For a residential district heating or cooling (RDHC) system in the “part-time part-space (PTPS)” service mode, the thermal load demands of end users are notably stochastic. To reducing the energy consumption of centralized variable-speed pumps (VSPs) in RDHC systems, this study presents a stochastic load demand-oriented synergetic optimal control strategy for VSPs in RDHC systems. In this strategy, the spatial and temporal distribution of the stochastic load demands (SLDs) in a residential district is predicted using the stochastic occupant behavior (SOB) method. The operation regulation of the RDHC system is mathematically described based on graph theory. A synergetic optimization mathematical model is developed based on each end user’s SLD as well as the operation regulation characteristics of the RDHC system. This synergetic optimization mathematical model is subsequently solved using particle swarm optimization (PSO) algorithm. Thus, an optimal hourly pump frequency ratio is obtained. A case study is performed to comprehensively compare the proposed synergetic optimal control (SOC) strategy with the conventional constant pressure difference control (CPDC) strategy. The results show that for the same load demands, the energy consumption of the pumps throughout the heating season under the SOC strategy is 62.9% lower than that under the CPDC strategy.

对于处于“兼职部分空间（PTPS）”服务模式的住宅区供热或制冷（RDHC）系统，最终用户的热负荷需求明显是随机的。 为了减少RDHC系统中的集中式变速泵（VSP）的能耗，本研究提出了一种随机负载导向的RDHC系统中VSP的协同最优控制策略。在此策略中，使用随机乘员行为（SOB）方法预测居民区中的随机负荷需求（SLD）的时空分布。RDHC系统的运行规律是基于图论的数学描述。基于每个最终用户的SLD以及RDHC系统的运行调节特性，开发了协同优化数学模型。随后使用粒子群优化（PSO）算法求解该协同优化数学模型。因此，获得了最佳的每小时泵浦频率比。进行了案例研究，以将所提出的协同最优控制（SOC）策略与常规恒压差控制（CPDC）策略进行全面比较。结果表明，在相同的负载需求下，SOC策略下泵在整个供暖季节的能耗比CPDC策略下低62.9％。