Kapacitetsbaserade prissignaler för effektkomponenten : Ett alternativ till tidsdifferentierade prissignaler

Sammanfattning

The increasing capacity challenges in electricity distribution networks, driven by electrification and changing consumption patterns, have created a growing need for mechanisms that can influence household electricity use and reduce peak demand. The aim of this study is to develop and analyze dynamic pricing models for active customers and evaluate their potential to reduce peak loads and improve capacity utilization in Mälarenergi’s electricity distribution network. The study is based on historical hourly consumption data from approximately 3,000 households connected to a distribution substation within Mälarenergi’s network during 2025. Initially, a regression analysis was conducted to identify the key factors influencing household load profiles. Three dynamic pricing models with different tariff structures and control principles were subsequently developed. Customer response was modeled using assumptions regarding price elasticity, demand flexibility, and the proportion of active customers. Scenario and sensitivity analyses were conducted using varying parameter combinations to evaluate model performance under different conditions. The results show that temperature is the dominant factor influencing load variations, while electricity price has only a limited direct effect on household consumption. Although the dynamic pricing models contributed to peak load reduction, the effects were generally limited and highly dependent on customer behavioral response, available flexibility, and tariff design. The findings further indicate that tariff design affects not only the strength of the pricing signal, but also the type of customer response required for the model to be effective. The study concludes that dynamic demand-based tariffs can serve as a relevant tool for supporting a more flexible electricity distribution system, but that their practical effectiveness as a standalone measure is limited. Achieving greater flexibility will likely require a combination of appropriate tariff design, technical automation, and increased customer adaptation.