Solar star projects SAM version 2017.9.5 PVwats version 5 model case study & validation

Abstract

Very Large Photovoltaic Solar Power Plants (VLPVPPs) are a major revolutionary step up not only for economies of scale, but also for %100 renewable power Global Grid. Their designs and investments should be performed in an environmentally friendly, fair, open to very large to small private investors, transparent and reducing relative income inequality approaches. Their investments will easily be possible with new investment models (%0 interest load, %100 private equity, open investment for ordinary people, project developers, private companies etc. with a constraint-based shareholder structuring). These revolutionary investment models will play an important and game changing role. VLPVPPs' early engineering and investment analysis can be performed in many software. Therefore, validation and verification efforts of those software in advance on the operational PVPPs are essential. This research study aims to present a validation and verification accomplishment of the Solar Star Projects (597 MWAC, 747.3 MWDC) (Solar Star I: 318 MWAC, 397.8 MWDC & Solar Star II: 279 MWAC, 349.5 MWDC) in Antelope Valley near Rosamond, Kern and Los Angeles counties, California, United States with the PVWatts Version 5 model of the National Renewable Energy Laboratory (NREL) System Advisor Model (SAM) Version 2017.9.5. The location and resource, system design data and information on the Solar Star Projects (I & II) are presented based on open source information and personal communications. The Solar Star Projects SAM software models' are simulated on a personal computer (PC) (Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHZ, 6,00 GB RAM) with the internet connection. The results of eight simple simulations, one parametric simulation and one stochastic simulation are compared with the actual generation data by the help of some statistical performance measures (e.g. annual model/actual: 100,0%, annual model/actual: 100,1%, absolute maximum forecast error 39.276 MWh, mean absolute error 11.554 MWh, geometric mean absolute error 8.924 MWh, mean square error 2.662.330.229 MWh, root mean square error 51.597 MWh).

Improving the Efficiency of Stand-Alone Solar PV Power Plants

“…Using concentrators in solar PV plants increases the solar energy specific flow incident on the solar photovoltaic cell, thereby reducing the solar photovoltaic cell area, and consequently, the cost of solar photovoltaic arrays of the same power. Concentrated radiation increases the efficiency of converting solar radiation from a solar photovoltaic cell, which is also noted in the works of other authors [24][25][26][27][28].…”

Development progress of power prediction robot and platform: Its world level very long term prototyping example

“…The main development and demonstration limitations during this study are as follows: (1) data and information still depends on other parties, organizations or similar like IEA; (2) electricity consumption data and information is collection manually or semi-manually, there is not any integration yet; (3) there is not any attention and interest of any international and national organizations for this fully automatized free open data and information sharing approach yet, none of the organizations pay attention and interest to (GP2S) idea or its principles yet, each organization has its own and very different ideas and approaches, chaos (chaotic); (4) attention and interest of researchers and academics cannot be taken for (GP2S) idea or its principles yet, nobody pays attention and interest to this idea yet, each and everybody has their own and very different ideas and approaches; (5) everybody and every organization have their own agenda, there is not any common approach or unification, the World is inefficient and thriftless; (6) there is still only one free open source scripting software used, R; (6) there is still only one free open source GIS software used, QGIS; (7) there are still only two free open source wire-framing and prototyping tools used, Quant UX and penpot, everything is presented on Quant UX, (8) there is not any web address and web page yet, (9) there are still only a few R packages used, there is not any comparison of all available packages yet, (10) a new specific package have not been designed and built yet, (11) full optimization algorithm is still under development and demonstration phase, (12) there is still only one researcher who has proposed and worked in this robot and platform.…”

“…The crucial technical experiences in the previous D3&D activities of (GP2S) are shortly as follows [5][6][7][11][12][13][14][15][16][17]:…”

“…It consists of 16 main blocks such as (common) (1) clearing workspace and setting working directory, (2) checking missing packages and installing them, (4) finding data file directory or location and reading data files; (ex-post, backwardlooking, historical analysis) (5) summarizing data, (6) executing, summarizing, and printing exploratory data analysis (EDA), ( 8) executing models for backward (e.g. linear regression, fuzzy, grey, ARIMA), (11) printing forecasting accuracy (forecast accuracy metrics universe); (ex-ante, forward-looking, projections) ( 14) executing models for forward (e.g. linear regression, fuzzy, grey, ARIMA) (ESM: GP2SWorldRScriptLongVersion.docx, GP2SWorldRCode.R).…”

“…Moreover, those experts will help other engineers and researchers in the inexperienced countries and grids (interconnected, separate or remote) or directly design all elements in the inexperienced countries for free (charge free) (e.g. Afghanistan, Chad, Congo, Iraq, Libya, Mozambique, Uganda, Rwanda, Somalia, Sudan, Syria) [7,[11][12][13][14][15][16]26,43,44]. In short, the second aim and motivation of this publication are to contribute a small part of the huge (GP2S) prototype related to its very-long-term time horizon (up to 500-years) with only 1-year interval at the World level region example in the Global Power Consumption Prediction Systems (GPCPS) subsystems (Appendix.A.1, ESM.3).…”