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All Journal PROCEEDING IC-ITECHS 2014
Lu, Junjie
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Computer Science & IT Education

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Optimized Scheme for the Combination of Shared Energy Storage Business Models Liu, Bulong; Lu, Junjie
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1559

Abstract

The Stackelberg game model between shared energy storage stations and renewable energy generators optimizes pricing strategies to maximize both parties' profits. The energy storage station, acting as the leader, sets pricing strategies for capacity leasing, peak-valley arbitrage, and frequency regulation services, while the renewable energy generator, as the follower, selects the optimal service package based on the station's pricing schemes. This paper employs a Genetic Algorithm (GA) to optimize the energy storage station’s profits and applies NSGA-II to assist the generators in finding the optimal package combination, thereby improving overall profitability. The study demonstrates that this approach effectively facilitates optimal collaboration under complex market conditions and in the experimental validation, the interactions between the energy storage station and the generators were simulated under different market scenarios. The results show that the proposed game model significantly improves station profitability and optimizes generator strategies. The model’s potential applications and future optimization possibilities are discussed, providing theoretical support for the practical operation of shared energy storage business models.
Intelligent Visual Inspection System of Ship Draft Based on UAV Lai, ChuangXin; Lu, JunJie
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1565

Abstract

Accurately calculating the weight of cargo after shipping is an important issue in ocean transportation. Existing methods mainly indirectly calculate the weight of cargo by obtaining the draft of the ship. To estimate the draft of ships passing through waterways and ports, it is necessary to collect sounding line data. However, the current methods for collecting sounding line data mainly rely on manual measurement, which is inefficient, inaccurate, and has safety hazards. To address these issues, this paper combines artificial intelligence and deep learning technologies to design an unmanned aerial vehicle-based intelligent detection system for ship drafts. Detailed research is conducted on image acquisition, image segmentation, image recognition algorithms, etc., and an intelligent measurement platform for ship drafts is developed.
Co-Authors Lai, ChuangXin Liu, Bulong