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Provision of secondary frequency regulation by coordinated dispatch of industrial loads and thermal power plants
Demand responsive industrial loads with high thermal inertia have potential to provide ancillary service for frequency regulation in the power market. To capture the benefit, this study proposes a new hierarchical framework to coordinate the demand responsive industrial loads with thermal power plants in an industrial park for secondary frequency control. In the proposed framework, demand responsive loads and generating resources are coordinated for optimal dispatch in two-time scales: (1) the regulation reserve of the industrial park is optimally scheduled in a day-ahead manner. The stochastic regulation signal is replaced by the specific extremely trajectories. Furthermore, the extremely trajectories are achieved by the day-ahead predicted regulation mileage. The resulting benefit is to transform the stochastic reserve scheduling problem into a deterministic optimization; (2) a model predictive control strategy is proposed to dispatch the industry park in real time with an objective to maximize the revenue. The proposed technology is tested using a real-world industrial electrolysis power system based upon Pennsylvania, Jersey, and Maryland (PJM) power market. Various scenarios are simulated to study the performance of the proposed approach to enable industry parks to provide ancillary service into the power market. The simulation results indicate that an industrial park with a capacity of 500 MW can provide up to 40 MW ancillary service for participation in the secondary frequency regulation. The proposed strategy is demonstrated to be capable of maintaining the economic and secure operation of the industrial park while satisfying performance requirements from the real world regulation market
Stable Wireless Network Control Under Service Constraints
We consider the design of wireless queueing network control policies with
particular focus on combining stability with additional application-dependent
requirements. Thereby, we consequently pursue a cost function based approach
that provides the flexibility to incorporate constraints and requirements of
particular services or applications. As typical examples of such requirements,
we consider the reduction of buffer underflows in case of streaming traffic,
and energy efficiency in networks of battery powered nodes. Compared to the
classical throughput optimal control problem, such requirements significantly
complicate the control problem. We provide easily verifyable theoretical
conditions for stability, and, additionally, compare various candidate cost
functions applied to wireless networks with streaming media traffic. Moreover,
we demonstrate how the framework can be applied to the problem of energy
efficient routing, and we demonstrate the aplication of our framework in
cross-layer control problems for wireless multihop networks, using an advanced
power control scheme for interference mitigation, based on successive convex
approximation. In all scenarios, the performance of our control framework is
evaluated using extensive numerical simulations.Comment: Accepted for publication in IEEE Transactions on Control of Network
Systems. arXiv admin note: text overlap with arXiv:1208.297
Finding Minima in Complex Landscapes: Annealed, Greedy and Reluctant Algorithms
We consider optimization problems for complex systems in which the cost
function has a multivalleyed landscape. We introduce a new class of dynamical
algorithms which, using a suitable annealing procedure coupled with a balanced
greedy-reluctant strategy drive the systems towards the deepest minimum of the
cost function. Results are presented for the Sherrington-Kirkpatrick model of
spin-glasses.Comment: 30 pages, 12 figure
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