A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources Athanasios Aris Panagopoulos 1 Supervisor: Georgios Chalkiadakis 1 Technical University of Crete, Greece A thesis submitted to the Department of Electronic and Computer Engineering in partial fulfillment of the requirements for the degree of Diploma in Engineering 1 Electronic and Computer Engineering, Technical University of Crete, Greece; emails: {apanagopoulos, gchalkiadakis} @isc.tuc.gr 1 of 30
Motivation Renewable energy sources need to get integrated into the electricity grid: – Inherently Intermittent – Potentially Distributed Smart Grid Technologies are the key for: – The successful integration of the numerous distributed energy resources – Decision-making regarding energy production and/or consumption A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 2 of 30
Virtual Power Plants (VPPs) AI and MAS research has been increasingly preoccupying itself with building intelligent systems for the Smart Grid Virtual Power Plants (VPPs) Coalitions of energy producers, consumers and/or 'prosumers' e.g. wind turbines, solar panels, electric vehicles’ batteries A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 3 of 30
Virtual Power Plants (VPPs) AI and MAS research has been increasingly preoccupying itself with building intelligent systems for the Smart Grid . Virtual Power Plants (VPPs) Coalitions of energy producers, consumers and/or 'prosumers' e.g. wind turbines, solar panels, electric vehicles’ batteries Equipping VPPs with an algorithmic framework and a web-based tool for dependable power output prediction of Photovoltaic Systems (PVSs) and Wind Turbines Generators (WTGs) across the Mediterranean Belt Our methods use free-to-all meteorological data A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 4 of 30
PVS Power Output Prediction Forecasting PV systems output can be linked to the task of forecasting solar irradiance estimates. Drawbacks of existing approximation methods: – They rely on expensive meteorological forecasts. – Many such methods produce clear sky prediction models only – Usually no strict approximation performance guarantees A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 5 of 30
PVS Power Output Prediction Forecasting PV systems output can be linked to the task of forecasting solar irradiance estimates. Drawbacks of existing approximation methods: – They rely on expensive meteorological forecasts. – Many such methods produce clear sky prediction models only – Usually no strict approximation performance guarantees – They are made up of components that have been evaluated only in isolation – Their performance has been evaluated only in a narrow geographic region – Examples: SVMs, MLP networks etc A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 6 of 30
Overview of Main Contributions Novel non-linear approximation methods for solar irradiance falling on a surface, given cloud coverage A generic PVS power output estimation model – combining our solar irradiance model with existing models calculating various PV systems losses Cheap methods: only require weather data readily available to all for free, via meteo websites Methods applicable to a wide region – Evaluation based on real data coming from across the Mediterranean belt (Med-Belt) Error propagation procedure to estimate our method’s total error for the entire Med-Belt RENES: a web-based, interactive DER output estimation tool incorporates our PVS power output estimation methods also produces WTG power output estimates A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 7 of 30
Overview of Main Contributions First work to use a generic and low-cost methodology i ncorporating solar irradiance estimation and free-to-all weather data Evaluated in a wide region RENES: a web-based, interactive DER output estimation tool : – Incorporates our PVS power output estimation methods – Also produces WTG power output estimates RENES: a convenient user-interactive tool for: – simulations and experiments – comes complete with an API and XML responses – VPPs operating A paper based on this work, entitled “ Predicting the Power Output of Distributed Renewable Energy Resources within a Broad Geographical Region ” and co-authored by Aris- Athanasios Panagopoulos, Dr. Georgios Chalkiadakis and Dr. Eftichios Koutroulis , was awarded the best student paper award in the Prestigious Applications of Intelligent Systems (PAIS) track of the 2012 European Conference on Artificial Intelligence (ECAI 2012) A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 8 of 30
A PVS Power Output Estimation Model The method for predicting the energy output of PV systems consists of the estimation steps: I. Developing a solar irradiance model to predict the incident radiation , , on the PV module II. Estimating the amount of incident radiation actually absorbed by the PV module , III. Predicting the module’s operating temperature , IV. Calculating the PV module’s maximum power output , V. Predicting the PV system’s actual power output , A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 9 of 30
A PVS Power Output Estimation Model The method for predicting the energy output of PV systems consists of the estimation steps/submodels: A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 10 of 30
An All-Sky Solar Irradiance Model stands for the total incident radiation on an arbitrarily oriented surface given a cloud coverage level N . It consists of three components: Beam Sky-diffuse Ground reflected . A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 11 of 30
An All-Sky Solar Irradiance Model++ , \. . For β=0 => => A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 12 of 30
Estimating the Cloud Transmittance Coefficients For we need to estimate the cloud transmittance coefficients and Note that: There is no direct way to calculate and However measurements are relatively commonplace I. Develop a Cloud cover Radiation Model (CRM), to estimate II. Decompose the estimated back to and , employing a known Diffuse Ratio Model (DRM) A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 13 of 30
Non-Linear Equation Models (CRM) They attempt to approximate the ratio (as it is independent of the season and solar elevation) Coefficients determined through least-squares fitting A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 14 of 30
Informed Non-Linear Equation Models (CRM) Air transparency depends on dew point temperature The difference between and is expected to be less dependent on location and season/time: Incorporating in our model: Coefficients determined through least-squares fitting A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 15 of 30
An MLP Network We also trained a MLP neural network with one hidden layer The network computes the quantity given: • The level of cloud coverage, N • The estimated quantity (in components) • The environmental temperature, • The relative humidity, RH A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 16 of 30
Our Cloud Cover Radiation Model (CRM) The nine (9) CRM approaches are: – Four (4) non-linear equation models – Four (4) informed non-linear equation models , trained on top of the “simple” non-linear equation models – An MLP network Trained and evaluated with the purpose of adopting one for our CRM in our region of interest A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources” ” 17 of 30
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