Load forecasting, dynamic pricing and DSM in smart grid: A review

Load forecasting (LF) plays important role in planning and operation of power systems. It is envisioned that future smart grids will utilize LF and dynamic pricing based techniques for effective Demand Side Management (DSM). This paper presents a comprehensive and comparative review of the LF and dynamic pricing schemes in smart grid environment. Real Time Pricing (RTP), Time of Use (ToU) and Critical Peak Pricing (CPP) are discussed in detail. Two major categories of LF: mathematical and artificial intelligence based computational models are elaborated with subcategories. Mathematical models including auto recursive, moving average, auto recursive moving average, auto recursive integrated moving average, exponential smoothing, iterative reweighted mean square, multiple regression, etc. used for effective DSM are discussed. Neural networks, fuzzy logic, expert systems of the second major category of LF models have also been described.

[1]  Jianwei Huang,et al.  Demand Response Management via Real-Time Electricity Price Control in Smart Grids , 2013 .

[2]  Muhammad Asim Qayyum,et al.  SHORT-TERM PEAK DEMAND FORECASTING IN FAST DEVELOPING UTILITY WITH INHERIT DYNAMIC LOAD CHARACTERISTICS , 1990 .

[3]  Thillainathan Logenthiran,et al.  Demand Side Management in Smart Grid Using Heuristic Optimization , 2012, IEEE Transactions on Smart Grid.

[4]  R.E. Brown,et al.  Impact of Smart Grid on distribution system design , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[5]  Yoh-Han Pao,et al.  Unsupervised/supervised learning concept for 24-hour load forecasting , 1993 .

[6]  Hye-Jin Kim,et al.  Energy Consumption Scheduler for Demand Response Systems in the Smart Grid , 2012, J. Inf. Sci. Eng..

[7]  Saifur Rahman,et al.  Analysis and Evaluation of Five Short-Term Load Forecasting Techniques , 1989, IEEE Power Engineering Review.

[8]  Y. Hwang,et al.  Experimental investigation on energy and exergy performance of adsorption cold storage for space cooling application , 2014 .

[9]  Ying Sun,et al.  A demand side management model based on advanced metering infrastructure , 2011, 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT).

[10]  Saifur Rahman,et al.  A generalized knowledge-based short-term load-forecasting technique , 1993 .

[11]  Vincent W. S. Wong,et al.  Tackling the Load Uncertainty Challenges for Energy Consumption Scheduling in Smart Grid , 2013, IEEE Transactions on Smart Grid.

[12]  Lingfeng Wang,et al.  Integration of plug-in hybrid electric vehicles into building energy management system , 2011, 2011 IEEE Power and Energy Society General Meeting.

[13]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[14]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[15]  Yong Tae Yoon,et al.  Designing a critical peak pricing scheme for the profit maximization objective considering price responsiveness of customers , 2015 .

[16]  David Infield,et al.  Optimal smoothing for trend removal in short term electricity demand forecasting , 1998 .

[17]  Jun Zhu,et al.  Comparison of different approaches to short-term load forecasting , 1995 .

[18]  J. D. McDonald,et al.  A real-time implementation of short-term load forecasting for distribution power systems , 1994 .

[19]  Tai-Lang Jong,et al.  A Novel Direct Air-Conditioning Load Control Method , 2008, IEEE Transactions on Power Systems.

[20]  H. Mori,et al.  Optimal fuzzy inference for short-term load forecasting , 1995 .

[21]  E. H. Barakat,et al.  New model for peak demand forecasting applied to highly complex load characteristics of a fast developing area , 1992 .

[22]  Masanobu Kii,et al.  The Effects of Critical Peak Pricing for Electricity Demand Management on Home-based Trip Generation , 2014 .

[23]  Z. S. Elrazaz,et al.  Unified weekly peak load forecasting for fast growing power system , 1989 .

[24]  Mahmud Fotuhi-Firuzabad,et al.  Improving direct load control implementation by an inititative load control method , 2013, 18th Electric Power Distribution Conference.

[25]  J. E. Payne,et al.  A survey of the electricity consumption-growth literature , 2010 .

[26]  Nadeem Javaid,et al.  Pakistan's overall energy potential assessment, comparison of LNG, TAPI and IPI gas projects , 2014 .

[27]  Y. Hwang,et al.  Review of cold storage materials for air conditioning application , 2012 .

[28]  Hong-Tzer Yang,et al.  A load forecasting method for HEMS applications , 2013, 2013 IEEE Grenoble Conference.

[29]  Derek W. Bunn,et al.  Large neural networks for electricity load forecasting: Are they overfitted? , 2005 .

[30]  Tomonobu Senjyu,et al.  Smart pricing scheme: A multi-layered scoring rule application , 2014, Expert Syst. Appl..

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

[32]  C.W. Gellings,et al.  The concept of demand-side management for electric utilities , 1985, Proceedings of the IEEE.

[33]  J. Nizami,et al.  A regression model for electric-energy-consumption forecasting in Eastern Saudi Arabia , 1994 .

[34]  Ivan Stojmenovic,et al.  A novel game-based demand side management scheme for smart grid , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[35]  Taskin Koçak,et al.  Smart Grid Technologies: Communication Technologies and Standards , 2011, IEEE Transactions on Industrial Informatics.

[36]  O. Hyde,et al.  An adaptable automated procedure for short-term electricity load forecasting , 1997 .

[37]  V. Vittal,et al.  A Framework for Evaluation of Advanced Direct Load Control With Minimum Disruption , 2008, IEEE Transactions on Power Systems.

[38]  Giuseppe Tommaso Costanzo,et al.  A System Architecture for Autonomous Demand Side Load Management in Smart Buildings , 2012, IEEE Transactions on Smart Grid.

[39]  Jin-ho Kim,et al.  Common failures of demand response , 2011 .

[40]  L.A.L. de Almeida,et al.  Demand side management using artificial neural networks in a smart grid environment , 2015 .

[41]  Hongzhan Nie,et al.  Hybrid of ARIMA and SVMs for Short-Term Load Forecasting , 2012 .

[42]  Guy R. Newsham,et al.  The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review , 2010 .

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

[44]  Gang Li,et al.  Review of cold storage materials for subzero applications , 2013 .

[45]  Chao-Ming Huang,et al.  Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting , 1995 .

[46]  Farrokh Rahimi,et al.  Demand Response as a Market Resource Under the Smart Grid Paradigm , 2010, IEEE Transactions on Smart Grid.

[47]  A. S. Anees Grid integration of renewable energy sources: Challenges, issues and possible solutions , 2012, 2012 IEEE 5th India International Conference on Power Electronics (IICPE).

[48]  Hamidreza Zareipour,et al.  Home energy management systems: A review of modelling and complexity , 2015 .

[49]  Damir Novosel,et al.  Grids get smart protection and control , 1997 .

[50]  Kishan Bhushan Sahay,et al.  Day ahead hourly load and price forecast in ISO New England market using ANN , 2013, 2013 Annual IEEE India Conference (INDICON).

[51]  Anil Pahwa,et al.  Economic evaluation of small wind generation ownership under different electricity pricing scenarios , 2010, North American Power Symposium 2010.

[52]  Saifur Rahman,et al.  Impact of TOU rates on distribution load shapes in a smart grid with PHEV penetration , 2010, IEEE PES T&D 2010.

[53]  Tongquan Wei,et al.  Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home , 2013, IEEE Transactions on Smart Grid.

[54]  Hamidreza Zareipour,et al.  Environmental benefits of plug-in hybrid electric vehicles: The case of Alberta , 2009, 2009 IEEE Power & Energy Society General Meeting.

[55]  Tomonobu Senjyu,et al.  Next day load curve forecasting using recurrent neural network structure , 2004 .

[56]  Michael T. Manry,et al.  Comparison of very short-term load forecasting techniques , 1996 .

[57]  Gang Li,et al.  Review of Thermal Energy Storage Technologies and Experimental Investigation of Adsorption Thermal Energy Storage for Residential Application , 2013 .

[58]  Jun Wang,et al.  Smart grid technologies , 2009, IEEE Industrial Electronics Magazine.

[59]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

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

[61]  S. Muto,et al.  Regression based peak load forecasting using a transformation technique , 1994 .

[62]  H. T. Mouftah,et al.  TOU-Aware Energy Management and Wireless Sensor Networks for Reducing Peak Load in Smart Grids , 2010, 2010 IEEE 72nd Vehicular Technology Conference - Fall.

[63]  V. Lo Brano,et al.  Forecasting daily urban electric load profiles using artificial neural networks , 2004 .

[64]  S. Widergren,et al.  Real-time pricing demand response in operations , 2012, 2012 IEEE Power and Energy Society General Meeting.

[65]  M. El-Hawary,et al.  Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation , 1993 .

[66]  Yue Yuan,et al.  Analysis of the environmental benefits of Distributed Generation , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[67]  Nadeem Javaid,et al.  A review of wireless communications for smart grid , 2015 .

[68]  S. Huang,et al.  Short-term load forecasting using threshold autoregressive models , 1997 .

[69]  Chuin-Shan Chen,et al.  Customer short term load forecasting by using ARIMA transfer function model , 1995, Proceedings 1995 International Conference on Energy Management and Power Delivery EMPD '95.

[70]  M. P. Abdullah,et al.  Time-based electricity pricing for Demand Response implementation in monopolized electricity market , 2012, 2012 IEEE Student Conference on Research and Development (SCOReD).

[71]  Hoseong Lee,et al.  Experimental investigation of energy and exergy performance of short term adsorption heat storage for residential application , 2014 .

[72]  R.-H. Liang,et al.  Fuzzy linear programming: an application to hydroelectric generation scheduling , 1994 .