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Peer-to-peer decentralized energy trading in industrial town considering central shared energy storage using alternating direction method of multipliers algorithm

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IET RENEWABLE POWER GENERATION 2022年第12期16卷 2579-2589页

作者： Aminlou, Ali Mohammadi-Ivatloo, Behnam Zare, Kazem Razzaghi, Reza Anvari-Moghaddam, Amjad Univ Tabriz Fac Elect & Comp Engn Tabriz *** Iran Monash Univ Dept Elect & Computet Syst Engn Melbourne Vic Australia Aalborg Univ Dept Energy AAU Energy Aalborg Denmark

Distributed energy resources are being progressively deployed by industry. The penetration of distributed generation in low voltage (LV) networks can position traditional consumers as market participants. With the improvements in communication networks and the introduction of new energy markets, these prosumers are incentivized to sell their excess production to other industries by participating in peer-to-peer (P2P) energy markets. This market paves the way for developing new technologies such as a shared battery energy storage system (SBESS). In this paper, a central storage unit rents its capacity for the prosumers to reduce the overall peak-load of the microgrid. Each user requests the required SBESS capacity and calculates the best charging and discharging times to reduce their cost. To this end, this paper organized a P2P energy trading paradigm with the presence of SBESS. The optimization problem was simulated and solved using the alternating direction method of multipliers (ADMM) algorithm. Results demonstrate how combining features of P2P energy trading and SBESS can save up to 29% for the industrial town.

关键词： power engineering computing central shared energy storage distributed power generation low voltage networks ADMM algorithm central storage unit power markets shared battery energy storage system power generation economics convex programming SBESS industrial town peer-to-peer computing distributed energy resources P2P energy trading paradigm SBESS capacity peer-to-peer energy markets communication networks peer-to-peer decentralized energy trading alternating direction method of multipliers algorithm distributed generation battery storage plants market participants

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One Variant of the Objective Function Parameterization Method and Its Implementation

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LOBACHEVSKII JOURNAL OF MATHEMATICS 2023年第1期44卷 483-490页

作者： Zabotin, I. Ya. Kazaeva, K. E. Shulgina, O. N. Kazan Volga Region Fed Univ Inst Computat Math & Informat Technol Kazan 420008 Tatarstan Russia

A method for solving the convex programming problem, which is conceptually close to the well-known method of parameterization of the objective function, is proposed. The method has certain advantages over the mentioned classical method, in particular, the possibilities of its practical use. The proposed method is characterized by the fact that it solves the minimization problem on a simple set or on the entire space of some auxiliary function to find each iteration point. This function is constructed as the sum of the cutoffs of the constraint functions and the parameterized objective function. The article provides a research of some properties of the method, the proof of its convergence. Practically convenient implementations of the method based on finite cut-off procedures are proposed and substantiated.

关键词： conditional minimization objective function iteration point method convergence function epigraph method implementation approximating sets cutting planes convex programming

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Prosumer-centric energy storage system and high voltage distribution network topology Co-optimisation for urban grid congestion management

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IET SMART GRID 2022年第4期5卷 297-307页

作者： Zhang, Xi Liu, Youbo Zeng, Yu Wu, Gang Liu, Junyong Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China State Grid Sichuan Econ Res Inst Chengdu Peoples R China

The surge of electricity load and the growing number of renewable energy sources (RESs) in urban power grid result in severe and frequent congestion for transmission networks. This causes the transmission system operators (TSOs) perform the high voltage distribution network (HVDN) reconfiguration frequently. The frequent reconfiguration of the HVDN will cause the fast degradation of high voltage circuit breakers, high maintenance and operational cost, and stability problems. To reduce the frequency of HVDN reconfiguration, this paper proposes a prosumer-centric energy storage system (ESS) and HVDN topology co-optimisation for transmission congestion management. Numerical results show that with the assist of ESS operational strategy, the reconfiguration frequency of HVDN can be significantly reduced while mitigating the transmission congestion.

关键词： convex programming distribution network energy storage energy source load shedding renewable

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Distributed online adaptive subgradient optimization with dynamic bound of learning rate over time-varying networks

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IET CONTROL THEORY AND APPLICATIONS 2022年第18期16卷 1834-1846页

作者： Fang, Runyue Li, Dequan Shen, Xiuyu Anhui Univ Sci & Technol Sch Math & Big Data Huaina Peoples R China Anhui Univ Sci & Technol Sch Artificial Intelligence Huainan 232000 Peoples R China Southeast Univ Sch Transportat Nanjing Peoples R China

Adaptive online optimization algorithms, such as Adam, RMSprop, and AdaBound, have recently been tremendously popular as they have been widely applied to address the issues in the field of deep learning. Despite their prevalence and prosperity, however, it is rare to investigate the distributed versions of these adaptive online algorithms. To fill the gap, a distributed online adaptive subgradient learning algorithm over time-varying networks, called DAdaxBound, which exponentially accumulates long-term past gradient information and possesses dynamic bounds of learning rates under learning rate clipping is developed. Then, the dynamic regret bound of DAdaxBound on convex and potentially nonsmooth objective functions is theoretically analysed. Finally, numerical experiments are carried out to assess the effectiveness of DAdaxBound on different datasets. The experimental results demonstrate that DAdaxBound compares favourably to other competing distributed online optimization algorithms.

关键词： time-varying networks distributed online adaptive subgradient learning algorithm possesses dynamic bounds subgradient optimization adaptive online algorithms gradient information deep learning learning (artificial intelligence) learning rate convex programming deep learning (artificial intelligence) optimisation adaptive online optimization algorithms rate clipping distributed versions competing distributed online gradient methods called dadaxbound dynamic regret

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Fast algorithm for box-constrained fractional-order total variation image restoration with impulse noise

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IET IMAGE PROCESSING 2022年第12期16卷 3359-3373页

作者： Zhu, Jianguang Wei, Juan Hao, Binbin Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao Peoples R China China Univ Petr Coll Sci Qingdao 266580 Peoples R China

In this paper, a novel variational model with box constraints is proposed to restore images corrupted by impulse noise. The proposed model is composed of fractional-order total variation regularization and L-p-fidelity term (0 < p < 1). Moreover, the new model possesses the advantages of preserving sharp edges and removing blocking effect. To solve the proposed model, some auxiliary variables are first introduced to transform it into some easy-to-solve subproblems. Further, the alternating direction method of multipliers, iteratively re-weighted l(1) algorithm and fast iteration technique are adopted to solve the related subproblems. Numerical results show that the proposed model performs better in comparison with the several existing methods, in terms of both quantitative evaluation and visual quality.

关键词： variational model image restoration box-constrained fractional-order total variation image restoration iterative methods alternating direction method of multipliers visual quality variational techniques algorithm Lp-fidelity term blocking effect removal image denoising sharp edge preservation iteratively reweighted 𝓁 convex programming fractional-order total variation regularization impulse noise fast iteration technique

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Optimal Design and Control of a Dual-Clutch SPHEV Powertrain for a Plug-in Delivery Truck 5

Optimal Design and Control of a Dual-Clutch SPHEV Powertrain...

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5th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Ma, Zetao Tian, Tian Cui, Qiong Shu, Jie Chinese Acad Sci Guangzhou Inst Energy Convers Key Lab Renewable Energy Guangzhou Peoples R China

ISBN: (纸本)9781665490542

This paper presents the comprehensive evaluation of a dual-clutch series-parallel plug-in hybrid electric truck (SP-PHET) from the perspective of optimal design and control, entailing components sizing and performance fulfillment. Two essential metrics are concerned in the evaluation, the fuel economy in charge-sustaining operation and the electricity consumption in zero emission operation. To achieve global optimization under the constraint of velocity tracking error, the maximal control is applied to regenerate the attainable driving cycles from the given ones for selected components sizes. The regenerated driving cycles are applied into the minimization of the two metrics, using a bi-level optimization algorithm combining convex programming (CP) and dynamic programming (DP), where the CP solves the optimal power/torque split and the DP picks the optimal operation modes. Pareto results are given out and comparisons are conducted to highlight the advantages of this powertrain in truck application.

关键词： dual-clutch series-parallel hybrid electric truck convex programming dynamic programming optimal design

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A smoothing gradient-based neural network strategy for solving semidefinite programming problems

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NETWORK-COMPUTATION IN NEURAL SYSTEMS 2022年第3-4期33卷 187-213页

作者： Nikseresht, Asiye Nazemi, Alireza Shahrood Univ Technol Fac Math Sci Shahrood Iran

Linear semidefinite programming problems have received a lot of attentions because of large variety of applications. This paper deals with a smooth gradient neural network scheme for solving semidefinite programming problems. According to some properties of convex analysis and using a merit function in matrix form, a neural network model is constructed. It is shown that the proposed neural network is asymptotically stable and converges to an exact optimal solution of the semidefinite programming problem. Numerical simulations are given to show that the numerical behaviours are in good agreement with the theoretical results.

关键词： Gradient-based neural network semidefinite programming convex programming convergent and stability

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Single-channel phaseless blind source separation

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TELECOMMUNICATION SYSTEMS 2022年第3期80卷 469-475页

作者： Hameed, Humera Ahmed, Ali Fayyaz, Ubaid U. Informat Technol Univ Punjab Dept Elect Engn Lahore Pakistan Univ Engn & Technol Dept Elect Engn Lahore Pakistan

In this letter, we consider a novel problem of blind source separation from observed magnitude-only measurements of their convolutive mixture in different communication systems. The problem setups correspond to a blind receiver architecture that either does not have phase information in the measurements or has excessive phase noise that cannot be easily recovered. We have formulated the problem as a matrix recovery problem by using the lifting technique and proposed a convex programming-based solution for joint recovery of the unknown channel and source signals. We have implemented the proposed solution using the alternating direction method of multipliers (ADMM). We have plotted a phase transition diagram for random Gaussian subspaces that shows, for s source signals each of length n and channel of length k, the minimum measurements required for exact recovery are m >= 1.19(sn + k) log(2)m that is in accord with our theoretical result. We have also plotted a phase transition diagram for the case where the channel delays matrix is deterministic (consisting of the first k columns of the identity matrix) that shows the minimum measurements required for exact recovery are m >= 2.86(sn + k) log(2)m which are higher than random subspaces.

关键词： Blind deconvolution Phase retrieval Demixing convex programming

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A proximal gradient splitting method for solving convex vector optimization problems

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OPTIMIZATION 2022年第1期71卷 33-53页

作者： Bello-Cruz, Yunier Melo, Jefferson G. Serra, Ray V. G. Northern Illinois Univ Dept Math Sci De Kalb IL 60115 USA Univ Fed Goias Inst Math & Stat Goiania Go Brazil

In this paper, a proximal gradient splitting method for solving nondifferentiable vector optimization problems is proposed. The convergence analysis is carried out when the objective function is the sum of two convex functions where one of them is assumed to be continuously differentiable. The proposed splitting method exhibits full convergence to a weakly efficient solution without assuming the Lipschitz continuity of the Jacobian of the differentiable component. To carry this analysis, the popular Beck-Teboulle's line-search procedure is extended to the vectorial setting under mild assumptions. It is also shown that the proposed scheme obtains an epsilon-approximate solution to the vector optimization problem in at most O(1/epsilon) terations.

关键词： convex programming full convergence proximal gradient method Vector optimization

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On the Design of Optimal Structured and Sparse Feedback Gains via Sequential convex programming

On the Design of Optimal Structured and Sparse Feedback Gain...

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American Control Conference

作者： Makan Fardad Mihailo R. Jovanovic Department of Electrical Engineering and Computer Science Syracuse University Syracuse NY 13244 Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN 55455

ISBN: (纸本)9781479932757

We consider the problem of finding optimal feedback gains in the presence of structural constraints and/or sparsity-promoting penalty functions. Such problems are known to be difficult due to their lack of convexity. We provide an equivalent reformulation of the optimization problem such that its source of nonconvexity is isolated in one nonconvex matrix inequality of the form Y ≤ X~(-1). Furthermore, we preserve the feedback gain as an optimization variable in the reformulated problem. Via linearizations of the nonconvex constraint, we introduce an iterative algorithm that solves a semidefinite program at every stage and for which the nonconvex constraint is satisfied upon convergence. We elaborate on the modular nature of the proposed scheme and show that it can be used in a wide range of network control problems.

关键词： feedback gain convex programming Iterative algorithms convexity network control

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