1,845 research outputs found
Generalized Nash Equilibrium Seeking Algorithm Design for Distributed Constrained Multi-Cluster Games
The multi-cluster games are addressed in this paper, where all players team
up with the players in the cluster that they belong to, and compete against the
players in other clusters to minimize the cost function of their own cluster.
The decision of every player is constrained by coupling inequality constraints,
local inequality constraints and local convex set constraints. Our problem
extends well-known noncooperative game problems and resource allocation
problems by considering the competition between clusters and the cooperation
within clusters at the same time. Besides, without involving the resource
allocation within clusters, the noncooperative game between clusters, and the
aforementioned constraints, existing game algorithms as well as resource
allocation algorithms cannot solve the problem. In order to seek the
variational generalized Nash equilibrium (GNE) of the multi-cluster games, we
design a distributed algorithm via gradient descent and projections. Moreover,
we analyze the convergence of the algorithm with the help of variational
analysis and Lyapunov stability theory. Under the algorithm, all players
asymptotically converge to the variational GNE of the multi-cluster game.
Simulation examples are presented to verify the effectiveness of the algorithm
Optimal Planning of Virtual Inertia Installations to Improve the Power System Frequency Response
In recent years, the power system has seen a fast transformation from one primarily based on fossil energy to one where renewable energy, especially wind and solar power, takes a more significant proportion in the energy profile. With the shift in energy profile come the changes in the electricity generation units. The solar panels and wind turbines replace the synchronous generators in electricity generation. Most solar and wind generation units are converter-interfaced. In contrast, the synchronous generator is connected to the power grid directly. For this reason, the future power system of a high level of renewable penetration will exhibit dynamic properties different from the traditional power system, which poses many challenges. One of the challenges is related to frequency stability.
The frequency stability of a traditional power system is secured with a three-level frequency control scheme. The scheme is composed of three frequency regulation mechanisms at different time scales. The fastest control mechanism, named primary frequency control, needs about 5 s to be fully deployed to arrest the frequency drops or overshoots. After that, the other two frequency secondary and tertiary frequency control mechanisms are then slowly deployed to bring the system frequency back to the nominal value. Under this control scheme, the overall active power generation and consumption in a power system get balanced, and the power frequency variation is limited within a narrow range around a nominal value.
However, before the primary frequency control is sufficiently deployed, the system relies on the natural inertia response of the synchronous generators to maintain the active power balance at the sacrifice of changes in the generators' rotational speeds. As the power frequency is decided collectively by the rotational speeds of all synchronous generators in the system,
larger system inertia means smaller power frequency variation when subject to the same disturbance.
Since there is no lack of system inertia in a synchronous generator-dominant power system, the power frequency variation with the help of the tertiary control scheme is usually contained within a limited range. For a future power system with more and more synchronous generators being displaced by converter-interfaced generation (CIG) units, the system inertia decreases.
The tertiary frequency control scheme alone can no longer limit the power frequency variation within an acceptable range. For this reason, techniques were proposed to emulate inertia response on a converter-interfaced generation unit.
Apart from the level of total system inertia, studies show that the spatial distribution of system inertia can also influence the frequency response. Under this context, a well-planned virtual inertia installation at selected locations can achieve a satisfactory level of improvement on frequency response at a low investment cost. This thesis work aims at developing a systematic method to search for the most economical plan of virtual inertia installations while ensuring a satisfactory level of frequency response.
In order to derive the most economical plan of virtual installation, a mathematical optimization problem is proposed with constraints formulated with the help of a newly proposed metric of inertia response that quantifies the influence of inertia on the system frequency response. The formulation of the optimization problem considers all possible combinations of loading and renewable generation profiles.
Two methods are proposed to solve the optimization problem of the mixed-integer type. The first one is based on the classic scheme of dynamic programming. The second method adopts a relaxation technique based on the sparsity promotion or Majorize-Minimization (MM) method. Furthermore, parallel and cloud programming techniques are used to facilitate computation speed.
Other minor contributions include a design of a supplementary controller on top of the inertia emulation control to improve the voltage stability of a converter-interfaced generation unit.
Finally, case studies were conducted on a modified Southeast Australian power system against different types of faults to validate the performance and investment cost of the virtual inertia installation plan givens by the proposed method in comparison with two other methods. The result shows that the virtual inertia installation plan given by the proposed method produces better performance while at lower investment costs
Effectiveness of Reconfigurable Intelligent Surfaces to Enhance Connectivity in UAV Networks
Reconfigurable intelligent surfaces (RISs) are expected to make future 6G
networks more connected and resilient against node failures, due to their
ability to introduce controllable phase-shifts onto impinging electromagnetic
waves and impose link redundancy. Meanwhile, unmanned aerial vehicles (UAVs)
are prone to failure due to limited energy, random failures, or targeted
failures, which causes network disintegration that results in information
delivery loss. In this paper, we show that the integration between UAVs and
RISs for improving network connectivity is crucial. We utilize RISs to provide
path diversity and alternative connectivity options for information flow from
user equipments (UEs) to less critical UAVs by adding more links to the
network, thereby making the network more resilient and connected. To that end,
we first define the criticality of UAV nodes, which reflects the importance of
some nodes over other nodes. We then employ the algebraic connectivity metric,
which is adjusted by the reflected links of the RISs and their criticality
weights, to formulate the problem of maximizing the network connectivity. Such
problem is a computationally expensive combinatorial optimization. To tackle
this problem, we propose a relaxation method such that the discrete scheduling
constraint of the problem is relaxed and becomes continuous. Leveraging this,
we propose two efficient solutions, namely semi-definite programming (SDP)
optimization and perturbation heuristic, which both solve the problem in
polynomial time. For the perturbation heuristic, we derive the lower and upper
bounds of the algebraic connectivity obtained by adding new links to the
network. Finally, we corroborate the effectiveness of the proposed solutions
through extensive simulation experiments.Comment: 14 pages, 8 figures, journal paper. arXiv admin note: text overlap
with arXiv:2308.0467
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