scholarly journals Energy-Efficient Distributed Packet Scheduling Optimization Strategy in Cooperative Vehicle Infrastructure Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liang Dai ◽  
Tianquan Chen ◽  
Yiming Zhai ◽  
Guiping Wang
Keyword(s):  
Energy Consumption ◽  
Optimization Model ◽  
Queue Length ◽  
Packet Scheduling ◽  
Optimization Theory ◽  
Optimization Strategy ◽  
Scheduling Optimization ◽  
Delay Guarantee ◽  
Self Powered ◽  
Knapsack Algorithm

In the cooperative vehicle infrastructure system (CVIS), due to the limitation of deployment conditions, some roadside units (RSUs) need to use renewable energy to supply power and transmit the fused sensor network’s data to the backbone network through the passing vehicles. Aiming at the problem of energy consumption and time delay guarantee of multiple self-powered RSUs in the CVIS, a distributed packet scheduling optimization strategy for energy-delay trade-off in self-powered RSUs is proposed. The strategy can minimize the system energy consumption by constraining the packet queue length of the self-powered RSUs. A dynamic optimization model of distributed packet adaptive scheduling for multiple self-powered RSUs is established based on the Lyapunov optimization theory. Based on the knapsack algorithm, the analytical algorithm of the optimization model is proposed. The simulation results show that the packet scheduling strategy can reduce the energy consumption and delay of the system by satisfying the upper limit of the packet queue length.

scholarly journals Optimization Strategy of Configuration and Scheduling for User-Side Energy Storage

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 120
Author(s):  
Yushan Liu ◽  
Qianqian Liu ◽  
Huaimin Guan ◽  
Xiao Li ◽  
Daqiang Bi ◽  
...  
Keyword(s):  
Energy Storage ◽  
Optimization Model ◽  
Lithium Iron Phosphate ◽  
Economic Benefits ◽  
Forecast Errors ◽  
Loop Scheduling ◽  
Optimization Strategy ◽  
Scheduling Optimization ◽  
Power Fluctuations ◽  
The Impact

In order to reduce the impact of load power fluctuations on the power system and ensure the economic benefits of user-side energy storage operation, an optimization strategy of configuration and scheduling based on model predictive control for user-side energy storage is proposed in this study. Firstly, considering the cost and benefits of energy storage comprehensively, an energy storage configuration optimization model with the highest annualized net income as the goal is built to determine the parameters for configuring energy storage. Then, with the goal of maximizing the profit during the scheduling period, pre-month scheduling optimization model, day-ahead scheduling optimization model and intra-day scheduling optimization model are established. The goal of the pre-month scheduling optimization model is to determine the maximum monthly demand; part of the scheduling results in the day-ahead scheduling optimization model directly participate in the intra-day scheduling; the intra-day rolling optimization relies on the advantages of real-time feedback and closed-loop scheduling to smooth out power fluctuations caused by load forecast errors. Finally, the configuration and economic benefit of lithium iron phosphate batteries, lead-carbon batteries and sodium-sulfur batteries are analyzed and compared, and scheduling analysis is performed. The simulation results show that the proposed optimization method can cut peaks and fill valleys, ensure the economic benefits of users, and provide guidance for users to invest in energy storage.

Probabilistic and Fuzzy based Efficient Routing Protocol for Mobile Ad Hoc Networks

2020 ◽  
Vol 13 (3) ◽  
pp. 422-432
Author(s):  
Madan Mohan Agarwal ◽  
Hemraj Saini ◽  
Mahesh Chandra Govil
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Routing Protocol ◽  
Queue Length ◽  
Ad Hoc ◽  
Fuzzy Controller ◽  
Second Phase ◽  
Path Distance ◽  
Fixed Threshold ◽  
Multiple Paths

Background: The performance of the network protocol depends on number of parameters like re-broadcast probability, mobility, the distance between source and destination, hop count, queue length and residual energy, etc. Objective: In this paper, a new energy efficient routing protocol IAOMDV-PF is developed based on the fixed threshold re-broadcast probability determination and best route selection using fuzzy logic from multiple routes. Methods: In the first phase, the proposed protocol determines fixed threshold rebroadcast probability. It is used for discovering multiple paths between the source and the destination. The threshold probability at each node decides the rebroadcasting of received control packets to its neighbors thereby reducing routing overheads and energy consumption. The multiple paths list received from the first phase and supply to the second phase that is the fuzzy controller selects the best path. This fuzzy controller has been named as Fuzzy Best Route Selector (FBRS). FBRS determines the best path based on function of queue length, the distance between nodes and mobility of nodes. Results: Comparative analysis of the proposed protocol named as "Improved Ad-Hoc On-demand Multiple Path Distance Vector based on Probabilistic and Fuzzy logic" (IAOMDV-PF) shows that it is more efficient in terms of overheads and energy consumption. Conclusion: The proposed protocol reduced energy consumption by about 61%, 58% and 30% with respect to FF-AOMDV, IAOMDV-F and FPAOMDV routing protocols, respectively. The proposed protocol has been simulated and analyzed by using NS-2.

scholarly journals A Multiobjective Scheduling Optimization Model for Multienergy Complementary System Integrated by Wind-Photovoltaic-Convention Gas Turbines considering Demand Response

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Zhang Lihui ◽  
Xin He ◽  
Ju Liwei
Keyword(s):  
Demand Response ◽  
Optimization Model ◽  
Gas Turbines ◽  
Risk Level ◽  
Objective Functions ◽  
Entropy Weight ◽  
Scheduling Optimization ◽  
Power Sources ◽  
Complementary System ◽  
Scheduling Model

To utilize the complementary feature of different power sources, wind power plant (WPP), and solar photovoltaic power (PV), convention gas turbines (CGT) and incentive-based demand response (IBDR) are integrated into a multienergy complementary system (MECS) with the implementation of price-based demand response (PBDR). Firstly, the power output model of WPP, PV, and CGT is constructed and the mathematical model of DR is presented. Then, a multiobjective scheduling model is proposed for MECS operation under the objective functions of the maximum economic benefit, the minimum abandoned energy, and the minimum risk level. Thirdly, the payoff table of objective functions is put forward for converting the multiobjective model into a single objective model by using entropy weight method to calculate weighting coefficients of different objective functions. Finally, the improved IEEE 30 bus system is taken as the simulation system with four simulation scenarios for comparatively analyzing the influence of PBDR and IBDR on MECS operation. The simulation results show the following: (1) The MECS fully utilized the complementarity of different power sources; CGT and IBDR can provide peaking service for WPP and PV to optimize overall system operation. (2) The proposed algorithm can solve the MECS multiobjective scheduling optimization model, and the system scheduling results in the comprehensive optimal mode can take into account different appeal. And the total revenue, abandoned energy capacity, and load fluctuation are, respectively, 108009.30¥, 11.62 MW h, and 9.74 MW. (3) PBDR and IBDR have significant synergistic optimization effects, which can promote the grid connection of WPP and PV. When they are both introduced, the peak-to-valley ratio of the load curve is 1.19, and the abandoned energy is 5.85 MW h. Therefore, the proposed MECS scheduling model and solution algorithm could provide the decision basis for decision makers based on their actual situation.

scholarly journals Energy-saving potential of intermittent heating system: Influence of composite phase change wall and optimization strategy

2020 ◽  
pp. 014459872096921
Author(s):  
Yanru Li ◽  
Enshen Long ◽  
Lili Zhang ◽  
Xiangyu Dong ◽  
Suo Wang
Keyword(s):  
Energy Consumption ◽  
Thermal Comfort ◽  
Phase Change ◽  
Air Temperature ◽  
Indoor Air ◽  
Thermal Environment ◽  
Optimization Strategy ◽  
Indoor Thermal Environment ◽  
Indoor Thermal Comfort ◽  
Intermittent Heating

In the Yangtze River zone of China, the heating operation in buildings is mainly part-time and part-space, which could affect the indoor thermal comfort while making the thermal process of building envelope different. This paper proposed to integrate phase change material (PCM) to building walls to increase the indoor thermal comfort and attenuate the temperature fluctuations during intermittent heating. The aim of this study is to investigate the influence of this kind of composite phase change wall (composite-PCW) on the indoor thermal environment and energy consumption of intermittent heating, and further develop an optimization strategy of intermittent heating operation by using EnergyPlus simulation. Results show that the indoor air temperature of the building with the composite-PCW was 2–3°C higher than the building with the reference wall (normal foamed concrete wall) during the heating-off process. Moreover, the indoor air temperature was higher than 18°C and the mean radiation temperature was above 20°C in the first 1 h after stopping heating. Under the optimized operation condition of turning off the heating device 1 h in advance, the heat release process of the composite-PCW to the indoor environment could maintain the indoor thermal environment within the comfortable range effectively. The composite-PCW could decrease 4.74% of the yearly heating energy consumption compared with the reference wall. The optimization described can provide useful information and guidance for the energy saving of intermittently heated buildings.

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