Peak shaving: a planning alternative to reduce investment costs in distribution systems?
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In the future, the foreseen increase of residential electricity consumption will force the Distribution System Operators to reinforce their networks at great expense. Through the emergence of ICT solutions and the increase of electric consumption flexibility at residential level, peak shaving has become an interesting alternative for reducing the investment costs in a distribution grid facing a load increase. This can be achieved with energy management systems (EMS) installed at residential level. Specifically, this work aims at considering peak shaving as an alternative to network reinforcement in a 20-year distribution planning study. For this purpose, the present work incorporates an optimal peak shaving approach to an accurate Convex DistFlow-based planning approach. Based on this, it quantifies how peak shaving can economically compete with network reinforcements for 12 real UK distribution networks under various flexibility scenarios. The results highlight that peak shaving is a competitive alternative to line reinforcement if the maximum initial line loading at the initial year of the planning study is under 80% of its nominal thermal rating value. It is also shown that EMS devices with a cost between 10 and 250 £/unit are economically competitive with network reinforcements depending on the considered network. Finally, this work proposes a planning decision metric, the initial line loading (ILL), measured at the beginning of the planning study, on the basis of which reinforcement decisions can be made.
KeywordsDistribution planning Energy management system Flexibility Peak shaving
The authors gratefully acknowledge BMBF (German Federal Ministry of Education and Research) for providing financial support, promotional reference 13N13297.
- 1.Element Energy Limited: Further analysis of data from the household electricity usage study: correlation of consumption with low carbon technologies. In: Final report for Department of Energy and Climate Change and Department for the Environment Food and Rural Affairs (2014)Google Scholar
- 3.Molzahn, D.K., Hiskens, I.A.: A survey of relaxations and approximations of the power flow equations. Found, Trends Electr Energy Syst (2017)Google Scholar
- 4.Farivar, M., Clarke, C.R., Low, S.H., Chandy, K.M.: Inverter VAR control for distribution systems with renewables. In: 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 457–462 (2011)Google Scholar
- 6.Martin, B., De Rua, P., De Jaeger, E., Glineur, F.: Loss reduction in a windfarm participating in primary voltage control using an extension of the Convex DistFlow OPF (Forthcoming). In: 20th Power Systems Computation Conference, (Dublin) (2018)Google Scholar
- 8.Dugan, R.C., McDermott, T.E., Ball, G.J.: Distribution planning for distributed generation. In Rural Electric Power Conference, IEEE, pp. C4–1 (2000)Google Scholar
- 10.Neimane, V.: On development planning of electricity distribution networks. PhD thesis, Royal Institute of Technology, Department of Electrical Engineering, Stockholm (2001)Google Scholar
- 11.Pilo, F., Jupe, S., Abbey, C., Baitch, A., Bak-Jensen, B., Carter-Brown, C., Celli, G., El Bakari, K., Fan, M., Georgilakis, P., Hearne, T., Ochoa, L., Petretto, G., Taylor, J.: Planning and optimization methods for active distribution systems. CIGRE, C6.19 working groupGoogle Scholar
- 12.Gemine, Q., Ernst, D., Cornlusse, B.: Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution, (2014). ArXiv preprint arXiv:1405.2806
- 17.Oudalov, A., Cherkaoui, R., Beguin, A.: Sizing and optimal operation of battery energy storage system for peak shaving application. In: 2007 IEEE Lausanne Power Tech, pp. 621–625 (2007)Google Scholar
- 18.Rahimi, A., Zarghami, M., Vaziri, M., Vadhva, S.: A simple and effective approach for peak load shaving using battery storage systems. In: 2013 North American Power Symposium (NAPS), pp. 1–5 (2013)Google Scholar
- 19.Molderink, A., Bakker, V., Bosman, M.G.C., Hurink, J.L., Smit, G.J.M.: Domestic energy management methodology for optimizing efficiency in Smart Grids. In: 2009 IEEE Bucharest PowerTech, pp. 1–7 (2009)Google Scholar
- 22.Mets, K., Verschueren, T., Turck, F., Develder, C.: Exploiting v2g to optimize residential energy consumption with electrical vehicle (dis)charging. In: 2011 IEEE First International Workshop on Smart Grid Modeling and Simulation (SGMS) (2011)Google Scholar
- 23.Feron, B.: An agent based approach for virtual power plant valuing thermal flexibility in energy markets. In: IEEE Powertech (2017)Google Scholar
- 24.Molitor, C.: Residential city districts as flexibility resource analysis simulation and decentralized coordination algorithms. PhD Thesis (2015)Google Scholar
- 26.Yu, T., Kim, D.S., Son, S.-Y.: Optimization of scheduling for home appliances in conjunction with renewable and energy storage resources. Int. J. Smart Home 7(4), 261–272 (2013)Google Scholar
- 29.Mathieu, J.: Modeling, analysis, and control of demand response resources. PhD thesis, University of California, Berkeley (2012)Google Scholar
- 30.Martin, B., De Jaeger, E., Glineur, F.: A comparison of convex formulations for the joint planning of microgrids. CIRED-Open Access Proc. J. (2017)Google Scholar
- 33.Espinosa, A.N.: Dissemination document low voltage networks models and low carbon technology profiles. Technical report, University of Manchester and Electricity North West Limited, UK (2015)Google Scholar
- 34.Electricity North West Limited: Statement of methodology and charges for connection of Electricity North West Limited’s electricity distribution. Tech, Rep (2015)Google Scholar