Due to time-varying utility prices, peak demand charges, and variable-efficiency equipment, optimal operation of heating ventilation, and air conditioning systems in campuses or large buildings is nontrivial. Given forecasts of ambient conditions and utility prices, system energy requirements can be reduced by optimizing heating/cooling load within buildings and then choosing the best combination of large chillers, boilers, etc., to meet that load while accounting for switching constraints and equipment performance. With the presence of energy storage, utility costs can be further reduced by temporally shifting production, which adds an additional layer of complexity. Furthermore, due to changes in market and weather conditions, it is necessary to revise a given schedule regularly as updated information is received, which means the problem must be tractable in real time (e.g., solvable within 15 min). In this paper, we present a mixed-integer linear programming model for this problem along with reformulations, decomposition approaches, and approximation strategies to improve tractability. Simulations are presented to illustrate the effectiveness of these methods. By removing symmetry from identical equipment, decomposing the problem into subproblems, and approximating longer-timescale behavior, large instances can be solved in real time to within 1% of the true optimal solution.

中文翻译：

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大型HVAC系统在线调度的混合整数优化方法

由于公用事业价格随时间变化，高峰需求费用和可变效率的设备，在校园或大型建筑物中供暖通风和空调系统的最佳运行并不容易。给定环境条件和公用事业价格的预测，可以通过优化建筑物内的供暖/制冷负荷，然后选择大型冷水机组，锅炉等的最佳组合来满足该负荷，同时考虑转换限制和设备性能，从而降低系统能源需求。在存在能量存储的情况下，可以通过临时转移生产来进一步降低公用事业成本，这增加了额外的复杂性。此外，由于市场和天气条件的变化，有必要在收到更新信息后定期修改给定时间表，这意味着问题必须实时解决（例如，在15分钟内可以解决）。在本文中，我们提出了针对此问题的混合整数线性规划模型，以及重新设计，分解方法和近似策略，以提高可处理性。仿真表明了这些方法的有效性。通过消除相同设备上的对称性，将问题分解为子问题，并近似较长的时间尺度行为，可以将大型实例实时求解到真正最佳解决方案的1％之内。仿真表明了这些方法的有效性。通过消除相同设备上的对称性，将问题分解为子问题，并近似较长的时间尺度行为，可以将大型实例实时求解到真正最佳解决方案的1％之内。仿真表明了这些方法的有效性。通过消除相同设备上的对称性，将问题分解为子问题，并近似较长的时间尺度行为，可以将大型实例实时求解到真正最佳解决方案的1％之内。