Virtual power plant-based distributed control strategy for multiple distributed generators

A distributed control strategy is developed to control the output of multiple distributed generators (DGs) in a coordinated fashion such that these generators develop into a virtual power plant (VPP) in a distribution network. To this end, cooperative control methodology from network control theory is used to make the VPP converge and operate at an optimal output, which is determined by the DGs' costs and the necessary service assigned by the distribution network. For each DG, the strategy only requires information from its neighbouring units, making communication networks (CNs) among the DGs simple and robust. A set of sufficient conditions under which the proposed method is valid are provided. It is shown that the proposed strategy has the advantages that the corresponding CNs are local and there is no central station collecting global information from the DGs. These features enable the VPP to have both self-organising and adaptive coordination properties. The proposed method is simulated using the IEEE standard 34-bus distribution network.

[1]  S. M. Moghaddas-Tafreshi,et al.  Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part I: Problem Formulation , 2011, IEEE Transactions on Power Systems.

[2]  M.E. Baran,et al.  A Multiagent-Based Dispatching Scheme for Distributed Generators for Voltage Support on Distribution Feeders , 2007, IEEE Transactions on Power Systems.

[3]  Danny Pudjianto,et al.  Virtual power plant and system integration of distributed energy resources , 2007 .

[4]  Tai C Yang,et al.  Networked control system: a brief survey , 2006 .

[5]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

[6]  Zhihua Qu,et al.  Cooperative control with improvable network connectivity , 2010, Proceedings of the 2010 American Control Conference.

[7]  E. Vittal,et al.  Enhanced Utilization of Voltage Control Resources With Distributed Generation , 2011, IEEE Transactions on Power Systems.

[8]  Anders Rantzer,et al.  A distributed power coordination scheme for fatigue load reduction in wind farms , 2011, Proceedings of the 2011 American Control Conference.

[9]  Gareth Harrison,et al.  Network Distributed Generation Capacity Analysis Using OPF With Voltage Step Constraints , 2010 .

[10]  D. Qi,et al.  Cooperative control strategy for multiple photovoltaic generators in distribution networks , 2011 .

[11]  M. Johansson,et al.  Accelerated gradient methods for networked optimization , 2011 .

[12]  Stephen P. Boyd,et al.  Optimal Scaling of a Gradient Method for Distributed Resource Allocation , 2006 .

[13]  Zhihua Qu,et al.  A Self-Organizing Strategy for Power Flow Control of Photovoltaic Generators in a Distribution Network , 2011, IEEE Transactions on Power Systems.

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

[15]  Christoforos N. Hadjicostis,et al.  Coordination and Control of Distributed Energy Resources for Provision of Ancillary Services , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[16]  Mo-Yuen Chow,et al.  Incremental cost consensus algorithm in a smart grid environment , 2011, 2011 IEEE Power and Energy Society General Meeting.

[17]  P. Olver.  Nonlinear Systems , 2013 .

[18]  Jingcheng Wang,et al.  Stochastic observer-based guaranteed cost control for networked control systems with packet dropouts , 2008 .

[19]  Amit Bhaya,et al.  Stability of asynchronous dynamical systems with rate constraints and applications , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[20]  Nikos D. Hatziargyriou,et al.  Centralized Control for Optimizing Microgrids Operation , 2008 .

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