Active management of distributed generation based on component thermal properties

Power flows within distribution networks are expected to become increasingly congested with the proliferation of distributed generation (DG) from renewable energy resources. Consequently, the size, energy penetration and ultimately the revenue stream of DG schemes may be limited in the future. This research seeks to facilitate increased renewable energy penetrations by utilising power system component thermal properties together with DG power output control techniques. The real-time thermal rating of existing power system components has the potential to unlock latent power transfer capacities. When integrated with a DG power output control system, greater installed capacities of DG may be accommodated within the distribution network. Moreover, the secure operation of the network is maintained through the constraint of DG power outputs to manage network power flows. The research presented in this thesis forms part of a UK government funded project which aims to develop and deploy an on-line power output control system for wind-based DG schemes. This is based on the concept that high power flows resulting from wind generation at high wind speeds could be accommodated since the same wind speed has a positive effect on component cooling mechanisms. The control system compares component real-time thermal ratings with network power flows and produces set points that are fed back to the DG for implementation. The control algorithm comprises: (i) An inference engine (using rule-based artificial intelligence) that decides when DG control actions are required; (ii) a DG set point calculator (utilising predetermined power flow sensitivity factors) that computes updated DG power outputs to manage distribution network power flows; and (iii) an on-line simulation tool that validates the control actions before dispatch. A section of the UK power system has been selected by ScottishPower EnergyNetworks to form the basis of field trials. Electrical and thermal datasets from the field are used in open loop to validate the algorithms developed. The loop is then closed through simulation to automate DG output control for increased renewable energy penetrations.

[1]  Liana Mirela Cipcigan,et al.  Investigation of the reverse power flow requirements of high penetrations of small-scale embedded generation , 2007 .

[2]  D. A. Douglass,et al.  Field application of a dynamic thermal circuit rating method , 1997 .

[3]  Mark O'Malley,et al.  Planning and operating non-firm distributed generation , 2009 .

[4]  J.G. Slootweg,et al.  Incorporating weather statistics in determining overhead line ampacity , 2005, 2005 International Conference on Future Power Systems.

[5]  Andrea Michiorri,et al.  Investigation into the influence of environmental conditions on power system ratings , 2009 .

[6]  J.R. Abbad,et al.  Assessment of energy distribution losses for increasing penetration of distributed generation , 2006, IEEE Transactions on Power Systems.

[7]  Mark Lutz,et al.  Learning Python, 3rd Edition , 2007 .

[8]  J.W. Bialek,et al.  Optimal power flow as a tool for fault level-constrained network capacity analysis , 2005, IEEE Transactions on Power Systems.

[9]  Allan Collinson,et al.  SOLUTIONS FOR THE CONNECTION AND OPERATION OF DISTRIBUTED GENERATION , 2003 .

[10]  H. E. House,et al.  Current-Carrying Capacity of ACSR , 1958, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems.

[11]  Tony Yip,et al.  Dynamic line rating protection for wind farm connections , 2009 .

[12]  A. R. Wallace,et al.  Optimal power flow evaluation of distribution network capacity for the connection of distributed generation , 2005 .

[13]  G. Ejebe,et al.  Automatic Contingency Selection , 1979, IEEE Transactions on Power Apparatus and Systems.

[14]  E. C. Bascom,et al.  Dynamic thermal ratings realize circuit load limits , 2000 .

[15]  D. A. Douglass,et al.  Real-time monitoring and dynamic thermal rating of power transmission circuits , 1996 .

[16]  F. Gubina,et al.  Congestion management approach after deregulation of the Slovenian power system , 2002, IEEE Power Engineering Society Summer Meeting,.

[17]  Francisco D. Galiana,et al.  A survey of the optimal power flow literature , 1991 .

[18]  Goran Andersson,et al.  Power flow control by use of controllable series components , 1993 .

[19]  Phil Taylor,et al.  Strategies for the control of multiple distributed generation schemes , 2009 .

[20]  Damian Flynn,et al.  Increasing Wind Farm Capacity , 2006 .

[21]  Andrea Michiorri,et al.  Coordinated output control of multiple distributed generation schemes , 2010 .

[22]  S. Arnalte,et al.  Automatic Generation Control of a Wind Farm with Variable Speed Wind Turbines , 2002, IEEE Power Engineering Review.

[23]  Phil Taylor,et al.  Assessing the value of active constrained connection managers for distribution networks , 2008 .

[24]  Goran Strbac,et al.  Tracing active and reactive power between generators and loads using real and imaginary currents , 1999 .

[25]  Graham Ault,et al.  Methodology for determination of economic connection capacity for renewable generator connections to distribution networks optimised by active power flow management , 2006 .

[26]  Peter Fox-Penner.  Easing Gridlock on the Grid: Electricity Planning and Siting Compacts , 2001 .

[27]  M. H. McGrath,et al.  The calculation of the temperature rise and load capability of cable systems , 1957, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems.

[28]  C. L. Masters.  Voltage rise: the big issue when connecting embedded generation to long 11 kV overhead lines , 2002 .

[29]  Goran Strbac,et al.  Electric power systems research on dispersed generation , 2007 .

[30]  A. Geschiere,et al.  Future network planning and grid control , 2005, 2005 International Conference on Future Power Systems.

[31]  Wai-yu. Ng.  Generalized Generation Distribution Factors for Power System Security Evaluations , 1981, IEEE Transactions on Power Apparatus and Systems.

[32]  Andrea Michiorri,et al.  An Evaluation of Distributed Generation Constrained Connection Managers , 2008 .

[33]  K Etsu.  OVERCOMING BARRIERS TO SCHEDULING EMBEDDED GENERATION TO SUPPORT DISTRIBUTION NETWORKS , 2000 .

[34]  Graham Ault,et al.  TECHNIQUES FOR MANAGING POWER FLOWS IN ACTIVE DISTRIBUTION NETWORKS WITHIN THERMAL CONSTRAINTS , 2009 .

[35]  Andrea Michiorri,et al.  Overhead line real-time rating estimation algorithm: Description and validation , 2010 .

[36]  A. Papalexopoulos,et al.  Transmission congestion management in competitive electricity markets , 1998 .

[37]  Andrea Michiorri,et al.  Dynamic thermal rating and active control for improved distribution network utilisation , 2010 .

[38]  D. Shepard.  A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.

[39]  J.A.P. Lopes,et al.  Optimum generation control in wind parks when carrying out system operator requests , 2006, IEEE Transactions on Power Systems.

[40]  Phil Taylor,et al.  Distributed generation output control for network power flow management , 2009 .

[41]  Yonghua Song,et al.  Modern optimisation techniques in power systems , 1999 .

[42]  Pierluigi Siano,et al.  Assessing the strategic benefits of distributed generation ownership for DNOs , 2009 .

[43]  Matti Lehtonen,et al.  Dynamic thermal modelling of power transformers , 2004 .

[44]  Vincent T. Morgan,et al.  The thermal rating of overhead-line conductors Part I. The steady-state thermal model , 1982 .

[45]  A. Piccolo,et al.  Exploring the Tradeoffs Between Incentives for Distributed Generation Developers and DNOs , 2007, IEEE Transactions on Power Systems.

[46]  C.D. Vournas,et al.  Application of interruptible contracts to increase wind-power penetration in congested areas , 2004, IEEE Transactions on Power Systems.

[47]  T.C. Green,et al.  Sizing of distributed generation plant through techno-economic feasibility assessment , 2006, 2006 IEEE Power Engineering Society General Meeting.

[48]  Jian Ma,et al.  Operational Impacts of Wind Generation on California Power Systems , 2009, IEEE Transactions on Power Systems.

[49]  Janusz Bialek,et al.  Tracing the flow of electricity , 1996 .

[50]  Miloš Pantoš,et al.  A flow-tracing method for transmission networks , 2005 .

[51]  R. Rohrer.  The Generalized Adjoint Network and Network Sensitivities , 1969 .

[52]  Bruce David Stedall.  The hierarchical control and protection of power systems , 1994 .

[53]  Janusz Bialek,et al.  Generation curtailment to manage voltage constraints in distribution networks , 2007 .

[54]  Z. Vukic,et al.  Speed and active power control of hydro turbine unit , 2005, IEEE Transactions on Energy Conversion.

[55]  C. Abbey,et al.  Global survey on planning and operation of active distribution networks - Update of CIGRE C6.11 working group activities , 2009 .

[56]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[57]  J.R. McDonald,et al.  UK research activities on advanced distribution automation , 2005, IEEE Power Engineering Society General Meeting, 2005.

[58]  A. Michiorri,et al.  Increasing the Energy Yield of Generation from New and Renewable Energy Resources , 2009 .

[59]  Julio Usaola,et al.  Optimal controllability of wind generators in a delegated dispatch , 2007 .

[60]  K. Khan,et al.  Meeting the energy challenge: a white paper on nuclear power , 2009 .

[61]  Graham Ault,et al.  Active power-flow management utilising operating margins for the increased connection of distributed generation , 2007 .