A systematic review of supply and demand side optimal load scheduling in a smart grid environment

Abstract Optimal load scheduling is an important optimization problem in the power system, which can bring significant economic benefits for all the market participants and environmental benefits for the society. As two main links of power system, supply side and demand side play important roles for the operation management of electricity market. In this regard, we present a systematic review of the optimal load scheduling models and methods from power supply and demand side. First, the optimal load scheduling of supply side is discussed from two aspects, i.e., unit commitment and optimal load dispatch of microgrid. Then, we focus on the optimal load scheduling of demand side under the environment of price-based and incentive-based demand response (DR). In addition, the joint optimal load scheduling of supply and demand side is further discussed. Also, the methods for solving the optimal load scheduling models are summarized, including conventional mathematical optimization, heuristic optimization and data-driven optimization methods.

[1]  F. Magnago,et al.  Impact of demand response resources on unit commitment and dispatch in a day-ahead electricity market , 2015 .

[2]  Zeyi Sun,et al.  Customer-side electricity load management for sustainable manufacturing systems utilizing combined heat and power generation system , 2015 .

[3]  B. Vahidi,et al.  A Solution to the Unit Commitment Problem Using Imperialistic Competition Algorithm , 2012, IEEE Transactions on Power Systems.

[4]  W. L. Kling,et al.  Day-ahead residential load forecasting with artificial neural networks using smart meter data , 2013, 2013 IEEE Grenoble Conference.

[5]  Noel Augustine,et al.  Economic dispatch for a microgrid considering renewable energy cost functions , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[6]  Dong Wei,et al.  Multi-objective economic dispatch model for a microgrid considering reliability , 2010, The 2nd International Symposium on Power Electronics for Distributed Generation Systems.

[7]  Shanlin Yang,et al.  Demand side management in China: The context of China’s power industry reform , 2015 .

[8]  Amin Kargarian,et al.  Partition-based bus renumbering effect on interior point-based OPF solution , 2018, 2018 IEEE Texas Power and Energy Conference (TPEC).

[9]  Shahram Jadid,et al.  Stochastic operational scheduling of smart distribution system considering wind generation and demand response programs , 2014 .

[10]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[11]  Xin Li,et al.  On the suitability of plug-in hybrid electric vehicle (PHEV) charging infrastructures based on wind and solar energy , 2009, 2009 IEEE Power & Energy Society General Meeting.

[12]  Carlos Silva,et al.  Optimal electricity dispatch on isolated mini-grids using a demand response strategy for thermal storage backup with genetic algorithms , 2015 .

[13]  Jiyong Eom,et al.  Demand responses of Korean commercial and industrial businesses to critical peak pricing of electricity , 2015 .

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

[15]  Zhi Chen,et al.  Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization , 2012, IEEE Transactions on Smart Grid.

[16]  Ma Li.  Smart Grid and Electricity Market , 2010 .

[17]  Zita Vale,et al.  A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers , 2015 .

[18]  M. Carrion,et al.  A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem , 2006, IEEE Transactions on Power Systems.

[19]  Mohammad Norouzi,et al.  Mixed integer programming of multi-objective security-constrained hydro/thermal unit commitment , 2014 .

[20]  Shanlin Yang,et al.  Multi-objective optimal load dispatch of microgrid with stochastic access of electric vehicles , 2018, Journal of Cleaner Production.

[21]  Arif S. Malik.  Modelling and economic analysis of DSM programs in generation planning , 2001 .

[22]  Prakash Kumar Hota,et al.  Economic emission load dispatch through fuzzy based bacterial foraging algorithm , 2010 .

[23]  Vassilios G. Agelidis,et al.  Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand , 2016 .

[24]  Haitao Liu,et al.  Multi-Objective Dynamic Economic Dispatch of Microgrid Systems Including Vehicle-to-Grid , 2015 .

[25]  M. Parsa Moghaddam,et al.  Modeling and prioritizing demand response programs in power markets , 2010 .

[26]  Zhongfu Tan,et al.  Joint optimization model of generation side and user side based on energy-saving policy , 2014 .

[27]  Wilfried Elmenreich,et al.  Residential demand response scheme based on adaptive consumption level pricing , 2016 .

[28]  Ming Ding,et al.  Dynamic economic dispatch of a microgrid: Mathematical models and solution algorithm , 2014 .

[29]  Iain MacGill,et al.  Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services , 2010, IEEE Transactions on Smart Grid.

[30]  N. Nwulu,et al.  Implementing a model predictive control strategy on the dynamic economic emission dispatch problem with game theory based demand response programs , 2015 .

[31]  Daniel S. Cohan,et al.  Potential emissions reductions from grandfathered coal power plants in the United States , 2011 .

[32]  George Gross,et al.  A conceptual framework for the vehicle-to-grid (V2G) implementation , 2009 .

[33]  Ong Hang See,et al.  A review of residential demand response of smart grid , 2016 .

[34]  A. A. Abou El-Ela,et al.  Maximal optimal benefits of distributed generation using genetic algorithms , 2010 .

[35]  Shoorangiz Shams Shamsabad Farahani,et al.  Using Exponential Modeling for DLC Demand Response Programs in Electricity Markets , 2012 .

[36]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[37]  Sung-Kwan Joo,et al.  Holiday Load Forecasting Using Fuzzy Polynomial Regression With Weather Feature Selection and Adjustment , 2012, IEEE Transactions on Power Systems.

[38]  Marija D. Ilic,et al.  Smart residential energy scheduling utilizing two stage Mixed Integer Linear Programming , 2015, 2015 North American Power Symposium (NAPS).

[39]  S. Pachauri,et al.  Elasticities of electricity demand in urban Indian households , 2004 .

[40]  Moataz Elsied,et al.  Optimal economic and environment operation of micro-grid power systems , 2016 .

[41]  S. Sitharama Iyengar,et al.  A Panorama of Future Interdependent Networks: From Intelligent Infrastructures to Smart Cities , 2018 .

[42]  M. Lijesen.  The real-time price elasticity of electricity , 2007 .

[43]  Johanna L. Mathieu,et al.  Data-driven optimization approaches for optimal power flow with uncertain reserves from load control , 2015, 2015 American Control Conference (ACC).

[44]  Ali Mohammadi,et al.  Spectrum allocation using fuzzy logic with optimal power in wireless network , 2014, 2014 4th International Conference on Computer and Knowledge Engineering (ICCKE).

[45]  A. Schroeder.  Modeling storage and demand management in power distribution grids , 2011 .

[46]  Zita Vale,et al.  Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming , 2010 .

[47]  Jan T. Bialasiewicz,et al.  Power-Electronic Systems for the Grid Integration of Renewable Energy Sources: A Survey , 2006, IEEE Transactions on Industrial Electronics.

[48]  Gwo-Ching Liao,et al.  The optimal economic dispatch of smart Microgrid including Distributed Generation , 2013, 2013 International Symposium on Next-Generation Electronics.

[49]  Tomonobu Senjyu,et al.  Optimal Thermal Unit Commitment Integrated with Renewable Energy Sources Using Advanced Particle Swarm Optimization , 2009 .

[50]  Z. Vale,et al.  Demand response in electrical energy supply: An optimal real time pricing approach , 2011 .

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

[52]  Katarina Kostkova,et al.  An introduction to load management , 2013 .

[53]  Gwo-Ching Liao,et al.  Solve environmental economic dispatch of Smart MicroGrid containing distributed generation system – Using chaotic quantum genetic algorithm , 2012 .

[54]  H. Asanol,et al.  Economic Analysis of Microgrids , 2007, 2007 Power Conversion Conference - Nagoya.

[55]  Naveed Arshad,et al.  Integrating renewables economic dispatch with demand side management in micro-grids: a genetic algorithm-based approach , 2014 .

[56]  Shanlin Yang,et al.  Understanding household energy consumption behavior: The contribution of energy big data analytics , 2016 .

[57]  Zhou Kai-l,et al.  Power Economic Dispatch of Microgrid Based on Genetic Algorithm , 2014 .

[58]  Abdollah Kavousi-Fard,et al.  Impact of plug-in hybrid electric vehicles charging demand on the optimal energy management of renewable micro-grids , 2014 .

[59]  Abbas Khosravi,et al.  A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources , 2015 .

[60]  Kincho H. Law,et al.  Bayesian Ascent: A Data-Driven Optimization Scheme for Real-Time Control With Application to Wind Farm Power Maximization , 2016, IEEE Transactions on Control Systems Technology.

[61]  P. K. Chattopadhyay,et al.  Evolutionary programming techniques for economic load dispatch , 2003, IEEE Trans. Evol. Comput..

[62]  F. Chan,et al.  Optimal scheduling of household appliances for smart home energy management considering demand response , 2017, Natural Hazards.

[63]  Martin Wietschel,et al.  Integration of intermittent renewable power supply using grid-connected vehicles – A 2030 case study for California and Germany , 2013 .

[64]  Lu Qian,et al.  Typical Applications and Prospects of Game Theory in Power System , 2014 .

[65]  Eric Campo,et al.  A review of smart homes - Present state and future challenges , 2008, Comput. Methods Programs Biomed..

[66]  Theocharis Tsoutsos,et al.  Renewable energy sources project appraisal under uncertainty: the case of wind energy exploitation within a changing energy market environment , 2002 .

[67]  W.L. Kling,et al.  Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch , 2007, IEEE Transactions on Energy Conversion.

[68]  Massimo Filippini,et al.  Swiss residential demand for electricity by time of use: An application of the almost ideal demand system , 1995 .

[69]  Guang Li,et al.  Two-stage network constrained robust unit commitment problem , 2014, Eur. J. Oper. Res..

[70]  Lin Li,et al.  “Just-for-Peak” buffer inventory for peak electricity demand reduction of manufacturing systems , 2013 .

[71]  V. Ganesh,et al.  Implementation of clustering based unit commitment employing imperialistic competition algorithm , 2016 .

[72]  Jiangfeng Zhang,et al.  Optimal scheduling of household appliances for demand response , 2014 .

[73]  Lennart Söder,et al.  Distributed generation : a definition , 2001 .

[74]  Ahad Kazemi,et al.  The optimization of demand response programs in smart grids , 2016 .

[75]  Shahram Jadid,et al.  Integrated scheduling of renewable generation and demand response programs in a microgrid , 2014 .

[76]  Heikki N. Koivo,et al.  Multiobjective optimization using modified game theory for online management of microgrid , 2011 .

[77]  Amin Kargarian,et al.  Diagonal Quadratic Approximation for Decentralized Collaborative TSO+DSO Optimal Power Flow , 2019, IEEE Transactions on Smart Grid.

[78]  Giri Venkataramanan,et al.  Financial incentives to encourage demand response participation by plug-in hybrid electric vehicle owners , 2010, 2010 IEEE Energy Conversion Congress and Exposition.

[79]  S. Gyamfi,et al.  Residential peak electricity demand response—Highlights of some behavioural issues , 2013 .

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

[81]  Narayana Prasad Padhy,et al.  Thermal unit commitment using binary/real coded artificial bee colony algorithm , 2012 .

[82]  Xiaohua Xia,et al.  Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs , 2015 .

[83]  C. T. Ng,et al.  Electricity time-of-use tariff with consumer behavior consideration , 2013 .

[84]  Muhammad Babar,et al.  A Novel Algorithm for Demand Reduction Bid based Incentive Program in Direct Load Control , 2013 .

[85]  Shanlin Yang,et al.  Optimal load distribution model of microgrid in the smart grid environment , 2014 .

[86]  Massimo Filippini,et al.  Swiss Residential Demand for Electricity by Time-of-Use: An Application of the Almost Ideal Demand System* , 1995 .

[87]  Peter J. Fleming,et al.  Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid , 2016 .

[88]  Makoto Tanaka.  Real-time pricing with ramping costs: A new approach to managing a steep change in electricity demand , 2006 .

[89]  D. Bertsimas,et al.  Robust and Data-Driven Optimization: Modern Decision-Making Under Uncertainty , 2006 .

[90]  Chuan-Ping Cheng,et al.  Unit commitment by Lagrangian relaxation and genetic algorithms , 2000 .

[91]  Atila Novoselac,et al.  Demand response for residential buildings based on dynamic price of electricity , 2014 .

[92]  Tobias Boßmann,et al.  Model-based assessment of demand-response measures—A comprehensive literature review , 2016 .

[93]  Shuangxia Niu,et al.  A scenario of vehicle-to-grid implementation and its double-layer optimal charging strategy for minimizing load variance within regional smart grids , 2014 .

[94]  Lingfeng Wang,et al.  A demand side management based simulation platform incorporating heuristic optimization for management of household appliances , 2012 .

[95]  Ronnie Belmans,et al.  Distributed generation: definition, benefits and issues , 2005 .

[96]  Anthony Papavasiliou,et al.  Applying High Performance Computing to Transmission-Constrained Stochastic Unit Commitment for Renewable Energy Integration , 2015, IEEE Transactions on Power Systems.

[97]  Geza Joos,et al.  Multiobjective Optimization Dispatch for Microgrids With a High Penetration of Renewable Generation , 2015, IEEE Transactions on Sustainable Energy.

[98]  Hamdi Abdi,et al.  Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem , 2016 .

[99]  Babak Mozafari,et al.  Designing time-of-use program based on stochastic security constrained unit commitment considering reliability index , 2012 .

[100]  Kaile Zhou,et al.  Multi-objective optimal dispatch of microgrid containing electric vehicles , 2017 .

[101]  Malin Song,et al.  Environmental efficiency and energy consumption of highway transportation systems in China , 2016 .

[102]  G. Lambert-Torres,et al.  Anomaly detection in power system control center critical infrastructures using rough classification algorithm , 2009, 2009 3rd IEEE International Conference on Digital Ecosystems and Technologies.

[103]  Yasunori Mitani,et al.  Optimization of a battery energy storage system using particle swarm optimization for stand-alone microgrids , 2016 .

[104]  Jungin Choi,et al.  Implementation of the Big Data Management System for Demand Side Energy Management , 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.

[105]  Xiangning Lin,et al.  Optimal placement of different types of RDGs based on maximization of microgrid loadability , 2017 .

[106]  R.J. Thomas,et al.  Demand-Side Bidding Agents: Modeling and Simulation , 2008, IEEE Transactions on Power Systems.