A novel cluster-based spinning reserve dynamic model for wind and PV power reinforcement

• Published in: Energy (Oxford) 2021-11, Vol.234, p.121270, Article 121270
• Main Authors: Nikolaidis, Pavlos, Poullikkas, Andreas
• Format: Article
• Language: English
• Subjects: Clean energy; Computer applications; Dynamic models; Electric power systems; Electrical loads; Electricity storage; Hybrid optimization; Load shedding; Optimization; Photovoltaic cells; Reliability; Renewable energy; Renewable resources; Robustness (mathematics); Spinning-reserve clusters; Sustainable yield; Unit commitment; Variable renewable energy sources
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2021.121270

The share of low-carbon energy in the electricity-production industry is increasing, creating reliability disturbances in modern power systems. Globally, the various renewable resources are distinguished by their origin into firm, variable and uncertain. To cope with the impact of variable and uncertain renewables on residual load, system operators need to plan-ahead adequate spinning reserves. In this work, we introduce a new paradigm for addressing the dynamic spinning reserve formulation, that is capable of accounting for the largely unaddressed challenge of the volatile behavior of different power inputs in the presence of storage. Based on realistic models and spinning reserve clusters, our solution leverages widely adopted robust approaches in the field, providing optimum cost/risk trade-off without deteriorating the computational burden. The proposed framework relies on a hybrid optimization mechanism to enable the effective unit commitment and allow for the minimization of spinning reserve deficits, renewable energy curtailment and load shedding. In the presence of storage, our formulation improves not only the annual total cost, but also allows for renewable generation enhancement at the maximum reliability level. The annual improvement accounts for 1.18% increases in renewable penetration, reduced costs in the range of €9M-€27 M and 3.75–9.45 GWh of services that are not withheld. •Hybrid techniques provide a pathway to multi-objective optimization in complex problems.•Cluster-based spinning-reserve formulation shows optimum cost/risk trade-offs.•Properly determined storage size allows for addressing the volatile renewable energy sources at least cost.