Multiobjective robust fuzzy stochastic approach for sustainable smart grid design

Abstract Smart grids are an effective solution for the rapidly increasing power demand. This study attempts to solve the sustainable smart grid design problem, where three indispensable sustainability dimensions, economy, environment, and society, are considered simultaneously. A multiobjective robust fuzzy stochastic programming approach is presented to minimize the economic, environmental and social costs of the network under uncertain scenarios and parameters. The objective is to determine the optimal number, location and capacity of renewable distributed generation units as well as the dynamic electricity pricing and energy resources scheduling in a sustainable smart grid. The proposed method is applied to a Vietnamese power company in the residential sector. The results indicate that demand response with dynamic pricing reduces environmental and social costs by 3%. Moreover, the proposed model efficiently tackles both uncertainties in scenarios and parameters with a total cost lower than other models.

[1]  Dipti Srinivasan,et al.  Game-Theory based dynamic pricing strategies for demand side management in smart grids , 2017 .

[2]  Yan Shi,et al.  Multiobjective optimization technique for demand side management with load balancing approach in smart grid , 2016, Neurocomputing.

[3]  Hong Zhou,et al.  Retail market pricing design in smart distribution networks considering wholesale market price uncertainty , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.

[4]  Mohamad Saeed Jabalameli,et al.  A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty , 2011, OR Spectrum.

[5]  Guohe Huang,et al.  A fuzzy-stochastic power system planning model: Reflection of dual objectives and dual uncertainties , 2017 .

[6]  Lang Tong,et al.  Dynamic Pricing and Distributed Energy Management for Demand Response , 2016, IEEE Transactions on Smart Grid.

[7]  Zita Vale,et al.  Dynamic electricity pricing for electric vehicles using stochastic programming , 2017 .

[8]  Leonardo Santiago,et al.  On the trade-off between real-time pricing and the social acceptability costs of demand response , 2018 .

[9]  Shahram Jadid,et al.  Stochastic multi-objective operational planning of smart distribution systems considering demand response programs , 2014 .

[10]  André Bardow,et al.  Robust multi-objective optimization for sustainable design of distributed energy supply systems , 2017, Comput. Chem. Eng..

[11]  Kit Po Wong,et al.  Recent Advancements on Smart Grids in China , 2014 .

[12]  M. Talaei,et al.  A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry , 2016 .

[13]  Wayes Tushar,et al.  Quantifying economic benefits in the ancillary electricity market for smart appliances in Singapore households , 2015 .

[14]  Mir Saman Pishvaee,et al.  A robust optimization approach to closed-loop supply chain network design under uncertainty , 2011 .

[15]  Pierluigi Mancarella,et al.  Regulatory capital and social trade-offs in planning of smart distribution networks with application to demand response solutions , 2016 .

[16]  Li Pan,et al.  Smart grids in China , 2014 .

[17]  Electric Construction,et al.  Feasibility study-110kv transmission line and substation projects in southern Vietnam , 2005 .

[18]  R. Tarjanne,et al.  Comparison of electricity generation costs , 2008 .

[19]  Armin Jabbarzadeh,et al.  Green and Resilient Design of Electricity Supply Chain Networks: A Multiobjective Robust Optimization Approach , 2019, IEEE Transactions on Engineering Management.

[20]  Heidar Ali Shayanfar,et al.  Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant , 2019, Energy.

[21]  Heidar Ali Shayanfar,et al.  A novel stochastic energy management of a microgrid with various types of distributed energy resources in presence of demand response programs , 2018, Energy.

[22]  Hêriş Golpîra,et al.  A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty , 2019, Energy.

[23]  P. Siano,et al.  Scenario-based stochastic framework for coupled active and reactive power market in smart distribution systems with demand response programs , 2017 .

[24]  Simona Bigerna,et al.  Socio-economic acceptability for smart grid development – a comprehensive review , 2016 .

[25]  Zita Vale,et al.  A stochastic model for energy resources management considering demand response in smart grids , 2017 .

[26]  Hongbo Zhu,et al.  Real-time pricing considering different type of smart home appliances based on Markov decision process , 2019, International Journal of Electrical Power & Energy Systems.

[27]  Maojiao Ye,et al.  Game Design and Analysis for Price-Based Demand Response: An Aggregate Game Approach , 2015, IEEE Transactions on Cybernetics.

[28]  Kashem M. Muttaqi,et al.  Sustainable energy system design with distributed renewable resources considering economic, environmental and uncertainty aspects , 2015 .

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

[30]  G. Verbong,et al.  Smart grids or smart users? Involving users in developing a low carbon electricity economy , 2013 .

[31]  Robert J. Vanderbei,et al.  Robust Optimization of Large-Scale Systems , 1995, Oper. Res..

[32]  G Hamed Shakouri,et al.  Multi-objective cost-load optimization for demand side management of a residential area in smart grids , 2017 .

[33]  Mohammad Saeid Atabaki,et al.  Multi-objective optimization for sustainable development of the power sector: An economic, environmental, and social analysis of Iran , 2018, Energy.

[34]  Oa Us Epa.  Sources of Greenhouse Gas Emissions , 2015 .