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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

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A Novel Method for Predicting the Power Output

  • f Distributed Renewable Energy Resources

Athanasios Aris Panagopoulos1

Supervisor: Georgios Chalkiadakis1

1 Electronic and Computer Engineering, Technical University of Crete, Greece; emails: {apanagopoulos, gchalkiadakis} @isc.tuc.gr

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A thesis submitted to the Department of Electronic and Computer Engineering in partial fulfillment of the requirements for the degree of Diploma in Engineering

Technical University of Crete, Greece

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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

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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

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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”

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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

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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

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Overview of Main Contributions

  • A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources”

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  • 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
  • utput estimation methods also produces WTG power output estimates
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Overview of Main Contributions

  • A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources”

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  • First work to use a generic and low-cost methodology incorporating 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)

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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,

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A PVS Power Output Estimation Model

  • The method for predicting the energy output of PV systems consists of the estimation

steps/submodels:

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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

 .

An All-Sky Solar Irradiance Model

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 ,  \.  .

  • For β=0 => =>

An All-Sky Solar Irradiance Model++

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  • 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)

Estimating the Cloud Transmittance Coefficients

  • A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources”

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  • They attempt to approximate the ratio (as it is independent of the season

and solar elevation)

  • Coefficients determined through least-squares fitting

Non-Linear Equation Models (CRM)

  • A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources”

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  • 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

Informed Non-Linear Equation Models (CRM)

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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
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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

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Incorporating Real Data

  • Meteorological data drawn from the Weather Underground database for 9 regions in the Med-

Belt, and 1 region in Northern Europe:

  • sky condition (qualitative observations)
  • solar radiation (i.e., )
  • ambient temperature ( )
  • relative humidity (%)

I. At least one year worth of observation data during 2009-2012 was collected in each city II. Quality control tests were performed III. Reduction of the larger datasets by progressively retaining every second observation

  • IV. All Med-Belt sets were collated
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The Final Dataset

  • From this we derive with training, testing and validation set
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Least-Squares Fitting and MLP Training

  • Least-squares fitting of the non-linear curves

– Choice of unique “mid-point” quantitative values to characterize each cloud coverage level – Computation of the sample mean of the corresponding for each of these values of N – Least square fitting over the pairs

  • A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources”

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Least-Squares Fitting and MLP Training

  • Least-squares fitting of the informed non-linear curves

– Choice of unique “mid-point” quantitative values to characterize each cloud coverage level – Computation of the sample mean of the corresponding for each of those values of N. – Least square fitting over the pairs .

  • A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources”

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Least-Squares Fitting and MLP Training

  • Training the MLP network

– MLP comprising of 4 nodes in the hidden layer was found to present the best network architecture. – Normalized values in the range of [-1,1] for the quantities at the input nodes – The MLP training used the back propagation learning algorithm with the batch method and uniform learning – Overfitting is avoided via the early stopping neural network training technique

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Evaluating Cloud Cover Radiation Model (CRM)

  • Comparison outside the

Med-Belt

  • ANOVA Tests
  • Local Training and

Evaluation

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Local Cloud Cover Radiation Model (CRM)

  • Example results:
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Error Propagation Methodology

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Error Propagation Methodology

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=> => => => => => => => => => => => => =>

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Error Propagation Methodology

  • Final power output prediction performance guarantees:
  • South-facing, 45◦ slope angle
  • rientation: worst-case bound for

rMAE in the order of 40%

  • No known comparable generic prediction methodology for the Med-Belt
  • Our method’s irradiance forecasting error is comparable to or lower than that of several other

more expensive methods evaluated in southern Spain

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WTG Power Output Estimation

  • Wind speed forecasts are commonplace
  • WTGs power output depends on the so-called power curve

Inside the range of:

  • Cut-in wind speed limit
  • Cut-out wind speed limit
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RENES: A Web-Based DER Output Estimation Tool

  • http://www.intelligence.tuc.gr/renes

 WTG and PVS power output prediction  User clickable map  Automatically populated parameters' values  API with XML responses

  • A. A. Panagopoulos “A Novel Method for Predicting the Power Output of Distributed Renewable Energy Resources”

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Conclusions and Future Work

  • This work is the first to provide low-cost power prediction estimates via a method applied to a

wide region, incorporating solar irradiance forecasts in the process

  • We implemented a web-based, interactive DER power output estimation tool (RENES)
  • Method and tool are extensible

– Can incorporate any other “intermediate-step” techniques deemed appropriate for particular sub-regions

  • RENES is a convenient user-interactive tool for simulations and experiments, and can be of use

to VPPs / wider public – We plan to employ RENES in VPPs-related simulations/ experiments – …and also to get readings for simulations related to optimal sun-tracking

Thank you! Any questions?

Department of Electronic and Computer Engineering,

Technical University of Crete, Greece

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