2 research outputs found
Distributed Coordination and Optimisation of Network-Aware Electricity Prosumers
Electricity networks are undergoing a transformation brought on
by new technologies, market pressures and environmental concerns.
This includes a shift from large centralised generators to
small-scale distributed generators. The dramatic cost reductions
in rooftop solar PV and battery storage means that prosumers
(houses and other entities that can both produce and consume
electricity) will have a large role to play in future networks.
How can networks be managed going forward so that they run as
efficiently as possible in this new prosumer paradigm? Our
vision is to treat prosumers as active participants by developing
a mechanism that incentivises them to help balance power and
support the network. The whole process is automated to produce a
near-optimal outcome and to reduce the need for human
involvement.
The first step is to design an autonomous energy management
system (EMS) that can optimise the local costs of each prosumer
in response to network electricity prices. In particular, we
investigate different optimisation strategies for an EMS in an
uncertain household environment. We find that the uncertainty
associated with weather, network pricing and occupant behaviour
can be effectively handled using online optimisation techniques
using a forward receding horizon.
The next step is to coordinate the actions of many EMSs spread
out across the network, in order to minimise the overall cost of
supplying electricity. We propose a distributed algorithm that
can efficiently coordinate a network with thousands of prosumers
without violating their privacy. We experiment with a range of
power flow models of varying degrees of accuracy in order to test
their convergence rate, computational burden and solution quality
on a suburb-sized microgrid. We find that the higher accuracy
model, although non-convex, converges in a timely manner and
produces near-optimal solutions. We also develop simple but
effective techniques for dealing with residential shiftable loads
which require discrete decisions.
The final part of the problem we explore is prosumer manipulation
of the coordination mechanism. The receding horizon nature of
our algorithm is great for managing uncertainty, but it opens up
unique opportunities for prosumers to manipulate the actions of
others. We formalise this form of receding horizon manipulation
and investigate the benefits manipulative agents can obtain. We
find that indeed strategic agents can harm the system, but only
if they are large enough and have information about the behaviour
of other agents. For the rare cases where this is possible, we
develop simple privacy-preserving identifiers that monitor agents
and distinguish manipulation from uncertainty.
Together, these components create a complete solution for the
distributed coordination and optimisation of network-aware
electricity prosumers