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The ongoing transition to a net-zero emissions energy system is driven by what can be called a fourth wave of electrification, where fossil fuel-dependent processes are increasingly replaced by electric-powered appliances. At the same time, a substantial rise in electricity production is coming from renewable energy sources. Both developments are critical for a successful energy transition over the coming decades. A key aspect of this transition is understanding the load demand of electricity users.

The aim of this thesis is to analyze and assess the impact of different electricity users on the local grid, focusing on their consumption behavior. Conducted in collaboration with Uppsala Municipality, the research emphasizes a public perspective, user-friendliness, and a technologically-agnostic presentation of results. Three research objectives have been pursued throughout the thesis: (1) analyzing the impact of a single user's behavior in detail on the grid, incorporating peak shaving and distributed energy resources, in addition to a broader analysis to assess multiple users through a peak load correlation analysis; (2) proposing a framework to generate typical load profiles for various users, using time series clustering and a qualitative clustering step to model their demand patterns throughout the year; and (3) designing an index to quantify and compare the flexibility potential of electricity users, based on a rankable index developed using limited information.        

The results reveal considerable variation among users in both peak load contributions and flexibility potential. The peak load analysis and the flexibility index both identify users that could benefit from behavioral changes or offer potentially valuable flexibility to support grid stability. To test the framework of generating typical load profiles, a case study of elementary schools in Uppsala Municipality was conducted that showed that these institutions exhibited similar load patterns. The most typical load profile was rescaled to represent load variability based on the heated indoor area of an arbitrary school, showing a reasonable accuracy. These profiles can inform the selection of distributed energy resources, such as photovoltaic systems, or facilitate the co-location of users with complementary consumption patterns to minimize grid impacts.

The methods, tools, and frameworks presented in this thesis are versatile and can serve as valuable inputs for strategic decision-making by municipalities, businesses, and other stakeholders. By providing insights into user behavior, these tools can guide focused interventions to support the continued evolution of the energy transition.