Next Wave of Energy Systems

Why understanding the next wave of energy systems in a whole systems context is important?

There is a need to understand how the development of smart local energy systems (SLES) will relate to the national energy system as a whole and the extent to which our future investment in our national grids for electricity and gas will be altered by widespread deployment of SLES. What opportunities do SLES present to create a better overall way of providing the nation with its energy needs? What challenges will emerge at national scale that might limit or slow the development of SLES?  

The creation of SLES to deliver energy services from local resources clearly changes the context of how national energy systems are operated and planned to achieve goals such as security, cost and sustainability. How often will SLES need to look to the national system to provide services such as providing cover the unavailability of a local energy resource? This a key part of ensuring secure supplies for everyone. Conversely, will SLES be able to offer services to the national system, such as a reduction in net electricity demand when the national system faces a sudden shortage of generation? Will SLES be able to provide cover (sometimes call reserve) for each other rather than looking to the national system?

Answering these questions will help to find out whether the roll-out of SLES will lead to reduced costs at national scale, and therefore for individuals also. The answers will also show how the cost balance between local and national scale will change and this will help to provide an l assessment of overall benefits and costs of SLES.  There are many benefits associated with SLES including improved health, reduced fuel poverty, building a sense of community which all need to be considered alongside the findings on economic, security and carbon-emissions implications. The breadth of expertise within the EnergyREV consortium together with the interaction with the PFER demonstrators will help to explore trade-offs between locally and nationally driven examples of energy systems. Models and analysis are needed to enable the assessment of how successful SLES are in driving towards a successful future energy system at national level.

How will EnergyREV deliver unique and useful insights?

There are a lot of energy models available. These are often described as ‘system models’ or ‘whole system models’ and traditionally fit into two categories which have advantages and limitations:

  • Models from the economic community which look at how various types of investors behave, how investment decisions are made, or how different economic solutions emerge at different times in different localities. These models do not take into account technical issues of running a system 24 hours a day, 365 days a year, but do capture the diversity of decision making when investing in SLES.
  • Technical models that can be used to find a configurations of energy networks that have a minimised cost but which still deliver a specified level of security of supply (such as avoidance of power cuts) and meet energy policy targets such as limits on greenhouse gas emissions.

These traditional models need to be brought together and be used to inform each other so that we can understand how SLES deliver nationwide benefits and how investors can be incentivised to adopt them.

EnergyREV also includes expertise in policy formulation and the adoption of new technologies across businesses or households across markets. This additional knowledge will assist in identifying contextual factors that enable or act as barriers to the success of SLES and help to build market and policy factors into models.

What will the next wave of energy systems in a whole systems team do to deliver on these aims?

The EnergyREV team will co-evolve three energy models and a database that together will provide the answers we seek. These models are:

  • BRAIN – an agent-based investment model - will be enhanced such that the policy and regulation agents are given options to promote SLES and so that range of investor agents includes not only corporate investors in energy systems but also organisations that champion SLES on behalf of communities. https://www.ucl.ac.uk/energy-models/models/brain-energy
  • WESIM - a detailed model of energy networks that is able to plan the best set of investments to make to adapt the networks for a different future use - will be enhanced to include the technical characteristics of SLES such as self-consumption, flexibility and demand-response. http://www.wholesem.ac.uk/documents/icl-model-summary
  • Energy Hub – a model of the operation and performance of LES – will be used to characteristise self-consumption, flexibility and demand-response of SLES from the calibration of existing LES examples and to create summaries of these characteristics for WESIM.

We will also be analysing a database of LES examples from different geographies to understand what pilot deployments of LES can teach us about how well they are received and how their use spreads once first established.

The reason to use this co-evolution approach is based on using common starting assumptions and data. This allows cross-referencing of boundary conditions and calibration of assumptions made in one model with detail provided from one of the other models that has greater detail on the feature in question. We have chosen this approach as a fully-integrated model would raise computational and convergence challenges that may not have a solution or may lead to very slow production of results.

All of the outcomes of the research will be published in briefing papers that will set out our methodologies and interim findings.