Why Do the Advanced Features Exist?

We know that the “advanced features” outlined in How to Use Advanced Features may be unintuitive, so we assure you that we had very good reasons for writing each of them.

Most of the advanced features are designed to handle the peculiarities of computing a monthly electricity bill in a time window of only a few days for moving horizon optmization.

Why Use Moving Horizon Optimization?

computational complexity
avoid perfect foresight

What Is the Purpose of prev_demand_dict and prev_consumption_dict?

FILL IN STUB: when performing moving horizon optimization, we need to know the previous consumption and costs

WHY BOTH?: for some demand charges there are different costs at different times, so we cannot just use the prev_consumption_dict

What Is the Purpose of consumption_estimate?

If a utility uses tiered charges, then the price per unit of electricity varies depending on total monthly consumption (What Makes Electricity Tariffs so Complex?). This means that the correct tier is a function of the monthly electricity consumption profile, which is a variable in the optimization problem. This makes computing the cost of electricity directly non-convex, so we assume the total monthly consumption using the consumption_estimate flag to simplify the problem into a convex form. Moving horizon optimization omplicates this issue even further, since the total monthly consumption will not be known in a single time window.

We have found that the convex simplification works quite well in our energy flexibility research since the timing but not magnitude of electricity consumption changes between simulations. However, in other applications this convex approxmiation may not fare as well.

What Is the Purpose of demand_scale_factor?

[cite Bolorinos ES&T (2023) & Chapin ES&T (2025)]: gist is that it is necessary for moving horizon optimization because the certainty around demand charges and therefore relative weight between demand and energy charges varies throughout the month.

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