# Learning Large Electrical Loads via Flexible Contracts with Commitment

## TL;DR

This paper introduces a novel flexible contract scheme for large electricity consumers that improves capacity planning by enabling information sharing and risk sharing, while maintaining customer autonomy.

## Contribution

It proposes a new flexible contract design that reveals customer demand information, addresses non-convex optimization challenges, and offers computationally efficient sub-optimal solutions with provable performance.

## Key findings

- Flexible contracts enable better demand information sharing.
- Proposed solutions are computationally efficient and robust.
- Achieves significant performance gains over traditional methods.

## Abstract

Large electricity customers (e.g., large data centers) can exhibit huge and variable electricity demands, which poses significant challenges for the electricity suppliers to plan for sufficient capacity. Thus, it is desirable to design incentive and coordination mechanisms between the customers and the supplier to lower the capacity cost. This paper proposes a novel scheme based on flexible contracts. Unlike existing demand-side management schemes in the literature, a flexible contract leads to information revelation. That is, a customer committing to a flexible contract reveals valuable information about its future demand to the supplier. Such information revelation allows the customers and the supplier to share the risk of future demand uncertainty. On the other hand, the customer will still retain its autonomy in operation. We address two key challenges for the design of optimal flexible contracts: i) the contract design is a non-convex optimization problem and is intractable for a large number of customer types, and ii) the design should be robust to unexpected or adverse responses of the customers, i.e., a customer facing more than one contract yielding the same benefit may choose the contract less favorable to the supplier. We address these challenges by proposing sub-optimal contracts of low computational complexity that can achieve a provable fraction of the performance gain under the global optimum.