Stochastic Optimal Investment Strategy for Net-Zero Energy Houses

TL;DR

This paper develops stochastic optimization models for net-zero energy houses, comparing individual versus shared energy storage strategies to minimize costs and achieve carbon neutrality in local power systems.

Contribution

It introduces two novel optimization frameworks—stochastic convex optimization and cooperative game—for energy sharing in net-zero energy houses.

Findings

Shared storage reduces overall costs compared to individual storage.

Monte Carlo SAA effectively handles stochastic variables in the models.

Practical implementation demonstrated in Japanese neighborhood.

Abstract

In this research, we investigate Net-Zero Energy Houses (ZEH), which harness regionally produced electricity from photovoltaic(PV) panels and fuel cells, integrating them into a local power system in pursuit of achieving carbon neutrality. This paper examines the impact of electricity sharing among users who are working towards attaining ZEH status through the integration of PV panels and battery storage devices. We propose two potential scenarios: the first assumes that all users individually invest in storage devices, hence minimizing their costs on a local level without energy sharing; the second envisions cost minimization through the collective use of a shared storage device, managed by a central manager. These two scenarios are formulated as a stochastic convex optimization and a cooperative game, respectively. To tackle the stochastic challenges posed by multiple random variables, we apply the Monte Carlo sample average approximation (SAA) to the problems. To demonstrate the practical applicability of these models, we implement the proposed scenarios in the Jono neighborhood in Kitakyushu, Japan.