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Optical wireless power transfer (OWPT) is a technology that wirelessly transmit light energy from an optical transmitter to an optical receiver, usually a solar cell. In order to achieve the highest transmission efficiency, the solar cell receiver should be accurately aligned with the optical transmitter. Hitherto, only a few works have been existed for solar cell recognition in presence of complex backgrounds. In this paper, we employ a deep learning approach based on Yolov5-Lite for the solar cell recognition purpose, due to its lightweight, fast and easy to deploy on hardware characteristics. Our tests show a high accuracy of the employed deep learning model with the highest F1 score of 91% and mAP of 94.8%. Therefore, this deep learning model is highly promising for use in OWPT systems to precisely align optical transmitters and solar cell receivers., Comment: In Proceedings of The International Council on Electrical Engineering (ICEE) Conference 2024

This research studies a non-convex geometric optimization problem arising from the field of optical wireless power transfer. In the considered optimization problem, the cost function is a sum of negatively and fractionally powered distances from given points arbitrarily located in a plane to another point belonging to a different plane. Therefore, it is a strongly nonlinear and non-convex programming, hence posing a challenge on the characterization of its optimizer set, especially its set of global optimizers. To tackle this challenge, the bifurcation theory is employed to investigate the continuation and bifurcation structures of the Hessian matrix of the cost function. As such, two main results are derived. First, there is a critical distance between the two considered planes such that beyond which a unique global optimizer exists. Second, the exact number of maximizers is locally derived by the number of bifurcation branches determined via one-dimensional isotropic subgroups of a Lie group acting on $\mathbb{R}^2$, when the inter-plane distance is smaller than the above-mentioned critical distance. Consequently, numerical simulations and computations of bifurcation points are carried out for various configurations of the given points, whose results confirm the derived theoretical outcomes.

Building on the carbon reduction targets agreed in the Paris Agreements, many nations have renewed their efforts toward achieving carbon neutrality by the year 2050. In line with this ambitious goal, nations are seeking to understand the appropriate combination of technologies which will enable the required reductions in such a way that they are appealing to investors. Around the globe, solar and wind power lead in terms of renewable energy deployment, while carbon capture and storage (CCS) is scaling up toward making a significant contribution to deep carbon cuts. Using Japan as a case study nation, this research proposes a linear optimization modeling approach to identify the potential contributions of renewables and CCS toward maximizing carbon reduction and identifying their economic merits over time. Results identify that the combination of these three technologies could enable a carbon dioxide emission reduction of between 55 and 67 percent in the energy sector by 2050 depending on resilience levels and CCS deployment regimes. Further reductions are likely to emerge with increased carbon pricing over time.

Peer-to-peer (P2P) energy systems have recently emerged as a promising approach for integrating renewable and distributed energy resources into energy grids to reduce carbon emissions. However, market-clearing energy price and amounts, resulted from solving optimal P2P energy management problems, might not be satisfactory for peers/agents. This is because peers/agents in practice do not know how to set their cost function parameters when participating into P2P energy markets. To resolve such drawback, this paper proposes a novel approach, in which an inverse optimization problem is formulated for peers/agents to cooperatively learn to choose their objective function parameters, given their intervals of desired energy prices and amounts. The result is that peers/agents can set their objective function parameters in the intervals computed analytically from the lower and upper bounds of their energy price and amounts, if the ratio of their maximum total buying and selling energy amounts lies in a certain interval subject to be learned by them. A case study is then carried out, which validates the effectiveness of the proposed approach., Comment: Full version with title changed and additional contents has been accepted for publication in IEEE Access. DOI: 10.1109/ACCESS.2021.3125031

In this paper, we consider consensus problems over a network of nodes, where the network is divided into a number of clusters. We are interested in the case where the communication topology within each cluster is dense as compared to the sparse communication across the clusters. Moreover, each cluster has one leader which can communicate with other leaders in different clusters. The goal of the nodes is to agree at some common value under the presence of communication delays across the clusters. Our main contribution is to propose a novel distributed two-time-scale consensus algorithm, which pertains to the separation in network topology of clustered networks. In particular, one scale is to model the dynamic of the agents in each cluster, which is much faster (due to the dense communication) than the scale describing the slowly aggregated evolution between the clusters (due to the sparse communication). We prove the convergence of the proposed method in the presence of uniform, but possibly arbitrarily large, communication delays between the leaders. In addition, we provided an explicit formula for the convergence rate of such algorithm, which characterizes the impact of delays and the network topology. Our results shows that after a transient time characterized by the topology of each cluster, the convergence of the two-time-scale consensus method only depends on the connectivity of the leaders. Finally, we validate our theoretical results by a number of numerical simulations on different clustered networks.

This paper proposes a novel conceptual system of optical wireless power transfer between objects, whether stationary or in-motion. Different from the currently existing optical wireless power transfer systems, where the optical transmitter and receiver are two distinct devices, the proposed system in this paper employs a single device - an optical transceiver, which is capable of working as both light absorption and emission. This optical transceiver is fabricated from a metal halide perovskite which yields superior features such as low costs, capability of flexibly attached on curved surfaces, thin and light weight. Therefore, the whole system size and cost can be significantly reduced, while perovskite transceivers can be made adaptive to any surface. This will contribute to realize a thing-to-thing optical wireless power transfer system, in which surfaces of objects/things are covered (fully or partially) by perovskite transceivers, enabling them to wirelessly charge or discharge from the others.

This paper studies the optimal energy management in a group of dwellings having micro fuel cell combined heat and power systems. To increase the self-sufficiency and resilience of such local community, a peer-to-peer energy trading system between dwellings is proposed in which output powers from fuel cells working under their rated powers can be sold to those already reach their rated outputs but still lack powers. The arising optimization problem from this optimal peer-to-peer energy trading system is non-convex due to the nonlinear dependence of power and heat efficiencies on fuel cell output power. Therefore, a linearization method is proposed to convexify the problem. Consequently, a distributed ADMM approach is introduced to solve the convexified optimization problem in parallel at each dwelling. A case study for a group of six dwellings based on realistic electric consumption data is then presented to demonstrate the proposed approach performance and positive impacts of the P2P energy trading system. More specifically, the proposed distributed ADMM approach is reasonably fast in convergence and is scalable well with system size. In addition, P2P electricity trading system helps operate fuel cells at a higher efficiency and increase the self-sufficiency of such dwellings., Comment: Accepted for publication in the International Journal of Electrical Power and Energy Systems

This paper studies the optimal clearing problem for prosumers in peer-to-peer (P2P) energy markets. It is proved that if no trade weights are enforced and the communication structure between successfully traded peers is connected, then the optimal clearing price and total traded powers in P2P market are the same with that in the pool-based market. However, if such communication structure is unconnected, then the P2P market is clustered into smaller P2P markets. If the trade weights are imposed, then the derived P2P market solutions can be significantly changed. Next, a novel decentralized optimization approach is proposed to derive a trading mechanism for P2P markets, based on the alternating direction method of multipliers (ADMM) which naturally fits into the bidirectional trading in P2P energy systems and converges reasonably fast. Analytical formulas of variable updates reveal insightful relations for each pair of prosumers on their individually traded prices and powers with their total traded powers. Further, based on those formulas, decentralized learning schemes for tuning parameters of prosumers cost functions are proposed to attain successful trading with total traded power amount as desired. Case studies on a synthetic system and the IEEE European Low Voltage Test Feeder are then carried out to verify the proposed approaches., Comment: Accepted for publication in IEEE Transactions on Power Systems

This research details outcomes from a global model which estimates future hydrogen penetration into a carbon constrained energy system to the year 2050. Focusing on minimum and maximum penetration scenarios, an investigation of global fuel cell vehicle (FCV) deployment is undertaken, cognizant of optimal economic deployment at the global level and stakeholder preferences in a case study of Japan. The model is mathematically formulated as a very large-scale linear optimization problem, aiming to minimize system costs, including generation type, fuel costs, conversion costs, and carbon reduction costs, subject to the constraint of carbon dioxide reductions for each nation. Results show that between approximately 0.8% and 2% of global energy consumption needs can be met by hydrogen out to the year 2050, with city gas and transport emerging as significant use cases. Passenger FCVs and hydrogen buses account for almost all of the hydrogen-based transportation sector, leading to a global deployment of approximately 120 million FCVs by 2050. Hydrogen production is reliant on fossil fuels, and OECD nations are net importers - especially Japan with a 100% import case. To underpin hydrogen production from fossil fuels, carbon capture and storage (CCS) is required in significant quantities when anticipating a large fleet of FCVs. Stakeholder engagement suggests optimism toward FCV deployment while policy issues identified include necessity for large-scale future energy system investment and rapid technical and economic feasibility progress for renewable energy technologies and electrolyzers.

This paper investigates the problem of bidirectional energy exchange between electric vehicles (EVs) and road lanes embedded with wireless power transfer technologies called wireless charging-discharging lanes (WCDLs). As such, EVs could provide better services to the grid, especially for balancing the supply-demand, while bringing convenience for EV users, because no cables and EV stops are needed. To enable this EV--WCDL energy exchange, a novel decentralized peer-to-peer (P2P) trading mechanism is proposed, in which EVs directly negotiate with a WCDL to reach consensus on the energy price and amounts to be traded. Those energy price and amounts are solutions of an optimization problem aiming at optimizing private cost functions of EVs and WCDL. The negotiation process between EVs and WCDL is secured by a privacy-preserving consensus mechanism. Further, to assure successful trading with desired energy price and amounts, an analytical and systematic method is proposed to select cost function parameters by EVs and WCDL in a fully decentralized manner. Simulations are then carried out to validate developed theoretical results, which confirm the effectiveness and scalability of the proposed algorithm., Comment: submitted to Energy

This article proposes a novel system concept named universal wireless power transfer, in which power can be wirelessly transferred between different entities (e.g. vehicles, robots, homes, grid facilities, consumer electronic devices, etc.) equipped with proper energy transmitters and receivers, whether stationary or in motion. This concept generalizes individually existing wireless power transfer systems, where a specific wireless power transfer technology is used, and where the wireless energy transmitter or receiver is fixed. As a result, energy mobility, flexibility, and convenience are significantly improved by the proposed universal wireless power transfer concept in this study. Moreover, factors relevant to system energy efficiency are analyzed according to each utilized wireless power transfer technology. Necessary market mechanisms for such a concept to be successfully deployed are also introduced, along with an analysis of the benefits engendered in terms of improving energy systems, the environment, human comfort, and convenience. Finally, a discussion of the proposed concept, policy implications and recommendations for future research directions which will underpin universal wireless power transfer systems are presented., Comment: Preliminary self report of authors

This paper proposes novel approaches to design hierarchical decentralized robust controllers for homogeneous linear multi-agent systems (MASs) perturbed by disturbances/noise. Firstly, based on LQR method, we present a systematic procedure to design hierarchical decentralized optimal stabilizing controllers for MASs without disturbances/noise. Next, a method for deriving reduced-order hierarchical decentralized stabilizing controllers is presented by suitable selections of the weighting matrices in the LQR performance index. Secondly, the hierarchical decentralized robust controller designs in terms of $H_{\infty}$ and $H_{2}$ norms are introduced, which include two different scenarios namely general and LQR-based synthesis. For the general synthesis, the robust controller gains are computed as solutions of a distributed convex optimization problem with LMI constraints. On the other hand, for the LQR-based design, the robust controller gains obtained from the general synthesis are further verified as LQR stabilizing gains to be unified with the LQR-based design when there are no disturbances/noise. This results in a hierarchical decentralized inverse optimal control problem, for which we will propose a new method to resolve it. Finally, several numerical examples are presented to illustrate the effectiveness of the proposed approaches., Comment: revised version submitted to IEEE Transactions on Control of Network Systems

In this paper, we propose a new and systematic design framework for output consensus in heterogeneous Multi-Input Multi-Output (MIMO) general nonlinear Multi-Agent Systems (MASs) subjected to directed communication topology. First, the input-output feedback linearization method is utilized assuming that the internal dynamics is Input-to-State Stable (ISS) to obtain linearized subsystems of agents. Consequently, we propose local dynamic controllers for agents such that the linearized subsystems have an identical closed-loop dynamics which has a single pole at the origin whereas other poles are on the open left half complex plane. This allows us to deal with distinct agents having arbitrarily vector relative degrees and to derive rank-$1$ cooperative control inputs for those homogeneous linearized dynamics which results in a minimum rank distributed dynamic consensus controller for the initial nonlinear MAS. Moreover, we prove that the coupling strength in the consensus protocol can be arbitrarily small but positive and hence our consensus design is non-conservative. Next, our design approach is further strengthened by tackling the problem of randomly switching communication topologies among agents where we relax the assumption on the balance of each switched graph and derive a distributed rank-$1$ dynamic consensus controller. Lastly, a numerical example is introduced to illustrate the effectiveness of our proposed framework., Comment: Revised version submitted to IEEE Transactions on Control of Network Systems

This paper proposes a new approach to analyze and design distributed robust consensus control protocols for general linear leaderless multi-agent systems (MASs) in presence of relative-state constraints or uncertainties. First, we show that the MAS robust consensus under relative-state constraints or uncertainties is equivalent to the robust stability under state constraints or uncertainties of a transformed MAS. Next, the transformed MAS under state constraints or uncertainties is reformulated as a network of Lur'e systems. By employing S-procedure, Lyapunov theory, and Lasalle's invariance principle, a sufficient condition for robust consensus and the design of robust consensus controller gain are derived from solutions of a distributed LMI convex problem. Finally, numerical examples are introduced to illustrate the effectiveness of the proposed theoretical approach., Comment: submitted to IEEE Transactions on Automatic Control

This paper proposes a new approach to analyze and synthesize robust consensus control laws for general linear leaderless multi-agent systems (MASs) subjected to input constraints or uncertainties. First, the MAS under input constraints or uncertainties is reformulated as a network of Lur'e systems. Next, two scenarios of communication topology are considered, namely undirected and directed cyclic structures. In each case, a sufficient condition for consensus and the design of consensus controller gain are derived from solutions of a distributed LMI convex problem. Finally, a numerical example is introduced to illustrate the effectiveness of the proposed theoretical approach., Comment: submitted to Automatica. arXiv admin note: text overlap with arXiv:1605.03647

This paper proposes a novel approach to design reduced-order distributed consensus controllers for multi-agent systems (MASs) with identical linear dynamics of agents. A new model namely edge dynamics representing the differences on agents' states is first presented. Then the distributed consensus controller design is shown to be equivalent to the synthesis of a distributed stabilizing controller for this edge dynamics. Consequently, based on LQR approach, the globally optimal and locally optimal distributed stabilizing controller designs are proposed, of which the locally distributed stabilizing controller for the edge dynamics results in a distributed consensus controller for the MAS with no conservative bound on the coupling strength. This approach is next further developed to obtain reduced-order distributed consensus controllers for linear MASs. Several numerical examples are introduced to illustrate the theoretical results., Comment: submitted to IEEE Transactions on Automatic Control