predicting the power output of distributed renewable ... · – the mlp training used the back...

A Novel Method for Predicting the Power Outputof 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

Motivation

Renewable energy sources need to getintegrated 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 increasinglypreoccupying itself with building intelligentsystems 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


Virtual Power Plants (VPPs)

AI and MAS research has been increasinglypreoccupying itself with building intelligentsystems 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 outputprediction of Photovoltaic Systems (PVSs) andWind Turbines Generators (WTGs) acrossthe Mediterranean Belt

Our methods use free-to-all meteorological data


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


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


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

Overview of Main Contributions


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)

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 PVS Power Output Estimation Model

The method for predicting the energy output of PV systems consists of the estimation steps/submodels:


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++


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


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)


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:



Informed Non-Linear Equation Models (CRM)


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


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


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


The Final Dataset

From this we derive with training, testing and validation set


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



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 .



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


Evaluating Cloud Cover Radiation Model (CRM)

Comparison outside the Med-Belt

ANOVA Tests

Local Training and Evaluation


Local Cloud Cover Radiation Model (CRM)

Example results:


Error Propagation Methodology




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Final power output prediction performance guarantees:

South-facing, 45◦ slope angleorientation: worst-case bound forrMAE 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


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


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




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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