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Encouraging citizens to invest in small-scale renewable resources is crucial for transitioning towards a sustainable and clean energy system. Local energy communities (LECs) are expected to play a vital role in this context. However, energy scheduling in LECs presents various challenges, including the preservation of customer privacy, adherence to distribution network constraints, and the management of computational burdens. This paper introduces a novel approach for energy scheduling in renewable-based LECs using a decentralized optimization method. The proposed approach uses the Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) method, significantly reducing the computational effort required for solving the mixed integer programming (MIP) problem. It incorporates network constraints, evaluates energy losses, and enables community participants to provide ancillary services like a regulation reserve to the grid utility. To assess its robustness and efficiency, the proposed approach is tested on an 84-bus radial distribution network. Results indicate that the proposed distributed approach not only matches the accuracy of the corresponding centralized model but also exhibits scalability and preserves participant privacy.
G. Pleßmann and P. Blechinger, “How to meet EU GHG emission reduction targets? A model based decarbonization pathway for europe's electricity supply system until 2050,”Energy Strategy Reviews, vol. 15, pp. 19–32, Mar. 2017.
J. Lowitzsch, C. E. Hoicka, and F. J. Van Tulder, “Renewable energy communities under the 2019 European clean energy package–governance model for the energy clusters of the future?" Renewable and Sustainable Energy Reviews, vol. 122, pp. 109489, Apr. 2020.
T. Sousa, T. Soares, P. Pinson, F. Moret, T. Baroche, and E. Sorin, “Peer-to-peer and community-based markets: A comprehensive review,”Renewable and Sustainable Energy Reviews, vol. 104, pp. 367–378, Apr. 2019.
M. Dolatabadi, P. Siano, and A. Soroudi, “Assessing the scalability and privacy of energy communities by using a large-scale distributed and parallel real-time optimization,”IEEE Access, vol. 10, pp. 69771–69787, Jun. 2022.
S. P. Burger, J. D. Jenkins, S. C. Huntington, and I. J. Perez-Arriaga, “Why distributed?: A critical review of the tradeoffs between centralized and decentralized resources,”IEEE Power and Energy Magazine, vol. 17, no. 2, pp. 16–24, Mar. /Apr. 2019.
R. Faia, J. Soares, T. Pinto, F. Lezama, Z. Vale, and J. M. Corchado, “Optimal model for local energy community scheduling considering peer to peer electricity transactions,”IEEE Access, vol. 9, pp. 12420–12430, Jan. 2021.
S. Malik, M. Duffy, S. Thakur, B. Hayes, and J. Breslin, “A priority-based approach for peer-to-peer energy trading using cooperative game theory in local energy community,”International Journal of Electrical Power & Energy Systems, vol. 137, pp. 107865, May 2022.
A. Shrestha, R. Bishwokarma, A. Chapagain, S. Banjara, S. Aryal, B. Mali, R. Thapa, D. Bista, B. P. Hayes, A. Papadakis, and P. Korba, “Peer-to-peer energy trading in micro/mini-grids for local energy communities: A review and case study of Nepal,”IEEE Access, vol. 7, pp. 131911–131928, Sep. 2019.
B. V. Rao, M. Stefan, T. Brunnhofer, R. Schwalbe, R. Karl, F. Kupzog, G. Taljan, F. Zeilinger, P. Stern, and M. Kozek, “Optimal capacity management applied to a low voltage distribution grid in a local peer-to-peer energy community,”International Journal of Electrical Power & Energy Systems, vol. 134, pp. 107355, Jan. 2022.
P. Siano, G. De Marco, A. Rolán, and V. Loia, “A survey and evaluation of the potentials of distributed ledger technology for peer-to-peer transactive energy exchanges in local energy markets,”IEEE Systems Journal, vol. 13, no. 3, pp. 3454–3466, Sep. 2019.
R. Rana, K. Berg, M. Z. Degefa, and M. Löschenbrand, “Modelling and simulation approaches for local energy community integrated distribution networks,”IEEE Access, vol. 10, pp. 3775–3789, Jan. 2022.
Y. Zheng, Y. Song, D. J. Hill, and Y. X. Zhang, “Multiagent system based microgrid energy management via asynchronous consensus ADMM,”IEEE Transactions on Energy Conversion, vol. 33, no. 2, pp. 886–888, Jun. 2018.
Y. Liu, Y. Z. Li, H. B. Gooi, Y. Jian, H. H. Xin, X. C. Jiang, and J. F. Pan, “Distributed robust energy management of a multimicrogrid system in the real-time energy market,”IEEE Transactions on Sustainable Energy, vol. 10, no. 1, pp. 396–406, Jan. 2019.
S. Lilla, C. Orozco, A. Borghetti, F. Napolitano, and F. Tossani, “Day-ahead scheduling of a local energy community: An alternating direction method of multipliers approach,”IEEE Transactions on Power Systems, vol. 35, no. 2, pp. 1132–1142, Mar. 2020.
C. Orozco, A. Borghetti, B. De Schutter, F. Napolitano, G. Pulazza, and F. Tossani, “Intra-day scheduling of a local energy community coordinated with day-ahead multistage decisions,”Sustainable Energy, Grids and Networks, vol. 29, pp. 100573, Mar. 2022.
F. García-Muñoz, S. Dávila, and F. Quezada, “A Benders decomposition approach for solving a two-stage local energy market problem under uncertainty,”Applied Energy, vol. 329, pp. 120226, Jan. 2023.
Z. F. Yang, H. W. Zhong, A. Bose, Q. Xia, and C. Q. Kang, “Optimal power flow in AC–DC grids with discrete control devices,”IEEE Transactions on Power Systems, vol. 33, no. 2, pp. 1461–1472, Mar. 2018.
P. Pareek and A. Verma, “Piecewise linearization of quadratic branch flow limits by irregular polygon,”IEEE Transactions on Power Systems, vol. 33, no. 6, pp. 7301–7304, Nov. 2018.
B. Veiga, G. Santos, T. Pinto, R. Faia, C. Ramos, and Z. Vale, “Simulation tools for electricity markets considering power flow analysis,”Energy, vol. 275, pp. 127494, Jul. 2023.
A. Ostrowska, T. Sikorski, A. Burgio, and M. Jasiński, “Modern use of prosumer energy regulation capabilities for the provision of microgrid flexibility services,”Energies, vol. 16, no. 1, pp. 469, Jan. 2023.
S. Backe, S. Zwickl-Bernhard, D. Schwabeneder, H. Auer, M. Korpås, and A. Tomasgard, “Impact of energy communities on the European electricity and heating system decarbonization pathway: Comparing local and global flexibility responses,”Applied Energy, vol. 323, pp. 119470, Oct. 2022.
S. Y. Zhou, Z. J. Hu, W. Gu, M. Jiang, and X. P. Zhang, “Artificial intelligence based smart energy community management: A reinforcement learning approach,”CSEE Journal of Power and Energy Systems, vol. 15, no. 1, pp. 1–10, Mar. 2019.
H. Nagpal, I. I. Avramidis, F. Capitanescu, and A. G. Madureira, “Local energy communities in service of sustainability and grid flexibility provision: Hierarchical management of shared energy storage,”IEEE Transactions on Sustainable Energy, vol. 13, no. 3, pp. 1523–1535, Jul. 2022.
Y. Zhou, J. Z. Wu, G. Y. Song, and C. Long, “Framework design and optimal bidding strategy for ancillary service provision from a peer-to-peer energy trading community,”Applied Energy, vol. 278, pp. 115671, Nov. 2022.
Y. Z. Zhou, Z. N. Wei, G. Q. Sun, K. W. Cheung, H. X. Zang, and S. Chen, “A robust optimization approach for integrated community energy system in energy and ancillary service markets,”Energy, vol. 148, pp. 1–15, Apr. 2018.
F. García-Muñoz, F. Teng, A. Junyent-Ferré, F. Díaz-González, and C. Corchero, “Stochastic energy community trading model for day-ahead and intraday coordination when offering DER's reactive power as ancillary services,”Sustainable Energy, Grids and Networks, vol. 32, pp. 100951, Dec. 2015.
H. Firoozi, H. Khajeh, and H. Laaksonen, “Optimized operation of local energy community providing frequency restoration reserve,”IEEE Access, vol. 8, pp. 180558–180575, Sep. 2020.
P. Siano and D. Mohammad, “MILP Optimization model for assessing the participation of distributed residential PV-battery systems in ancillary services market,”CSEE Journal of Power and Energy Systems, vol. 7, no. 2, pp. 348–357, Mar. 2021.
M. Dolatabadi and P. Siano, “A scalable privacy preserving distributed parallel optimization for a large-scale aggregation of prosumers with residential PV-battery systems,”IEEE Access, vol. 8, pp. 210950–210960, Nov. 2020.
M. Dolatabadi, A. Borghetti, and P. Siano, “Scalable distributed optimization combining conic projection and linear programming for energy community scheduling,”Journal of Modern Power Systems and Clean Energy, vol. 11, no. 6, pp. 1814–1826, Nov. 2023.
K. Zhang, S. Troitzsch, S. Hanif, and T. Hamacher, “Coordinated market design for peer-to-peer energy trade and ancillary services in distribution grids,”IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 2929–2941, Jul. 2020.
L. L. Wang, Q. Zhou, Z. Xiong, Z. A. Zhu, C. W. Jiang, R. N. Xu, and Z. Y. Li, “Security constrained decentralized peer-to-peer transactive energy trading in distribution systems,”CSEE Journal of Power and Energy Systems, vol. 8, no. 1, pp. 188–197, Jan. 2022.
T. Morstyn and M. D. McCulloch, “Multiclass energy management for peer-to-peer energy trading driven by prosumer preferences,”IEEE Transactions on Power Systems, vol. 34, no. 5, pp. 4005–4014, Sep. 2019.
H. Z. Sheng, C. F. Wang, X. M. Dong, K. Meng, and Z. Y. Dong, “Incorporating P2P trading into DSO's decision-making: a DSO-prosumers cooperated scheduling framework for transactive distribution system,”IEEE Transactions on Power Systems, vol. 38, no. 3, pp. 2362–2375, May 2023.
M. Baran and F. F. Wu, “Optimal sizing of capacitors placed on a radial distribution system,”IEEE Transactions on Power Delivery, vol. 4, no. 1, pp. 735–743, Jan. 1989.
A. Likas, N. Vlassis, and J. J. Verbeek, “The global k-means clustering algorithm,”Pattern Recognition, vol. 36, no. 2, pp. 451–461, Feb. 2003.
R. Tibshirani, G. Walther, and T. Hastie, “Estimating the number of clusters in a data set via the gap statistic,”Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 63, no. 2, pp. 411–423, Feb. 2001.
R. H. Byrd, J. Nocedal, and R. B. Schnabel, “Representations of quasi-Newton matrices and their use in limited memory methods,”Mathematical Programming, vol. 63, no. 1–3, pp. 129–156, Jan. 1994.
O. L. Mangasarian and R. R. Meyer, “Nonlinear perturbation of linear programs,”SIAM Journal on Control and Optimization, vol. 17, no. 6, pp. 745–752, Nov. 1979.
A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,”SIAM Journal on Imaging Sciences, vol. 2, no. 1, pp. 183–202, Jan. 2009.
D. P. Bertsekas, Nonlinear Programming, 3rd ed., Athena Scientific, 2016.
S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge: Cambridge University Press, 2004.
C. Wang and H. Z. Cheng, “Optimization of network configuration in large distribution systems using plant growth simulation algorithm,”IEEE Transactions on Power Systems, vol. 23, no. 1, pp. 119–126, Feb. 2008.
M. Q. Wang and H. B. Gooi, “Spinning reserve estimation in microgrids,”IEEE Transactions on Power Systems, vol. 26, no. 3, pp. 1164–1174, Aug. 2011.
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