Frode Lie-Jensen, Andreas Aannø, E. Aleksandrova, Anders Westli, Morten Nielsen, Tiina Komulainen
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引用次数: 2
Abstract
District heating system (DHS) is a widely used and increasingly popular energy source in cities. The uncertainty in the heat load (HL) due to customer demand fluctuations makes unit commitment (UC) and heat production unit (HPU) control a complex task. This case study of the DHS at Fortum Oslo Varme AS (FOV) aims to find a strategy to optimize and fully automate UC and HPU. Our results suggests this can be accomplished by using model predictive control (MPC) to control HPU power and flow rate, mixed integer linear programming (MILP) optimization to solve UC problem, and multiple linear regression (MLR) model to predict the HL. We also show that the fuel cost can be reduced significantly.
区域供热系统(DHS)是一种应用广泛且日益普及的城市能源。由于用户需求波动引起的热负荷不确定性，使得机组承诺(UC)和产热机组(HPU)控制成为一个复杂的任务。Fortum Oslo Varme AS (FOV)的DHS案例研究旨在找到一种优化和完全自动化UC和HPU的策略。我们的研究结果表明，可以通过模型预测控制(MPC)来控制HPU功率和流量，混合整数线性规划(MILP)优化来解决UC问题，多元线性回归(MLR)模型来预测HL。我们还表明，燃料成本可以显著降低。
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