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Optimal battery sizing for a distribution network in Austria to maximise profits and reliability

Optimal battery sizing for a distribution network in Austria...

引用

CIRED 2020 Berlin Workshop (CIRED 2020)

作者： Da Huo Marcos V. Santos David M. Greenwood Neal S. Wade Markus P. Resch School of Engineering Newcastle University Newcastle upon Tyne NE1 7RU UK Energie Güssing GmbH Wiener Straße 23 7540 Güssing Austria

Energy storage systems (ESSs) can benefit distribution networks by providing multiple services to the distribution system operator and contributing to system reliability. Given the high capital costs of ESS, it is beneficial to optimally design them for their intended applications; however, this can be computationally expensive due to the non-convex formulation of the AC power flow, the complexity of business use cases and the simulation of multi-period operations. This study develops a comprehensive model to size the ESS to minimise the system lifetime costs and maximise reliability. The non-convex AC power flow model is modified using a convex relaxation, which yields efficient and globally optimal results. The battery degradation is simulated by a global wear coefficient considering the effect of depth of discharge. The sizing study is implemented to fulfil multiple applications on the Güssing distribution network in Austria.

关键词： distribution networks profitability reliability energy storage power system reliability load flow convex programming concave programming battery storage plants power distribution economics

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Covariance Control for Discrete-Time Stochastic Linear Systems with Incomplete State Information

Covariance Control for Discrete-Time Stochastic Linear Syste...

引用

American Control Conference

作者： Efstathios Bakolas Department of Aerospace Engineering and Engineering Mechanics The University of Texas at Austin

ISBN: (纸本)9781509045839

This work deals with a finite-horizon covariance control problem for discrete-time stochastic linear systems with incomplete state information subject to constraints. We show that under the assumption that the class of admissible control policies for this stochastic optimal control problem is comprised of sequences of non-anticipative (causal) control laws that can be expressed as linear combinations of the past and present output measurements of the system, then the covariance control problem can be reduced to a finite-dimensional, deterministic nonlinear program with a convex performance index. In addition, we show that the nonlinear program can be associated with a convex program via a simple relaxation technique that allows us to express the non-convex matrix equality constraint induced by the boundary condition on the terminal state covariance as a positive semi-definite (convex) constraint.

关键词： autocovariance measurement system Nonlinear programming stochastic optimal control convex programming Relaxation Techniques Linear system discrete-time convexity

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Stochastic Filtering in Jump Systems With State Dependent Mode Transitions

Stochastic Filtering in Jump Systems With State Dependent Mo...

引用

2009 American Control Conference (ACC 2009)

作者： Agostino Capponi Concetta Pilotto Division of Engineering and Applied Sciences California Institute of Technology

ISBN: (纸本)9781424445233

We introduce a new methodology to construct a Gaussian mixture approximation to the true filter density in hybrid Markovian switching systems. We relax the assumption that the mode transition process is a Markov chain and allow it to depend on the actual and unobservable state of the system. The main feature of the method is that the Gaussian densities used in the approximation are selected as the solution of a convex programming problem which trades off sparsity of the solution with goodness of fit. A meaningful example shows that the proposed method can outperform the widely used interacting multiple model (IMM) filter in terms of accuracy at the expenses of an increase in computational time.

关键词： Tracking Kalman filtering estimation transition process Markov chain convex programming approximation computational time jump Filtration Solutions Gauss Filters Switched systems Stochastic IMM

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Internal Quadratic Invariance and Decentralized Control

Internal Quadratic Invariance and Decentralized Control

引用

American Control Conference

作者： Laurent Lessard Sanjay Lall Department of Aeronautics and Astronautics at Stanford University Stanford CA 94305 USA Department of Electrical Engineering and Aeronautics and Astronautics at Stanford University Stanford CA 94305 USA

ISBN: (纸本)9781424474264

For decentralized control systems with quadratically invariant information constraints, the set of achievable closed-loop maps is affine, and thus the associated minimum-norm controller synthesis problem is amenable to a convex programming approach. In this paper, we show that a strictly broader class of problems we call internally quadratically invariant, also yields an affine set of achievable closed-loop maps. We treat systems represented by rational as well as proper rational transfer functions and present an illustrative example.

关键词： decentralised control In-law Rational number TRANSFER FUNCTIONS invariance Affine Distributed control systems realizable Invariant Quadratic maps convex programming

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Energy-Efficient Resource Allocation for Latency-Sensitive Mobile Edge Computing

Energy-Efficient Resource Allocation for Latency-Sensitive M...

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IEEE Vehicular Technology Conference

作者： Xihan Chen Yunlong Cai Qingjiang Shi Minjian Zhao Guanding Yu Zhejiang University College of ISEE Hangzhou China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing 210016 China

ISBN: (纸本)9781538663592;9781538663585

This paper investigates a multiuser mobile edge computing system under interference channels, where mobile users can offload their latency-sensitive (computation-intensive) tasks to the mobile edge server via a base station (BS). In this work, we seek to jointly optimize the user selection indicators for offloading and the computation resources, as well as the transmit power level of the offloading users in order to minimize the system energy consumption under latency-sensitive, computation and transmit power budget, transmission quality, and user selection constraints. The proposed optimization problem is nonconvex and highly coupled, which is difficult to solve. By exploiting binary relaxation and introducing auxiliary variables, we first convert this problem into a more tractable form. We then propose a concave-convex procedure (CCCP) based algorithm to obtain the resulting problem. Furthermore, a simplified algorithm is proposed to reduce the computational complexity. Simulation results are proposed to verify the proposed algorithms.

关键词： computational complexity concave programming convex programming energy conservation mobile computing multi-access systems radiofrequency interference resource allocation telecommunication power management wireless channels Resource utilization Multi access systems Radiofrequency interference wireless channels convex programming Mobile Computing complexity classes Energy conservation UNLOADING management of resources system energy consumption Mobile Interference channels power level

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ROBUST ADAPTIVE BEAMFORMING BASED ON MULTI-DIMENSIONAL COVARIANCE FITTING

ROBUST ADAPTIVE BEAMFORMING BASED ON MULTI-DIMENSIONAL COVAR...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Michael Rubsamen Alex B. Gershman Communication Systems Group Technische Universitat Darmstadt Merckstr. 25 64283 Darmstadt Germany

ISBN: (纸本)9781424442959

Robust adaptive beamforming based on worst-case performance optimization is known to provide a substantially improved robustness against signal self-nulling as compared to the traditional adaptive beamforming techniques. The worst-case performance optimization based beamformers of [1] and [2] can be alternatively obtained by solving the one-dimensional (1D) covariance fitting problem [3]. In this paper, we show that the robustness of this approach can be significantly improved by extending it to multi-dimensional (MD) covariance fitting.

关键词： Robust adaptive beamforming multi-dimensional covariance fitting difference of convex programming Adaptive Beam forming Covariance convex programming MULTIDIMENSIONAL MACHINE SHOP PRACTICE beamformers Robustness

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Sensor and Actuator Placement for Large-Scale Systems: A Projection-Based Formulation

Sensor and Actuator Placement for Large-Scale Systems: A Pro...

引用

American Control Conference

作者： Nan Xue Chengzhi Yuan North Carolina State University Raleigh NC 27695 USA University of Rhode Island Kingston RI 02881 USA

ISBN: (纸本)9781538679012;9781538679265

The problem of sensor and actuator placement aims at achieving near-optimal control performance with only a small number of sensors or actuators. In this paper we present a new formulation and design for the problem of sensor and actuator placement as a structural-constrained H_2 optimization. The structural constraint is defined by two projections which indicate the placement of sensors and actuators respectively. The advantage of this formulation is that the associated structural constraint always satisfies quadratic invariance condition, and allows convex reformulation for the constrained H_2 optimization in model matching form. We provide an optimality gap between the constrained H_2 problem with selected sensors and actuators and the unconstrained H_2 problem with full feedback, and subsequently propose the minimization problem to tighten the gap. The overall computational complexity for our proposed design is shown to be O(n~3), which can be very scalable for large-scale applications. We illustrate our design with simulations of a IEEE prototype power system model.

关键词： computational complexity control system synthesis convex programming feedback interconnected systems large-scale systems minimisation optimal control optimisation sensor placement

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The Finite Convergence of the Relaxation Method for Solving Systems of Inequalities

The Finite Convergence of the Relaxation Method for Solving ...

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作者： Goffin, J. L California Univ Berkeley Operations Research Center Office of Naval Research Mathematical Sciences Division Arlington VA OFFICE OF NAVAL RESEARCH ARLINGTON VA

The main concern of this work is to rejuvenate the relaxation method for solving linear inequalities, which uses as primitive the notion of hyperplanes, instead of the more derived concept of vertices or bases. The main result is that the method converges finitely for a wide range of values of the relaxation parameter. The smooth enough property is defined, and it delineates a class of problems where the method works particularly well. It is hoped that the relaxation method might become a powerful alternative to the decomposition, or column generation, techniques for large scale programs in which the theoretical finiteness of the simplex method breaks down to a practical transfiniteness.

关键词： Linear programming Simplex method convex bodies convex sets Engineering Real numbers Algorithms California Computational science New york Operations research Optimization Universities Bodies convex programming Theorems Topology

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On State Estimation with Bad Data Detection

On State Estimation with Bad Data Detection

引用

IEEE Conference on Decision and Control

作者： Weiyu Xu Meng Wang Ao Tang School of ECE Cornell University Ithaca NY 14853

ISBN: (纸本)9781612848006

We consider the problem of state estimation through observations corrupted by both bad data and additive observation noises. A mixed l_(1) and l_(2) convex programming is used to separate both sparse bad data and additive noises from the observations. Using the almost Euclidean property for a linear subspace, we derive a new performance bound for the state estimation error under sparse bad data and additive observation noises. Our main contribution is to provide sharp bounds on the almost Euclidean property of a linear subspace, using the "escape-through-a-mesh" theorem from geometric functional analysis. We also propose and numerically evaluate an iterative convex programming approach to solve bad data detection problems in electrical power networks.

关键词： State estimation data detection Power grids convex programming Additive noise Euclidean Subspace

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Policy Gradient for Coherent Risk Measures 15

Policy Gradient for Coherent Risk Measures

引用

Annual Conference on Neural Information Processing Systems

作者： Aviv Tamar Yinlam Chow Mohammad Ghavamzadeh Shie Mannor UC Berkeley Stanford University Adobe Research & INRIA Technion

ISBN: (纸本)9781510825024

Several authors have recently developed risk-sensitive policy gradient methods that augment the standard expected cost minimization problem with a measure of variability in cost. These studies have focused on specific risk-measures, such as the variance or conditional value at risk (CVaR). In this work, we extend the policy gradient method to the whole class of coherent risk measures, which is widely accepted in finance and operations research, among other fields. We consider both static and time-consistent dynamic risk measures. For static risk measures, our approach is in the spirit of policy gradient algorithms and combines a standard sampling approach with convex programming. For dynamic risk measures, our approach is actor-critic style and involves explicit approximation of value function. Most importantly, our contribution presents a unified approach to risk-sensitive reinforcement learning that generalizes and extends previous results.

关键词： Policies convex programming gradient methods

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