Quantifying the Methods of Monitoring Timers in Electric Water Heater for Grid Balancing on Demand Side Management: A Systematic Mapping Review

Authors: Yamamah Abdulrazaq, Lahieb A. Abrahim, Samuel E. Davies, Iain Shewring

Abstract:

Electric water heater (EWH) is a powerful appliance that uses electricity in residential, commercial, and industrial settings, and the ability to control them properly will result in cost savings and the prevention of blackouts on the national grid. This article discusses the usage of timers in EWH control strategies for demand-side management (DSM). To the authors' knowledge, there is no systematic mapping review focusing on the utilization of EWH control strategies in DSM has yet been conducted. Consequently, the purpose of this research is to identify and examine main papers exploring EWH procedures in DSM by quantifying and categorizing information with regard to publication year and source, kind of methods, and source of data for monitoring control techniques. In order to answer the research questions, a total of 31 publications published between 1999 and 2023 were selected depending on specific inclusion and exclusion criteria. The data indicate that direct load control (DLC) has been somewhat more prevalent than indirect load control (ILC). Additionally, the mix method is much lower than the other techniques, and the proportion of real-time data (RTD) to non-real-time data (NRTD) is about equal.

Keywords: Demand side management, direct load control, electric water heater, indirect load control, non-real-time data, real time data.

References:

[1] Brereton, P., Kitchenham, B.A., Budgen, D., Turner, M. and Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain. Journal of Systems and Software, 80(4), pp.571–583.
[2] Popoola, O.M. and Burnier, C.B. (2017). Solar water heater contribution to energy savings in higher education institutions: Impact analysis. Journal of Energy in Southern Africa, 25(1), pp.51–58.
[3] Pimm, A.J., Cockerill, T.T. and Taylor, P.G. (2018). The potential for peak shaving on low voltage distribution networks using electricity storage. Journal of Energy Storage, 16, pp.231–242.Journal of Energy in Southern Africa, 23(1), pp.39–45.
[4] Chiloane, L.L., Kirui, G.K. and Yen, Y.-C.J. (2021). Demand Reduction of Electric Water Heaters for Load-Shedding Recovery using Stochastic Control. 2021 IEEE AFRICON.
[5] Tabari, M. and Yazdani, A. (2015). An Energy Management Strategy for a DC Distribution System for Power System Integration of Plug-In Electric Vehicles. IEEE Transactions on Smart Grid, pp.1–1.
[6] Langerudy, A.T. and Mousavi G, S.M. (2016). Hybrid railway power quality conditioner for high‐capacity traction substation with auto‐tuned DC‐link controller. IET Electrical Systems in Transportation, 6(3), pp.207–214.
[7] Rabiee, A., Mohseni-Bonab, S.M., Parniani, M. and Kamwa, I. (2019). Optimal Cost of Voltage Security Control Using Voltage Dependent Load Models in Presence of Demand Response. IEEE Transactions on Smart Grid, 10(3), pp.2383–2395.
[8] Xia, X., Zhang, J. and Cass, W. (2012). Energy management of commercial buildings - A case study from a POET perspective of energy efficiency. Journal of Energy in Southern Africa,
[online] 23(1), pp.23–31.
[9] Strbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36(12), pp.4419–4426.
[10] Ghent, B.A. (2005). Demand side management of water heater systems. (online) Available at: https://patents.google.com/patent/US6861621B2/en (Accessed 18 Mar. 2023).
[11] Kaseke, N. and Hosking, S.G. (2013). Sub-Saharan Africa Electricity Supply Inadequacy: Implications. Eastern Africa Social Science Research Review, 29(2), pp.113–132.
[12] Aalami, H.A., Moghaddam, M.P. and Yousefi, G.R. (2010). Demand response modeling considering Interruptible/Curtailable loads and capacity market programs. Applied Energy, (online) 87(1), pp.243–250.
[13] Najafi, F. and Fripp, M. (2020). Stochastic optimization of comfort-centered model of electrical water heater using mixed integer linear programming. Sustainable Energy Technologies and Assessments, 42, p.100834.
[14] LaMeres, B.J., Nehrir, M.H. and Gerez, V. (1999). Controlling the average residential electric water heater power demand using fuzzy logic. Electric Power Systems Research, 52(3), pp.267–271.
[15] Moreau, A. (2011). Control Strategy for Domestic Water Heaters during Peak Periods and its Impact on the Demand for Electricity. Energy Procedia, 12, pp.1074–1082.
[16] Jianming Chen, Lee, F.N., Breipohl, A.M. and Adapa, R. (1995). Scheduling direct load control to minimize system operation cost. IEEE Transactions on Power Systems, 10(4), pp.1994–2001.
[17] Kondoh, J. (2011). Direct load control for wind power integration. (online) IEEE Xplore. Available at: https://ieeexplore.ieee.org/abstract/document/6039480 (Accessed 18 Mar. 2023).
[18] Nehrir, M.H., Jia, R., Pierre, D.A. and Hammerstrom, D.J. (2007). Power Management of Aggregate Electric Water Heater Loads by Voltage Control. (online) IEEE Xplore. Available at: https://ieeexplore.ieee.org/abstract/document/4275790 (Accessed 18 Mar. 2023).
[19] Nazir, M.S., Galiana, F.D. and Prieur, A. (2016). Unit Commitment Incorporating Histogram Control of Electric Loads With Energy Storage. IEEE Transactions on Power Systems, 31(4), pp.2857–2866.
[20] Atwa, Y.M., El-Saadany, E.F. and Salama, M.M. (2007). DSM Approach for Water Heater Control Strategy Utilizing Elman Neural
[21] Network. (online) IEEE Xplore. Available at: https://ieeexplore.ieee.org/abstract/document/4520362 (Accessed 18 Mar. 2023).
[22] Faille, D., Mondon, C. and Al-Nasrawi, B. (2007). mCHP Optimization by Dynamic Programming and Mixed Integer Linear Programming. (online) IEEE Xplore. Available at: https://ieeexplore.ieee.org/abstract/document/4441677 (Accessed 18 Mar. 2023).
[23] Pedersen, T. (2014). Forecasting Model of Electricity Demand in the Nordic Countries. lup.lub.lu.se. (online) Available at: http://lup.lub.lu.se/student-papers/record/4394382 (Accessed 18 Mar. 2023).
[24] Goh, C.H.K. and Apt, J., 2004, February. Consumer strategies for controlling electric water heaters under dynamic pricing. In Carnegie Mellon Electricity Industry Center Working Paper.
[25] Alvarez, M.A.Z., Agbossou, K., Cardenas, A., Kelouwani, S. and Boulon, L. (2020). Demand Response Strategy Applied to Residential Electric Water Heaters Using Dynamic Programming and K-Means Clustering. IEEE Transactions on Sustainable Energy, 11(1), pp.524–533.
[26] Lee, S.H. and Wilkins, C.L. (1983). A Practical Approach to Appliance Load Control Analysis: A Water Heater Case Study. IEEE Power Engineering Review, PER-3(5), pp.64–64.
[27] Negnevitsky, M., Voropai, N., Kurbatsky, V., Tomin, N. and Panasetsky, D. (2013). Development of an intelligent system for preventing large-scale emergencies in power systems. (online) IEEE Xplore. Available at: https://ieeexplore.ieee.org/abstract/document/6672099 (Accessed 18 Mar. 2023).
[28] Rautenbach, B. and Lane, I.E. (1996). The multi-objective controller: a novel approach to domestic hot water load control. IEEE Transactions on Power Systems, 11(4), pp.1832–1837.
[29] Ayele, G.T., Mabrouk, M.T., Haurant, P., Laumert, B. and Lacarrière, B. (2021). Optimal heat and electric power flows in the presence of intermittent renewable source, heat storage and variable grid electricity tariff. Energy Conversion and Management, 243, p.114430.
[30] Parvin, K., Hannan, M.A., Al-Shetwi, A.Q., Ker, P.J., Roslan, M.F. and Mahlia, T.M.I. (2020). Fuzzy Based Particle Swarm Optimization for Modeling Home Appliances Towards Energy Saving and Cost Reduction Under Demand Response Consideration. IEEE Access, (online) 8, pp.210784–210799. Available at: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9268107 (Accessed 11 Dec. 2022).
[31] Yildiz, B., Roberts, M., Bilbao, J.I., Heslop, S., Bruce, A., Dore, J., MacGill, I., Egan, R.J. and Sproul, A.B. (2021). Assessment of control tools for utilizing excess distributed photovoltaic generation in domestic electric water heating systems. Applied Energy, 300, p.117411.
[32] Backe, S., Kara, G. and Tomasgard, A. (2020). Comparing individual and coordinated demand response with dynamic and static power grid tariffs. Energy, 201, p.117619.
[33] Hong, S.H., Yu, M. and Huang, X. (2015). A real-time demand response algorithm for heterogeneous devices in buildings and homes. Energy, (online) 80, pp.123–132. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0360544214013085 (Accessed 18 Mar. 2023)
[34] Moradzadeh, M. and Abdelaziz, M., 2020, August. Optimal Demand Control of Electric Water Heaters to Accommodate the Integration of Plug-in Electric Vehicles in Residential Distribution Networks. In 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1-6). IEEE.
[35] Holland, L., Karayaka, H.B., Tanaka, M.L. and Ball, A., 2014, February. An empirical method for estimating thermal system parameters based on operating data in smart grids. In ISGT 2014 (pp. 1-5). IEEE.
[36] Abiri-Jahromi, A. and Bouffard, F., 2015. Contingency-type reserve leveraged through aggregated thermostatically-controlled loads—Part II: Case studies. IEEE Transactions on Power Systems, 31(3), pp.1981-1989.
[37] Daly, P., Qazi, H.W. and Flynn, D., 2019. Rocof-constrained scheduling incorporating non-synchronous residential demand response. IEEE Transactions on Power Systems, 34(5), pp.3372-3383.
[38] Motalleb, M., Thornton, M., Reihani, E. and Ghorbani, R., 2016. Providing frequency regulation reserve services using demand response scheduling. Energy Conversion and Management, 124, pp.439-452.
[39] Lakshmanan, V., Sæle, H. and Degefa, M.Z., 2021. Electric water heater flexibility potential and activation impact in system operator perspective–Norwegian scenario case study. Energy, 236, p.121490.
[40] Tejero-Gómez, J.A. and Bayod-Rújula, A.A. (2021). Energy management system design oriented for energy cost optimization in electric water heaters. Energy and Buildings, 243, p.111012.
[41] Shen, G., Lee, Z.E., Amadeh, A. and Zhang, K.M. (2021). A data-driven electric water heater scheduling and control system. Energy and Buildings, (online) 242, p.110924. Available at: https://www.sciencedirect.com/science/article/pii/S0378778821002085.
[42] Kitchenham, B. and Charters, S. (2007) Guidelines for Performing Systematic Literature Reviews in Software Engineering, Technical Report EBSE 2007-001, Keele University and Durham University Joint Report.
[43] Idri, A., Abnane, I. and Abran, A., 2018. Evaluating Pred (p) and standardized accuracy criteria in software development effort estimation. Journal of Software: Evolution and Process, 30(4), p.e1925.
[44] Balaid, A., Abd Rozan, M.Z., Hikmi, S.N. and Memon, J. (2016). Knowledge maps: A systematic literature review and directions for future research. International Journal of Information Management, 36(3), pp.451–475.
[45] Rekik, R., Kallel, I., Casillas, J. and Alimi, A.M. (2018). Assessing web sites quality: A systematic literature review by text and association rules mining. International Journal of Information Management, 38(1), pp.201–216.
[46] Zahedi, M., Shahin, M. and Ali Babar, M. (2016). A systematic review of knowledge sharing challenges and practices in global software development. International Journal of Information Management, (online) 36(6), pp.995–1019.
[47] Webster, J. and Watson, R.T., 2002. Analyzing the past to prepare for the future: Writing a literature review. MIS quarterly, pp.xiii-xxiii.
[48] Iden, J. and Eikebrokk, T.R. (2013). Implementing IT Service Management: A systematic literature review. International Journal of Information Management, 33(3), pp.512–523.
[49] Mohan, K. and Ahlemann, F. (2013). Understanding acceptance of information system development and management methodologies by actual users: A review and assessment of existing literature. International Journal of Information Management, 33(5), pp.831–839.
[50] Tranfield, D., Denyer, D. and Smart, P., 2003. Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British journal of management, 14(3), pp.207-222.
[51] Busalim, A.H., 2016. Understanding social commerce: A systematic literature review and directions for further research. International Journal of Information Management, 36(6), pp.1075-1088.
[52] Clift, D.H., Stanley, C., Hasan, K.N. and Rosengarten, G. (2022). Assessment of advanced demand response value streams for water heaters in renewable-rich electricity markets. Energy, p.126577.
[53] Shen, G., Lee, Z.E., Amadeh, A. and Zhang, K.M. (2021). A data-driven electric water heater scheduling and control system. Energy and Buildings, (online) 242, p.110924. Available at: https://www.sciencedirect.com/science/article/pii/S0378778821002085 (Accessed 13 Oct. 2021).
[54] Moradzadeh, M. and Abdelaziz, M., 2018, May. Mitigating the impact of plug-in electric vehicles on distribution systems using demand-side management of electric water heaters. In 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE) (pp. 1-5). IEEE.
[55] Ahmed, M.T., Faria, P. and Vale, Z., 2018, October. Financial benefit analysis of an electric water heater with direct load control in demand response. In 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (pp. 1-6). IEEE.
[56] Ahmed, M.T., Faria, P., Abrishambaf, O. and Vale, Z., 2018, July. Electric water heater modelling for direct load control demand response. In 2018 IEEE 16th International Conference on Industrial Informatics (INDIN) (pp. 490-495). IEEE.
[57] Benchekroun, A., Davigny, A., Courtecuisse, V., Coutard, L., Hassam-Ouari, K. and Robyns, B., 2019, October. Demand-Side Management Strategy for Electric Vehicles and Electric Water Heaters Connected to Distribution Grids. In International Conference on Emerging and Renewable Energy: Generation and Automation (ICEREGA’19).
[58] Nel, P.J.C., Booysen, M.T. and van der Merwe, B., 2015, July. Using thermal transients at the outlet of electrical water heaters to recognise consumption patterns for heating schedule optimization. In 2015 7th International Conference on New Technologies, Mobility and Security (NTMS) (pp. 1-5). IEEE.
[59] Wong, K. and Negnevitsky, M., 2013, September. Optimization of switching programs for demand side management of domestic hot water load. In 2013 Australasian Universities Power Engineering Conference (AUPEC) (pp. 1-6). IEEE.
[60] Saleh, S.A., Ozkop, E., Castillo-Guerra, E. and Pijnenburg, P.C., 2019. Developing and testing a unit-commitment-based controller of bus-split aggregated residential electric water heaters. IEEE Transactions on Industry Applications, 56(2), pp.1124-1135.
[61] Roux, M., Apperley, M. and Booysen, M.J. (2018). Comfort, peak load and energy: Centralised control of water heaters for demand-driven prioritization. Energy for Sustainable Development, 44, pp.78–86.
[62] Nehrir, M.H., LaMeres, B.J. and Gerez, V., 1999, January. A customer-interactive electric water heater demand-side management strategy using fuzzy logic. In IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No. 99CH36233) (Vol. 1, pp. 433-436). IEEE.
[63] Kapsalis, V. and Hadellis, L. (2017). Optimal operation scheduling of electric water heaters under dynamic pricing. Sustainable Cities and Society, 31, pp.109–121.
[64] Xue, S., Che, Y., He, W., Zhao, Y. and Zhang, R., 2019. Control strategy of electric heating loads for reducing power shortage in power grid. Processes, 7(5), p.273.
[65] Al-Jabery, K., Wunsch, D.C., Xiong, J. and Shi, Y., 2014, November. A novel grid load management technique using electric water heaters and Q-learning. In 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm) (pp. 776-781). IEEE.
[66] Ruelens, F., Claessens, B.J., Quaiyum, S., De Schutter, B., Babuška, R. and Belmans, R., 2016. Reinforcement learning applied to an electric water heater: From theory to practice. IEEE Transactions on Smart Grid, 9(4), pp.3792-3800.
[67] Halbe, S., Chowdhury, B. and Abbas, A., 2019, October. Mitigating rebound effect of demand response using battery energy storage and electric water heaters. In 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT) (pp. 095-099). IEEE.
[68] Khan, M.W., Mahmood, A., Ayaz, M.H., Waseem, M., Razzaq, S. and Javaid, N., 2015, November. Optimized Energy Management System Using Electric Water Heater. In 2015 10th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA) (pp. 311-317). IEEE.
[69] Wong, K. and Negnevitsky, M., 2013, July. Development of an evaluation tool for demand side management of domestic hot water load. In 2013 IEEE Power & Energy Society General Meeting (pp. 1-5). IEEE.
[70] Booysen, M.J. and Cloete, A.H., 2016, August. Sustainability through intelligent scheduling of electric water heaters in a smart grid. In 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech) (pp. 848-855). IEEE.
[71] Fan, W., Liu, N. and Zhang, J., 2015, November. Multi-objective optimization model for energy mangement of household micro-grids participating in demand response. In 2015 IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA) (pp. 1-6). IEEE.
[72] Shad, M., Momeni, A., Errouissi, R., Diduch, C.P., Kaye, M.E. and Chang, L., 2015. Identification and estimation for electric water heaters in direct load control programs. IEEE Transactions on Smart Grid, 8(2), pp.947-955.
[73] Vrettos, E., Koch, S. and Andersson, G., 2012, October. Load frequency control by aggregations of thermally stratified electric water heaters. In 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) (pp. 1-8). IEEE.
[74] Sepulveda, A., Paull, L., Morsi, W.G., Li, H., Diduch, C.P. and Chang, L., 2010, August. A novel demand side management program using water heaters and particle swarm optimization. In 2010 IEEE Electrical Power & Energy Conference (pp. 1-5). IEEE.