---
title: "Optimal Prosumer Decision-Making Using Factored MDPs"
authors:
  - "Angelos Angelidakis"
  - "Georgios Chalkiadakis"
doi: ""
url: "https://ijcai.org/proceedings/2016/612"
pdf: "https://ijcai.org/proceedings/2016/0612.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "4110-4113"
track: ""
keywords:
---

# Optimal Prosumer Decision-Making Using Factored MDPs

Angelos Angelidakis, Georgios Chalkiadakis

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 4110-4113.

## Abstract

Tackling the decision-making problem faced by a prosumer (i.e., a producer that is simultaneously a consumer) when selling and buying energy in the emerging smart electricity grid, is of utmost importance for the economic profitability of such a business entity. In this work, we model, for the first time, this problem as a factored Markov Decision Process. By so doing, we are able to rep-resent the problem compactly, and provide an ex-act optimal solution via dynamic programming — notwithstanding its large size. Our model success-fully captures the main aspects of the business decisions of a prosumer corresponding to a community microgrid of any size. Moreover, it includes appropriate sub-models for prosumer production and consumption prediction. Experimental simulations verify the effectiveness of our approach; and show that our exact value iteration solution matches that of a state-of-the-art method for stochastic planning in very large environments, while outperforming it in terms of computation time.