Constrained consumption shifting management in the distributed energy resources scheduling considering demand response

Demand response concept has been gaining increasing importance while the success of several recent implementations makes this resource benefits unquestionable. This happens in a power systems operation environment that also considers an intensive use of distributed generation. However, more adequate approaches and models are needed in order to address the small size consumers and producers aggregation, while taking into account these resources goals. The present paper focuses on the demand response programs and distributed generation resources management by a Virtual Power Player that optimally aims to minimize its operation costs taking the consumption shifting constraints into account. The impact of the consumption shifting in the distributed generation resources schedule is also considered. The methodology is applied to three scenarios based on 218 consumers and 4 types of distributed generation, in a time frame of 96 periods.

[1]  Zita Vale,et al.  Demand response design and use based on network locational marginal prices , 2014 .

[2]  Hyung-Geun Kwag,et al.  Reliability modeling of demand response considering uncertainty of customer behavior , 2014 .

[3]  Z. Vale,et al.  Distributed generation and demand response dispatch for a virtual power player energy and reserve provision , 2014 .

[4]  Zita Vale,et al.  An integrated approach for distributed energy resource short-term scheduling in smart grids considering realistic power system simulation , 2012 .

[5]  Roy Billinton,et al.  Effects of Load Sector Demand Side Management Applications in Generating Capacity Adequacy Assessment , 2012, IEEE Transactions on Power Systems.

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

[7]  Hai Lu,et al.  Energy quality management for building clusters and districts (BCDs) through multi-objective optimization , 2014 .

[8]  Linfeng Zhang,et al.  Energy management in a microgrid with distributed energy resources , 2014 .

[9]  Pedro Faria,et al.  Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling , 2013, IEEE Transactions on Smart Grid.

[10]  Scott Backhaus,et al.  Safe control of thermostatically controlled loads with installed timers for demand side management , 2014 .

[11]  Marnix C. Vlot,et al.  Economical Regulation Power Through Load Shifting With Smart Energy Appliances , 2013, IEEE Transactions on Smart Grid.

[12]  Pedro Faria,et al.  Demand response programs definition using demand price elasticity to define consumers aggregation for an improved remuneration structure , 2013, IEEE PES ISGT Europe 2013.

[13]  Kumudhini Ravindra,et al.  Decentralized demand–supply matching using community microgrids and consumer demand response: A scenario analysis , 2014 .

[14]  Hans Christian Gils,et al.  Assessment of the theoretical demand response potential in Europe , 2014 .

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

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

[17]  Chi-Keung Woo,et al.  EditorialGuest editors' introduction , 2010 .

[18]  P. Postolache,et al.  Customer Characterization Options for Improving the Tariff Offer , 2002, IEEE Power Engineering Review.

[19]  Sarah Busche,et al.  Power systems balancing with high penetration renewables: The potential of demand response in Hawaii , 2013 .

[20]  W. D. Rosehart,et al.  Long-Term Market Equilibrium Model With Strategic, Competitive, and Inflexible Generation , 2012, IEEE Transactions on Power Systems.

[21]  Karin Alvehag,et al.  Further exploring the potential of residential demand response programs in electricity distribution , 2014 .