Predicting cloud cover and solar intensity at PV sites to improve performance of the Low Voltage distribution network

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Title
Predicting cloud cover and solar intensity at PV sites to improve performance of the Low Voltage distribution network

CoPED ID
a99c74fc-feff-47da-9d38-75420f3776df

Status
Closed


Value
£213,820

Start Date
March 1, 2018

End Date
Feb. 28, 2019

Description

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"The rapid rise in the installation of photovoltaic power generation (PV) is leading to operational problems for the Distribution Network Operators (DNOs) and impacting the electricity demand profile. These issues restrict installation of additional PV capacity, increase operating costs for the DNO's, complicate forecasting and balancing of the electricity network and impact margins of the PV operators.

This project tests the feasibility of developing a smart system to create short term (up to 1 hour ahead at 5 to 15 minute intervals) predictions of cloud cover and solar intensity at specific locations. These predictions will update every 15 minutes as new satellite imagery becomes available Using these predictions, the project will:

1. Predict site power output for PV sites.
2. Model the integration of these predictions into the Demand Side Response (DSR) market to predict short term electricity output from PV farms and properties, with and without on-site battery storage.
3. Review how these predictions can optimise on-site generation and demand, and enable the creation of a micro grid to optimise local electricity demand.

The project is geographically focused on the South West but the system will be suitable for any location in the UK.

The benefits of the project being:

1. To help balance local power fluctuations in the local distribution network
2. To help solar farms that have fixed output contracts identify shortfalls and have some opportunity to manage this shortfall
3. To help solar farms that have (or are considering) battery storage to improve the revenue (or business case) of such an asset by better managing the combination of storage and solar output, for example generating additional revenue by participating in DSR markets.

The innovation in the project lies primarily in the integration of the short term cloud cover/PV output predictions with DSR capability to deliver a system/service that can be exploited by the commercial PV community.

The project team comprises; Cornwall Council, a PV farm owner who is impacted significantly by grid imbalances; BRE -- National Solar Centre, a leading consultant in PV installation and monitoring providing solar advice and expertise; Open Energi, a leading specialist in the DSR market with existing expertise in the PV sector; Meniscus, a real time Big Data analytics specialist who is the project lead. The project calls on specific expertise from two sub-contractors; Pixalytics Ltd a satellite acquisition specialist providing processed imagery; Bath University with expertise in cloud prediction algorithms."

Meniscus Systems Ltd. LEAD_ORG
Meniscus Systems Ltd. PARTICIPANT_ORG
Open Energi Limited PARTICIPANT_ORG
Cornwall Council PARTICIPANT_ORG
Building Research Establishment Limited PARTICIPANT_ORG

Mike Everest PM_PER
Mike Everest PM_PER

Subjects by relevance
  1. Forecasts
  2. Solar energy
  3. Projects
  4. Production of electricity
  5. Electricity market
  6. Electrical power networks
  7. Electricity
  8. Renewable energy sources
  9. Optimisation
  10. Costs

Extracted key phrases
  1. Short term cloud cover
  2. Pv output prediction
  3. Short term electricity output
  4. Site power output
  5. Cloud prediction algorithms.&quot
  6. Pv site
  7. Solar output
  8. Pv farm
  9. Pv installation
  10. Solar intensity
  11. Additional pv capacity
  12. Solar farm
  13. Site battery storage
  14. Low Voltage distribution network
  15. Project lead

Related Pages

UKRI project entry

UK Project Locations