scheduling of distributed generations in microgrids for reducing system peak load based on load shifting. Sustainable Energy, Grids and Networks 2020; 23: 100368. DOI: 10.1016/j.segan.2020.100368.
23. [23] Harsh, P, Das, D. Energy management in microgrid using incentive-based demand response and reconfigured network considering uncertainties in renewable energy sources. Sustainable Energy Technologies and Assessments 2021; 46: 101225. DOI: 10.1016/j.seta.2021.101225.
24. [24] Ayub, S, Ayob, SM., Tan, CW., Ayub, L, Bukar, AL. Optimal residence energy management with time and device-based preferences using an enhanced binary grey wolf optimization algorithm. Sustainable Energy Technologies and Assessments 2020; 41: 100798. DOI: 10.1016/j.seta.2020.100798.
25. [25] Yang, Y, Wang, S. Resilient residential energy management with vehicle-to-home and photovoltaic uncertainty. International Journal of Electrical Power & Energy Systems 2021; 132: 107206. DOI: 10.1016/j.ijepes.2021.107206.
26. [26] Abdulaal, A, Asfour, S. A linear optimization-based controller method for real-time load shifting in industrial and commercial buildings. Energy and Buildings 2016; 110: 269-83. DOI: 10.1016/j.enbuild.2015.10.046.
27. [27] Schreiber, T, Eschweiler, S, Baranski, M, Müller, D. Application of two promising Reinforcement Learning algorithms for load shifting in a cooling supply system. Energy and Buildings 2020; 229: 110490. DOI: 10.1016/j.enbuild.2020.110490.
28. [28] Nyong-Bassey, BE, Giaouris, D, Patsios, C, Papadopoulou, S, Papadopoulos, AI, Walker, S, Voutetakis, S, Seferlis, P, Gadoue, S. Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty. Energy 2020; 193: 116622. DOI: 10.1016/j.energy.2019.116622.
29. [29] Giaouris, D, Papadopoulos, AI, Seferlis, P, Voutetakis, S, Papadopoulou, S. Power grand composite curves shaping for adaptive energy management of hybrid microgrids. Renewable Energy 2016; 95: 433-48. DOI: 10.1016/j.renene.2016.04.028
30. [30] Rozali, NE, Zaki, SA, Ho, WS, Liu, WH, Alwi, SW, Manan, ZA, Klemes, JJ. Study of the effects of peak/off-peak load shifting on hybrid power system storage using Power Pinch Analysis. Chemical Engineering Transactions 2017; 61: 1519-1524. DOI: 10.3303/CET1761251.
31. [31] Rozali, NE, Ho, WS, Alwi, SR, Manan ZA, Klemeš JJ, Yunus MN, Zaki SA. Peak-off-peak load shifting for optimal storage sizing in hybrid power systems using Power Pinch Analysis considering energy losses. Energy. 2018; 156: 299-310. DOI: 10.1016/j.energy.2018.05.020.
32. [32] Hu, RL, Skorupski R, Entriken R, Ye Y. A mathematical programming formulation for optimal load shifting of electricity demand for the smart grid. IEEE Transactions on Big Data 2016; 6: 638-651. DOI: 10.1109/TBDATA.2016.2639528
33. [33] Giaouris, D, Papadopoulos, AI, Ziogou, C, Ipsakis D, Voutetakis, S, Papadopoulou, S, Seferlis, P, Stergiopoulos, F, Elmasides, C. Performance investigation of a hybrid renewable power generation and storage system using systemic power management models. Energy 2013; 61: 621-35. DOI: 10.1016/j.energy.2013.09.016.
34. [34] Nyong-Bassey, BE, Giaouris, D, Papadopoulos, AI, Patsios, H, Papadopoulou, S, Voutetakis, S, Seferlis, P, Walker, S, Taylor, P, Gadoue S. Adaptive power pinch analysis for energy management of hybrid energy storage systems. In: ISCAS 2018. IEEE International Symposium on Circuits and Systems May 27 2018: IEEE, pp. 1-5.
35. [35] Ayodele, TR, Ogunjuyigbe, AS, Akpeji, KO, Akinola, OO. Prioritized rule-based load management technique for residential building powered by PV/battery system. Engineering science and technology, an international journal 2017; 20: 859-73. DOI: 10.1016/j.jestch.2017.04.003.
36. [36] Nyong-Bassey, BE, Giaouris, D, Probabilistic adaptive power pinch analysis for islanded hybrid energy storage systems. Journal of Energy Storage 2022; 54: 105224. DOI:10.1016/j.est.2022.105224.