Peak shaving: a planning alternative to reduce investment costs in distribution systems?

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.

[1]  Won Cheol Lee,et al.  An Optimal Power Scheduling Method Applied in Home Energy Management System Based on Demand Response , 2013 .

[2]  Reza Ghorbani,et al.  Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration , 2016 .

[3]  S. Vadhva,et al.  A simple and effective approach for peak load shaving using Battery Storage Systems , 2013, 2013 North American Power Symposium (NAPS).

[4]  M. F. Abdullah,et al.  A review on peak load shaving strategies , 2018 .

[5]  Zhenpo Wang,et al.  Grid Power Peak Shaving and Valley Filling Using Vehicle-to-Grid Systems , 2013, IEEE Transactions on Power Delivery.

[6]  Antonello Monti,et al.  Residential city districts as flexibility resource : analysis, simulation, and decentralized coordination algorithms , 2015 .

[7]  N. C. Sahoo,et al.  Recent advances on power distribution system planning: a state-of-the-art survey , 2013 .

[8]  K. Mani Chandy,et al.  Inverter VAR control for distribution systems with renewables , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[9]  Damien Ernst,et al.  Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution , 2014, 1405.2806.

[10]  Ian A. Hiskens,et al.  A Survey of Relaxations and Approximations of the Power Flow Equations , 2019, Foundations and Trends® in Electric Energy Systems.

[11]  Tooraj Jamasb,et al.  Distributed Generation Storage, Demand Response, and Energy Efficiency as Alternatives to Grid Capacity Enhancement , 2014 .

[12]  Lingfeng Wang,et al.  Smart charging and appliance scheduling approaches to demand side management , 2014 .

[13]  Johanna L. Mathieu Modeling, Analysis, and Control of Demand Response Resources , 2012 .

[14]  Roger C. Dugan,et al.  Distribution planning for distributed generation , 2000, 2000 Rural Electric Power Conference. Papers Presented at the 44th Annual Conference (Cat. No.00CH37071).

[15]  Alejandro Navarro-Espinosa,et al.  Probabilistic Impact Assessment of Low Carbon Technologies in LV Distribution Systems , 2016, IEEE Transactions on Power Systems.

[16]  Arkadi Nemirovski,et al.  On Polyhedral Approximations of the Second-Order Cone , 2001, Math. Oper. Res..

[17]  A. Oudalov,et al.  Sizing and Optimal Operation of Battery Energy Storage System for Peak Shaving Application , 2007, 2007 IEEE Lausanne Power Tech.

[18]  Sung-Yong Son,et al.  Optimization of Scheduling for Home Appliances in Conjunction with Renewable and Energy Storage Resources , 2013 .

[19]  L.F. Ochoa,et al.  Distribution network capacity assessment: Variable DG and active networks , 2010, IEEE PES General Meeting.

[20]  François Glineur,et al.  A comparison of convex formulations for the joint planning of microgrids , 2017 .

[21]  Antonello Monti,et al.  An agent based approach for Virtual Power Plant valuing thermal flexibility in energy markets , 2017, 2017 IEEE Manchester PowerTech.

[22]  Chris Develder,et al.  Exploiting V2G to optimize residential energy consumption with electrical vehicle (dis)charging , 2011, 2011 IEEE First International Workshop on Smart Grid Modeling and Simulation (SGMS).

[23]  Hamed Mohsenian Rad,et al.  Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments , 2010, IEEE Transactions on Smart Grid.

[24]  Karen Herter Residential implementation of critical-peak pricing of electricity , 2007 .

[25]  Ufuk Topcu,et al.  Exact Convex Relaxation of Optimal Power Flow in Radial Networks , 2013, IEEE Transactions on Automatic Control.

[26]  Viktoria Neimane,et al.  On Development Planning of Electricity Distribution Networks , 2001 .

[27]  Kashem M. Muttaqi,et al.  A Controllable Local Peak-Shaving Strategy for Effective Utilization of PEV Battery Capacity for Distribution Network Support , 2014, IEEE Transactions on Industry Applications.

[28]  Albert Molderink,et al.  Domestic energy management methodology for optimizing efficiency in Smart Grids , 2009, 2009 IEEE Bucharest PowerTech.

[29]  François Glineur,et al.  Loss Reduction in a Windfarm Participating in Primary Voltage Control Using an Extension of the Convex DistFlow OPF , 2018, 2018 Power Systems Computation Conference (PSCC).