Dispatch strategies for large-scale heat pump based district heating under high renewable share and risk-aversion: A multistage stochastic optimization approach
研究采用多阶段随机优化方法，最小化大型热泵区域供热的总期望热生产成本，考虑电力市场价格随机性、高可再生能源渗透率及供热运营商的风险偏好，发现热泵需与电锅炉等灵活技术耦合，且自愿平衡市场投标可降低期望成本28-59%。
Heat pumps play an essential role in decarbonizing the heating sector, and their adoption is projected to rise significantly. The high share of large-scale heat pumps in district heating exposes heating utilities to uncertainty in electricity markets. This challenge is further exacerbated by 1) future high share of renewable energy resulting in increased uncertainty of electricity prices, and 2) introduction of voluntary energy bids for balancing energy markets (or regulation market). Despite the importance of this issue, little research has been conducted to address it. Therefore, this paper investigates the optimal dispatch of large-scale heat pump based district heating by adopting multi-stage stochastic optimization approach to minimize the total expected heat generation cost, taking sequential electricity markets prices as stochastic. We also evaluate the impact of high renewable energy penetration on this optimal dispatch strategy. Furthermore, we include the risk preference of district heating operator by using conditional value at risk in the objective function. We conclude that heat pump should be coupled with flexible technologies like electric boilers as more renewable energy and balancing market trading increases flexible units' dispatch. Also, most of the heat produced (50%) in our case study is used flexibly via thermal storage. We find more renewable could lead to negative expected costs and voluntary bids in balancing market further reduces the expected cost by 28–59%. Furthermore, trading risk increases significantly with renewable energy penetration, which can be mitigated to a limited extent by bidding in up and down balancing market. Risk aversion shifts trading from intraday to day-ahead market and promotes heat pump dispatch.