Dynamic Spectrum Sharing for the Coexistence of Smart Utility Networks and WLANs in Smart Grid Communications

Ma, Ruofei; Chen, Hsiao‐Hwa; Meng, Weixiao

doi:10.1109/mnet.2016.1500262nm

Cited by 26 publications

(7 citation statements)

References 14 publications

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“…The MOP is formulated as (4), and (7) ∀c ∈ A SF (11) where x denotes the decision variable vector that contains decision variables h b , p h c , p h ev , and u h for all b, c, and h. The objective functions are conflicting, so the global optimal solution that optimizes both objective functions simultaneously does not exist. To solve the MOP in (11), Pareto optimality is adopted [81].…”

Section: Pareto Optimal Demand Response Programmentioning

confidence: 99%

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Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid

Chiu

Hsieh

Chen

2020

IEEE Trans. Ind. Inf.

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Demand response for residential users is essential to the realization of modern smart grids. This paper proposes a multiobjective approach to designing a demand response program that considers the energy costs of residential users and the load factor of the underlying grid. A multiobjective optimization problem (MOP) is formulated and Pareto optimality is adopted. Stochastic search methods of generating feasible values for decision variables are proposed. Theoretical analysis is performed to show that the proposed methods can effectively generate and preserve feasible points during the solution process, which comparable methods can hardly achieve. A multiobjective evolutionary algorithm is developed to solve the MOP, producing a Pareto optimal demand response (PODR) program. Simulations reveal that the proposed method outperforms the comparable methods in terms of energy costs while producing a satisfying load factor. The proposed PODR program is able to systematically balance the needs of the grid and residential users.

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Section: Pareto Optimal Demand Response Programmentioning

confidence: 99%

“…Digital Object Identifier 10.1109/TII.2019.2928520 flow between customers and suppliers, and reliably defend against attacks or natural disasters [3], [4]. In a smart grid, the real-time energy consumption profiles of residential users are crucial to power suppliers [5].…”

Section: Introductionmentioning

confidence: 99%

Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid

Chiu

Hsieh

Chen

2020

IEEE Trans. Ind. Inf.

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“…To commemorate in-house or local point-to-point broadcasting service for sensory and automated actuation devices, Zigbee technology proffers smart network congestion supervision with high data rate transfer within low bandwidth spectrum. It uses a unique periodic logging of data (nonbeacon and beacon mode) operations which results in low energy performance suitable for low-powered electric appliances [15], [16]. However, Zigbee solution only performs at its best for short range wireless transmitting/receiving mesh deployments targeted for in-house connection.…”

Section: A Wide-area Wireless Information and Communication For Singlementioning

confidence: 99%

“…Here, the algorithm engages non-CL Agent to forecast desirable online load capacity at the next time stamp. Therefrom, (16) and 17formulate load minimization approach that proffers optimal electrification criterion where scheduling of shiftable loads are strategically shed based on generation availability. The time constraints dictated for computations were restricted to half-hour time span.…”

Section: ) Formulate Agents' Reinforce Learning and Policies For Emmentioning

confidence: 99%

A Nano-Biased Energy Management Using Reinforced Learning Multi-Agent on Layered Coalition Model: Consumer Sovereignty

et al. 2019

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Trends in energy management schema have advanced into legislating consumer-centered solutions due to inclination interests for personal owned distributed energy resources at the low-voltage level. Thence, this paper proposes a tailorable energy manager tool that empowers Prosumer(s) in a nanostructured distribution network to take sole precedence when prosuming optimal services to the energy system. It too acts as an aggregator that attests cooperative energy management processes amongst Prosumers to enhance demand-side responses and economics. The suggested nano-biased energy manager engages multi-agent network as the basis coordinator for peer-to-peer advocacy in a decentralized environment. The agents were then programmed with reinforcement and extreme learning machine intelligence on a layered coalition model to compute joint decision-making processes with constraint relaxation relaxed decision constraints and policies. The problem formulations assure engagement of energy management in the liberalized market is sustainable, reliable, and non-discriminated. Computational validations were analyzed using MATLAB and Java agent development framework on four aggregated Nanogrids representing the residential, commercial, and industrial building. Results have shown positive eco-strategic managerial avenues where cooperative assets scheduling and bidding-abled decorum were autonomously acquired. Reduced operating costs were gained from energy trading profit margin due to strategic use/sell of electricity based on real-time tariff and conferred incentive packages but constrained within the mandatory obligation to demand-side management. The subsidiary, the inauguration of meshed communication infrastructure has shown adequate monitoring and commanding resolutions for decentralized Agent(s) to function collaboratively. INDEX TERMS Demand-side management, multi-agent systems, adaptive scheduling, hybrid power system, stochastic processes and nanostructured power grid. NOMENCLATURE DER Distributed Renewable/Energy Resource DSO Distributed Network Operator ELM Extreme Learning Machine EM Energy Management JADE Java Agent DEvelopment LCM Layered Coalition Model MAN Multi-Agent Network MG Microgrid The associate editor coordinating the review of this manuscript and approving it for publication was Ramakrishnan Srinivasan.

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“…For instance, a channel change may be sufficient when an interfering Wi-Fi Access Point is detected. Ma et al [25] investigate the interference arising with multiple Wi-Fi networks for smart utility networks. They only address internal interference by allocating orthogonal channels to each WLAN.…”

Section: Interference Among Different Technologiesmentioning

confidence: 99%

Cooperative resynchronization to improve the reliability of colocated IEEE 802.15.4 -TSCH networks in dense deployments

2017

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With the large adoption of small and smart objects, the interest of researchers to define new protocols to meet Internet of Things (IoT) requirements is growing. In particular, the Industrial Internet of Things (IIoT) aims to provide high reliability and upper bounded end-to-end latency while interconnecting a large collection of sensors and actuators. The TimeSlotted Channel Hopping (TSCH) mode of IEEE 802.15.4 exploits a slow channel hopping approach to combat efficiently external interference (e.g. Wi-Fi, Bluetooth). We envision in the future very dense deployments and we investigate here the mutual influence among different colocated TSCH networks. We first study analytically with a simple model the performance of several TSCH networks, able or not to be synchronized with each other. We emphasize the multiplication of collisions, exacerbated by clock drifts when no cooperation is achieved. We also highlight experimentally that a mutual synchronization improves very significantly the reliability. We propose here a mechanism to re-synchronize distributively the border routers of different TSCH networks. Our thorough experimental evaluation illustrates the relevance of our approach, able to guarantee high-reliability, even in very dense topologies.

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Dynamic Spectrum Sharing for the Coexistence of Smart Utility Networks and WLANs in Smart Grid Communications

Cited by 26 publications

References 14 publications

Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid

Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid

A Nano-Biased Energy Management Using Reinforced Learning Multi-Agent on Layered Coalition Model: Consumer Sovereignty

Cooperative resynchronization to improve the reliability of colocated IEEE 802.15.4 -TSCH networks in dense deployments

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Dynamic Spectrum Sharing for the Coexistence of Smart Utility Networks and WLANs in Smart Grid Communications

Cited by 26 publications

References 14 publications

Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid

Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid

A Nano-Biased Energy Management Using Reinforced Learning Multi-Agent on Layered Coalition Model: Consumer Sovereignty

Cooperative resynchronization to improve the reliability of colocated IEEE 802.15.4 -TSCH networks in dense deployments

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Cooperative resynchronization to improve the reliability of colocated IEEE 802.15.4 -TSCH networks in dense deployments