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Scheduling a residential building short-term to optimize the electricity billcan be difficult with the inclusion of capacity-based grid tariffs. Schedulingthe building based on a proposed measured-peak (MP) grid tariff, which is acost based on the highest peak power over a period, requires the user toconsider the impact the current decision-making has in the future. Therefore,the authors propose a mathematical model using stochastic dynamic programming(SDP) that tries to represent the long-term impact of current decision-making.The SDP algorithm calculates non-linear expected future cost curves (EFCC) forthe building based on the peak power backwards for each day over a month. Theuncertainty in load demand and weather are considered using a discrete Markovchain setup. The model is applied to a case study for a Norwegian building withsmart control of flexible loads, and compared against methods where the MP gridtariff is not accurately represented, and where the user has perfectinformation of the whole month. The results showed that the SDP algorithmperforms 0.3 % better than a scenario with no accurate way of presenting futureimpacts, and performs 3.6 % worse compared to a scenario where the user hadperfect information.

Representing Long-term Impact of Residential Building Energy Management using Stochastic Dynamic Programming