A novel energy service model and optimal scheduling algorithm for residential distributed energy res

Abstract We propose a novel decision-support tool that aims to optimize the provision of residential energy services from the perspective of the end-user. The tool is composed of a novel energy service model and a novel distributed energy resources scheduling algorithm. The proposed model takes into account the time-varying demand and benefit that end-users derive from different services, and assigns the benefit to the energy that realizes the service. The scheduling algorithm determines how distributed energy resources available to the end-users and under their control should be operated so that the net benefit of energy services is maximized based on the energy service models, and their technical characteristics and capabilities. The scheduling is a challenging optimization problem; hence, a heuristic simulation-based approach based around cooperative particle swarm optimization is used. The paper presents a case study where this decision-support tool is used to optimize the provision of desired energy services in a ‘smart’ home that includes a number of controllable loads, energy storage and photovoltaic generation.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[3]  I. Jacob Raglend,et al.  Comparison of Intelligent Techniques to Solve Economic Load Dispatch Problem with Line Flow Constraints , 2009, 2009 IEEE International Advance Computing Conference.

[4]  John Randolph,et al.  Energy for Sustainability: Technology, Planning, Policy , 2008 .

[5]  M. Pedrasa,et al.  Scheduling of Demand Side Resources Using Binary Particle Swarm Optimization , 2009, IEEE Transactions on Power Systems.

[6]  Marija D. Ilic,et al.  Distributed electric power systems of the future: Institutional and technological drivers for near-optimal performance , 2007 .

[7]  Jorge Valenzuela,et al.  Strategic bidding in electricity markets using particle swarm optimization , 2009 .

[8]  Francisco Jurado,et al.  Optimization of biomass fuelled systems for distributed power generation using Particle Swarm Optimization , 2008 .

[9]  Amory B. Lovins,et al.  Small is profitable: The Hidden economic benefits of making electrical resources the right size , 2003 .

[10]  Carlos A. Coello Coello,et al.  THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART , 2002 .

[11]  E. D. Spooner,et al.  Improved energy services provision through the intelligent control of distributed energy resources , 2009, 2009 IEEE Bucharest PowerTech.

[12]  Harry J. Sauer,et al.  Principles of Heating, Ventilating and Air Conditioning , 1993 .

[13]  Mohammed E. El-Telbany,et al.  Short-term forecasting of Jordanian electricity demand using particle swarm optimization , 2008 .

[14]  Stéphane Ploix,et al.  Tabu search for the optimization of household energy consumption , 2006, 2006 IEEE International Conference on Information Reuse & Integration.

[15]  Nikos D. Hatziargyriou,et al.  Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities , 2007 .

[16]  Nebojsa Nakicenovic,et al.  Towards sustainability of energy systems: A primer on how to apply the concept of energy services to identify necessary trends and policies , 2008 .

[17]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[18]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..

[19]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[20]  X. Yao,et al.  Scaling up fast evolutionary programming with cooperative coevolution , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[21]  Donald E. Grierson,et al.  Comparison among five evolutionary-based optimization algorithms , 2005, Adv. Eng. Informatics.

[22]  Gilbert M. Masters,et al.  Renewable and Efficient Electric Power Systems , 2004 .

[23]  B. Mozafari,et al.  Optimal operation of distribution system with regard to distributed generation: a comparison of evolutionary methods , 2005, Fourtieth IAS Annual Meeting. Conference Record of the 2005 Industry Applications Conference, 2005..