Chapter 7 Conclusion and Further Work
7.2 Suggestions for Further Work
During the course of the work, several avenues were explored which later had to be curtailed, and they are presented here as possible further work. The first of these is a temporal look at the instantaneous power output, and a look at how this affects the long period power output. During the models it was assumed that the generators, no-matter how closely placed, were independent of each other. In truth, the generators are
not entirely independent, and this is a function of the placement of the generators and the time. It would be very good to get this investigated.
Another avenue which had to be curtailed is the distributions used to describe the windspeed and insolation variations. The work above suggests either a Gaussian or a uniform distribution, but acknowledges that neither of these is a true fit, but both are close. Work by others suggests the Kolmogorov spectrum is an accurate fit to the wind spectra, and the accuracy of the predictions of the models contained within would be improved by using more fitting distributions, whichever distributions they may be.
A form of probabilistic load-flow would also be very useful for this DG work, as the outputs from the DG units are probabilistic. This would eliminate the simplification used to apply the models to the network, and provide useful data for both the instantaneous power flow, and the long period power flow.
As well as these avenues, model improvements such as park effects, wind shading, solar shading, temperature troughs, and a whole host of other effects produced by introducing complex terrain would benefit the models accuracy. The question, when introducing complex terrain, is whether or not the benefits in accuracy are greater or outweighed by the model complexity (in terms of computing power), and the ease of creation and use of the models. The programs created using the models are fast to execute and easy to add to or modify, but the introduction of terrain, buildings, and landscape features adjusts not only the operational speed of the programs, but also the difficulty of use.
References
[1] “Kyoto Protocol, Status of Ratification”, United Nations Framework on Climate Change, retrieved April 2010. Available:
http://unfccc.int/files/essential_background/kyoto_protocol/status_of_ratification/appl ication/pdf/kpstats.pdf
[2] “Proposal for a Decision of the European Parliament and of the Council on the effort of Member States to reduce their greenhouse gas emissions to meet the Community’s greenhouse gas emission reduction commitment up to 2020”, Commission of the European Communities
[3] International Net Electricity Generation Tables, Most Recent Annual Estimate by Type, 2006, U.S. Energy Information Administration, available from:
http://www.eia.doe.gov
[4] Towards Smart Power Networks - Lessons Learned from European research FP5 projects. [Online]. available http://www.smartgrids.eu
5 Kaplan, S. M., Sissine, F., ‘Smart Grid: Modernizing Electric Power Transmission and Distribution”, The Capitol Net Inc, 2009, p217
[6] Pudjiano, D., Ramsay, C. and Strbac, G., ‘The Virtual Power Plant and System Integration of Distributed Energy Resources’, IET Renew. Power Gener., 2007, 1, (1), pp. 10-16
[7] Nørgaard, P., Holttinen, H., ‘A Multi-Turbine Power Curve Approach’, presented at Nordic Wind Power Conf., Chalmers University of Technology, March 2004 [8] Lange, M., Focken, U., ‘New Developments in Wind Energy Forecasting’, 2008, Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, IEEE
[9] Lorenz, E., Girodo, M., Engel, E., Heinemann, D.: ‘Solar Irradiance Forecasting for the Management of Solar Energy Systems’, presented at Solar 2006, Denver, July 2006
[10] Kirschen, D., Strbac,. G., ‘Fundamentals of Power System Economics’, 2004, John Wiley & Sons
[11] Tovey, N. K., ‘Developments in the Electricity Markets in the UK: The move towards BETTA’, Operational Experience and Practice of the European Electricity
Markets 4th International conference, available
http://www2.env.uea.ac.uk/gmmc/energy/energy_links/electricity_supply.htm [12] C. Obersteiner, L. Weißensteiner, R. Haas et al., “Market potentials, trends and marketing options for Distributed Generation in Europe”, deliverable D2.1 of the project MASSIG, Energy Economics Group, Vienna University of Technology, November 2008, available http://www.iee-massig.eu
[13] Brookes, L. G., Motamen, Homa., ‘The Economics of Nuclear Energy’, 1984, Chapman and Hall
[14] Kutz, M., ‘Environmentally Conscious Transportation’, 2008, John Wiley &
Sons
[15] List of Common conversion factors (Engineering conversion factors), IOR Energy Pty Ltd
[16] Lithium Sulphur Rechargeable Battery Data Sheet. [Online]. available:
http://www.sionpower.com/pdf/sion_product_spec.pdf
[17] Borbely, A., Kreider, J. D., ‘Distributed generation: the power paradigm for the new millennium’, 2001, CRC Press LLC
[18] Harrison, G. P., Wallace, A. R., “OPF evaluation of distribution network capacity for the connection of distributed generation”, Proc. Inst. Elect. Eng., Gen. Transm.
Dist, 2005
[19] Nimpitiwan, N., Heydt, G. T., ‘Fault Current Issues for Market Driven Power Systems with Distributed Generation’, IEEE PES Gen. Meet., 2005
[20] Masters, G. M.: ‘Renewable and Efficient Electric Power Systems’, 2004, John Wiley & Sons
[21] Bockelie, M., Swensen, D., Denison, M., Sarofin, A., ‘A Computational Workbench Environment for Virtual Power Plant Simulation’, Office of Scientific and Technical Information, U.S. Department of Energy, 2001. Available:
http://www.osti.gov/bridge/servlets/purl/832878-NkuRkC/native/832878.pdf [22] Ausubel, L., Cramton, P., ‘Virtual Power Plant Auctions’, working paper, University of Maryland, 2009
[23] Dielmann, K., van der Velden, A., 'Virtual Power Plants (VPP) - A New Perspective for Energy Generation?', MTT 2003, Proc. 9th. Intl. Scientific and Practical Conf. of Students, Post-grad. and Young Scientists, 2003, pp18-20.
[24] Pudjianto, D., Ramsay, C., Strbac, G., 'Virtual Power Plant and System
Integration of Distributed Energy Resources', IET Renew. Power Gener., 2007, 1, (1), pp10-16
[25] Gianfranco, C., Mancarella, P., ‘Distributed Multi-Generation: A Comprehensive View’, Renewable and Sustainable Energy Review, 2007, 3, (3), pp535-551
[26] Braun, M., 'Technological Control Capabilities of DER to Provide Future Ancillary Services', Int. Jour. Dist. Energy Res., 2007, 3, (3), pp191-206
[27] Braun, M., 'Reactive Power Control in Mini-Grids', 4th European PV-Hybrid and Mini-Grid Conf., Otti e.V., May 2008
[28] Braun, M., 'Reactive Power Supplied by Wind Energy Converters - Cost-Benefit-Analysis', EWEC 2008, Brussels
[29] Bignucolo, F., Caldon, R., Prandoni, V., Spelta, S., Vezzola, M., 'The Voltage Control on MV Distribution Networks with Aggregated DG Units', Proc. 41st Intl.
UPEC ’06, 2006, 1, (1), pp187-192
[30] Braun, M., Ringelstein, J., 'Voltage Control in Electric Distribution Grids by Aggregation of Distributed Energy Units', 6th Intl. Conf. European Energy Market, 2009, Katholieke Universiteit Leuven
[31] Bertling, F., Soter, S., 'Improving Grid Voltage Quality by Decentral Injection of Current Harmonics', IECON 2005, 31st Annual Conf. of IEEE, 2005
[32] Kok, K., ‘Short-Term Economics of Virtual Power Plants’, 20th Int. Conf. Elec.
Dist., CIRED, 2009
[33] Roossien, B., Hommelberg, M., Warmer, C., Kok, K., Turkstra, J., 'Virtual Power Plant Field Experiment Using 10 Micro-CHP Units at Consumer Premises', Paper 86, CIRED Seminar 2008: SmartGrids for Distribution, 2008, Frankfurt
[34] Kamphuis, R., Warmer, C., Hommelberg, M., Kok, K., 'Massive Coordination of Dispersed Generation using PowerMatcher Based Software Agents', Paper 447, 19th Int. Conf. on Elec. Dist., 2009, CIRED
[35] Franke, M., Rolli, D., Kamper, A., Dietrich, A,. Geyer-Schulz, A., Lockemann, P., Schmeck, H., Weinhardt, C., 'Impacts of Distributed Generation from Virtual Power Plants', Proc. Annual Intl. Sustainable Development Research Conf., 2005, 11, (1), pp1-12
[36] Zhang, X., P., ‘A Framework for Operation and Control of Smart Grids with Distributed Generation’, 2008 IEEE Power and Energy Society General Meeting – Conversion and Delivery of Electrical Energy in the 21st Century, 2008, pp1-5 [37] Breuer, W., Povh, D., Retzmann, D., Urbanke, Ch., Weinhold, M., 'Prospects of Smart Grid Technologies for a Sustainable and Secure Power Supply', 20th World Energy Conference & Exhibition, 2007, Rome
[38] Braun, M., Degner, T., Engler. A., 'Analysis of Inverter-Controlled Island Grids', ISET e.V., 2006
[39] Cipcigan, L. M, Taylor, P. C., 'A Generic Model of a Virtual Power Station Consisting of Small Scale Energy Zones', CIRED Conference 2007, Vienna, Paper 692
[40] Sebastian, M., Marti, J., Lang, P., ‘Evolution of DSO Control Centre Tool in Order to Maximize the Value of Aggregated Distributed Generation in Smart Grid’, CIRED Seminar 2008: SmartGrids for Distribution, Paper 34
[41] Bel, I., Valenti, A., Maire, J., Corera, J., Lang, P., ‘Innovative Operation with Aggregated Distributed Generation’, 19th Int. Conf. on Elec. Dist., CIRED, 2007, Paper 461
[42] Braun, M., ‘Virtual Power Plants in Real Applications’, Intl. ETG Congress 2009, Düsseldorf, 2009
[43] Dimeas, A. L., Hatziargyriou, N. D., ‘Agent Based Control of Virtual Power Plants’, 14th Int. Conf. Intelligent Sys. App. to Power Sys., 2007, ISAP
[44] Pipattanasomporn, M., Feroze, H., Rahman, S., ‘Multi-Agent Systems in a Distributed Smart Grid: Design and Implementation’, Proc. IEEE PES 2009 Power Syst. Conf. and Expo., 2009
[45] Schultz, C., Röder, G., Kurrat, M., 'Virtual Power Plants with Combined Heat and Power Micro-Units', 2005 Intl. Conf. Future Power Systems, Amsterdam, 2005 [46] Bakker, V., Molderink, A., Hurink, J. L., Smit, G. J. M., ‘Domestic Heat Demand Prediction Using Neural Networks’, 19th Int. Conf. on Sys. Eng., 2008, pp189-194
[47] Danish Wind Industry Association.: Betz’ Law. [Online]. Available:
http://www.windpower.org/en/tour/wres/betz.htm [Cited 21st August 2007]
[48] Danish Wind Industry Association. Wind Turbines and the Environment.
[Online]. Available: http://www.windpower.org/en/tour/env/index.htm [Cited 21st August 2007]
[49] Danish Wind Industry Association. Birds and Wind Turbines. [Online].
Available: http://www.windpower.org/en/tour/env/birds.htm [Cited 21st August 2007 [50] British Wind Energy Association. Small Wind Frequently Asked Questions.
[Online]. Available: http://www.bwea.com/small/faq.html [Cited 21st August 2007]
[51] BWEA - Small Wind and Frequently Asked Question. [Online]. available http://www.bwea.com/small/faq#cost
[52] Department of Trade and Industry UK, The Energy Challenge, Appendix B, 2006 available http://www.berr.gov.uk/files/file32014.pdf
[53] Hand, M. M., ‘Mitigation of Wind Turbine/Vortex Interaction Using Disturbance Accommodating Control’, NREL, 2003
[54] Frank, D., “Blowing in the Wind”, Popular Mechanics, Vol. 168, No. 8, 1991, Hearst Magazines
[55] Schaeffer, J., Pratt, D., “Gaiam Real Goods Solar Living Sourcebook: Your Complete Guide to Renewable Energy Technologies and Sustainable Living”, 2005, New Society Publishers Limited
[56] Wu, Y-K., Hong J-S., ‘A Literature Review of Wind Forecasting Technology in the World’, Power Tech 2007 IEEE, 2007
[57] Potter, C.W., Negnevitsky, M., ‘Very short-term Wind Forecasting for Tasmania Power Generation’, IEEE Trans. Power Systems, vol. 21, 2006
[58] National Renewable Energy Laboratory, USA
[59] Energyfuture. energyfuture - What’s the cost? [Online]. Available:
http://www.energyfuture.org.uk/index.php?option=com_content&task=view&id=116
&Itemid=134 [Cited 21st August 2007]
[60] Solar Photo-Voltaic Grants. | Government Grants. [Online]. available:
http://www.government-grants.co.uk/photo-voltaic-grants.shtml
[61] “Solar Energy: Growth Opportunities for the Semiconductor Industry”, 2009, IC Insights
[62] Sharman, H., Constable, J., ‘Electricity Prices in the United Kingdom, Fundamental Drivers and Probable Trends, 2008 to 2020’, Renewable Energy Foundation 2008, available from http://www.ref.org.uk
[63] Stull, R. B., “An Introduction to Boundary Layer Meteorology”, 1988, Kluwer Academic Publishers
[64] Sözen, A., Arcaklioğlu, E., İzalp, M., Çağlar, N., ‘Forecasting Based on Neural Approach of Solar Potential in Turkey’, Renewable Energy, vol. 30, June 2005 [65] Sfetsos, A., Coonick, A.H., ‘Univariate and Multivariate Forecasting of Hourly Solar Radiation with Artificial Intelligence Techniques’, Solar Energy, vol. 68, February 2000
[66] Mellit, A., Benghanem, M., Kalogirou, S.A., ‘An Adaptive Wavelet-Network Model for Forecasting Daily Total Solar-Radiation’, Applied Energy, vol 83, July 2006
[67] Yona, A., Senjyu, T., Saber, A.Y., Funabashi, T., Sekine, H., Chul-Hwan Kim,
‘Application of Neural Network to 24-hour-ahead Generating Power Forecasting for a PV System’, Power and Energy Soc. Gen. Meeting – Conversion and Delivery of Elec. Ener. in the 21st Century, July 2008
[68] Green Consumer Guide. How Much Does a WhisperGen Cost? [Online].
Available:
http://www.greenconsumerguide.com/powergenminisite/whispergenunit.htm [Cited 31st August 2007]
[69] Robinson, P. R, ‘North Carolina weather & climate’, 2005, North Carolina Press [70] UK Met Office, ‘Met Office: Numerical Weather Prediction’,
www.metoffice.gov.uk/research/nwp/, retrieved 10th September 2008
[71] Mortensen, N.G., L. Landberg, I. Troen, and E.L. Petersen: Wind Atlas Analysis and Application Program (WAsP) – User’s Guide. Risø-I-666(EN)(v.2), Risø
National Laboratory, Roskilde, Denmark
[72] UK Met Office, ‘Met Office: Ensemble Prediction Sample Forecast’,
http://www.metoffice.gov.uk/research/nwp/ensemble/prob-examples.html, retrieved 10th September 2008
[73] Burton, T., Sharpe, D., Jenkins, N., Bossanyi, E., ‘Wind Energy Handbook’, 2001, John Wiley & Sons
[74]Van der Hoven, I., ‘Power spectrum of horizontal wind speed in the frequency range from 0.0007 to 900 cycles per hour’, J. Meteor, 14, 1957
[75] Zingerle, C., Reichert, B. K., Träger-Chatrerjee, C., ‘Satellite Data for Monitoring NWP in Operational Environments and for Model Development’
[76] Stull, R., ‘An Introduction to Boundary Layer Meteorology’, 1988, Springer [77] Murty, M. K. S., “Dynamic Response of Lattice Towers and Guyed Masts”, 2002, ASCE
[78] Mathew, S., ‘Wind energy: fundamentals, resource analysis and economics, Volume 1’, 2006, Springer
[79] Masters, G. M.: ‘Renewable and Efficient Electric Power Systems’, 2004, John Wiley & Sons
[80] B. Ai et Al.: ‘Computer-aided design of PV/wind hybrid system.’, Elsevier Science Ltd., Dec 2002
[81] Zingerle, C., Reichert, B. K., Träger-Chatrerjee, C., ‘Satellite Data for Monitoring NWP in Operational Environments and for Model Development’
[82] Ransome, S., J., ‘How well do PV modelling algorithms really predict performance?’, 22nd PVSEC, Milan, 2007. Available from:
http://www.bp.com/liveassets/bp_internet/solar/bp_solar_global_new/STAGING/loca l_assets/downloads_pdfs/pq/PV_Modelling_Algorithms.pdf
[83] Siegenthaler, J., ‘Modern Hydronic Heating for Residential and Light Commercial Buildings, 2nd Edition’, 2004, Thomson Delmar Learning
[84] Nussbaumer, H. J., ‘Fast Fourier Transform and Convolution Algorithms’, Second Edition, 1982, Springer
[85] Giebel, G. et Al.: ‘Wind Power Prediction Using Ensembles’, Risø National Laboratory, Roskilde, Denmark, September 2005
[86] Lorenz, E.N.: ‘Deterministic Nonperiodic Flow’, J Atmospheric Sciences 20, pp.
130-141 (1963)
[87] Landberg, L. et Al: ‘Short-term Prediction of Regional Wind Power Production’, JOR3–CT98–0272 (Joule III programme), Commission of the European Communities [88] Messenger, R. A., Ventre, J., ‘Photovoltaic Systems Engineering’, 2nd Ed., 2004, CRC Press LLC
[89] Warkentin, D., ‘Electric Power Industry in Nontechnical Language’, 1998, PennWell
[90] Weedy, B. M.: ‘Electric Power Systems’, 3rd Ed., New York: Wiley, 1979 [91] Hoff, T. E. at Al.: ‘Distributed Generation: An Alternative to Electric Utility Investments in System Capacity’, Fuel and Energy Abstracts, Vol 37, Number 2 , March 1996, Elsevier
[92] BS EN 50160: ‘Voltage characteristics of electricity supplied by public
distribution systems’, British Standards Institution, 2000
[93] Kulmala, A., Repo, S., Järventausta, P., ‘Increasing Penetration of Distributed Generation in Existing Distribution Networks Using Coordinated Voltage Control’, Int. Jour. Dist. Energy Res., Vol 5, Number 3, 2009
[94] Ghosh, A., Ledwich, G., ‘Power Quality Enhancement Using Custom Power Devices’, 2002, Kluwer Academic Publishers
[95] Rehtanz, C., ‘Autonomous systems and intelligent agents in power system control and operation’, 2003, Springer
[96] Weedy, B. M. ‘Electric Power Systems’, 3rd ed., New York: Wiley, 1979, pp.
227-228
[97] Nedic, D.: ‘Tap Adjustment in AC Load Flow’, UMIST, September 2002, Available:
http://www.eee.manchester.ac.uk/research/groups/eeps/publications/reportstheses/aoe/
nedic_tech%20rep_2002.pdf
[98] ‘UK GDS’, http://monaco.eee.strath.ac.uk/ukgds/
[99] Borkowska, B: ‘Probabilistic Load Flow’, IEEE Trans. Power App. Syst., vol.
PAS93, pp. 752-759, 1974.
[100] Dopazo, J. F. at Al: ‘Stochastic Load Flows’ IEEE Trans. Power App. Syst., vol. 94, pp. 299-309, 1975.
[101] Allan, R. N. et Al: ‘Probabilistic AC load flow’, Proc. IEE, 1976, 123, pp.531-537
[102] Allan, R. N. et Al: ‘Probabilistic techniques in a.c. load-flow analysis’, Proc.
Inst. Elect. Eng., vol. 124, pp. 154-160, 1977.
[103] Leite da Silva, A. M., Ribiero, S. M. P., Arienti, V. L., Allan, R. N., Do Cuotto Filho, M. B., “Probabilistic Load Flow Techniques Applied to Power System
Expansion Planning”, IEEE Trans. Power Systems, vol. 5, no. 4, pp.1047-1053, Nov.
1990
[104] Chen, P., Chen, Z., Bak-Jensen, B., “Probabilistic load flow: A review”, Third Intl. Conf. Electric Utility Deregulation and Restructuring and Power Tech., 2008 [105] Hatziargyriou, N. D., Lorentzou, M. I., ‘Voltage Control Settings to Increase Wind Power on Probabilistic Load Flow’, Probabilistic Methods Applied to Power Systems, 2004 Intl. Conf., September 2004, pp737-741
[106] Infield, M. and Thomson, M.: ‘Impact of widespread photovoltaics generation on distribution systems’, IET Renew. Power Gener., 2007, 1, pp. 33-40
[107] Jaisingh, J., ‘Statistics for the utterly confused’, 2nd Ed., 2005, McGraw-Hill [108] Mazer, A., ‘Electric Power Planning for Regulated and Deregulated Markets’, 2007, IEEE
[109] Von Meier, A., ‘Electric Power Systems: a Conceptual Introduction’, 2006, John Wiley & Sons
[110] ‘Middelgrunden Wind Turbine Co-operative’, available:
http://www.middelgrunden.dk/mg_uk/project_info/location.htm
[111] Salmon, J. R. and Walmsley, J. L.: ‘A two-site correlation model for wind speed, direction and energy estimates’, J. Wind Eng. Indus. Aerodyn, 1999, 79, pp233-268
[112] Apt, J., ‘The Spectrum of power from wind turbines’, J., Journal of Power Sources, Volume 169, Issue 2, 2007, pp.369-374
[113] ‘Wind Energy Basics’, American Wind Energy Association, available:
http://www.awea.org/faq/wwt_basics.html
[114] UK Electricity Market Data from Elexon, UK Balancing and Settlements Code Company, www.elexon.co.uk, retrieved for 12-2008
[115] UK Electricity Suppliers that will buy back solar generated electricity, http://www.totalsolarenergy.co.uk/electricity-suppliers.html, accessed 01-08-2009 [116] NFPA | Power Auctions, http://www.nfpa.co.uk/auctionprices.html, accessed data for February 2009
[117] Energy Saving Trust, UK Gov., “Feed in tariffs”,
http://www.energysavingtrust.org.uk/Generate-your-own-energy/Sell-your-own-energy/Feed-in-Tariff-scheme, accessed July 2010
[118] The Electricity (Standards of Performance) Regulations 2005, UK Statute Law, Statuatory Instrument 2005 № 1019
[119] Roos, F. and Lindahl, S.: ‘Distribution System Component Failure Rates and Repair Times – An Overview’, Nordic Distrib. and Asset Manag. Conf., Espoo, Finland, August 2004
Appendix A Building the Toolset
In order to apply the methods developed above, a toolset was developed. This chapter gives a brief overview of the tools, their function, and a little of the background functions, as a means to explain the tools and their usage and an aid to those trying to reproduce the methods.
The Basic Power Aggregator Tool
The basic aggregator tool is the most simplistic of the tools, as it runs without any attachment to a network. The DG may be thought of as being attached to an infinite bus for this tool. Onto this bus, the DG is added, and the VPP output is determined by inputting weather forecast conditions. This begins with the input stage.
The input for the process is the parameter data for the DG technologies. The output data is statistical data (to be built into a graph) recording the probability for power generation. Working through the process, the flow of information is as follows,
- User action to begin the output process,
- Output parameters chosen by means of a parameter input window, - Output parameters read in and temporarily saved,
- Output data for individual generators compiled using appropriate data,
- Output data compiled by combining each data set for each individual generator,
- Output data written to screen.
This cycle is completed by adding the steps:
- User action to add a new type of generator, of user selected technology, - Input data chosen by means of a parameters window,
- Input data read in for the generator(s),
- Input data stored within the program under the correct DG technology.
This may be considered to be an un-optimized flow, but this is the most basic flow of information which will produce the output. The flow may be analysed from start to finish, starting with the first window the user is presented with.
Inputting Data
Figure A.1: Main DG Window
The window shown above is the first window the user is presented with. Contained within this window are the means by which to add new generators of the user selected type, means by which to start the process of the compilation of DG data and the power output, means by which to view and edit the input data, and means by which to view the output data. The saving and loading of DG parameter lists is also available.
The entire process is laid out logically, from left to right. Adding a new generator is done by pressing one of the buttons in the bottom left of the screen, labelled functionally as ‘Add Wind’, ‘Add PV’ and ‘Add CHP’.
Figure A.2: Add DG Window
Pressing any one of these buttons opens up a new window, which contains parameters specific to the technology, and moves the process along to the next step - choosing the input parameters. The window shown to the left, for instance, is specific to micro wind turbines, and is not the same as the window shown for PV and microCHP. All the required generator parameters are inputted here; none of them need to
be added elsewhere in the process.
Also visible in Figure A.2 is a numeric entry box labelled ‘Number’, which allows the generator with parameters as selected to be added into the model multiple times. This is a feature to facilitate fast data entry; if a scenario has 100 identically specified generators it is tedious to go through this process 100 times.
When the user presses the ‘Done’ button the window is closed and the user is returned to the previous window (Figure A.1). This moves the process along to the next 2 steps, reading the data, and saving the data. The data chosen by the user to be the DG parameters is saved temporarily, along with the number required. The correct DG list is then found in memory according to the DG technology selected. The program then appends generators to the list with the chosen parameters, stored temporarily, until the number of generators appended is equal to the number of generators chosen. The temporary parameters are then deleted.
The user can, however, always press the X in the top right-hand of the window to stop
The user can, however, always press the X in the top right-hand of the window to stop