Abstract
Renewable sources are taking center stage in electricity generation. Due to the intermittent nature of these renewable resources, the problem of the demand-supply gap arises. To solve this problem, several techniques have been proposed in the literature in terms of cost (adding peaker plants), availability of data (Demand Side Management “DSM”), hardware infrastructure (appliance controlling DSM) and safety (voltage reduction). However, these solutions are not fair in terms of electricity distribution. In many cases, although the available supply may not match the demand in peak hours, however, the total aggregated demand remains less than the total supply for the whole day. Load shedding (complete blackout) is a commonly used solution to deal with the demand-supply gap, which can cause substantial economic losses. To solve the demand-supply gap problem, we propose a solution called Soft Load Shedding (SLS), which assigns electricity quota to each household in a fair way. We measure the fairness of SLS by defining a function for household satisfaction level. We model the household utilities by parametric function and formulate the problem of SLS as a social welfare problem. We also consider revenue generated from the fair allocation as a performance measure. To evaluate our approach, extensive experiments have been performed on both synthetic and real-world datasets, and our model is compared with several baselines to show its effectiveness in terms of fair allocation and revenue generation.
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References
Aalami, H.A., Parsa Moghaddam, M., Yousefi, G.R.: Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl. Energy 87(1), 243–250 (2010)
Aleksandrov, M.D., Aziz, H., Gaspers, S., Walsh, T.: Online fair division: analysing a food bank problem. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 2540–2546 (2015)
Ali, S., Mansoor, H., Arshad, N., Khan, I.: Short term load forecasting using smart meter data. In: International Conference on Future Energy Systems (e-Energy), pp. 419–421 (2019)
Ali, S., Mansoor, H., Khan, I., Arshad, N., Khan, M.A., Faizullah, S.: Hour-ahead load forecasting using AMI data. arXiv reprint arXiv:1504.06975 (2019)
Altman, E., Avrachenkov, K., Garnaev, A.: Generalized \(\alpha \)-fair resource allocation in wireless networks. In: IEEE Conference on Decision and Control, pp. 2414–2419 (2008)
Aslam, T., Arshad, N.: Soft load shedding: an efficient approach to manage electricity demand in a renewable rich distribution system. In: International Conference on Smart Cities and Green ICT Systems, SMARTGREENS, pp. 101–107 (2018)
Bashir, N., Sharani, Z., Qayyum, K., Syed, A.A.: Design and evaluation of a smart demand-response system for highly-stressed grids. arXiv preprint arXiv:1504.06975 (2015)
Bashir, N., Sharani, Z., Qayyum, K., Syed, A.A.: Delivering smart load-shedding for highly-stressed grids. In: International Conference on Smart Grid Communications (SmartGridComm), pp. 852–858 (2015)
Bonald, T., Massoulié, L.: Impact of fairness on Internet performance. SIGMETRICS Perform. Eval. Rev. 29, 82–91 (2001)
Bredel, M., Fidler, M.: Understanding fairness and its impact on quality of service in IEEE 802.11. In: International Conference on Computer Communications INFOCOM, pp. 1098–1106 (2009)
L’uboš, B., Rui, C.: Controlling congestion on complex networks: fairness, efficiency and network structure. Sci. Rep. 7(1), 9152 (2017)
Chandan, V., Ganu, T., Wijaya, T.K., Minou, M., Stamoulis, G., Thanos, G., Seetharam, D.P.: iDR: consumer and grid friendly demand response system. In: International Conference on Future Energy Systems, pp. 183–194 (2014)
Chen, L., Li, N., Low, S.H., Doyle, J.C.: Two market models for demand response in power networks. In: International Conference on Smart Grid Communications, pp. 397–402 (2010)
Chiang, M.: Networked Life: 20 Questions and Answers. Cambridge University Press, Cambridge (2012)
Connolly, D., Lund, H., Mathiesen, B.V., Leahy, M.: The first step towards a 100. Appl. Energy 88, 502–507 (2011)
Craciun, D., Ichim, S., Besanger, Y.: A new soft load shedding: power system stability with contribution from consumers. In: IEEE Bucharest PowerTech, pp. 1–6 (2009)
Dutta, G., Mitra, K.: A literature review on dynamic pricing of electricity. J. Oper. Res. Soc. 68(10), 1131–1145 (2017)
Commission for Energy Regualtion (CER): CER smart metering project - electricity customer behaviour trail, 2009–2010 (2012). https://www.ucd.ie/issda/data/commissionforenergyregulationcer
Gerossier, A., Girard, R., Kariniotakis, G., Michiorri, A.: Probabilistic day-ahead forecasting of household electricity demand. CIRED-Open Access Proc. J. 2017(1), 2500–2504 (2017)
Jain, R.K., Chiu, D.M.W., Hawe, W.R.: A quantitative measure of fairness and discrimination. Eastern Research Laboratory, Digital Equipment Corporation, pp. 2–7 (1984)
Javed, F., Arshad, N., Wallin, F., Vassileva, I., Dahlquist, E.: Forecasting for demand response in smart grids: an analysis on use of anthropologic and structural data and short term multiple loads forecasting. Appl. Energy 96, 150–160 (2012)
Kell, A., McGough, A.S., Forshaw, M.: Segmenting residential smart meter data for short-term load forecasting. In: International Conference on Future Energy Systems, pp. 91–96 (2018)
Kelly, F.: Charging and rate control for elastic traffic. Eur. Trans. Telecommun. 8(1), 33–37 (1997)
Lan, T., Kao, D., Chiang, M., Sabharwal, A.: An axiomatic theory of fairness in network resource allocation (2010)
Le Boudec, J.Y.: Rate adaptation, congestion control and fairness: a tutorial. Web page, November 2005
Liu, Y., Yuen, C., Rong, Y., Zhang, Y., Xie, S.: Queuing-based energy consumption management for heterogeneous residential demands in smart grid. IEEE Trans. Smart Grid 7(3), 1650–1659 (2016)
Lusis, P., Khalilpour, K.R., Andrew, L., Liebman, A.: Short-term residential load forecasting: impact of calendar effects and forecast granularity. Appl. Energy 205, 654–669 (2017)
Mansoor, H., Khan, I., Arshad, N.: Market model for demand response under block rate pricing (2020, Preprint)
Mohd, A., Ortjohann, E., Schmelter, A., Hamsic, N., Morton, D.: Challenges in integrating distributed energy storage systems into future smart grid. In: International Symposium on Industrial Electronics, pp. 1627–1632 (2008)
Oluwasuji, O.I., Malik, O., Zhang, J., Ramchurn, S.D., et al.: Algorithms for fair load shedding in developing countries. In: International Joint Conferences on Artificial Intelligence (IJCAI), pp. 1590–1596 (2018)
Ćosić, B., Krajacic, G., Duic, N.: Towards 100. In: Conference on Sustainable Development of Energy, Water and Environment Systems (2011)
Roberts, B.P., Sandberg, C.: The role of energy storage in development of smart grids. Proc. IEEE 99(6), 1139–1144 (2011)
Shi, H., Minghao, X., Li, R.: Deep learning for household load forecasting–a novel pooling deep RNN. IEEE Trans. Smart Grid 9(5), 5271–5280 (2018)
Vardakas, J.S., Zorba, N., Verikoukis, C.V.: A survey on demand response programs in smart grids: pricing methods and optimization algorithms. IEEE Commun. Surv. Tutor. 17(1), 152–178 (2015)
Walsh, T.: Challenges in resource and cost allocation. In: AAAI Conference on Artificial Intelligence, pp. 4073–4077 (2015)
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Ali, S., Mansoor, H., Khan, I., Arshad, N., Faizullah, S., Khan, M.A. (2021). Fair Allocation Based Soft Load Shedding. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-55187-2_32
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