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This paper evaluates the optimal bidding strategy for demand response (DR) aggregator in day-ahead (DA) markets. Because of constraint of minimum power quantity requirement, small-sized customers have to become indirect participants of electricity markets via the DR aggregator, who could offer various contracts accessing customers' demand reduction capacity in advance. In day-ahead markets, DR aggregator...
Given a physical substrate network and a collection of requests of virtual networks, the Virtual Network Embedding problem (VNE) calls for the embedding onto the physical substrate of a selection of virtual networks in such a way that the profit is maximized. The embedding corresponds to a virtual-to-physical mapping of nodes and links, subject to capacity constraints. Since, in practical scenarios,...
With increasing concerns on environmental issues, vast renewable energy is integrated into power systems worldwide, especially wind power. However, its essential uncertainty introduces great challenges into power system operation. Additionally, the emission-constrained generation dispatch problem need to be further studied in the background of restructured power systems, as just the total emission...
As power systems become more reliant on intermittent resources, system operators are faced with the fact that the availability of intermittent resources is beyond human control and largely unpredictable. Due to the lack of compliance from intermittent resources to follow strict dispatch instructions, the dispatch instruction for an intermittent resource is proposed to be a desired dispatch range or...
In this paper, we address the OpenFlow virtual network (VN) design problem. Unlike most of the existing approaches that directly performing bandwidth slicing on the physical networks, we take traffic uncertainly and statistical multiplexing into consideration. In our system, a user can specify the desired VN topology and the capacity for each virtual link (VL). The bandwidth descriptor for a VL consists...
Robust optimization, as a powerful paradigm for optimization under uncertainty, has recently attracted increasing attention in power system operations. In particular, adaptive robust optimization models have been proposed for the day-ahead unit commitment as well as real-time economic dispatch problems in power systems with a high penetration level of renewable energy sources. In this paper, we review...
This paper analyzes laws of disaster evolution and characteristics of disaster situation information and divides the problem into two phases. Under the background of two phases and multi kinds of warehouses, this paper studies how to distribute relief supplies in an effectively and fairly manner to meet the needs of disaster victims and reduce the loss. In black decision phase, without the determining...
We study a surgical cases assignment problem (SSA) of determining whether patients can be operated in the planning period and, if so, to determine the surgery date and location with the objective of maximizing the revenue of surgical suite. A major difficulty stems from the fact that the service time for each surgery is an uncertain parameter, and even small deviations occur between the actual and...
In hot rolling production process of iron and steel plant, the control of the strip shape is a challenging problem, since the set of the rolling schedule in operation optimization is dynamic with uncertainty, with consideration of the disturbance of the incoming information, the errors of the calculation models and the variations of the production parameters. In this paper, a model based on the shape...
The heat and power outputs of Combined Heat and Power (CHP) units are jointly constrained. Hence, the optimal management of systems including CHP units is a multi-commodity optimization problem. Problems of this type are stochastic, owing to the uncertainty inherent both in the demand for heat and in the electricity prices that owners of CHP units receive for the power they sell in the market. In...
In this paper, robust optimization based AC optimal power flow (ROPF) considering wind and solar power uncertainty is proposed. ROPF is used to determine optimal power dispatch and locational marginal prices in a day-ahead market while limiting the risk of dispatch cost variation. ROPF is tested on PJM 5-bus system integrating wind and solar PV generation. Simulation results indicate that ROPF results...
The increasing penetration of renewable generation poses a challenge to the power system operator's task of balancing demand with generation due to the increased inter-temporal variability and uncertainty from renewables. Recently major system operators have been testing approaches to managing inter-temporal ramping requirement. In this paper we propose a robust optimization based economic dispatch...
The traditional triangulation algorithms in multiview geometry problems have the drawback that its solution is locally optimal. Robust Optimization is a specific and relatively novel methodology for handling optimization problems with uncertain data. The key idea of robust optimization is to find the best possible performance in the worst case. In this paper, we propose a novel approach which solves...
Order execution for algorithmic trading has been studied in the literature as a means of determining the optimal strategy by minimizing a trade-off between expected execution cost and risk. However, the variance has been recognized not to be practical since it is a symmetric measure of risk and, hence, penalizes the low-cost events. In this paper, we propose the use of the conditional value-at-risk...
In this paper, based on the robust optimization techniques in Bertsimas and Sim[8], we propose a computationally tractable robust mean absolute deviation portfolio model. The purpose is to consider parameter uncertainty by controlling the impact of estimation errors on the portfolio strategy performance. The remarkable characteristic of the new method is that the robust optimization model retains...
In this paper, we present a novel approach to find the robust optimum of integrated photonic devices affected by implementation error. We apply robust optimization on a cheap surrogate model of an expensive integrated photonic device simulation. Robust optimization is the process of finding the best design point, in the presence of uncertainties, by minimizing the maximum realizable value of the objective...
The Capacitated Arc Routing Problem (CARP) is a widely investigated classic combinatorial optimization problem. Being a deterministic model, it is far away from the real world. A more practical problem model of CARP is the Uncertain CARP (UCARP), with the objective of finding a robust solution which performs well in all possible environments. There exist few algorithms for UCARP in previous work....
This paper describe a electromagnetic optimization technique using Taguchi's method. Taguchi's method was developed on the basis of the orthogonal array (OA) concept, which offers systematic and efficient characteristics. In manufacturing, the dielectric constant of High-speed PCB's dielectric material is uncontrollable factor. The paper carries out a comprehensive study of the impacts of the different...
Design of embedded systems involves a number of architecture decisions which have a significant impact on its quality. Due to the complexity of today's systems and the large design options that need to be considered, making these decisions is beyond the capabilities of human comprehension and makes the architectural design a challenging task. Several tools and frameworks have been developed, which...
Beamforming for multi-input multi-output (MIMO) cognitive networks is considered in the presence of channel uncertainty induced by errors in estimating cognitive-to-primary channels. A robust beamforming problem is formulated to optimize an appropriate cognitive radio network-wide performance metric, while enforcing protection of the primary system. In spite of the non-convexity of the resultant optimization...
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