Long-term energy system planning considering short-term operational constraints

• Published in: Energy strategy reviews Vol. 26; p. 100383
• Main Authors: Gaur, Ankita Singh, Das, Partha, Jain, Anjali, Bhakar, Rohit, Mathur, Jyotirmay
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2019; Elsevier
• Subjects: Operational constraints; Power system planning; Renewable intermittency; TIMES energy model; Unit commitment; TIMES energy model; Renewable intermittency; Power system planning; Unit commitment; Operational constraints
• ISSN: 2211-467X; 2211-467X
• DOI: 10.1016/j.esr.2019.100383

The intermittent nature of renewable energy sources (RESs) brings formidable challenges in the operation of power system. Long-term energy system planning models overlook the impact of renewable intermittency on system operations due to the computational burden associated with large model size and long planning horizon. Hence, strategies such as soft-linking multiple models are developed, but they do not assure the convergence and optimality of such incoherent modeling framework. In this context, this paper utilizes unit commitment (UC) extension of TIMES modeling framework to integrate operational constraints directly in a long-term power system planning model. This strategy eliminates the complexity of handling multiple models. Results indicate that incorporation of UC constraints improve the performance of conventional generators in terms of increased capacity utilization, and help to assess flexibility requirements with high RESs. Energy storage provides the balancing and flexibility needs with stringent generator constraints. Sensitivity analysis shows that improved flexibility of thermal generators enables increased renewable penetrations. •Long-term planning models give an insight into possible energy scenarios and do not examine technologies in detail.•Renewable energy sources have spatial and temporal intermittency that causes challenges for short-term system operations.•Including operational details of generators directly in a planning model reduces intricacy of handling separate models.•Operational constraints increase usage of conventional generators and reduce overall capacity needs in planning model.•Higher operational flexibility of conventional generators allows greater renewable penetration and reduces storage needs.