AIChE Journal · 2022 · 20 citations · 44 references
Natural ResourcesEconomicsIndustrial EngineeringEnergy EconomicsOptimizationSchedulingDemand ResponseElectricity MarketsEnergy PricingOptimization Under UncertaintyStochastic OptimizationStochastic ProgrammingDay-ahead Electricity MarketsDemand Response OptimizationDemand Response SchedulingOptimization Under Demand UncertaintyProcess Parameter EffectsProcess Parameter OptimizationProcess ParametersProcess Parameters Optimization
Abstract Time‐varying electricity prices on the day‐ahead and intraday market incentivize demand response of industrial processes. In prior work (Schäfer et al. AIChE J . 2020;66:1‐14), we studied the demand response potential with a generalized process model, but neglected the intraday market. Extending our prior investigation, we account for uncertain intraday prices in a mixed‐integer linear stochastic programming‐based scheduling, that is, we minimize expected cost and conditional value‐at‐risk in a bi‐objective optimization. We find that for very broad variations of the generalized process parameters, the conditional value‐at‐risk can be reduced significantly without drastically increasing the expected cost. Furthermore, simultaneously improving multiple process parameter leads to synergetic benefits. Moreover, the savings of three electrolysis processes can be more than doubled by marketing flexibility on the intraday market in addition to the day‐ahead market. Overall, our model allows for a rapid early assessment of the demand response potential considering the two markets.
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