Month: January 2021

What factors affect transition to smart local energy systems from the Local Authorities perspective?

The below model captures the causes and impacts between a set of factors that emerged form the case study of Bristol city. This model is to be used as an exploration tool to build a better understanding of impact propagation and co-dependencies. It is not for quantification of any kind.  

Press Remix then Play to exercise the below model by

increasing/decreasing impact of various factors through arrows in cycled factors 

Note: this is only a partial model; it is based only on the information from a qualitative study of Bristol’s local authority. Thus, it is both:

• limited to inputs provided by the qualitative study participants;
• limited to the circumstances of Bristol itself.
• It is underspecified in terms of the magnitudes of change, as well as in terms of elements and links between them. For example, when multiple loops execute simultaneously, only the length of the link between nodes shows which loop will eventually dominate, which, clearly is not any reflection of reality.





What Skills and Training do householders require for better adoption of smart local energy services (like the present DSR service)?

Based on the case study of Bristol’s citizens, we suggest that: there is no single skill or point of training provision. Instead skills and support needs to be provided at each point where the householders face an automation impact factor. The support is needed both where any impediments are expected, but also where the positive impact factors can be amplified.

In accordance with this systemic view of automation adoption by the citizens shown below, the expected environmental, financial and community benefits, as well as personal convenience and trust towards the local authorities and local pro-environmental companies, foster adoption of the Smart Local Energy Services, like Demand-Side Response (DSR). On the other hand, mistrust towards 3rd party service providers, lack of technical and smart local energy systems skills, as well as safety and security risks, hamper such adoption.

Press Remix then Play to exercise the below model by

increasing/redesign impact of various factors through arrows in cycled factors

Note: this is only a partial model; it is based only on the information extracted from a qualitative study of 30 Bristol residents. Thus, it is both:

• limited to inputs provided by the qualitative study participants;
• limited to the circumstances of Bristol itself.
• It is underspecified in terms of the magnitudes of change, as well as in terms of elements and links between them. For example, when multiple loops execute simultaneously, only the length of the link between nodes shows which loop will eventually dominate, which, clearly is not any reflection of reality.