THESIS
2017
xiii, 120 pages : illustrations ; 30 cm
Abstract
With the increased penetration of intermittent renewable energy sources in the power
grid, power system operators need to maintain more ancillary services (AS) to ensure system
reliability. Recently, demand-side management (DSM) has been proposed as a novel approach
for AS provision in future smart grids. The idea is to change the power consumption
behavior at the demand-side to achieve certain objectives like active power balancing and
reactive power support. In this thesis, we study and tackle three issues related to the existing
challenges in the implementation of DSM for AS provision.
Firstly, we focus on the issue of operating reserves provision and show how to use load
aggregators to enable residential users to provide operating reserves. A hierarchical structure
is int...[
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With the increased penetration of intermittent renewable energy sources in the power
grid, power system operators need to maintain more ancillary services (AS) to ensure system
reliability. Recently, demand-side management (DSM) has been proposed as a novel approach
for AS provision in future smart grids. The idea is to change the power consumption
behavior at the demand-side to achieve certain objectives like active power balancing and
reactive power support. In this thesis, we study and tackle three issues related to the existing
challenges in the implementation of DSM for AS provision.
Firstly, we focus on the issue of operating reserves provision and show how to use load
aggregators to enable residential users to provide operating reserves. A hierarchical structure
is introduced. In the lower level, we propose an incentive compatible and social optimal
mechanism for user aggregation. In the upper level, the reserve market is modeled using
the conjectured supply function approach. We prove the existence and uniqueness of the
market equilibrium, and analyze its efficiency. A distributed algorithm is also developed that
converges to the equilibrium. We find that by applying our aggregation mechanism, both
power system operators and residential users can benefit financially.
We also investigate the control problem of charging electric vehicles (EV) under uncertainty. Both synchronous and asynchronous distributed algorithms are proposed to solve
this stochastic optimization problem using the historical information only. The computation
burden is distributed to the EVs, thus ensuring the scalability of the algorithms. We found
the convergence rate of the synchronous algorithm and obtain a sparse charging solution so
that the EV battery degradation caused by frequent charging can be mitigated. Simulation
results validate our proposed schemes, and show how the algorithm shaves the load peak.
Finally, we consider the issue of supporting reactive power from demand-side users with
photovoltaic (PV) inverters in a distribution system. A Stackelberg game is proposed to
model this problem. As the game leader, the system operator solves an optimal power
flow problem to determine the reactive power prices with the knowledge of the reaction
of users. As game followers, users apply an online algorithm to control the battery and PV
inverter to maximize the time average revenue based on techniques in Lyapunov optimization.
This algorithm has low complexity and does not need prior knowledge of the uncertain
parameters. We prove that it can achieve near-optimal performance when the battery size is
large. Simulation results show the increased user revenue, reduced power loss and improved
voltage profile in the distribution network.
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