scholarly journals An Application of a Deep Q-Network Based Dynamic Fare Bidding System to Improve the Use of Taxi Services during Off-Peak Hours in Seoul

2021 ◽  
Vol 13 (16) ◽  
pp. 9351
Author(s):  
Yunji Cho ◽  
Jaein Song ◽  
Minhee Kang ◽  
Keeyeon Hwang
Keyword(s):  
Real Time ◽  
Dynamic Pricing ◽  
Supply And Demand ◽  
Time Zone ◽  
Taxi Drivers ◽  
Operating Profit ◽  
Pricing Scheme ◽  
Multi Agent ◽  
Adjustment System ◽  
Varying Demand

The problem of structural imbalance in terms of supply and demand due to changes in traffic patterns by time zone has been continuously raised in the mobility market. In Korea, unlike large overseas cities, the waiting time tolerance increases during the daytime when supply far exceeds demand, resulting in a large loss of operating profit. The purpose of this study is to increase taxi demand and further improve driver’s profits through real-time fare discounts during off-peak daytime hours in Seoul, Korea. To this end, we propose a real-time fare bidding system among taxi drivers based on a dynamic pricing scheme and simulate the appropriate fare discount level for each regional time zone. The driver-to-driver fare competition system consists of simulating fare competition based on the multi-agent Deep Q-Network method after developing a fare discount index that reflects the supply and demand level of each region in 25 districts in Seoul. According to the optimal fare discount level analysis in the off-peak hours, the lower the OI Index, which means the level of demand relative to supply, the higher the fare discount rate. In addition, an analysis of drivers’ profits and matching rates according to the distance between the origin and destination of each region showed up to 89% and 65% of drivers who actively offered discounts on fares. The results of this study in the future can serve as the foundation of a fare adjustment system for varying demand and supply situations in the Korean mobility market.

scholarly journals A Demand-Response Scheme Using Multi-Agent System for Smart DC Microgrid

2019 ◽  
Vol 10 (1) ◽  
pp. 48-68 ◽  
Author(s):  
Diana Severine Rwegasira ◽  
Imed Saad Ben Dhaou ◽  
Aron Kondoro ◽  
Anastasia Anagnostou ◽  
Amleset Kelati ◽  
...  
Keyword(s):  
Demand Response ◽  
Dynamic Pricing ◽  
Supply And Demand ◽  
Load Shedding ◽  
Multi Agent System ◽  
Solar Panels ◽  
Remote Communities ◽  
Agent System ◽  
Dc Microgrid ◽  
Multi Agent

This article describes a framework for load shedding techniques using dynamic pricing and multi-agent system. The islanded microgrid uses solar panels and battery energy management system as a source of energy to serve remote communities who have no access to the grid with a randomized type of power in terms of individual load. The generated framework includes modeling of solar panels, battery storage and loads to optimize the energy usage and reduce the electricity bills. In this work, the loads are classified as critical and non-critical. The agents are designed in a decentralized manner, which includes solar agent, storage agent and load agent. The load shedding experiment of the framework is mapped with the manual operation done at Kisiju village, Pwani, Tanzania. Experiment results show that the use of pricing factor as a demand response makes the microgrid sustainable as it manages to control and monitor its supply and demand, hence, the load being capable of shedding its own appliances when the power supplied is not enough.

Energy trading and control of islanded DC microgrid using multi-agent systems

2021 ◽  
Vol 17 (2) ◽  
pp. 113-128
Author(s):  
Diana Rwegasira ◽  
Imed Ben Dhaou ◽  
Masoumeh Ebrahimi ◽  
Anders Hallén ◽  
Nerey Mvungi ◽  
...  
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Dynamic Pricing ◽  
Energy Sector ◽  
Load Shedding ◽  
Dc Microgrid ◽  
Energy Trading ◽  
Development Framework ◽  
Demand Response Program ◽  
Multi Agent

The energy sector is experiencing a revolution that is fuelled by a multitude of factors. Among them are the aging grid system, the need for cleaner energy and the increasing demands on energy sector. The demand-response program is an advanced feature in smart grid that strives to match suppliers to their demands using price-based and incentive programs. The objective of the work is to analyse the performance of the load shedding technique using dynamic pricing algorithm. The system was designed using multi-agent system (MAS) for a DC microgrid capable of real-time monitoring and controlling of power using price-based demand-response program. As a proof of concept, the system was implemented using intelligent physical agents, Java Agent Development Framework (JADE), and agent simulation platform (REPAST) with two residential houses (non-critical loads) and one hospital (critical load). The architecture has been implemented using embedded devices, relays, and sensors to control the operations of load shedding and energy trading in residential areas that have no access to electricity. The measured results show that the system can shed the load with the latency of less than 600 ms, and energy cost saving with an individual houses by 80% of the total cost with 2USD per day. The outcome of the studies demonstrates the effectiveness of the proposed multi-agent approach for real-time operation of a microgrid and the implementation of demand-response program.

scholarly journals Optimized Charging of PV-Batteries for Households Using Real-Time Pricing Scheme: A Model and Heuristics-Based Implementation

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 113 ◽  
Author(s):  
Robert Basmadjian
Keyword(s):  
Real Time ◽  
Dynamic Pricing ◽  
Real Life ◽  
Solar Panels ◽  
Smart Meters ◽  
Technological Advances ◽  
Pricing Scheme ◽  
Attractive Option ◽  
Real Time Pricing ◽  
Pv Generation

The combination of solar panels (PV) with energy storage systems (ESS) has been becoming more and more a common practice for households. In this context, the battery of ESS satisfies the needs of the household when PV generation is not present. Recently, dynamic pricing became one of the measures taken to shift the demand. Thanks to technological advances (e.g., smart meters), real-time pricing (RTP) has shown to be the most attractive option in the market, due to the ease of estimating price elasticity over various time periods. We studied a PV-battery system for the case of households which are under RTP scheme. To this end, we described and modeled the underlying system, and compiled an objective function having as an optimization goal, the minimization of the charging cost of the battery. Furthermore, we propose a heuristics-based algorithm that schedules the charging process during cheap periods. To evaluate the amount of savings, we considered a real-life testbed and implemented the proposed algorithm by taking into account different scenarios. The results demonstrate the benefits of households adhering to real-time pricing scheme, where the savings reached 50% in certain cases.

scholarly journals A Real-Time Pricing Scheme for Energy Management in Integrated Energy Systems: A Stackelberg Game Approach

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2858 ◽  
Author(s):  
Tengfei Ma ◽  
Junyong Wu ◽  
Liangliang Hao ◽  
Huaguang Yan ◽  
Dezhi Li
Keyword(s):  
Real Time ◽  
Stackelberg Game ◽  
Supply And Demand ◽  
Energy System ◽  
Stackelberg Equilibrium ◽  
Energy Hub ◽  
Pricing Scheme ◽  
Real Time Pricing ◽  
Aggregated Load ◽  
Energy Provider

This paper proposes a real-time pricing scheme for the demand response management between one energy provider and multiple energy hub operators. A promising energy trading scenario has been designed for the near future integrated energy system. The Stackelberg game approach was employed to capture the interactions between the energy provider (leader) and energy consumers (follower). A distributed algorithm was proposed to derive the Stackelberg equilibrium, then, the best strategies for the energy provider and each energy hub operator were explored in order to maximize their benefits. Simulation results showed that the proposed method can balance the energy supply and demand, improve the payoffs for all players, as well as smooth the aggregated load profiles of all energy consumers.

Regular estimates of paddock pasture mass can improve profitability on New Zealand dairy farms

2015 ◽  
Vol 77 ◽  
pp. 29-34 ◽  
Author(s):  
P.C. Beukes ◽  
S. Mccarthy ◽  
C.M. Wims ◽  
A.J. Romera
Keyword(s):  
Supply And Demand ◽  
Grazing Management ◽  
Average Error ◽  
Value Proposition ◽  
Perfect Knowledge ◽  
Operating Profit ◽  
A Value ◽  
Main Driver ◽  
Imperfect Knowledge ◽  
Farm Scale

Paddock selection is an important component of grazing management and is based on either some estimate of pasture mass (cover) or the interval since last grazing for each paddock. Obtaining estimates of cover to guide grazing management can be a time consuming task. A value proposition could assist farmers in deciding whether to invest resources in obtaining such information. A farm-scale simulation exercise was designed to estimate the effect of three levels of knowledge of individual paddock cover on profitability: 1) "perfect knowledge", where cover per paddock is known with perfect accuracy, 2) "imperfect knowledge", where cover per paddock is estimated with an average error of 15%, 3) "low knowledge", where cover is not known, and paddocks are selected based on longest time since last grazing. Grazing management based on imperfect knowledge increased farm operating profit by approximately $385/ha compared with low knowledge, while perfect knowledge added a further $140/ha. The main driver of these results is the level of accuracy in daily feed allocation, which increases with improving knowledge of pasture availability. This allows feed supply and demand to be better matched, resulting in less incidence of under- and over-feeding, higher milk production, and more optimal post-grazing residuals to maximise pasture regrowth. Keywords: modelling, paddock selection, pasture cover

Perancangan Sistem Kontrol Lampu Lalulintas Cerdas Dengan Menggunakan Mikrokontroler dan Kamera

Jurnal MIPA ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 200
Author(s):  
Tjerie Pangemanan ◽  
Arnold Rondonuwu
Keyword(s):  
Real Time ◽  
Digital Image ◽  
Red Light ◽  
Green Light ◽  
Processing Method ◽  
Traffic Lights ◽  
Traffic System ◽  
Road Users ◽  
Adjustment System ◽  
System Time

Masalah lalu lintas  merupakan salah satu  masalah yang sangat sulit diatasi dengan hanya menggunakan system waktu (timer). Oleh sebab itu diperlukan suatu system pengaturan otomatis yang bersifat real-time sehingga waktu pengaturan lampu lalu lintas dapat disesuaikan dnegan keadaan di lapangan. Penelitian ini bertujuan mengembangkan suatu simulasi sistem yang mampu mengestimasi panjang antrian kendaraan menggunakan metoda pengolahan citra digital hanya dengan menggunakan satu kamera untuk dijadikan parameter masukan  dalam menghitung lama waktu nyala lampu merah dan lampu hijau. Oleh karena itu, sistem lalulintas sangatlah diperlukan, sebagai sarana dan prasarana untuk menjadikan lalulintas lancar, aman, bahkan sebagai media pembelajaran disiplin bagi masyarakat pengguna jalan raya. Penelitian ini penulis menggunakan sistem pengontrolan berbasis citra digital dimana camera sebagai sensor. Untuk aplikasi dari  semua metode dalam penelitian ini digunakan Microcontroller AurdinoTraffic problems is one of the problems that is very difficult to overcome by only using the system time (timer). Therefore we need an automatic real-time adjustment system so that the time settings for traffic lights can be adjusted according to the conditions on the ground. This study aims to develop a system simulation that is able to estimate the length of the vehicle queue using a digital image processing method using only one camera to be used as input parameters in calculating the length of time the red light and green light. Therefore, the traffic system is very necessary, as a means and infrastructure to make traffic smooth, safe, even as a medium for disciplined learning for road users. In this study the authors used a digital image-based control system where the camera as a sensor. For the application of all methods in this study, Aurdino Microcontroller is used

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