Stochastic Optimization of Residential PV Battery Systems Under a Wholesale Market-Linked Retailer Model

Abstract

The rapid growth of distributed energy resources (DERs) is creating opportunities for residential customers to participate more actively in electricity markets. Emerging wholesale-linked retail models, which expose customers to time-varying wholesale prices while automating device operation, provide a new mechanism for coordinating behind-the-meter resources for mutual customer and system benefits. This paper develops a stochastic optimization framework for an emerging wholesale-linked retail paradigm, where residential customers receive exposure to time-varying wholesale electricity prices and automated control coordinates flexible devices. The proposed optimization framework is designed around an operational wholesale-linked retail model, allowing the interaction between customer decision-making and real-time wholesale price signals to be examined under realistic market conditions. The methodological contribution of the study is the formulation of a two-stage stochastic linear program for day-ahead residential PV–battery dispatch under a wholesale market-linked retail arrangement, incorporating uncertainty in wholesale prices, household load and PV generation. It adds an important facet to the wholesale market-linked retailer model to operate the battery more conservatively as spot price outcomes in particular can be highly uncertain. Australia provides an important case study due to its high rooftop PV penetration, five-minute settlement in the National Electricity Market (NEM), significant wholesale price volatility, and emerging retail innovation. The framework is positioned alongside existing DER coordination mechanisms, including Virtual Power Plants (VPP) and peer-to-peer trading (P2P). A stochastic linear programming formulation is developed to optimize household solar utilization, grid imports and exports, and BTM BESS charging and discharging decisions under uncertainty in electricity prices, demand, and PV generation. Using actual household demand and rooftop PV data from an Adelaide customer, the case study demonstrates how optimized battery operation can capture arbitrage opportunities, reduce exposure to high wholesale prices, and reduce electricity bills, while supporting system-level flexibility. Simulation results for a representative Adelaide household indicate that a 10 kW PV system combined with a 20 kWh battery, with an assumed total capital cost of AUD 30,000, could provide an annual benefit exceeding AUD 5,000 under the modelled conditions. This corresponds to a simple first-year return of approximately 16%, before accounting for battery degradation, financing costs and other lifecycle considerations. Batteries can play a particularly critical role in exploiting arbitrage opportunities that may generate very high savings up to ${\$}$ 194 in a single day with high prices, and also provide a hedge to customers against such prices. The framework provides a foundation for extending wholesale-linked retail models to incorporate network constraints, ancillary services, electric vehicles, and other flexible residential loads.