Efficient operation of energy hubs in time-of-use and dynamic pricing electricity markets

Natural gas can support the electricity grid through integration of the existing natural gas and electric networks into energy hubs. This paper elaborates on the design of an efficient algorithm for the EMS (energy management system) inside a residential energy hub. We study the strategic operation of the energy hubs in a competitive electricity market. Each energy hub aims to jointly minimize the operating cost and the discomfort cost caused by modifying the electrical and thermal load profiles. We show that there exists a competitive equilibrium for the energy hubs. A linear time EMS scheduling algorithm is proposed to determine that equilibrium. The optimal strategy of the energy hubs are compared for two widely used electricity pricing mechanisms, namely the TOU (time-of-use) and the dynamic pricing schemes. Furthermore, we study the effects of the electrical storage system and renewable resources on the hubs' optimal strategies. Simulations are performed for a group of ten residential energy hubs in a competitive electricity market. It is shown that the energy hubs' daily cost will be reduced by using the proposed scheduling algorithm. It is also shown that the dynamic pricing scheme can better motivate the energy hubs toward modifying their daily operation.

[1]  I Potts,et al.  Integrated distributed energy evaluation software (IDEAS) simulation of a micro-turbine based CHP system , 2007 .

[2]  Zhu Han,et al.  Autonomous Demand Response Using Stochastic Differential Games , 2015, IEEE Transactions on Smart Grid.

[3]  Anil Kumar Sao,et al.  Solar radiation forecasting with multiple parameters neural networks , 2015 .

[4]  Haisheng Chen,et al.  Comparative study of the influences of different water tank shapes on thermal energy storage capacity and thermal stratification , 2016 .

[5]  L. Friedrich,et al.  Modeling and optimization of renewables: applying the Energy Hub approach , 2008, 2008 IEEE International Conference on Sustainable Energy Technologies.

[6]  Farhad Kamyab,et al.  Demand Response Program in Smart Grid Using Supply Function Bidding Mechanism , 2016, IEEE Transactions on Smart Grid.

[7]  Shahram Jadid,et al.  Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system , 2015 .

[8]  Ali Mohammad Ranjbar,et al.  An autonomous demand response program for electricity and natural gas networks in smart energy hubs , 2015 .

[9]  Kristina Orehounig,et al.  Integration of decentralized energy systems in neighbourhoods using the energy hub approach , 2015 .

[10]  Ali Mohammad Ranjbar,et al.  A cloud computing framework on demand side management game in smart energy hubs , 2015 .

[11]  O. Perpiñán,et al.  PV power forecast using a nonparametric PV model , 2015 .

[12]  Michelangelo Scorpio,et al.  Experimental analysis of a micro-trigeneration system composed of a micro-cogenerator coupled with an electric chiller , 2014 .

[13]  G. Andersson,et al.  Energy hubs for the future , 2007, IEEE Power and Energy Magazine.

[14]  Shahram Jadid,et al.  Smart microgrid energy and reserve scheduling with demand response using stochastic optimization , 2014 .

[15]  Chul-Hwan Kim,et al.  Application of Neural Network to One-Day-Ahead 24 hours Generating Power Forecasting for Photovoltaic System , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[16]  Chen Changsong,et al.  Forecasting power output for grid-connected photovoltaic power system without using solar radiation measurement , 2010, The 2nd International Symposium on Power Electronics for Distributed Generation Systems.

[17]  Kwang Y. Lee,et al.  Determining PV Penetration for Distribution Systems With Time-Varying Load Models , 2014, IEEE Transactions on Power Systems.

[18]  Shahab Bahrami,et al.  Game Theoretic Based Charging Strategy for Plug-in Hybrid Electric Vehicles , 2014, IEEE Transactions on Smart Grid.

[19]  Yongjun Sun,et al.  Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming , 2015 .

[20]  Bangyin Liu,et al.  Smart energy management system for optimal microgrid economic operation , 2011 .

[21]  Alfredo Vaccaro,et al.  A robust optimization approach to energy hub management , 2012 .

[22]  Albert Moser,et al.  Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response , 2014 .

[23]  Shahab Bahrami,et al.  A Financial Approach to Evaluate an Optimized Combined Cooling, Heat and Power System , 2013 .

[24]  Carlo Roselli,et al.  Distributed microtrigeneration systems , 2012 .

[25]  Massimiliano Renzi,et al.  Simulation of hybrid renewable microgeneration systems for variable electricity prices , 2014 .

[26]  Yongping Yang,et al.  Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines , 2012 .

[27]  G. Andersson,et al.  Optimal Power Flow of Multiple Energy Carriers , 2007, IEEE Transactions on Power Systems.

[28]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[29]  Francisco J. Batlles,et al.  Solar radiation forecasting in the short- and medium-term under all sky conditions , 2015 .

[30]  Kankar Bhattacharya,et al.  Optimal Operation of Residential Energy Hubs in Smart Grids , 2012, IEEE Transactions on Smart Grid.

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

[32]  Jean-Jacques Roux,et al.  Peak load reductions: Electric load shifting with mechanical pre-cooling of residential buildings with low thermal mass , 2015 .

[33]  Shahab Bahrami,et al.  From Demand Response in Smart Grid Toward Integrated Demand Response in Smart Energy Hub , 2016, IEEE Transactions on Smart Grid.

[34]  Matti Lehtonen,et al.  Home load management in a residential energy hub , 2015 .

[35]  Na Li,et al.  Two Market Models for Demand Response in Power Networks , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[36]  Risto Lahdelma,et al.  Modelling and optimization of CHP based district heating system with renewable energy production and energy storage , 2015 .