Background

Methodological approach:

Our researchers designed a unique framework that incorporates both quantitative and qualitative metric data to derive a useful practical output. The conceptual model presented below on Fig 1.0, shows a graphical representation of how our existing user input parameters integrate a tried and tested energy modelling simulation concept for building digital tools. From this illustration, the key outputs we intend to derive are:
1). A comprehensive environmental, technical, and financial report assessing the viability of solar PV grid projects anywhere in the world,
2). A detailed and well-structured debt finance cash flow model for solar energy investments.



Fig 1: Conceptual Model

Identifying the indicators:

PVDATA indicator selector was categorised into 3 segments. 1). Financial indicators, 2). Technical indicators, 3). Environmental, Social and Governance indicators (ESG). As incorporating qualitative ESG indicators to our software was important to us, we followed the following sustainable frameworks for identifying ESG: UN Principles for Responsible Investment (PRI) framework and Sustainability Accounting Standards Board (SASB) framework.
Over 50 unique indicators were identified and incorporated to the PVDATA software. Some of the indicators identified and used on PVDATA are Flood data, Internal rate of return (IRR), Energy affordability, biodiversity risk, regulatory policy risks, levelized cost of energy (LCOE) and many more.

Primary and secondary data:

Our primary and secondary datasets are all opensource and available to the public. It was important to us that we retrieved these datasets from credible organisations with a strong track record of data integrity and data accuracy. As such, we focused on the following organisations for all our opensource datasets: the world bank, NASA, Google, Trading Economics, the European Union, the United Nations, Imperial College London, Bloomberg, Global Forest Watch, Climate Investment Fund (CIF) and others. Below on table 1, is a further breakdown on where our selected datasets are retrieved from:

Table 1: Primary dataset

Data Type	Indicator	Regular Updates	Source
Geographical	Location coordinate API	Daily	Google
	Temperature data	Daily	NASA
	Irradiance data	Daily	NASA
	Rainfall data	Daily	NASA
	Flood risk API	Daily	NASA, Imperial College London
Financial	Currency Names	Daily	The World Bank
	Currency exchange rates	Daily	Currency freaks
	Interest rate API	Daily	Trading Economics
	Corporate tax API	Daily	Trading Economics
	GDP/Capita data	Annually	The World Bank
Utilities	Electricity utilities data	Quarterly	Bloomberg, European Commission, Statista, Individual country database on electric utility.
	Power market data	Quarterly	European Commission, Individual country database on electric utility (see country list).
	Electricity consumed per household	Quarterly	The World Bank, world data, Individual country database on electric utility.
	Electricity affordability index	Quarterly	The World Bank
Environmental/ Social	Biodiversity risk	Annually	The World Bank, Imperial College London
	Grid carbon intensity data	Annually	Ecometrica
	Population data	Annually	The World Bank
	Sustainable land use index	Annually	Imperial College London
	Natural disaster risk	Annually	weltrisikobericht.de
	Regulatory policies	Quarterly	IEA, UNFCCC, Bloomberg, Climate Investment Fund (CIF).
	Deforestation	Annually	Global forest watch

Tertiary data:

Our tertiary data category sourced data directly from solar energy stakeholders in the form of interviews, workshops or academic journals or articles. For this process, we conducted and or read over 1,000 interviews, workshops, and journals collectively. The main goal for this category was to know what is going on in the solar energy market from all the different viewpoints across the world. For example, how does a policy framework for solar in the United States affect profitability, or how does an environmental protection policy in Brazil affect market players in the region. This is very key to the PVDATA software, to ensure validity and real time data reporting derived from our software. Some of the journals and stakeholder workshops we covered to retrieved useful datasets are highlighted on table 2. The table is classified into five groups: Journal Paper (JP), Energy Financial Models (EFM), Official Reports (OR), Stakeholder interviews (SI) and other which refers to reviews from solar trade associations. Furthermore, data from official reports (OR), were retrieved predominantly from the International Energy Agency (IEA), the World Economic Forum (WEF) and the United Nations (UN).

Table 2: Tertiary data

Source

Year

Country

JP

EFM

OR

INT

OTHER

C1

C2

C3

C4

Hernandez et al.

2014

USA, Spain

×

×

Powell & Bender

2009

Canada

×

×

Schaeffer & Szklo

2005

Brazil

×

×

×

×

Wescott

2011

USA

×

×

Tsoutsos & Frantzeskaki

2005

Greece

×

×

×

World Bank

2013

USA

×

×

SolarCentury

2016

UK

×

×

Vera & Langlois

2007

Austria

×

×

×

×

Evans & Strezov

2009

Australia

×

×

×

×

Quaintglobal

2016

Nigeria

×

×

Stamford

2012

UK

×

×

×

×

×

Martínez

2013

Croatia

×

×

×

×

NREL

2015

USA

×

×

×

IRENA

2012

Germany

×

×

European Commission

2011

Belgium

×

×

Low Carbon

2016

UK

×

×

Liu

2014

China

×

×

×

×

Tejeda & Ferreira

2014

USA

×

×

Phillips

2013

UK

×

×

×

Green-Giraffe

2016

UK

×

×

Wahid

2016

India

×

×

World Economic Forum, n.d.

2013

Switzerland

×

×

×

×

EPIA

2010

Belgium

×

×

FirstSolar

2016

Brazil

×

×

Ngan & Tan

2012

Malaysia

×

×

Prakash & Bhat

2009

India

×

×

Juwi

2016

Germany

×

×

Kemmler & Spreng

2007

Switzerland

×

×

×

×

PWC

2015

UK

×

×

×

Evans et al.

2009

Australia

×

×

×

×

D’Amico & Petroni

2015

Italy

×

×

Raadal & Vold

2014

Norway

×

×

Ngan & Tan

2012

Malaysia

×

×

Mann & Teilmann

2013

Denmark

×

×

Leung & Yang

2012

China

×

×

Augusta&Co

2016

UK

×

×

Sciences

2007

USA

×

×

Tabassum & Premalatha

2014

India

×

×

×

Saidur & Rahim

2011

Malaysia

×

×

Dai & Bergot

2015

China/USA

×

×

Kunz et al.

2007

USA

×

×

Oliveira & Fernandes

2011

Portugal

×

×

Zhang

2010

USA

×

×

Bidwell

2013

USA

×

×

×

×

Kaldellis

2005

Greece

×

×

Wustenhagen & Wolsink

2007

Switzerland

×

×

Enevoldsen & Sovacool

2016

Denmark

×

×

Gallego & Carrera

2010

Germany

×

×

D’Souza & Yiridoe

2014

Australia

×

×

Khorsand & Kormos

2015

Canada

×

×

Fielding & Whitfield

2006

UK

×

×

Torres Sibille

2009

Spain

×

×

Default Values Assumptions:

The PVDATA software allows users to select system default values which are fetched from our API directly to the user’s dashboard. Although we do prefer users entering their own unique input parameters to get a more bespoke output of their solar energy investment, we give the user the option of requesting for data they do not have or not too familiar with by clicking on our system default toggle on the PVDAT software. Table 3 below gives the source of the data.

Table 3: Default data

Data	Number of Sources	Main source Retrieved	Type of Storage on PVDATA	Number of countries
Solar cost per MW ($USD)	25	Bloomberg	Server	Global
Land size per MW (Ha)	70	NREL	Server	Global
Photovoltaic Design	151	NREL	Server	Global
Financing structure	214	PWc	Server	Global
Operation	51	Juwi Solar	Server	Global
Live API Fetch	1	Trading Economics	Server	Global

How we calculate our annual solar energy yield

We follow the global formula for estimating the electricity generated from a solar photovoltaic system, where the formula is:

E = A * r * H * PR

E = Energy (kWh)

A = Total solar panel area (m2)

R = Solar panel efficiency (%)

H = Annual average solar irradiance on tilted panels (shading not included)

PR = Performance ratio, coefficient of losses

Countries covered by the PVDATA Software:

The PVDATA software holds more than 100,000+ unique datasets. Of which, we cover every single geological location in all 96 countries listed. This means that users can structure a solar energy project/investment in any region, town, state, or community within the listed countries below:

Angola

Argentina

Armenia

Australia

Austria

Azerbaijan

Bangladesh

Belarus

Belgium

Bolivia

Botswana

Brazil

Bulgaria

Cambodia

Cameroon

Canada

Chile

China

Colombia

Costa Rica

Côte d Ivoire

Cyprus

Czech Republic

Denmark

Dominican Republic

Ecuador

Egypt

El Salvador

Ethiopia

Finland

France

Gabon

Germany

Ghana

Greece

Guatemala

Hungary

India

Indonesia

Israel

Italy

Japan

Jordan

Kazakhstan

Kenya

Kuwait

Latvia

Lebanon

Lithuania

Luxembourg

Madagascar

Malaysia

Mali

Mexico

Mongolia

Morocco

Mozambique

Namibia

Netherlands

New Zealand

Nigeria

Norway

Oman

Pakistan

Panama

Paraguay

Peru

Philippines

Poland

Portugal

Qatar

Romania

Russia

Rwanda

Saudi Arabia

Senegal

Sierra Leone

Singapore

Slovenia

South Africa

Spain

Sri Lanka

Sweden

Switzerland

Thailand

Togo

Turkey

Uganda

Ukraine

United Arab Emirates

United Kingdom

United States

Uruguay

Yemen

Zambia

Zimbabwe