New skills and training

Why are new skills and training important for smart local energy systems?

The transition to smart local energy systems (SLES) will lead to new opportunities and occupations across energy and associated systems, each coming with a need for new/updated employee skillsets.

To support the transition to SLES, the content and level of education and training provision to the existing and emerging energy system workforce needs to be updated, any skills gaps identified and redressed, and new skills needs anticipated. This would then ensure that the workforce is well trained and ready to take up the new opportunities as they arise.

EnergyREV’s Skills Needs Assessment is working on identifying the skills that are expected to be in high demand with the advent of SLES so as to inform training provision and related policy decisions. The work takes into consideration local circumstances in order to factor in different regional characteristics and needs.

What are the EnergyREV team doing in this area?

The Skills Needs Assessment work package first reviewed the methods used for assessing skills needs and shortages. Given that low carbon energy is not classified as a separate sector, there is no consistent data collected for the sector’s skills. As a result, the work package chose to use case studies.

So far, case studies have been completed for the city of Bristol and for the Energy Superhub Oxford PFER project. Through these case studies skillsets have been identified that are expected to be in high demand in the next five to seven years. Factors that propel or impede transition to SLES within each study locality have been analysed. We have also identified training provision modes.

Two further case studies are in the pipeline, together with focus groups to validate the emerging findings. Finally, a review of training provision needs will be undertaken.

How is EnergyREV exploring these issues?

The case studies have commenced with:

  • SLES-related grey literature review for each locality (e.g., local government reports, business evets and publications, blogs, news article, etc.). This was used to build an understanding of the local context, for example what SLES projects are carried out; which business and organisations are engaged; what is the local authority’s stand, etc.
  • Using this background knowledge, a number of relevant local SLES participants were recruited for interview and focus group discussions.
  • The material was analysed to distil the list of skills expected to be in short supply in the given locality, as well as the local drivers and obstacles to SLES.
What are the emerging insights?

A key insight is not to view SLES as a single system, but as a set of loosely interconnected, semi-independent sub-systems that form a system-of-systems.

The key sub-systems in a SLES (though not all always present) are:

  • Energy supply and distribution which expand the traditional energy system
  • ICT infrastructure for digital energy services; data exchange for decision making and control
  • Local (and central) government which sets (local) policies and regulations
  • Community energy groups with distributed generation and other energy activities
  • Buildings and Retrofitting businesses that enable heat retention and control, as well as install household-level energy storage and generation, etc.
  • Transport and Mobility services that draw on the electricity resources (e.g., for EV charging), while, also, providing stored energy to the grid (e.g., through feeding back EV battery charge), and deliver biogas, or hydrogen etc.  
  • Citizens who would take up smart energy services and share their data and routine flexibility with the SLES environment.

Skills training is necessary both:

(a) Within each of the above subsystems, and

(b) Across subsystems.

The skills in high demand in SLES range from specialist power system design and integration needs to generic communication and management.

Despite the varied context of the case studies, a common set of skills likely to become scarce in SLES is identified.