Although relatively small, with 7,600 inhabitants, Péronne is like any other town in Europe. Its inhabitants are eager to make changes which will help protect the environment and promote sustainability. An important part of this is the uptake of residential photovoltaics (PV) for solar energy.
Gazelec undertook a technology research project with the aim of providing actionable and timely insight on the actual and projected impacts of PV introduction on the low voltage grid. This project was supported by the French government through the “Investissements d’avenir” program, entrusted to the ADEME.
The project, called ‘Utilit-e’, showcased the combination of the latest generation of smart meters with leading-edge AI based grid analytics tools to:
- Increase efficiency of distribution and so reduce energy losses
- Optimise the grid PV capacity and connection request
- Demonstrate green credentials
- Optimise CAPEX investment in grid.
Getting to the Information
Step 1 was to obtain the information required to drive the analytics solution.
Gazelec had invested in smart meters provided by Networked Energy Services (NES). These meters, based on Open Smart Grid Protocol (OSGP) communications, act as remote sensors deep within the low-voltage grid. Without the need for any dedicated monitoring and communications infrastructure, Gazelec had access to more than 25 power quality and grid health parameters exposed by each NES smart meter.
The NES smart meters can also gather information from smart gas and water meters and expose this information to analytics at the back-end to expand on the value of the solution.
Reliability and security of this source of information is supported by NES operational tooling – ensuring that data is always available at the right time for analytics processing.
Generating the Business Insight
Step 2 was to have the AI based analytics platform by Odit-e process this information to generate the kinds of practical insight that Gazelec could apply to achieve and verify business benefits.
Odit-e’s solution creates a digital twin of the grid based solely on information from the smart meters, identifies network constraints through a load and balancing analysis, and simulates different PV uptake scenarios and capacity models to allow Gazelec to optimise their low-voltage grid.
A key point of the solution is its independence of pre-existing topology records. Whilst many DSOs have high quality records, maintaining them is an overhead. And many DSOs in emerging markets, where PV will play a big part in micro-grid deployment, will not have this information for the low-voltage grid. Creating the digital twin without needing pre-existing topology records unblocks analytics solutions for many of the DSOs just starting on their smart grid journey.
Gazelec now has a network analysis and a PV hosting capacity map that allows them to plan out rebalancing for the grid and optimise strategic infrastructure investment.
Importantly, Péronne now has a practical plan to support PV capacity of up to 9,000 kW peak. That’s very respectable for a population of 7,600.
In addition, Gazelec now has models which can be refined through constant testing and re-assessment. This ensures that projections for PV uptake can be trusted. In practical terms, this means that the stability of the low-voltage grid is assured as it transitions from supporting consumers to enabling prosumers.
PV is just the first challenge to be addressed. Next will come the introduction of residential Electric Vehicle (EV) charging points and the inevitable impact on the grid to cover demand.
Gazelec will also need to implement strategies for load-control in response to changing demand. All of these require both the means to manage the supply and demand at the consumer/prosumer, and the ability to rapidly develop actionable insight to optimise within a highly dynamic environment.
Gazelec’s first step to address PV will be the first of many to ensure that the residence of Péronne can take pride in their local DSOs record for sustainability and protection of the environment.