When considering the design of electricity-intensive industrial processes, a challenge is that future electricity prices are highly uncertain. Design decisions made before construction can affect operations decades into the future. We thus explore whether including electricity price uncertainty into the design process affects design decisions.

We apply stochastic optimization to the design and operations of a chlor-alkali plant, an electrochemical process that produces chlorine, caustic soda, and hydrogen. Chlor-alkali production is electricity intensive and can be operated flexibly based on fluctuating electricity prices. We consider participation in the 5-min real time market and consider each day as a scenario in the stochastic program.

We find that flexible plant designs that oversize certain plant components can enhance participation in electricity markets and increase profits. When electricity-price uncertainty is considered by using stochastic optimization, the optimal system design includes fuel cell and hydrogen storage capacity, which allow the plant to hedge against price uncertainty. We furthermore find that scenario reduction techniques, which are used to reduce computational complexity, in our example approximate the expected objective function value well, but lead to error in terms of optimal design decision variables. This error ranges from not building some components (fuel cell and hydrogen storage capacity) to overestimating their capacities by 50%.

在考虑电力密集型工业流程的设计时，面临的挑战是未来的电价高度不确定。在施工之前做出的设计决策可能会影响未来数十年的运营。因此，我们探讨了将电价不确定性纳入设计过程是否会影响设计决策。

我们将随机优化应用于氯碱工厂的设计和运营中，这是一种产生氯气，苛性钠和氢气的电化学过程。氯碱生产需要大量电力，可以根据波动的电价灵活地进行操作。我们考虑参与5分钟的实时市场，并将每天作为随机计划中的一种情况。

我们发现，灵活的工厂设计会放大某些工厂组件的尺寸，可以增强对电力市场的参与并增加利润。当通过随机优化考虑电价不确定性时，最佳系统设计包括燃料电池和氢气存储容量，这使工厂可以对冲价格不确定性。此外，我们发现，在我们的示例中，用于减少计算复杂性的场景缩减技术很好地逼近了预期目标函数值，但在最佳设计决策变量方面却导致了错误。此错误的范围从不建立某些组件（燃料电池和氢气存储容量）到高估其容量50％。