PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
June 1, 2024Heliyon9 citationsOpen Access

Improving power ramp rate of a coal-fired power plant by a bypass steam accumulator

View Full Paper
HDHongyu DingSDShuangfeng DingQTQingxiong Tan

Key Points

Key points are not available for this paper at this time.

Abstract

The increasing penetration of high-volatility renewable energy sources in the power system presents higher demands for flexibility from coal-fired power plant (CFPP). To enhance the flexibility of CFPPs, researchers have conducted a significant amount of thermal-system-level research in recent years on increasing system peak shaving depth. However, the load ramp rate of CFPPs under deep peak shaving is rarely discussed, despite its significance to the overall flexibility performance of CFPPs. This paper proposes a steam accumulator storage system integrating to the turbine's bypass system. The steam accumulator charges directly with working fluid from the live steam or reheat systems and discharge to the turbine, responding quickly to power ramp commands. A steady state model and a dynamic model of the proposed system were built and validated, and the calculation shows that the proposed scheme provides a load change of +2.13 % Pe and -8.3%Pe during a round-trip with a power efficiency of 63.6 % at a unit load of 40 % THA. The unit's load increase rate under coordinated control was enhanced by 1.5 % Pe/min, reaching 3 % Pe/min, using the proposed steam accumulator without revising the original controls, and the load decrease rate reached at least 5 % Pe/min. The results indicate that the proposed system provides a straightforward, easy-to-implement, and efficient solution for enhancing the load ramp rate of CFPPs at low loads.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Ding et al. (2024) studied this question.

synapsesocial.com/papers/68e66b13b6db6435875f61echttps://doi.org/10.1016/j.heliyon.2024.e32412
Ask AI
Helpful
Bookmark
Share
View Full Paper