Implementation of Demand Response Programs on Unit Commitment Problem

Peak electrical demand usually occurs in hot weather conditions due to the synchronous connection of the air-conditioners to local power grids. Hence, if a demand-side management (DSM) strategy is not applied on consumption patterns of customers, load-generation imbalance may lead to voltage collapse, cascaded outages, and catastrophic blackouts. This chapter presents a mathematical model for application of time-amount-based DSM program on a cost-based unit commitment problem in order to minimize the energy procurement cost. The ramp down/up rate limit, start-up and shutdown costs, generation capacity, minimum up- and downtimes, minimum and maximum time-dependent operating limits, and power balance criterion are considered as constraints. Simulations are conducted on a 10-unit test system and solved as a mixed integer nonlinear problem under general algebraic mathematical modeling system to find the optimum operating point of thermal units without and with implementation of a DSM strategic program.

[1]  Juan M. Morales,et al.  Real-Time Demand Response Model , 2010, IEEE Transactions on Smart Grid.

[2]  Behnam Mohammadi-Ivatloo,et al.  Risk-constrained day-ahead economic and environmental dispatch of thermal units using information gap decision theory , 2019 .

[3]  Danny H. K. Tsang,et al.  A Two-Stage Approach for Network Constrained Unit Commitment Problem With Demand Response , 2018, IEEE Transactions on Smart Grid.

[4]  Yungang Liu,et al.  A multiobjective hybrid bat algorithm for combined economic/emission dispatch , 2018, International Journal of Electrical Power & Energy Systems.

[5]  Mehdi Rahmani-andebili,et al.  Modeling nonlinear incentive-based and price-based demand response programs and implementing on real power markets , 2016 .

[6]  Pierluigi Siano,et al.  Demand response and smart grids—A survey , 2014 .

[7]  Giambattista Gruosso,et al.  Limiting gaming opportunities on incentive-based demand response programs , 2018, Applied Energy.

[8]  Rajesh Kumar,et al.  A New Binary Variant of Sine–Cosine Algorithm: Development and Application to Solve Profit-Based Unit Commitment Problem , 2018 .

[9]  Abbas Rabiee,et al.  Corrective Voltage Control Scheme Considering Demand Response and Stochastic Wind Power , 2014, IEEE Transactions on Power Systems.

[10]  Mathijs de Weerdt,et al.  Robust unit commitment with dispatchable wind power , 2018 .

[11]  J. Oyarzabal,et al.  A Direct Load Control Model for Virtual Power Plant Management , 2009, IEEE Transactions on Power Systems.

[12]  M. A. Abido Environmental/economic power dispatch using multiobjective evolutionary algorithms , 2003 .

[13]  Leehter Yao,et al.  Optimal Purchase Strategy for Demand Bidding , 2018, IEEE Transactions on Power Systems.

[14]  Amir Abdollahi,et al.  Optimal reserve market clearing considering uncertain demand response using information gap decision theory , 2018 .

[15]  Lingfeng Wang,et al.  Stochastic economic emission load dispatch through a modified particle swarm optimization algorithm , 2008 .

[16]  Jiyong Eom,et al.  Variability of electricity load patterns and its effect on demand response: A critical peak pricing experiment on Korean commercial and industrial customers , 2016 .

[17]  J. Fuller,et al.  Time-of-Use Pricing in Electricity Markets Under Different Market Structures , 2012, IEEE Transactions on Power Systems.

[18]  Mostafa Kazemi,et al.  Security constrained unit commitment with flexibility in natural gas transmission delivery , 2015 .

[19]  J. T. Wood,et al.  Potential impacts of clean air regulations on system operations , 1995 .

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

[21]  Behnam Mohammadi-Ivatloo,et al.  Self-Scheduling of Demand Response Aggregators in Short-Term Markets Based on Information Gap Decision Theory , 2019, IEEE Transactions on Smart Grid.

[22]  Filipe Joel Soares,et al.  Optimal supply and demand bidding strategy for an aggregator of small prosumers , 2017 .

[23]  Behnam Mohammadi-Ivatloo,et al.  Risk-based bidding of large electric utilities using Information Gap Decision Theory considering demand response , 2014 .

[24]  S. Sivasubramani,et al.  Multi-objective dynamic economic and emission dispatch with demand side management , 2018 .

[25]  M. Sailaja Kumari,et al.  Demand response and pumped hydro storage scheduling for balancing wind power uncertainties: A probabilistic unit commitment approach , 2016 .

[26]  Jong-Keun Park,et al.  Stochastic security-constrained unit commitment with wind power generation based on dynamic line rating , 2018, International Journal of Electrical Power & Energy Systems.

[27]  Behnam Mohammadi-Ivatloo,et al.  Optimal stochastic energy management of retailer based on selling price determination under smart grid environment in the presence of demand response program , 2017 .

[28]  Gevork B. Gharehpetian,et al.  Integration of smart grid technologies in stochastic multi-objective unit commitment: An economic emission analysis , 2018, International Journal of Electrical Power & Energy Systems.

[29]  Jaspreet Singh Dhillon,et al.  Fuzzy satisfying stochastic multi-objective generation scheduling by weightage pattern search methods , 2004 .

[30]  Jaspreet Singh Dhillon,et al.  Profit based unit commitment using hybrid optimization technique , 2018 .

[31]  Abbas Rabiee,et al.  Information gap decision theory approach to deal with wind power uncertainty in unit commitment , 2017 .

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

[33]  Behnam Mohammadi-Ivatloo,et al.  Stochastic optimization of energy hub operation with consideration of thermal energy market and demand response , 2017 .

[34]  Lingfeng Wang,et al.  Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm , 2007 .

[35]  Rajesh Kumar,et al.  Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets , 2019 .

[36]  A. Rezaee Jordehi,et al.  Optimisation of demand response in electric power systems, a review , 2019, Renewable and Sustainable Energy Reviews.

[37]  O. P. Malik,et al.  Environmentally constrained unit commitment , 1992 .

[38]  Gongguo Tang,et al.  A game-theoretic approach for optimal time-of-use electricity pricing , 2013, IEEE Transactions on Power Systems.

[39]  Guohe Huang,et al.  A nonlinear fractional programming approach for environmental–economic power dispatch , 2016 .

[40]  Pandian Vasant,et al.  A holistic review on optimization strategies for combined economic emission dispatch problem , 2018 .

[41]  Bijaya K. Panigrahi,et al.  Binary Grey Wolf Optimizer for large scale unit commitment problem , 2018, Swarm Evol. Comput..

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

[43]  He Li,et al.  Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: A case study in China , 2018, Applied Energy.