Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
Open Access
- 10 June 2021
- Vol. 14 (12), 3453
- https://doi.org/10.3390/en14123453
Abstract
The growing adoption of decentralised renewable energy generation (such as solar photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain experienced by the distribution networks in the near future. In such a scenario, energy storage is becoming a key alternative to traditional expensive reinforcements to network infrastructure, due to its flexibility, decreasing costs and fast deployment capabilities. In this work, an end-to-end data-driven solution to optimally design the control of a battery unit with the aim of reducing the peak electricity demand is presented. The proposed solution uses state-of-the-art machine learning methods for forecasting electricity demand and PV generation, combined with an optimisation strategy to maximise the use of photovoltaic energy to charge the energy storage unit. To this end, historical demand, weather, and solar energy generation data collected at the Stentaway Primary substation near Plymouth, UK, and at other six locations were employed.Funding Information
- UK Engineering and Physical Sciences Research Council (EP/S001387/1, EP/T013206/1, EP/L015099/1)
This publication has 32 references indexed in Scilit:
- Probabilistic electric load forecasting: A tutorial reviewInternational Journal of Forecasting, 2016
- Assessing the economics of large Energy Storage Plants with an optimisation methodologyEnergy, 2015
- Short-term load forecasting using regression based moving windows with adjustable window-sizesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- GEFCom2012 hierarchical load forecasting: Gradient boosting machines and Gaussian processesInternational Journal of Forecasting, 2014
- Global Energy Forecasting Competition 2012International Journal of Forecasting, 2014
- Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demandEnergy Policy, 2007
- Greedy function approximation: A gradient boosting machine.The Annals of Statistics, 2001
- Random ForestsMachine Learning, 2001
- Interior-point methodsJournal of Computational and Applied Mathematics, 2000
- Electric load forecasting using an artificial neural networkIEEE Transactions on Power Systems, 1991