Feasibility Project 4
Getting the right spatial & social mix: improved methods for planning community Renewable Energy facilities
Alexis Comber*1, Jen Dickie2, 1University of Leeds, 2University of Stirling, *Corresponding author: email@example.com
This project has a number of objectives:
- to understand the biomass, Renewable Energy (RE) planning and policy landscape in Scotland;
- to develop and extending current tools for optimally locating RE facilities;
- to identify the spatialities of RE demand: hotspots and coldspots.
The project has taken a Fuzzy Cognitive Mapping (FCM) approach for scenario testing to understand the factors involved in RE supply, demand, resourcing and planning. FCM captures domain knowledge from actors and stakeholders by getting them to identify the important factors in RE and, critically, how they are related to each other. In this project, standard FCM approaches have been extended in 2 ways to support objective-focused scenarios. First, optimisation routines have been applied to identify sets of factors, which if changed would result in particular outcomes (for example, increasing green energy demand, reducing the impacts of varying oil price on RE developments). Second, graph-partitioning approaches have also been used to identify groups of factors (clusters) from analysis of their inter-dependencies and links. These clusters suggest RE-related factors to be considered and evaluated together in order to increase RE uptake and developments.
The project has also extended location-allocation approaches to facility planning that seek to match supply and demand, so that they a) consider the spatial distribution of resource supply, b) suggest the optimal mix of different sizes (capacities) and types of RE facility (Biogas, CHP, Anaerobic Digester), and c) competing demands on the land resource.
The project has a Scotland focus. This is because the Scottish Government is taking a world-leading role in developing community resilience to climate change: it initiated the global climate justice fund, it is leading initiatives towards a low carbon climate resilient economy and it recognises the need for developed countries to mitigate their own carbon emissions. The Renewables Routemap sets out the targets, including the ambition to meet the equivalent of 100% electricity demand from renewable sources by 2020, and for at least 500 MW of renewables to be in community or local ownership by 2020.
Thus further objectives for this project are to identify what information is required to support decisions on investment by rural communities in land based renewable energy schemes (for example based around biomass and / or anaerobic digestion) at different scales: from national to community scale.
An example of the FCM from a workshop and its translation to an adjacency matrix is shown in the following figure:
The final report can be viewed below: