Demand-side flexibility (DSF) has been touted as a possible solution to the challenges in power system operation arising from increasing intermittent renewables penetration and the emergence of electric vehicles. In Singapore, where around 24 to 60% of electricity demand in buildings could be attributed to heating, ventilation, and air conditioning (HVAC) purposes, air-conditioned buildings represent a significant flexibility resource which could be used to provide DSF and help tackle these challenges. This study aims to investigate the DSF potential of Singapore’s building stock and to explore how this potential could be realized through demand-side bidding. To this end, a building energy modeling tool with explicit modeling of the relationship between occupant comfort and HVAC load with model predictive control, CoBMo, is used. CoBMo allows optimal load scheduling to be expressed as a linear programming problem: minimizing overall electricity cost while maintaining occupant comfort. A price-based market clearing model is developed to evaluate demand-side bidding implementation, for which a case study on Singapore’s Downtown Core district is developed. Three scenarios with possible future utility-scale photovoltaic (PV) penetration in Singapore’s electricity system are explored, alongside a sensitivity analysis and a comparison between centralized dispatch and demand-side bidding with price-quantity pairs and linear curves. Results of the analysis show that DSF potential varies between building types, depending on cooling load and occupancy schedule. When extreme price fluctuations happen in future Singapore electricity market with 10 GWp PV penetration, demand-side bidding could aid consumers to utilize their DSF potential by encouraging more effective energy use and in turn, reducing their total electricity cost.

需求侧灵活性（DSF）被认为是应对因间歇性可再生能源渗透率不断提高和电动汽车的出现而引起的电力系统运营挑战的一种可能解决方案。在新加坡，建筑物中约有24％到60％的电力需求可归因于供暖，通风和空调（HVAC）用途，空调建筑物代表着巨大的灵活性资源，可用于提供DSF并帮助解决这些问题挑战。本研究旨在调查新加坡建筑存量的DSF潜力，并探讨如何通过需求方招标来实现这一潜力。为此，使用了具有模型预测控制CoBMo的建筑能耗建模工具，该工具具有对乘员舒适度和HVAC负荷之间关系的显式建模的功能。CoBMo允许将最佳负荷计划表述为线性规划问题：在保持乘员舒适性的同时，将总用电成本降至最低。开发了基于价格的市场清算模型以评估需求方招标的实施，为此，以新加坡市中心区为例进行了研究。探索了三种可能在新加坡电力系统中使用的公用事业规模光伏（PV）未来方案，并进行了敏感性分析，并通过价格量对和线性曲线比较了集中调度和需求方投标之间的比较。分析结果表明，取决于制冷负荷和使用时间表，建筑物类型之间的DSF潜力会有所不同。当未来的新加坡电力市场发生10 GWp光伏渗透的极端价格波动时，