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Entangled Pair Resource Allocation under Uncertain Fidelity Requirements

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arXiv 2023年

作者： Kaewpuang, Rakpong Xu, Minrui Turner, Stephen John Niyato, Dusit Yu, Han Kim, Dong In School of Computer Science and Engineering Nanyang Technological University Singapore School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Thailand School of Information and Communication Engineering Sungkyunkwan University Korea Republic of

In quantum networks, effective entanglement routing facilitates remote entanglement communication between quantum source and quantum destination nodes. Unlike routing in classical networks, entanglement routing in quantum networks must consider the quality of entanglement qubits (i.e., entanglement fidelity), presenting a challenge in ensuring entanglement fidelity over extended distances. To address this issue, we propose a resource allocation model for entangled pairs and an entanglement routing model with a fidelity guarantee. This approach jointly optimizes entangled resources (i.e., entangled pairs) and entanglement routing to support applications in quantum networks. Our proposed model is formulated using two-stage stochastic programming, taking into account the uncertainty of quantum application requirements. Aiming to minimize the total cost, our model ensures efficient utilization of entangled pairs and energy conservation for quantum repeaters under uncertain fidelity requirements. Experimental results demonstrate that our proposed model can reduce the total cost by at least 20% compared to the baseline model. © 2023, CC0.

关键词： stochastic programming

Data-driven Optimization Approaches to the Power System Planning under Uncertainty

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arXiv 2022年

作者： Lyu, Shuhan Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany

In order to protect the environment and address fossil fuel scarcity, renewable energy is increasingly used for power generation. However, due to the uncertainties it brings to electricity production, deterministic optimization is no longer sufficient for operational needs. Therefore, a large number of optimization techniques under uncertainty have been proposed, which provide good ways to address uncertainties. This paper selects three of the more important optimization techniques under uncertainty to introduce: stochastic programming (SP), robust optimization (RO), and a novel approach named distributionally robust optimization (DRO) based on the first two. We explain each approach’s basic framework and general process using specific examples. The focus is on how each method addresses the uncertainties. In addition, we also compare their strengths and weaknesses and discuss future research directions. © 2022, CC BY.

关键词： stochastic programming

Source-storage interaction optimization and scheduling based on centralized control virtual power plant

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Source-storage interaction optimization and scheduling based...

第三十八届中国控制会议

作者： Hu Yu Gao Shan Liu Yu chool of Electrical Engineering Southeast University Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment

In view of the coordinated control and scheduling of distributed energy, such as controllable units, energy storage devices and fans in distribution networks, a virtual power plant source and load storage optimization scheduling model with uncertainties, demand response and network security constraints under centralized control is proposed. Firstly, the wind turbine, gas turbine, energy storage battery and load are integrated into a virtual power plant, and price-type demand response measures are implemented on the user side to improve the load curve. Then, aiming at maximizing the operating profit of the virtual power plant, the opportunity constraint model is used to describe the uncertainty of the wind turbine, load forecasting and internal power balance, and consider the operational constraints and network security constraints of each unit. The scheduling scheme is generated based on the reasonable control and coordination of the output of each component. Finally, the quantum particle swarm optimization algorithm with inertia weight adaptive adjustment is used to solve the model. The feasibility of the model and the effectiveness of the algorithm are verified by an example analysis of the IEEE 9 node improved system.

关键词： virtual power plant demand response stochastic programming quantum particle swarm optimal scheduling

Controllability and Flexibility of HVDC Converters in Hybrid AC/DC Grids: A stochastic Approach

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TechRxiv

TechRxiv 2023年

作者： Yurtseven, Kaan Koirala, Arpan Ergun, Hakan Van Hertem, Dirk Electa Department of Electrical Engineering KU Leuven Leuven Belgium EnergyVille Genk Belgium

With the rapid adoption of intermittent energy sources, HVDC transmission plays an increasingly significant role in power systems. To optimize the utilization of AC/DC grids with a high share of RES, there is a critical need for an accurate quantification tool that can effectively represent the uncertain operational characteristics of RES while incorporating the control capabilities of HVDC converter stations. This paper addresses this need by introducing a general polynomial chaos based non-sampling, one-shot mathematical framework capable of solving the stochastic optimal power flow problem in hybrid AC/DC grids. The proposed framework not only incorporates HVDC controllability and accounts for uncertainties caused by demand and renewable generation, but it also offers the additional capability of assessing the hosting capacity in meshed AC/DC grids. To show the effectiveness of the framework, three case studies are conducted on 5-bus and 67-bus AC/DC test systems. The 5-bus system is used for comparison with Monte Carlo simulations, while the studies on 67-bus system emphasize the enhanced flexibility of HVDC converters and explore the effects of various confidence levels. The findings of this research contribute to optimizing grid operation in RES-rich systems, offering guidance for the optimal utilization of hybrid AC/DC grids. © 2023, CC BY.

关键词： stochastic programming

Modelling and analysis of multi-timescale uncertainty in energy system planning

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arXiv 2024年

作者： Zhang, Hongyu Heir, Erlend Nisi, Asbjørn Tomasgard, Asgeir Department of Industrial Economics and Technology Management Norwegian University of Science and Technology Høgskoleringen 1 Trondheim7491 Norway

Recent developments in decomposition methods for multi-stage stochastic programming with block separable recourse enable the solution to large-scale stochastic programs with multi-timescale uncertainty. Multi-timescale uncertainty is important in energy system planning problems. Therefore, the proposed algorithms were applied to energy system planning problems to demonstrate their performance. However, the impact of multi-timescale uncertainty on energy system planning is not sufficiently analysed. In this paper, we address this research gap by comprehensively modelling and analysing short-term and long-term uncertainty in energy system planning. We use the REORIENT model to conduct the analysis. We also propose a parallel stabilised Benders decomposition as an alternative solution method to existing methods. We analyse the multi-timescale uncertainty regarding stability, the value of the stochastic solution, the rolling horizon value of the stochastic solutions and the planning decisions. The results show that (1) including multi-timescale uncertainty yields an increase in the value of the stochastic solutions, (2) long-term uncertainty in the right-hand side parameters affects the solution structure more than cost coefficient uncertainty, (3) parallel stabilised Benders decomposition is up to 7.5 times faster than the serial version. © 2024, CC BY-NC-ND.

关键词： stochastic programming

A Quantile Neural Network Framework for Two-stage stochastic Optimization

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arXiv 2024年

作者： Alcántara, Antonio Ruiz, Carlos Tsay, Calvin Department of Statistics University Carlos III of Madrid Spain Department of Computing Imperial College London United Kingdom

Two-stage stochastic programming is a popular framework for optimization under uncertainty, where decision variables are split between first-stage decisions, and second-stage (or recourse) decisions, with the latter being adjusted after uncertainty is realized. These problems are often formulated using Sample Average Approximation (SAA), where uncertainty is modeled as a finite set of scenarios, resulting in a large "monolithic" problem, i.e., where the model is repeated for each scenario. The resulting models can be challenging to solve, and several problem-specific decomposition approaches have been proposed. An alternative approach is to approximate the expected second-stage objective value using a surrogate model, which can then be embedded in the first-stage problem to produce good heuristic solutions. In this work, we propose to instead model the distribution of the second-stage objective, specifically using a quantile neural network. Embedding this distributional approximation enables capturing uncertainty and is not limited to expected-value optimization, e.g., the proposed approach enables optimization of the Conditional Value at Risk (CVaR). We discuss optimization formulations for embedding the quantile neural network and demonstrate the effectiveness of the proposed framework using several computational case studies including a set of mixed-integer optimization problems. © 2024, CC BY-NC-ND.

关键词： stochastic programming

Existence of an optimal strategy for stochastic train energy-efficient operation problem

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Existence of an optimal strategy for stochastic train energy...

第六届中国智能计算大会

作者： Xiang Li Ziyou Gao Wenzhe Sun State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing 100044China

When we formulate the optimal train control model for optimizing the operation strategy,it is necessary to consider the uncertain disturbances arising from the whether,route,and locomotive rolling *** energy-efficient operation is proved to be an efficient approach to reduce the effect of uncertainty by estimating the resistance coefficients as random *** purpose of this paper is to prove the existence of an optimal operation ***,we prove the existence of a feasible operation strategy satisfying the nonnegativity,boundary conditions,trip distance,and the motion ***,we prove the Lipschitz continuity for the stochastic objective ***,we prove the existence theorem for an optimal operation strategy.

关键词： Optimal train control Energy-efficient operation stochastic programming

Optimal Sizing of Energy Storage System for Wind Power Plants

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Optimal Sizing of Energy Storage System for Wind Power Plant...

IEEE Power and Energy Society General Meeting

作者： Zhen Shu Panida Jirutitijaroen Department of Electrical & Computer Engineering National University of Singapore 21 Lower Kent Ridge Road 119077 Singapore

ISBN: (纸本)9781467327275

This paper proposes a stochastic programming approach to the solution of sizing problem of energy storage system applied to grid-connected wind power plants. The objective is to maximize expected daily profit though time shifting of renewable energy production. The problem is formulated considering stochastic behaviors of renewable power as well as electricity prices. We apply stochastic programming framework with Sample Average Approximation (SAA). We conduct a case study using data from the Electric Council Reliability Council of Texas (ERCOT). It is shown that considerable profit can be achieved with a suitable energy storage size.

关键词： renewable energy energy storages stochastic programming Monte Carlo sampling wind power correlations Wind power plants Wind farms Energy storage systems Cache Memory System Time shifting SIZING stochastic programming stochastic models random behavior

A Multi-Objective Process Optimization Procedure under Uncertainty for Sustainable Process Design

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A Multi-Objective Process Optimization Procedure under Uncer...

The 2nd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2008)(第二届生物信息与生物医学工程国际会议)

作者： Li Sun JiPing Pan Anqi Wang Department of Chemical Engineering Dalian University of Technology Dalian Liaoning 116023 China

Sustainable chemical process design can be formulated as a multi-objective optimization (MOO) problem covering economic, environmental and societal aspects. Moreover, uncertainties are unavoidable during the process design. So, uncertainties should be involved in the optimization. In this work, authors work on the basis of stochastic programming to deal with uncertainty factors, and integrate MOO deterministic algorithms to identify the optimal process design for the improvement of sustainability from a number of alternatives. The efficacy of the procedure is demonstrated by design of 1-hexene separation process.

关键词： sustainability uncertainty stochastic programming multi-objective optimization