Efficient Algorithms for Renewable Energy Allocation to Delay Tolerant Consumers

TL;DR

This paper develops robust, efficient algorithms for allocating renewable energy to delay-tolerant consumers, minimizing costs and maximizing profits without needing supply or demand statistics.

Contribution

It introduces two stochastic optimization algorithms using Lyapunov techniques for renewable energy allocation, handling unpredictable supply and demand.

Findings

Algorithms effectively minimize costs and maximize profits.

Methods do not require prior knowledge of supply/demand statistics.

Approach is robust to arbitrary supply and demand variations.

Abstract

We investigate the problem of allocating energy from renewable sources to flexible consumers in electricity markets. We assume there is a renewable energy supplier that provides energy according to a time-varying (and possibly unpredictable) supply process. The plant must serve consumers within a specified delay window, and incurs a cost of drawing energy from other (possibly non-renewable) sources if its own supply is not sufficient to meet the deadlines. We formulate two stochastic optimization problems: The first seeks to minimize the time average cost of using the other sources (and hence strives for the most efficient utilization of the renewable source). The second allows the renewable source to dynamically set a price for its service, and seeks to maximize the resulting time average profit. These problems are solved via the Lyapunov optimization technique. Our resulting algorithms do not require knowledge of the statistics of the time-varying supply and demand processes and are robust to arbitrary sample path variations.