检索结果-内蒙古大学图书馆

Flexible Ramping Product Procurement in Day-Ahead Markets

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Yurdakul, Ogun Ela, Erik Albayrak, Sahin Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Power Delivery and Utilization Department Electric Power Research Institute Palo AltoCA94304-1395 United States

This article puts forward a methodology for procuring flexible ramping products (FRPs) in the day-ahead market (DAM). The proposed methodology comprises two market passes, the first of which employs a stochastic unit commitment (SUC) model that explicitly evaluates the uncertainty and the intra-hourly and inter-hourly variability of net load so as to minimize the expected total operating cost. The second pass clears the DAM while imposing FRP requirements. The cornerstone of our work is to set the FRP requirements at levels that drive the DAM decisions toward the optimal SUC decisions. Our methodology provides an economic underpinning for the stipulated FRP requirements, and it brings forth DAM awards that reduce the costs toward the expected total cost under SUC, while conforming to the chief DAM design principles. By preemptively considering the dispatch costs before awarding FRPs, it can further avert unexpectedly high costs that could result with the deployment of procured FRPs. We conduct numerical studies and lay out the relative merits of the proposed methodology vis-à-vis selected benchmarks based on various evaluation metrics. © 2022, CC BY.

关键词： stochastic programming

Regularized sample average approximation for high-dimensional stochastic optimization under low-rankness

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Liu, Hongcheng Hernandez, Charles Lee, Hung Yi Department of Industrial and Systems Engineering University of Florida GainesvilleFL32611 United States

This paper concerns a high-dimensional stochastic programming problem of minimizing a function of expected cost with a matrix argument. To this problem, one of the most widely applied solution paradigms is the sample average approximation (SAA), which uses the average cost over sampled scenarios as a surrogate to approximate the expected cost. Traditional SAA theories require the sample size to grow rapidly when the problem dimensionality increases. Indeed, for a problem of optimizing over a p-by-p matrix, the sample complexity of the SAA is given by O(1) • p2/ϵ2polylog(1/ϵ) to achieve an o-suboptimality gap, for some poly-logarithmic function polylog(•) and some quantity O(1) independent of dimensionality p and sample size n. In contrast, this paper considers a regularized SAA (RSAA) with a low-rankness-inducing penalty. We demonstrate that the sample complexity of RSAA is O(1) • p/ϵ3polylog(p, 1/ϵ), which is almost linear in p and thus indicates a substantially lower dependence on dimensionality. Therefore, RSAA can be more advantageous than SAA especially for larger scale and higher dimensional problems. Due to the close correspondence between stochastic programming and statistical learning, our results also indicate that high-dimensional low-rank matrix recovery is possible generally beyond a linear model, even if the common assumption of restricted strong convexity is completely *** Codes 62J02, 65K05 Copyright © 2019, The Authors. All rights reserved.

关键词： stochastic programming

Learn and pick right nodes to offload

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Zhu, Zhaowei Liu, Ting Jin, Shengda Luo, Xiliang School of Information Science and Technology ShanghaiTech University Shanghai China

Task offloading is a promising technology to exploit the benefits of fog computing. An effective task offloading strategy is needed to utilize the computational resources efficiently. In this paper, we endeavor to seek an online task offloading strategy to minimize the long-term latency. In particular, we formulate a stochastic programming problem, where the expectations of the system parameters change abruptly at unknown time instants. Meanwhile, we consider the fact that the queried nodes can only feed back the processing results after finishing the tasks. We then put forward an effective algorithm to solve this challenging stochastic programming under the non-stationary bandit model. We further prove that our proposed algorithm is asymptotically optimal in a non-stationary fog-enabled network. Numerical simulations are carried out to corroborate our designs. Copyright © 2018, The Authors. All rights reserved.

关键词： stochastic programming

Efficient presolving methods for the influence maximization problem

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Chen, Sheng-Jie Chen, Wei-Kun Dai, Yu-Hong Yuan, Jian-Hua Zhang, Hou-Shan Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Mathematics and Statistics Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing100081 China School of Science Beijing University of Posts and Telecommunications Beijing100876 China

MSC Codes 90C10, 90C15We consider the influence maximization problem (IMP) which asks for identifying a limited number of key individuals to spread influence in a network such that the expected number of influenced individuals is maximized. The stochastic maximal covering location problem (SMCLP) formulation is a mixed integer programming formulation that effectively approximates the IMP by the Monte-Carlo sampling. For IMPs with a large-scale network or a large number of samplings, however, the SMCLP formulation cannot be efficiently solved by existing exact algorithms due to its large problem size. In this paper, we attempt to develop presolving methods to reduce the problem size and hence enhance the capability of employing exact algorithms in solving large-scale IMPs. In particular, we propose two effective presolving methods, called strongly connected nodes aggregation (SCNA) and isomorphic nodes aggregation (INA), respectively. The SCNA enables to build a new SMCLP formulation that is potentially much more compact than the existing one, and the INA further eliminates variables and constraints in the SMCLP formulation. A theoretical analysis on two special cases of the IMP is provided to demonstrate the strength of the SCNA and INA in reducing the problem size of the SMCLP formulation. We integrate the proposed presolving methods, SCNA and INA, into the Benders decomposition algorithm, which is recognized as one of the state-of-the-art exact algorithms for solving the IMP. We show that the proposed SCNA and INA provide the possibility to develop a much faster separation algorithm for the Benders cuts. Numerical results demonstrate that with the SCNA and INA, the Benders decomposition algorithm is much more effective in solving the IMP in terms of solution time. Copyright © 2021, The Authors. All rights reserved.

关键词： stochastic programming

Data-Driven Learning of Two-Stage Beamformers in Passive IRS-Assisted Systems with Inexact Oracles

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Pougkakiotis, Spyridon Hashmi, Hassaan Kalogerias, Dionysis The Department of Mathematics King’s College London United Kingdom The Department of Electrical and Computer Engineering Yale University New HavenCT United States

We develop an efficient data-driven and model-free unsupervised learning algorithm for achieving fully passive intelligent reflective surface (IRS)-assisted optimal short/long-term beamforming in wireless communication networks. The proposed algorithm is based on a zeroth-order stochastic gradient ascent methodology, suitable for tackling two-stage stochastic nonconvex optimization problems with continuous uncertainty and unknown (or "black-box") terms present in the objective function, via the utilization of inexact evaluation oracles. We showcase that the algorithm can operate under realistic and general assumptions, and establish its convergence rate close to some stationary point of the associated two-stage (i.e., short/long-term) problem, particularly in cases where the second-stage (i.e., short-term) beamforming problem (e.g., transmit precoding) is solved inexactly using an arbitrary (inexact) oracle. The proposed algorithm is applicable on a wide variety of IRS-assisted optimal beamforming settings, while also being able to operate without (cascaded) channel model assumptions or knowledge of channel statistics, and over arbitrary IRS physical configurations;thus, no active sensing capability at the IRS(s) is needed. Our algorithm is numerically demonstrated to be very effective in a range of experiments pertaining to a well-studied MISO downlink model, including scenarios demanding physical IRS tuning (e.g., directly through varactor capacitances), even in large-scale regimes. © 2024, CC BY.

关键词： stochastic programming

Robust Optimization Based Bidding Strategy for Virtual Power Plants in Electricity Markets

学校读者我要写书评

暂无评论

Robust Optimization Based Bidding Strategy for Virtual Power...

IEEE Power and Energy Society General Meeting

作者： Zheming Liang Yuanxiong Guo School of Electrical and Computer Engineering Oklahoma State University

ISBN: (纸本)9781509041695

The variable generation from renewable energy sources makes them challenging to be integrated into electricity markets. Virtual power plant (VPP) is proposed to integrate various distributed energy resources (DERs) so as to participate into the electricity market as a single entity. In this paper, we investigate a VPP consisting of several thermal generation units and renewable generation units. An optimal bidding strategy of the VPP in pool-based electricity markets is proposed based on the robust optimization. Compared with the previous studies which almost exclusively use the stochastic programming approach, our approach only requires a deterministic uncertainty set, rather than the hard-to-obtain probability distribution on the uncertain data. Moreover, the computational cost of our approach is much smaller than that of the stochastic programming based approach. A realistic case study is presented and the results obtained verify the effectiveness of our proposed approach.

关键词： power markets virtual power VP-P protocol Power Plants stochastic programming stochastic models Renewable energy renewable energy sources

Reserve Requirements in Ancillary Markets Using Consensus-Based Cooperative Model Considering Renewable Resources

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Xiu, Yan Magounder, Subior Barose, Stefanous Hamdi, Mohammd IEEE Department of Mechanical Engineering KTH University Sweden

From an economic point of view, a common criterion for assessing the merits of a reserve investment is its impacts on social welfare. The underlying assumption in using this criterion is that side payments may be used to distribute the social gains among all market players. In reality, however, since the impacts of an electricity reserve project on different players may vary, such side payments are rather difficult to implement. We present a three-stage scenario-based programming model for committing reserves in power markets with large amounts of wind power. We describe wind power generation in terms of a representative set of appropriately weighted scenarios, and we present a dual decomposition algorithm for solving the resulting stochastic program. We test our scenario generation methodology on a model of 118 bus system, and we show that the stochastic programming unit commitment policy outperforms common reserve *** Codes 60 Copyright © 2017, The Authors. All rights reserved.

关键词： stochastic programming

TOWARDS A SUSTAINABLE MICROGRID ON ALDERNEY ISLAND USING A PYTHON-BASED ENERGY PLANNING TOOL

学校读者我要写书评

暂无评论

TOWARDS A SUSTAINABLE MICROGRID ON ALDERNEY ISLAND USING A P...

The 12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2020)

作者： S. Dehghan A. M Nakiganda J. Lancaster P. Aristidou School of Electronic and Electrical Engineering University of Leeds Leeds UK Alderney Electricity Ltd Alderney Channel Islands

In remote or islanded communities, the use of microgrids (MGs) is necessary to ensure electrification and resilience of supply. However, even in small-scale systems, it is computationally and mathematically challenging to design low-cost, optimal, sustainable solutions taking into consideration all the uncertainties of load demands and power generations from renewable energy sources (RESs). This paper uses the open-source Python-based Energy Planning (PyEPLAN) tool, developed for the design of sustainable MGs in remote areas, on the Alderney island, the 3 rd largest of the Channel Islands with a population of about 2000 people. A two-stage stochastic model is used to optimally invest in battery storage, solar power, and wind power units. Moreover, the AC power flow equations are modelled by a linearised version of the DistFlow model in PyEPLAN, where the investment variables are here-and-now decisions and not a function of uncertain parameters while the operation variables are wait-and-see decisions and a function of uncertain parameters. The k-means clustering technique is used to generate a set of best (risk-seeker), nominal (risk-neutral), and worst (risk-averse) scenarios capturing the uncertainty spectrum using the yearly historical patterns of load demands and solar/wind power generations. The proposed investment planning tool is a mixed-integer linear programming (MILP) model and is coded with Pyomo in PyEPLAN.

关键词： power grids integer programming investment linear programming wind power plants load flow distributed power generation stochastic programming renewable energy sources power generation planning power generation economics Python sustainable development battery storage plants solar power stations power engineering computing statistical analysis

Simple Inference on Functionals of Set-Identified Parameters Defined by Linear Moments

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Cho, JoonHwan Russell, Thomas M. Department of Economics University of Toronto 150 St. George Street TorontoONM5S3G7 Canada Department of Economics Carleton University 1125 Colonel By Drive OttawaONK1S5B6 Canada

This paper proposes a new approach to obtain uniformly valid inference for linear functionals or scalar subvectors of a partially identified parameter defined by linear moment inequalities. The procedure amounts to bootstrapping the value functions of randomly perturbed linear programming problems, and does not require the researcher to grid over the parameter space. The low-level conditions for uniform validity rely on genericity results for linear programs. The unconventional perturbation approach produces a confidence set with a coverage probability of 1 over the identified set, but obtains exact coverage on an outer set, is valid under weak assumptions, and is computationally simple to implement. Copyright © 2018, The Authors. All rights reserved.

关键词： stochastic programming