Resumen:
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Consulta en la Biblioteca ETSI Industriales (Riunet)
[EN] In the next few years, it can be expected that many battery systems will
be installed in the distribution grid to cope with the high in-feed of photovoltaics.
In particular, battery systems can help stabilize the ...[+]
[EN] In the next few years, it can be expected that many battery systems will
be installed in the distribution grid to cope with the high in-feed of photovoltaics.
In particular, battery systems can help stabilize the power system
by managing short-term power
uctuations caused by sudden changes in
demand and supplies from RES, as well as reduce grid's overall operation
costs. However, the time-varying state of charge from batteries induces a
time dependent behaviour which leads to a greater complexity of the optimization
problem with hard energy capacity constraints and dynamic couplings.
Therefore, new control strategies are needed to dispatch a large
number of battery systems.
In this Thesis, to reduce the problem complexity, a distributed control
strategy based on the OCD algorithm is developed and applied to interconnected
LV grids. This metholodogy divides the central dispatch problem
into several smaller iteratively solvable subproblems, which are solved
independently but coordinated whereas taking the grid constraints into account.
Each subproblem comprises one LV grid and is coordinated with
the neighbouring LV grids by exchanging updated variables and Lagrangian
multipliers from the bordering nodes. This exchange is done after one iteration.
In each LV grid, PV and battery systems are assigned to households
and distributed over the grid nodes. Additionally, to deal with forecast uncertainties
and batteries' dynamic behaviour, the proposed approach has
been extended by applying a Model Predictive Control which allows the optimization
problem to be solved over a prediction horizon and handle the
dependency between consecutive time steps.
As an outcome of this thesis, the developed distributed control strategy
is compared with the centralized problem in terms of computation e ort
and optimality. The simulation results show that the distributed control
problem converges to the centralized solution, however, a higher number of
iterations is needed. For large-scale systems, a parallel algorithm presents
the advantage of needing less computation time than a centralized strategy.
As the interior point algorithm is used, a high dependency is seen between
convergence and the barrier term. Furthermore, due to the radial structure,
convergence is strongly related to the feeder's control variables for time steps
with low PV generation.
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