A mathematical modelling framework for optimal demand response incentives and retrofit

In this paper, a mathematical optimization model is presented for a demand response program in South Africa. The demand response scheme termed Standard Product Program encourages customers to replace inefficient energy appliances with energy efficient ones. The state owned utility, pays an incentive to willing and participating customers. The incentive to be offered by the utility is very important and there is a need for it to be optimal, attractive to customers and at the same time achieve desired program objectives. The mathematical model presented in this paper is a multi-objective optimization problem with the first objective to minimize the utility's levelized cost and the second to maximize the total power saved. Model validation is done using data from the South African Standard Product Program. Obtained results indicate the robustness, practicality and accuracy of the developed model.

[1]  Nnamdi I. Nwulu,et al.  A neural network model for optimal demand management contract design , 2011, 2011 10th International Conference on Environment and Electrical Engineering.

[2]  Florian Leuthold,et al.  Feed-In Tariffs for Photovoltaics: Learning by Doing in Germany , 2009 .

[3]  Vincent W. S. Wong,et al.  Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid , 2010, IEEE Transactions on Smart Grid.

[4]  Paolo Fantini,et al.  A Method for Assisting the Study of Pareto Solutions in Multi-Objective Optimization , 2007 .

[5]  Xiaohua Xia,et al.  A multiple objective optimisation model for building energy efficiency investment decision , 2013 .

[6]  X. Xia,et al.  Energy management of commercial buildings - A case study from a POET perspective of energy efficiency , 2017 .

[7]  Xiaohua Xia,et al.  Mathematical description for the measurement and verification of energy efficiency improvement , 2013 .

[8]  David W. Coit,et al.  Practical solutions for multi-objective optimization: An application to system reliability design problems , 2007, Reliab. Eng. Syst. Saf..

[9]  F. Alvarado,et al.  Designing incentive compatible contracts for effective demand management , 2000 .

[10]  J. Sweeney,et al.  Learning-by-Doing and the Optimal Solar Policy in California , 2008 .

[11]  Johannes Bisschop,et al.  AIMMS - Optimization Modeling , 2006 .

[12]  Fernando L. Alvarado,et al.  Using utility information to calibrate customer demand management behavior models , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[13]  M. P. Moghaddam,et al.  Demand response modeling considering Interruptible/Curtailable loads and capacity market programs , 2010 .

[14]  Andrew Higgins,et al.  Evaluating intervention options to achieve environmental benefits in the residential sector , 2012, Sustainability Science.

[15]  Hassan Abniki,et al.  Load profile reformation through demand response programs using Smart Grid , 2010, 2010 Modern Electric Power Systems.

[16]  Nnamdi I. Nwulu,et al.  Investigating a Ranking of Loads in Avoiding Potential Power System Outages , 2012 .

[17]  Nnamdi I. Nwulu,et al.  A soft computing approach to projecting locational marginal price , 2012, Neural Computing and Applications.

[18]  G.R. Yousefi,et al.  Demand Response model considering EDRP and TOU programs , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[19]  Hanne Sæle,et al.  Demand Response From Household Customers: Experiences From a Pilot Study in Norway , 2011, IEEE Transactions on Smart Grid.

[20]  C. Goldman,et al.  Option value of electricity demand response , 2005 .

[21]  Ulf Schlichtmann,et al.  A Successive Approach to Compute the Bounded Pareto Front of Practical Multiobjective Optimization Problems , 2009, SIAM J. Optim..