Multistage stochastic demand-side management for price-making major consumers of electricity in a co-optimized energy and reserve market

In this paper, we take an optimization-driven heuristic approach, motivated by dynamic programming, to solve a class of non-convex multistage stochastic optimization problems. We apply this to the problem of optimizing the timing of energy consumption for a large manufacturer who is a price-making major consumer of electricity. We introduce a mixed-integer program that co-optimizes consumption bids and interruptible load reserve offers, for such a major consumer over a finite time horizon. By utilizing Lagrangian methods, we decompose our model through approximately pricing the constraints that link the stages together. We construct look-up tables in the form of consumption-utility curves, and use these to determine optimal consumption levels. We also present heuristics, in order to tackle the non-convexities within our model, and improve the accuracy of our policies. In the second part of the paper, we present stochastic solution methods for our model in which, we reduce the size of the scenario tree by utilizing a tailor-made scenario clustering method. Furthermore, we report on a case study that implements our models for a major consumer in the (full) New Zealand Electricity Market and present numerical results.