  As the energy landscape evolves toward sustainability, the accelerating
integration of distributed energy resources poses challenges to the operability
and reliability of the electricity grid. One significant aspect of this issue
is the notable increase in net load variability at the grid edge. Transactive
energy, implemented through local energy markets, has recently garnered
attention as a promising solution to address the grid challenges in the form of
decentralized, indirect demand response on a community level. Given the nature
of these challenges, model-free control approaches, such as deep reinforcement
learning, show promise for the decentralized automation of participation within
this context. Existing studies at the intersection of transactive energy and
model-free control primarily focus on socioeconomic and self-consumption
metrics, overlooking the crucial goal of reducing community-level net load
variability. This study addresses this gap by training a set of deep
reinforcement learning agents to automate end-user participation in ALEX, an
economy-driven local energy market. In this setting, agents do not share
information and only prioritize individual bill optimization. The study unveils
a clear correlation between bill reduction and reduced net load variability in
this setup. The impact on net load variability is assessed over various time
horizons using metrics such as ramping rate, daily and monthly load factor, as
well as daily average and total peak export and import on an open-source
dataset. Agents are then benchmarked against several baselines, with their
performance levels showing promising results, approaching those of a
near-optimal dynamic programming benchmark.
