RENEWABLE ELECTRICITY PROJECTS ANALYSIS

Power Station

TABLE OF CONTENTS

OBJECTIVES

This project contains data from the Renewable Energy Planning Database (REPD), managed by Barbour ABI on behalf of the Department of Business, Energy and Industrial Strategies (BEIS). The last update of the database was for the first quarter of 2024. It tracks the progress of renewable electricity projects in the United Kingdom from inception, through planning, construction, operation and decommissioning. It contains information on all Renewable Electricity and CHP (Combined Heat and Power) Projects in the United Kingdom.

The project aims to analyse:

DATA SOURCE

The data used for this project was pulled from the Renewable Energy Planning Database (REPD), the database has over 50 columns but I only needed to pull columns that had the following data:

The data for this project can be downloaded from the Renewable Energy Planning Database (REPD)

TOOLS

Tools Purpose
Microsoft Excel Data Exploration
SQL Server Data cleaning and transformation
Microsoft Power BI Data transformation and visualization via interactive dashboard
GitHub Hosting project documentation and version control

DESIGN

Dashboard Requirements

The dashboard requirements formed the basis of my analysis, and they asked the following question:

STEPS

Algorithm Outline

The general approach to the project is listed below:

Data Exploration

At first glance, the downloaded data from the Renewable Energy Planning Database (REPD) was about 3.58 MB in size with the table having over fifty columns ane more than ten thousand rows of data, the data was not consistent with formatting and it had missing values in some columns.

The data was accurate but incomplete due to the missing values. I immediately started thinking of ways to clean, correct and upgrade the data in terms of accuracy, quality, completeness and reliability.

The columns had the basic data needed as regards project location, operational status and power capacity, they just needed to be cleaned, prepared and properly formatted.

Data Cleaning

In context, Clean data should be consistent with uniform formats and standardized values, completeness is also ensured by having no missing values or handling them appropriately. Clean data should also be unique and be free of duplicates and only contain relevant information for analysis.

The aim was to refine the dataset to ensure proper structure and analysis. The clean data should meet the following requirements:

Steps and SQL scripts for data cleaning

--VIEW WHOLE TABLE
SELECT *
FROM repd_II;
GO

-- REMOVE UNWANTED COLUMNS
SELECT
    Old_Ref_ID, Record_Last_Updated_dd_mm_yyyy, Operator_or_Applicant,
    Site_Name, Technology_Type, Storage_Type, Installed_Capacity_MWelec,
    CHP_Enabled, CfD_Allocation_Round, CfD_Capacity_MW, Turbine_Capacity_MW,
    No_of_Turbines, Mounting_Type_for_Solar, Development_Status_short,
    County, Region, Country, Post_Code, X_coordinate, Y_coordinate,
    Planning_Authority
FROM repd_II;
GO

-- RENAME COLUMNS
SELECT
    Old_Ref_ID AS Ref_ID, Record_Last_Updated_dd_mm_yyyy AS Updated,
    Operator_or_Applicant AS Operator, Site_Name AS Site,
    Technology_Type AS Tech, Storage_Type AS Storage, 
    Installed_Capacity_MWelec AS 'Capacity Installed(MW)', CfD_Capacity_MW AS 'Capacity Agreed(MW)',
    CHP_Enabled AS 'CHP?', CfD_Allocation_Round AS 'Allocation Round',    
    No_of_Turbines AS Turbines,
    Development_Status_short AS Status,
    County, Region, Country,
    Post_Code, X_coordinate, Y_coordinate,
    Planning_Authority
FROM repd_II
WHERE Record_Last_Updated_dd_mm_yyyy is not null;

Creating SQL view for import into Power BI for visualization

-- CREATE VIEW
CREATE VIEW RENEWABLE AS
SELECT
	Old_Ref_ID AS Ref_ID, Record_Last_Updated_dd_mm_yyyy AS Updated,
	Operator_or_Applicant AS Operator, Site_Name AS Site,
	Technology_Type AS Tech, Storage_Type AS Storage, 
	Installed_Capacity_MWelec AS 'Capacity Installed(MW)', CfD_Capacity_MW AS 'Capacity Agreed(MW)',
	CHP_Enabled AS 'CHP?', CfD_Allocation_Round AS 'Allocation Round',	
	No_of_Turbines AS Turbines,
	Development_Status_short AS Status,
	County, Region, Country,
	Post_Code, X_coordinate,Y_coordinate,
	Planning_Authority
FROM repd_II
WHERE Record_Last_Updated_dd_mm_yyyy is not null

VISUALIZATION

Results

Power Station

Power Station

Power Station

Three interactive dashboards were created showing the analysis results regarding the power installed, station status and regional analysis of the power stations.

Dax Measures

Total Projects = COUNTROWS(RENEWABLE)

Total power capacity = SUM(RENEWABLE[Capacity Installed(MW)])

Station Status = IF(RENEWABLE[Status] = "Operational",
                    "Operational",
                    "Not Operational")

Operational Projects = CALCULATE(
                        COUNT(RENEWABLE[Station Status]),
			RENEWABLE[Station Status] = "Operational")

Non-Operational Projects = CALCULATE(
				COUNT(RENEWABLE[Station Status]),
				RENEWABLE[Station Status] = "Not Operational")

ANALYSIS

As of the first quarter of 2024, 9,844 projects were planned to cater for 214.916 MW of renewable power across the United Kingdom. The data points out the demand in England as it has been penned down to receive over 60% (129,000 MW) of the total renewable power with Scotland coming in second taking a distant 31% (68,000 MW), Wales and Northern Ireland taking planned to take a lowly 6.5% (14,000 MW) and 1.9% (4,000 MW) respectfully.
The projects planned to have the highest capacity were The East Anglia Array, Homsea 4, Dogger Bank, Homsea 3 and the Dinorwig, all built between 2018 and 2023. This was made obvious with the power trend going up with over 200% increase between 2018 and 2023, all these projects except for Dinorwig were built in England.
England has 6766 projects across its regions, with less than 30% operational and the rest having a non-operational status to their name. Wales and Northern Ireland have 619 and 415 projects respectively but have better operational projects status of 39% and 40%.

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