We study the optimal operation of air-conditioning loads for commercial consumers based on the time-of-use (TOU) ($/kWh) and demand ($/kW) pricing plan. The problem is formulated as a dynamic program that aims to adjust consumer needs for electricity charges and comfort flexibly. Moreover, the Monte Carlo method is used to imitate the environmental uncertainties due to predicted outdoor temperatures. To determine the day-ahead temperature schedules for air-conditioning loads, a quantum-behaved particle swarm optimization based on space compression strategy (QPSO-SC) is presented in this paper. Space compression and particle re-initialization by chaotic initialization enhance the performance of the QPSO-SC. Finally, we test the proposed method for the TOU and demand pricing plan from Duke Energy. Thermostatical, daily energy charge saving, and monthly charge saving strategies are first compared, and the QPSO-SC is further compared with the particle swarm optimization, genetic algorithm, differential evolution, and other optimization algorithms. From the extensive simulations, the QPSO-SC is observed to be capable of yielding higher-quality solutions stably and efficiently than the other optimization algorithms; the proposed optimal strategy can also achieve a better balance between electricity charge and customer comfort, considering environmental uncertainties.

中文翻译：

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分时需求定价方案下商用空调负荷的日前优化策略

我们研究了基于使用时间 (TOU) ($/kWh) 和需求 ($/kW) 定价计划的商业消费者空调负载的最佳运行。该问题被制定为一个动态程序，旨在灵活调整消费者对电费和舒适度的需求。此外，蒙特卡罗方法用于模拟由于预测的室外温度引起的环境不确定性。为了确定空调负载的日前温度计划，本文提出了一种基于空间压缩策略的量子行为粒子群优化（QPSO-SC）。通过混沌初始化进行空间压缩和粒子重新初始化提高了 QPSO-SC 的性能。最后，我们测试了杜克能源公司为 TOU 和需求定价计划提出的方法。恒温，日常节能充电，首先比较了月费节省策略，并进一步将QPSO-SC与粒子群优化、遗传算法、差分进化等优化算法进行了比较。从广泛的模拟中，观察到 QPSO-SC 能够比其他优化算法稳定有效地产生更高质量的解决方案；考虑到环境的不确定性，所提出的最优策略还可以在电费和客户舒适度之间取得更好的平衡。观察到 QPSO-SC 能够比其他优化算法稳定有效地产生更高质量的解决方案；考虑到环境的不确定性，所提出的最优策略还可以在电费和客户舒适度之间取得更好的平衡。观察到 QPSO-SC 能够比其他优化算法稳定有效地产生更高质量的解决方案；考虑到环境的不确定性，所提出的最优策略还可以在电费和客户舒适度之间取得更好的平衡。