…another thing

On rereading my previous post, ‘Rebooting marine renewables’, it occurred to me that it is mostly about why the technologies need to be ‘rebooted’, and not about how it should be done. It was already too long by the time I got to that bit and I just wanted to finish it off as quickly as possible. I think, therefore, that it would be worthwhile expanding on this point, as it’s actually the most important part of the whole thing. Read more

Rebooting marine renewables

Last Monday evening (8 February) I watched an online broadcast of a lecture entitled ‘Marine Renewables – Crossing the Valley of Death’, given at the Institution of Civil Engineers in London by Clare Lavelle, who is head of energy consulting in Scotland for Arup. Before that Clare worked for Pelamis Wave Power and before that for Scottish Power on its wave and tidal projects. Her presentation was very much a statement of the standard wave and tidal narrative that if only the government would spend enough money the technology would succeed. She even name-checked Professor Salter’s conspiracy theory and the one about how the UK lost its world lead in wind energy to the Danes. Read more

A critique of anti-renewables rhetoric – part 1

These days more and more people seem to be railing against renewable energy—in the media, on the internet and down the pub. Here are some recent examples:

  • Wind farms ‘will never keep the lights on’: Study claims turbines are ‘expensive and deeply inefficient’ (Daily Mail, 27 October 2014). [Poulter, 2014]
  • Green energy costs ‘far higher than ministers admit’—report claims renewable energy is ‘the most expensive domestic policy disaster in modern British history’ (Daily Telegraph, 18 March 2015). [Gosden, 2015]
  • The windfarm delusion—The government has finally seen through the wind-farm scam – but why did it take them so long? (The Spectator, 3 March 2012). [Ridley, 2012]

This kind of stuff is common in the mainstream media, as the above examples testify. It often contains emotive terms like ‘disaster’ and ‘scam’, as the above examples also testify. The regular drip feed of it seems to be turning ‘Middle England’ against renewables and the government is responding by rolling back the measures it had in place to encourage them. However, I’ve yet to see a fact-based critique of these arguments anywhere. Here’s my attempt fill the gap.

Read more

VBA Monte Carlo risk analysis spreadsheet with correlation using the Iman-Conover method. Part 5

Welcome to the fifth and final part of my series of blog posts about how to build a VBA application in Microsoft Excel that performs Monte Carlo simulation. It follows on from my post on 11 February, which assembled into a working prototype the parts developed in earlier posts. (If you haven’t read any of the other posts in this series, it would probably be best to read those first, starting with Post 1.) Read more

VBA Monte Carlo risk analysis spreadsheet with correlation using the Iman-Conover method. Part 3a

Introduction

Welcome to the third of five (sort of) posts about Monte Carlo risk analysis using Excel VBA. Actually, what was going to have been today’s post has turned out to be much longer than my target of around 3,000 words, so I have decided to split it in two, with the second half appearing tomorrow.

Yesterday we looked at how to generate arrays of random numbers using a combination of inverse transform and ‘latin hypercube’ sampling. Today we get to the heart of the problem and consider how to induce correlation onto our random sample using the Iman-Conover method. This is based on the idea of rank-order correlation. It involves the following steps: Read more

VBA Monte Carlo risk analysis spreadsheet with correlation using the Iman-Conover method. Part 2

Welcome back! This is number two in a series of five blog posts that describe how to construct a Monte Carlo risk analysis application in Excel VBA. It follows on from yesterday’s, where I gave an overview of the problem and how I propose to tackle it. Today’s post describes how to generate random numbers according to arbitrarily chosen probability distributions using the inverse transform method. Read more